Adverse impact of polyphasic sleep patterns in humans: Report of the National Sleep Foundation sleep timing and variability consensus panel

Affiliations.

  • 1 Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA; Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA.
  • 2 Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Clayton, Australia.
  • 3 Sleep & Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • 4 Department of Child Health, University of Missouri, Columbia, Missouri, USA.
  • 5 Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA; Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • 6 Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA; Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA; Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Clayton, Australia.
  • 7 Institute for Medical Psychology, Ludwig Maximilian University of Munich, Munich, Germany.
  • 8 Department of Neuroscience, The University of Texas Southwestern Medical Center, Dallas, Texas, USA; Howard Hughes Medical Institute, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • 9 Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, USA.
  • 10 Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA.
  • 11 Laboratory of Genetics, The Rockefeller University, New York, New York, USA.
  • 12 Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA; Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: [email protected].
  • PMID: 33795195
  • DOI: 10.1016/j.sleh.2021.02.009

Polyphasic sleep is the practice of distributing multiple short sleep episodes across the 24-hour day rather than having one major and possibly a minor ("nap") sleep episode each day. While the prevalence of polyphasic sleep is unknown, anecdotal reports suggest attempts to follow this practice are common, particularly among young adults. Polyphasic-sleep advocates claim to thrive on as little as 2 hours of total sleep per day. However, significant concerns have been raised that polyphasic sleep schedules can result in health and safety consequences. We reviewed the literature to identify the impact of polyphasic sleep schedules (excluding nap or siesta schedules) on health, safety, and performance outcomes. Of 40,672 potentially relevant publications, with 2,023 selected for full-text review, 22 relevant papers were retained. We found no evidence supporting benefits from following polyphasic sleep schedules. Based on the current evidence, the consensus opinion is that polyphasic sleep schedules, and the sleep deficiency inherent in those schedules, are associated with a variety of adverse physical health, mental health, and performance outcomes. Striving to adopt a schedule that significantly reduces the amount of sleep per 24 hours and/or fragments sleep into multiple episodes throughout the 24-hour day is not recommended.

Keywords: Circadian misalignment; Health; Performance; Polyphasic sleep; Sleep patterns.

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.
  • Mental Health*
  • Young Adult

Grants and funding

  • R01 HL114088/HL/NHLBI NIH HHS/United States
  • R01 HL128538/HL/NHLBI NIH HHS/United States
  • HHMI/Howard Hughes Medical Institute/United States
  • Open access
  • Published: 17 June 2021

Relationship between sleep habits and academic performance in university Nursing students

  • Juana Inés Gallego-Gómez 1 ,
  • María Teresa Rodríguez González-Moro 1 ,
  • José Miguel Rodríguez González-Moro 2 ,
  • Tomás Vera-Catalán 1 ,
  • Serafín Balanza 1 ,
  • Agustín Javier Simonelli-Muñoz 3 &
  • José Miguel Rivera-Caravaca 4  

BMC Nursing volume  20 , Article number:  100 ( 2021 ) Cite this article

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Sleep disorders are composed of a group of diseases of increasing prevalence and with social-health implications to be considered a public health problem. Sleep habits and specific sleep behaviors have an influence on the academic success of students. However, the characteristics of sleep and sleep habits of university students as predictors of poor academic performance have been scarcely analyzed. In the present study, we aimed to investigate sleep habits and their influence on academic performance in a cohort of Nursing Degree students.

This was a cross-sectional and observational study. An anonymous and self-administered questionnaire was used, including different scales such as the ‘Morningness and Eveningness scale’, an author-generated sleep habit questionnaire, and certain variables aimed at studying the socio-familial and academic aspects of the Nursing students. The association of sleep habits and other variables with poor academic performance was investigated by logistic regression. The internal consistency and homogeneity of the ‘sleep habits questionnaire’ was assessed with the Cronbach’s alpha test.

Overall, 401 students (mean age of 22.1 ± 4.9 years, 74.8 % females) from the Nursing Degree were included. The homogeneity of the ‘sleep habits questionnaire’ was appropriate (Cronbach’s alpha = 0.710). Nursing students were characterized by an evening chronotype (20.2 %) and a short sleep pattern. 30.4 % of the Nursing students had bad sleep habits. Regarding the academic performance, 47.9 % of the students showed a poor one. On multivariate logistic regression analysis, a short sleep pattern (adjusted OR = 1.53, 95 % CI 1.01–2.34), bad sleep habits (aOR = 1.76, 95 % CI 1.11–2.79), and age < 25 years (aOR = 2.27, 95 % CI 1.30–3.98) were independently associated with a higher probability of poor academic performance.

Conclusions

Almost 1/3 of the Nursing students were identified as having bad sleep habits, and these students were characterized by an evening chronotype and a short sleep pattern. A short sleep pattern, bad sleep habits, and age < 25 years, were independently associated with a higher risk of poor academic performance. This requires multifactorial approaches and the involvement of all the associated actors: teachers, academic institutions, health institutions, and the people in charge in university residences, among others.

Peer Review reports

Introduction

Sleep is a complex phenomenon resulting from the interaction between the neuroendocrine system, biological clock and biochemical processes, with environmental, social and cultural aspects that are very relevant in the life stages of adolescence and youth [ 1 ]. Indeed, the chronic lack of sleep is a recent worry among adolescents and young university students and it is associated with worse health and clinical outcomes [ 2 , 3 ].

Among biological factors determining sleep, there are “chronotypes” and sleep patterns. The first term refers to the personal preferences of scheduling the sleep-wake cycle, emphasizing three basic chronotypes: morning (early-risers), and evening (night-owls) and those who are intermediate, defined as those who do not have clear preferences towards any of the extreme schedules for the fulfilling of their activities [ 4 ]. The sleep pattern refers to the personal schedule of bedtime and wake-up time. In this sense, a circadian rhythm is a natural, internal process, driven by a circadian clock that repeats roughly every 24 h and regulates the sleep-wake cycle [ 5 ].

On the other hand, the sleep habits are in the intersection between biological and cultural values. Endogenous, exogenous or environmental factors are included here, as well as those activities that are developed by the population to induce or maintain sleep, with its study and care becoming a challenge for Nursing [ 6 ]. Currently, spontaneous abusive behaviors regarding sleep habits are becoming frequent, leading to a state of chronic sleep deprivation, which translates to fatigue and somnolence during the day [ 7 ]. Hence, there is a high prevalence of sleep disorders in university students, especially those that affect the wake-sleep rhythm [ 2 ]. For this reason,the interest in establishing relationships between sleep and cognitive processes such as memory, learning ability and motivation, has gained attention during the last years. However, studies that relate sleep with academic problems are scarce, despite previous authors have shown that the reduction of sleep time in teenagers and university students was associated with poor academic performance, accidents and obesity [ 8 , 9 ]. Since good-quality sleep does not only imply sleeping well at night but also an adequate level of attention during the day for performing different tasks, appropriate sleep has an influence in efficient learning processes in university students [ 10 , 11 , 12 ].

Although some scientific evidence has shown a relationship between sleep and low academic performance [ 13 , 14 ], so far, there are no questionnaires to specifically evaluate sleep habits in Nursing students. Considering that this population has special characteristics, they are mostly young, combine hospital training at the same time they attend classes at the university, they present lifestyles that can negatively influence the academic performance. To study the sleep habits using a specific tool, in addition to analyze the sleep pattern and chronotype, could help to identify students with inappropriate sleep habits for developing interventions to modify these habits. This might have a positive impact on their academic performance and avoid potentially serious negative consequences for their physical and mental health. In the present research, we aimed (a) to design a ‘sleep habits questionnaire’, (b) to analyze the sleep habits, sleep pattern and chronotype, and (c) to investigate sleep habits and their influence on academic performance, in a cohort of Nursing Degree students.

Design and study population

This was an observational, prospective and cross-sectional study involving Nursing students, all of them distributed among the 4 years of the Nursing Degree. There were no inclusion criteria, i.e. all Nursing students were suitable for the study, unless those who did not attend class on the day of data collection, or those who did not wish to participate (from 420 students, 19 refused to participate in the study). The study was fully carried out during the first semester of the 2019–2020 academic year.

Study Variables

Circadian rhythm: the reduced “horne & östberg morningness-eveningness questionnaire”.

Preferences of schedule for the sleep-wake cycle and its influence on academic performance were assessed using the reduced version of the Horne & Östberg Morningness-Eveningness Questionnaire (rMEQ) proposed by Adan & Almirall [ 15 ], translated to Spanish, that is composed of 5 items. The score determines the following five types of schedule: clearly morning type (22–25 points), moderately morning type (18–21 points), no preference (12–17 points), moderately evening type (8–11 points), and clearly evening type (4–7 points). The internal consistency of the circadian rhythm scale assessed using the rMEQ by Adan & Almirall is good, as the scores from all the items are correlated among themselves [ 15 , 16 ].

Sleep habits questionnaire

For the initial design of the sleep habits questionnaire, a panel of 10 voluntary experts was included. This panel was composed of 5 registered nurses and 5 physicians, with a minimum of 5 years of experience in sleep. All of them were interviewed and informed individually about the study. Items composing of the questionnaire were obtained according to the scientific literature and the main factors influencing sleep habits as the discretion of the expert panel [ 14 , 17 , 18 ]. Eleven questions were finally included in a self-reported questionnaire, each ranging from 1 to 4 (never (1), sometimes (2), usually (3), always (4)) ( Supplementary file ). Sleep habits, including sleep routines, study schedule preference, and napping were also evaluated. The overall score of the questionnaire ranges from 11 to 44 points, with the highest scores indicating the worst sleep habits. As there is no specific cut-off point for this questionnaire, students over the fourth quartile (4Q, i.e. ≥25 points) were categorized as having inappropriate habits. Therefore, these Nursing students were included in the “bad sleeping habits” group.

  • Academic performance

The academic performance was measured by the ratio “failed exams/performed exams” and checked in the student’s academic records. A good academic performance was considered if the final grade of every exam completed during the Nursing Degree was ≥ 5 (in a 0–10 range, where an exam is considered passed if the score is ≥ 5).

Other variables

Other variables such as gender, age and hours of sleep (sleep pattern), were analyzed. To describe the sleep pattern of the Nursing students, we used the classification described by Miró et al. (2002) [ 19 ]. This classification was composed of three categories as a function of the hours slept, so that we found subjects that had a short sleep pattern (< 6 h per day), subjects with a long sleep pattern (≥ 9 h per day), and subjects with an intermediate sleep pattern (6–9 h per day).

Ethical considerations

The study protocol was approved by an accredited Ethics Committee (Reference: CE-6191) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. All students were informed and gave consent to participation in the study. The anonymity and confidentiality were guaranteed.

Statistical analysis

The sample size was calculated by a non-probabilistic sampling technique using Ene 2.0 (GlaxoSmithKline) with a precision ± 5 % and α error = 0.05. This calculation was based on the estimation that the prevalence of bad sleep habits in Nursing students of our university was 30.4 %, which resulted in a minimum sample of 229 subjects.

Categorical variables were expressed as frequencies and percentages. Continuous variables were presented as mean ± standard deviation (SD) or median and interquartile range (IQR), as appropriate.

The Pearson Chi-squared test was used to compare proportions whereas comparison of continuous variables was performed using the Student t test. Correlations between different scales were performed using the Pearson’s correlation test.

In order to investigate if sleep habits and other variables were independently associated with poor academic performance, a logistic regression model (with odds ratios [OR] and two-sided 95 % confidence intervals [CI]) was performed. To measure the internal consistency and homogeneity of the sleep habits questionnaire, the Cronbach’s alpha test was performed.

A p -value < 0.05 was accepted as statistically significant. Statistical analyses were performed using SPSS v. 21.0 (SPSS, Inc., Chicago, IL, USA).

We included 401 Nursing students (100 students from 1st year, 105 from 2nd year, 101 from 3rd year, and 95 from 4th year) in the study. The students were characterized for being predominantly females (300, 74.8 %), with a mean age of 22.1 ± 4.9 years, and the majority of them (88.5 %) were singles.

Sleep habits of the Nursing students were examined using our previously designed (as described in the Methods section) self-reported ‘sleep habits questionnaire’. The homogeneity of the questionnaire was appropriate, with a Cronbach’s alpha value of 0.710. The mean score in the questionnaire was 22.3 ± 3.9, and 30.4 % of the Nursing students had bad sleep habits (i.e. score > 4Q), which were characterized by a clear preference of studying at night, easily lose a night of sleep for work-related or academic tasks that imply staying up late, and showing difficulties in maintaining sleep routines.

Table  1 shows the summarized results for each question of the sleep habits questionnaire.

The Nursing students in our sample were characterized by an evening chronotype (20.2 %, 81) and a short sleep pattern (i.e. <6 h of sleep daily), with 51.1 % (205) of the students sleeping less than 6 h/day, 42.1 % (169) sleeping 6–9 h/day, and 6.7 % (27) sleeping more than 9 h/day. The mean duration of sleep found in the Nursing students was 6.52 ± 1.4 h.

Of note, most of the Nursing students that had an evening chronotype were < 25 years old (22.2 %, p  = 0.011). In addition, age showed a positive association with circadian rhythm and as age increased, the students tended to have a predominantly morning chronotype ( R  = 0.223, p  < 0.001). Nursing students < 25 years of age had also worse sleep habits according to the sleep habits questionnaire than those ≥ 25 years (22.61 ± 3.79 vs. 21.19 ± 4.37, p  = 0.005). A negative correlation was found between the overall sleep habits questionnaire score and age as a continuous variable ( R = -0.105, p  = 0.03).

In addition, 29.5 % of patients that had bad sleep habits ( p  = 0.001), and 23.9 % that had poor academic performance ( p  = 0.020), had also an evening chronotype (Table  2 ). A significant negative correlation was found between the sleep pattern and sleep habits ( R = -0.293, p  < 0.001), and between circadian rhythm and sleep habits, hence Nursing students with good sleep habits have predominantly a morning circadian rhythm ( R = -0.201, p  < 0.001).

Regarding the academic performance, 93 % (373) of the Nursing students attended all the exams planned, and 47.9 % (192) of the students showed poor academic performance. When we investigated specifically if the sleep habits, as assessed by the ‘sleep habits questionnaire’, influenced the academic performance, we found that 32 % (140) of the Nursing students that had bad sleep habits obtained poor academic results ( p  < 0.001). Those that had the worst academic results were the ones that did not have a regular hour for waking up and going to sleep (2.66 ± 1.03, p  = 0.031), presented difficulties to maintain the sleep during the night (1.73 ± 0.77, p  = 0.003), and preferred to study for an exam at night (1.33 ± 0.48, p  = 0.030), as well as going to bed late to obtain better results (1.46 ± 0.51, p  = 0.041). Also, those students with poorer academic results where those listening to music before going to bed (1.84 ± 1.10, p  = 0.007), and going out at night even if they had to get-up early the next day (1.58 ± 0.72, p  = 0.012). Overall, those Nursing students whose work or academic activities entailed going to bed late to attain their objectives, had the lowest academic performance (2.25 ± 1.01, p  = 0.001). Lastly, we can confirm that the Nursing students that had better academic performance were the ones who had the best sleep habits. Indeed, the overall ‘sleep habits questionnaire’ score was significantly lower compared to those Nursing students who had poor academic performance (21.91 ± 3.90 vs. 24.18 ± 3.55, p  < 0.001) (Table  3 ).

Finally, the profile of Nursing students with more failed courses was characterized by an evening circadian rhythm ( R = -0.134, p  = 0.007), bad sleep habits ( R  = 0.216, p  < 0.001), and less hours of sleep daily ( R = -0.211, p  < 0.001).

To confirm these observations, a multivariate logistic regression analysis was performed. Therefore, a short sleep pattern (adjusted OR = 1.53, 95 % CI 1.01–2.34), bad sleep habits (adjusted OR = 1.76, 95 % CI 1.11–2.79), and age < 25 years (adjusted OR = 2.27, 95 % CI 1.30–3.98) were independently associated with a higher probability of poor academic performance (Table  4 ).

Sleep is an excellent indicator of the health status and an element that favors good quality of life [ 20 ], but entering university is a change that highly impacts the student in every dimension, including sleep habits [ 21 , 22 ]. A potential barrier for maximizing performance during the university stage is the irregular sleep schedule, which lead to sleep deficit and high prevalence of somnolence during the day [ 23 ]. A review by Shochat et al. (2014) [ 24 ] examined the consequences of lack of sleep among Nursing students, and confirmed the relationship between sleep disorders and changes in sleep patterns with a reduced academic performance. Other studies have established that sleep has an integral role in learning and memory consolidation [ 25 , 26 ]. Therefore, despite some scientific evidence has shown a relationship between sleep and low academic performance [ 13 , 14 ], the originality of our study was to examine the influence that sleep characteristics exert (chronotypes and sleep patterns), as well as sleep habits of the university population on academic performance.

Overall, the academic performance of our Nursing students was suboptimal. When analyzing how sleep pattern, sleep habits, and circadian rhythms influenced this academic performance, we observed that all of them may be determine factors for learning, as other studies have done [ 27 ].

Concerning the sleep pattern, it should be noted that most of the students enrolled in the Nursing Degree slept less than 6 h per day. Of note, our results seem to establish a relationship between the hours slept and the academic performance during the first semester, as gathered from the academic records. This finding is in accordance to observations by other authors in university students from Medicine [ 9 ], Pharmacy [ 2 ] or Nursing [ 28 ], which also showed evidence between the hours slept and the academic achievement. In a previous study, we already observed that university students from the Faculty of Nursing attributed the hours slept with academic performance [ 29 ]. Indeed, it should be highlighted that chronic lack of sleep is not only associated with alterations of attention and academic performance, but also to a series of adverse consequences for health such as risky behaviors, depression, anxiety, alterations in social relations, and obesity, among others [ 30 ].

In addition, our study has evidenced how the sleep habits directly influenced the academic performance of these Nursing students, and approximately 1/3 of the students with bad sleep habits obtained poor academic results. Certainly, the sleep pattern and inadequate sleep habits could be related. Good sleep hygiene includes aspects such as a regular sleep-wake schedule, adequate environment, avoiding stimulating activities before going to bed, and limiting the use of technology in bed or immediately before going to bed. In the present study, 30.4 % of the students had bad sleep habits, characterized by having a clear preference for studying at night, often losing a night of sleep for work or academic activities that imply go to bed late, and show difficulties in maintaining sleep routines. An important proportion of our Nursing degree students declared that they watched television, listened to music, worked or read academic documents during the last hour before going to bed. In this sense, LeBourgeois et al. (2017) [ 31 ] have described the university population as great consumers of technology, and have associated the frequent use of technology before going to bed with problems to sleep and daytime somnolence.

Finally, age was another factor that should be considered in the analysis of sleep habits. According to our results, the Nursing students that were < 25 years of age had the worst sleep habits and used to have more difficulties in maintaining sleep routines, modifying them on the weekends and holidays, preferring to stay up late to obtain better study results, and going out at night without considering that they had to get up early. As other studies [ 21 ], we observed that social activities were a priority in the life of the university adolescents and the substituting of hours of sleep for enjoying and sharing activities with friends and classmates did not constitute a problem for them. These behaviors were added to the physiological delay of the start of sleep that is typical in this stage of life and might unleash deprivation or a chronic deficit of sleep, maintained throughout the entire week. The students then tried to compensate for this lack of sleep by increasing their hours of sleep during the weekend. We agree with previous studies that this circumstance, far from minimizing or compensating the effects of sleep deprivation, aggravates them, worsening the pattern and the quality of sleep of the students [ 22 ].

Further, we found an association between age and circadian type. We observed that most of the university students with evening chronotypes were aged < 25, had bad sleep habits, and a poor academic performance. Physiologically, adolescents and adults tend to have delayed circadian preferences and are “lovers of the night” [ 23 ]. In our study, 20.2 % of students had an evening chronotype, which is lower than that reported in other studies, where 59 % of the students between 18 and 29 years of age described themselves as night owls [ 32 ]. Our results also showed a clear normalization of the evening behaviors of the students. These data are in agreement with other authors who highlighted the influence exerted by the aforementioned normalization of evening habits among the youth on the quality of sleep, leading to a medium to long-term sleep deficit [ 20 ]. As Crowley et al. (2018) [ 33 ], we think that evening behavior leads to asynchrony between the biological rhythm and the social life of the student, having negative consequences on the academic performance. However, how this really affects academic results requires extending researches, since the circadian rhythm was not significantly associated with academic performance.

The results of this study evidence the need to seriously take into consideration the sleep deficits that are associated with inadequate sleep habits, with the aim of developing preventative and educational initiatives to improve the sleep habits of the university population. The challenge ahead starts with the social awareness of the importance of having good-quality sleep since many times, adequate knowledge about sleep does not translate into a change of sleep habits [ 23 ].

Limitations

Some limitations should be noted. Due to the cross-sectional design of the study, we could not establish an exact causal relationship between sleep pattern and academic performance. In addition, it should be note that the ‘sleep habits questionnaire’ is a subjective questionnaire, and therefore the result could be biased if the student did not answer honestly. Another limitation is the difficulty in conceptualizing academic performance, due to its complex and multi-causal character, where many factors intervene. The factors include attitudes, habits, the character of the staff, methodologies, family environment, organization of the educational system, socio-economic condition, as well as other social, economic, and psychological aspects [ 34 ]. Finally, the study was conducted only in Nursing students, so our results must be prospectively validated in University students from a larger variety of academic sectors. Similarly, this study was conducted in a single University, so more studies involving other Universities are also necessary. Despite these circumstances, we believe that our hypothesis that the duration of sleep could lead to better academic performance is based on current scientific data.

Using the 11-item ‘sleep habits questionnaire’, 30.4 % of the Nursing students were identified as having bad sleep habits. In addition, Nursing students included in this research were characterized by an evening chronotype and a short sleep pattern. Regarding academic performance, half of the Nursing students showed a poor one. A short sleep pattern, bad sleep habits, and younger age, were independently associated with a higher risk of poor academic performance. This requires multifactorial approaches and the involvement of all the associated actors: teachers, academic institutions, health institutions, and the people in charge in university residences, among others.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Matricciani L, Bin YS, Lallukka T, Kronholm E, Wake M, Paquet C, Dumuid D, Olds T. Rethinking the sleep-health link. Sleep Health. 2018;4(4):339–348. doi: https://doi.org/10.1016/j.sleh.2018.05.004 .

Article   PubMed   Google Scholar  

Zeek ML, Savoie MJ, Song M, Kennemur LM, Qian J, Jungnickel PW, Westrick SC. Sleep Duration and Academic Performance Among Student Pharmacists. Am J Pharm Educ. 2015;79(5):63. doi: https://doi.org/10.5688/ajpe79563 .

Article   PubMed   PubMed Central   Google Scholar  

Dijk DJ, Landolt HP. Sleep Physiology, Circadian Rhythms, Waking Performance and the Development of Sleep-Wake Therapeutics. Handb Exp Pharmacol. 2019;253:441–481. doi: https://doi.org/10.1007/164_2019_243 .

Article   CAS   PubMed   Google Scholar  

Zerbini G, Merrow M. Time to learn: How chronotype impacts education. Psych J. 2017;6(4):263–276. doi: https://doi.org/10.1002/pchj.178 .

Huang W, Ramsey KM, Marcheva B, Bass J. Circadian rhythms, sleep, and metabolism. J Clin Invest. 2011;121(6):2133–41. doi: https://doi.org/10.1172/JCI46043 .

Owens H, Christian B, Polivka B. Sleep behaviors in traditional-age college students: A state of the science review with implications for practice. J Am Assoc Nurse Pract. 2017; 29(11):695–703. doi: https://doi.org/10.1002/2327-6924.12520 .

Becerra MB, Bol BS, Granados R, Hassija C. Sleepless in school: The role of social determinants of sleep health among college students. J Am Coll Health. 2020; 68(2):185–191. doi: https://doi.org/10.1080/07448481.2018.1538148 .

Kozak AT, Pickett SM, Jarrett NL, Markarian SA, Lahar KI, Goldstick JE. Project STARLIT: protocol of a longitudinal study of habitual sleep trajectories, weight gain, and obesity risk behaviors in college students. BMC Public Health. 2019;19(1):1720. doi: https://doi.org/10.1186/s12889-019-7697-x .

El Hangouche AJ, Jniene A, Aboudrar S, Errguig L, Rkain H, Cherti M, Dakka T. Relationship between poor sleep quality, excessive daytime sleepiness and poor academic performance in medical students. Adv Med Educ Pract. 2018; 9: 631–638. doi: 10.2147 / AMEP.S162350.

Article   Google Scholar  

Makino K, Ikegaya Y. Learning Paradigms for the Promotion of Memory, and Their Underlying Principles. Brain Nerve. 2018;70(7):821–828. doi: https://doi.org/10.11477/mf.1416201083 .

Haile YG, Alemu SM, Habtewold TD. Insomnia and Its Temporal Association with Academic Performance among University Students: A Cross-Sectional Study. Biomed Res Int. 2017;2017:2542367. doi: https://doi.org/10.1155/2017/2542367 .

Gianfredi V, Nucci D, Tonzani A, Amodeo R, Benvenuti AL, Villarini M, Moretti M. Sleep disorder, Mediterranean Diet and learning performance among nursing students: inSOMNIA, a cross-sectional study. Ann Ig. 2018; 30(6):470–481. doi: https://doi.org/10.7416/ai.2018.2247 .

Zhao K, Zhang J, Wu Z, Shen X, Tong S, Li S. The relationship between insomnia symptoms and school performance among 4966 adolescents in Shanghai, China. Sleep Health. 2019;5(3):273–279. doi: https://doi.org/10.1016/j.sleh.2018.12.008 .

Alotaibi AD, Alosaimi FM, Alajlan AA, Bin Abdulrahman KA. The relationship between sleep quality, stress, and academic performance among medical students. J Family Community Med. 2020;27(1):23–28. doi: https://doi.org/10.4103/jfcm.JFCM_132_19 .

Adan, A.; Almirall, H. Horne & Östberg Morningnees-Eveningnees Questionnaire: a reduced scale. Pers Individ Dif. 1991, 12, 241–53. doi: https://doi.org/10.1016/0191-8869(91)90110-W

Randler C. German version of the reduced Morningness-Eveningness Questionnaire (rMEQ). Biological Rhythm Research. 2013;44(5):730–736. doi: https://doi.org/10.1080/09291016.2012.739930

Peach H, Gaultney JF. Charlotte Attitudes Towards Sleep (CATS) Scale: A validated measurement tool for college students. J Am Coll Health. 2017;65(1):22–31. doi: https://doi.org/10.1080/07448481.2016.1231688 .

Al-Kandari S, Alsalem A, Al-Mutairi S, Al-Lumai D, Dawoud A, Moussa M. Association between sleep hygiene awareness and practice with sleep quality among Kuwait Zhao University students. Sleep Health. 2017;3(5):342–347. doi: https://doi.org/10.1016/j.sleh.2017.06.004 .

Miró E, Iáñez MA, Cano-Lozano MC. Sleep and health patterns. Int J Clin Health Psychol. 2002;2:301–326.

Google Scholar  

Zohal MA, Yazdi Z, Kazemifar AM, Mahjoob P, Ziaeeha M. Sleep Quality and Quality of Life in COPD Patients with and without Suspected Obstructive Sleep Apnea. Sleep Disord. 2014;2014:508372. doi: https://doi.org/10.1155/2014/508372.21

Núñez P, Perillan C, Arguelles J, Diaz E. Comparison of sleep and chronotype between senior and undergraduate university students. Chronobiol Int. 2019;36(12):1626–1637. doi: https://doi.org/10.1080/07420528.2019.1660359 .

Phillips AJK, Clerx WM, O’Brien CS, Sano A, Barger LK, Picard RW, Lockley SW, Klerman EB, Czeisler CA. Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing. Sci Rep. 2017;7(1):3216. doi: https://doi.org/10.1038/s41598-017-03171-4 .

Niño García JA, Barragán Vergel MF, Ortiz Labrador JA, Ochoa Vera ME, González Olaya HL. Factors Associated with Excessive Daytime Sleepiness in Medical Students of a Higher Education Institution of Bucaramanga. Rev Colomb Psiquiatr. 2019;48(4):222–231. doi: https://doi.org/10.1016/j.rcp.2017.12.002 .

Shochat T, Cohen-Zion M, Tzischinsky O. Functional consequences of inadequate sleep in adolescents: a systematic review. Sleep Med Rev. 2014;18:75–87. doi: https://doi.org/10.1016/j.smrv.2013.03.005

Yang G, Lai CS, Cichon J, Ma L, Li W, Gan WB. Sleep promotes branch-specific formation of dendritic spines after learning. Science. 2014;344(6188):1173–8. doi: https://doi.org/10.1126/science.1249098 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bruin EJ, van Run C, Staaks J, Meijer AM. Effects of sleep manipulation on cognitive functioning in adolescents: a systematic review. Sleep Med Rev. 2017; 32: 45–57. doi: https://doi.org/10.1016/j.smrv.2016.02.006 .

Arbabi T, Vollmer C, Dörfler T, Randler C The influence of timing and intelligence on academic performance in elementary school is mediated by awareness, sleep midpoint and motivation. Chronobiol Int. 2015;32(3):349–57. doi: https://doi.org/10.3109/07420528.2014.980508

Menon B, Karishma HP, Mamatha IV. Sleep quality and health complaints among nursing students. Ann Indian Acad Neurol. 2015;18(3):363–4. doi: https://doi.org/10.4103/0972-2327.157252 .

Simonelli-Muñoz AJ, Balanza S, Rivera-Caravaca JM, Vera-Catalán T, Lorente AM, Gallego-Gómez JI. Reliability and validity of the student stress inventory-stress manifestations questionnaire and its association with personal and academic factors in university students. Nurse Educ Today. 2018;64:156–160. doi: https://doi.org/10.1016/j.nedt.2018.02.019 .

Begdache L, Kianmehr H, Sabounchi N, Marszalek A, Dolma N. Principal component regression of academic performance, substance use and sleep quality in relation to risk of anxiety and depression in young adults. Trends Neurosci Educ. 2019;15:29–37. doi: https://doi.org/10.1016/j.tine.2019.03.002 .

LeBourgeois MK, Hale L, Chang AM, Akacem LD, Montgomery-Downs HE, Buxton OM. Digital Media and Sleep in Childhood and Adolescence. Pediatrics. 2017;140(Suppl 2):S92-S96. doi: https://doi.org/10.1542/peds.2016-1758J .

Talero-Gutiérrez C, Durán-Torres F, Pérez-Olmos I. Sleep: general characteristics Physiological and pathophysiological patterns in adolescence. Revista Ciencias de la Salud. 2013;11(3):333–348.

Crowley SJ, Wolfson AR, Tarokh L, Carskadon MA. An update on adolescent sleep: New evidence informing the perfect storm model. J Adolesc. 2018;67:55–65. doi: https://doi.org/10.1016/j.adolescence.2018.06.001 .

Suardiaz-Muro M, Morante-Ruiz M, Ortega-Moreno M, Ruiz MA, Martín-Plasencia P, Vela-Bueno A. Sleep and academic performance in university students: a systematic review. Rev Neurol. 2020;71(2):43–53. doi: https://doi.org/10.33588/rn.7102.2020015 .

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Gallego-Gómez, J.I., González-Moro, M.T.R., González-Moro, J.M.R. et al. Relationship between sleep habits and academic performance in university Nursing students. BMC Nurs 20 , 100 (2021). https://doi.org/10.1186/s12912-021-00635-x

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Daily rhythms of the sleep-wake cycle

  • Jim Waterhouse 1 ,
  • Yumi Fukuda 2 &
  • Takeshi Morita 2  

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The amount and timing of sleep and sleep architecture (sleep stages) are determined by several factors, important among which are the environment, circadian rhythms and time awake. Separating the roles played by these factors requires specific protocols, including the constant routine and altered sleep-wake schedules. Results from such protocols have led to the discovery of the factors that determine the amounts and distribution of slow wave and rapid eye movement sleep as well as to the development of models to determine the amount and timing of sleep. One successful model postulates two processes. The first is process S, which is due to sleep pressure (and increases with time awake) and is attributed to a 'sleep homeostat'. Process S reverses during slow wave sleep (when it is called process S'). The second is process C, which shows a daily rhythm that is parallel to the rhythm of core temperature. Processes S and C combine approximately additively to determine the times of sleep onset and waking. The model has proved useful in describing normal sleep in adults. Current work aims to identify the detailed nature of processes S and C. The model can also be applied to circumstances when the sleep-wake cycle is different from the norm in some way. These circumstances include: those who are poor sleepers or short sleepers; the role an individual's chronotype (a measure of how the timing of the individual's preferred sleep-wake cycle compares with the average for a population); and changes in the sleep-wake cycle with age, particularly in adolescence and aging, since individuals tend to prefer to go to sleep later during adolescence and earlier in old age. In all circumstances, the evidence that sleep times and architecture are altered and the possible causes of these changes (including altered S, S' and C processes) are examined.

Sleep in adults

Most adults take a consolidated 7-hour sleep during the night [ 1 ]. The reasons for sleeping at night are partly because the environment is quiet and also it would be unconventional to arrange meetings or meet friends at this time. Moreover, we are diurnal creatures and after a normal day when we have been awake and active for some time, we feel tired in the evening and ready for sleep. It is possible to sleep at other times, as is evident from the lifestyle of night workers but, even in a quiet environment, daytime sleep tends to be more fragmented and shorter than nocturnal sleep. That is, the ability to get to sleep and sleep uninterruptedly for long enough shows a daily rhythm. This rhythm of ease of getting to sleep (sleep propensity) is clear if individuals miss a night's sleep; they feel tired during the night but, in spite of having had no sleep, they then feel less tired as the new day dawns and, during the afternoon, will feel surprisingly alert. However, by the evening, the sensation of fatigue increases markedly and becomes increasingly difficult to resist. This result indicates that there is an increasing drive to sleep as the amount of time awake continues to rise but that it is mixed with a rhythmic component that varies during the course of the 24 hours.

A good sleep is recuperative and removes the feelings of fatigue (and also produces an improvement in cognitive ability); individuals then feel ready to face the rigors of a new day. Intuitively, the concept of increasing 'sleep pressure' with increasing time awake is not difficult to appreciate (even if the detailed nature of sleep pressure is not understood). However, the presence of daily rhythms in the desire to sleep, staying asleep and waking up might be less easy to understand. In fact, when repeated measurements are made over the course of 24 hours in subjects who are living normally (active in the daytime and sleeping at night), all physiological and biochemical variables show daily rhythms. Some properties of such rhythmicity need to be considered, and they will be illustrated by the rhythm of core temperature (which is representative of biological rhythms in general).

Basic chronobiology

Endogenous and exogenous components of a rhythm.

Figure 1 shows the 24-hour rhythm of core temperature in a group of subjects living normally. The temperature is higher in the daytime and lower at night. A priori , it would be supposed that such rhythms result from the behavioral changes that are associated with the sleep-activity cycle. During the daytime, individuals are physically and mentally active, and eat and drink in an environment that is busy and stimulating; all of these factors will tend to raise core temperature. By contrast, at night, individuals fast, are inactive, sleep and choose a quiet, dark environment.

figure 1

The daily rhythm of core temperature in a group of eight young men . Full line: under normal conditions (sleep from 12 a.m. to 7 a.m., indicated by bar). Dashed line: undergoing a 24-hour constant routine starting at 4 a.m. (From [ 2 ]).

In fact, such an explanation is only partially correct; the standard method for demonstrating this is the 'constant routine'. In this protocol, a subject is required to remain awake and sedentary (or lying down) and relaxed for at least 24 hours in an environment of constant temperature, humidity and lighting; to engage in similar activities, generally reading or listening to music; and to take identical meals at regularly-spaced intervals. Such a protocol removes any rhythmic changes due to the individual's lifestyle or environment. When this protocol is undertaken, it is observed (Figure 1 ) that the rhythm of core temperature does not disappear, even though its amplitude decreases. The rhythm shows a peak around 4 p.m. to 6 p.m. and a minimum around 4 a.m. to 5 a.m. The rhythm is quite symmetrical, with the time of most rapid rise being in the period 7 a.m. to 10 a.m. and that of most rapid fall around 10 a.m. to 1 a.m.

By considering the two temperature profiles shown in Figure 1 , the following deductions can be made [ 2 , 3 ]:

The rhythm observed during the constant routine arises internally; it is the endogenous component of the temperature rhythm and its generation is attributed to a 'body clock'.

Since the two rhythms are not identical, effects due to the environment and lifestyle are also present when the participants live conventionally; the difference between the two rhythms is the exogenous component of the rhythm. The exogenous component of core temperature is dominated by the sleep-wake cycle, activity raising temperature and lying down and sleeping decreasing it.

The two components are in phase. During the daytime, body temperature is raised by the body clock acting in synchrony with a more dynamic environment and increased physical and mental activities; during the night, the clock, a more restful environment and relaxed lifestyle all act to reduce core temperature.

These deductions are general, insofar as all rhythms show a mixture of endogenous and exogenous components when compared under normal living conditions and during a constant routine. However, when different rhythms are investigated, differences do exist, as described below.

The origin of the exogenous component

For example: the exogenous component is the sleep-wake cycle in the cases of heart rate and blood pressure, as with the rhythm of core temperature; it is the light-dark cycle for melatonin secretion; it is the rhythms of posture and fluid intake for urine flow; and it is the rhythm of food intake for plasma insulin concentration.

The relative size of the endogenous and exogenous components

As Figure 1 indicates, these two components are of similar size for core temperature. By contrast, the exogenous component is larger than the endogenous component in the cases of heart rate, blood pressure, urine flow and insulin secretion. For melatonin secretion, provided light levels are low (as normally found domestically) - the exogenous component is smaller than the endogenous component. Because the relative sizes of the endogenous components of the rhythms of core temperature and, particularly, melatonin are small, these rhythms are often used as 'clock markers'.

The body clock and its adjustment by zeitgebers

The body clock consists of paired suprachiasmatic nuclei at the base of the hypothalamus; many details of its genetics and molecular biochemistry are now known [ 4 – 6 ]. It is sited close to areas that exert widespread effects upon the body (for example, temperature regulation, hormone secretion and the feeding cycle) and sends outflows to these areas, so producing rhythmicity throughout the body.

When individuals are studied in time-free environments (such as an underground cave), the body clock and rhythms produced by it continue to be manifest but with a period closer to 25 than 24 hours. Such rhythms are called circadian (Latin: about a day) and the timing system is described as 'free-running'. This period of about 25 hours does not reflect the intrinsic period of the body clock (tau) exactly, due to effects of light exposure during waking. Current evidence (from protocols using very dim light during waking or from blind subjects) indicates that the true value of tau averages about 24.3 hours [ 7 ].

Whatever the exact value for tau in an individual, a body clock will be of value only if it and the rhythms it drives are synchronized to a solar (24-hour) day. This adjustment is due to zeitgebers (German: time-giver), rhythms in the environment and/or the individual's behavior which show a 24-hour period. For humans, the most important zeitgebers are the light-dark cycle and rhythmic secretion of pineal melatonin during the dark. Rhythms of physical activity, social factors and food intake appear to play comparatively minor roles [ 8 – 10 ]. Nevertheless, whatever the details of the components of a 'zeitgeber package', all potential zeitgebers normally act harmoniously to adjust the body clock to the 24-hour (solar) day.

The effect of light exposure upon the body clock depends upon the time of exposure relative to the temperature minimum (normally around 4 a.m. to 5 a.m., see Figure 1 ). Light exposure in the 6 hours after this minimum advances the body clock to an earlier time, in the 6 hours before the minimum, delays it, and at other times exerts no effect upon the clock [ 11 ]. Outdoor light is more effective at adjusting the body clock than is the dimmer light found indoors (that is, it tends to produce larger phase shifts), even though domestic lighting is normally adequate to act as a zeitgeber. The relationship between the time when a zeitgeber is presented and the shift of the body clock that is produced is called a phase-response curve.

Melatonin ingestion also adjusts the body clock; in the afternoon and early evening, melatonin ingestion advances the clock and, in the second half of sleep and during the early morning, delays it. Bright light inhibits endogenous melatonin secretion, and the clock-shifting effects of these two zeitgebers reinforce each other; bright light in the hours immediately after the temperature minimum advances the body clock directly via the phase-response curve (see above) and also indirectly (by suppressing melatonin secretion and so preventing the phase-delaying effect that melatonin would have exerted at this time) [ 12 ].

Sleep rhythms

Laboratory-based results.

A laboratory environment is advantageous in that conditions conducive to sleep (quiet, comfort, and so on) can be standardized. The standardization of such factors is equivalent to standardizing the exogenous components of the rhythms of sleep. Studies in sleep laboratories have systematically investigated the separate effects upon sleep and sleep architecture (the distribution of sleep stages) of time of day and time awake, which together constitute the endogenous component of the sleep rhythms. Several protocols have been used, including the constant routine, multiple sleep latency tests and modifying sleep-wake protocols (by changing the time of day while maintaining time awake constant or changing the amount of time awake at the same times of day). These protocols have been discussed in detail in Reilly and Waterhouse [ 13 ]. Some of the main findings from these investigations are detailed below.

Firstly, falling asleep is easiest if core temperature is falling or low (evening and night) and most difficult if it is high or rising (morning and afternoon). Secondly, waking up spontaneously tends to be the opposite of falling asleep; it is easiest if core temperature is rising or high, and most difficult when it is falling or low. These last results explain why, if individuals have gone to bed earlier or later than normal, even though they will feel less or more fatigued, respectively, than normal, they are likely to have a longer or shorter sleeps, respectively, than normal. Thirdly, the above two results stress that the ability to initiate and sustain sleep shows rhythmic changes during the course of the 24-hour day, and that these are associated with the rhythm of core temperature. However, superimposed upon these effects is that (if time-of-day effects are taken into account) sleep propensity - measured subjectively (fatigue, alertness) or objectively (polysomnography) - increases in proportion to time awake. This finding means that the endogenous component of sleep rhythms, like those of alertness, fatigue and cognition, is a mixture of two main elements, time of day and time awake. Finally, combining these findings indicates that an unbroken sleep is most likely to occur if it starts in the late evening and ends sometime after 7 a.m., the individual having slept through the trough of core temperature (see Figure 1 ). Sleep onset will occur as core temperature falls, melatonin secretion begins, fatigue increases and alertness falls, the individual having been awake for about 16 hours; sleep will end when core temperature rises and melatonin secretion falls.

Possible cause of rhythms of the sleep-wake cycle

The association between the sleep-wake cycle (sleep propensity, sleep maintenance and waking up) and the rhythm of core temperature has been interpreted as a causal link, incorporating temperature-induced changes in brain metabolism (see, for example, [ 14 ]). However, this might be an oversimplification of the position since there are many other rhythms associated with the sleep-wake rhythm [ 3 , 15 ]. These rhythms include: reciprocal activity of the sympathetic and parasympathetic branches of the autonomic nervous system (sympathetic activity paralleling core temperature and parasympathetic activity showing a profile that is the mirror image); levels of plasma adrenaline (parallel to core temperature); and plasma melatonin, concentrations of which are low in daytime light, start to rise around 9 p.m., peak during sleep in the dark, and fall on awakening in the morning. Melatonin is known to cause cutaneous vasodilatation (and so promote a fall of core temperature) as well as to increase fatigue. It is likely that many factors contribute to the physiological preparations that need to be accomplished to feel ready for sleep in the evening, to being able to maintain unbroken sleep at night, to making preparations for waking up in the morning, and to being physically and mentally active in the daytime. Core temperature is only one of these factors and should be seen as only one of the factors that reflect the activity of the body clock and rhythmicity of the body as a whole.

The value of the rhythms of core temperature and melatonin secretion as convenient markers of the timing of the body clock has already been mentioned. Alertness and fatigue, important indicators of sleep-mediated recuperation and the need for sleep, respectively, are also affected by the body clock. Once again, however, many other factors also contribute to these subjective feelings (for example, boredom and excitement with tasks in hand and, particularly, time awake). The effects of boredom and interest mean that alertness and fatigue can be misleading indicators as to the physiological need for sleep (the build-up of sleep pressure) at any time. Even so, the principles involved in modeling the rhythm of sleep times (see below) have also been used to model rhythms of alertness, fatigue and mental activity (for details of which, see [ 16 – 20 ]).

Sleep stages

Sleep is not homogeneous, and this has been investigated by recording surface electrical activity on the scalp using an electroencephalogram (EEG). In normal sleep, there is an ultradian rhythm of cycling between slow wave sleep (SWS) and rapid-eye-movement (REM) sleep stages, the REM-nonREM cycle. This cycle lasts approximately 90 minutes, about five cycles occurring during the course of a normal night's sleep. The composition of successive cycles varies, with the amount of SWS decreasing and the amount of REM sleep increasing.

Modeling sleep rhythms and the distribution of sleep stages

The basic model.

One simple, and yet very effective, model of sleep rhythms is the two-process model of sleep homeostasis of Borbély [ 21 ] (Figure 2 ). In this model, it is postulated that one of the processes, sleep pressure (S), increases during waking as an exponential saturating function, and then decreases exponentially during sleep (now being termed S'). The other process, C, is rhythmic with a period of about 24 hours and consists of two parallel components, an upper and lower component, both approximately in phase with core temperature.

figure 2

Times of sleep and wake (top, separated by the double vertical lines) . The C component is represented by two curves (Upper C and Lower C). Sleep pressure increases exponentially in the wake phase (Process S, dotted line) and decreases at a faster exponential rate in the sleep phase (S', dashed line). For more details, see text. (Based on [ 21 ]).

Sleep onset occurs when the rising value of S intercepts the upper C function, normally during the phase of rapid decline and some hours before the minimum of the upper C function. During sleep, the level of S' falls exponentially (but with a shorter half-time constant than exists for S) until it meets the lower C function, at which point waking occurs and the cycle starts again. Waking normally occurs after about 7 hours of sleep, a few hours after the minimum of the lower C function when it is rising quickly. Factors S and C are assumed to act additively (but see [ 22 , 23 ]).

A three-component model of sleep regulation, based upon the two-component model but incorporating 'sleep inertia', has been developed. Sleep inertia is the phenomenon whereby, immediately after waking from sleep, the beneficial effects of sleep upon cognitive performance and mood are not immediately apparent [ 24 – 26 ].

Interpreting physiological and biochemical correlates of the sleep model

Many investigations have been performed to define the physiological and biochemical correlates of processes S and S' and the rhythmic function C, and others are in progress. As with investigations of sleep times, studies of processes S and S' generally consist of measuring aspects of waking or sleep (subjective estimates of fatigue and alertness, or objective measures of EEG during waking and sleep, for example) after the sleep-wake schedules of volunteers have been altered in some way.

The general view (summarized in [ 22 , 27 ]) is that the amount of sleep pressure accumulated during the wake time (process S) is reflected in the amount of SWS that occurs during the following sleep. Such 'deep sleep' has been regarded for some time as the type of sleep that best reflects its recuperative role. The actual dissipation of sleep pressure (process S') is associated with the amount of slow wave activity (SWA), assessed from the power in the low-frequency band of the sleep EEG. Increasing sleep pressure during the daytime when the subject is awake can be measured by assessing sleep propensity (the opposite of sleep latency) and the power in the theta-alpha band of the waking EEG. Sleep pressure is also related to increasing subjective fatigue and decreasing alertness. SWS and SWA are little affected by core temperature; by contrast, the amount of REM sleep is inversely proportional to core temperature and little affected by prior wake time. It is the difference between the factors associated with SWS and REM sleep that leads, during a normal nocturnal sleep, to the first REM-nonREM cycles being richer in SWS (beginning the process of recuperation) and the later ones, closer to the temperature trough, richer in REM sleep.

However, the detailed nature of the links involved remains unresolved. Comments already made about other variables being temporally associated with core temperature (and, therefore, the C component), and all of them possibly being reflections of some more fundamental process, apply here also. The fundamental reasons why increased wake time leads to an increased need for sleep and change in sleep architecture remain to be elucidated. One approach to this problem has been to investigate substances that accumulate in the brain during the waking period and the neurophysiological changes that take place during this time [ 28 ]. However, detailed explanations of the associations between sleep stages and time awake and core temperature are still awaited.

Using the two-component model to explain altered sleep patterns in adults

Possible causes of differences in sleep patterns.

With regard to adjustment of the body clock by zeitgebers, this process need not be precise on any particular day. Daily differences in detailed timing of biological rhythms will arise due to slight variations in the body clock and timing of the zeitgebers on a particular day. Moreover, different individuals will show biological variation with regard to the exact intrinsic period of their body clock (tau), their exposure to zeitgebers and details of their phase-response curves to different zeitgebers. That is, there will be both intra- and inter-individual variation in the timing of the circadian clock and the rhythms it drives.

Such variation will also apply when considering either the sleep homeostat (S and S') or rhythmic (C) components of the two-component model of sleep (Figure 2 ), all of which is predicted to lead to changes in the detailed timing of the sleep-wake cycle. Thus if the timing of the upper or lower components is changed, this will alter times of falling asleep or waking (times when the S and S' curves intercept the C curves); curves phased earlier will tend to cause earlier times of sleep onset and offset, and vice versa. If the amplitude of the C rhythms is decreased, the angle between the S and C curves will be less, and this will result in any changes in the curves causing increased variations in the exact timing of the points of intersection; this will in turn cause an increased variability in sleep onset and offset times. If process S increases less with time awake (its half-time constant is increased), then sleep onset will be delayed. Finally, if process S' is less marked (its half-time constant is increased), then sleep can be considered to be less recuperative (SWS and SWA will decline) and will tend to last longer.

These predicted changes can then form the basis of attempts to explain some abnormalities of sleep behavior, even though the understanding is incomplete in the absence of knowledge as to the exact correlates of the S, S' and C components (see above).

Adults who are poor sleepers and short-sleepers

Because, like any biological variable, the S, S' and C components will show inter- and intra-individual variation, there is the implication that the sleep-wake cycle will differ between individuals and between days in a single individual. It is common for individuals to delay their sleep-wake cycle over the weekend (generally due to increased social activities) and to find difficulty in readjusting it to weekday requirements on the Monday. There is also evidence that those with less regular lifestyles have more sleep problems [ 29 ]. These are external factors, and so contribute to the exogenous component of sleep rhythms, and readily modified by a change in lifestyle. The issue is if differences in the internal factors (S, S' and C) are present in some cases of altered sleep.

There are differences in the amount of sleep habitually taken by individuals because individuals choose to do this (external factors), seem to need less sleep ('short sleepers'), or get less sleep than they want and so feel tired in the daytime ('poor sleepers'). Studies using bed-rest and sleep deprivation have been used to investigate if poor sleepers show systematic differences from 'normal' sleepers [ 30 ], but they have not reliably shown that the sleep homeostat or rhythm of core temperature is different. For example, the temperature rhythm and responses to the multiple sleep latency tests following these changed schedules were normal in poor sleepers. Also, when short sleepers (< 6 hours per night) and long sleepers (> 9 hours per night) were compared, both responded in the same way with regard to the effect of prior wake time upon the amount of SWS and the kinetics of SWS during recovery sleep [ 31 ]. Therefore, it has been suggested that short sleepers endure higher sleep pressure, and that poor sleepers are not particularly susceptible to sleep pressure.

Even though these differences from normal sleep generally produce no more than some degree of inconvenience, they can be more troublesome if daytime fatigue is marked and cognitive function impaired. In these cases, treatment is sometimes contemplated, which generally consists of attempts to strengthen the individual's exposure to zeitgebers (particularly bright light) or regular ingestion of melatonin a few hours before sleep is desired [ 32 – 34 ]. In this regard, treatment is very similar to that for aged subjects (see below).

The effect of chronotype

The measurement of a person's chronotype score is now commonplace, the original questionnaire having been translated into several languages (and then validated upon local populations of subjects) and also adapted for cultures that, for example, routinely rise early. A population's chronotype scores are distributed normally, with those who tend towards the 'morningness' part of the distribution choosing to perform important activities in the morning, and those tending towards 'eveningness' choosing to do them in the evening. The tails of the distribution - the extreme morning types (larks) and extreme evening types (owls) - can find normal lifestyles difficult to participate in fully and effectively. This difference might be due to habits (and so influence the times of exposure to zeitgebers) and/or the body clock (tau values differing between individuals) and/or to the phase response curves (which might differ in the size of advances and delays in the body clock that zeitgebers produce), but few data on this issue are available. What can be stated is that individuals are likely to have problems if they adopt an early lifestyle (retiring and rising earlier than average) but have a circadian system that tends to run later than average (for whatever reason), or vice versa. Such disparities can be important for those working shift systems.

The observation [ 35 ] that temperature rhythms in morning- and evening-types during constant routines are phased about 1 hour earlier or later than average, respectively, implies that some endogenous component (differences in adjustment of the body clock? differences in tau?) is involved, but it might also be caused partly by the phase differences (due to different lifestyles) that were present in the days before the constant routine.

The S and C components of the two-process sleep model have been compared in morning and evening types, using the core temperature rhythm (a reflection of the C component) and daytime measures of subjective sleepiness and alpha-theta activity (reflections of the S component). The core temperature was phased earlier, and the build-up of sleepiness and alpha-theta activity was more rapid, in the morning types; that is, both components of the two-component model were different [ 36 ]. In other studies, morning-types had more SWA at the start of a recovery sleep following a night of sleep disruption, and also showed a more rapid decay of SWA [ 37 , 38 ]. These results indicate that both processes S and S' might be more rapid in morning types, in addition to an earlier phasing of the body clock (see above), and all factors might contribute to earlier times of retiring and rising.

There is evidence that some of the differences between chronotypes and in processes S, S' and C have a genetic basis. Evidence from a large study upon adults in which sleepiness, chronotype, quality of life and sleep times were compared [ 39 ] indicated that associations existed that could not be explained by common lifestyles (those sharing the same household, for example); this implies a genetic component. One of the clock genes, PERIOD3, shows polymorphism and individuals homozygous for one variant, the five-repeat allele, PER3(5/5), are not only more likely to be a morning type but also more susceptible to the effects of sleep loss. By contrast, individuals homozygous for another variant, PER3(4/4), are more likely to be an evening type. Subjects homozygous for PER3(5/5) also showed more waking alpha-theta activity, more SWA during sleep and a greater decline in cognitive function when sleep-deprived [ 40 ]. A previous study [ 41 ] had also shown that individuals could be divided into 'resistant' and 'non-resistant' on the basis of deterioration of their brain function following sleep loss; since this division was independent of any difference in phasing of core temperature, it seems to relate to the sleep homeostat rather than process C.

Two comparatively rare syndromes have been described: delayed sleep phase syndrome and advanced sleep phase syndrome. In the former, the internal relationship between daily rhythms and the sleep-wake cycle is normal, except that the whole system is delayed with regard to external time; for example, the core temperature rhythm shows a minimum around 8 a.m., and melatonin secretion begins around midnight (both rhythms being delayed about 4 hours compared with normal). Subjects tend to wish to retire around 4 a.m. and rise about noon, and this lifestyle can be very inconvenient [ 42 ]. The opposite changes in timing of daily rhythms and chosen sleep-wake cycle apply to advanced sleep phase syndrome. Behaviorally, the individuals can be considered as pathologically extreme examples of evening or morning types. Whether the syndromes result from extreme values of tau, abnormal phase-response curves, and/or abnormalities with regard to the sleep homeostat is unknown.

In summary, evidence is beginning to be obtained that indicates individuals' chronotypes partly reflect some aspects of the timing of their body clock and sleep homeostat, and that a genetic component contributes to these differences.

Differences in sleep patterns with age

Neonates and infants up to adolescence.

Immediately after birth, neither full-term nor premature babies show clear circadian rhythms of the sleep-wake cycle or any other variable [ 43 – 45 ]. Instead, bouts of sleep and waking alternate several times during the course of a 24-hour period. Therefore, the concept of sleep pressure, accumulating with substantial periods of waking, cannot be applied in the same way. Further, EEG results indicate that the nature of sleep is different, there being two types of sleep, 'quiet' and 'active' [ 46 – 48 ]. That is, the basic two-component model of sleep cannot be applied to individuals in newborn babies.

Even if the sleep-wake rhythm and other circadian rhythms developed progressively during the first years of life, to become firmly established when the child is five years of age [ 49 – 52 ], there are no studies in which components of the two-component model have been investigated in children of this age. The reasons for this are easy to understand - requiring infants to take part in such studies would be unethical.

Adolescents

In practice, therefore, the earliest age at which substantial amounts of data have been collected is when the child reaches adolescence, though here also experimental sleep-deprivation experiments have not been performed and no constant routine data are available. The data that are available for this age group (10 to 17 years) are dominated by the observation that they go to bed considerably later in the evening (particularly if they have access to television or live in latitudes where summer evenings can be very long) and so tend to be sleep-deprived on school days [ 53 – 64 ]. At weekends, they catch up on lost sleep by extended time spent in bed (lie-ins). At the weekends also, the melatonin rhythm is phase delayed compared with during the week. Adolescents tend towards evening types, therefore, though this change is more marked in some than others and occurs at a slightly earlier age in girls, possibly due to their earlier onset of puberty [ 65 ]. The sleep restriction that is experienced during weekdays causes concern with regard to school performance, particularly when school starts early in the morning and the degree of eveningness of an individual is marked [ 66 – 71 ].

Some of these points are illustrated by some of our unpublished data, which show changes in chronotype and sleep quality in primary and secondary school students in Japan (7 to 18 years old; 404 boys, 411 girls). The Japanese versions of Horne and Ostberg's Morningness-Eveningness Questionnaire (MEQ) and the Pittsburgh Sleep Quality Index (PSQI) were used. Figure 3 shows the MEQ scores. Although most of the students were neither morning type nor evening type, that is, they were 'intermediate type', the scores decreased significantly with age, indicating a change towards becoming evening type (boys: r = -0.99, P < 0.001; girls: r = -0.83, P = 0.041, Pearson's correlation coefficients). Female students older than 10 years tended to be more evening types than their male counterparts, but this difference decreased with age and was reversed in students aged18 years old. There was a clear linear relationship between the MEQ scores and age of male students, while this relationship was less clear in the female students and appeared to be affected other factors, especially around 10 years of age. This change in female MEQ scores at around 10 years might relate to menarche.

figure 3

Morningness-Eveningness Questionnaire (chronotype) scores in male and female school children of different ages . Filled bars, boys; open bars, girls. Scores indicate: range 16 to 30, definitely evening-type; 31 to41, moderately evening-type; 42 to 58, neither or intermediate-type; 59 to 69, moderately morning-type; and 70 to 86, definitely morning-type.

Figure 4 shows that their PSQI scores tended to increase significantly with age, indicating a deterioration in sleep quality (boys: r = 0.96, P < 0.01; girls: r = 0.95, P < 0.01, Pearson's correlation coefficients), girls tending to be worse than boys. Given that all students had to rise at the same time in the morning to attend school, the poorer sleep observed in the older children was consistent with the tendency for the chronotype in older children to become progressively more orientated towards that of an evening type.

figure 4

Pittsburgh Sleep Quality Index scores in male and female school children of different ages . Filled bars, boys; open bars, girls. Scores > 5 indicate some problems with sleep.

Attempts have been made to incorporate the findings regarding lifestyle and chronotype in adolescents into the two-component model of sleep regulation [ 72 – 74 ]. It is believed that both components change. The evidence for changes in the C component is the later phasing of core temperature and melatonin rhythms; the evidence for a change in the sleep homeostat (S and S' components) is an increase in sleep latency and a decline in delta power density. From a circadian viewpoint, the delayed rhythms imply that early school hours will be too soon after the trough of the core temperature rhythm and time of high melatonin secretion (resulting in poorer cognitive performance), and the fall in core temperature and onset of melatonin secretion later in the evening will delay sleep onset. The observed changes in the sleep homeostat appear to enable these later bedtimes to occur.

Why such changes should exist during and just after puberty is unclear; attributing them to the marked maturational changes, in the brain as well as the pituitary-gonadal axis, is an obvious possibility, but details of the mechanisms involved are unknown [ 75 , 76 ]. It is also possible that the individual's increasing independence at this age means that personal interests might delay bedtime, and this will lead to a delay in the circadian system. If this exogenous factor were important, then curtailing evening activities and retiring earlier would provide a suitable remedy; but if the driving force behind the change were neurologically or hormonally-based (and internally-based), then advising earlier retiring times would be ineffective. Whatever the cause, recommendations that school hours and examination times should be delayed are common [ 66 , 68 , 70 , 71 ].

Older persons

There has been a large amount of research upon the sleep-wake cycle and associated rhythms in aged individuals, generally 55 years or older (for recent reviews, see [ 77 – 79 ]. Volunteers for such investigations have come from two main sources; those who come to the laboratory and those who live in homes for the elderly. The type of person studied in these two cases might not be the same. Subjects volunteering for laboratory-based studies are likely to be healthy, active and independent; those studied in homes are more likely to be less active and independent, and also more likely to suffer from some of the problems associated with old age - forms of dementia, for example. The concepts of 'survivor' and 'frailty', respectively, have sometimes been used to describe these two types of volunteer [ 80 ], and differences between the results given by them might be expected.

Nevertheless, there are the following general findings [ 58 , 81 – 88 ]: daytime is associated with a greater number of naps and night with greater sleep latency and more fractionated sleep (sleep efficiency declines); the increased frequency of waking is associated with a need to empty the bladder; and times of retiring and rising tend to become slightly earlier, individuals become more of a morning type. These changes to sleep and the sleep-wake cycle have been observed in longitudinal [ 89 , 90 ] as well as transverse studies, and made use of measurements that have come from self-report diaries, answering questionnaires, reports of care-givers or objective measures of activity (by actimetry) or sleep (EEG and polysomnography).

Other factors that might be involved with changed sleep-wake cycles are also found in aged individuals. Poorer thermoregulation [ 91 ] and cognitive function [ 92 ] are present but, as in younger adults, a nap improves cognitive performance [ 93 ]. Poorer sleep at night and increased daytime naps will decrease the normal degree of dichotomy between daytime activity and nocturnal rest. Associated with this decrease is likely to be decreased regularity of lifestyle and smaller exogenous components of circadian rhythms. Against this scenario, however, are the results from studies that have shown an increase in lifestyle regularity [ 94 – 96 ]. Also, following a simulated time-zone transition, aged persons, like younger adults, suffer from sleep loss and deterioration in cognitive performance - but older subjects seem better able to pace themselves and maintain cognitive performance at times of sleep loss and low core temperature [ 97 , 98 ]. There is also other evidence that alertness in the aged seems to be less dependent upon core temperature than is the case in younger subjects [ 99 ]. In other words, some of the behavioral changes observed in aged subjects accord with the view that individuals try to adopt lifestyles that oppose the difficulties that arise with aging.

Circadian rhythms alter with aging; those of core temperature and melatonin secretion having been studied most. The amplitudes of these rhythms, when measured in aged individuals living their normal lifestyles, normally show lower values when compared with younger controls and phase advances in the order of 1 hour [ 85 , 100 ]. There is also evidence that aged individuals who suffer from poor sleep at night have a raised core temperature at this time [ 101 ], but this is in contrast to the results from an earlier study that had found that altered sleep could not be accounted for by changed rhythms of core temperature [ 102 ]. Such deterioration in circadian rhythms is not observed equally in all aging subjects [ 103 , 104 ] but tends to be more marked when other problems (such as pain, failing cognitive powers or senile dementia) are also present, and less marked if a partner or companion is present.

Determining the cause of the observed changes is difficult because the links that exist between circadian rhythms and the sleep-wake cycle are complex. For example, the need for recuperation during nocturnal sleep might be lessened if, during the daytime, an individual is less physically and mentally active and takes naps. Also, even though decreased activity of the suprachiasmatic nucleus (producing a rhythmic output that is lower in amplitude) could account for many of the observed changes, so too could, for example, decreased exposure to or perception of zeitgebers, daytime activity and exposure to the natural light-dark cycle being compromised by decreased mobility (not being able to venture outdoors) and/or poorer eyesight.

Poorer vision might be particularly important, causing individuals to lack confidence and further restrict their daytime activities, both indoors (reading, for example) and outdoors (walking). The cornea and lens become more opaque with age and light transmission, particularly of the shorter (bluer) wavelengths, decreases [ 105 ]. This acquired tritanomaly can be assessed objectively by a desaturated 15-hue test, in which patients are required to distinguish between very similar shades of blue. This partial loss of perception of blue colors can also be seen in some paintings in which, as the artist gets older, there is more interest in reds and yellows than greens or blues. These changes in the perception of blue hues can be marked in those who suffer from cataracts.

Light of shorter wavelengths is also important in the control of body temperature, melatonin secretion and the sleep-wake rhythm. Melatonin secretion in the late evening and night promotes sleep, partly due to the fall in core temperature that it produces. This secretion is suppressed by light, particularly light at the blue end of the visible spectrum [ 106 – 108 ]. Accordingly, it seems possible that changing transmission of blue light through the lens in aged subjects, particularly those suffering from cataracts, will change the pattern of melatonin secretion and times of sleep. For example, the declining transmission of blue wavelengths in the evening might lead to an earlier rise of melatonin secretion in the evening, contributing to earlier sleep onset in aged individuals. In addition, it has been observed [ 109 ] that daytime exposure to bright light in young adults raises the secretion of melatonin at night, causes a greater fall of core temperature at this time, and improves sleep; such effects might be less marked if the lens of the eye has reduced ability to transmit light at some, or all, of the visible wavelengths.

Therefore, cataract surgery will not only improve individuals' sight but it might also alter the quality of their sleep. Asplund and Lindblad [ 110 , 111 ] found that a considerable proportion of the patients who had undergone cataract surgery reported subjective improvements in their sleep after surgery. This has recently been re-examined [ 112 ]. Fifteen patients were studied before and one month after cataract surgery, in which UV light-cutting intra-ocular lenses had been implanted. Vision and color perception both improved. After recovery from the surgery, they also demonstrated a change in nocturnal sleep, though this improvement varied between patients. After surgery, patients showed a negative correlation between later wake-up or retiring times and sleep efficiency; that is, sleep efficiency of patients with earlier wake-up and retiring times was higher than in those with later wake-up and retiring times. It seems that, after surgery, the patients' retinas received more light of shorter wavelengths and this affected sleep efficiency.

Whilst the detailed implications of these results with regard to the link between light perception and sleep are still unclear, they do confirm that light, melatonin secretion and sleep are linked. It is also noteworthy that, in aged individuals who do not suffer visual problems, the phase shifts produced by exposure to bright light are the same as in younger controls [ 113 ]. Also, a 3-hour bout of exercise at the start of normal sleep time delayed the melatonin rhythm equally in young and old adults [ 114 ]. That is, there is no evidence that the increased morningness found in aged individuals can be attributed to altered adjustment of the body clock by zeitgebers.

Attempts to interpret the causes of altered sleep patterns in the aged in terms of the two-component model of sleep provide some evidence that both the C and S components are altered, but interpretation of such results is not without problems. For example, whilst many circadian rhythms show decreased amplitude, this decline can be due to a fall in the endogenous or the exogenous component of the rhythm, or both. That is, the observed fall in amplitude of the rhythm of core temperature (which implies a change in the C component) might reflect decreased output from the suprachiasmatic nucleus (the endogenous component of the rhythm), decreased secretion of melatonin (which causes core temperature to fall due to cutaneous dilatation), or a decrease in the dichotomy between daytime activity and nocturnal inactivity (the exogenous component). Further, the tendency for aged individuals to become more morning-orientated might be due to an altered body clock and/or timing of individuals' lifestyles (factors that might reflect the C component of the two-component model in particular), or the more rapid build-up of sleep pressure (the S component). A further difficulty of interpretation arises because older individuals might go to bed earlier because their body clock is running faster, their poorer eyesight restricts what they can do (read or watch television), their declining mental faculties mean they get bored more easily, sleep pressure builds up more quickly or they fear a poor sleep at night and so want to attempt it sooner.

It is only the endogenous component of the rhythm of core temperature that is directly associated with component C. Since the endogenous and exogenous components of a circadian rhythm normally act together to produce a measured rhythm, it is necessary to distinguish between them. The constant routine protocol is believed to enable the circadian output from the body clock to be assessed more directly. However, it must be remembered that this protocol assumes that any effects of the sleep-wake cycle upon the amplitude and phase of the core temperature rhythm on normal days (when the exogenous component is present) do not continue into the constant routine but disappear immediately it is undertaken. There is some evidence to support this assumption, at least with regard to amplitude [ 115 ]. However, when aged participants undergo constant routines and their core temperature rhythm or secretion of melatonin in dim light is investigated (two common markers of the body clock), there is evidence for a reduced output from the body clock insofar as the amplitude of these rhythms is reduced; also, the temperature profile has been found to advance [ 116 , 117 ]. These results suggest the presence of a body clock with a declining output and an advanced timing. However, when the free-running period of the body clock was examined in aged individuals who were blind (twice in each individual, the occasions of study being separated by a period of 10 years), where effects of light upon tau are absent, the period lengthened [ 118 ]. Other studies also have failed to find a consistent phase advance of the core temperature rhythm in sighted individuals when using the constant routine protocol. Only an advance of phase of the core temperature rhythm, possibly due to a reduction in tau, would indicate an endogenous cause for the increasing tendency to morningness with aging (rather than an exogenous cause such as preferring to get up earlier and go to bed earlier). Therefore, the general position with regard to the change in the endogenous component of the temperature rhythm in aged individuals is unclear. Again, differences between survivors and the frail might be important.

The changes in sleep with aging might be explained in terms of altered S and S' processes. Investigations of responses to altered sleep-wake schedules (advancing or delaying bedtime, for example) have shown that changes similar to those found in younger adults under the same circumstances are found for many sleep variables, including sleep efficiency and time awake after sleep onset [ 119 ]. The increase in sleep latency with age has been taken to imply that sleep pressure decreases but, again, contradictory results have been obtained. SWS during the first part of a night's sleep is normally decreased in the aged [ 120 , 121 ]. The rate of dissipation of S (S', as assessed from SWA) does not differ from that found in younger adults and, as in them, the amount of SWS increases following sleep-deprivation [ 122 ] and decreases in nocturnal sleeps taken after daytime naps [ 123 ]. On the other hand, another study claimed that the relationship between SWS and prior wakefulness was different between aged participants and younger controls [ 116 ] and that daytime naps led to a fall in sleep efficiency and earlier waking [ 124 ]. Several differences in the power of different frequency bands in the EEG have been found in aged individuals compared with younger controls [ 120 ], though their detailed meaning is unclear.

It has already been mentioned that those who seem to experience fewer difficulties with sleep also seem to have more regular lifestyles. It is stressed that this increased regularity might reflect a relatively stronger output from the body clock (process C) or stronger activity of the sleep homeostat (processes S and S') - or it might be that increasing the exogenous components of circadian rhythms, attempting to be active and so promote accumulation of sleep pressure when awake, and having regular exposure to zeitgebers, can all be considered as ways of combating a declining influence of endogenous components. Whatever the exact cause of the decreased daily variability in some aged individuals, it becomes translated into several ways of improving the quality of life of the aged, at least in those living in homes for the elderly [ 125 ].

The general aim of these procedures is to increase the dichotomy between daytime activities in the light and nocturnal sleep in the dark [ 1 , 126 – 137 ]. Possibilities include increasing daytime physical activity, outdoors in natural light, if possible and increasing daytime mental activities, by providing an interest or getting individuals to discuss topics of interest to them (their childhood, favorite food or films, for example). Increasing artificial lighting levels is also used, not only to promote conditions suitable for taking part in activities but also to inhibit napping. Coupled with these courses of action can be encouragement to individuals to stay in bed at night, even if they cannot sleep, with restricted use of lights (as long as requirements for safety and care are met). In addition, some studies have indicated that the regular use of a mild soporific is effective (melatonin, for example), though the long-term use of any drug requires medical advice [ 135 , 138 , 139 ].

Many of these treatments have been found to be effective; it often being stressed that individuals should be encouraged to see some form of regular daytime activity as a simple means of improving their quality of life. The possible mechanisms which might cause the treatments to be effective have been considered above; they might act on the exogenous or endogenous component of circadian rhythms by promoting accumulation of sleep pressure, and promote sleep pressure directly. Which mechanism(s) are important is not yet known.

A rather different approach to the problem of improving sleep in older individuals is based upon the decline in efficiency of thermoregulation with age [ 91 ]. Part of the problem is that, due to impaired cutaneous thermal sensitivity, aged individuals are less able to perceive the temperature of their surroundings, and so are unable to forestall falls or rises of body temperature by suitable behavior. Additionally, sleep onset is inhibited by cold hands and feet, and this is observed in all age groups. Warming the feet by wearing bed-socks was found to decrease sleep latency in aged individuals [ 140 ].

Conclusions

The two-process model of sleep has enabled the processes that determine sleep times and some aspects of sleep architecture to be described. Equally, the constant routine protocol remains the standard way of distinguishing between endogenous and exogenous components of circadian rhythms. Taken together, these concepts of a sleep homeostat and endogenous and exogenous components of circadian rhythms have enabled the quantity, timing and quality of sleep to be understood better. Such understanding has been applied successfully to understanding the timing of sleep in healthy individuals and in the changed circumstances of adolescence and old age. However, details of the nature of the S, S' and C components of the sleep model, and unraveling the interactions between these components and those of the circadian rhythms, are still required. Obtaining such details will improve our knowledge of differences between healthy individuals at different stages of their life-span and when suffering from some sleep disorders. Such deeper understanding will then give a firmer rationale to advice and treatment, with the hope that they will become more successful.

Ancoli-Israel S, Ayalon L, Salzman C: Sleep in the elderly: normal variations and common sleep disorders. Harv Rev Psychiatry. 2008, 16: 279-286. 10.1080/10673220802432210.

Article   PubMed   Google Scholar  

Minors D, Waterhouse J: Circadian Rhythms and the Human. 1981, Bristol, UK: John Wright

Google Scholar  

Reilly T, Atkinson G, Waterhouse J: Biological Rhythms and Exercise. 1997, Oxford: Oxford University Press

Clayton J, Kyriacou C, Reppert S: Keeping time with the human genome. Nature. 2001, 409: 829-831. 10.1038/35057006.

Article   CAS   PubMed   Google Scholar  

Reppert S, Weaver D: Molecular analysis of mammalian circadian rhythms. Ann Rev Physiol. 2001, 63: 647-678. 10.1146/annurev.physiol.63.1.647.

Article   CAS   Google Scholar  

Jolma I, Laerum O, Lillo C, Ruoff P: Circadian oscillators in eukaryotes. Wiley Interdiscip Rev Syst Biol Med. 2010, 2: 533-549.

Shanahan T, Czeisler C: Physiological effects of light on the human circadian pacemaker. Semin Perinatol. 2000, 24: 299-320. 10.1053/sper.2000.9123.

Mrosovsky N: Critical assessment of methods and concepts in nonphotic phase shifting. Biol Rhythm Res. 1999, 30: 135-148. 10.1076/brhm.30.2.135.1418.

Article   Google Scholar  

Mistlberger R, Skene D: Social influences on mammalian circadian rhythms: animal and human studies. Biol Rhythm. 2004, 79: 533-556.

Edwards B, Reilly T, Waterhouse J: Zeitgeber effects of exercise on human circadian rhythms: what are alternative approaches to investigating the existence of a phase-response curve to exercise?. Biol Rhythm Res. 2009, 40: 53-69. 10.1080/09291010802067072.

Khalsa S, Jewett M, Cajochen C: A phase response curve to single bright light pulses in human subjects. J Physiol. 2003, 549: 945-952. 10.1113/jphysiol.2003.040477.

Article   PubMed Central   CAS   PubMed   Google Scholar  

Lewy A, Bauer V, Ahmed S: The human phase response curve (PRC) to melatonin is about 12 hours out of phase with the PRC to light. Chronobiol Int. 1998, 15: 71-83. 10.3109/07420529808998671.

Reilly T, Waterhouse J: Sports performance: is there evidence that the body clock plays a role?. Eur J Appl Physiol. 2009, 106: 321-332. 10.1007/s00421-009-1066-x.

Kleitman N: Sleep and Wakefulness. 1963, Chicago: University of Chicago Press

Van Someren E: More than a marker: Interaction between the circadian regulation of temperature and sleep, age-related changes, and treatment possibilities. Chronobiol Int. 2000, 17: 313-354. 10.1081/CBI-100101050.

Åkerstedt T, Folkard S: The three-process model of alertness and its extension to performance, sleep latency, and sleep length. Chronobiol Int. 1997, 14: 115-123. 10.3109/07420529709001149.

Åkerstedt T: Altered sleep/wake patterns and mental performance. Physiol Behav. 2007, 90: 209-218. 10.1016/j.physbeh.2006.09.007.

Article   PubMed   CAS   Google Scholar  

Beersma D, Gordij M: Circadian control of the sleep-wake cycle. Physiol Behav. 2007, 90: 190-195. 10.1016/j.physbeh.2006.09.010.

McCauley P, Kalachev L, Smith A, Belenky G, Dinges D, Van Dongen H: A new mathematical model for the homeostatic effects of sleep loss on neurobehavioral performance. J Theor Biol. 2009, 256: 227-239. 10.1016/j.jtbi.2008.09.012.

Article   PubMed Central   PubMed   Google Scholar  

Van Dongen H: Comparison of mathematical model predictions to experimental data of fatigue and performance. Aviat Space Environ Med. 2004, 75 (3 Suppl): A15-A36.

PubMed   Google Scholar  

Borbély A: A two process model of sleep regulation. Hum Neurobiol. 1982, 1: 195-204.

Achermann P: Sleep homeostasis and models of sleep regulation. J Biol Rhythms. 1999, 14: 557-568.

Van Dongen H, Dinges D: Investigating the interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance. J Sleep Res. 2003, 12: 181-187. 10.1046/j.1365-2869.2003.00357.x.

Naitoh P, Kelly T, Babkoff H: Sleep inertia: best time not to wake up?. Chronobiol Int. 1993, 10: 109-118. 10.3109/07420529309059699.

Åkerstedt T, Billiard M, Bonnet M, Ficca G, Garma L, Mariotti M, Salzarulo P, Schulz H: Awakening from sleep. Sleep Med Rev. 2002, 6: 267-286. 10.1053/smrv.2001.0202.

Krauchi K, Knoblauch V, Wirz-Justice A, Cajochen C: Challenging the sleep homeostat does not influence the thermoregulatory system in men: evidence from a nap vs. sleep-deprivation study. Amer J Physiol Regul Integr Comp Physiol. 2006, 290: R1052-R1061.

Puente-Munoz A, Perez-Martinez D, Villalibre-Valderrey I: The role of slow wave sleep in the homeostatic regulation of sleep. Rev Neurol. 2002, 34: 211-215.

CAS   PubMed   Google Scholar  

Inoue S, Honda K, Komodo Y: Sleep as neuronal detoxification and restitution. Behav Brain Res. 1995, 69: 91-96. 10.1016/0166-4328(95)00014-K.

Monk T, Reynolds C, Buysse D, DeGrazia J, Kupfer D: The relationship between lifestyle regularity and subjective sleep quality. Chronobiol Int. 2003, 20: 97-107. 10.1081/CBI-120017812.

Benoit O, Aguirre A: Homeostatic and circadian aspects of sleep regulation in young poor sleepers. Clin Neurophysiol. 1996, 26: 40-50. 10.1016/0987-7053(96)81533-4.

Aeschbach D, Postolache T, Sher L, Matthews J, Jackson M, Wehr T: Evidence from the waking electroencephalogram that short sleepers live under higher homeostatic sleep pressure than long sleepers. Neuroscience. 2001, 102: 493-502. 10.1016/S0306-4522(00)00518-2.

Yang C, Spielman A, D'Ambrosio P, Serizawa S, Nunes J, Birnbaum J: A single dose of melatonin prevents the phase delay associated with a delayed weekend sleep pattern. Sleep. 2001, 24: 272-281.

Zisapel N: Circadian rhythm sleep disorders - pathophysiology and potential approaches to management. CNS Drugs. 2001, 15: 311-328. 10.2165/00023210-200115040-00005.

Arendt J, Van Someren E, Appleton R, Skene D, Akerstedt T: Clinical update: melatonin and sleep disorders. Clin Med. 2008, 8: 381-383.

Kerkhof G, Van Dongen H: Morning-type and evening-type individuals differ in the phase position of their endogenous circadian oscillator. Neurosci Lett. 1996, 218: 153-156. 10.1016/S0304-3940(96)13140-2.

Taillard J, Philip P, Coste O, Sagaspe P, Bioulac B: The circadian and homeostatic modulation of sleep pressure during wakefulness differs between morning and evening chronotypes. J Sleep Res. 2003, 12: 275-282. 10.1046/j.0962-1105.2003.00369.x.

Mongrain V, Carrier J, Dumont M: Circadian and homeostatic sleep regulation in morningness-eveningness. J Sleep Res. 2006, 15: 162-168.

Mongrain V, Dumont M: Increased homeostatic response to behavioral sleep fragmentation in morning types compared to evening types. Sleep. 2007, 30: 773-780.

PubMed Central   PubMed   Google Scholar  

Evans D, Snitker S, Wu S, Mody A, Njajou O, Perlis M, Gehrman P, Shuldiner A, Hsueh W: Habitual sleep/wake patterns in the old order Amish: heritability and association with non-genetic factors. Sleep. 2011, 34: 661-669.

Dijk D-J, Archer S: PERIOD3, circadian phenotypes, and sleep homeostasis. Sleep Med Rev. 2010, 14: 151-160. 10.1016/j.smrv.2009.07.002.

Vandewalle G, Archer S, Wuillaume C, Balteau E, Degueldre C, Luxen A, Maquet P, Dijk D-J: Functional Magnetic Resonance Imaging-assessed brain responses during an executive task depend on interaction of sleep homeostasis, circadian phase, and PER3 genotype. J Neurosci. 2009, 29: 7948-7956. 10.1523/JNEUROSCI.0229-09.2009.

Weitzman E, Czeisler C, Coleman R, Spielman A, Zimmerman J, Dement W, Richardson G: Delayed Sleep Phase Syndrome - a chronobiological disorder with sleep-onset insomnia. Arch Gen Psychiatry. 1981, 38: 737-746. 10.1001/archpsyc.1981.01780320017001.

Shimada M, Takahashi K, Segawa M, Higurashi M, Samejim M, Horiuchi K: Emerging and entraining patterns of the sleep-wake rhythm in preterm and term infants. Brain Develop. 1999, 21: 468-473. 10.1016/S0387-7604(99)00054-6.

Mirmiran M, Maas Y, Ariagno R: Development of fetal and neonatal sleep and circadian rhythms. Sleep Med Rev. 2003, 7: 321-334. 10.1053/smrv.2002.0243.

Goessel-Symank R, Grimmer I, Korte J, Siegmund R: Actigraphic monitoring of the activity-rest behavior of preterm and full-term infants at 20 months of age. Chronobiol Int. 2004, 21: 661-671. 10.1081/CBI-120039208.

Peirano P, Algarin C, Uauy R: Sleep-wake states and their regulatory mechanisms throughout early human development. J Pediatr. 2003, 143: S70-S79. 10.1067/S0022-3476(03)00404-9.

Biagioni E, Boldrini A, Giganti F, Guzzetta A, Salzarulo P, Cioni G: Distribution of sleep and wakefulness EEG patterns in 24-h recordings of preterm and full-term newborns. Early Hum Develop. 2005, 81: 333-339. 10.1016/j.earlhumdev.2004.09.001.

McLaughlin Crabtree V, Williams NA: A normal sleep in children and adolescents. Child Adolesc Psychiatr Clin N Am. 2009, 18: 799-809. 10.1016/j.chc.2009.04.013.

Rivkees S: A developing circadian rhythmicity in infants. Pediatrics. 2003, 112: 373-381. 10.1542/peds.112.2.373.

Mirmiran M, Baldwin R, Ariagno R: Circadian and sleep development in preterm infants occurs independently from the influences of environmental lighting. Pediatr Res. 2003, 53: 933-938. 10.1203/01.PDR.0000061541.94620.12.

Pringuey D, Tible O, Cherikh F: [Ontogenesis of circadian rhythms in humans]. Encephale. 2009, 35: S46-S52.

Zornoza-Moreno M, Fuentes-Hernandez S, Sanchez-Solis M, Rol M, Larque E, Madrid J: Assessment of circadian rhythms of both skin temperature and motor activity in infants during the first 6 months of life. Chronobiol Int. 2011, 28: 330-337. 10.3109/07420528.2011.565895.

Mantz J, Muzet A, Winter A: The characteristics of sleep-wake rhythm in adolescents aged 15-20 years: a survey made at school during ten consecutive days. Arch Pediatr. 2000, 7: 256-262. 10.1016/S0929-693X(00)88741-2.

Takeuchi H, Inoue M, Watanabe N, Yamashita Y, Hamada M, Kadota G, Harada T: Parental enforcement of bedtime during childhood modulates preference of Japanese junior high school students for eveningness chronotype. Chronobiol Int. 2001, 18: 823-829. 10.1081/CBI-100107517.

Aronen E, Fjallberg M, Paavonen E, Soininen M: Day length associates with activity level in children living at 60 degrees north. Child Psychiatry Hum Dev. 2002, 32: 217-226. 10.1023/A:1017956706208.

Vinha D, Cavalcante J, Andrade M: Sleep-wake patterns of student workers and non-workers. Biol Rhythm Res. 2002, 33: 417-426. 10.1076/brhm.33.4.417.8803.

Louzada F, Menna-Barreto LL: Sleep-wake cycle in rural populations. Biol Rhythm Res. 2004, 35: 153-157. 10.1080/09291010412331313304.

Ohayon M, Carskadon M, Guilleminault C, Vitiello M: Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep. 2004, 27: 1255-1273.

Chung K, Cheung M: Sleep-wake patterns and sleep disturbance among Hong Kong Chinese adolescents. Sleep. 2008, 31: 185-194.

Hagenauer M, Perryman J, Lee T, Carskadon M: Adolescent changes in the homeostatic and circadian regulation of sleep. Develop Neurosci. 2009, 31: 276-284. 10.1159/000216538.

Peixoto C, da Silva A, Carskadon M, Louzada F: Adolescents living in homes without electric lighting have earlier sleep times. Behav Sleep Med. 2009, 7: 73-80. 10.1080/15402000902762311.

Randler C, Frech D: Young people's time-of-day preferences affect their school performance. J Youth Stud. 2009, 12: 653-667. 10.1080/13676260902902697.

Crowley S, Carskadon M: Modifications to weekend recovery sleep delay circadian phase in older adolescents. Chronobiol Int. 2010, 27: 1469-1492. 10.3109/07420528.2010.503293.

Figueiro M, Rea M: Evening daylight may cause adolescents to sleep less in spring than in winter. Chronobiol Int. 2010, 27: 1242-1258. 10.3109/07420528.2010.487965.

Figueiro M, Rea M: Lack of short-wavelength light during the school day delays dim light melatonin onset (DLMO) in middle school students. Neuroendocrinol Lett. 2010, 31: 92-96.

Fischer F, Radosevic-Vidacek B, Koscec A, Teixeira L, Moreno C, Lowden A: Internal and external time conflicts in adolescents: sleep characteristics and interventions. Mind Brain Educ. 2008, 2: 17-23. 10.1111/j.1751-228X.2008.00024.x.

Fischer F, Nagai R, Teixeira L: Explaining sleep duration in adolescents: The impact of socio-demographic and lifestyle factors and working status. Chronobiol Int. 2008, 25: 359-372. 10.1080/07420520802110639.

Koscec A, Radosevic-Vidacek B, Bakotic M: Regulation of wakefulness and sleep in adolescence: biological and social aspects. Suvremena Psiholgija. 2008, 11: 223-239.

Natal C, Lourenco T, Silva L, Boscolo R, Silva A, Tufik S, de Mello M: Gender differences in the sleep habits of 11-13 year olds. Revista Brasileira Psiquiatria. 2009, 31: 358-361.

Besoluk S, Onder I, Deveci I: Morningness-eveningness preferences and academic achievement of University students. Chronobiol Int. 2011, 28: 118-125. 10.3109/07420528.2010.540729.

Kirby M, Maggi S, D'Angiulli A: School start times and the sleep-wake cycle of adolescents: a review and critical evaluation of available evidence. Educ Res. 2011, 40: 56-61. 10.3102/0013189X11402323.

Carskadon M, Acebo C, Jenni O: Regulation of adolescent sleep: implications for behavior. Ann N Y Acad Sci. 2004, 1021: 276-291. 10.1196/annals.1308.032.

Campbell I, Higgins L, Trinidad J, Richardson P, Feinberg I: The increase in longitudinally measured sleepiness across adolescence is related to the maturational decline in low-frequency EEG power. Sleep. 2007, 30: 1677-1687.

Crowley S, Acebo C, Carskadon M: Sleep, circadian rhythms, and delayed phase in adolescence. Sleep Med. 2007, 8: 602-612. 10.1016/j.sleep.2006.12.002.

Laberge L, Petit D, Simard C, Vitaro F, Tremblay R, Montplaisir J: Development of sleep patterns in early adolescence. J Sleep Res. 2001, 10: 59-67. 10.1046/j.1365-2869.2001.00242.x.

Taylor D, Jenni O, Acebo C, Carskadon M: Sleep tendency during extended wakefulness: insights into adolescent sleep regulation and behavior. J Sleep Res. 2005, 14: 239-244. 10.1111/j.1365-2869.2005.00467.x.

Pandi-Perumal S, Seils L, Kayumov L, Ralph M, Lowe A, Moller H, Swaab D: Senescence, sleep, and circadian rhythms. Age Res Rev. 2002, 1: 559-604. 10.1016/S1568-1637(02)00014-4.

Arbus C, Cochen V: Sleep changes with aging. Psychol Neuropsychiat Vieil. 2010, 8: 7-14.

Roepke S, Ancoli-Israel S: Sleep disorders in the elderly. Ind J Med Res. 2010, 131: 302-310.

Cochen V, Arbus C, Soto M, Villars H, Tiberge M, Montemayor T, Hein C, Veccherini M, Onen S, Ghorayeb I, Verny M, Fitten L, Savage J, Dauvilliers Y, Vellas B: Sleep disorders and their impacts on healthy, dependent, and frail older adults. J Nutr Health Aging. 2009, 13: 322-329. 10.1007/s12603-009-0030-0.

De La Calzada M: Modifications in sleep with aging. Rev Neurol. 2000, 30: 577-580.

Fetveit A, Bjorvatn B: Sleep disturbances among nursing home residents. Int J Geriatr Psychiatry. 2002, 17: 604-609. 10.1002/gps.639.

Huang Y, Liu R, Wang Q, Van Someren E, Xu H, Zhou J: Age-associated difference in circadian sleep-wake and rest-activity rhythms. Physiol Behav. 2002, 76: 597-603. 10.1016/S0031-9384(02)00733-3.

Park Y, Matsumoto K, Seo Y, Kang M, Nagashima H: Effects of age and gender on sleep habits and sleep trouble for aged people. Biol Rhythm Res. 2002, 33: 39-51. 10.1076/brhm.33.1.39.1327.

Münch M, Cajochen C, Wirz-Justice A: Sleep and circadian rhythms in ageing. Z Gerontol Geriatr. 2005, 38: 21-23. 10.1007/s00391-005-1106-z.

Hoekert M, Riemersma-van der Lek R, Swaab D, Kaufer D, Van Someren E: Comparison between informant-observed and actigraphic assessments of sleep-wake rhythm disturbances in demented residents of homes for the elderly. Am J Geriatr Psychiatry. 2006, 14: 104-111. 10.1097/01.JGP.0000192481.27931.c5.

Martin J, Webber A, Alam T, Harker J, Josephson K, Alessi C: Daytime sleeping, sleep disturbance, and circadian rhythms in the nursing home. Am J Geriatr Psychiatry. 2006, 14: 121-129. 10.1097/01.JGP.0000192483.35555.a3.

Monk T, Thompson W, Buysse D, Hall M, Nofzinger E, Reynolds C: Sleep in healthy seniors: a diary study of the relation between bedtime and the amount of sleep obtained. J Sleep Res. 2006, 15: 256-260. 10.1111/j.1365-2869.2006.00534.x.

Bliwise D, Ansari F, Straight L, Parker K: Age changes in timing and 24-hour distribution of self-reported sleep. Am J Geriatr Psychiatry. 2005, 13: 1077-1082.

Loerbroks A, Debling D, Amelang M, Sturmer T: Nocturnal sleep duration and cognitive impairment in a population-based study of older adults. Int J Geriatr Psychiatry. 2010, 25: 100-109.

Raymann R, Van Someren E: Diminished capability to recognize the optimal temperature for sleep initiation may contribute to poor sleep in elderly people. Sleep. 2008, 31: 1301-1309.

Oosterman J, Van Someren E, Vogels R, Van Harten B, Scherder E: Fragmentation of the rest-activity rhythm correlates with age-related cognitive deficits. J Sleep Res. 2009, 18: 129-135. 10.1111/j.1365-2869.2008.00704.x.

Campbell S, Murphy P, Stauble T: Effects of a nap on nighttime sleep and waking function in older subjects. J Am Geriatr Soc. 2005, 53: 48-53. 10.1111/j.1532-5415.2005.53009.x.

Minors D, Atkinson G, Bent N, Rabbitt P, Waterhouse J: The effects of age upon some aspects of lifestyle and implications for studies on circadian rhythmicity. Age Ageing. 1998, 27: 67-72. 10.1093/ageing/27.1.67.

Minors D, Rabbitt P, Worthington H, Waterhouse J: Variation in meals and sleep-activity patterns in aged subjects; its relevance to circadian studies. . Chronobiol Int. 1989, 6: 139-146. 10.3109/07420528909064624.

Monk T, Buysse D, Hall M, Nofzinger E, Thompson W, Mazumdar S, Reynolds C: Age-related differences in the lifestyle regularity of seniors experiencing bereavement, care-giving, insomnia, and advancement into old-old age. Chronobiol Int. 2006, 23: 831-841. 10.1080/07420520600827152.

Monk T, Kupfer D: Circadian rhythms in healthy aging - effects downstream from the pacemaker. Chronobiol Int. 2000, 17: 355-368. 10.1081/CBI-100101051.

Monk T: Aging human circadian rhythms: conventional wisdom may not always be right. J Biol Rhythms. 2005, 20: 366-374. 10.1177/0748730405277378.

Monk T, Buysse D, Carrier J, Kupfer D: Inducing jet-lag in older people: directional asymmetry. J Sleep Res. 2000, 9: 101-116. 10.1046/j.1365-2869.2000.00184.x.

Pandi-Perumal S, Zisapel N, Srinivasan V, Cardinali D: Melatonin and sleep in aging population. Exp Gerontol. 2005, 40: 911-925. 10.1016/j.exger.2005.08.009.

Lushington K, Dawson D, Lack L: Core body temperature is elevated during constant wakefulness in elderly poor sleepers. Sleep. 2000, 23: 504-510.

Carrier J, Monk T, Reynolds C, Buysse D, Kupfer D: Are age differences in sleep due to phase differences in the output of the circadian timing system?. Chronobiol Int. 1999, 16: 79-91. 10.3109/07420529908998714.

Fetveit A, Bjorvatn B: Sleep duration during the 24-hour day is associated with the severity of dementia in nursing home patients. Int J Geriatr Psychiatry. 2006, 21: 945-950. 10.1002/gps.1587.

Crowley K: Sleep and sleep disorders in older adults. Neuropsychol Rev. 2011, 21: 41-53. 10.1007/s11065-010-9154-6.

Brainard G, Rollag M, Hanifin J: Photic regulation of melatonin in humans: ocular and neural signal transduction. J Biol Rhythms. 1997, 2: 537-546.

Brainard G, Hanifin J, Greeson J, Byrne B, Glickman G, Gerner E, Rollag M: Action spectrum for melatonin regulation in humans: Evidence for a novel circadian photoreceptor. J Neurosci. 2001, 21: 6405-6412.

Thapan K, Arendt J, Skene D: An action spectrum for melatonin suppression: evidence for a novel non-rod, non-cone photoreceptor system in humans. J Physiol. 2001, 535: 261-267. 10.1111/j.1469-7793.2001.t01-1-00261.x.

Herjevic M, Middleton B, Thapan K, Skene D: Light-induced melatonin suppression: age-related reduction in response to short wavelength light. Exp Gerontol. 2005, 40: 237-242. 10.1016/j.exger.2004.12.001.

Tokura H, Rutkowska D, Kim H, Aizawa S, Park S-J, Zhang P, Teramoto Y: Physiological significance of light for behavioral and autonomic temperature regulation in humans in terms of circadian rhythm. Biological Clocks. Mechanisms and Applications. Edited by: Touitou Y. 1998, Amsterdam: Elsevier Science, 209-216.

Asplund R, Lindblad B: The development of sleep in persons undergoing cataract surgery. Arch Gerontol Geriatr. 2002, 35: 179-187. 10.1016/S0167-4943(02)00022-5.

Asplund R, Lindblad B: Sleep and sleepiness 1 and 9 months after cataract surgery. Arch Gerontol Geriatr. 2004, 38: 69-75.

Tanaka M, Hosoe K, Hamada T, Morita T: Change in sleep state of the elderly before and after cataract surgery. J Physiol Anthropol. 2011, 29: 219-224.

Benloucif S, Green K, L'Hermite-Baleriaux M, Weintraub S, Wolfe L, Zee P: Responsiveness of the aging circadian clock to light. Neurobiol Aging. 2006, 27: 1870-1879. 10.1016/j.neurobiolaging.2005.10.011.

Baehr E, Eastman C, Revelle W, Olson S, Wolfe L, Zee P: Circadian phase-shifting effects of nocturnal exercise in older compared with young adults. Am J Physiol. 2003, 284: R1542-R1550.

Waterhouse J, Minors D, Folkard S, Owen D, Atkinson G, MacDonald I, Nevill A, Reilly T, Sytnik N, Turner P, Weinert D: Lack of evidence that feedback from lifestyle alters the amplitude of the circadian pacemaker in humans. Chronobiol Int. 1999, 16: 93-107. 10.3109/07420529908998715.

Dijk D, Duffy J, Czeisler C: Contribution of circadian physiology and sleep homeostasis to age-related changes in human sleep. Chronobiol Int. 2000, 17: 285-311. 10.1081/CBI-100101049.

Harper D, Volicer L, Stopa E, McKee A, Nitta M, Satlin A: Disturbance of endogenous circadian rhythm in aging and Alzheimer disease. Am J Geriatr Psychiatry. 2005, 13: 359-368.

Kendall A, Lewy A, Sack R: Effects of aging on the intrinsic circadian period of totally blind humans. J Biol Rhythms. 2001, 16: 87-95. 10.1177/074873040101600110.

Monk T, Buysse D, Begley A, Billy B, Fletcher M: Effects of a two-hour change in bedtime on the sleep of healthy seniors. Chronobiol Int. 2009, 26: 526-543. 10.1080/07420520902821119.

Dijk D, Duffy J, Riel E, Shanahan T, Czeisler C: Ageing and the circadian and homeostatic regulation of human sleep during forced desynchrony of rest, melatonin and temperature rhythms. J Physiol. 1999, 516: 611-627. 10.1111/j.1469-7793.1999.0611v.x.

Niggemyer K, Begley A, Monk T, Buysse D: Circadian and homeostatic modulation of sleep in older adults during a 90-minute day study. Sleep. 2004, 27: 1535-1541.

Cajochen C, Münch M, Knoblauch V, Blatter K, Wirz-Justice A: Age-related changes in the circadian and homeostatic regulation of human sleep. Chronobiol Int. 2006, 23: 461-474. 10.1080/07420520500545813.

Campbell I, Feinberg I: Homeostatic sleep response to naps is similar in normal elderly and young adults. Neurobiol Aging. 2005, 26: 135-144. 10.1016/j.neurobiolaging.2004.02.021.

Monk T, Buysse D, Carrier J, Billy B, Rose L: Effects of afternoon "siesta" naps on sleep, alertness, performance, and circadian rhythms in the elderly. Sleep. 2001, 24: 680-687.

Vinzio S, Ruellan A, Perrin A, Schlienger J, Goichot B: Actigraphic assessment of the circadian rest-activity rhythm in elderly patients hospitalized in an acute care unit. Psychiatry Clin Neurosci. 2003, 57: 53-58. 10.1046/j.1440-1819.2003.01079.x.

Gasio P, Krauchi K, Cajochen C, Van Someren E, Amrhein I, Pache M, Savaskan E, Wirz-Justice A: Dawn-dusk simulation light therapy of disturbed circadian rest-activity cycles in demented elderly. Exp Gerontol. 2003, 38: 207-216. 10.1016/S0531-5565(02)00164-X.

Benloucif S, Orbeta L, Ortiz R, Janssen I, Finkel S, Bleiberg J, Zee P: Morning or evening activity improves neuropsychological performance and subjective sleep quality in older adults. Sleep. 2004, 27: 1542-1551.

Fetveit A, Bjorvatn B: The effects of bright-light therapy on actigraphical measured sleep last for several weeks post-treatment. A study in a nursing home population. J Sleep Res. 2004, 13: 153-158-

Valtonen M, Niskanen L, Kangas A, Koskinen T: Effect of melatonin-rich night-time milk on sleep and activity in elderly institutionalized subjects. Nordic J Psychiatry. 2005, 59: 217-221. 10.1080/08039480510023034.

Conn D, Madan R: Use of sleep-promoting medications in nursing home residents - risks versus benefits. Drugs Aging. 2006, 23: 271-287. 10.2165/00002512-200623040-00001.

Grandner M, Kripke D, Langer R: Light exposure is related to social and emotional functioning and to quality of life in older women. Psychiatry Res. 2006, 143: 35-42. 10.1016/j.psychres.2005.08.018.

Martin J, Marler M, Harker J, Josephson K, Alessi C: A multicomponent nonpharmacological intervention improves activity rhythms among nursing home residents with disrupted sleep/wake patterns. J Gerontol A Biol Sci Med Sci. 2007, 62: 67-72. 10.1093/gerona/62.1.67.

Fetveit A: Late-life insomnia: a review. Geriatr Gerontol Int. 2009, 9: 220-234. 10.1111/j.1447-0594.2009.00537.x.

Friedman L, Zeitzer J, Kushida C, Zhdanova I, Noda A, Lee T, Schneider B, Guilleminault C, Sheikh J, Yesavage J: Scheduled bright light for treatment of insomnia in older adults. J Am Geriatr Soc. 2009, 57: 441-452. 10.1111/j.1532-5415.2008.02164.x.

Wade A, Ford I, Crawford G, McConnachie A, Nir T, Laudon M, Zisapel N: Nightly treatment of primary insomnia with prolonged release melatonin for 6 months: a randomized placebo controlled trial on age and endogenous melatonin as predictors of efficacy and safety. BMC Med. 2010, 8: 51-10.1186/1741-7015-8-51.

Article   PubMed Central   PubMed   CAS   Google Scholar  

Zisberg A, Gur-Yaish N, Shochat T: Contribution of routine to sleep quality in community elderly. Sleep. 2010, 33: 509-514.

Lieverse R, Van Someren E, Nielen M, Uitdehaag B, Smit J, Hoogendijk W: Bright light treatment in elderly patients with nonseasonal major depressive disorder: a randomized placebo-controlled trial. Arch Gen Psychiatry. 2011, 68: 61-70. 10.1001/archgenpsychiatry.2010.183.

Mishima K, Okawa M, Shimizu T, Hishikawa Y: Diminished melatonin secretion in the elderly caused by insufficient environmental illumination. J Clin Endocrinol Metab. 2001, 86: 129-134. 10.1210/jc.86.1.129.

Rikkert M, Rigaud A: Melatonin in elderly patients with insomnia - a systematic review. Z Gerontol Geriatr. 2001, 34: 491-497. 10.1007/s003910170025.

Raymann R, Swaab D, Van Someren E: Skin temperature and sleep-onset latency: changes with age and insomnia. Physiol Behav. 2007, 90: 257-266. 10.1016/j.physbeh.2006.09.008.

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The preliminary draft of part of the section on adolescents was written by YF, of part of the section on old persons by TM, and of the rest of the review by JW. Thereafter, all authors were equally involved in the several revisions that were undertaken. All authors read and approved the final manuscript.

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Waterhouse, J., Fukuda, Y. & Morita, T. Daily rhythms of the sleep-wake cycle. J Physiol Anthropol 31 , 5 (2012). https://doi.org/10.1186/1880-6805-31-5

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The association of smartphone screen time with sleep problems among adolescents and young adults: cross-sectional findings from India

  • Chanda Maurya 1 ,
  • T. Muhammad   ORCID: orcid.org/0000-0003-1486-7038 2 ,
  • Priya Maurya 2 &
  • Preeti Dhillon 1  

BMC Public Health volume  22 , Article number:  1686 ( 2022 ) Cite this article

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Although sleep problem is a growing public health issue with the advancement of technology, especially among adolescents and young adults, it received little attention. The study aimed to examine the association of screen time on smartphone with sleep problems among adolescents and young-adults in India.

We used data from the “Understanding the lives of adolescents and young-adults” (UDAYA, 2018). The effective sample size for the study was 16,292 adolescents and young adults (males-4428 and females-11,864). Descriptive statistics and bivariate analysis with percentages and chi-square test were used to report the preliminary results. Multivariable logistic regression analysis was conducted to examine the association between smartphone screen time and sleep problems, separately for adolescents and young adults.

Nearly 15.6% of males and 23.5% of females had sleep problems in their adolescence in the last 15 days, while these percentage were high among young-adults (18.4% males and 33.24% females). Adolescents [AOR: 1.55; CI: 1.21-1.99] and young adults [AOR: 1.48; CI: 1.24-1.75], who spent more than 2 h on smartphone had higher odds of reporting sleep problems than those who did not use smartphone in the last 24 hours. Adolescent females who used smartphone for less or equal to 2 h and three or more hours respectively, had 2.11 [AOR: 2.11; CI: 1.63-2.73] and 2.94 times [AOR: 2.94; CI: 1.97-4.38] higher odds of reporting sleep problems than adolescent males who did not use smartphones. Additionally, among the young adult females, the odds of sleep problems were 1.66 times [AOR: 1.66; CI: 1.55-2.38] and 2.36 times [AOR: 2.36; CI: 1.59-3.51] greater than the non-users young adult males.

The increased time spent on mobile phones’s screen among adolescents and young-adults, particularly in females is associated with a higher likelihood of reporting sleeping problems. The current findings have important implications for adolescence and young-adults’ mental health programmes. The findings can also be used to further inform how different strategies need to be developed for better sleep outcome during adolescence and young-adults.

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Adolescence is a unique transitional phase between dependent childhood to independent adulthood, which delineates the foundation of good health of an individual’s life. Adolescents also experience rapid physical, cognitive and psychological growth [ 1 ]. Sleep is a physiological phenomenon as well as behavioural process that affects the growth, cognitive development, learning and good health of children and adolescents [ 2 , 3 ]. According to the US National Sleep Foundation, the required sleep for adolescents (aged 14-17 years) is 8-10 hours and for young adults (aged 18-25 years) is around 7 to 9 hours per night to promote basic optimal health and development [ 4 ]. Unfortunately, it is an easily compromised part of daily routine. Insufficient sleep or disturbance in sleep has become a common problem among youth and adolescents worldwide [ 5 ]. Poor sleep has multiple effects on adolescent health, including depression, excessive daytime sleepiness, and metabolic dysfunctions [ 2 , 3 , 6 ]. Previous evidence identified various social, environmental, cultural and family-related factors responsible for sleep disorders among adolescents and young adults [ 5 , 7 , 8 , 9 ].

Since the beginning of the new millennium, the countries around the world have witnessed an era of continued technological advancement, resulting in an increase in people’s screen viewing of digital devices such as television, mobile phones, other portable electronic devices, and the internet [ 10 , 11 ]. Although the internet penetration rate, which is the percentage of total population of a country or region using internet, is high in the developed world (86.6%) than developing world (47%) and least developed countries (19.1%), a rapid increase has been observed in low and middle-income countries in recent years [ 11 ]. A recent survey in India reported that 92.8% of the households had mobile ownership [ 12 ], and 35% of the country’s total population had internet access in 2017 [ 11 ]. Particularly, more than two-third (67%) of internet users belonged to the age group of 12 to 29 years and about one-third (32%) of them were from 12 to 19 age cohort in the country [ 13 , 14 ]. Increased availability of smartphones and accessibility of internet come up with access and use of the internet during daytime and bedtime, further which leads to internet addiction [ 15 , 16 , 17 ]. In 2016, the new Canadian 24-hour Movement Guidelines recommended that screen time/day should be less than 2 h for children and adolescents [ 18 ].

The use of smartphones has a wide range of positive impacts, such as updated information and improved academic performance; however, the negative impacts include substance abuse and addiction to the device affecting individuals’ social and personal life [ 8 , 15 , 17 , 19 ]. One of the major negative impacts is the sleep disturbance that leads to other problems among adolescents and young adults. Problematic use of smartphones has been attributed to time displacement, such as smartphone use that transcends to delay in bedtime due to surfing the content of the media, causing arousal and interfering with the ability to fall asleep. Additionally, it has a biological impact through light emission of the device in the blue spectrum resulting in melatonin suppression which further leads to difficulty in sleep initiation and non-restorative sleep [ 6 , 7 , 8 , 16 , 17 , 19 ]. With the advancement of technology and the use of smartphones, the sleep problem has become a growing concern. Age, gender, physical activities and substance use are other potential risk factors for sleep disorder among adolescents [ 20 , 21 , 22 ]. A systematic review revealed that adolescents sleep less as they get older. The same study also mentioned that females sleep more than males but females’ bed time is decreasing at a larger rate than males for each year of increasing age [ 20 ]. Heavy drinking behaviour,smoking and physical activities are significantly associated with sleep disorders among adolescents [ 21 ].

Although sleep problem is a growing public health issue with the advancement of technology, especially among adolescents and young adults, it received little attention in India. Study reported that magnitude of smartphone addiction among teens and youngsters in India ranged from 39 to 44% [ 23 ]. Similarly, sleep disorders are usually prevalent among adolescents and young adults due to lifestyle factors, dietary habits and hormonal and emotional disturbances [ 24 ]. However, sleep problem independently associated with smartphone screentime is a neglected topic among youngsters in India. This study can help to understand the linkage between screen time and sleep problems during adolescence and early adulthood. Also, itmay suggests policy implications that can help Indian adolescents and youth improve their mental health and achieve better academic performance through limited screen time and good sleep behaviour. Thus, the present study aimed to examine the association of smartphone screen time with sleep problems among adolescents and young adults in India after controlling for a large number of confounders.

Materials and methods

The data for this study were derived from the survey of “Understanding the lives of adolescents and young adults” (UDAYA, 2018), conducted by the Population Council, New Delhi and funded by the Bill and Melinda Gates Foundation and the David and Lucile Packard Foundation. The UDAYA survey is a longitudinal study conducted in Uttar Pradesh and Bihar following a cohort of adolescents aged 10-19 years.

The UDAYA study used both cross-sectional and longitudinal designs for sampling at wave- 1, and a multi-stage systematic sampling design was employed during the sample selection. The UDAYA was designed to provide estimates at two time-points for the state as well as for the urban and rural areas of the state for each of the five categories of respondents, namely younger males in the age group 10–14 years, older males in the age group 15–19 years, younger females in the ages group 10–14 years, unmarried older females in the age group 15–19 years, and married older females in the age group 15–19 years. A total of 150 primary sampling units (PSUs), 75 for rural and 75 for urban respondents, were sampled in each state using the probability proportional to size (PPS) technique. PSUs’ list was stratified using four variables, namely, region, village/ward size, the proportion of the population belonging to scheduled castes and scheduled tribes, and female literacy. The household sample in rural areas was selected in three stages, while in urban areas, it was selected in four stages.

Data collection for wave-1 was done during 2015-16, and after 3 years, wave-2 data were collected during 2018-19. This paper analysed smartphone screen time in the past 24 hours, and this information was collected only in wave-2. Hence, for the current study, a cross-sectional sample of only wave-2 was used, consisting of 12- 23 years old adolescents and young adults. The effective sample size for the study was 16,292 adolescents and young adults (males-4428 and females-11,864).

Variable description

Outcome variable.

The sleep problem was coded as 1 “having sleep problems in the last 15 days” and 0 “not having sleep problem in the last 15 days”.

Explanatory variables

Key explanatory variable was time spent on smartphone in the past 24 hours that was coded as 0 “not users of smartphone in past 24 hours”, 1 “one to two hour of smarphone use” and 3 “three or more hours of smartphone use” [ 25 ]. Socio-demographic variables included age, that was grouped into “12-18 years” and “19-23 years”; current marital status, that was coded as “single” and “married”; educational level, that was coded as 0 “illiterate”, 1 “primary and middle for up to 8 years of schooling” and 3 “higher for nine and more years of schooling”. Other predictor variables were social media exposure, that was coded as 0 “no exposure” and 1 “exposure”; physical activity, that was coded as 0 “no physical activity in the past week”, low activity “less than 7 hours of physical activity in the past week” and high activity “7 or more hours of physical activity in the past week” [ 26 ]; sedentary behaviour, that was coded as “no sedentary behaviour in the past week”, “less than or equal to 14 hours of sedentary behaviour in the past week”, and “more than 14 hours of sedentary behaviour” [ 26 ]; substance use, that included consumption of either tobacco products or alcohol, which was coded as “no” and “yes”; parent’s co-residence with the respondents, that was coded as “both parents co-reside”, “one parent co-resides”, and “no parents co-reside”; paid work in the past 12 months, that was coded as “no” and “yes”. The control variables also included religion, that was coded as “Hindu” and “non-Hindu”; caste, that was coded as “Scheduled Caste/Scheduled Tribe (SC/ST)”, “Other background class (OBC)”, and “other”; mother’s education, that was coded as “no” and “yes”; the wealth index, that was coded as “poor”, “middle”, and “rich”; and the place of residence, that was coded as “rural” and “urban”.

Statistical analysis

Descriptive statistics and bivariate analysis with percentages and chi-square test were used to report the preliminary results. Two multivariable logistic regression models were used to analyse the association between the binary outcome variable and explanatory variables, separately for adolescents and young adults. The outcome variable was (0 “not sleep problem” and 1 “having sleep problem”). To assess the reliability of the models we calculated the Hosmer-Lemeshow goodness-of-fit statistic [ 27 ]. Both regression models showed good calibration (Hosmer-Lemeshow P value of 0.52 and 0.59, respectively). The results were presented in the form of the adjusted odds ratio (OR) with 95% confidence interval (CI) and interaction term was used to identify the interaction effects of predictors of sex and time spent on smartphones on sleep problem. The logistic regression model is usually put into a more compact form as follows:

Where β0… βM are regression coefficients indicating the relative effect of a particular explanatory variable on the outcome. These coefficients change as per the context in the analysis in the study.

Socio-demographic profile of adolescent and young adult males and females are presented in Table  1 . A higher proportion of adolescent males (98.68%) and females (82.02%) were single, while these percentage were low among young adult males (88.62%) and females (37.27%). A proportion of 48.21 and 78.69% of the adolescents and young adult males and 52.11 and 56.17% of the adolescent and young adult females, respectively had high school and above educational level. Fourteen percent of males and only 4 % of females in their adolescence spent three or more hours on smartphone, while these percentages were higher among young adults (25.59% males and 5.79% females). Nearly 58 and 53% of adolescent males and females reported smartphone screentime of less than 2 h. There were significant sex differences in the exposure to social media among both adolescents and young adults. One-fifth of the adolescent males and a quarter of the adolescent females were exposed to the social media, while 44% of the males and 5% of the females were exposed to social media in their young adulthood. Approximately 15.64 and 23.52% of adolescent males and females respectively had sleep problems in the last 15 days of the interview, while these percentages were higher among young adults (18.4% males and 33.24% females).

Figure  1 depicts the average time (in minutes) spent on smartphones increases with age, irrespective of sex of the respondents. Adolescent males spent more time on smartphones than adolescent females, and the sex difference in smartphone screentime increased with age. For instance, at age 23 years, adolescent males spent around 3 hours (153 minutes), and adolescent females spent an hour (56 minutes) on smartphones in the last 24 hours preceding the survey.

figure 1

Average screentime spent on smartphone among adolescents and young-adults, stratified by sex

Figure  2 depicts the type of activities adolescents and young adults did on a smartphone. Only 7% of males and 3% of females used their smartphones for educational purposes, whereas, a higher percentage of males and females used their smartphones for phone calls. Half of the males and one out of five females used their smartphones for surfing social media.

figure 2

Things adolescents and young adults did on smartphone, stratified by sex

The association of sleep problem among adolescents and young adults with background characteristics are shown in Table  2 . The results in the table showed clear sex differentials for sleep problems among adolescents and young adults. Result revealed that a higher percentage of adolescents who used smartphone for three or more hours suffered from sleep problem in last 15 days (Males: 22.46% and females: 38.51%) and a higher percentage of young adults who used smartphone for three or more hours had sleep problem (males: 23.47% and females: 44.34%). It was revealed that both adolescents and younger adult females who used smartphone for three or more hours had higher percentage of sleep problem in comparison their male counterpartsmale.

Moreover, a higher percentage of females (29.75 39.23%) who never attended school suffered from sleep in their adolescence and young adulthood, respectivily. There were higher and significant sex differences in sleep problems (13.40%) among the young adults who had higher educational attainments. Significant gender diffecences found in sleep problems among young adults who were exposed to social media. The prevalence of sleep problems was higher among adolescent and young adult males (20.3 and 29%) and females (36.8 and 48.9%) with low physical activity.

Furthermore, adolescents (males: 18.3% and females: 36.7%) and young adults (males:23.4% and females:42.7%) who used substances had a significantly higher prevalence of sleep problems. Adolescent and young adult males and females who had one to four or more than four friends had a significantly higher prevalence of sleep problems than those who did not have friends. The adolescents (males: 19.1% and females: 30.7%) and young adults (males: 23.4%% and females: 36.9%) who belonged to an urban residence had a substantially higher prevalence of sleep problems than those who belonged to a rural place.

Estimates from logistic regression analysis of sleep problems across explanatory variables among adolescent and young adult males and females are presented in Table  3 . Adolescents and young adults who used smartphones for more than 2 h had 1.55 times [AOR: 1.55; CI: 1.21-1.99] and 1.48 times [AOR: 1.48; CI: 1.24-1.75] higher odds of suffering from sleep problems in comparisonto those who did not use smartphone in last 24 hours. Moreover, there was a considerable sex differentce in reporting sleep problem. The odds of sleep problems were 2.08 times [AOR: 2.08; CI: 1.77-2.44] and 2.55 times [AOR: 2.55; CI: 2.17-2.99] higher among adolescent and young adult females compared to their male counterparts.

Estimates from logistic regression on reporting sleep problems among adolescents and young adults by interaction between sex and time spent on smartphone are presented in Table  4 . The interaction analysis found that the odds of sleep problems among young adult males who used smartphones for 1 or 2 h were lower [AOR: 0.63; CI: 0.44-0.92] than non-users from young adult males. Adolescent females who used smartphone for less or equal to 2 h and three or more hours respectively, had 2.11 [AOR: 2.11; CI: 1.63-2.73] and 2.94 times [AOR: 2.94; CI: 1.97-4.38] higher odds of reporting sleep problems than adolescent males who did not use smartphones. Among the young adult females, the odds of sleep problem were 1.66 times [AOR: 1.66; CI: 1.55-2.38] and 2.36 times [AOR: 2.36; CI: 1.59-3.51] greater than the non-users from young adult males.

The aim of this study was to examine the prevalence of screen time and sleep problems and associations of time spent per day on screen, and the reporting of sleep problems in adolescents and young adults in India. A proportion of 13.90 and 25.59% of adolescent and young males and 3.76 and 5.79% of adolescent and young females reported increased time spent on mobile phones per day, which was higher than the recommended total screen time to minimise the negative health effects in previous studies [ 7 , 28 ]. Screen time is negatively associated with markers of health in adolescents and young adults in developed countries [ 9 , 29 , 30 ], but very little is known about such relationships in these populations in low- and middle-income countries. Studies in China found that increased screen time is associated with adolescents’ unhealthy behaviours and undesirable psychological states that can contribute to sleep problems and poor quality of life [ 31 , 32 , 33 ]. Consistently, the current findings showed that adolescents and young adults who reported increased screen time had higher odds of sleep problems, and greater odds were observed among adolescents than young adults. Another study in the US showed that screen time on mass media such as reading news online and social media was associated with increased odds of short sleep duration [ 34 ]. Similarly, the likelihood to have insufficient sleep was higher for adolescents who engaged in excessive screentime behaviours when compared to those who did not engage in such behaviours [ 35 ].

Given the number of studies demonstrating the adverse effects of insufficient sleep on adolescents’ and young adults’ physical and mental health, the increasing proportion of those who do not get the recommended hours of sleep raises public health concerns. On the other hand, a large body of literature has shown that adolescent and young females suffer more frequently from sleep disorders than males [ 36 , 37 , 38 ], and the current study has supported this suggesting a call for special attention for the development of gender-based initiatives by health-decision makers and policy experts in the country. Excessive screen exposure in some studies was shown to be associated with poor psychosocial well-being, and sleep played a mediating role [ 39 ]. Similarly, multiple processes have been identified as potential mechanisms responsible for the negative impacts of screen time on sleep, including displacement of sleep time, increases in arousal that harm sleep quality, re-entrainment of circadian rhythms due to light exposure, and increases in depressive symptoms [ 40 , 41 , 42 , 43 ].

Furthermore, beyond the screen time- sleep problem association, we were able to demonstrate different associations for both adolescent and young males and females by doing interaction analyses. In concordance with earlier findings [ 44 ], the current study showed that adolescent and young females who spent more time on screen had a higher likelihood of reporting sleep problems in comparison to adolescent and young males who reported no screen time. The observed gender difference may be attributed to discrepancies in their patterns of use and the nature of the content. For example, gender difference can be noticed in the motivations behind attending or listening to music; while males may consider music as a means to create a more positive image of themselves or boost their energy level, whereas females tend to listen to music as a reflection of their current negative emotional state including feeling lonely or depressed [ 45 , 46 ]. Therefore, while considering the future studies on gender differences in the observed association, the types of media and content as markers of such gender difference should be analysed.

In addition to excessive screentime behaviors, the following factors were also found to be associated with reporting sleep problems: increasing age, physical activity, substance use, higher number of friends, engaged in paid work, and urban place of residence. As prior research documented that increasing age is negatively associated with sleep quality and positively associated with sleep problems [ 47 , 48 , 49 , 50 ], the finding of our study consistently showed that reporting of sleep problem varied by age, with early to mid adolescents (12-17 years of age) reporting lesser sleep problem than late adolescents (18–23 years of age). Physical activity in the current study was positively associated with sleep problems which is inconsistent with multiple previous studies showing that physical activity has a protective effect on insufficient sleep and related stress during adolescence [ 51 , 52 ]. The inconsistent finding may partially be explained by the possible reverse causality that suggests that earlier onset of sleep predicts increased sedentary behaviour and less physical activity in the next day [ 53 ].

The cross-sectional design of the study precludes determining the causation in the observed associations. Besides, we did not separate out the types of screen time and content and sleep on school-day and non-school-day or weekend and weekday. These are important while concluding the findings; however, research has to be further conducted covering these aspects. The self-reported nature of screen time and sleep problems is subject to measurement error due to recall and social desirability bias. Further studies by including subjective and objective measures of both variables and detailed information on sleep disorder such as shortened sleep duration, longer sleep latency, and more mid-sleep awakenings need to be undertaken. A major limitation of the study is to evaluate cell phone use only in the previous 24 hours. Similarly, we evaluated sleep-related problems in the last 15 days. However, peculiar situations could modify the sleep referred to moment, since it is a short period, thus modifying the result found.

Furthermore, it is plausible that the mechanisms by which increased time spent on mobile phones is related to reporting sleep problems, which in turn is related to behavioural health, may differ depending on the adolescents’ developmental stage. For example, late adolescents may engage in more screen time and deliberately avoid sleep, whereas young adolescents may be overstimulated by the games and other online activities and, therefore, have more difficulty settling in when it is time to sleep. Future investigation is warranted on the trajectories of sleep disorders during adolescence. On the other hand, although the current results support a possible causal inference that increased screen time per day (more than 2 hours) is responsible for reporting sleep disorder using large scale survey data, there is a need for longitudinal and randomised-control intervention studies that may strengthen the causal inferences and explore specific processes responsible for this influence. The study was conducted during pre-CoVID-19 pandemic, and therefore, with the increase in online learning and reduction in outdoor activities, the screen time use might have increased among adolescence. Further, the study setting is from the lower socio economic states with prominent rural areas, screen time use in other states and urban areas of India may be much higher than that is reported in the present study. Therefore, the effect of screentime use on sleep disorders may be higher in current scenario in Indian setting than the reported in present study.

Conclusions

The increased time spent on the mobile phone among adolescents and in females, in particular, is associated with a higher likelihood of reporting sleeping problems. The current findings have important implications for health practitioners and families with adolescent members and mental health programmes in adolecence. The findings can also be used to inform further how different strategies need to be developed for sleep health during adolescence. Future studies are required to explore the potential interventions that uniquely target adolescents who have poor sleep health.

Availability of data and materials

The study utilizes a secondary source of data that is freely available in the public domain through: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/RRXQNT .

The necessary ethical approval has been taken by the respective organizations involved in the data collection process.

WHO. Adolescent health. Geneva: World Health Organization; 2021. https://www.who.int/news-room/fact-sheets/detail/adolescents-health-risks-and-solutions .

Singh R, Suri JC, Sharma R, et al. Sleep Pattern of Adolescents in a School in Delhi , India : Impact on their Mood and Academic Performance. Indian J Pediatr. 2018;85:841–8.

Article   PubMed   Google Scholar  

Dewald JF, Meijer AM, Oort FJ, et al. The influence of sleep quality , sleep duration and sleepiness on school performance in children and adolescents: a meta-analytic review. Sleep Med Rev. 2010;14:179–89.

National Sleep Foundation. How Much Sleep Do We Really Need? National Sleep Foundation; 2020. https://www.sleepfoundation.org/how-sleep-works/why-do-we-need-sleep .

Gradisar M, Gardner G, Dohnt H. Recent worldwide sleep patterns and problems during adolescence: a review and meta-analysis of age, region, and sleep. Sleep Med. 2011;12:110–8.

Hysing M, Pallesen S, Stormark KM, Jakobsen R, Lundervold AJ, Sivertsen B. Sleep and use of electronic devices in adolescence: results from a large population-based study. BMJ open. 2015;5(1):e006748.

Twenge JM, Hisler GC, Krizan Z. Associations between screen time and sleep duration are primarily driven by portable electronic devices: evidence from a population-based study of U.S. children ages 0–17. Sleep Med. 2019;56:211–8.

Argiansya F, Soedjadhi R, Indra RM, et al. Electronic media use and sleep disorders among adolescents during the COVID-19 pandemic. Sleep Disorders. 2021;2021:5–9.

Article   Google Scholar  

Thomée S, Härenstam A, Hagberg M. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC Public Health. 2011;11:1–11.

ITU. Measuring digital development facts and figures. Geneva: Geneva Internet Platform Dig Watch; 2019.

Murugiah P. Internet usage in India: the global analytics. In: Measuring and Implementing Altmetrics in Library and Information Science Research. India: IGI Global; 2020. p. 29–37.

Mohan D, Juste J, Bashingwa H, et al. Does having a mobile phone matter ? Linking phone access among women to health in India : an exploratory analysis of the National Family Health Survey. PLoS One. 2020;15:1–16.

Article   CAS   Google Scholar  

Statista. Internet usage in India – Statistics and Facts 2020 https://www.statista.com/statistics/309866/india-digital-population/ .

Google Scholar  

Population Council. Exploring the digital divide access to and use of mobile phones, the internet, and social media by adolescents and young adults in Bihar. New Delhi: Population Council; 2020.

Prabhakaran M-A, Patel V, Ganjiwale D, et al. Factors associated with internet addiction among school-going adolescents in Vadodara. J Fam Med Prim Care. 2016;5:765.

Kuss DJ, Lopez-Fernandez O. Internet addiction and problematic internet use: a systematic review of clinical research. World J Psychiatry. 2016;6:143.

Article   PubMed   PubMed Central   Google Scholar  

Karki K, Singh DR, Maharjan D, et al. Internet addiction and sleep quality among adolescents in a peri-urban setting in Nepal: a cross-sectional school-based survey. PLoS One. 2021;16:1–10.

Janssen I, Garriguet D, Colley RC, et al. Physical activity and sedentary behavior during the early years in Canada: a cross-sectional study. Int J Behav Nutr Phys Act. 2013;10:54.

Griffiths MD, Lopez-fernandez O, Throuvala M, et al. Excessive and problematic use of social media in adolescence: a brief overview; 2018. Epub ahead of print 2018. https://doi.org/10.13140/RG.2.2.11280.71682 .

Book   Google Scholar  

Olds T, Blunden S, Petkov J, et al. The relationships between sex , age , geography and time in bed in adolescents : A meta-analysis of data from 23 countries. Sleep Med Rev. 2010;14:371–8.

World Health Organization. WHO technical meeting on sleep and health. 2004.

Bartel K, Williamson P, van Maanen A, et al. Protective and risk factors associated with adolescent sleep : findings from Australia, Canada, and The Netherlands. Sleep Med. 2016;26:97–103.

Davey S, Davey A. Assessment of smartphone addiction in Indian adolescents: a mixed method study by systematic-review and meta-analysis approach. Int J Prev Med. 2014;5:1500.

PubMed   PubMed Central   Google Scholar  

Kabel AM, Al Thumali AM, Aldowiala KA, et al. Sleep disorders in adolescents and young adults: insights into types, relationship to obesity and high altitude and possible lines of management. Diabetes Metab Syndr. 2018;12:777–81.

Cao H, Qian Q, Weng T, et al. Screen time, physical activity and mental health among urban adolescents in China. Prev Med. 2011;53:316–20.

Chaput JP, Willumsen J, Bull F, et al. 2020 WHO guidelines on physical activity and sedentary behaviour for children and adolescents aged 5–17 years: summary of the evidence. Int J Behav Nutr Phys Act. 2020;17:1–9.

Lemeshow S, Sturdivant RX, Hosmer DW Jr. Applied logistic regression: Wiley; 2013.

Li S, Jin X, Wu S, et al. The impact of media use on sleep patterns and sleep disorders among school-aged children in China. Sleep. 2007;30:361–7.

Ghekiere A, Van Cauwenberg J, Vandendriessche A, et al. Trends in sleeping difficulties among European adolescents: are these associated with physical inactivity and excessive screen time? Int J Public Health. 2019;64:487–98.

Hale L, Guan S. Screen time and sleep among school-aged children and adolescents: a systematic literature review. Sleep Med Rev. 2015;21:50–8.

Wu X, Tao S, Zhang Y, et al. Low physical activity and high screen time can increase the risks of mental health problems and poor sleep quality among Chinese college students. PLoS One. 2015;10:1–10.

Yan H, Zhang R, Oniffrey TM, et al. Associations among screen time and unhealthy behaviors, academic performance, and well-being in Chinese adolescents. Int J Environ Res Public Health. 2017;14:1–15.

Song Y, Li L, Xu Y, et al. Associations between screen time, negative life events, and emotional and behavioral problems among Chinese children and adolescents. J Affect Disord. 2020;264:506–12.

Twenge JM, Krizan Z, Hisler G. Decreases in self-reported sleep duration among U.S. adolescents 2009–2015 and association with new media screen time. Sleep Med. 2017;39:47–53.

Baiden P, Tadeo SK, Peters KE. The association between excessive screen-time behaviors and insufficient sleep among adolescents: findings from the 2017 youth risk behavior surveillance system. Psychiatry Res. 2019;281:112586.

Lan QY, Chan KC, Yu KN, et al. Sleep duration in preschool children and impact of screen time. Sleep Med. 2020;76:48–54.

Lange K, Cohrs S, Skarupke C, et al. Electronic media use and insomnia complaints in German adolescents: gender differences in use patterns and sleep problems. J Neural Transm. 2017;124:79–87.

Rajab AM, Rajab TM, Basha AC, et al. Gender differences in sleep and mental health among Saudi adolescents. Sleep Disord. 2021;2021:1–8.

Zhao J, Zhang Y, Jiang F, et al. Excessive screen time and psychosocial well-being: the mediating role of body mass index, sleep duration, and parent-child interaction. J Pediatr. 2018;202:157–62 e1.

Twenge JM, Joiner TE, Rogers ML, et al. Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clin Psychol Sci. 2018;6:3–17.

Chang AM, Aeschbach D, Duffy JF, et al. Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proc Natl Acad Sci U S A. 2015;112:1232–7.

Article   CAS   PubMed   Google Scholar  

Gradisar M, Wolfson AR, Harvey AG, et al. The sleep and technology use of Americans: findings from the National Sleep Foundation’s 2011 sleep in America poll. J Clin Sleep Med. 2013;9:1291–9.

Owens J, Au R, Carskadon M, et al. Insufficient sleep in adolescents and young adults: an update on causes and consequences. Pediatrics. 2014;134:e921–32.

Zhu R, Fang H, Chen M, et al. Screen time and sleep disorder in preschool children: identifying the safe threshold in a digital world. Public Health. 2020;186:204–10.

Miranda D. The role of music in adolescent development: much more than the same old song. Int J Adolesc Youth. 2013;18:5–22.

Tiggemann M. Television and adolescent body image: the role of program content and viewing motivation. J Soc Clin Psychol. 2005;24:361–81.

Buxton OM, Chang A-M, Spilsbury JC, et al. Sleep in the modern family: protective family routines for child and adolescent sleep. Physiol Behav. 2016;176:100–6.

Sivertsen B, Harvey AG, Pallesen S, et al. Trajectories of sleep problems from childhood to adolescence: a population-based longitudinal study from Norway. J Sleep Res. 2017;26:55–63.

Kuula L, Pesonen AK, Merikanto I, et al. Development of late circadian preference: sleep timing from childhood to late adolescence. J Pediatr. 2018;194:182–189.e1.

Şimşek Y, Tekgül N. Sleep quality in adolescents in relation to age and sleep-related habitual and environmental factors. J Pediatr Res. 2019;6:307–13.

Lang C, Kalak N, Brand S, et al. The relationship between physical activity and sleep from mid adolescence to early adulthood. A systematic review of methodological approaches and meta-analysis. Sleep Med Rev. 2016;28:32–45.

Park S. Associations of physical activity with sleep satisfaction, perceived stress, and problematic internet use in Korean adolescents. BMC Public Health. 2014;14:1–6.

Master L, Nye RT, Lee S, et al. Bidirectional, daily temporal associations between sleep and physical activity in adolescents. Sci Rep. 2019;9:1–14.

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Conception and design: CM, PM and TM; Data analysis: CM; Writing the first draft: CM, TM and PM; review and editing: TM and PD, supervision: PD. All the authors of this paper have read and approved the final manuscript.

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Maurya, C., Muhammad, T., Maurya, P. et al. The association of smartphone screen time with sleep problems among adolescents and young adults: cross-sectional findings from India. BMC Public Health 22 , 1686 (2022). https://doi.org/10.1186/s12889-022-14076-x

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Clearly, sleep is important. But despite the evidence, there continues to be a shortage of psychiatrists or other doctors trained in sleep medicine, leaving many to educate themselves. So what happens to our mental health if we aren’t getting enough sleep, and what can be done about it?

How does poor sleep affect your mood?

When people have trouble sleeping, it changes how they experience stress and negative emotions, said Aric Prather, a sleep researcher at the University of California, San Francisco, who treats patients with insomnia. “And for some, this can have a feed-forward effect — feeling bad, ruminating, feeling stressed can bleed into our nights,” he said.

Carly Demler, 40, a stay-at-home mother in North Carolina, said she went to bed one night and never fell asleep . From that point onward, she would be up at least once a week until 3 or 4 a.m. It continued for more than a year.

She became irritable, less patient and far more anxious.

Hormone blood work and a sleep study in a university lab offered her no answers. Even after taking Ambien, she stayed up most of the night. “It was like my anxiety was a fire that somehow jumped the fence and somehow ended up expanding into my nights,” she said. “I just felt I had no control.”

In the end, it was cognitive behavioral therapy for insomnia , or C.B.T.-I., that brought Ms. Demler the most relief. Studies have found that C.B.T.-I. is more effective than sleep medications are over the long term: As many as 80 percent of the people who try it see improvements in their sleep.

Ms. Demler learned not to “lay in bed and freak out.” Instead, she gets up and reads so as not to associate her bedroom with anxiety, then returns to bed when she’s tired.

“The feeling of gratitude that I have every morning, when I wake up and feel well rested, I don’t think will ever go away,” she said. “That’s been an unexpected silver lining.”

Adults need between 7 and 9 hours of sleep a night, according to the Centers for Disease Control and Prevention . Teenagers and young children need even more.

It’s not just about quantity. The quality of your sleep is also important. If it takes more than 30 minutes to fall asleep, for example, or if you regularly wake up in the middle of the night, it is harder to feel rested, regardless of the number of hours you spend in bed.

But some people “have a tendency to think they’re functioning well even if they’re sleepy during the day or having a harder time focusing,” said Lynn Bufka, a clinical psychologist and spokeswoman for the American Psychological Association.

Ask yourself how you feel during the day: Do you find that you’re more impatient or quick to anger? Are you having more negative thoughts or do you feel more anxious or depressed? Do you find it harder to cope with stress? Do you find it difficult to do your work efficiently?

If so, it’s time to take action.

How to stop the cycle.

We’ve all heard how important it is to practice good sleep hygiene , employing the daily habits that promote healthy sleep. And it’s important to speak with your doctor, in order to rule out any physical problems that need to be addressed, like a thyroid disorder or restless legs syndrome.

But this is only part of the solution.

Conditions like anxiety, post-traumatic stress disorder and bipolar disorder can make it harder to sleep, which can then exacerbate the symptoms of mental illness, which in turn makes it harder to sleep well.

“It becomes this very difficult to break cycle,” Dr. Bufka said.

Certain medications, including psychiatric drugs like antidepressants, can also cause insomnia. If a medication is to blame, talk to your doctor about switching to a different one, taking it earlier in the day or lowering the dose, said Dr. Ramaswamy Viswanathan, a professor of psychiatry and behavioral sciences at State University of New York Downstate Health Sciences University and the incoming president of the American Psychiatric Association.

The cycle can afflict those without mental health disorders too, when worries worsen sleep and a lack of sleep worsens mood.

Emily, who worked in the big law firm, would become so concerned about her inability to sleep that she didn’t even want to get into bed.

“You really start to believe ‘I’m never going to sleep,’” she said. “The adrenaline is running so high that you can’t possibly do it.”

Eventually she came across “Say Goodnight to Insomnia” by Gregg D. Jacobs. The book, which uses C.B.T.-I. techniques, helped Emily to reframe the way she thought about sleep. She began writing down her negative thoughts in a journal and then changing them to positive ones. For example: “What if I’m never able to fall asleep again?” would become “Your body is made to sleep. If you don’t get enough rest one night, you will eventually.” These exercises helped her stop catastrophizing.

Once she started sleeping again, she felt “way happier.”

Now, at 43, nearly 20 years after she moved to New York, she is still relying on the techniques she learned, and brings the book along whenever she travels. If she doesn’t sleep well away from home, “I catch up on sleep for a few days if necessary,” she said. “I’m way more relaxed about it.”

Christina Caron is a Times reporter covering mental health. More about Christina Caron

Managing Anxiety and Stress

Stay balanced in the face of stress and anxiety with our collection of tools and advice..

These simple and proven strategies will help you manage stress , support your mental health and find meaning in the new year.

First, bring calm and clarity into your life with these 10 tips . Next, identify what you are dealing with: Is it worry, anxiety or stress ?

Persistent depressive disorder is underdiagnosed, and many who suffer from it have never heard of it. Here is what to know .

If you notice drastic shifts in your mood during certain times of the year, you could have seasonal affective disorder. Here are answers to your top questions about the condition .

How much anxiety is too much? Here is how to establish whether you should see a professional about it .

Drawing, music and writing can elevate your mood and benefit your mental health. Here are some easy ways to welcome them into your life .

Stress is unavoidable in modern life, but it doesn’t have to get you down. This guide can help you keep in check .

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Stanford Medicine study identifies distinct brain organization patterns in women and men

Stanford Medicine researchers have developed a powerful new artificial intelligence model that can distinguish between male and female brains.

February 20, 2024

sex differences in brain

'A key motivation for this study is that sex plays a crucial role in human brain development, in aging, and in the manifestation of psychiatric and neurological disorders,' said Vinod Menon. clelia-clelia

A new study by Stanford Medicine investigators unveils a new artificial intelligence model that was more than 90% successful at determining whether scans of brain activity came from a woman or a man.

The findings, published Feb. 20 in the Proceedings of the National Academy of Sciences, help resolve a long-term controversy about whether reliable sex differences exist in the human brain and suggest that understanding these differences may be critical to addressing neuropsychiatric conditions that affect women and men differently.

“A key motivation for this study is that sex plays a crucial role in human brain development, in aging, and in the manifestation of psychiatric and neurological disorders,” said Vinod Menon , PhD, professor of psychiatry and behavioral sciences and director of the Stanford Cognitive and Systems Neuroscience Laboratory . “Identifying consistent and replicable sex differences in the healthy adult brain is a critical step toward a deeper understanding of sex-specific vulnerabilities in psychiatric and neurological disorders.”

Menon is the study’s senior author. The lead authors are senior research scientist Srikanth Ryali , PhD, and academic staff researcher Yuan Zhang , PhD.

“Hotspots” that most helped the model distinguish male brains from female ones include the default mode network, a brain system that helps us process self-referential information, and the striatum and limbic network, which are involved in learning and how we respond to rewards.

The investigators noted that this work does not weigh in on whether sex-related differences arise early in life or may be driven by hormonal differences or the different societal circumstances that men and women may be more likely to encounter.

Uncovering brain differences

The extent to which a person’s sex affects how their brain is organized and operates has long been a point of dispute among scientists. While we know the sex chromosomes we are born with help determine the cocktail of hormones our brains are exposed to — particularly during early development, puberty and aging — researchers have long struggled to connect sex to concrete differences in the human brain. Brain structures tend to look much the same in men and women, and previous research examining how brain regions work together has also largely failed to turn up consistent brain indicators of sex.

test

Vinod Menon

In their current study, Menon and his team took advantage of recent advances in artificial intelligence, as well as access to multiple large datasets, to pursue a more powerful analysis than has previously been employed. First, they created a deep neural network model, which learns to classify brain imaging data: As the researchers showed brain scans to the model and told it that it was looking at a male or female brain, the model started to “notice” what subtle patterns could help it tell the difference.

This model demonstrated superior performance compared with those in previous studies, in part because it used a deep neural network that analyzes dynamic MRI scans. This approach captures the intricate interplay among different brain regions. When the researchers tested the model on around 1,500 brain scans, it could almost always tell if the scan came from a woman or a man.

The model’s success suggests that detectable sex differences do exist in the brain but just haven’t been picked up reliably before. The fact that it worked so well in different datasets, including brain scans from multiple sites in the U.S. and Europe, make the findings especially convincing as it controls for many confounds that can plague studies of this kind.

“This is a very strong piece of evidence that sex is a robust determinant of human brain organization,” Menon said.

Making predictions

Until recently, a model like the one Menon’s team employed would help researchers sort brains into different groups but wouldn’t provide information about how the sorting happened. Today, however, researchers have access to a tool called “explainable AI,” which can sift through vast amounts of data to explain how a model’s decisions are made.

Using explainable AI, Menon and his team identified the brain networks that were most important to the model’s judgment of whether a brain scan came from a man or a woman. They found the model was most often looking to the default mode network, striatum, and the limbic network to make the call.

The team then wondered if they could create another model that could predict how well participants would do on certain cognitive tasks based on functional brain features that differ between women and men. They developed sex-specific models of cognitive abilities: One model effectively predicted cognitive performance in men but not women, and another in women but not men. The findings indicate that functional brain characteristics varying between sexes have significant behavioral implications.

“These models worked really well because we successfully separated brain patterns between sexes,” Menon said. “That tells me that overlooking sex differences in brain organization could lead us to miss key factors underlying neuropsychiatric disorders.”

While the team applied their deep neural network model to questions about sex differences, Menon says the model can be applied to answer questions regarding how just about any aspect of brain connectivity might relate to any kind of cognitive ability or behavior. He and his team plan to make their model publicly available for any researcher to use.

“Our AI models have very broad applicability,” Menon said. “A researcher could use our models to look for brain differences linked to learning impairments or social functioning differences, for instance — aspects we are keen to understand better to aid individuals in adapting to and surmounting these challenges.”

The research was sponsored by the National Institutes of Health (grants MH084164, EB022907, MH121069, K25HD074652 and AG072114), the Transdisciplinary Initiative, the Uytengsu-Hamilton 22q11 Programs, the Stanford Maternal and Child Health Research Institute, and the NARSAD Young Investigator Award.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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Sleep improves ability to recall complex events

Sleep helps consolidate our memory of complex associations, thus supporting the ability to complete memories of whole events.

Researchers had known for some time that sleep consolidates our memories of facts and episodic events. However, the research to date has concentrated mainly on simple associations -- that is to say, connections between elements, such as we make when learning new vocabulary. "But in real life, events are generally made up of numerous components -- for example, a place, people, and objects -- which are linked together in the brain," explains Dr. Nicolas Lutz from LMU's Institute of Medical Psychology. These associations can vary in strength and some elements might be connected with each other only indirectly. "Thanks to the neural connections that underlie these associations, a single cue word is often all it takes for somebody to recall not only individual aspects of an event but multiple aspects at once." This process, which is known as pattern completion, is a fundamental feature of episodic memory. Lutz is lead author of a study recently published in the journal Proceedings of the National Academy of Sciences (PNAS) , which investigated the effect of sleep on memory of such complex events.

After the study participants had learned events with complex associations, in one condition they spent the night in a sleep laboratory, where they were allowed to sleep as usual, while in another condition, they had to stay up all night. In both conditions, the participants were allowed to spend the following night at home to recover. Then they were tested on how well they could recall different associations between elements of the learned events. "We were able to demonstrate that sleep specifically consolidates weak associations and strengthens new associations between elements that were not directly connected with each other during learning. Moreover, the ability to remember multiple elements of an event together, after having been presented with just a single cue, was improved after sleep compared to the condition in which the participants had stayed awake," summarizes Nicolas Lutz. This demonstrates the importance of sleep for completing partial information and processing complex events in the brain.

By monitoring the brain activity of the study participants during sleep, the authors of the study were also able to show that the improvement in memory performance is connected with so-called sleep spindles -- bursts of neural oscillatory activity during sleep, which are associated with the active consolidation of memory contents. This occurs through reactivation of the underlying neural structures while sleeping. "This finding suggests that sleep spindles play an important role in the consolidation of complex associations, which underlie the completion of memories of whole events," says Professor Luciana Besedovsky, lead researcher of the study.

According to Lutz and Besedovsky, the identified effects of sleep on memory can be seen as an important adaptation of the human brain, because they help people draw a more coherent picture of their environment, which in turn enables them to make more comprehensive predictions of future events. "And so our results reveal a new function by which sleep can offer an evolutionary advantage," reckons Luciana Besedovsky. "Furthermore, they open up new perspectives on how we store and access information about complex multielement events."

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Journal Reference :

  • Nicolas D. Lutz, Estefanía Martínez-Albert, Hannah Friedrich, Jan Born, Luciana Besedovsky. Sleep shapes the associative structure underlying pattern completion in multielement event memory . Proceedings of the National Academy of Sciences , 2024; 121 (9) DOI: 10.1073/pnas.2314423121

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  • Published: 24 June 2019

Effects of Relaxing Music on Healthy Sleep

  • Maren Jasmin Cordi 1 , 2 ,
  • Sandra Ackermann 1 &
  • Björn Rasch   ORCID: orcid.org/0000-0001-7607-3415 1 , 2  

Scientific Reports volume  9 , Article number:  9079 ( 2019 ) Cite this article

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  • Human behaviour
  • Quality of life

Sleep is vital for human health and wellbeing, and sleep disturbances are comorbid to many mental and physiological disorders. Music consistently improves subjective sleep quality, whereas results for objective sleep parameters diverge. These inconsistencies might be due to inter-individual differences. Here, 27 female subjects listened to either music or a control text before a 90 minutes nap in a within-subjects design. We show that music improved subjective sleep quality as compared to the text condition. In all participants, music resulted in a reduced amount of sleep stage N1 during the nap. In addition, music significantly increased the amount of slow-wave sleep (SWS) and increased the low/high frequency power ratio. However, these effects occurred only in participants with a low suggestibility index. We conclude that listening to music before a nap can improve subjective and objective sleep parameters in some participants.

Introduction

Sleep plays an important role for maintaining physical and mental health 1 , 2 , 3 , 4 , 5 , and is critical for general well-being 6 , 7 . However, sleep disturbances are highly common in our society 8 , with increased prevalence in ageing 9 as well as among people at risk of or suffering from a psychiatric disorder 10 . The use of sleep enhancing medicine is problematic, as its effectiveness decreases across time and may lead to addiction. Consequently, researchers need to empirically validate the effectiveness of non-pharmacological and easy to implement tools to support healthy sleep.

Listening to music is a widely used tool to improve sleep. In an online survey in a general population 62% (out of n = 651 respondents) stated to have at least once used music to help them sleep 11 . In a survey in over 500 patients with sleep disorders, over 50% reported to use music as sleep aid 12 . In a meta-analysis based on six included studies in a total of 314 patients, Jespersen and colleagues reported that music helped to improve subjective sleep quality in insomnia patients 13 . Similarly, sedative music while resting could effectively improve subjectively rated sleep in patients with sleep complaints 14 . In 20 included trials, another meta-analysis showed positive effects of music based interventions before bedtime on PSQI scores, sleep onset latency and sleep efficiency in patients suffering from primary insomnia 15 . Positive effects of listening to music on subjectively evaluated sleep were found in toddlers when listening throughout the naptime, preschool children and young adults listening 45 minutes at naptime and/or bedtime 16 , 17 , 18 . Also in older women, two further studies reported shorter sleep onset time, less nocturnal awakenings and better sleep quality and satisfaction as measured with a questionnaire and sleep logs when listening to music when going to bed 19 , 20 . In sum, the positive effects of listening to music on subjective ratings of sleep quality are well established across different age groups including both healthy participants and patients.

In contrast to subjective sleep quality, empirical findings on the effects of music on objectively measured sleep are scarce and inconsistent. For example, Lazic and Ogilvie 21 did not find differences in polysomnographic measures when subjects in a within design either listened to music, tones or neither tones nor music after lights off until continuous sleep was observed. Similarly, Chang et al . 22 did not observe any positive effects of music playing when lying in bed on objective measures of sleep onset, total sleep time, sleep interruption and sleep efficiency, in spite of positive effects of music on subjective sleep quality. Only Chen et al . 23 reported effects of music on objective sleep parameters: One hour of listening to music after subjects went to bed significantly decreased the amount of stage N2 sleep, and increased deep SWS only in a subgroup of participants with long sleep latencies. However, further, more fine grained analyses of sleep-related oscillations are missing in this study. This paper points towards the possibilities that the effects of music on objective sleep parameters might depend on certain individual differences.

One possible factor being involved in individual differences in the effects of non-pharmacological interventions on objective sleep may be suggestibility. Suggestibility describes the ability of a person to respond to suggestions in terms of perceptual, cognitive, neural and bodily processes 24 . Suggestibility accounts for strong individual differences in the effects of hypnotic interventions, but also outside of hypnosis, like for instance “placebo” effects 25 (but see 26 ). Also, a suggestion to not attribute meaning to Stroop words reduced the Stroop effect in highly suggestible subjects irrespective of whether a hypnotic state was established before or not 27 . It was even shown that suggestions impact on memories, beliefs and behaviors 28 . In a series of studies 29 , we have shown that hypnotic suggestion to sleep deeper increases deep SWS and slow wave activity (SWA) in high suggestible healthy participants. No enhancement of SWS was observed for low suggestible females. A similar pattern of results occurred in a conceptual replication of this study in elderly women 30 . As we here applied the same study design as in those previous experiments, we were interested if the effect of music on sleep also depends on expectations and self-suggestions. We expect that the effect of music on objective sleep parameters and sleep related oscillations in the electroencephalogram (EEG) is stronger in high vs. low suggestible participants.

In the current study we tested if listening to music as compared to a spoken text before a midday nap improves sleep quality in high vs. low suggestible females. We measured sleep objectively using polysomnography. The experimental design was identical to our previous studies on the effects of hypnotic suggestions on sleep, except that a musical piece instead of the standardized hypnotic suggestion was used. The musical piece was composed by Dr. Lee Bartel for promoting sleep, called “Drifting into Delta”track 2 , 31 . It was pre-selected in a pilot study due to its best effects on subjective ratings of sleep quality. As it is supposed to induce deep sleep through its frequencies of 0.01–2 Hz we also analyzed brain activity during listening to the sound. To foster the role of subjective expectations and beliefs, subjects were explicitly informed that the music composition used was specifically designed to deepen sleep. Consequently we also assumed to find increased low and reduced high frequency EEG activity during the nap following the music condition. In order to test possible consequences of altered sleep on cognitive performance, we additionally introduced memory tests before and after the nap.

Subjective sleep measures

As expected from previous studies, subjects reported better subjective sleep after music (3.69 ± 0.16, scale from 1 to 5) as compared to the control text (3.28 ± 0.18) as indicated by a significant main effect of sound on the dependent variable subjective sleep quality ( F (1, 24) = 4.36, p  = 0.048, eta 2  = 0.15). The interaction with suggestibility did not reach significance ( p  = 0.15).

Objective sleep measures

For objective measures of sleep architecture, only one parameter revealed a significant effect: Listening to music before the 90 min nap decreased the time spent in N1 sleep (6.13 ± 0.73 min) as compared to the control condition (8.00 ± 0.78 min, main effect sound: F (1, 25) = 5.39, p  = 0.029, eta 2  = 0.18, see Fig.  1A ). In all other sleep stages, the main effect of sound was non-significant (all p  ≥ 0.15 see Table  1 ). Additionally, we observed a significant interaction between the type of sound and suggestibility on SWS increase (where the amount of SWS in the control condition was set to 100%): In low suggestibles, music increased the percentage of SWS by +46.18% ( p  = 0.035), whereas a non-significant reduction of SWS (−9.84%, p  = 0.60) occurred in high suggestible participants (interaction F (1, 25) = 4.37, p  = 0.047, eta 2  = 0.15), See Fig.  1B . In absolute values, low suggestibles spent 31.35 ± 3.83 min in SWS after listening to music as compared to 20.58 ± 3.58 min after listening to the control text ( p  = 0.027). In contrast, no difference in time spent in SWS occurred for high suggestible participants (23.11 ± 3.50 min vs. 23.86 ± 4.27 min, for music vs. control condition, p  = 0.89). Both results were not affected when considering that during listening to music, 9 low and 11 high suggestible subjects fell asleep while 4 low and 3 high suggestibles did not (all p  ≥ 0.25 for this factor).A chi-square test comparing the number of subjects falling asleep and suggestibility was non-significant (Chi 2 (1) = 0.31, p  = 0.58). During listening to the text, 11 low and 11 high suggestibles fell asleep, while 2 low and 3 high suggestibles did not (Chi 2 (1) = 0.69, p  = 0.16).

figure 1

Effect of music on sleep stages. ( A ) Both groups benefitted from reduced minutes spent in N1 after music compared to text condition. The within-group comparison was significant only for low suggestible subjects. ( B ) The interaction between sound and suggestibility in SWS increase was driven by low suggestibles showing a higher increase in SWS after music compared to text while high suggestibles did not benefit from music. The x-axes distinguish low (LS) and high suggestible (HS) subjects. The graphs show the values for N1 minutes and SWS increase in percent in the text (black bar) and music condition (grey bar). Asterisks indicate p values ≤ 0.05. Mean + /− standard errors of the mean (SEM) are displayed.

Additionally, the interaction between sound and suggestibility for REM sleep was significant ( F (1, 25) = 6.39, p  = 0.018, eta 2  = 0.20), as demonstrated in Table  1 . However, only few participants had actually reached REM sleep (high suggestible: 9 vs. 4 participants after music vs. text, low suggestibles: 3 vs. 5 participants). Although the Chi-square for a difference from an equal distribution was not significant ( p  > 0.15), we refrained from further analyses of this finding due to the very low sample size in this analysis.

Please note that differences in total sleep time (see Table  1 ) might rather be a product of divergent sleep latencies as time in bed was limited to 90 minutes. In general, high suggestible participants spent more time in N2 sleep and were less time awake after sleep onset as compared to low suggestibles (both p  = 0.05, see Table  1 ).

Poweranalyses during non-REM (NREM) sleep

For a more fine grained analysis of the effects of music on sleep, we compared power of sleep-related brain oscillations during NREM sleep stages N2 and SWS between the music and the control condition. The analyses were conducted using repeated measure ANOVAs with within-subject factors FCP (frontal, central, parietal), hemisphere (right vs. left) and type of sound (music vs. text) and between-subjects factor suggestibility (high vs. low). Total power (i.e., 0.5–50 Hz) did not differ significantly between the two experimental sessions ( p  > 0.16). Therefore, the poweranalyses are based on the absolute muV values in the single frequency bands.

We focused on the ratio between low and high frequencies during NREM sleep, as this marker has been discussed as indicator for restorative sleep (SWA divided by beta activity, see 32 and 33 ). Higher values thus mean a higher proportion of low than high frequency power in the signal during NREM sleep and might be associated with more restorative sleep. Music significantly increased the low/high frequency ratio during sleep as compared to the control text only in low suggestible subjects (interaction: F (1, 25) = 5.72, p  = 0.025, eta 2  = 0.19). The post-hoc t-test showed a significantly higher ratio after music (154.07 ± 32.69) than after text (89.67 ± 15.46), t (12) = −2.45, p  = 0.031), while this difference was non-significant in high suggestible subjects ( p  = 0.65). The effects were wide-spread across the whole scalp, see Fig.  2 , upper panel, although the ANOVA indicated that this interaction was dependent on localization (three-way interaction between sound, suggestibility and FCP F (2, 50) = 3.90, p  = 0.027, eta 2  = 0.14). Following up on this revealed that in high suggestibles, FCP * sound was non-significant ( p  = 0.64), while the interaction was significant in low suggestible subjects ( F (2, 24) = 3.40, p  = 0.05, eta 2  = 0.22. Here, all post-hoc tests were significant and showed a higher ratio in frontal ( t (12) = 2.25, p  = 0.044), central ( t (12) = 2.57, p  = 0.024) and parietal electrodes ( t (12) = 2.71), p  = 0.019). All other effects with sound or suggestibility in this ANOVA were non-significant ( p  > 0.06).

figure 2

Power analyses during NREM sleep. Displays t-values of the analyses on SWA/beta Ratio (upper panel) and Sigma power (lower panel). The 1 st column shows comparisons on music versus text in low suggestibles, while higher values indicate higher power after music. The 2 nd column shows the results of the same analysis for high suggestibles. The 3 rd column indicates results of the group comparison high versus low suggestibles on the difference music – text. Positive values mean higher power value differences in low compared to high suggestibles. Significant electrodes (non-corrected for multiple comparisons) are indicated with white dots.

Analyzing SWA alone revealed a trend in the interaction between suggestibility and sound ( F (1, 25) = 3.37, p  = 0.08, eta 2  = 0.12). No other main effects or interaction with those two factors were significant (all p  > 0.10). When only investigating SWA in SWS this pattern remained. Please note however, that in this analysis 2 low suggestible subjects had to be excluded as they did not show SWS in one of the sessions. Examining beta power alone revealed a significant three-way interaction between sound, suggestibility and FCP ( F (2, 50) = 6.55, p  = 0.003, eta 2  = 0.21). While in high suggestibles, beta power did not differ depending on sound ( p  > 0.80) or FCP* sound ( p  > 0.10), a main effect of sound ( F (1, 12) = 5.46, p  = 0.038, eta 2  = 0.31) and its interaction with FCP ( F (2, 24) = 4.56, p  = 0.021, eta 2  = 0.28) were found in low suggestibles. After listening to music, beta power was lower in low suggestibles (0.59 ± 0.09) than after text (0.67 ± 0.08). This difference was most pronounced in frontal electrodes ( t (12) = −2.55, p  = 0.026)) and central electrodes ( t (12) = −2.26, p  = 0.043) and not significant in parietal area ( t (12) = −1.99, p  = 0.070)).

We did neither find effects of sound in theta nor in alpha power (all p  > 0.20).

Interestingly, power in the sigma band was reduced after music (1.19 ± 0.09) compared to the text condition (1.26 ± 0.10), F (1, 25) = 6.06, p  = 0.02, eta 2  = 0.20. The interaction between sound and suggestibility was a trend ( F (1, 25) = 3.90, p  = 0.06, eta 2  = 0.14) and further specified by the three-way interaction between sound, FCP and suggestibility ( F (2, 50) = 6.20, p  = 0.004, eta 2  = 0.20). In low suggestibles, sigma band power was higher in frontal and central electrodes after text compared to music ( t (12) = −2.69, p  = 0.02 and t (12) = −2.31, p  = 0.04, respectively), while in high suggestibles, all direct comparisons were non-significant (all p  > 0.20), see Fig.  2 , lower panel. All other effects were non-significant (all p  > 0.20). Due to significant effects in the power range of spindle activity, we analyzed number and density of spindles during NREM sleep. All effects were however non-significant (all p  > 0.20, see Table  1 ). When restricting the analysis to frontal slow spindles, low suggestibles had descriptively reduced spindle density after music listening (3.02 ± 0.49) as compared to the control text (3.37 ± 0.60). However, the interaction between sound and suggestibility was not significant ( p  > 0.20). Also no significant main effects occurred ( p  > 0.07, see Table  1 ). The number of slow spindles also revealed no significant results ( p  > 0.50). All effects of density and number of parietal fast spindles were p  > 0.50.

Cardiac responses and poweranalyses during listening

As low suggestible participants profited more from relaxing music, it might have been possible that music induced stronger responses on the autonomic and central nervous system already during the listening period. In the ANOVA with sound and suggestibility on heart rate during listening, the interaction was not significant ( p  > 0.80). Also, neither the main effect of sound nor suggestibility reached significance (both p  > 0.60). Similarly, during subsequent sleep, heart rate did neither differ depending on sound ( p  = 0.44), suggestibility ( p  > 0.70) nor sound * suggestibility ( p  > 0.60).

With respect to the power analysis of EEG activity during listening, we restricted our analysis to epochs during listening, in which the subjects were awake. Thus, all epochs containing stage N1 or stage N2 were excluded.

In this analysis, we observed a significant three-way interaction between sound, suggestibility and hemisphere for the SWA/beta ratio ( F (1, 25) = 4.79, p  = 0.038, eta 2  = 0.16). In low suggestibles, the main effect of sound and all its interactions were p  > 0.08 with non-significantly higher values during music than sound. In high suggestibles all follow-up tests were p  > 0.20. The same pattern was true for the four-way interaction with FCP ( F (2, 50) = 4.21, p  = 0.020, eta 2  = 0.14) and its follow-up tests (all p  > 0.06 in low and p  > 0.30 in high suggestibles). All main effects and interactions with sound or suggestibility were p  ≥ 0.09 in the alpha and p  > 0.10 in the theta and sigma band (see the Supplementary Material for the results of the power analyses of the entire listening period).

Cognitive tasks

To investigate effects of altered sleep pattern on post-nap cognitive performance, we included vigilance task after sleep. Here, performance was equal in both groups and after both conditions concerning reaction times, number of reactions and error rate ( p  > 0.09). Thus, attentional processes after sleep did not differ dependent on condition.

Also, we measured memory consolidation across sleep by presenting paired associate learning task (PAL) before and after the nap. For this parameter, a main effect of sound ( F (1, 24) = 4.45, p  = 0.05, eta 2  = 0.16) demonstrated that maintenance of memory performance across sleep was higher after listening to the text (99.89% ± 1.24) than across sleep after listening to music (94.95% ± 1.61). The other effects were p  > 0.10. PAL performance across sleep with presleep memory set to 100% were 96.00 ± 1.87% after music and 101.25 ± 2.09% after text in low suggestibles and 94.04 ± 2.56% after music and 98.72 ± 1.46% after text in high suggestibles. Both differences were p  > 0.10. The difference between PAL performance across nap after listening to music vs. text did not correlate with the difference in any sleep stage (all p  > 0.15, uncorrected) nor subjective sleep quality ( p  > 0.20), nor the difference in any power band (all p  > 0.10, uncorrected).

Here we tested if listening to relaxing music before a nap helps improving subjective and objective sleep quality of a nap in young healthy females. Our results showed a generally improving effect of listening to music on subjectively rated sleep quality. Subjects reported better sleep quality after having listened to music compared to listening to a control text. This is in line with previous studies showing beneficial effects of music on sleep ratings 14 , 18 . While subjective sleep is highly relevant for experienced wellbeing and the diagnosis of for instance insomnia, it does not necessarily correspond to objective sleep measures. We hence additionally measured sleep with polysomnography and compared sleep patterns after listening to music to sleep patterns after listening to a control text. Objectively, subjects spent less time in the sleep/wake transition phase N1 after music compared to the text condition. More fine-grained analyses showed that power in the sigma band (11–15 Hz) was also reduced during NREM sleep in the nap after listening to music versus listening to a text. Thus, overall, it can be concluded that listening to relaxing music before a nap can reduce time spent in N1 and the power of high frequency bands.

As we have seen in previous studies on the effect of hypnotic suggestions on a nap in healthy young and older females, it was important to consider subjects’ suggestibility. This measure indicates how sensitive subjects react to suggestions given externally. Concerning hypnosis, only those who were responsive for suggestions benefitted from the verbal, hypnotic intervention. To investigate whether suggestibility is also a relevant factor when testing the effect of music on sleep, we included this additional factor. We found that low suggestible subjects benefited from an increase in the amount of SWS of about 46% compared to their sleep after listening to the text. Whether this increase was to the detriment of the amount of REM sleep cannot be answered in this study due to a very limited amount of subjects reaching REM sleep at all during this nap. Additionally, as this was only a nap study we cannot make substantiated statements concerning REM sleep which usually takes place late in a sleep epoch. To confirm a possible trade-off between SWS and REM sleep the study should follow a nighttime design.

Also in a more fine grained analysis of oscillatory power bands during sleep, the level suggestibility significantly altered the effects of music. As studies have shown that insomniacs often experience low sleep quality when a high amount of high frequency penetrates their sleep 33 , 34 , we expected reduced high and increased low frequency power after listening to the relaxing music before sleep. A ratio quantifying the proportion of high and low frequency power in the NREM signal was calculated by dividing SWA power by beta power. Thus, higher values indicate a higher proportion of low compared to high frequencies in the signal. Low values had been referred to as an indication of worse sleep quality 32 , 34 and less restorative sleep 35 . Here we found higher SWA/beta ratios during sleep after listening to music than after listening to the text in low suggestible subjects. No changes were observed in high suggestible participants. These results using objective sleep data suggest that low suggestibles might have experienced a more restorative sleep after listening to music.

In spite of more restorative sleep, sigma power in frontal and central brain regions during sleep was significantly reduced in low suggestibles after listening to music. However, we did not observe a reduction in sleep spindles in this condition, in spite of the reduction in sigma power. Previous studies have reported changes in spindle density without changes in sigma power (e.g. 36 ), while here we report the opposite case. A possible explanation is that music reduces more long-lasting sigma power in low suggestible which might be unrelated to discrete sleep spindles (lasting maximal 3 seconds).

Interestingly, memory consolidation across the sleep interval was significantly reduced after listening to music as compared to the control text in the entire sample. The reduction in memory is particularly puzzling, because listening to non-verbal music after learning should induce less interference than the verbal control text. As SWS has been implicated in processes of memory consolidation during sleep 37 , 38 , increases in SWS and SWA /beta ratios in low suggestible participants should have led to improvements in declarative memory consolidation during sleep in low suggestible participants. It might be possible that the impairment in memory consolidation is related to the general reduction in sigma power in the music condition, as sleep spindles have suggested to be a critical factor for successful consolidation of memories during sleep 39 . However, as discrete sleep spindles were not altered by music in our study and changes in sigma power did not correlate with changes in memory performance, this possibility remains highly speculative and requires further examination. Note that memory consolidation was also not improved in spite of increases in SWS and SWA after hypnotic suggestions in high suggestibles in our previous studies 29 , 30 . In sum, low suggestibles benefitted more from relaxing music for their nap than high suggestibles. This is contrary to what we expected, as usually, a high level of suggestibility indicates more pronounced behavioral reactions to suggestions or expectations. Here we briefed the subjects about our expectations to find improved sleep after music compared to text. High suggestibles would have rather expected to be positively influenced by this information. However, also mere expectations without hypnotic induction did not lead to SWS increase in our nap study 29 . This suggests that high suggestibles show greater effects when the procedure is framed as hypnosis and a hypnotic induction actually takes place. On the contrary, low suggestibles seem to prefer non-verbal relaxation to hypnotic suggestions. This might result from a concern of being manipulated by direct and concrete instruction when being confronted with a spoken text. Under this assumption it could be even more effective to increase self-control by for instance letting subjects choose their own music. Trahan et al . (2018) discussed that familiar, self-chosen in contrast to given music might be less analgesic and anxiolytic 11 . These authors showed that particularly subjects with musical engagement use music to improve sleep. In our data, the distribution of subjects playing an instrument did not differ between high and low suggestible subjects. However, it might have possibly required a more precise measure of familiarity with music to test this assumption. Besides, effectiveness of a sleep-related intervention depends on whether sleep destabilization is triggered by psychological (i.e. mood, thoughts) or physical (i.e. arousal) factors. Being explicitly and verbally guided by hypnotic suggestions could thus be more expedient when rumination must be stopped while reducing physical tension could be better achieved by a more indirect and open form of relaxation intervention. Unfortunately, we did not measure those aspects in our sample to test group differences in this respect. Cardiac responses during the listening period and brain activity during wakeful listening remained unaffected. Probably, a combination of both, suggestions shaded with relaxing music could be beneficial for both needs and thus, all subjects could improve their sleep.

The importance of focusing on subjective and objective measures when investigating effects of any intervention on sleep was demonstrated by the fact that in neither of the groups the ratio or the amount of SWS was correlated with subjective sleep quality rating. Possibly separate mechanisms affect subjective and objective measures leading to converging results.

In sum, here we show that subjective sleep quality in young healthy females’ naps can be improved by relaxing music to some extent. Music reduced time spent in N1 and arousing high frequency power during following NREM sleep while leading to improved subjective sleep quality. This method seemed to be particularly beneficial for subjects low in suggestibility, although this conclusion should be tested critically in further studies and possible other confounding factors should be considered. Taken together, our results support the conclusion that music is a non-pharmacological, low-risk and low– cost tool to improve sleep on a subjective and objective level. To what extent this is applicable to sleep disturbances cannot yet be answered.

Thirty-two healthy, right-handed women (19–35 years, mean age 23.81, SD = 4.28) participated. To avoid gender effects we excluded males. Three subjects had to be excluded due to lacking sleep in one of the sessions, reporting naps in the week before the experiment or heavy cough during the session. In two subjects suggestibility could not be measured, one subject did not correctly understand the declarative memory test and was excluded in that analysis. The final sample consisted of 27 subjects (aged 23.22 ± 3.85 years). Ten of them indicated to play an instrument (equally distributed between high and low suggestibles, Chi squared p  = 0.29), four indicated to practice any kind of relaxation exercise. The subjects were German natives or had advanced German skills. They did neither regularly take naps, nor suffer from a diagnosed sleep disorder nor consume drugs. Hormonal contraceptives were allowed. None did shift work or intercontinental flights within 6 weeks before participation. For the experimental days they were asked to refrain from caffeine and alcohol, get up between 7 and 8 a.m. and to not do any sports until the session. Subjects received oral and written study information and gave their written informed consent before participation. They were paid 140 CHF for full participation. The Ethic Committee of the University of Zurich approved the study and the experiment was performed in accordance with the existing regulations.

The study consisted of four sessions. In the introduction group session, the study flow and its purpose were explained. We explicitly mentioned the expectation that music should improve sleep quality. Besides, questionnaires assessed sleep quality, demographic data and suggestibility. In the second session, subjects took a nap in the sleep laboratory to become familiar with the setup and sleep environment. The sleep diary was handed out to record sleep behavior in the week before the first experimental session. The experimental sessions took place at the same day of the week, separated by one week and started at 1 p.m. After performing the paired associate task and the finger tapping task, subjects went to bed. Depending on randomization, either music or the control text was presented via loudspeakers from the bedside cabinet when subjects lay in bed directly after switching the lights off. Participants were allowed to fall asleep at any time, but asked to listen to the sound. Listening and nap were recorded for a total of 90 minutes with polysomnography (PSG). Subjects were awakened 90 minutes after switching the lights off and asked to fill out the sleep quality questionnaire and perform again on the memory tasks. After each session, the sleep diary for the upcoming week was handed out.

Audio recordings

Music: Before the experimental naps, subjects either listened to a music tape or a spoken text. The selection of the musical piece was performed in a pilot study: Five 15 minutes-pieces of music, composed to induce sleep, were selected according to internet evaluations (google, youtube, amazon). Fifteen subjects listened to this selection before falling asleep and rated the tapes the next morning. According to subjectively reported enhancement of sleep quality, shortened sleep latency and better recovery, the best rated tape was chosen for the study. It was a piece composed by Dr. Lee Bartel for promoting sleep, called “Drifting into Delta”track 2 , 31 . It is supposed to induce deep sleep through its frequencies of 0.01–2 Hz, based on treatises on the influence of auditory pulsing of 0.25 to 2 Hz frequencies on oscillatory coherence 40 . Please find the spectrogram displaying the contained frequencies in the Supplementary Material. The series “Music to Promote Sleep” out of which we took this piece was tested before in patients suffering from fibromyalgia and showed positive effects on subjective sleep quality 41 .

Text: The text in the control condition was taken from Cordi et al . 29 and was a documentary about mineral deposits. A text was chosen as control to exclude a simple effect of listening. It was spoken by a male voice in normal speed and intonation.

Both tapes played for about 14 minutes. They were presented at a volume of 45–50 dB through loudspeakers before falling asleep. The order was determined randomly across subjects. Before playing the sound we instructed the subjects to relax and to get themselves into the tape, trying to let pass any thoughts that might come up and to not stuck on them. Subjects were allowed to fall asleep whenever possible.

Questionnaires

Standardized Sleep Inventory for the Assessment of Sleep – Revised Version, SFA – R: It assesses subjectively reported sleep behavior within the last sleep period (adapted to a nap here) on several dimensions 42 . As a measure for subjective sleep quality we analyzed the mean of an item asking to rate the previous sleep on 7 adjectives. We excluded the adjective “extensive” as the nap was limited in time by protocol. Higher values indicate better ratings. Data of one subject is missing.

Pittsburg Sleep Quality Index, PSQI: The PSQI measures general subjective sleep quality within the last month 43 . The global score ranges from 0 to 21, while 5 is the cutoff value for sleep difficulties. Overall, the sample scored 4.41 ± 2.26 ranging from 1 to 10 (5 subjects scoring >5). High and low suggestible subjects did not differ on this score ( p  = 0.83).

Sleep diary: To control sleep behavior one week before each of the experimental sessions, a sleep diary was used. We used the information about the wake up time to verify that subjects got up between 7 and 8 a.m. on experimental days.

Harvard Group Scale of Hypnotic Susceptibility, Form A, HGSHS: A: This is a widely used, standardized tool to determine hypnotic suggestibility 44 . After listening to a tape with a recorded hypnosis, subjects are confronted with a questionnaire on their experiences during listening. According to their rating about how strongly they had reacted to the given suggestions, they were grouped as high (score 7 or higher, mean = 8.50 ± SD = 1.23, n = 14) or low suggestible (scores 0–6, mean = 4.85 ± 1.21, n = 13). The groups differed significantly on those means, t (25) = −7.78, p  < 0.001, but not on age ( p  > 0.50).

Memory measures

Word pair associate learning task, PAL: In this episodic memory task, subjects are asked to remember as many of the presented word pairs as possible for a later cued recall 45 . The words are presented consecutively in EPrime in black font on a white computer screen. After a 500 ms fixation cross, the first word of the pair was presented for 1000 ms, followed by a blank interval of 200 ms separating it from the according second word of the pair. Another blank interval of 500 ms was displayed before the next fixation cross separating the previous from the following word pair. During cued recall, the first word of the pair was displayed until the subject remembered the according second word or indicated that it was forgotten. No feedback was given. The order during recall was different from the order during learning, but stable across subjects. Recall was tested directly before the nap and afterwards in the same order. Memory performance was defined as the number of correctly recalled words after sleep relative to the presleep performance which was set to 100%.

The procedural memory task and its results are reported in the Supplementary Material.

Psychomotor vigilance task (PVT): To overcome sleep inertia and to control for possible attentional differences before the memory tests after the nap, a vigilance task was applied. Subjects were confronted with a black screen on which a timer started to count upwards at unforeseen times. As soon as this was recognized, subjects were asked to press the space bar. Error rate and reaction time were analyzed.

Sleep recordings, scoring and EEG data processing

Sleep was measured with the Geodesic EEG System 400 series (Electrical Geodesics, Inc.) including high density electroencephalogram (EEG) with 128 electrodes, electromyogram (EMG), electrooculogram (EOG) and electrocardiogram (ECG). Data was collected with a sampling rate of 500 Hz, impedances were kept below 50 kΩ. Data was filtered and scored according to the criteria of the American Association of Sleep Medicine (AASM) (i.e., 0.1 Hz lowpassfilter, 35 Hz highpassfilter). Three coworkers blind to condition scored the data at the electrodes F4, C4, P4, O2, HEOG, VEOG and EMG. In case of disagreement, a forth blind scorer was consulted.

Power analyses were based on Fast Fourier Transformations (FFT). During data preprocessing we excluded the electrodes on the outer edge of the EEG cap (i.e. electrode numbers 17, 48, 49, 56, 63, 68, 73, 81, 88, 94, 99, 107, 113, 119, 125), filtered the data between 0.1 and 50 Hz and applied a 50 Hz Notch Filter. We re-referenced data to the average of both mastoids. The signal that was recorded while the music and the control file were presented was segmented into epochs of 4096 data points (~8 seconds) with an overlap of 409 points to account for the later applied Hamming-window of 10% and semi-automatically corrected for artefacts within the wake periods in the 14 minutes in which subjects listened to the tape. The same was done for segments scored as NREM sleep. Afterwards, the FFT was calculated with a Hamming-window of 10% and a resolution of 0.2 Hz. From this analysis, we exported the power in the respective areas (muV*Hz) for slow-wave activity, SWA (0.5–4.5 Hz), theta (4.5–8 Hz), alpha (8–11 Hz), sigma (11–15 Hz), beta (15–30 Hz) and the total power (0.5–50 Hz). Similarly to Krystal 32 and Maes et al . 33 we focused on the ratio between low and high frequencies during NREM sleep as an indicator for restorative sleep. SWA power was divided by beta power and hence higher values would indicate slowing of the EEG activity.

For the analyses on spindle density, we selected the derivations Fz, Cz and Pz for the analysis of the spindles across all sleep stages. On those, we performed a frequency extraction for frequency power of 11–15 Hz and added Spindle on and off markers to identify its on- and offset. Before automatic artifact correction detecting a maximal allowed difference of 200 muV within 200 ms in the EMG channel, we segmented the data into 1024 data point wide epochs (~2 seconds). Spindle events were counted for which the power signal exceeded a fixed threshold (±10 muV) for an interval lasting 0.5–3 sec. We extracted the amount of detected spindles (=spindle count) in those epochs separately for each sleep stage and separately for fast (13–15 Hz) and slow spindles (11–13 Hz). We then calculated spindle density per 1 minute within each sleep stage, also within NREM sleep (N2 + N3).

Heart rate: We analyzed electrocardial data with Kubios HRV Version 3.1 2. We first ran an automatic artifact correction on the unfiltered data that eliminated ectopic beats and artifacts based in dRR series. Afterwards, we excluded further visually detectable artifacts resulting from e.g. movements. Mean heart rate (HR) was measured for the time in which the subjects listened to the tapes and the following sleep episode separately. We could include all except 2 subjects for who the signal was too bad in one of the two sessions (1 low, 1 high suggestible).

Statistical analysis

The study followed a crossover design with a within-subjects comparison of the two naps. Data was analyzed using SPSS 23. The repeated measures analysis of variance (ANOVA) included the within subjects factor “sound” (music versus text) and the between subjects factor “suggestibility” (high versus low). When the Mauchly-Test was significant, we adapted values with Greenhouse-Geisser. We also adapted values when the Levene Test indicated uneven variances. Only the significant main effects and interactions were further investigated using paired samples t-tests according to Fisher’s protected LSD test. The level of significance was set to p  = 0.05.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Lange, T., Dimitrov, S. & Born, J. Effects of sleep and circadian rhythm on the human immune system. Ann. N. Y. Acad. Sci. 1193 , 48–59 (2010).

Article   ADS   CAS   Google Scholar  

Xie, L. et al . Sleep drives metabolite clearance from the adult brain. Science 342 , 373–7 (2013).

Strine, T. W. & Chapman, D. P. Associations of frequent sleep insufficiency with health-related quality of life and health behaviors. Sleep Med. 6 , 23–27 (2005).

Article   Google Scholar  

Kaneita, Y. et al . Associations between sleep disturbance and mental health status: A longitudinal study of Japanese junior high school students. Sleep Med. 10 , 780–786 (2009).

Stein, M. B., Belik, S.-L., Jacobi, F. & Sareen, J. Impairment Associated With Sleep Problems in the Community: Relationship to Physical and Mental Health Comorbidity. Psychosom. Med. 70 , 913–919 (2008).

Sano, A. et al . Influence of sleep regularity on self-reported mental health and wellbeing. Sleep 39 , A68 (2016).

Google Scholar  

Magnavita, N. & Garbarino, S. Sleep, Health and Wellness at. Work: A Scoping Review. Int. J. Environ. Res. Public Health 14 , 1347 (2017).

BFS. Schweizerische Gesundheitsbefragung 2012 . Bundesamt für Statistik BFS (2013).

Ohayon, M. M., Carskadon, M. A., Guilleminault, C. & Vitiello, M. V. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: Developing normative sleep values across the human lifespan. Sleep 27 , 1255–1273 (2004).

Melo, M. C. A. et al . Sleep and circadian alterations in people at risk for bipolar disorder: A systematic review. J. Psychiatr. Res. 83 , 211–219 (2016).

Trahan, T., Durrant, S. J., Müllensiefen, D. & Williamson, V. J. The music that helps people sleep and the reasons they believe it works: A mixed methods analysis of online survey reports. PLoS One 13 , e0206531 (2018).

Huang, C.-Y., Chang, E.-T. & Lai, H.-L. Use of integrative medicine approaches for treating adults with sleep disturbances. Appl. Nurs. Res. 43 , 49–55 (2018).

Jespersen, K. V, Koenig, J., Jennum, P. & Vuust, P. Music for insomnia in adults. Cochrane Database Syst . Rev , https://doi.org/10.1002/14651858.CD010459.pub2 (2015).

De Niet, G., Tiemens, B., Lendemeijer, B. & Hutschemaekers, G. Music-assisted relaxation to improve sleep quality: Meta-analysis. Journal of Advanced Nursing 65 , 1356–1364 (2009).

Feng, F. et al . Can music improve sleep quality in adults with primary insomnia? A systematic review and network meta-analysis. International Journal of Nursing Studies 77 , 189–196 (2018).

Field, T. Music Enhances Sleep in Preschool Children. Early Child Dev. Care 150 , 65–68 (1999).

Tan, L. P. The effects of background music on quality of sleep in elementary school children. J. Music Ther. 41 , 128–50 (2004).

Harmat, L., Takács, J. & Bódizs, R. Music improves sleep quality in students. J. Adv. Nurs. 62 , 327–335 (2008).

Johnson, J. E. The use of music to promote sleep in older women. J. Community Health Nurs. 20 , 27–35 (2003).

Lai, H.-L. & Good, M. Music improves sleep quality in older adults. J. Adv. Nurs. 49 , 234–244 (2005).

Lazic, S. E. & Ogilvie, R. D. Lack of efficacy of music to improve sleep: A polysomnographic and quantitative EEG analysis. Int. J. Psychophysiol. 63 , 232–239 (2007).

Chang, E.-T., Lai, H.-L., Chen, P.-W., Hsieh, Y.-M. & Lee, L.-H. The effects of music on the sleep quality of adults with chronic insomnia using evidence from polysomnographic and self-reported analysis: a randomized control trial. Int. J. Nurs. Stud. 49 , 921–30 (2012).

Chen, C.-K. et al . Sedative music facilitates deep sleep in young adults. J. Altern. Complement. Med. 20 , 312–7 (2014).

Raz, A. Suggestibility and hypnotizability: Mind the gap. Am. J. Clin. Hypn. 49 , 205–210 (2007).

Sheiner, E. O., Lifshitz, M. & Raz, A. Placebo response correlates with hypnotic suggestibility. Psychol. Conscious. Theory, Res. Pract. 3 , 146–153 (2016).

Lifshitz, M., Sheiner, E. O., Olson, J. A., Thériault, R. & Raz, A. On Suggestibility and Placebo: A Follow-Up Study. Am. J. Clin. Hypn. 59 , 385–392 (2017).

Raz, A., Kirsch, I., Pollard, J. & Nitkin-Kaner, Y. Suggestion reduces the Stroop effect. Psychol. Sci. 17 , 91–95 (2006).

Bernstein, D. M., Laney, C., Morris, E. K. & Loftus, E. F. False Memories About Food Can Lead to Food Avoidance. Soc. Cogn. 23 , 11–34 (2005).

Cordi, M. J., Schlarb, A. A. & Rasch, B. Deepening sleep by hypnotic suggestion. Sleep 37 , 1143–1152 (2014).

Cordi, M. J., Hirsiger, S., Mérillat, S. & Rasch, B. Improving sleep and cognition by hypnotic suggestion in the elderly. Neuropsychologia 69 , 176–182 (2015).

Bekker, H. & Bartel, L. R. Drifting into delta. (2004).

Krystal, A. D. Non-REM Sleep EEG Spectral Analysis in Insomnia. Psychiatr. Ann. 38 , 615–620 (2008).

Maes, J. et al . Sleep misperception, EEG characteristics and Autonomic Nervous System activity in primary insomnia: A retrospective study on polysomnographic data. Int. J. Psychophysiol. 91 , 163–171 (2014).

Article   CAS   Google Scholar  

Krystal, A. D. & Edinger, J. D. Measuring sleep quality. Sleep Med. 9 (Suppl 1), S10–7 (2008).

Moldofsky, H., Scarisbrick, P., England, R. & Smythe, H. Musculosketal symptoms and non-REM sleep disturbance in patients with ‘fibrositis syndrome’ and healthy subjects. Psychosom. Med. 37 , 341–351 (1975).

Gais, S., Mölle, M., Helms, K. & Born, J. Learning-dependent increases in sleep spindle density. J . Neurosci , 20026697 (2002).

Tononi, G. & Cirelli, C. Sleep and synaptic down-selection. In Research and Perspectives in Neurosciences 99–106 (John Wiley & Sons, Ltd (10.1111), https://doi.org/10.1007/978-3-319-28802-4_8 (2016)

Rasch, B. & Born, J. About sleep’s role in memory. Physiol. Rev. 93 , 681–766 (2013).

Mednick, S. C. et al . The critical role of sleep spindles in hippocampal-dependent memory: a pharmacology study. J. Neurosci. 33 , 4494–504 (2013).

Berger, J. & Turow, G. Music, science, and the rhythmic brain: Cultural and clinical implications. Music . Sci . Rhythm . Brain Cult . Clin . Implic , 1–215, https://doi.org/10.4324/9780203805299 (2012).

Book   Google Scholar  

Picard, L. M. et al . Music as a sleep aid in fibromyalgia. Pain Res. Manag. 19 , 97–101 (2014).

Görtelmeyer, R. SF-A/R und SF-B/R - Schlaffragebogen A und B - Revidierte Fassung . (Hogrefe, 2011).

Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R. & Kupfer, D. J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res. 28 , 193–213 (1989).

Bongartz, W. Scale of Hypnotic Susceptibility, Form A. Int. J. Clin. Exp. Hypn. 33 , 131–139 (1985). German norms for the Harvard Group.

Rasch, B., Born, J. & Gais, S. Combined blockade of cholinergic receptors shifts the brain from stimulus encoding to memory consolidation. J. Cogn. Neurosci. 18 , 793–802 (2006).

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Acknowledgements

We thank Loredana Lucartuorto for helping to setup and design the study and to lead the pilot study. We thank Carmen Hättenschwiler, Noemi Marti Georg Rahn and Raphael Zeltner for helping to collect data. Grant information: This study was supported by a grant from University of Zurich Clinical research priority project “Sleep and Health” and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 677875).

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All authors designed the experiment, S.A. led data collection, M.C. analyzed the data, all authors wrote the manuscript.

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Correspondence to Björn Rasch .

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Cordi, M.J., Ackermann, S. & Rasch, B. Effects of Relaxing Music on Healthy Sleep. Sci Rep 9 , 9079 (2019). https://doi.org/10.1038/s41598-019-45608-y

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‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice

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The gap between research findings and clinical practice is well documented and a range of strategies have been developed to support the implementation of research into clinical practice. The objective of this study was to update and extend two previous reviews of systematic reviews of strategies designed to implement research evidence into clinical practice.

We developed a comprehensive systematic literature search strategy based on the terms used in the previous reviews to identify studies that looked explicitly at interventions designed to turn research evidence into practice. The search was performed in June 2022 in four electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched from January 2010 up to June 2022 and applied no language restrictions. Two independent reviewers appraised the quality of included studies using a quality assessment checklist. To reduce the risk of bias, papers were excluded following discussion between all members of the team. Data were synthesised using descriptive and narrative techniques to identify themes and patterns linked to intervention strategies, targeted behaviours, study settings and study outcomes.

We identified 32 reviews conducted between 2010 and 2022. The reviews are mainly of multi-faceted interventions ( n  = 20) although there are reviews focusing on single strategies (ICT, educational, reminders, local opinion leaders, audit and feedback, social media and toolkits). The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Furthermore, a lot of nuance lies behind these headline findings, and this is increasingly commented upon in the reviews themselves.

Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been identified. We need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of research perspectives (including social science) in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed.

Peer Review reports

Contribution to the literature

Considerable time and money is invested in implementing and evaluating strategies to increase the implementation of research into clinical practice.

The growing body of evidence is not providing the anticipated clear lessons to support improved implementation.

Instead what is needed is better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice.

This would involve a more central role in implementation science for a wider range of perspectives, especially from the social, economic, political and behavioural sciences and for greater use of different types of synthesis, such as realist synthesis.

Introduction

The gap between research findings and clinical practice is well documented and a range of interventions has been developed to increase the implementation of research into clinical practice [ 1 , 2 ]. In recent years researchers have worked to improve the consistency in the ways in which these interventions (often called strategies) are described to support their evaluation. One notable development has been the emergence of Implementation Science as a field focusing explicitly on “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice” ([ 3 ] p. 1). The work of implementation science focuses on closing, or at least narrowing, the gap between research and practice. One contribution has been to map existing interventions, identifying 73 discreet strategies to support research implementation [ 4 ] which have been grouped into 9 clusters [ 5 ]. The authors note that they have not considered the evidence of effectiveness of the individual strategies and that a next step is to understand better which strategies perform best in which combinations and for what purposes [ 4 ]. Other authors have noted that there is also scope to learn more from other related fields of study such as policy implementation [ 6 ] and to draw on methods designed to support the evaluation of complex interventions [ 7 ].

The increase in activity designed to support the implementation of research into practice and improvements in reporting provided the impetus for an update of a review of systematic reviews of the effectiveness of interventions designed to support the use of research in clinical practice [ 8 ] which was itself an update of the review conducted by Grimshaw and colleagues in 2001. The 2001 review [ 9 ] identified 41 reviews considering a range of strategies including educational interventions, audit and feedback, computerised decision support to financial incentives and combined interventions. The authors concluded that all the interventions had the potential to promote the uptake of evidence in practice, although no one intervention seemed to be more effective than the others in all settings. They concluded that combined interventions were more likely to be effective than single interventions. The 2011 review identified a further 13 systematic reviews containing 313 discrete primary studies. Consistent with the previous review, four main strategy types were identified: audit and feedback; computerised decision support; opinion leaders; and multi-faceted interventions (MFIs). Nine of the reviews reported on MFIs. The review highlighted the small effects of single interventions such as audit and feedback, computerised decision support and opinion leaders. MFIs claimed an improvement in effectiveness over single interventions, although effect sizes remained small to moderate and this improvement in effectiveness relating to MFIs has been questioned in a subsequent review [ 10 ]. In updating the review, we anticipated a larger pool of reviews and an opportunity to consolidate learning from more recent systematic reviews of interventions.

This review updates and extends our previous review of systematic reviews of interventions designed to implement research evidence into clinical practice. To identify potentially relevant peer-reviewed research papers, we developed a comprehensive systematic literature search strategy based on the terms used in the Grimshaw et al. [ 9 ] and Boaz, Baeza and Fraser [ 8 ] overview articles. To ensure optimal retrieval, our search strategy was refined with support from an expert university librarian, considering the ongoing improvements in the development of search filters for systematic reviews since our first review [ 11 ]. We also wanted to include technology-related terms (e.g. apps, algorithms, machine learning, artificial intelligence) to find studies that explored interventions based on the use of technological innovations as mechanistic tools for increasing the use of evidence into practice (see Additional file 1 : Appendix A for full search strategy).

The search was performed in June 2022 in the following electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched for articles published since the 2011 review. We searched from January 2010 up to June 2022 and applied no language restrictions. Reference lists of relevant papers were also examined.

We uploaded the results using EPPI-Reviewer, a web-based tool that facilitated semi-automation of the screening process and removal of duplicate studies. We made particular use of a priority screening function to reduce screening workload and avoid ‘data deluge’ [ 12 ]. Through machine learning, one reviewer screened a smaller number of records ( n  = 1200) to train the software to predict whether a given record was more likely to be relevant or irrelevant, thus pulling the relevant studies towards the beginning of the screening process. This automation did not replace manual work but helped the reviewer to identify eligible studies more quickly. During the selection process, we included studies that looked explicitly at interventions designed to turn research evidence into practice. Studies were included if they met the following pre-determined inclusion criteria:

The study was a systematic review

Search terms were included

Focused on the implementation of research evidence into practice

The methodological quality of the included studies was assessed as part of the review

Study populations included healthcare providers and patients. The EPOC taxonomy [ 13 ] was used to categorise the strategies. The EPOC taxonomy has four domains: delivery arrangements, financial arrangements, governance arrangements and implementation strategies. The implementation strategies domain includes 20 strategies targeted at healthcare workers. Numerous EPOC strategies were assessed in the review including educational strategies, local opinion leaders, reminders, ICT-focused approaches and audit and feedback. Some strategies that did not fit easily within the EPOC categories were also included. These were social media strategies and toolkits, and multi-faceted interventions (MFIs) (see Table  2 ). Some systematic reviews included comparisons of different interventions while other reviews compared one type of intervention against a control group. Outcomes related to improvements in health care processes or patient well-being. Numerous individual study types (RCT, CCT, BA, ITS) were included within the systematic reviews.

We excluded papers that:

Focused on changing patient rather than provider behaviour

Had no demonstrable outcomes

Made unclear or no reference to research evidence

The last of these criteria was sometimes difficult to judge, and there was considerable discussion amongst the research team as to whether the link between research evidence and practice was sufficiently explicit in the interventions analysed. As we discussed in the previous review [ 8 ] in the field of healthcare, the principle of evidence-based practice is widely acknowledged and tools to change behaviour such as guidelines are often seen to be an implicit codification of evidence, despite the fact that this is not always the case.

Reviewers employed a two-stage process to select papers for inclusion. First, all titles and abstracts were screened by one reviewer to determine whether the study met the inclusion criteria. Two papers [ 14 , 15 ] were identified that fell just before the 2010 cut-off. As they were not identified in the searches for the first review [ 8 ] they were included and progressed to assessment. Each paper was rated as include, exclude or maybe. The full texts of 111 relevant papers were assessed independently by at least two authors. To reduce the risk of bias, papers were excluded following discussion between all members of the team. 32 papers met the inclusion criteria and proceeded to data extraction. The study selection procedure is documented in a PRISMA literature flow diagram (see Fig.  1 ). We were able to include French, Spanish and Portuguese papers in the selection reflecting the language skills in the study team, but none of the papers identified met the inclusion criteria. Other non- English language papers were excluded.

figure 1

PRISMA flow diagram. Source: authors

One reviewer extracted data on strategy type, number of included studies, local, target population, effectiveness and scope of impact from the included studies. Two reviewers then independently read each paper and noted key findings and broad themes of interest which were then discussed amongst the wider authorial team. Two independent reviewers appraised the quality of included studies using a Quality Assessment Checklist based on Oxman and Guyatt [ 16 ] and Francke et al. [ 17 ]. Each study was rated a quality score ranging from 1 (extensive flaws) to 7 (minimal flaws) (see Additional file 2 : Appendix B). All disagreements were resolved through discussion. Studies were not excluded in this updated overview based on methodological quality as we aimed to reflect the full extent of current research into this topic.

The extracted data were synthesised using descriptive and narrative techniques to identify themes and patterns in the data linked to intervention strategies, targeted behaviours, study settings and study outcomes.

Thirty-two studies were included in the systematic review. Table 1. provides a detailed overview of the included systematic reviews comprising reference, strategy type, quality score, number of included studies, local, target population, effectiveness and scope of impact (see Table  1. at the end of the manuscript). Overall, the quality of the studies was high. Twenty-three studies scored 7, six studies scored 6, one study scored 5, one study scored 4 and one study scored 3. The primary focus of the review was on reviews of effectiveness studies, but a small number of reviews did include data from a wider range of methods including qualitative studies which added to the analysis in the papers [ 18 , 19 , 20 , 21 ]. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. In this section, we discuss the different EPOC-defined implementation strategies in turn. Interestingly, we found only two ‘new’ approaches in this review that did not fit into the existing EPOC approaches. These are a review focused on the use of social media and a review considering toolkits. In addition to single interventions, we also discuss multi-faceted interventions. These were the most common intervention approach overall. A summary is provided in Table  2 .

Educational strategies

The overview identified three systematic reviews focusing on educational strategies. Grudniewicz et al. [ 22 ] explored the effectiveness of printed educational materials on primary care physician knowledge, behaviour and patient outcomes and concluded they were not effective in any of these aspects. Koota, Kääriäinen and Melender [ 23 ] focused on educational interventions promoting evidence-based practice among emergency room/accident and emergency nurses and found that interventions involving face-to-face contact led to significant or highly significant effects on patient benefits and emergency nurses’ knowledge, skills and behaviour. Interventions using written self-directed learning materials also led to significant improvements in nurses’ knowledge of evidence-based practice. Although the quality of the studies was high, the review primarily included small studies with low response rates, and many of them relied on self-assessed outcomes; consequently, the strength of the evidence for these outcomes is modest. Wu et al. [ 20 ] questioned if educational interventions aimed at nurses to support the implementation of evidence-based practice improve patient outcomes. Although based on evaluation projects and qualitative data, their results also suggest that positive changes on patient outcomes can be made following the implementation of specific evidence-based approaches (or projects). The differing positive outcomes for educational strategies aimed at nurses might indicate that the target audience is important.

Local opinion leaders

Flodgren et al. [ 24 ] was the only systemic review focusing solely on opinion leaders. The review found that local opinion leaders alone, or in combination with other interventions, can be effective in promoting evidence‐based practice, but this varies both within and between studies and the effect on patient outcomes is uncertain. The review found that, overall, any intervention involving opinion leaders probably improves healthcare professionals’ compliance with evidence-based practice but varies within and across studies. However, how opinion leaders had an impact could not be determined because of insufficient details were provided, illustrating that reporting specific details in published studies is important if diffusion of effective methods of increasing evidence-based practice is to be spread across a system. The usefulness of this review is questionable because it cannot provide evidence of what is an effective opinion leader, whether teams of opinion leaders or a single opinion leader are most effective, or the most effective methods used by opinion leaders.

Pantoja et al. [ 26 ] was the only systemic review focusing solely on manually generated reminders delivered on paper included in the overview. The review explored how these affected professional practice and patient outcomes. The review concluded that manually generated reminders delivered on paper as a single intervention probably led to small to moderate increases in adherence to clinical recommendations, and they could be used as a single quality improvement intervention. However, the authors indicated that this intervention would make little or no difference to patient outcomes. The authors state that such a low-tech intervention may be useful in low- and middle-income countries where paper records are more likely to be the norm.

ICT-focused approaches

The three ICT-focused reviews [ 14 , 27 , 28 ] showed mixed results. Jamal, McKenzie and Clark [ 14 ] explored the impact of health information technology on the quality of medical and health care. They examined the impact of electronic health record, computerised provider order-entry, or decision support system. This showed a positive improvement in adherence to evidence-based guidelines but not to patient outcomes. The number of studies included in the review was low and so a conclusive recommendation could not be reached based on this review. Similarly, Brown et al. [ 28 ] found that technology-enabled knowledge translation interventions may improve knowledge of health professionals, but all eight studies raised concerns of bias. The De Angelis et al. [ 27 ] review was more promising, reporting that ICT can be a good way of disseminating clinical practice guidelines but conclude that it is unclear which type of ICT method is the most effective.

Audit and feedback

Sykes, McAnuff and Kolehmainen [ 29 ] examined whether audit and feedback were effective in dementia care and concluded that it remains unclear which ingredients of audit and feedback are successful as the reviewed papers illustrated large variations in the effectiveness of interventions using audit and feedback.

Non-EPOC listed strategies: social media, toolkits

There were two new (non-EPOC listed) intervention types identified in this review compared to the 2011 review — fewer than anticipated. We categorised a third — ‘care bundles’ [ 36 ] as a multi-faceted intervention due to its description in practice and a fourth — ‘Technology Enhanced Knowledge Transfer’ [ 28 ] was classified as an ICT-focused approach. The first new strategy was identified in Bhatt et al.’s [ 30 ] systematic review of the use of social media for the dissemination of clinical practice guidelines. They reported that the use of social media resulted in a significant improvement in knowledge and compliance with evidence-based guidelines compared with more traditional methods. They noted that a wide selection of different healthcare professionals and patients engaged with this type of social media and its global reach may be significant for low- and middle-income countries. This review was also noteworthy for developing a simple stepwise method for using social media for the dissemination of clinical practice guidelines. However, it is debatable whether social media can be classified as an intervention or just a different way of delivering an intervention. For example, the review discussed involving opinion leaders and patient advocates through social media. However, this was a small review that included only five studies, so further research in this new area is needed. Yamada et al. [ 31 ] draw on 39 studies to explore the application of toolkits, 18 of which had toolkits embedded within larger KT interventions, and 21 of which evaluated toolkits as standalone interventions. The individual component strategies of the toolkits were highly variable though the authors suggest that they align most closely with educational strategies. The authors conclude that toolkits as either standalone strategies or as part of MFIs hold some promise for facilitating evidence use in practice but caution that the quality of many of the primary studies included is considered weak limiting these findings.

Multi-faceted interventions

The majority of the systematic reviews ( n  = 20) reported on more than one intervention type. Some of these systematic reviews focus exclusively on multi-faceted interventions, whilst others compare different single or combined interventions aimed at achieving similar outcomes in particular settings. While these two approaches are often described in a similar way, they are actually quite distinct from each other as the former report how multiple strategies may be strategically combined in pursuance of an agreed goal, whilst the latter report how different strategies may be incidentally used in sometimes contrasting settings in the pursuance of similar goals. Ariyo et al. [ 35 ] helpfully summarise five key elements often found in effective MFI strategies in LMICs — but which may also be transferrable to HICs. First, effective MFIs encourage a multi-disciplinary approach acknowledging the roles played by different professional groups to collectively incorporate evidence-informed practice. Second, they utilise leadership drawing on a wide set of clinical and non-clinical actors including managers and even government officials. Third, multiple types of educational practices are utilised — including input from patients as stakeholders in some cases. Fourth, protocols, checklists and bundles are used — most effectively when local ownership is encouraged. Finally, most MFIs included an emphasis on monitoring and evaluation [ 35 ]. In contrast, other studies offer little information about the nature of the different MFI components of included studies which makes it difficult to extrapolate much learning from them in relation to why or how MFIs might affect practice (e.g. [ 28 , 38 ]). Ultimately, context matters, which some review authors argue makes it difficult to say with real certainty whether single or MFI strategies are superior (e.g. [ 21 , 27 ]). Taking all the systematic reviews together we may conclude that MFIs appear to be more likely to generate positive results than single interventions (e.g. [ 34 , 45 ]) though other reviews should make us cautious (e.g. [ 32 , 43 ]).

While multi-faceted interventions still seem to be more effective than single-strategy interventions, there were important distinctions between how the results of reviews of MFIs are interpreted in this review as compared to the previous reviews [ 8 , 9 ], reflecting greater nuance and debate in the literature. This was particularly noticeable where the effectiveness of MFIs was compared to single strategies, reflecting developments widely discussed in previous studies [ 10 ]. We found that most systematic reviews are bounded by their clinical, professional, spatial, system, or setting criteria and often seek to draw out implications for the implementation of evidence in their areas of specific interest (such as nursing or acute care). Frequently this means combining all relevant studies to explore the respective foci of each systematic review. Therefore, most reviews we categorised as MFIs actually include highly variable numbers and combinations of intervention strategies and highly heterogeneous original study designs. This makes statistical analyses of the type used by Squires et al. [ 10 ] on the three reviews in their paper not possible. Further, it also makes extrapolating findings and commenting on broad themes complex and difficult. This may suggest that future research should shift its focus from merely examining ‘what works’ to ‘what works where and what works for whom’ — perhaps pointing to the value of realist approaches to these complex review topics [ 48 , 49 ] and other more theory-informed approaches [ 50 ].

Some reviews have a relatively small number of studies (i.e. fewer than 10) and the authors are often understandably reluctant to engage with wider debates about the implications of their findings. Other larger studies do engage in deeper discussions about internal comparisons of findings across included studies and also contextualise these in wider debates. Some of the most informative studies (e.g. [ 35 , 40 ]) move beyond EPOC categories and contextualise MFIs within wider systems thinking and implementation theory. This distinction between MFIs and single interventions can actually be very useful as it offers lessons about the contexts in which individual interventions might have bounded effectiveness (i.e. educational interventions for individual change). Taken as a whole, this may also then help in terms of how and when to conjoin single interventions into effective MFIs.

In the two previous reviews, a consistent finding was that MFIs were more effective than single interventions [ 8 , 9 ]. However, like Squires et al. [ 10 ] this overview is more equivocal on this important issue. There are four points which may help account for the differences in findings in this regard. Firstly, the diversity of the systematic reviews in terms of clinical topic or setting is an important factor. Secondly, there is heterogeneity of the studies within the included systematic reviews themselves. Thirdly, there is a lack of consistency with regards to the definition and strategies included within of MFIs. Finally, there are epistemological differences across the papers and the reviews. This means that the results that are presented depend on the methods used to measure, report, and synthesise them. For instance, some reviews highlight that education strategies can be useful to improve provider understanding — but without wider organisational or system-level change, they may struggle to deliver sustained transformation [ 19 , 44 ].

It is also worth highlighting the importance of the theory of change underlying the different interventions. Where authors of the systematic reviews draw on theory, there is space to discuss/explain findings. We note a distinction between theoretical and atheoretical systematic review discussion sections. Atheoretical reviews tend to present acontextual findings (for instance, one study found very positive results for one intervention, and this gets highlighted in the abstract) whilst theoretically informed reviews attempt to contextualise and explain patterns within the included studies. Theory-informed systematic reviews seem more likely to offer more profound and useful insights (see [ 19 , 35 , 40 , 43 , 45 ]). We find that the most insightful systematic reviews of MFIs engage in theoretical generalisation — they attempt to go beyond the data of individual studies and discuss the wider implications of the findings of the studies within their reviews drawing on implementation theory. At the same time, they highlight the active role of context and the wider relational and system-wide issues linked to implementation. It is these types of investigations that can help providers further develop evidence-based practice.

This overview has identified a small, but insightful set of papers that interrogate and help theorise why, how, for whom, and in which circumstances it might be the case that MFIs are superior (see [ 19 , 35 , 40 ] once more). At the level of this overview — and in most of the systematic reviews included — it appears to be the case that MFIs struggle with the question of attribution. In addition, there are other important elements that are often unmeasured, or unreported (e.g. costs of the intervention — see [ 40 ]). Finally, the stronger systematic reviews [ 19 , 35 , 40 , 43 , 45 ] engage with systems issues, human agency and context [ 18 ] in a way that was not evident in the systematic reviews identified in the previous reviews [ 8 , 9 ]. The earlier reviews lacked any theory of change that might explain why MFIs might be more effective than single ones — whereas now some systematic reviews do this, which enables them to conclude that sometimes single interventions can still be more effective.

As Nilsen et al. ([ 6 ] p. 7) note ‘Study findings concerning the effectiveness of various approaches are continuously synthesized and assembled in systematic reviews’. We may have gone as far as we can in understanding the implementation of evidence through systematic reviews of single and multi-faceted interventions and the next step would be to conduct more research exploring the complex and situated nature of evidence used in clinical practice and by particular professional groups. This would further build on the nuanced discussion and conclusion sections in a subset of the papers we reviewed. This might also support the field to move away from isolating individual implementation strategies [ 6 ] to explore the complex processes involving a range of actors with differing capacities [ 51 ] working in diverse organisational cultures. Taxonomies of implementation strategies do not fully account for the complex process of implementation, which involves a range of different actors with different capacities and skills across multiple system levels. There is plenty of work to build on, particularly in the social sciences, which currently sits at the margins of debates about evidence implementation (see for example, Normalisation Process Theory [ 52 ]).

There are several changes that we have identified in this overview of systematic reviews in comparison to the review we published in 2011 [ 8 ]. A consistent and welcome finding is that the overall quality of the systematic reviews themselves appears to have improved between the two reviews, although this is not reflected upon in the papers. This is exhibited through better, clearer reporting mechanisms in relation to the mechanics of the reviews, alongside a greater attention to, and deeper description of, how potential biases in included papers are discussed. Additionally, there is an increased, but still limited, inclusion of original studies conducted in low- and middle-income countries as opposed to just high-income countries. Importantly, we found that many of these systematic reviews are attuned to, and comment upon the contextual distinctions of pursuing evidence-informed interventions in health care settings in different economic settings. Furthermore, systematic reviews included in this updated article cover a wider set of clinical specialities (both within and beyond hospital settings) and have a focus on a wider set of healthcare professions — discussing both similarities, differences and inter-professional challenges faced therein, compared to the earlier reviews. These wider ranges of studies highlight that a particular intervention or group of interventions may work well for one professional group but be ineffective for another. This diversity of study settings allows us to consider the important role context (in its many forms) plays on implementing evidence into practice. Examining the complex and varied context of health care will help us address what Nilsen et al. ([ 6 ] p. 1) described as, ‘society’s health problems [that] require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies’. This will help us shift implementation science to move, ‘beyond a success or failure perspective towards improved analysis of variables that could explain the impact of the implementation process’ ([ 6 ] p. 2).

This review brings together 32 papers considering individual and multi-faceted interventions designed to support the use of evidence in clinical practice. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been conducted. As a whole, this substantial body of knowledge struggles to tell us more about the use of individual and MFIs than: ‘it depends’. To really move forwards in addressing the gap between research evidence and practice, we may need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of perspectives, especially from the social, economic, political and behavioural sciences in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed. Harvey et al. [ 53 ] suggest that when context is likely to be critical to implementation success there are a range of primary research approaches (participatory research, realist evaluation, developmental evaluation, ethnography, quality/ rapid cycle improvement) that are likely to be appropriate and insightful. While these approaches often form part of implementation studies in the form of process evaluations, they are usually relatively small scale in relation to implementation research as a whole. As a result, the findings often do not make it into the subsequent systematic reviews. This review provides further evidence that we need to bring qualitative approaches in from the periphery to play a central role in many implementation studies and subsequent evidence syntheses. It would be helpful for systematic reviews, at the very least, to include more detail about the interventions and their implementation in terms of how and why they worked.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Before and after study

Controlled clinical trial

Effective Practice and Organisation of Care

High-income countries

Information and Communications Technology

Interrupted time series

Knowledge translation

Low- and middle-income countries

Randomised controlled trial

Grol R, Grimshaw J. From best evidence to best practice: effective implementation of change in patients’ care. Lancet. 2003;362:1225–30. https://doi.org/10.1016/S0140-6736(03)14546-1 .

Article   PubMed   Google Scholar  

Green LA, Seifert CM. Translation of research into practice: why we can’t “just do it.” J Am Board Fam Pract. 2005;18:541–5. https://doi.org/10.3122/jabfm.18.6.541 .

Eccles MP, Mittman BS. Welcome to Implementation Science. Implement Sci. 2006;1:1–3. https://doi.org/10.1186/1748-5908-1-1 .

Article   PubMed Central   Google Scholar  

Powell BJ, Waltz TJ, Chinman MJ, Damschroder LJ, Smith JL, Matthieu MM, et al. A refined compilation of implementation strategies: results from the Expert Recommendations for Implementing Change (ERIC) project. Implement Sci. 2015;10:2–14. https://doi.org/10.1186/s13012-015-0209-1 .

Article   Google Scholar  

Waltz TJ, Powell BJ, Matthieu MM, Damschroder LJ, et al. Use of concept mapping to characterize relationships among implementation strategies and assess their feasibility and importance: results from the Expert Recommendations for Implementing Change (ERIC) study. Implement Sci. 2015;10:1–8. https://doi.org/10.1186/s13012-015-0295-0 .

Nilsen P, Ståhl C, Roback K, et al. Never the twain shall meet? - a comparison of implementation science and policy implementation research. Implementation Sci. 2013;8:2–12. https://doi.org/10.1186/1748-5908-8-63 .

Rycroft-Malone J, Seers K, Eldh AC, et al. A realist process evaluation within the Facilitating Implementation of Research Evidence (FIRE) cluster randomised controlled international trial: an exemplar. Implementation Sci. 2018;13:1–15. https://doi.org/10.1186/s13012-018-0811-0 .

Boaz A, Baeza J, Fraser A, European Implementation Score Collaborative Group (EIS). Effective implementation of research into practice: an overview of systematic reviews of the health literature. BMC Res Notes. 2011;4:212. https://doi.org/10.1186/1756-0500-4-212 .

Article   PubMed   PubMed Central   Google Scholar  

Grimshaw JM, Shirran L, Thomas R, Mowatt G, Fraser C, Bero L, et al. Changing provider behavior – an overview of systematic reviews of interventions. Med Care. 2001;39 8Suppl 2:II2–45.

Google Scholar  

Squires JE, Sullivan K, Eccles MP, et al. Are multifaceted interventions more effective than single-component interventions in changing health-care professionals’ behaviours? An overview of systematic reviews. Implement Sci. 2014;9:1–22. https://doi.org/10.1186/s13012-014-0152-6 .

Salvador-Oliván JA, Marco-Cuenca G, Arquero-Avilés R. Development of an efficient search filter to retrieve systematic reviews from PubMed. J Med Libr Assoc. 2021;109:561–74. https://doi.org/10.5195/jmla.2021.1223 .

Thomas JM. Diffusion of innovation in systematic review methodology: why is study selection not yet assisted by automation? OA Evid Based Med. 2013;1:1–6.

Effective Practice and Organisation of Care (EPOC). The EPOC taxonomy of health systems interventions. EPOC Resources for review authors. Oslo: Norwegian Knowledge Centre for the Health Services; 2016. epoc.cochrane.org/epoc-taxonomy . Accessed 9 Oct 2023.

Jamal A, McKenzie K, Clark M. The impact of health information technology on the quality of medical and health care: a systematic review. Health Inf Manag. 2009;38:26–37. https://doi.org/10.1177/183335830903800305 .

Menon A, Korner-Bitensky N, Kastner M, et al. Strategies for rehabilitation professionals to move evidence-based knowledge into practice: a systematic review. J Rehabil Med. 2009;41:1024–32. https://doi.org/10.2340/16501977-0451 .

Oxman AD, Guyatt GH. Validation of an index of the quality of review articles. J Clin Epidemiol. 1991;44:1271–8. https://doi.org/10.1016/0895-4356(91)90160-b .

Article   CAS   PubMed   Google Scholar  

Francke AL, Smit MC, de Veer AJ, et al. Factors influencing the implementation of clinical guidelines for health care professionals: a systematic meta-review. BMC Med Inform Decis Mak. 2008;8:1–11. https://doi.org/10.1186/1472-6947-8-38 .

Jones CA, Roop SC, Pohar SL, et al. Translating knowledge in rehabilitation: systematic review. Phys Ther. 2015;95:663–77. https://doi.org/10.2522/ptj.20130512 .

Scott D, Albrecht L, O’Leary K, Ball GDC, et al. Systematic review of knowledge translation strategies in the allied health professions. Implement Sci. 2012;7:1–17. https://doi.org/10.1186/1748-5908-7-70 .

Wu Y, Brettle A, Zhou C, Ou J, et al. Do educational interventions aimed at nurses to support the implementation of evidence-based practice improve patient outcomes? A systematic review. Nurse Educ Today. 2018;70:109–14. https://doi.org/10.1016/j.nedt.2018.08.026 .

Yost J, Ganann R, Thompson D, Aloweni F, et al. The effectiveness of knowledge translation interventions for promoting evidence-informed decision-making among nurses in tertiary care: a systematic review and meta-analysis. Implement Sci. 2015;10:1–15. https://doi.org/10.1186/s13012-015-0286-1 .

Grudniewicz A, Kealy R, Rodseth RN, Hamid J, et al. What is the effectiveness of printed educational materials on primary care physician knowledge, behaviour, and patient outcomes: a systematic review and meta-analyses. Implement Sci. 2015;10:2–12. https://doi.org/10.1186/s13012-015-0347-5 .

Koota E, Kääriäinen M, Melender HL. Educational interventions promoting evidence-based practice among emergency nurses: a systematic review. Int Emerg Nurs. 2018;41:51–8. https://doi.org/10.1016/j.ienj.2018.06.004 .

Flodgren G, O’Brien MA, Parmelli E, et al. Local opinion leaders: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2019. https://doi.org/10.1002/14651858.CD000125.pub5 .

Arditi C, Rège-Walther M, Durieux P, et al. Computer-generated reminders delivered on paper to healthcare professionals: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2017. https://doi.org/10.1002/14651858.CD001175.pub4 .

Pantoja T, Grimshaw JM, Colomer N, et al. Manually-generated reminders delivered on paper: effects on professional practice and patient outcomes. Cochrane Database Syst Rev. 2019. https://doi.org/10.1002/14651858.CD001174.pub4 .

De Angelis G, Davies B, King J, McEwan J, et al. Information and communication technologies for the dissemination of clinical practice guidelines to health professionals: a systematic review. JMIR Med Educ. 2016;2:e16. https://doi.org/10.2196/mededu.6288 .

Brown A, Barnes C, Byaruhanga J, McLaughlin M, et al. Effectiveness of technology-enabled knowledge translation strategies in improving the use of research in public health: systematic review. J Med Internet Res. 2020;22:e17274. https://doi.org/10.2196/17274 .

Sykes MJ, McAnuff J, Kolehmainen N. When is audit and feedback effective in dementia care? A systematic review. Int J Nurs Stud. 2018;79:27–35. https://doi.org/10.1016/j.ijnurstu.2017.10.013 .

Bhatt NR, Czarniecki SW, Borgmann H, et al. A systematic review of the use of social media for dissemination of clinical practice guidelines. Eur Urol Focus. 2021;7:1195–204. https://doi.org/10.1016/j.euf.2020.10.008 .

Yamada J, Shorkey A, Barwick M, Widger K, et al. The effectiveness of toolkits as knowledge translation strategies for integrating evidence into clinical care: a systematic review. BMJ Open. 2015;5:e006808. https://doi.org/10.1136/bmjopen-2014-006808 .

Afari-Asiedu S, Abdulai MA, Tostmann A, et al. Interventions to improve dispensing of antibiotics at the community level in low and middle income countries: a systematic review. J Glob Antimicrob Resist. 2022;29:259–74. https://doi.org/10.1016/j.jgar.2022.03.009 .

Boonacker CW, Hoes AW, Dikhoff MJ, Schilder AG, et al. Interventions in health care professionals to improve treatment in children with upper respiratory tract infections. Int J Pediatr Otorhinolaryngol. 2010;74:1113–21. https://doi.org/10.1016/j.ijporl.2010.07.008 .

Al Zoubi FM, Menon A, Mayo NE, et al. The effectiveness of interventions designed to increase the uptake of clinical practice guidelines and best practices among musculoskeletal professionals: a systematic review. BMC Health Serv Res. 2018;18:2–11. https://doi.org/10.1186/s12913-018-3253-0 .

Ariyo P, Zayed B, Riese V, Anton B, et al. Implementation strategies to reduce surgical site infections: a systematic review. Infect Control Hosp Epidemiol. 2019;3:287–300. https://doi.org/10.1017/ice.2018.355 .

Borgert MJ, Goossens A, Dongelmans DA. What are effective strategies for the implementation of care bundles on ICUs: a systematic review. Implement Sci. 2015;10:1–11. https://doi.org/10.1186/s13012-015-0306-1 .

Cahill LS, Carey LM, Lannin NA, et al. Implementation interventions to promote the uptake of evidence-based practices in stroke rehabilitation. Cochrane Database Syst Rev. 2020. https://doi.org/10.1002/14651858.CD012575.pub2 .

Pedersen ER, Rubenstein L, Kandrack R, Danz M, et al. Elusive search for effective provider interventions: a systematic review of provider interventions to increase adherence to evidence-based treatment for depression. Implement Sci. 2018;13:1–30. https://doi.org/10.1186/s13012-018-0788-8 .

Jenkins HJ, Hancock MJ, French SD, Maher CG, et al. Effectiveness of interventions designed to reduce the use of imaging for low-back pain: a systematic review. CMAJ. 2015;187:401–8. https://doi.org/10.1503/cmaj.141183 .

Bennett S, Laver K, MacAndrew M, Beattie E, et al. Implementation of evidence-based, non-pharmacological interventions addressing behavior and psychological symptoms of dementia: a systematic review focused on implementation strategies. Int Psychogeriatr. 2021;33:947–75. https://doi.org/10.1017/S1041610220001702 .

Noonan VK, Wolfe DL, Thorogood NP, et al. Knowledge translation and implementation in spinal cord injury: a systematic review. Spinal Cord. 2014;52:578–87. https://doi.org/10.1038/sc.2014.62 .

Albrecht L, Archibald M, Snelgrove-Clarke E, et al. Systematic review of knowledge translation strategies to promote research uptake in child health settings. J Pediatr Nurs. 2016;31:235–54. https://doi.org/10.1016/j.pedn.2015.12.002 .

Campbell A, Louie-Poon S, Slater L, et al. Knowledge translation strategies used by healthcare professionals in child health settings: an updated systematic review. J Pediatr Nurs. 2019;47:114–20. https://doi.org/10.1016/j.pedn.2019.04.026 .

Bird ML, Miller T, Connell LA, et al. Moving stroke rehabilitation evidence into practice: a systematic review of randomized controlled trials. Clin Rehabil. 2019;33:1586–95. https://doi.org/10.1177/0269215519847253 .

Goorts K, Dizon J, Milanese S. The effectiveness of implementation strategies for promoting evidence informed interventions in allied healthcare: a systematic review. BMC Health Serv Res. 2021;21:1–11. https://doi.org/10.1186/s12913-021-06190-0 .

Zadro JR, O’Keeffe M, Allison JL, Lembke KA, et al. Effectiveness of implementation strategies to improve adherence of physical therapist treatment choices to clinical practice guidelines for musculoskeletal conditions: systematic review. Phys Ther. 2020;100:1516–41. https://doi.org/10.1093/ptj/pzaa101 .

Van der Veer SN, Jager KJ, Nache AM, et al. Translating knowledge on best practice into improving quality of RRT care: a systematic review of implementation strategies. Kidney Int. 2011;80:1021–34. https://doi.org/10.1038/ki.2011.222 .

Pawson R, Greenhalgh T, Harvey G, et al. Realist review–a new method of systematic review designed for complex policy interventions. J Health Serv Res Policy. 2005;10Suppl 1:21–34. https://doi.org/10.1258/1355819054308530 .

Rycroft-Malone J, McCormack B, Hutchinson AM, et al. Realist synthesis: illustrating the method for implementation research. Implementation Sci. 2012;7:1–10. https://doi.org/10.1186/1748-5908-7-33 .

Johnson MJ, May CR. Promoting professional behaviour change in healthcare: what interventions work, and why? A theory-led overview of systematic reviews. BMJ Open. 2015;5:e008592. https://doi.org/10.1136/bmjopen-2015-008592 .

Metz A, Jensen T, Farley A, Boaz A, et al. Is implementation research out of step with implementation practice? Pathways to effective implementation support over the last decade. Implement Res Pract. 2022;3:1–11. https://doi.org/10.1177/26334895221105585 .

May CR, Finch TL, Cornford J, Exley C, et al. Integrating telecare for chronic disease management in the community: What needs to be done? BMC Health Serv Res. 2011;11:1–11. https://doi.org/10.1186/1472-6963-11-131 .

Harvey G, Rycroft-Malone J, Seers K, Wilson P, et al. Connecting the science and practice of implementation – applying the lens of context to inform study design in implementation research. Front Health Serv. 2023;3:1–15. https://doi.org/10.3389/frhs.2023.1162762 .

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Acknowledgements

The authors would like to thank Professor Kathryn Oliver for her support in the planning the review, Professor Steve Hanney for reading and commenting on the final manuscript and the staff at LSHTM library for their support in planning and conducting the literature search.

This study was supported by LSHTM’s Research England QR strategic priorities funding allocation and the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. Grant number NIHR200152. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or Research England.

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Boaz, A., Baeza, J., Fraser, A. et al. ‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice. Implementation Sci 19 , 15 (2024). https://doi.org/10.1186/s13012-024-01337-z

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Effects of Mobile Use on Subjective Sleep Quality

Nazish rafique.

1 Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

Lubna Ibrahim Al-Asoom

Ahmed abdulrahman alsunni, farhat nadeem saudagar, latifah almulhim.

2 College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

Gaeda Alkaltham

Associated data.

  • Patel N. Cell phone radiations and its effects in public health - Comparative review study. MOJ Public Health . 2018;7(2):14–17. doi: 10.15406/Mojph.2018.07.00197 [ CrossRef ]

The objective of this study was to find out the association between mobile use and physiological parameters of poor sleep quality. It also aimed to find out the prevalence of mobile-related sleep risk factors (MRSRF) and their effects on sleep in mobile users.

Materials and Methods

This cross-sectional study was conducted on 1925 students (aged 17–23yrs) from multiple Colleges of Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. The study tools used were Pittsburgh sleep quality index (PSQI) and MRSRF online questionnaires.

The mean age (±SD) of participants was 19.91 ± 2.55 years. Average mobile screen usage time was 8.57±4.59/24 hours, whereas average mobile screen usage time in the bed after the lights have been turned off was 38.17±11.7 minutes. Only 19.7% of subjects used airplane mode, while 70% kept the mobile near the pillow while sleeping. The blue light filter feature was used by only 4.2% of the participants. “Screen usage time of ≥8 hours” was positively correlated with sleep disturbances and decrease in the length of actual sleeping time (p =0.023 and 0.022). “Using the mobile for at least 30 minutes (without blue light filter) after the lights have been turned off” showed positive correlation with poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency (p= 0.003, 0.004 and 0.001). “Keeping the mobile near the pillow while sleeping” was also positively correlated with daytime sleepiness, sleep disturbances and increased sleep latency (p =0.003, 0.004 and 0.001).

This study concludes that using mobile screen ≥8 hours/24 hours, using the mobile for at least 30 minutes before sleeping after the lights have been turned off and keeping the mobile near the pillow are positively associated with poor sleep quality. Moreover, we observed that MRSRF were highly prevalent amongst the mobile users.

Introduction

Sleep is a physiological state of unawareness which is regulated homeostatically. 1 Almost one-third of our lives are spent while sleeping. 2 Sleep plays an important role in cognitive and physical functions, cellular toxin removal, disease prevention and restoration of both mind and body. 3 – 5 A major decline in the sleep hours and its strong correlation with obesity, diabetes, and other chronic debilitating diseases have been documented in the past 20–30 years. 5 , 6

Proper sleep is especially important for children and adolescents. 6 Lack of sleep in adolescents is becoming an important health issue worldwide. 7 Many factors can affect sleep hygiene 8 but the role of mobile use in causing sleep problems in adolescence has gained huge attention in the past few years. 9 A recent review by Sohn et al reported that one in every four children and young people are suffering from Problematic cell phone use (PSU), which is linked to depression, anxiety and poor sleep quality. 10 Current metaanalysis by Carter et al showed that bedtime use of media devices was positively associated with poor sleep quality and excessive daytime sleepiness. 11

Mobile use at bedtime (after the lights have been turned off), can cause poor sleep quality (PSQ) by various mechanisms. 12 Due to technology revolution, most of the mobile phone users now have smartphones which enable them to access internet and social networks, watching videos, online chatting and playing games. 13 This results in exposure to stimulating content, mobile phone overuse and phone addiction thus contributing to hyper arousal in pre bedtime period and poor sleep quality. 14

A major factor which can contribute to PSQ is the blue light emitted by screens of mobile phones. 15 This blue light can decrease the production of melatonin, the hormone which controls the sleep/wake cycle or circadian rhythm. Reduction in melatonin makes it difficult to fall and stay asleep. 16 Some studies have found that exposure to blue light increases brain alertness 17 and can stimulate cognitive functions, which in turn can lead to PSQ. 18

Moreover, the mobile phones receive and transmit the signals through radiofrequency electromagnetic fields (RF-EMFs). 19 It is well documented that RF-EMFs can pass through the skull, and reach the brain. 20 Therefore, this technology may pose dangers for human health, of particular interest are its effects on sleep parameters and sleep electroencephalogram (EEG). 21 Some studies have reported that RF-EMFs exposure can result in changes in EEG during rapid eye movement (REM) sleep, non-REM sleep, and sleep latency. 22 – 24 All these findings further strengthen the role of mobile in causing PSQ.

Limited availability of the data regarding the “Prevalence of mobile use and its association with sleep quality in the Saudi population” compelled us to design this project. To our knowledge, the current study recruited the largest number of samples of young Saudi population for investigating the link between mobile phone use and sleep quality. We hypothesize that a positive association exists between mobile use and poor sleep quality. We also aimed to find out the prevalence of mobile-related sleep risk factors (MRSRF) in mobile users and their effects on sleep, ie, using mobile before sleeping after the lights have been turned off, not enabling airplane mode on mobiles, putting the mobiles near or below the pillows and bedside while sleeping. As no previous studies are available to highlight these important findings.

This cross-sectional study was conducted from January 2018 till August 2019 on 1925 students (aged 17–23yrs) from multiple colleges of Imam AbdulRahman Bin Faisal University, Dammam (IAU).

Sample size calculation was done by using open source epidemiologic statistics for public health tools software (accessed at: http://epitools.ausvet.com.au/content.php?page=1Proportion&Proportion ). The calculation was based on estimated prevalence of poor sleep quality in mobile users in a target population of 5000 students and desired precision as 0.02 (2%), confidence interval as 0.95 (95%). The calculated sample size was 2017.

The study tool included two questionnaires: mobile-related sleep risk factors Questionnaire (MRSRF) and Pittsburg sleep quality index (PSQI) ( Supplementary material ).

Identification of Mobile-Related Sleep Risk Factors (MRSRF)

This online questionnaire (generated by using Google forms) was designed by the authors based on relevant required information, extracted from few previous studies. 8 , 13 , 14 , 21 The face validity of the questionnaire was confirmed by professors of physiology and respiratory therapy at IAU, whereas test retest technique was used to verify the reliability (interval of three weeks) with a group of 30 students (P = 0.002; r = 0.84).

MRSRF Questionnaire includes seven items which focus on the following areas: Total duration of mobile use/day, using mobile while in the bed when the lights have been turned off, using blue light filters on mobile, keeping the mobile under pillow, keeping the mobile 2 meters away from the bed and putting the mobile on airplane mode while sleeping.

Identification of Sleep Quality

The pittsburgh sleep quality index (psqi).

Various questionnaires are used to identify the sleep quality. But PSQI has been found to be most effective in terms of reliability and validity. It includes 19 self-rated items, which focus on seven main areas including: subjective sleep quality, sleep latency (time taken to fall asleep), sleep duration, habitual sleep efficiency (the ratio of total sleep time to time in bed), sleep disturbances, the use of sleep-inducing medicines and daytime dysfunction. 25

PSQI Scoring

The PSQI includes a scoring key for calculating a patient’s seven subscores, each of which ranges from 0 to 3.

  • A score of 0 indicates no difficulty.
  • A score of 3 indicates severe difficulty.

The 7 component scores are then added to make a global score with a range of 0–21.

  • A score of 0 means no difficulty.
  • A score of 5 or more indicates poor sleep quality.
  • A score of 21 means severe difficulties in all areas.
  • (The higher the score, the worse the quality).

Data Collection

Data were collected by convenience sampling technique, and response rate was 38.5%, as 1925 out of 5000 students volunteered and completed the questionnaire. A five minutes briefing session was given in the class to explain the rational of study and terminologies used in the questionnaire (Average strength of students/class was 50). The online questionnaire was shared with each class on their WhatsApp groups, and a time of 8 minutes was provided to the students to fill the questionnaire. The students were assured about the confidentiality of their personal information.

Inclusion Criteria

  • The students between 17 and 26 years who were willing to participate in the study.
  • The students who use mobile phone daily, even if they use it for a brief moment.

Exclusion Criteria

The students suffering from

  • Any diagnosed sleep disorder.
  • Any diagnosed chronic respiratory problem (including nasal congestion, chest infections, asthma, adenoids, allergic rhinitis)
  • Any chronic physical or mental illness, affecting their sleep.
  • Using any prescription medication for at least last 3 months.

Finally, 156 students were excluded, and 1925 were selected.

Ethical approval of the study was taken by Deanship of Scientific Research College of Medicine (IAU).

Statistical Analysis

The data were analyzed using Statistical Package for Social Sciences (SPSS) for Windows, Version 20.0. Descriptive statistics were used to determine the demographic data.

Comparison of sleep quality and sleep parameters in the participants with various “Mobile-related sleep risk factors” (MRSRF) was done by using Cross tab Chi square test for nominal variables, and independent t test for qualitative data. A p value of <0.05 was considered statistically significant.

Correlation of Poor sleep quality and various sleep parameters with MRSRF was done by using Pearson and Spearman tests.

Binary regression analysis test was run using the quality of sleep (poor PSQI>5 versus good PSQI<5) as the dependent variable and the following variables were the independent factors: age, gender, screen usage time >8 hours, using the mobile phone during two hours before sleep, using mobile in bed after lights are turned off, duration of cell phone use after the lights are turned off (minutes), keeping the mobile phone near the pillow while sleeping.

A shapiro-Wilk’s test (p>0.05) and visual inspection of histogram showed that the data were approximately normally distributed.

The mean age (±SD) of participants was 19.91 ± 2.55 years. Number of female participants was 1502 (77%), whereas the number of male participants was 423 (21%). 98% of the participants owned smart phones. Average screen usage time was 8.57±4.59/24 hours, and 38% of the participants reported of using mobile for more than 8/24 hours. Almost 88.7%of the subjects were using the mobile after the lights have been turned off with an average screen usage time of 38.17±11.7 minutes. Out of these 88.7% subjects, 84.5% were not using the blue light filters on their mobiles. Average time spent on watching videos was 1.8±1.74 hours. Poor sleep quality was seen in 33% of males and 37% of females. Comparison of these parameters between male and female subjects is given in Table 1 .

Comparison of Screen and Sleep-Related Parameters, Between Male and Female Subjects

Note: P value <0.05 is considered statistically significant.

Prevalence of mobile usage, and various MRSRF are shown in Table 2 . Only 19.7% of subjects used airplane mode, while 70% kept the mobile near the pillow while sleeping. The blue light filter feature was used by only 4.2% of the participants. It was observed that the greater number of females as compared to males keep the mobiles near their pillow while sleeping (p = 0.029). Whereas the number of females who use airplane mode on their mobiles while sleeping was greater than the number of males using this function (p = 0.021).

Prevalence of Mobile Usage and “Mobile-Related Sleep Risk Factors” in Study Participants

Abbreviation: MRSRF, mobile-related sleep risk factors.

Comparison of sleep quality in the participants with various MRSRF is shown in Table 3 . Data indicated that the subjects who use the mobile after the lights have been turned off for at least 30 minutes (without a blue light filter in mobile), and who put the mobile near their pillow while sleeping have a statistically significant poor sleep quality (P=0.001, 0.001) respectively.

Comparison of Sleep Quality in the Participants with Various “Mobile-Related Sleep Risk Factors”

Further analysis revealed a strong positive correlation of poor sleep quality with Using Mobile for at least 30 minutes after the lights are turned off (without a blue light filter in mobile) (p=0.018) Table 4 .

Correlation of Poor Sleep Quality with Various “Mobile-Related Sleep Risk Factors”

Correlation of various sleep parameters with MRSRF is highlighted in Table 5 . Screen usage time of >8 hours was positively but weakly correlated with sleep disturbances and decrease in the length of actual sleeping time (P value 0.023 and 0.022, respectively). Using the mobile after the lights have been turned off for at least 30 minutes (without a blue light filter in mobile) showed positive but weak correlation with daytime sleepiness, sleep disturbances and increased sleep latency (p= 0.003, 0.004 and 0.001). Keeping the mobile near the pillow while sleeping was also positively but weakly correlated with daytime sleepiness, sleep disturbances and increased sleep latency (p =0.003, 0.004 and 0.001).

Correlation of Various Sleep Parameters with “Mobile-Related Sleep Risk Factors”

Our study showed a high prevalence and prolonged duration of mobile use in young adults. Average mobile screen usage time was 8.57±4.59/24 hours, and 38% of the participants reported of using mobile for more than 8/24 hours. Almost similar results have been demonstrated by other authors, Rideout et al and Strasburger found that school-aged children and adolescents spend almost a 7 hours/day in front of a screen. 26 , 27 Moreover, a recent review of literature reported that one in every four children and young people are suffering from (PSU). 28

Using Mobile after the lights have been turned off was reported by 88.7% of the subjects (average duration 38 minutes). About 75% of study participants of Munezawa et al reported of using mobile after lights out. Whereas a large study conducted on 90,000 young participants showed that only 17% of their subject use mobile after the lights have been turned off. This difference may be due to the reason that their study population included younger subjects from grades 7 to 12 only, who are under strict supervision of their parents as compared to the older adults. 29

One supreme finding of this study was that using mobile for at least 30 minutes after the lights have been turned off (without a blue light filter in mobile) correlates with poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency. A study on 844 Flemish subjects (18–94 years old) also revealed that using mobile after lights out negatively affects PSQI scores, sleep latency, sleep efficiency and causes more sleep disturbance and daytime dysfunction. 30 Almost similar results were reported by a Japanese study on younger subjects (aged 13 to 19 years). 29 But these studies did not mention the exact duration of mobile use; moreover, they have not specified that either or not their subjects were using a blue light filter mode on their mobiles. The data regarding the use of blue light filers on mobile screens are scarce, but our study indicated that only 4.4% of the subjects are using this filter. So we can assume that most of the participants in the above-mentioned studies were also using their mobiles without a blue light filter.

Some recent studies have indicated that the blue light emitted by the mobile screens is the major culprit behind the PSQ in late night mobile users. As most of the mobile screens emit blue light in wave length between 400−495 nm and blue light in the range of 460–480 nm can cause a phase-shifting in human circadian clock by decreasing the production of melatonin. 31 , 32 Reduced melatonin levels have been linked to prolonged sleep latency and sleep disturbances. 16 Moreover, exposure to blue light increases brain alertness and stimulates cognitive functions, resulting in PSQ. 17 , 18 Our study findings were also supportive of the above-mentioned facts, we observed that participants who “used the mobile for at least 30 minutes after the lights have been turned off (without a blue light filter),” showed strong positive correlation with poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency. Moreover, a comparative study of Mortazavi et al found that using amber blue light filter in the mobiles, significantly improves the sleep quality, but this study used a small sample size of 43 participants only. 33 We therefore recommend further case control and experimental studies with larger sample size to confirm these findings.

In addition to the blue light effects of mobile screen, using mobile in pre bed time, ie, surfing the web, playing a game, seeing something exciting on facebook, or reading a negative email can also cause physical and psychological hyperexcitability contributing to hyper arousal state and PSQ. 13 , 14

Another important finding of this study was that putting the mobile near pillow while sleeping caused increased sleep latency, sleep disturbances and daytime sleepiness. These effects may be caused by; a continuous urge to see notifications and updates on the nearly placed phone, 14 disturbance created by the vibrations from receiving notifications and messages, heat generated by charging phones and RF-EMF exposure from the mobile phone. RF-EMF exposure can cause changes in EEG during REM and non-REM sleep. 21 – 24 During the sleep time, when the mobile phones are not in use, they still emit RF-EMFs; however, the levels are much lower than that of a phone call. Moreover, the smart phones are constantly scanning for signals, text updates, emails, and software updates. Even notifications that we receive through apps require a certain level of radiation to be released. 34 , 35 These RF-EMFs can cross the skull and reach the brain 20 causing neuronal hyper-excitability resulting in various sleep problems. 36 The above-mentioned findings may be the underlying reason of the sleep problems (daytime sleepiness, sleep disturbances and increased sleep latency) seen in our study subjects who placed their mobiles near their pillows while sleeping. But further experimental and case control studies are required to confirm this causal relationship.

To our knowledge, this is the first study which also aimed to find out the prevalence of MRSRF in mobile users. To our surprise, there was limited awareness about the possible hazardous effects of the mobile phone on human health, especially on sleep. As 88.7% of the subjects mentioned that they used mobile for at least 30 minutes after the lights are turned off, blue light filter feature was used by only 4.2% of the participants. Only 19.7% of the subjects used airplane mode, whereas 70% kept the mobile near their pillow while sleeping. Although we consider smartphone use to be a source of PSQ, many adolescents mistakenly believe that these media facilitate them to sleep. 37 It is therefore strongly recommended that health authorities should conduct seminars and awareness sessions in schools, colleges and universities. And students should be educated about the “hazardous effects of mobile phone use on sleep” and should be encouraged to implement the safety practices to prevent these effects.

This study incorporated a large sample size and showed a positive association between bedtime mobile use and poor sleep quality. It also provided an insight into the causal relationship between mobile use and poor sleep quality, ie, hazardous effects of RF-EMFs and blue light emitted from the mobiles phones on sleep. But due to study limitations, and lack of objective measures we were not able to measure these effects directly. So we recommend further experimental and case control studies to probe the role of these causal factors, especially (RF-EMF and Blue light emitted from mobile screens), in causing poor sleep quality.

Conclusions

This study concludes that

  • “Using the mobile for at least 30 minutes (without blue light filter) after the lights have been turned off” results in poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency.
  • “Keeping the mobile near the pillow while sleeping” positively correlates with daytime sleepiness, sleep disturbances and increased sleep latency.
  • Mobile-related sleep risk factors (MRSRF), ie, “using mobile before sleeping after the lights have been turned off, not using blue light filter, not using airplane mode, putting the mobile near the pillow while sleeping” were highly prevalent amongst the mobile users.

Acknowledgment

The authors are thankful to Dr. Afzal Haq Asif, Dr. Faisal Fahad Essa Alousi, Dr. Hina Khan, Saira Saeed, and Dr. Samina Bashir for their help in data collection.

The authors report no conflicts of interest in this work.

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