gadget addiction research paper

Gadget Addiction

by Ananth Indrakanti, Milan Chutake, Stephen Prouty, Venkat Sundaranatha, Vinod Koverkathu

Introduction

Technology and gadgets are now indispensable in our daily lives. In the past few years carrying a miniature computer (a smart phone) in a pocket has become commonplace. Technology helps advance the human race forward and makes doing mundane things more efficient and repeatable. Technology has helped create the information revolution.

With technological advances, devices have evolved to be so powerful and smart that it feels like having a super-computer on one’s hands. Humans now have an insatiable appetite for information at their fingertips. When technology makes this happen, the natural tendency is for this to become an expectation. When was the last time you printed a map or wrote a snail mail letter? If you did, then you belong to the elite endangered cadre of humans who are vanishing rapidly. Welcome to the information age! Before we frame our problem, we would like to ponder briefly over how our lives have changed with gadgets, compared to pre-digital era.

Life Without Gadgets

People born before the 1980’s would very well relate to life before the information age, when people had no access to internet or personal gadgets.  Let's briefly walk down the memory lane to relive those moments — a life without gadgets.

• Children played together outdoor — they had a lot of physical activity.
• People talked to each other more often, and verbal communication face-face was at its peak.
• Chat jargon did not exist and people knew their spellings well, as they read more books.
• People enjoyed spending more time outdoors with family and friends.
• It was commonplace to get the news from newspaper or radio.
• Entertainment came from playing board games, playing sports, going to the movies, watching VHS tapes, etc.
• Writers often used either a type-writer or a word processor on their computer.
• Computers were expensive and bulky.
• Doing research was hard; frequent visits to the library or scouring through plethora of papers, books, etc. were necessary.
• Communication was slow.

Life With Gadgets

Gadgets equipped with internet have transformed our lives in several ways and brought about a paradigm shift in our dependence on technology to perform key tasks in our everyday routine. To highlight a few:

• Use Google Maps to get directions, watch YouTube videos to learn to cook, sing, draw, learn science, etc.
• Health monitoring apps on the cell phone that would remind people to walk, run, bike, check BP periodically, etc.
• Capability to share daily life or special events instantly with thousands of people and see reaction in a matter of minutes, if not seconds
• Expedited research with access to information galore
• Ability to watch videos on demand from anywhere (Netflix, Amazon, etc.)
• Ability to read e-books online on demand — no more visits to library needed
• Use of mobile phones, tablets as pacifiers for kids
• Improved speed of communication by orders of magnitude leading to faster decision-making
• Existence of mobile apps for entertainment, social interaction through digital media, paying bills, accessing bank accounts, etc. (virtually for any purpose)

While there have been advantages to this information age and gadget revolution, it has created an insatiable appetite for information. It's now an expectation that information be readily available on demand from anywhere. This is the age of instant gratification.  While technology has fostered the human race, does our current consumption pattern adversely impact our analytical and creative abilities, lead to loss of focus in communication and make us just indexers of data rather than bearers of knowledge?  Are we addicted to our gadgets? Let's find out.

You’ve temporarily misplaced your cell phone and anxiously retrace your steps to try to find it. Or perhaps you never let go of your phone — it's always in your hand, your pocket, or your bag, ready to be answered or consulted at a moment’s notice.

Dr. Veronika Konok and her collaborators [1] cite evidence that supports the idea that “healthy, well-functioning adults also report significant emotional attachment to special objects.”

A quick survey showed that most people panicked when they had misplaced their smartphones (Fig. 1).

Figure 1 : Survey results from “Lookout”

Mobile Consumption Growth Trend

In the last decade, digital consumption on mobile devices has overtaken that on desktop devices. Between 2011 and 2016, about 300% growth [2] (Fig. 2) was seen with data  consumption on mobile devices, while that on desktop devices and other connected devices stayed relatively flat.  The growth in combined number of smartphone/tablet users is expected to grow from current 2.5 billion to about 3.13 billion by 2020 (about 23%).

Social networking, listening to music, watching videos and playing games represent the bulk of what people do with their smartphones and tablets. Essentially it’s about communication and entertainment, two things that help people to cope with the level of stress in today’s world.

Figure 2:  Time spent per adult user/day with digital media

Americans tend to spend more than 11 hours/day on a screen (mobile phone/desktop/tablet, etc.), be it for personal use or work-related activity.  About half of the screen time is spent on a mobile device. Statistics [3] show that 8% of the time spent on a mobile is on a browser while the majority (92%) of the time is spent on social networking/media, music and entertainment apps. (Fig. 3)

Figure 3:  Ratio of time spent on mobile by app category

Mobile App Usage Statistics

It will have been a decade since the establishment of the mobile app ecosystem by the summer of 2018. The total number of mobile app downloads touched 197 billion in 2017 [3] . The two biggest app stores, i.e., Apple’s iOS App store and Google’s Play store, have served as effective app distribution channels for the millions of app developers in the ecosystem.

Not surprisingly, Facebook app demonstrated the highest level of penetration among 18+ years age group with a whopping 81% in 2017, while YouTube came second with 71% penetration and Facebook Messenger was not too far behind with 68% penetration. It is interesting to note that the chart is completely dominated by Facebook-owned (Facebook, FB messenger and Instagram) and Google-owned (Google search, Google Maps, Gmail, Google Play) apps, with Snapchat and Pandora being the only exceptions.  It is also intriguing that social networking and entertainment are valued the most by app users worldwide.

Figure 4: Mobile apps penetration chart

The Invisible Problem

The business model of social networking and entertainment sites/apps like Facebook, Twitter, YouTube, Snapchat, etc. revolves around [4] :

• Sophisticated methods to seek attention of the maximum number of users and maximize the users’ time spent on these apps, i.e., make users interact and share their experiences, actions with the online community frequently and crave for virtual rewards (likes, comments)
• Enablement and empowerment of advertisers to target these users continually while scrolling through feeds in Facebook or browsing through videos on YouTube
• Learning from user interests (vacation preferences, activities etc.) and developing products that targets these users with AI driven personalized content, feeds and advertisements 24x7, where they start maneuvering the user behavior to their advantage.

While there is no denying that the business model is tuned to maximize users’ attention and time spent on these apps leading to gadget addiction and increased screen time, the other major problem that needs to be highlighted is that these platforms have no way to validate content being fed to users, i.e., fake news, articles generated to manipulate minds can be easily spread with these apps with no regulations or checks in place.

Let’s elaborate the point on how users that get initiated into these platforms develop the tendency to repeatedly visit them and ultimately get addicted, without any external force. How does this really work?

The Science of Addiction

Nir Eyal ’s Hooked model explains the four stages we run through as we use the platform [5] :

• Boredom acts as an internal trigger, and external notifications add to that.
• The action is dead simple: open the app or page in the browser.
• A great variability of rewards is bestowed upon us: photos, comments, likes, gossip, news, emotions, laughter. The wheel of fortune never disappoints.
• We invest more and more time and attention into interacting on the platform, which keeps us coming back.

Taken together, these elements are what have caused so many of us to spiral into addiction. The worst part is we do it to ourselves.

Effects of Gadget Addiction

While the business model of the top few app companies hinges on people spending more time with their gadgets every day, we need to recognize that the most important fallout of this induced behavior would be the rising epidemic of gadget addiction. A sense of urge to use the phone or any other gadget when bored or idle equates to addiction. Gadget addiction doesn't discriminate who is affected, it affects all age groups and people of all races. The effects range from mental, physical, emotional to even threatening our democracy.

Mental and Emotional Health

Dopamine is a neurochemical that largely controls the pleasure and reward centers of the brain. High levels of dopamine are usually associated with motivation and excitement to fulfil goals that would lead to recognized rewards and thus reinforcement of a sense of pleasure while achieving those goals. Procrastination, lack of enthusiasm and self-confidence, and boredom are linked to low levels of dopamine.

Research has shown that the brain gets “rewired” as excessive amounts of dopamine get released in the body on frequent interaction with a rewarding stimulus, i.e., using a smartphone app like Facebook [6] . Boredom triggers an interaction with the rewarding stimulus (Facebook app), which in turn results in wide variety of rewards in the form of likes, messages, photos, etc. causing high releases of dopamine in the body. Frequent cycles such as these cause the brain’s receptors to become more insensitive to dopamine, causing the body to experience less pleasure than before for the same natural reward. This leads the person down a spiral, where one has increased craving for the same reward to achieve normal levels of pleasure. If the increased craving cannot be satisfied, it would lead to anxiety, lack of motivation and depression. Gadget addiction is likened to addiction to alcohol or drugs since it results in similar negative consequences.

Studies [7] have shown that children's cognitive and emotional development can be adversely impacted by internet/gadget addiction. More screen time means more virtual interactions and rewards through social media (shares, likes) and less face time. Less face-to-face interaction with other people results in lack of empathy for fellow human beings. As social media glorify picture-perfect lives and well-toned physiques, children’s self-esteem and self-confidence are eroded. Lack of focus and more distraction during conversations is another expected negative impact.  A study on China high school students [8] demonstrated that children with moderate to severe risk of internet addiction are more than twice as likely to develop depressive symptoms than addiction-free counterparts.

Figure   5:  Dopamine level releases w.r.t time

Physical Health

Today’s children are immersed in technology right from a very young age. With more than half the schools in the US using smart devices as teaching tools in class, coupled with at-home smart device usage, the total screen exposure time of students in the age group 8-18 has exceeded ten hours a day [9] . There are obvious benefits to being exposed to technology right from a very young age, i.e., development of skills needed to be successful in technology-related areas in a future career. However, on the downside, there could be lack of development of social behavioral skills and high risk of obesity due to limited physical activity.

As one would also expect, one of the biggest health risks of excessive smart device usage is vision-related. The National Eye Institute [10] has found that the frequency of myopia (near-sightedness) has increased exponentially in Americans over the last few decades. The other effect on eyes was reduced blink rate leading to higher incidence of dry eye symptoms.  Based on these findings, the American Academy of Pediatrics [11] has revised recommendations for limiting screen time for kids at different ages.

Figure 6:  Recommended screen time for kids (American Academy of Pediatrics)

Listening to loud music through earbuds has detrimental effects on hearing ability  The National Institute of Deafness and Other Communication Disorders [12] reports that about 15% of Americans between the ages of 20-69 have a reduced capability to hear high frequency sounds due to exposure to loud sounds. Other negative effects on physical health from excessive gadget usage include lack of sleep and increased weight on the spine [13] as the head tilt increases to view the screen.

Figure 7:  The burden of starting at a smartphone

Human Behavior

Figure 8: Cognitive-behavioral therapy [14]

Cognitive-behavioral therapy depicts how emotions, thoughts and behaviors influence each other. This model has been very useful in treatment of substance abuse, addictions, gambling addiction, smoking cessation etc.With advent of social networks, our emotional dependence is on instant likes, brief instant text messages creating a virtual set of friends who may never be physically present. Opinions and judgements are made without actual human connection and in-depth in person discussions. The virtual instant digitized friend circle gives a sense of belonging and feeling of having many friends who care about us. The HOOK business model leverages this human emotional dependence feeling and transforms those feelings into behavior where one feels like constantly engaging with these social networking platforms seeking for instant gratifications. When one does not get the instant emotional support in the forms of likes, instant messages then one starts feeling anxious, lonely and moody. Lot of the younger generation seem to start losing self-esteem and self-confidence if their friends fail to like their picture or respond to their posts instantly. In a nutshell the human behavior is being digitized.

Our political discourse is shrinking to fit to our smartphone screens. The most classic example is when President Obama used Instagram to push forward his climate change agenda.

The HOOK business model has got us addicted to our gadgets to watch the next post or news on social media. Well, this hunger for information can have both positive and negative impacts on our society and democracy. Social media may not create our bad habits, but it feeds them, and for one reason alone: money. In 1920’s it was the radio that reduced people to their voices, then in 1960’s television gave people their bodies back. Today with public looking to smartphones for news and media we seem to be in the third wave of election engineering. A recent survey found that 37% of people trust the news that get from social media — that's half the share from print and magazine media.

Let's consider the positive impacts. Few years ago, touch was used to connect with people especially if you're not the outgoing type. These platforms allow us to tailor the message to the audience, do fundraising, and get feedback. The momentum for the movements to topple regimes in Libya and Tunisia [15] was powered by these platforms. The more visceral the message, the more quickly it goes viral and the longer it holds the darting public eye. Around the world, these platforms like social media are making it easier for people to have a voice in the government, to discuss issues, organize around causes, and hold leaders accountable.

The argument is not complete without the negative impacts. For example, bots are often used to amplify political messages. The financial crisis of 2007-2008 stoked public anger [16] when the wealthy left everyone behind. These culture wars have split voters by their identity rather than class. It is claimed that more than 146 million people could have potentially seen fake news in their feed during the 2016 election year. These companies have  moral responsibility to let users know that content might not be real. [17]

Don’t ask about the intentions, aspirations or responsibilities of social media companies. Just follow the money, that’s the basis of the HOOK business model.

How is society responding?

As we see the rise of ill effects of long term gadget use, rising health concerns amidst this drive to seek mindshare, finite attention of the same consumers there are groups of individuals who are now speaking up and taking a stand. These groups are investors, ex-employees of these companies and consumer groups. Starting 2018 these voices have amplified and there is a call for action and change is imminent.

Apple Investor's Open Letter

A pair of investors who hold about $2 billion in Apple stock are pushing the company to do more to protect its youngest users from the effects of digital technology [18] . In an open letter to Apple, the investors, the activist hedge fund Jana Partners and the California State Teachers’ Retirement System, voiced concerns that such technology might be hurting children and said Apple could help ease the damage even as it generates business.

Addressing the issue now could help Apple avoid an impending reckoning as unease grows over the role technology and social media play in our daily lives, the shareholders wrote. “There is a developing consensus around the world including Silicon Valley that the potential long-term consequences of new technologies need to be factored in at the outset, and no company can outsource that responsibility,” the investors wrote. The solution, they argued, is not to banish such devices from children’s hands, but to help parents help them understand how to use technology with care. The open letter highlights growing concern that Silicon Valley is damaging youth and urges new parental controls, child protection committee and release of data.

The Center for Humane Technology

A group of Silicon Valley technologists who were early employees at Facebook and Google [19] , concerned over the ill effects of social networks and smartphones, are getting together to challenge the very companies they helped build. They have come together to a union of concerned experts called the Center for Humane Technology [20] . It plans an anti-tech addiction lobbying effort and an ad campaign at 55,000 public schools in the United States. The campaign, titled The Truth About Tech, will be funded with $7 million from Common Sense and capital raised by the Center for Humane Technology.

One of the co-founders of Center for Humane Technology — Harris, a former design ethicist at Google — mentions [21] it is not enough to simply turn your phone to gray or to stop using these tools entirely. Always-on technology is now baked into the social fabric. The teen who quits Snapchat risks missing out on the primary way his peers communicate. The employee who declines to answer her boss's after-hours email risks losing career opportunities. Which is why Harris is calling on the companies themselves to redesign their products with ethics, not purely profits, in mind, and calling on Congress to write basic consumer protections into law.

How is the industry responding?

With the clamour for change, companies who have a larger part to play in this ecosystem have realised that they need to acknowledge and recognize that there is an issue and at least have controls in place to alleviate the impact and negative PR around these issues.

Based on our understanding of this ecosystem and bucketing the responses we expect the changes to come from the following groups:

Device Makers

Popular apps, standalone apps, regulations, self-awareness.

Device makers have a very large influence on this ecosystem. Availability of platform level features could make a big difference to the user experience, privacy and parental controls across apps and device interaction itself. Ever since these issues have got increased media attention has forced device makers to think of alternatives or at least options in place for concerned groups. Some of the options available natively on device are the following:

• Grayscale option
• Parental controls - purchases, time limitations, app usage limitations
• Night light - predominant on reading devices and reading apps

Shades of Gray

Tristan Harris from Center for Humane Tech proposed [22] using shades of gray is to make the glittering screen a little less stimulating. Based on a popular report, “We’re simple animals, excited by bright colors, it turns out.” Silicon Valley companies know this, and they have increasingly been turning to the field of applied neuroscience to see how exactly brains respond to color in the apps, what brings pleasure and what keeps the eye. New research shows how important color is to our understanding of priorities and emotion. Grayscale can make the display more readable for those who are color blind. Second, if your battery is running low and you know that it will be a while before you'll have the opportunity to charge it, grayscale can extend battery life. Third, some experts say that using grayscale on your iPhone might be the answer to the question of how to break phone addiction. Not so popular but turns out iOS and Android devices have controls to switch to grayscale mode. On the iPhone grayscale mode can be turned on from Accessibility controls. On Android it’s a slightly more difficult workflow to enable grayscale. First up, you'll need to enable the hidden "Developer Options" menu. Under the Hardware accelerated rendering section, choose "Monochromacy" on the popup, then your screen will immediately enter grayscale mode.

Apple: In January 2018, Apple said it would introduce new features to help parents control their children’s use of the company’s products [23] . The move came after two Apple shareholders posted an open letter pushing Apple to address what is seen as a “growing public health crisis” of smartphone addiction in young people. Now, Apple has a new page on its site that collects information about the company’s family features and parental controls in one place [24] .

The page showcases features including an Ask To Buy tool that lets parents approve or decline app purchases from their device; an app management feature that lets users automatically block in-app purchases automatically; and the option to limit adult content on kids’ devices and restrict browsing to only pre-approved websites. Apple’s Find My Friends can also help track locations and issue alerts when children leave or arrive somewhere.

Google: Google announced the launch of Family Link in March 2017 [25] , an application for parents that lets them establish a child’s first Google account, as well as utilize a series of  parental controls to manage and track screen time, daily limits, device “bedtimes,” and which apps kids can use.

While all the major mobile device providers – Apple, Google, and Amazon included – offer parental controls on their devices, Family Link is different because it’s a two-party system. Instead, it works more like the third-party parental control and monitoring software already on the market, where an app installed on a parent’s device is used to configure settings and keep an eye on kids’ digital behavior.

Parental Control devices: This is the other class of devices that can help you create a safe online environment for your kids over your home wireless network. The advantage is that any parental control settings you apply to a network will apply to all devices connected to the Wi-Fi. You don’t have to install software on each individual device, and you can filter content right at the source. The disadvantage, however, is that the parental control options are generally less flexible and only apply when the devices are used at home. Here are some of the popular parental control devices available in the market today:

• Circle with Disney [26]
• UnGlue [27]
• KoalaSafe [28]

Some of the popular apps have also taken steps to attempt to solve the “device addiction” problems. Here are some of the notable initiatives.

Facebook: In January 2018 Mark Zuckerberg announced [30] a major overhaul of Facebook’s News Feed algorithm that would prioritize “meaningful social interactions” over “relevant content” on Thursday, one week after he pledged to spend 2018 “making sure that time spent on Facebook is time well spent”. The social media platform will de-prioritize videos, photos, and posts shared by businesses and media outlets, which Zuckerberg dubbed “public content,” in favor of content produced by a user’s friends and family [31] .

Youtube: Google launched a service called YouTube Kids in February 2015 [32] , a new version of the internet’s leading destination for video aimed squarely at children. YouTube Kids limits the world of content on the service to curated, family-friendly videos, channels, and educational clips. It also includes features like timer settings to limit screen time and a search function. The search gives users access to YouTube’s main database of videos, but that YouTube Kids’ results are automatically filtered for safe content. The service also gives adults a range of parental controls, including the ability to disable search completely, limit screen time and cap the volume. Google has disabled comments on the service, but it does show some kid-friendly ads.

There are multiple independent, third party apps of varying quality. These are mostly from smaller startups with limited revenue. Some of these could be effective, but involves searching for the right app and downloading it on all devices. The illustration covers some of the apps in this space.

Figure 9:  Illustration of apps for limiting gadget usage

From our research on these topic regulations seems to have limited impact in helping reduce gadget addiction and usage. There have been multiple regulations, bans and reversals for usage of gadgets in schools.

A girls’ school is banning wearable activity trackers and smartwatches because of concerns that pupils are skipping lunch if they fail to meet their calorie and exercise targets [33] . This article also suggests that “Social media addiction is thought to affect around 5% of young people, with social media being described as more addictive than cigarettes and alcohol” - which ties in with the HOOK model.

The French government in Dec 2017 decided to ban students from using mobile phones in the country’s primary, junior and middle schools [34] . Children will be allowed to bring their phones to school, but not allowed to get them out at any time until they leave, even during breaks.

Although students have been using cell phones consistently in their daily lives for almost a decade, many public schools continue to resist allowing the devices into the classroom. Schools generally grapple with new technologies, but cell phones’ reputation as a nuisance and a distraction has been hard to dislodge. Recently, however, the acceptance of these devices has been growing. Beginning in March, New York City, the largest school district in the country with 1.1 million students, will reverse its long standing ban on cell phones in schools [35] .

Centre for Humane Tech suggests humane design and applying political pressure as two of the ways to move forward for making gadgets less addictive [36] . Regulation alone will not help drive change, regulation can help support the change.

Self-awareness is key to reducing gadget addiction. Consumer demand for change becomes a forcing function for companies — device makers and popular app makers to recognize this problem and work towards having better designed — “humane designed” technology that aids use.

Consumers do not want to use technology/products that they know are harmful, especially when it harms their kids. We should increase awareness, spread the message such that consumers recognize the difference between technology designed to extract the most attention from us, and technology whose goals are aligned with our own. Consumers need to take control of their digital lives with better tools, habits and demand to make this change.

Having a more aware set of consumers and users will force policy makers and also help push regulations and policies in the right direction. Being self-aware enables us to be mindful and enjoy life moments without being glued to our screens and spend quality time with our loved ones.

Recommendations — which of these will have more impact?

We compared gadget addiction with other addiction paradigms to see what has worked in that context so that we can use the learnings.

Other Addiction Paradigms  

Obesity: This has been a raging problem in the US, especially impacting the younger generation. Research has shown that shame campaigns like “fat = bad” has not worked. But the campaigns around positive reinforcement of healthy habits have seen resounding success.

Tobacco Addiction: In the US alone, we spend close to 240 billion dollars in treating tobacco addiction. This a growing problem. There have been several successful programs and some not so successful. The government regulation exists for tobacco manufacturers to have the Surgeon General's Warning on the ill-effects of using tobacco. While every smoker reads it they still continue to smoke, it has become an issue of passionate defiance, addiction - an emotional dependence as smokers feel it helps them cope with stress, anxiety etc. Smokers still act against their best interest. [37]

CDC has said that the campaigns against smoking is working, but need to be rolled out nationally and continuously. Their initial efforts have shown that up to 100,000 smokers quit from these campaigns — a good sign. The most common problem smokers cite is that everyone around them smoked. Moving away from these groups has also shown positive effects in quitting smoking.

Drug/Alcohol Addiction: This has become part and parcel of our typical public health landscape. We see campaigns like “Don’t drink and drive,” the fines and punishment for DUI, etc. There have been some successful campaigns that permeate our society which are commonly known as “Don’t let your friend drink and drive” and the “designated driver program.” Some of these have become the terminology that we adopted in our daily lives. Some of these campaigns do work effectively.

Based on other addiction paradigms, campaigns can work if they focus on the right habits and not focus on shaming. Governing bodies might bring in regulations for companies to address this area either through self-awareness campaigns or by regulating detection of device abuse. With that said, device makers will have the bulk share of the responsibility to integrate them into the devices. While the regulations will have an impact but the device and self-awareness campaigns will have a more pronounced effect as seen in other addiction paradigms.

Where can we expect change from?

Device makers have best reach/effectiveness. We feel device makers will enable capabilities for users to turn on device abuse and notifications. But this can only be useful if users are self-aware that they have a problem with addiction to a gadget. These two – device makers and self-awareness — are the biggest change drivers.

We expect policy and regulations to have reasonable impact, but they need to work along with users and device makers to work out a good balance. Standalone apps to reduce screen time, etc. need users to be aware to download these apps. Popular app makers have less incentive to change their freemium or advertising revenue by reducing screen time and so unless there is a strong awareness from the user base which is pushing for change, popular app makers have little incentive to change.

Figure 10: Sources of change and impact

How do we see this playing out in the future?

As we have more and more gadgets entering our daily life, we will accept, adapt and evolve to lead device interrupted life as the new norm. Right now we are seeing a big increase in the number of digital assistants. Interaction will move from keyboard to more spoken forms and gestures. Voice and gestures will be the primary interface in the future. Augmented Reality (AR) is technology that superimposes a computer-generated image on a user's view of the real world, thus providing a composite view. AR will provide contextual information just in time as we go through our daily routines.

Technology and gadgets would have become an integral part of human lifestyle, and will only continue to increase with years to come. The form factors of gadgets and how humans interact with them may change. However, fundamentally as a human society we should continue to be aware and make sure we do live a fulfilling life by not becoming addicted to machines and continue to emphasize and cherish the human connection in our lives.

Works Cited

[1] https://www.psychologytoday.com/us/blog/fulfillment-any-age/201609/is-why-we-cant-put-down-our-phones

[2] https://www.smartinsights.com/mobile-marketing/mobile-marketing-analytics/mobile-marketing-statistics/

[3] http://www.businessofapps.com/data/app-statistics/#3

[4] http://humanetech.com/problem/

[5] https://www.nirandfar.com/2012/03/how-to-manufacture-desire.html

[6] https://www.ama.org/publications/MarketingNews/Pages/feeding-the-addiction.aspx

[7] https://www.commonsensemedia.org/sites/default/files/uploads/research/csm_2016_technology_addiction_research_brief_0.pdf

[8] https://jamanetwork.com/journals/jamapediatrics/fullarticle/383813

[9] https://blog.chocchildrens.org/effects-of-screen-time-on-childrens-vision/

[10] https://www.cbsnews.com/news/screen-time-digital-eye-strain/

[11] https://blog.chocchildrens.org/effects-of-screen-time-on-childrens-vision/

[12] http://www.digitalresponsibility.org/technology-and-hearing-loss

[13] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5440123/

[14] https://en.wikipedia.org/wiki/Cognitive_behavioral_therapy

[15] https://www.theguardian.com/world/2011/feb/25/twitter-facebook-uprisings-arab-libya

[16] https://www.economist.com/news/leaders/21730871-facebook-google-and-twitter-were-supposed-save-politics-good-information-drove-out

[17] https://www.economist.com/news/leaders/21730871-facebook-google-and-twitter-were-supposed-save-politics-good-information-drove-out

[18] https://www.theguardian.com/technology/2018/jan/08/apple-investors-iphone-addiction-children

[19] https://www.nytimes.com/2018/02/04/technology/early-facebook-google-employees-fight-tech.html

[20] http://humanetech.com/

[21] https://www.wired.com/story/center-for-humane-technology-tech-addiction/

[22] https://www.nytimes.com/2018/01/12/technology/grayscale-phone.html

[23] https://9to5mac.com/2018/01/08/improved-parental-controls-ios/

[24] https://9to5mac.com/2018/03/14/apple-parental-controls-features/

[25] https://techcrunch.com/2017/03/15/google-introduces-family-link-its-own-parental-control-software-for-android/

[26] https://meetcircle.com/

[27] https://www.unglue.com/

[28] https://koalasafe.com/

[29] https://www.asecurelife.com/best-parental-controls-for-wireless-networks/#torch

[30] https://www.facebook.com/zuck/posts/10104413015393571

[31] https://www.theguardian.com/technology/2018/jan/11/facebook-news-feed-algorithm-overhaul-mark-zuckerberg

[32] https://techcrunch.com/2015/02/23/hands-on-with-youtube-kids-googles-newly-launched-child-friendly-youtube-app/

[33] https://www.theguardian.com/society/2017/jul/12/gloucestershire-school-clamps-down-smartphones-activity-trackers-pupils

[34] https://www.theguardian.com/world/2017/dec/11/france-to-ban-mobile-phones-in-schools-from-september

[35] https://www.wsj.com/articles/cellphone-ban-in-nyc-schools-to-end-1420602754

[36] http://humanetech.com/problem#the-way-forward

[37] https://www.cnn.com/2014/01/11/health/still-smoking/index.html

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Smartphone addiction in students: A qualitative examination of the components model of addiction using face-to-face interviews

Affiliations.

• 1 Faculty of Behavioral Sciences,SGT University, Gurugram,India.
• 2 Department of Psychology,Jamia Millia Islamia, New Delhi,India.
• 3 Psychology Department,Nottingham Trent University, Nottingham,UK.
• PMID: 31619046
• PMCID: PMC7044586
• DOI: 10.1556/2006.8.2019.57

Background and aims: Smartphone use has increased markedly over the past decade and recent research has demonstrated that a small minority of users experience problematic consequences, which in extreme cases have been contextualized as an addiction. To date, most research have been quantitative and survey-based. This study qualitatively examined the components model of addiction for both "addicted" and "non-addicted" users.

Methods: A screening tool comprising 10 dichotomous items was administered to 40 college students. Of these, six addicted and six non-addicted participants were identified on the basis of their score on the screening tool and were asked to participate in a semi-structured interview. The interview questions were based on the components model of addiction comprising six domains (i.e., salience, withdrawal, conflict, relapse and reinstatement, tolerance, and mood modification). Directed content analysis was used to analyze the transcribed data and subthemes as well as emerging themes for the study as a whole were established.

Results: There was some evidence of demarcation between smartphone addicts on the dimensions of salience, tolerance, withdrawal, and conflict. Mood modification was not much different in either group, and no participant reported relapse.

Conclusions: The non-addicted group had much greater control over their smartphone usage than the addicted group on four (of six) aforementioned dimensions of behavioral addiction. Consequently, the main findings of this study provided good support for the components model of behavioral addiction.

Keywords: behavioral addiction; component model; contextual factors; directed content analysis; smartphone addiction; social factors.

• Behavior, Addictive / physiopathology*
• Behavior, Addictive / psychology
• Qualitative Research
• Smartphone*
• Universities*
• Young Adult

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Internet Addiction: A Brief Summary of Research and Practice

Hilarie cash.

a reSTART Internet Addiction Recovery Program, Fall City, WA 98024

Cosette D Rae

Ann h steel, alexander winkler.

b University of Marburg, Department for Clinical Psychology and Psychotherapy, Gutenbergstraße 18, 35032 Marburg, Germany

Problematic computer use is a growing social issue which is being debated worldwide. Internet Addiction Disorder (IAD) ruins lives by causing neurological complications, psychological disturbances, and social problems. Surveys in the United States and Europe have indicated alarming prevalence rates between 1.5 and 8.2% [1]. There are several reviews addressing the definition, classification, assessment, epidemiology, and co-morbidity of IAD [2-5], and some reviews [6-8] addressing the treatment of IAD. The aim of this paper is to give a preferably brief overview of research on IAD and theoretical considerations from a practical perspective based on years of daily work with clients suffering from Internet addiction. Furthermore, with this paper we intend to bring in practical experience in the debate about the eventual inclusion of IAD in the next version of the Diagnostic and Statistical Manual of Mental Disorders (DSM).

INTRODUCTION

The idea that problematic computer use meets criteria for an addiction, and therefore should be included in the next iteration of the Diagnostic and Statistical Manual of Mental Disorders (DSM) , 4 th ed. Text Revision [ 9 ] was first proposed by Kimberly Young, PhD in her seminal 1996 paper [ 10 ]. Since that time IAD has been extensively studied and is indeed, currently under consideration for inclusion in the DSM-V [ 11 ]. Meanwhile, both China and South Korea have identified Internet addiction as a significant public health threat and both countries support education, research and treatment [ 12 ]. In the United States, despite a growing body of research, and treatment for the disorder available in out-patient and in-patient settings, there has been no formal governmental response to the issue of Internet addiction. While the debate goes on about whether or not the DSM-V should designate Internet addiction a mental disorder [ 12 - 14 ] people currently suffering from Internet addiction are seeking treatment. Because of our experience we support the development of uniform diagnostic criteria and the inclusion of IAD in the DSM-V [ 11 ] in order to advance public education, diagnosis and treatment of this important disorder.

CLASSIFICATION

There is ongoing debate about how best to classify the behavior which is characterized by many hours spent in non-work technology-related computer/Internet/video game activities [ 15 ]. It is accompanied by changes in mood, preoccupation with the Internet and digital media, the inability to control the amount of time spent interfacing with digital technology, the need for more time or a new game to achieve a desired mood, withdrawal symptoms when not engaged, and a continuation of the behavior despite family conflict, a diminishing social life and adverse work or academic consequences [ 2 , 16 , 17 ]. Some researchers and mental health practitioners see excessive Internet use as a symptom of another disorder such as anxiety or depression rather than a separate entity [e.g. 18]. Internet addiction could be considered an Impulse control disorder (not otherwise specified). Yet there is a growing consensus that this constellation of symptoms is an addiction [e.g. 19]. The American Society of Addiction Medicine (ASAM) recently released a new definition of addiction as a chronic brain disorder, officially proposing for the first time that addiction is not limited to substance use [ 20 ]. All addictions, whether chemical or behavioral, share certain characteristics including salience, compulsive use (loss of control), mood modification and the alleviation of distress, tolerance and withdrawal, and the continuation despite negative consequences.

DIAGNOSTIC CRITERIA FOR IAD

The first serious proposal for diagnostic criteria was advanced in 1996 by Dr. Young, modifying the DSM-IV criteria for pathological gambling [ 10 ]. Since then variations in both name and criteria have been put forward to capture the problem, which is now most popularly known as Internet Addiction Disorder. Problematic Internet Use (PIU) [ 21 ], computer addiction, Internet dependence [ 22 ], compulsive Internet use, pathological Internet use [ 23 ], and many other labels can be found in the literature. Likewise a variety of often overlapping criteria have been proposed and studied, some of which have been validated. However, empirical studies provide an inconsistent set of criteria to define Internet addiction [ 24 ]. For an overview see Byun et al . [ 25 ].

Beard [ 2 ] recommends that the following five diagnostic criteria are required for a diagnosis of Internet addiction: (1) Is preoccupied with the Internet (thinks about previous online activity or anticipate next online session); (2) Needs to use the Internet with increased amounts of time in order to achieve satisfaction; (3) Has made unsuccessful efforts to control, cut back, or stop Internet use; (4) Is restless, moody, depressed, or irritable when attempting to cut down or stop Internet use; (5) Has stayed online longer than originally intended. Additionally, at least one of the following must be present: (6) Has jeopardized or risked the loss of a significant relationship, job, educational or career opportunity because of the Internet; (7) Has lied to family members, therapist, or others to conceal the extent of involvement with the Internet; (8) Uses the Internet as a way of escaping from problems or of relieving a dysphoric mood (e.g., feelings of helplessness, guilt, anxiety, depression) [ 2 ].

There has been also been a variety of assessment tools used in evaluation. Young’s Internet Addiction Test [ 16 ], the Problematic Internet Use Questionnaire (PIUQ) developed by Demetrovics, Szeredi, and Pozsa [ 26 ] and the Compulsive Internet Use Scale (CIUS) [ 27 ] are all examples of instruments to assess for this disorder.

The considerable variance of the prevalence rates reported for IAD (between 0.3% and 38%) [ 28 ] may be attributable to the fact that diagnostic criteria and assessment questionnaires used for diagnosis vary between countries and studies often use highly selective samples of online surveys [ 7 ]. In their review Weinstein and Lejoyeux [ 1 ] report that surveys in the United States and Europe have indicated prevalence rates varying between 1.5% and 8.2%. Other reports place the rates between 6% and 18.5% [ 29 ].

“Some obvious differences with respect to the methodologies, cultural factors, outcomes and assessment tools forming the basis for these prevalence rates notwithstanding, the rates we encountered were generally high and sometimes alarming.” [ 24 ]

There are different models available for the development and maintenance of IAD like the cognitive-behavioral model of problematic Internet use [ 21 ], the anonymity, convenience and escape (ACE) model [ 30 ], the access, affordability, anonymity (Triple-A) engine [ 31 ], a phases model of pathological Internet use by Grohol [ 32 ], and a comprehensive model of the development and maintenance of Internet addiction by Winkler & Dörsing [ 24 ], which takes into account socio-cultural factors ( e.g. , demographic factors, access to and acceptance of the Internet), biological vulnerabilities ( e.g. , genetic factors, abnormalities in neurochemical processes), psychological predispositions ( e.g. , personality characteristics, negative affects), and specific attributes of the Internet to explain “excessive engagement in Internet activities” [ 24 ].

NEUROBIOLOGICAL VULNERABILITIES

It is known that addictions activate a combination of sites in the brain associated with pleasure, known together as the “reward center” or “pleasure pathway” of the brain [ 33 , 34 ]. When activated, dopamine release is increased, along with opiates and other neurochemicals. Over time, the associated receptors may be affected, producing tolerance or the need for increasing stimulation of the reward center to produce a “high” and the subsequent characteristic behavior patterns needed to avoid withdrawal. Internet use may also lead specifically to dopamine release in the nucleus accumbens [ 35 , 36 ], one of the reward structures of the brain specifically involved in other addictions [ 20 ]. An example of the rewarding nature of digital technology use may be captured in the following statement by a 21 year-old male in treatment for IAD:

“I feel technology has brought so much joy into my life. No other activity relaxes me or stimulates me like technology. However, when depression hits, I tend to use technology as a way of retreating and isolating.”

REINFORCEMENT/REWARD

What is so rewarding about Internet and video game use that it could become an addiction? The theory is that digital technology users experience multiple layers of reward when they use various computer applications. The Internet functions on a variable ratio reinforcement schedule (VRRS), as does gambling [ 29 ]. Whatever the application (general surfing, pornography, chat rooms, message boards, social networking sites, video games, email, texting, cloud applications and games, etc.), these activities support unpredictable and variable reward structures. The reward experienced is intensified when combined with mood enhancing/stimulating content. Examples of this would be pornography (sexual stimulation), video games (e.g. various social rewards, identification with a hero, immersive graphics), dating sites (romantic fantasy), online poker (financial) and special interest chat rooms or message boards (sense of belonging) [ 29 , 37 ].

BIOLOGICAL PREDISPOSITION

There is increasing evidence that there can be a genetic predisposition to addictive behaviors [ 38 , 39 ]. The theory is that individuals with this predisposition do not have an adequate number of dopamine receptors or have an insufficient amount of serotonin/dopamine [ 2 ], thereby having difficulty experiencing normal levels of pleasure in activities that most people would find rewarding. To increase pleasure, these individuals are more likely to seek greater than average engagement in behaviors that stimulate an increase in dopamine, effectively giving them more reward but placing them at higher risk for addiction.

MENTAL HEALTH VULNERABILITIES

Many researchers and clinicians have noted that a variety of mental disorders co-occur with IAD. There is debate about which came first, the addiction or the co-occurring disorder [ 18 , 40 ]. The study by Dong et al . [ 40 ] had at least the potential to clarify this question, reporting that higher scores for depression, anxiety, hostility, interpersonal sensitivity, and psychoticism were consequences of IAD. But due to the limitations of the study further research is necessary.

THE TREATMENT OF INTERNET ADDICTION

There is a general consensus that total abstinence from the Internet should not be the goal of the interventions and that instead, an abstinence from problematic applications and a controlled and balanced Internet usage should be achieved [ 6 ]. The following paragraphs illustrate the various treatment options for IAD that exist today. Unless studies examining the efficacy of the illustrated treatments are not available, findings on the efficacy of the presented treatments are also provided. Unfortunately, most of the treatment studies were of low methodological quality and used an intra-group design.

The general lack of treatment studies notwithstanding, there are treatment guidelines reported by clinicians working in the field of IAD. In her book “Internet Addiction: Symptoms, Evaluation, and Treatment”, Young [ 41 ] offers some treatment strategies which are already known from the cognitive-behavioral approach: (a) practice opposite time of Internet use (discover patient’s patterns of Internet use and disrupt these patterns by suggesting new schedules), (b) use external stoppers (real events or activities prompting the patient to log off), (c) set goals (with regard to the amount of time), (d) abstain from a particular application (that the client is unable to control), (e) use reminder cards (cues that remind the patient of the costs of IAD and benefits of breaking it), (f) develop a personal inventory (shows all the activities that the patient used to engage in or can’t find the time due to IAD), (g) enter a support group (compensates for a lack of social support), and (h) engage in family therapy (addresses relational problems in the family) [ 41 ]. Unfortunately, clinical evidence for the efficacy of these strategies is not mentioned.

Non-psychological Approaches

Some authors examine pharmacological interventions for IAD, perhaps due to the fact that clinicians use psychopharmacology to treat IAD despite the lack of treatment studies addressing the efficacy of pharmacological treatments. In particular, selective serotonin-reuptake inhibitors (SSRIs) have been used because of the co-morbid psychiatric symptoms of IAD (e.g. depression and anxiety) for which SSRIs have been found to be effective [ 42 - 46 ]. Escitalopram (a SSRI) was used by Dell’Osso et al . [ 47 ] to treat 14 subjects with impulsive-compulsive Internet usage disorder. Internet usage decreased significantly from a mean of 36.8 hours/week to a baseline of 16.5 hours/week. In another study Han, Hwang, and Renshaw [ 48 ] used bupropion (a non-tricyclic antidepressant) and found a decrease of craving for Internet video game play, total game play time, and cue-induced brain activity in dorsolateral prefrontal cortex after a six week period of bupropion sustained release treatment. Methylphenidate (a psycho stimulant drug) was used by Han et al . [ 49 ] to treat 62 Internet video game-playing children diagnosed with attention-deficit hyperactivity disorder. After eight weeks of treatment, the YIAS-K scores and Internet usage times were significantly reduced and the authors cautiously suggest that methylphenidate might be evaluated as a potential treatment of IAD. According to a study by Shapira et al . [ 50 ], mood stabilizers might also improve the symptoms of IAD. In addition to these studies, there are some case reports of patients treated with escitalopram [ 45 ], citalopram (SSRI)- quetiapine (antipsychotic) combination [ 43 ] and naltrexone (an opioid receptor antagonist) [ 51 ].

A few authors mentioned that physical exercise could compensate the decrease of the dopamine level due to decreased online usage [ 52 ]. In addition, sports exercise prescriptions used in the course of cognitive behavioral group therapy may enhance the effect of the intervention for IAD [ 53 ].

Psychological Approaches

Motivational interviewing (MI) is a client-centered yet directive method for enhancing intrinsic motivation to change by exploring and resolving client ambivalence [ 54 ]. It was developed to help individuals give up addictive behaviors and learn new behavioral skills, using techniques such as open-ended questions, reflective listening, affirmation, and summarization to help individuals express their concerns about change [ 55 ]. Unfortunately, there are currently no studies addressing the efficacy of MI in treating IAD, but MI seems to be moderately effective in the areas of alcohol, drug addiction, and diet/exercise problems [ 56 ].

Peukert et al . [ 7 ] suggest that interventions with family members or other relatives like “Community Reinforcement and Family Training” [ 57 ] could be useful in enhancing the motivation of an addict to cut back on Internet use, although the reviewers remark that control studies with relatives do not exist to date.

Reality therapy (RT) is supposed to encourage individuals to choose to improve their lives by committing to change their behavior. It includes sessions to show clients that addiction is a choice and to give them training in time management; it also introduces alternative activities to the problematic behavior [ 58 ]. According to Kim [ 58 ], RT is a core addiction recovery tool that offers a wide variety of uses as a treatment for addictive disorders such as drugs, sex, food, and works as well for the Internet. In his RT group counseling program treatment study, Kim [ 59 ] found that the treatment program effectively reduced addiction level and improved self-esteem of 25 Internet-addicted university students in Korea.

Twohig and Crosby [ 60 ] used an Acceptance & Commitment Therapy (ACT) protocol including several exercises adjusted to better fit the issues with which the sample struggles to treat six adult males suffering from problematic Internet pornography viewing. The treatment resulted in an 85% reduction in viewing at post-treatment with results being maintained at the three month follow-up (83% reduction in viewing pornography).

Widyanto and Griffith [ 8 ] report that most of the treatments employed so far had utilized a cognitive-behavioral approach. The case for using cognitive-behavioral therapy (CBT) is justified due to the good results in the treatment of other behavioral addictions/impulse-control disorders, such as pathological gambling, compulsive shopping, bulimia nervosa, and binge eating-disorders [ 61 ]. Wölfling [ 5 ] described a predominantly behavioral group treatment including identification of sustaining conditions, establishing of intrinsic motivation to reduce the amount of time being online, learning alternative behaviors, engagement in new social real-life contacts, psycho-education and exposure therapy, but unfortunately clinical evidence for the efficacy of these strategies is not mentioned. In her study, Young [ 62 ] used CBT to treat 114 clients suffering from IAD and found that participants were better able to manage their presenting problems post-treatment, showing improved motivation to stop abusing the Internet, improved ability to control their computer use, improved ability to function in offline relationships, improved ability to abstain from sexually explicit online material, improved ability to engage in offline activities, and improved ability to achieve sobriety from problematic applications. Cao, Su and Gao [ 63 ] investigated the effect of group CBT on 29 middle school students with IAD and found that IAD scores of the experimental group were lower than of the control group after treatment. The authors also reported improvement in psychological function. Thirty-eight adolescents with IAD were treated with CBT designed particularly for addicted adolescents by Li and Dai [ 64 ]. They found that CBT has good effects on the adolescents with IAD (CIAS scores in the therapy group were significant lower than that in the control group). In the experimental group the scores of depression, anxiety, compulsiveness, self-blame, illusion, and retreat were significantly decreased after treatment. Zhu, Jin, and Zhong [ 65 ] compared CBT and electro acupuncture (EA) plus CBT assigning forty-seven patients with IAD to one of the two groups respectively. The authors found that CBT alone or combined with EA can significantly reduce the score of IAD and anxiety on a self-rating scale and improve self-conscious health status in patients with IAD, but the effect obtained by the combined therapy was better.

Multimodal Treatments

A multimodal treatment approach is characterized by the implementation of several different types of treatment in some cases even from different disciplines such as pharmacology, psychotherapy and family counseling simultaneously or sequentially. Orzack and Orzack [ 66 ] mentioned that treatments for IAD need to be multidisciplinary including CBT, psychotropic medication, family therapy, and case managers, because of the complexity of these patients’ problems.

In their treatment study, Du, Jiang, and Vance [ 67 ] found that multimodal school-based group CBT (including parent training, teacher education, and group CBT) was effective for adolescents with IAD (n = 23), particularly in improving emotional state and regulation ability, behavioral and self-management style. The effect of another multimodal intervention consisting of solution-focused brief therapy (SFBT), family therapy, and CT was investigated among 52 adolescents with IAD in China. After three months of treatment, the scores on an IAD scale (IAD-DQ), the scores on the SCL-90, and the amount of time spent online decreased significantly [ 68 ]. Orzack et al . [ 69 ] used a psychoeducational program, which combines psychodynamic and cognitive-behavioral theoretical perspectives, using a combination of Readiness to Change (RtC), CBT and MI interventions to treat a group of 35 men involved in problematic Internet-enabled sexual behavior (IESB). In this group treatment, the quality of life increased and the level of depressive symptoms decreased after 16 (weekly) treatment sessions, but the level of problematic Internet use failed to decrease significantly [ 69 ]. Internet addiction related symptom scores significantly decreased after a group of 23 middle school students with IAD were treated with Behavioral Therapy (BT) or CT, detoxification treatment, psychosocial rehabilitation, personality modeling and parent training [ 70 ]. Therefore, the authors concluded that psychotherapy, in particular CT and BT were effective in treating middle school students with IAD. Shek, Tang, and Lo [ 71 ] described a multi-level counseling program designed for young people with IAD based on the responses of 59 clients. Findings of this study suggest this multi-level counseling program (including counseling, MI, family perspective, case work and group work) is promising to help young people with IAD. Internet addiction symptom scores significantly decreased, but the program failed to increase psychological well-being significantly. A six-week group counseling program (including CBT, social competence training, training of self-control strategies and training of communication skills) was shown to be effective on 24 Internet-addicted college students in China [ 72 ]. The authors reported that the adapted CIAS-R scores of the experimental group were significantly lower than those of the control group post-treatment.

The reSTART Program

The authors of this article are currently, or have been, affiliated with the reSTART: Internet Addiction Recovery Program [ 73 ] in Fall City, Washington. The reSTART program is an inpatient Internet addiction recovery program which integrates technology detoxification (no technology for 45 to 90 days), drug and alcohol treatment, 12 step work, cognitive behavioral therapy (CBT), experiential adventure based therapy, Acceptance and Commitment therapy (ACT), brain enhancing interventions, animal assisted therapy, motivational interviewing (MI), mindfulness based relapse prevention (MBRP), Mindfulness based stress reduction (MBSR), interpersonal group psychotherapy, individual psychotherapy, individualized treatments for co-occurring disorders, psycho- educational groups (life visioning, addiction education, communication and assertiveness training, social skills, life skills, Life balance plan), aftercare treatments (monitoring of technology use, ongoing psychotherapy and group work), and continuing care (outpatient treatment) in an individualized, holistic approach.

The first results from an ongoing OQ45.2 [ 74 ] study (a self-reported measurement of subjective discomfort, interpersonal relationships and social role performance assessed on a weekly basis) of the short-term impact on 19 adults who complete the 45+ days program showed an improved score after treatment. Seventy-four percent of participants showed significant clinical improvement, 21% of participants showed no reliable change, and 5% deteriorated. The results have to be regarded as preliminary due to the small study sample, the self-report measurement and the lack of a control group. Despite these limitations, there is evidence that the program is responsible for most of the improvements demonstrated.

As can be seen from this brief review, the field of Internet addiction is advancing rapidly even without its official recognition as a separate and distinct behavioral addiction and with continuing disagreement over diagnostic criteria. The ongoing debate whether IAD should be classified as an (behavioral) addiction, an impulse-control disorder or even an obsessive compulsive disorder cannot be satisfactorily resolved in this paper. But the symptoms we observed in clinical practice show a great deal of overlap with the symptoms commonly associated with (behavioral) addictions. Also it remains unclear to this day whether the underlying mechanisms responsible for the addictive behavior are the same in different types of IAD (e.g., online sexual addiction, online gaming, and excessive surfing). From our practical perspective the different shapes of IAD fit in one category, due to various Internet specific commonalities (e.g., anonymity, riskless interaction), commonalities in the underlying behavior (e.g., avoidance, fear, pleasure, entertainment) and overlapping symptoms (e.g., the increased amount of time spent online, preoccupation and other signs of addiction). Nevertheless more research has to be done to substantiate our clinical impression.

Despite several methodological limitations, the strength of this work in comparison to other reviews in the international body of literature addressing the definition, classification, assessment, epidemiology, and co-morbidity of IAD [ 2 - 5 ], and to reviews [ 6 - 8 ] addressing the treatment of IAD, is that it connects theoretical considerations with the clinical practice of interdisciplinary mental health experts working for years in the field of Internet addiction. Furthermore, the current work gives a good overview of the current state of research in the field of internet addiction treatment. Despite the limitations stated above this work gives a brief overview of the current state of research on IAD from a practical perspective and can therefore be seen as an important and helpful paper for further research as well as for clinical practice in particular.

ACKNOWLEDGEMENTS

Declared none.

CONFLICT OF INTEREST

The authors confirm that this article content has no conflict of interest.

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The acceleration of modern technology in the techno gadget era has led to the transformation of modern media highlighted by Generation Z. The aim of this study is to explore how gadget addiction influences the level of depression, anxiety, stress and sleep quality among the samples from the Ministry of Health Training Institution Sungai Buloh, Malaysia. Using the stratified sampling method, a total of 316 college students from various field are surveyed on the Smartphone Problematic Use Questionnaire, Depression Anxiety Stress Scale and Pittsburg Sleeping Quality Index. The Pearson correlation analysis and linear regression are adopted to find the relationship among the variables. Results show that gadget addiction is a predictor of depression, anxiety, stress and sleep disturbances. The findings of descriptive analysis show that the level of addiction, depression, anxiety and stress among the trainers is high. Positive correlations are found between the gadget addiction and levels of depression, anxiety, stress and sleep quality. Levels of depression, anxiety, stress and sleep quality among trainers are significant, with 13%, 10.7%, 12.5% and 3.4% experiencing symptoms of depression, anxiety, stress and sleeping disturbance respectively. Therefore, it is generally possible to know that the problem of gadget addiction can interfere with mental health of the users if the use of gadgets is not well controlled. The implications of this study are useful to academics and heavy gadget users and those who are hooked with their gadgets in their everyday life. Further studies in this area are needed to delve deeper into other issues related to each element of gadget addiction in order to reinforce the research framework which will in turn develop a standard guide for controlling gadget use in Malaysia .

Sleep Disorders , Depression , Anxiety , Stress , Addiction

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1. Introduction

A gadget is an important device and has become a necessity for human beings around the world. Tal and Torous (2017) said the rapid development of modern technology in the era of technological gadget has led to the revolution of modern media by the present generation Z (Gen Z). Previously, the use of gadget is very limited to professionals but nowadays, the use of these gadgets is not uncommon especially among Gen Z. More and more gadget users are able to own these devices because they are widely available and are commonly sold at affordable price. This study is generally aimed at identifying the extent of addiction affecting mental health (quality of sleep, depression, anxiety and stress) among Gen Z. In particular, the objectives of this study are to identify the level, relationship and contribution of quality of sleep, depression, anxiety and stress as well as gadget addiction among Gen Z users. The outcome of this study is expected to contribute to various parties especially teenagers and parents in identifying the elements that can lead to mental health and also serves as a guide in helping this group reduce their dependency on this gadget. All the sections in this study involve quantitative data collection.

2. Literature Review

2.1. Gadget and Gen Z

The literature review has documented that the use of a gadget can increase the community’s social status because it symbolizes a high-tech society. Without a gadget, someone will be perceived as an outdated person. A research conducted by Turner (2015) states that users who use gadgets are the most effective explorer in digital information. This is because the convenient strategy in searching for information helps users improve their search and sharing information methods. The findings of Issa Omar Malecela (2016) showed that the level of ownership and the usage can help increase the use of the gadgets in a more sophisticated and dynamic manner. The use of electronic software such as smartphones, iPad, and tablet helps users perform various activities such as sharing of information, internet surfing, document writing and other activities ( Khan et al., 2013 ). The study by Haque et al. (2016) states that with the availability of various applications on this technological device, it enables users to socialize, communicate, take pictures, record videos and some other sophisticated applications making it easy for users to perform an activity. According to Katz et al. (1973) in the theory of uses and gratification. Says that gadget users are active audience and they use the gadgets to meet specific needs. There is a need for gadgets to be transformed to various techno gadget features and thus providing different levels of satisfaction to its users. According to a topologist McQuail (1985) the uses and gratifications theory is a strategy to clarify the satisfaction obtained by gadget users while searching for information such as personal identity, social interaction and entertainment. The user satisfaction strategy is regarded as a method of using the gadget starting with setting the gadget’s objectives.

Mohd Mothar et al. (2013) has stated that in the selection of a gadget, it provides an avenue for identifying the advantages and disadvantages of the gadget’s usage, how it works in everyday life, how effective it is in searching, delivering and sharing of information and how users are able to control its usage. Thus, the use of gadget among Gen Z users is a strategy that helps this generation towards being high-tech savvy in the digital and techno gadget era.

The literature review shows that there is a disadvantage of gadget use among the Gen Z users. Elhai et al. (2017) revealed that Gen Z users spent 16 hours per day on average using a gadget. This implies the users cannot live without their electronic gadget. This is the feeling and resentment of gadget users whenever they have left their device at home or lose it. Many shortcomings need to be improved to achieve a more controlled level in use of the gadget.

Many Gen Z users are directly and indirectly involved in the effective and innovative use of these gadgets daily especially in their studies and daily lives. However, the dependency on the use of gadget without control is capable of contributing to problems in terms of social, academic and mental health. Thupayagale-Tshweneagae et al. (2014) state that people who are more frequent in using gadgets for the purpose of emotional support are likely inclined towards gadget addiction. Ranjan et al. (2013) state that gadget dependency is an unregulated behavioral problem and the inability of the user to use the gadget moderately. The extensive use may result in a stunted and non-functional daily life of the person. On the other hand, gadget addiction has the tendency to impel a person to be compulsive, characterized by an obsession to use the gadget. This distracts the ability of the person to live a normal life ( Mat Sharif & Omar, 2013 ).

Babadi-Akashe et al. (2014) state that problem in gadget usage is characterized as an individual’s nature and behaviour to regulate their dependence on the gadget and this nature raises the level of stress and creates problems in managing the individual’s daily activities. According to VandenBos & American Psychological Association (2015) , mental health is a fundamental field that describes emotional disturbance and disorientation that affects the functions of life. According to Gupta et al. (2013) a common symptom identified in the use of gadget is the inability to control the use of gadget, conceal the use of gadget and prolong use of the gadget on an ongoing basis without considering the effects of such behaviour. Mobile Phone dependency is a new emerging public health concern, due to the ill effects it predisposes on the younger generation. But, mobile phones can act as a boon also, when used effectively; hence adolescents need right motivation for the better usage of mobile phones ( Dilip & Javalkar, 2018 ).

According to Zulkefly & Baharudin (2009) , the use of gadgets among youth has experienced an increase from time to time causing symptoms of anxiety, depression and stress. Hassan et al. (2017) state that users who attempt to control and reduce the frequency of using the gadgets are found to suffer from mental problems. Gadget users who fail to control the excessive use of gadget suffer from symptoms such as restlessness, tiredness, feeling angry and extremely emotional especially gadget users who fail or unsuccessful in surfing the Internet ( Suki, 2013 ). The escalated number of social communication applications such as WhatsApp, Telegram, WeChat and social media applications such as Facebook, Instagram, Twitter as well as online games are the cause of serious gadget addiction among the users with significant increase in the number of current cases and also foreseeing the number will increase in the future ( Teong & Ang, 2016 : Ching et al., 2015 : Al-Barashdi et al., 2015 ).

The approach used in these studies primarily focus on how the role of these gadgets affects the level of dependency and the functionality among the Gen Z. The similarity in the studies is the use of gadget and mental problems among Gen Z. In other words, the prevalent use of the gadget will heighten their dependency and addiction and thus affect their lifestyle. Although there are studies that provide explicit and implied content on the gadget dependency, less emphasis is given on the impact of gadget addiction on mental health problems among Gen Z.

2.2. Conceptual Framework

This study uses a conceptual framework to assist researchers in conducting studies and to obtain information that is needed. This study adopts the concepts as shown in Figure 1 . For this study, the independent variable is the gadget addiction. Mental health elements are the dependent variable. The main component of the conceptual framework is the Gen Z users from the Ministry of Health Training Institution, Sungai Buloh, Malaysia. The second component is the gadget addiction among the Gen Z users which are based on four elements, namely, quality of sleep, depression, anxiety and stress which are the third component of the conceptual framework.

Based on the opinions expressed by previous scholars, a conceptual framework for this research is developed as shown in Figure 1 which provides an insight on the purpose of the study to identify the influence of gadget addiction on mental health and the risks among the Gen Z users.

3. Methodology

The design of the study used cross-sectional survey method. This study is conducted by using a survey where data is collected using a set of questionnaires. The data is analyzed using the descriptive statistic in finding percentages, standard deviation and mean score. This study is conducted at the Ministry of Health

Figure 1 . Conceptual framework gadget addiction and mental health among Gen Z.

Training Institution, (ILKKM), Malaysia. The study is targeted at all the trainees of the ILKKM, comprising of 325 trainees. A total of 316 people answered and returned the questionnaires. Stratified random sampling technique is used to analyze the data. As shown in Table 1 , a total of 165 male trainees (52.2%) and 151 female trainees (47.8%) participated in this study. From the perspective of the programmes, the participants involved in this survey are; 111 from the Assistant Environmental Health Officer programme, 89 from the nursing programme, 36 from the Occupational therapy programme, 27 people from the Physiotherapy programme, 26 people from the Radiography and Radiotherapy program, 21 people from the Assistant public health programme while the remaining 6 people are the trainees of the Assistant Pharmaceutical officers programme.

The “Smartphone Use questionnaire” developed by Rush (2011) is adapted to identify the respondents addiction to the use of gadgets from eight aspects, i.e. no control, withdraw, interpersonal and relapse conflicts, lost judgement, sense of success, emotional relations, attraction and declining productivity. This instrument has been modified and contains 44 items in the form of Likert scale, while for the survey of healthy mind screenings the “Depression Anxiety Stress Scale” is used. The questionnaire contains three subscales, namely depression, anxiety and pressure. To measure the level of sleep disorders, the “Pittsburg Sleep Quality Index’ survey is used. The questionnaire consists of 5 sections. Part A is based on respondents” demographics. This section aims to obtain information on the samples studied. Part B to part E is a survey related to the purpose of using gadgets, gadget addiction, mental filter and sleep disorders. The 7 point Likert scale which is a scale of seven points starting with “1 = very never”, “2 = very rare”, “3 = sometimes”, “4 = frequent”, “5 = Always” and “6 = very often” is used to identify the stage of use, addiction and filter healthy mind trainers. The mean value is referred from Plomp (2013) , whereby 1.00 - 2.33 mean score is considered low, mean score 2.34 - 3.66 is average and mean score 3.67 - 5.00 is considered high. The data collected is analyzed using SPSS version 22 which

Table 1 . Total number of research respondents and programme.

involves descriptive analysis, correlation and regression.

4. Result and Finding

The analysis shows three main factors are needed to be discussed in this study namely 1) quality level of sleep, depression, anxiety, stress and gadget addiction, 2) sleep quality and its relationship, depression, anxiety and stress and gadget addiction among Gen Z and 3) factors contributing to depression, anxiety and stress and addiction of gadget among Gen Z.

4.1. Gadgets Owners

The analysis conducted ( Table 2 ) has identified the number of gadgets owned by the respondents. As summarized in Table 2 , the majority of the respondents (n = 189, 59.8%) own two types of gadget, followed by 75 people (23.7%) with three types of gadgets. 40 respondents (12.7%) have more than one type of gadget while 10 (3.2%) respondents and 2 (0.6%) respondents have 4 and 5 gadgets respectively.

4.2. Level of Gadget Addiction, Sleep Quality, Depression, Anxiety and Stress among Gen Z

The results of the study as shown in Table 3 indicate that only 3 respondents or 0.9 percent have low-addictive level score, while the majority of respondents are at a high-level addiction score with 99.1 per cent or 313 respondents.

Table 4 shows the quality of sleep. The respondents who obtain a score of less

Table 2 . Number of gadgets owned by respondents.

Table 3 . Level of gadget addiction.

Table 4 . Level of sleep quality. Mean score for item: 1.00 - 2.33: Low, 2.34 - 3.67: Average, 3.68 - 5.00: High.

than level 5 are 273 or 86.4 percent, while 42 respondents or 13.3 percent are at level 5 and above. This indicates that most of the respondents are not facing sleep disorders because they have scored below 5.

Table 5 shows most of the respondents 170 (53.8%) are having severe depression, while 150 (47.5%) suffer severe stress. A total of 78 respondents (24.7%) experienced very severe depression, while 125 (39.6%) experienced very severe stress. Compared to the anxiety level, majority of the respondents experienced anxiety at a very severe level with 303 respondents (95.9%).

4.3. Relationship between Sleep Quality, Depression, Anxiety and Stress and Gadget Addiction

With reference to Table 6 , correlation analysis shows that gadget addiction has a significant but very weak relationship with quality of sleep and the level of anxiety. Instead, addiction has been found to have a significant and positive correlation with levels of depression and anxiety level. Hence, this suggests there is a linear and positive relationship between quality of sleep, depression, anxiety and stress with gadget addiction. This shows that gadget addiction can lead to mental health problems as mentioned above.

The value of Pearson coefficient for the quality of sleep showed r = 0.185, p = 0.001 where the strength of the relationship is very weak. The anxiety levels

Table 5 . Level of depression, anxiety and stress.

Table 6 . Pearson analysis between sleep quality and gadget addiction among Gen Z.

**p < 0.01 [Correlation is significant at the 0.01 (2-tailed)]; Interpreting Correlation Coefficients: 0.00 - 0.29: Very Low, 0.30 - 0.49: Low 0.50 - 0.69: Average, 0.70 - 0.89: High, 0.90 - 1.00: Very High.

show the correlation value R = 0.328, p = 0.000 where the strength of the relationship is positively low. The relationship for the two variables; depression and stress levels recorded R = 0.361, p = 0.000 for depression and r = 0.370, p = 0.000 for stress. This relationship indicates that the null hypothesis is rejected. The findings show that the relationship between quality of sleep, depression, anxiety and stress with gadget addiction among Gen Z is significant.

4.4. Contribution of Gadget Addiction Variance on the Element of Sleep Quality

This study uses linear regression analysis and it involves more than one independent variable. The hypothesis of the study tested four factors and criteria and a predictor that is expected to influence the tendency to gadget addiction. Linear regression test will not be able to test all of the relationships in one statistical test so a separate regression test is used to test the hypothesis completely ( Gefen et al., 2000 ). In the first regression analysis, the quality of sleep is a dependent variable while gadget addiction is the independent variable. The coefficient of determination (R²) evaluates the proportion of the variance of a dependent variable against the mean score explained by an independent variable or predictor ( Hair Jr et al., 2010 ). The higher the amount of R², thus the better the regression model fits the data. The result of linear regression analysis that identifies the relative contribution of gadget addiction to the quality of sleep is formulated in Table 7 and Table 8 . The findings show that independent variables contributed significantly (P < 0.05) to the total variants in quality of sleep. The free variable is the gadget addiction level. This variable contributed 3.4 percent to the variants in quality of the respondents’ sleep. Therefore, there is no significant contribution by the independent variable to the level of quality of sleep.

Based on the second test of regression, depression is a dependent variable while gadget addiction is the independent variable. Table 9 and Table 10 show the results of the regression test for the depression variables. The findings show that the gadget addiction level contributes significantly (p < 0.05) to the number

Table 7 . Variance analysis of gadget addiction with element of sleep quality.

Sig. at the 0.05 level; a Predictor: (Constant), Gadget Addiction; b Dependent Variable: Level of sleep quality.

Table 8 . Linear regression analysis showing gadget addiction influencing sleep quality.

R = 0.185 a ; R 2 = 0.0.34; Adjusted R 2 = 0.31; Standard Error = 1.390; Dependent Variable: Sleep Quality.

of variants in the level of depression. These two variables contributed 13.0 percent to the variant in the level of depression. Therefore, there are no significant contributions by the independent variables of the level of depression.

The third regression analysis is tested. Anxiety is a dependent variable while gadget addiction is the independent variable. Table 11 and Table 12 show the third regression variable. The test of the findings shows that gadget addiction had contributed significantly (p < 0.05) against the number of variants in predicting anxiety levels. The value of R 2 regression model for dependent variable level of anxiety is 102 (adjusted R 2 ), which means that 10.2% of the variance in the level of anxiety is explained by the regression model. This suggests that gadget addiction can contribute significantly to the extent of the respondents’ anxiety.

The fourth regression analysis is conducted. Stress is the dependent variable while gadget addiction is the independent variable. Table 13 and Table 14 show

Table 9 . Variance analysis of gadget addiction with element of depression.

Sig. at the 0.05 level; a Predictor: (Constant), Gadget addiction; b Dependent Variable: Level of Depression.

Table 10 . Linear regression analysis showing the influence of gadget addiction with the level of depression.

R = 0.361 a ; R 2 = 0.130; Adjusted R 2 = 0.122; Standard Error = 3.896; Dependent Variable: Depression.

Table 11 . Variance analysis showing the influence of gadget addiction on the element of anxiety.

Sig. at 0.05 level; a Predictor: (Constant), Gadget Addiction; b Dependent Variable: Level of Anxiety.

Table 12 . Linear regression analysis showing the influence of gadget addiction with level of anxiety.

R = 0.328 a ; R 2 = 0.107; Adjusted R 2 = 0.102; Standard Error = 3.460; Dependent Variable: Level of Anxiety.

Table 13 . Variance analysis showing the influence of gadget addiction with the element of stress.

Sig. at 0.05 level; a Predictor: (Constant), Gadget Addiction; b Dependent Variable: Level of Stress

Table 14 . Linear regression analysis showing the influence of gadget addiction with level of stress.

R = 0.370 a ; R 2 = 0.125; Adjusted R 2 = .0125; Standard Error = 3.507; Dependent Variable: Stress level.

the fourth variable for regression model. The results show that the gadget addiction level is important in predicting the level of stress against the use of gadget among Gen Z. The value of R 2 regression model for the dependent variable which is the stress level is 125, meaning that 12.5 percent of the variance in the stress level is explained by the regression model. This variable contributed 12.5 percent to a variant of the respondents’ stress level. Therefore, there is no significant contribution by independent variable which is the level of stress.

The major and highest predictor of gadget addiction is the stress level (β = 0.321, t = 5.996, and P = 0.000) and it contributes 12.5 per cent ( Table 14 ). This shows that for each unit which increases the stress score, the trainees’ gadget addiction score is increased by 0.321 units. This means that the increase in the levels of gadget addiction among trainees is the main factor contributing 12.5 percent of the variant to the increase in stress in mental health disturbances. The second predictor is the level of depression (β = 0.301, t = 5.594, and P = 0.000) and this contributes to the increase in depression at 13.0 per cent. This means that when the depression level increases a unit ( Table 10 ), gadget addiction level increases by 0.301 units. This finding clearly shows the increase in depression thus the gadget addiction level is also increased.

The following predictors recorded a value of β = 0.295, t = 5.476, and P = 0.000 and contribute to the increase in mental health disorder at 10.2 per cent ( Table 12 ). This means that when the anxiety level increases a unit, gadget addiction level also increases by 0.295 units. The findings clearly show that with the increase in anxiety, the gadget addiction level is also increased. On the other hand, the fourth predictor which is the quality of sleep records a value of β = 0.185, t = 3.335, and P = 0.000 and the level of contribution to the quality of sleep disruption was 3.4 per cent ( Table 8 ). This finding clearly shows the increase in the quality of sleep disruption; thus, the gadget addiction level is also increased. The balance of 60.4 per cent can be explained with the other variables which are not accounted for in this model. The findings indicate that there may be some other factors affecting gadget addiction that is not highlighted in this study ( Hair Jr et al., 2010 ; Pallant, 2009 ; Tabachnick & Fidell, 2007 ).

5. Discussion

Generation Z tends to adapt and easily understand the use of electronic devices, namely gadget. They are categorized as early adopters ( Gupta et al., 2013 ). For them, a gadget is a compelling tool, a device which encompasses the whole of entertainment. It is not only a communication tool but it also increases their sense of autonomy, identity and even their credibility ( Carbonell et al., 2018 ). The Gen Z gadget addiction level is an important issue because it is closely related to mental health as well as their overall welfare. Mental health is one of the indicators of productivity. The highly productive Gen Z is an important asset in the national development. The Gen Z is the country’s backbone in terms of the economy as this generation will soon be in the work force and will contribute to the productivity of the country. Hence, the study of gadget addiction on mental health is an important perspective in assisting to formulate policies related to mental health in higher education institutions and globally for the implementation of the policy that is for the Gen Z. Thus, the study aims to know and identify the distribution of generations that are experiencing gadget addiction, thus contributing to psychological and mental disturbances among Gen Z in Malaysia, particularly those from training institution of Ministry of Health Malaysia, and thus estimating the percentage of users with gadget addiction and its impact on their mental health.

From a descriptive analysis conducted, more than half of the total respondents (51.6%) use gadgets to surf the internet. Rush (2011) and Elhai et al. (2016) used the same instrument and recorded a lower rating among the respondents as compared to this study. However, the findings of the study are consistent with the findings of the study of by Zulkefly & Baharudin (2009) , which recorded over 50% respondents using the gadgets to surf the internet. The different findings of the studies are possibly due to the different environmental factors and the respondents’ lifestyle who are involved in these studies. Even the trainees are also very obsessed in the use of these gadgets in entertainment and media at 50.6%. The study conducted by Bruner & Kumar (2007) indicates that undergraduates in Malaysia are the users of active social networking sites and it has become part of their daily activities. The findings of Mohd Mothar et al. (2013) , found teenagers as die-hard users of gadgets in social networking sites where these addictions have disrupted their psychological well-being.

Based on the analysis of frequency, the level of gadget addiction among Gen Z shows 3 respondents are normal gadget users while 313 respondents are addicted to gadgets. Studies conducted by Salehan and Negahban (2013) , Zaremohzzabieh et al. (2014) , Teong & Ang (2016) and Carbonell et al. (2018) found the use of gadgets at normal level may provide positive benefits, but the use of gadget at extreme and critical level is capable of disrupting the daily life of gadget users resulting in the decline of mental health, lack of focus in learning and affect socialization and academic performance.

Based on the analysis of the findings, it is found that the score for sleep quality of Gen Z is at a moderate level. The outcome of this study clarifies that proper distribution and planning facilitates the well-being of a healthy mind which has a lot of tasks to be completed and activities that need to be participated. The findings of this study are supported by the study of Mohammadbeigi et al. (2016) and Liu et al. (2017) which justifies that effective time management can eschew the trainees from forfeiting their sleeping time to play gadgets and thus can ensure the trainees get enough rest for the days to come. Preety et al. (2018) explains that gadget addiction can cause the trainees to sacrifice their sleep time and hence will bring to serious mental problems among Gen Z. The trainees should have the awareness of the importance of adequate and quality sleep. This is because adequate sleep can help the trainees to stay focused and energetic. It can also avoid stress and other psychological problems.

The findings from correlation analysis explain that there is a significant and positive relationship between gadget addiction and mental health among Gen Z. The findings indicate that high gadget addiction can contribute to mental health problems. This finding is in line with the study conducted Ranjan et al. (2013) , Lepp et al. (2014) and Elhai et al. (2016) . Gadget addiction has a positive relationship with depression, anxiety and stress. A study by Akilandeswari & Sujatha (2018) found gadget addiction level among medical students is at a low level. However, the study indicates that the symptoms of gadget addiction are increasing among the respondents. Elhai et al. (2017) found that the excessive use of gadgets would cause students to procrastinate resulting in their inability to complete their assignments in time. Therefore Babadi-Akashe et al. (2014) emphasize that the use of gadgets apart from learning purposes among the students is considered unethical.

The results of the analysis of the study also found that as a whole, there is a significant and positive relationship between gadget addiction not only with depression but also anxiety and stress. The results of regression analysis show that gadget addiction affects 3.4% of sleep quality, 13% of depression, 10.7% anxiety and 12.5% stress faced by the trainees. The study by Soni et al. (2017) , state that the use of extreme gadget will have a negative impact on mental health where the higher the level of pathology, the lower the level of mental health among the users. A study by Carbonell et al. (2018) , says that constructively speaking, users who use gadgets frequently usually experience a decline in their academic performance, disruption in social relationships, face financial problems and affect their physical health. A research by Kwon et al. (2013) and Ching et al. (2015) , have expressed their concern that gadget usage will increase when users suffers from psychological disorders such as loneliness.

Previous studies have also found relationships between addiction and mental health among Gen Z ( Ozkan & Solmaz, 2015 ; Pundir et al., 2016 ; Vanitha & Javalkar, 2018 ). These past studies are consistent with the present study showing a significant difference in distribution between gadget addiction and mental health. However, the unadulterated effects of gadget addiction on mental health can only be identified when demographic, socioeconomic and environmental factors are able to be controlled. For example, a study made by Ozkan & Solmaz (2015) found that anxiety and depression affect the probability of getting stress. However, this relationship is absent when adjustments are made to other factors. Hence, the main contribution of this study is the estimation of the unadulterated effects of gadget addiction to mental health by using the econometric model.

Based on the findings of healthy mind screening, this study found the level of gadget addiction has a significant relationship in determining the level of mental health among Gen Z. The probability of getting mental health problems is high when the gadget addiction level keeps increasing. Apart from mental health, loneliness, technophobia and nomophobia also serve to determine the level of Gen Z mental health. Nevertheless, other variables are beyond the scope of this study.

6. Implication and Conclusion

The implication of this study can provide awareness and benefits to society especially for the Gen Z as the result of study contributes to the negative impact in their daily lives. The study by Carbonell et al. (2018) states that gadget dependencies will affect mental health such as depression, anxiety and stress among the users. In addition, parents and educators are able to identify users who suffer from gadget dependency resulting in impact of psychological problems contributing to the individual’s academic and mental health disorders. In conclusion, gadget addiction has an impact on the users’ academic, socialization and mental health. Gadget addiction among students not only distracts their academic performance, but also contributes to their physical, emotional and cognitive problems. Hence, efforts have been attempted to rationalize the smart and controlled use of the gadgets among Gen Z in order to enhance their time management, achieve good academic performance thus maintaining a more positive level of mental health. The implications of this study are useful to academics and heavy gadget users and those who are hooked with their gadgets in their everyday life. Further studies in this area are needed to delve deeper into other issues related to each element of gadget addiction in order to reinforce the research framework which will in turn develop a standard guide for controlling gadget use in Malaysia.

Acknowledgements

We are very grateful to experts for their appropriate and constructive suggestions to improve this article. I would like to express my gratitude to Faculty of Education Universiti Kebangsaan Malaysia, under Grant GG2019-020 for financially support this research.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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• Volume 7, Issue 1
• Gadget addiction among school-going children and its association to cognitive function: a cross-sectional survey from Bangladesh
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• Mowshomi Mannan Liza 1 , 2 ,
• http://orcid.org/0000-0002-9073-5451 Mohammad Azmain Iktidar 1 , 2 ,
• http://orcid.org/0000-0002-7124-5043 Simanta Roy 1 , 2 ,
• Musa Jallow 3 ,
• Sreshtha Chowdhury 1 , 2 ,
• Mustari Nailah Tabassum 2 , 4 ,
• Tarannum Mahmud 2 , 4
• 1 Department of Public Health , North South University , Dhaka , Bangladesh
• 2 Department of Public Health , School of Research , Chattogram , Bangladesh
• 3 Medical Research Council Unit The Gambia , London School of Hygiene and Tropical Medicine , Banjul , Gambia
• 4 Department of Medicine , Chittagong Medical College , Chittagong , Bangladesh
• Correspondence to Dr Mohammad Azmain Iktidar; sazmain{at}gmail.com

Background People are becoming more dependent on technology than ever before. Today’s children and adults are heavily plugged into electronics, which raises concerns for their physical and cognitive development. This cross-sectional study was conducted to assess the relationship between media usage and cognitive function among school-going children.

Methods This cross-sectional study was conducted in 11 schools in 3 of Bangladesh’s most populous metropolitan areas: Dhaka, Chattogram and Cumilla. A semistructured questionnaire with three sections was used to obtain data from the respondents: (1) background information, (2) PedsQL Cognitive Functioning Scale and (3) Problematic Media Use Measure Short Form. Stata (V.16) was used for statistical analysis. Mean and SD were used to summarise quantitative variables. Qualitative variables were summarised using frequency and percentage. The χ 2 test was used to explore bivariate association between categorical variables, and a binary logistic regression model was fit to investigate the factors associated with the cognitive function of the study participants after adjusting for confounders.

Results The mean age of total of 769 participants was 12.0±1.8 years, and the majority (67.31%) were females. The prevalence of high gadget addiction and poor cognitive function was 46.9% and 46.5%, respectively, among the participants. After adjusting the factors, this study found a statistically significant relationship (adjusted OR 0.4, 95% CI 0.3 to 0.7) between gadget addiction and cognitive function. In addition, the duration of breast feeding was a predictor of cognitive function as well.

Conclusion This study found digital media addiction as a predictor of decreased cognitive performance in children who use digital gadgets regularly. Although the cross-sectional design of the study precludes causal relationships from being determined, the study finding deserves further examination via longitudinal research.

• Child Psychiatry
• Epidemiology

Data availability statement

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

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

http://dx.doi.org/10.1136/bmjpo-2022-001759

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WHAT IS ALREADY KNOWN ON THIS TOPIC

School age is a time of rapid physical and mental growth for children.

Both children and adults are excessively immersed in electronic gadgets in today’s times.

Digital addiction has a detrimental effect on ’students' performance in the classroom.

Boys have a higher score of addiction to gadgets (66.3%).

WHAT THIS STUDY ADDS

This study found a significant proportion of school-going children are addicted to digital gadgets. Gadget addiction has a statistically significant relationship with the cognitive function of school-going children.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study recommends regular screening of gadget addictions among school-going children and future interventions and policies on daily recommended time limits of digital media device usage in children.

Introduction

Around the world, people are increasing their reliance on technology devices at a rate that has never been seen before. 1 Not only adults but also children are excessively immersed in electronic gadgets in today’s times, which generates issues and worries regarding the effects these devices have on children in terms of their physical and cognitive development. 2 3 Regarding the situation in Asia, a prior study that was carried out in six Asian nations concluded that children aged from 12 to 18 years held ownership of smartphones at a rate of 62% overall. 3

Numerous developments have taken place in the public sphere of the modern period, leading to an explosion of new forms of data transmission, social interaction and leisure time activities. As technology continues to grow on a global scale, it is nearly impossible to live without any digital screen. 4 Technological progress brings about inevitable lifestyle changes, particularly in children. These changes include the habit of playing with gadgets, eating habits, physical activity levels and the impacts of these changes. 5 There are identified benefits of digital device use, such as helping children acquire new vocabulary, languages and stay engaged in the classroom. 6 However, the possible negative impact of digital device use and its problematic usage is also common. A study has shown that digital addiction has a detrimental effect on students’ performance in the classroom. 7 Children who spend an excessive amount of time in front of screens may have decreased levels of productivity. 3 Above-mentioned studies indicate that there are a variety of advantages as well as drawbacks associated with the use of the various forms of the digital screen.

A cognitive function is any psychological process that is involved in the process of acquiring knowledge, the manipulation of information or the logical derivation of conclusions. 8 The capabilities of perceiving, remembering, learning, paying attention, deliberating and communicating are all included in the cognitive processes. 8 People who use digital screens for prolonged periods have been reported to have impaired cognitive regulation and cognitive inflexibility. 9 According to the findings of another study, digital addiction is connected with an increased number of reported cognitive failures. 10

School age is a time of rapid physical and mental growth for children. 11 There are increasing concerns about the effects of children’s excessive screen usage on their growth and development. 12 According to the results of a survey, around two-thirds of students use the digital screen while they should be paying attention in class, studying or completing assignments. 7 The distraction that is resulted from this multitasking is one of the factors that has been proven to have a negative impact on students’ academic performance. 7 There are limited evidences of digital addiction among children and its correlates in this geographic area. Therefore, this cross-sectional study was carried out to determine the extent of media use, and its association with cognitive function among school-going children in the study region.

Study design, setting and sample

This cross-sectional study was carried out among children aged 8–14 enrolled in grades 4–7 at five private schools, five public schools and one madrasah (a specially adapted institution for Islamic education and culture) in Bangladesh. The study locations were chosen using convenient sampling. A printed questionnaire with instructions was used to obtain information from the parent, while trained volunteers performed face-to-face interviews with the participant.

Participants in the selected schools were sent informational pamphlets, parental consent forms and questionnaires. In addition, the pamphlets included a contact number for any more inquiries. Cognitive function assessment interviews were conducted with (n=769) children who provided written parental consent and completed the questionnaire within 1 week.

A semistructured questionnaire with three sections was used for data collection. Section 1 included questions on sociodemographic factors (age, gender, residence, family type, family income and parental education status), birth order (the order in which the child is born in comparison to other sibling), method of delivery (how the child was given birth: normal vaginal delivery or caesarean section), Expanded Programme on Immunisation (EPI) vaccination status (If the child received all vaccination according to the EPI schedule), duration of breast feeding (for how long the child was breastfed) and deworming status (The interval at which the child received deworming medication: never, occasionally or regularly). Sections 2 and 3 included two validated questionnaires (PedsQL Cognitive Functioning Scale and Problematic Media Use Measure Short Form (PMUM-–SF)) for measuring cognitive function and gadget addiction, respectively. The parents received sections 1 and 3 with precise instructions for completion. The remainder of the questionnaire (section 2: PedsQL Cognitive Functioning Scale) was completed by a trained volunteer after the participant’s face-to-face interview.

PedsQL Cognitive Functioning Scale

The PedsQL Cognitive Functioning Scale consists of six questions (‘It is hard for me to keep my attention on things;’ ‘It is hard for me to remember what people tell me;’ ‘It is hard for me to remember what I just heard;’ ‘It is hard for me to think quickly;’ ‘I have trouble remembering what I was just thinking;’ ‘I have trouble remembering more than one thing at a time.’). This scale was developed through focus group discussions, cognitive interviews, pretesting and field-testing measurement development techniques. 13 A five-point Likert scale was used to assess this scale, with 0 denoting never, 1 denoting nearly never, 2 denoting sometimes, 3 denoting often and 4 denoting almost always. All responses were reverse-scored and then linearly translated to a 0–100 scale (0=100, 1=75, 2=50, 3=25, 4=0), in accordance with established scoring protocols. Any score below the mean was considered as poor cognitive functioning and higher scores indicated higher functioning.

Problematic Media Use Measure Short Form

The PMUM–SF was used to determine the level of screen addiction among all of the children in our study cohort. It includes nine components. Each answer was based on a five-point Likert scale: (1) never, (2) seldom, (3) sometimes, (4) often and (5) always. Children who scored 3 or higher on at least five questions were deemed to have a high level of device addiction.

A pretesting was done on 20 participants from government and private schools to check the feasibility and reliability of the study. Necessary modifications were made to simplify the data collection without affecting the data quality. The inclusion of a helpline number in leaflets was considered on the suggestions of the pilot participants.

Statistical analysis

All analyses were performed using Stata (V.16). Descriptive statistics were calculated as mean and SD for quantitative variables or frequency and relative frequency for categorical variables. The bivariate association of two categorical variables was explored using the χ 2 test. A binary logistic regression model was fitted to assess the association between cognitive function and gadget addiction. Variables with a p≤0.2 in the bivariate analysis entered in the multivariate model in a forward stepwise selection method. A two-tailed p<0.05 was considered statistically significant.

Public involvement

Members of the public were involved in several stages of the study including design and conduct. We received input from children and their parents and implemented them in our study design. We intend to disseminate the main results to study participants and will seek public involvement in the development of an appropriate method of dissemination.

Of the 836 questionnaires and consent forms provided to the participants, 67 were ineligible (30 did not meet inclusion criteria and 37 did not consent), resulting in 769 potential responders. A total of 769 responses out of 836 amounted to a response rate of 91.9%.

Background information of the study participants is presented in table 1 . Among the 769 participants, 67.3% were female and hailed from urban areas. About 78% of the participants were from nuclear families, and most of the participants’ birth orders were second or more. Most of the participants’ family income was in between BDT10 000 and BDT20 000 (42.4%). Regarding parental education, 40.9% of parents had 8–12 years of schooling. In terms of birth, 26.3% of participants’ modes of delivery were by caesarean section, and 67.8% were normal vaginal delivery. Most of the participants (90.6%) were EPI vaccinated. 10.8% of participants’ duration of breast feeding was less than 6 months, whereas 47.8% of participants were more than 24 months. About 3% of participants were never dewormed, whereas 49.08% were occasionally and 48.1% were regularly. The prevalence of high gadget addiction and poor cognitive function were 46.9% and 46.5%, respectively, among the participants ( figure 1 ).

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Background information of study participants (n=769)

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Prevalence of gadget addiction and cognitive function among school-going children (n=769)

Table 2 includes all the potential variables and demonstrates the adjusted result. After adjusting for age, gender, residence, family type, birth order, family income, parental education, mode of delivery, EPI vaccination status, duration of breast feeding and deworming status, participants with high gadget addiction had 56% less chance of good cognitive function than those with low gadget addiction. Also, participants whose duration of breast feeding was 6–12 months (adjusted OR, AOR 2.5, 95% CI 1.1 to 5.4, p=0.02), 12–24 months (AOR 2.0, 95% CI 1.0 to 4.2, p=0.05) and more than 24 months (AOR 2.4, 95% CI 1.0 to 4.7, p=0.01) had a higher chance of having good cognitive function than those who were breastfed for less than 6 months. Responses regarding the PMUM questionnaire are presented in table 3 .

Cognitive function of the study participants and associated factors (n=769)

Problematic media use measure questionnaire and responses of the participant

The objective of this study was to determine the prevalence of gadget addiction and its association with cognitive functions among school-going children in Bangladesh. Using a semistructured questionnaire, data were collected on background information, and data estimating cognitive functions and gadget addictions via the PedsQL Cognitive Functioning Scale and PMUM-SF, respectively. In this study, a high gadget addiction score (46.9%) was found in the participants; this result is similar to other studies reporting the growing prevalence of gadget addiction in different parts of the world. Similarly, previous research consisting of two systematic reviews and meta-analysis 2 14 confirm the increasing prevalence trend of gadget addiction over time in children and children. An Indian study among school-going children, where 57.55% were female, found that 10.69% of technology users were addicted, with 8.91% addicted solely to their phones. 15

The PMUM-SF scale is a validated and reliable tool used to estimate screen media addiction in children by measuring child screen time and psychosocial functioning. 16–18 The high gadget addiction score estimated by PMUM was found to be across all age groups, and of the total participants in this study, the median age was 12.0 years with females being the majority (67%). This is in contrast to a study conducted in India, which reported boys as having a higher gadget addiction score (66.3%) because they had longer screen time than girls. 19 Other studies suggest that the prevalence of problematic media use or gadget addiction among children and young adults often varies (ranging from 5% to 50%). 16 20

Although the significance could not be established, it was observed that majority of the participants were from urban areas, belonged to nuclear families, had family income ≥BDT15 000/month, and had parents with some level of education. These elements could potentially be indicative of higher socioeconomic status and, therefore, children born from such families are more at risk of excessive screen exposure and gadget addiction. A few studies have demonstrated the link between high family income and screen or internet addiction, thus confirming our theory. 21 22

Using the PedsQL Cognitive Functioning Scale which is a reliable and valid measure of cognitive functioning in children, 13 23 we estimated the cognitive function of all participants in the study and determine their association with children with gadget addiction. Overall, it was found that 53.5% of the children had a good cognitive function score, and children identified to have high gadget addiction scores had 57% less chance (AOR 0.4, 95% CI 0.30 to 0.6, p<0.001) of having a good cognitive function compared with those with low gadget addiction. The adjusted logistic regression analysis showed that as gadget addiction increases the level of poor cognitive function increases as well. A previous study conducted on children under 12 years of age in India, found that gadget media addiction has a close association with decreased cognitive function. 19 The study findings indicated that increased screen time and gadget addiction were significantly associated with parental concerns in some cognitive elements such as problem-solving, communication and personal-social development. 19 Previous research further supports this, reporting the significant association between increased screen time and delays in cognition, language and developmental motor milestones. 24 Similarly, there is evidence to show that parents who frequently use digital media devices to calm upset children lead to increase concerns in socialemotional development in toddlers. 25 A few studies observed increased ADHD problems in children with excessive televison (TV) use, 26 27 while the cognitive development of children was found to improve when screen time was reduced to less than 2 hours per day. 28 It was reported that the use of electronic media in preschool-age children was associated with behavioural difficulties over time. 29 Hyperactivity or inattention problems were associated with baseline use of mobile phones, while emotional and conduct problems were associated with internet or computer usage. 29

To the best of our knowledge, this is the first study to examine gadget addiction and its association with cognitive function in children in Bangladesh, using the PMUM-SF and PedsQL Cognitive Functioning Scales. The measurement of cognitive function may not be accurate considering the absence of clinical test. Still, the questionnaire used in this study was developed from validated scales, thus, enhancing the strength of our research. Another strength of this study is the large sample size used, which allows for greater precision and generalisability of the findings. One of the limitations of this study is that we could only present the association between gadget addiction and cognitive function, rather than causality due to our research methodology. Due to convenience sampling methods employed in this study, there may be sampling bias, however, we attempted to minimise this by sampling 769 children from 11 schools in three of Bangladesh’s most populous metropolitan areas of Bangladesh (Dhaka, Chattogram and Cumilla). Recall and social desirability bias are likely to have occurred since part of the data was drawn from parental reports. Future research is needed to establish cause and effect on this topic and, therefore, draw definitive conclusions.

We conclude that there is a positive association between gadget addiction and poor cognitive function among children who use digital devices frequently. Therefore, interventions and education programmes should be developed to increase public awareness of harmful gadget addictions in children. However, additional longitudinal research is required to obtain a clearer data.

Ethics statements

Patient consent for publication.

Consent obtained from parent(s)/guardian(s).

Ethics approval

Ethical approval for this study was obtained from the Institutional Review Board, North South University (Approval no-2022/0R-NSU/IRB/1005). All the participants were explained in detail about the aims and process of this study and informed consent was taken before data collection.

Acknowledgments

The authors would like to thank Dr. Azaz bin sharif (North South University), and Dr. Sanjana Zaman (North South University) for their assistance and time with this article.

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Contributors MML conceived the need for the survey, participated in its design, contributed to the interpretation of the results and is responsible for the overall content as guarantor. SR and SC participated in the design. MML, MAI and SR participated in data analysis of the study. MJ, SC, MAI, TM and MNT collaborated in data collection and writing up the manuscript. All authors read and approved the final manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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