essay addiction of mobile phones

Essay on Mobile Phone Addiction

Students are often asked to write an essay on Mobile Phone Addiction in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Mobile Phone Addiction

Introduction.

Mobile phones, once a luxury, are now a necessity. They are fantastic tools for communication, learning, and entertainment. However, excessive use can lead to addiction.

Understanding Mobile Phone Addiction

Mobile phone addiction is a dependence on your phone. It’s when you can’t stop checking it, even when it disrupts your life.

Effects of Addiction

This addiction can affect your mental health, leading to anxiety and depression. It can also impact your physical health, causing poor posture and eye strain.

It’s essential to balance phone use with other activities. This way, you can enjoy the benefits without the addiction.

250 Words Essay on Mobile Phone Addiction

Mobile phone addiction, a contemporary issue, is becoming increasingly prevalent, especially among college students. The phenomenon, a manifestation of behavioral addiction, is characterized by excessive or compulsive use of mobile phones, interfering with daily activities.

The Psychology Behind the Addiction

The addiction stems from various psychological factors. Firstly, the Fear of Missing Out (FOMO) drives the urge to stay constantly connected. Secondly, the dopamine-induced pleasure from receiving notifications or likes contributes to the addictive behavior.

Impacts of Mobile Phone Addiction

Mobile phone addiction can lead to serious consequences. It negatively affects mental health, causing anxiety and depression. It also leads to physical health issues like poor posture and eye strain. Furthermore, it can hamper academic performance and social relationships.

Addressing the Issue

Addressing this issue requires a multi-faceted approach. Self-regulation, digital detox, and mindfulness can help manage the addiction. Additionally, educational institutions can play a crucial role by promoting digital literacy and healthy technology use.

While mobile phones have become an indispensable part of our lives, it is essential to maintain a balance. Recognizing and addressing mobile phone addiction is crucial to ensure our well-being and productivity. It is not about completely eliminating the use of mobile phones, but about using them responsibly and mindfully.

500 Words Essay on Mobile Phone Addiction

The advent of mobile phones has undeniably brought about significant convenience and connectivity in our lives. However, the ubiquity of these devices has given rise to a contemporary issue – mobile phone addiction. This phenomenon is particularly prevalent among the younger generation, including college students, who are increasingly becoming reliant on their mobile devices for a wide range of activities.

Mobile phone addiction, also known as nomophobia, is characterized by an excessive and compulsive use of mobile phones, leading to a dependency that can interfere with daily life. It is a multifaceted problem, with roots in psychological, social, and technological aspects. The addictive nature of social media, games, and other applications, coupled with the Fear of Missing Out (FOMO), fuels this addiction.

The Impact of Mobile Phone Addiction

The impact of mobile phone addiction is manifold, with both psychological and physical consequences. Psychologically, it can lead to anxiety, depression, and stress, often resulting from the pressure to be constantly available and responsive. Physically, excessive screen time can lead to vision problems, sleep disorders, and even physical discomfort like neck and back pain.

The Social Aspect

Mobile phone addiction also has a significant social impact. It can lead to isolation, as individuals may prefer virtual interactions over real-life socializing. This addiction can also hamper interpersonal relationships, as excessive phone use can be perceived as disrespectful and can cause misunderstandings.

Addressing mobile phone addiction requires a multi-pronged approach. Firstly, awareness about the issue is crucial. Educational institutions can play a vital role in this by conducting workshops and seminars on the topic. Secondly, self-regulation strategies, such as setting screen time limits and having phone-free periods, can be beneficial. Therapy and counseling may also be necessary in severe cases.

Role of Technology Companies

Technology companies also have a role to play in curbing mobile phone addiction. By designing apps and features that promote digital wellbeing, such as screen time trackers and ‘do not disturb’ modes, they can help users manage their phone usage better.

In conclusion, while mobile phones have revolutionized the way we communicate and access information, their excessive use has led to the emergence of mobile phone addiction. This issue, though complex, is not insurmountable. With concerted efforts from individuals, educational institutions, and technology companies, we can address this problem and promote healthier digital habits.

That’s it! I hope the essay helped you.

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Cell Phone Addiction, Essay Example

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Nowadays, many individuals have become more and more addicted and dependable on phones, specifically smart call phone s. This often happens without these individuals realizing how the excessive and inappropriate use of their cell phones can create several of problems in their social and everyday lives. Because of this, cell phone addiction can easily be denied as an incredibly serious compulsive disorder that has been increasing with newly available technology. As technology has progressed throughout the years, innovate phones and intriguing apps make it almost impossible and irresistible for individuals to be able to put their phones down in social settings, such as a family dinner. In fact, scientific debates have recently arisen that question whether cell phone addiction, along with gambling, should be added to the new DSM-V addiction list (Choliz, 2010, p. 373). Having said this, it is imperative to make it known that the primary purpose of this research paper cognizant of the actuality that chronic cell phone usage can indeed be a huge problem in society today. This can lead to problems inside one ’ s own family as well as conflicts in the workplace. The result of the excessive cell phone usage creates social, behavioral, and affective problems in the lives of future teenagers all around the world.

Something that must be openly understood is the fact that a teenager ’ s social experience can deeply be affected by the manner in which he or she uses his or her cell phone. For example, for a teenager to be using his or her cell phone during a party makes this certain individual unsocial. This can result in a person only having friends online as opposed to having the real thing of having personal friends to interact with on a daily basis. While this controversy might seem worrisome to many older individuals, people should realize that if a teenager is not able to have a social life, he/she might end up leading a depressed lifestyle. Also, as W.K. Park points out, loneliness is “positively associated with mobile phone addiction” which shows that addicted persons tend to reduce their feelings of loneliness by using mobile devices, such as a cell phone (2005, p. 260).

This kind of behavior would not only affect the teenager, but also the teenager ’ s family members. The fact of the matter is that cell phones have become so addictive nowadays that teenagers do not realize that they are addicted to their cell phones until it is too late and they have already lost the majority of their friends because of the manner in which they have shut everybody out of their lives. It goes without saying that this type of behavior should be stopped immediately because it has a huge impact on the kind of individuals that will grow up to lead the world into a better tomorrow. However, this “ better tomorrow ” will not be able to be made if these teenagers grow up being socially awkward.

A second aspect that must be considered when talking about cell phone addiction is the fact that, due to the fact that cell phones have so many addictive applications on them, teenagers choose to use their cell phones for extended periods of time in order to pass certain levels on a video game. While there is nothing wrong with a teenager playing a simple video game on his or her cell phone, some of these games are extremely violent for young teenagers to be playing. These violent video games are often times difficult for parents to monitor because no teenagers wishes to have his or her parents looking through his or her phone. Without parental supervision, teenagers find it much easier to download violent video games or explicit content unto their phones. This kind of behavior creates grave problems for teenagers, as they lose sense of what is real and what is portrayed in their little phone screen. In order to avoid this type of behavior altogether, it should be considered each child ’ s parents ’ responsibility to monitor what his or her child is watching in his or her cell phone and ensure that nothing inside that cell phone could prove to be detrimental to the teenager ’ s behavior either at home at school.

The reason as to why a cell phone addiction might prove to be increasingly dangerous to some teenagers is because of the fact that some teenagers are not quite ready to know how to keep their social and behavioral life in shape. As a result of this, teenagers often times find it normal to spend unreasonable hours throughout their own respective day looking through their phone and talking to other individuals online. The problem that is brought forth with these kinds of actions is that it is only a matter of time until face-to-face interaction is considered taboo. When society reaches this point, it is a fair statement to say that cell phone addiction will have taken over the majority of society. In order to prevent teenagers from being prone to chronic cell phone addictions is by making sure that the teenager ’ s parents limit the amount of time allowed on the cell phone. Another method that could be used by parents is for them to ask their teenagers to turn in their phones by the end of the night to ensure that their children are getting their necessary sleep and are not spending all of their night on the phone instead.

The result of the excessive cell phone usage creates social, behavioral, and affective problems in the lives of future teenagers all around the world. Despite the fact that there is no definitive manner by which this addiction can be put to a stop once and for all, there are a number of alternatives that could be taken by certain parents in order to ensure that their children do not become prone to the kind of addiction that is often linked to yield unproductive teenagers who do not have much ambition in life. In today ’ s increasingly technological world, it is without a doubt that it would be invariably difficult to put a stop to the kind of addiction that is present in the world today. One way that would help would be for researchers to conduct more studies on cell phone addiction and related addictions through bibliographic databases that refer specifically to Internet, video games, and cell phone addiction ( Carbonell, Guardiola, Beranuy, & Bellés, 2009).

Carbonell, X., Guardiola, E., Beranuy, M., & Bellés, A. (2009). A bibliometric analysis of the scientific literature on Internet, video games, and cell phone addiction . Journal of the Medical Library Association: JMLA, (97) 2, 102-107. Retrieved from http://www. ncbi.nlm.nih.gov/pmc/articles/PMC2670219

Choliz, M. (2010). Mobile phone addiction: a point of issue. Addiction (105) 2, 373-374.

Grohol, J. (n.d.). Coping with cell phone addiction. Retrieved from http://psychcentral.com/ lib/coping-with-cell-phone-addiction/

Hersman, D. (2015). Cell phones: A potentially deadly addiction. Retrieved from http://www.huffingtonpost.com/deborah-hersman/cell-phones-a- potentially_b_7161074.html

Murdock, S. (2015). Our addiction to cell phones is costing lives: Here’s how we can stop it. Retrieved from http://www.huffingtonpost.com/2015/06/09/cell-phone-addiction- driving_n_7543464.html

Park, W. K. (2005). Mobile phone addiction. In R. Ling & P.E. Pedersen (Eds.) Mobile Communications , 253-272. London, UK: Springer.

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What Are the Signs of Phone Addiction?

Warning Signs and How to Help

Who Is at Risk
Health Effects
How to Break It
When to Seek Help

While cell phones are integral to our daily lives and have numerous benefits, many people have developed what some researchers consider phone addiction symptoms that can have negative effects on well-being. According to some research, roughly 27.9% of young adults are addicted to their cell phones.

Read on to find out more about how cell phone addiction is defined, the risks involved, and how to identify and treat it.

Georgijevic / Getty Images

What Is Phone Addiction?

Cell phone addiction may be categorized as a type of behavioral addiction that presents when a person can't go without their cell phone, their excessive use causes adverse consequences, or they experience symptoms similar to withdrawal when they do.

While cell phone addiction is very real for the people experiencing it, it is not officially recognized as a mental health illness or an addiction in the fifth edition of the " Diagnostic and Statistical Manual of Mental Health Disorders " (DSM-5). However, it does present with similar characteristics as other behavioral addictions, such as gambling .

What Is the DSM-5?

The DSM-5 is the American Psychiatric Association's official handbook that mental health professionals use to assess and diagnose a variety of mental health disorders.

There are reasons why smartphones are hard to put down. The sounds and vibrations catch our attention, and the games, notifications, social media, and messages provide instant gratification.

Smartphones offer a constant source of entertainment and distraction. Companies use persuasive design techniques with features like infinite scrolling, push notifications, and personalized content to keep us engaged and make it harder to disconnect.

Who Is at Risk of Phone Addiction?

The exact number of people addicted to their cell phones isn’t known. This is because it can be hard to quantify and many studies base their data on self-reporting methods.

Although anyone can be at risk for this type of addiction, it is most commonly found among adolescents. Some research indicates that about 20%–30% of adolescents and young adults have a phone addiction. Teens in particular use their phones with high frequency, while cell phone use tends to decrease gradually as a person gets older.

People who get phones at a younger age are also more likely to present with addictive behaviors than those who get them later in life.

Cell Phone Risk Between the Sexes

Both young boys and girls are at a higher risk of developing an addiction to their cell phones, but there may be somewhat different patterns of use. Girls typically use their phones for social interaction, while boys use phones for the same reason in addition to gaming applications. Males also show a higher tendency to use their phones in risky situations.

Social media addiction may go hand in hand with phone addiction. It is associated with poor sleep quality and depression. And, it is also correlated with body perception issues.

What Are the Symptoms of Phone Addiction?

Some new terms have emerged to describe the characteristics of phone addiction:

Nomophobia : Fear tied to going without one’s phone
Textaphrenia : Fear of the inability to receive or send text messages
Ringxiety : Feeling as though a notification has come through on your phone when it hasn’t
Textiety : Feeling anxious about receiving and responding to text messages immediately

Some symptoms of phone addiction include:

You are constantly reaching for your phone.
You spend much of your time on your phone.
You wake in the night to check if your phone has any notifications.
You feel negative emotions such as anger, sadness, or anxiety when you don’t have your phone or can’t check it.
Using your phone has led to an injury or accident, such as a car crash from texting while driving.
The amount of time you spend on your phone affects your professional or personal life.
When you try to limit your phone use, you end up relapsing in a short time.

Signs From Others

While it can be difficult to notice your own phone addiction, one telltale sign you are forming an addiction is if someone in your life mentions your phone overuse to you. They may express concern about how much you are on your phone or your behavior while you are not using it.

What Are the Effects of Phone Addiction?

Studies show that cell phone overuse can have a negative impact on your health in a variety of ways.

Excessive smartphone use has been associated with physical and mental health problems in adolescents and young adults, including:

Obsessive-compulsive disorder (OCD)
Attention deficit hyperactivity disorder (ADHD)
Alcohol use disorder
Difficulties in cognitive-emotion regulation
Impulsivity
Impaired cognitive function
Addiction to social networking
Low self-esteem

Some other effects of phone addiction include:

Muscle pain and stiffness
Blurry vision
Red or irritated eyes
Auditory illusions (hearing your phone ring or vibrate when it’s not)
Thumb or wrist pain
Loss of interest in other activities you once enjoyed
Insomnia and sleep disturbances
Worsened school or work performance
Heightened conflicts with your social group or family
Feelings of irritability or unease when you don’t have your phone
An increased risk of developing depression or anxiety
Putting yourself in dangerous situations by using your phone when you shouldn’t be
Feelings of guilt, helplessness, or loneliness when you go without your phone

Cell Phone Addiction and Dopamine

Cell phone addiction is similar to other types of addiction because of its effect on dopamine , a chemical in the body that causes feelings of pleasure. Cell phone use has been shown to stimulate the production and release of dopamine, which drives the need to use it more and more.

How to Break the Addiction

Breaking any type of addiction isn’t easy, but it is possible.

First, you must acknowledge the issues it's causing in your life. Once you have determined that you need to break your addiction, you can:

Identify the reasons : Research has found that people who are on their phones constantly may be trying to escape issues or problems in their lives. By determining if the root cause of your phone addiction is to escape problems, you can address and treat the underlying issues.
Consider therapy : Certain types of therapy , such as cognitive behavioral therapy (CBT) , have proven effective in helping people overcome addictions. Other types of effective therapies for addictions are contingency management, motivational interviewing , and couples counseling (if it is affecting your relationships).

Tips to Beat Phone Addiction on Your Own

While addictions often require professional help, not all people will want to go that route. If you want to try to get over phone addiction on your own you can:

Buy a cellphone lockbox that only opens after a set amount of time. This will limit your use.
Remove apps that take up the majority of your time.
Eliminate notifications on your phone so you aren’t summoned to check it every time a notification appears.
Charge your phone in an inaccessible place so it’s harder to get to.
Try to replace phone use with other activities you enjoy.
Switch to a non-smartphone.

How to Prevent Phone Addiction

The best prevention method for phone addiction is avoidance. If you have a phone, you can prevent becoming addicted by using it only when necessary. This means deleting any apps that don’t serve a purpose and using your time to connect with people in other ways.

For parents with young children, limit your child's phone use by only allowing them to use it on your terms, or avoid buying them a phone altogether until they are above a certain age. Since children in their teen years are most at risk, you could hold off on buying them a phone until it is absolutely necessary.

If your child must have a phone for safety reasons, consider buying a phone that doesn’t have the ability to download apps that may lead to addiction. This way they will still be able to contact you or their friends if they need to but will not have access to time-consuming apps.

When to Contact a Healthcare Provider

If you feel as though your phone use has begun to control your life, or your loved ones have mentioned their concerns to you, it may be time to seek out professional help.

You can do this by speaking to your healthcare provider for referrals to a therapist or by signing up for a digital detox—a time when you give up tech devices.

While not formally recognized by the DSM-5, problematic cell phone use shares many similarities with behavioral addictions. A person with a phone addiction will have difficulty staying off their phone and could lose interest in things they once enjoyed because of excessive phone use. Teens and young adults are most at risk of developing a phone addiction.

Signs of phone addiction include feeling irritable or negative when going without a phone, being unable to go without a phone for long periods, or using a phone so much that it negatively affects physical health or mental health.

While phone addiction does come with negative consequences, there are ways to beat it. A person can seek out professional help through a therapist or practice control techniques that limit phone use.

De-Sola Gutiérrez J, Rodríguez de Fonseca F, Rubio G. Cell-phone addiction: A review . Front Psychiatry. 2016;24(7):175. doi:10.3389/fpsyt.2016.00175

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders . Fifth Edition. American Psychiatric Association; 2013. doi:10.1176/appi.books.9780890425596

Lin YH, Chiang CL, Lin PH, Chang LR, Ko CH, Lee YH, Lin SH. Proposed diagnostic criteria for smartphone addiction . PLoS One. 2016;11(11):e0163010. doi:10.1371/journal.pone.0163010

Schulz van Endert T, Mohr PNC. Likes and impulsivity: Investigating the relationship between actual smartphone use and delay discounting . Xin B, ed. PLoS ONE. 2020;15(11):e0241383. doi: 10.1371/journal.pone.0241383

Chen X, Hedman A, Distler V, Koenig V. Do persuasive designs make smartphones more addictive? - A mixed-methods study on Chinese university students . Computers in Human Behavior Reports. 2023;10:100299. doi: 10.1016/j.chbr.2023.100299

Pew Research Center. Mobile Fact Sheet 2021 .

Shoukat S. Cell phone addiction and psychological and physiological health in adolescents . EXCLI J. 2019;18:47-50.

yi Lin L, Sidani JE, Shensa A, et al. Association between social media use and depression among U.S. young adults: research article: social media and depression . Depress Anxiety . 2016;33(4):323-331. doi:10.1002/da.22466

Çakmak S, Tanrıöver Ö. Is obesity and body perception disturbance related to social media addiction among university students? J Am Coll Health. 2022 Feb 14:1-8. doi:10.1080/07448481.2022.2034832

Addiction Center. Phone addiction: warning signs and treatment .

Wacks Y, Weinstein AM. Excessive Smartphone Use Is Associated With Health Problems in Adolescents and Young Adults . Front Psychiatry. 2021 May 28;12:669042. doi: 10.3389/fpsyt.2021.669042

Chun JW, Choi J, Kim JY, Cho H, Ahn KJ, Nam JH, Choi JS, Kim DJ. Altered brain activity and the effect of personality traits in excessive smartphone use during facial emotion processing . Sci Rep. 2017;7(1):12156. doi:10.1038/s41598-017-08824-y

Roberts JA, Yaya LH, Manolis C. The invisible addiction: cell-phone activities and addiction among male and female college students . J Behav Addict. 2014;3(4):254-265. doi:10.1556/JBA.3.2014.015

Seo HS, Jeong EK, Choi S, Kwon Y, Park HJ, Kim I. Changes of neurotransmitters in youth with internet and smartphone addiction: A comparison with healthy controls and changes after cognitive behavioral therapy . AJNR Am J Neuroradiol. 2020;41(7):1293-1301. doi:10.3174/ajnr.A6632

By Angelica Bottaro Angelica Bottaro is a professional freelance writer with over 5 years of experience. She has been educated in both psychology and journalism, and her dual education has given her the research and writing skills needed to deliver sound and engaging content in the health space.

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What is smartphone addiction?

Causes and effects of smartphone and internet addiction, signs and symptoms of smartphone addiction, self-help tips for smartphone addiction, modify your smartphone use, step-by-step, treatment for smartphone and internet addiction, helping a child or teen with smartphone addiction, smartphone and internet addiction.

Worried about your phone or internet use? These tips can help you break free of the habit and better balance your life, online and off.

While a smartphone, tablet, or computer can be a hugely productive tool, compulsive use of these devices can interfere with work, school, and relationships. When you spend more time on social media or playing games than you do interacting with real people, or you can’t stop yourself from repeatedly checking texts, emails, or apps—even when it has negative consequences in your life—it may be time to reassess your technology use.

Smartphone addiction, sometimes colloquially known as “nomophobia” (fear of being without a mobile phone), is often fueled by an internet overuse problem or internet addiction disorder. After all, it’s rarely the phone or tablet itself that creates the compulsion, but rather the games, apps, and online worlds it connects us to.

Smartphone addiction can encompass a variety of impulse-control problems, including:

Virtual relationships. Addiction to social networking , dating apps, texting, and messaging can extend to the point where virtual, online friends become more important than real-life relationships. We’ve all seen the couples sitting together in a restaurant ignoring each other and engaging with their smartphones instead. While the internet can be a great place to meet new people, reconnect with old friends, or even start romantic relationships, online relationships are not a healthy substitute for real-life interactions. Online friendships can be appealing as they tend to exist in a bubble, not subject to the same demands or stresses as messy, real-world relationships. Compulsive use of dating apps can change your focus to short-term hookups instead of developing long-term relationships.

Information overload. Compulsive web surfing, watching videos, playing games, or checking news feeds can lead to lower productivity at work or school and isolate you for hours at a time. Compulsive use of the internet and smartphone apps can cause you to neglect other aspects of your life, from real-world relationships to hobbies and social pursuits.

Cybersex addiction. Compulsive use of internet pornography, sexting, nude-swapping, or adult messaging services can impact negatively on your real-life intimate relationships and overall emotional health. While online pornography and cybersex addictions are types of sexual addiction, the internet makes it more accessible, relatively anonymous, and very convenient. It’s easy to spend hours engaging in fantasies impossible in real life. Excessive use of dating apps that facilitate casual sex can make it more difficult to develop long-term intimate relationships or damage an existing relationship.

Online compulsions, such as gaming, gambling, stock trading, online shopping, or bidding on auction sites like eBay can often lead to financial and job-related problems. While gambling addiction has been a well-documented problem for years, the availability of internet gambling has made gambling far more accessible. Compulsive stock trading or online shopping can be just as financially and socially damaging. eBay addicts may wake up at strange hours in order to be online for the last remaining minutes of an auction. You may purchase things you don’t need and can’t afford just to experience the excitement of placing the winning bid.

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While you can experience impulse-control problems with a laptop or desktop computer, the size and convenience of smartphones and tablets means that we can take them just about anywhere and gratify our compulsions at any time. In fact, most of us are rarely ever more than five feet from our smartphones. Like the use of drugs and alcohol, they can trigger the release of the brain chemical dopamine and alter your mood. You can also rapidly build up tolerance so that it takes more and more time in front of these screens to derive the same pleasurable reward.

Heavy smartphone use can often be symptomatic of other underlying problems, such as stress , anxiety, depression , or loneliness . At the same time, it can also exacerbate these problems. If you use your smartphone as a “security blanket” to relieve feelings of anxiety, loneliness, or awkwardness in social situations , for example, you’ll succeed only in cutting yourself off further from people around you. Staring at your phone will deny you the face-to-face interactions that can help to meaningfully connect you to others, alleviate anxiety, and boost your mood. In other words, the remedy you’re choosing for your anxiety (engaging with your smartphone), is actually making your anxiety worse.

Smartphone or internet addiction can also negatively impact your life by:

Increasing loneliness and depression. While it may seem that losing yourself online will temporarily make feelings such as loneliness, depression, and boredom evaporate into thin air, it can actually make you feel even worse. A 2014 study found a correlation between high social media usage and depression and anxiety. Users, especially teens, tend to compare themselves unfavorably with their peers on social media, promoting feelings of loneliness and depression.

Fueling anxiety. One researcher found that the mere presence of a phone in a work place tends to make people more anxious and perform poorly on given tasks. The heavier a person’s phone use, the greater the anxiety they experienced.

Increasing stress. Using a smartphone for work often means work bleeds into your home and personal life. You feel the pressure to always be on, never out of touch from work. This need to continually check and respond to email can contribute to higher stress levels and even burnout .

Exacerbating attention deficit disorders. The constant stream of messages and information from a smartphone can overwhelm the brain and make it impossible to focus attention on any one thing for more than a few minutes without feeling compelled to move on to something else.

Diminishing your ability to concentrate and think deeply or creatively. The persistent buzz, ping or beep of your smartphone can distract you from important tasks, slow your work, and interrupt those quiet moments that are so crucial to creativity and problem solving. Instead of ever being alone with our thoughts, we’re now always online and connected.

Disturbing your sleep. Excessive smartphone use can disrupt your sleep , which can have a serious impact on your overall mental health. It can impact your memory, affect your ability to think clearly, and reduce your cognitive and learning skills.

Encouraging self-absorption. A UK study found that people who spend a lot of time on social media are more likely to display negative personality traits such as narcissism . Snapping endless selfies, posting all your thoughts or details about your life can create an unhealthy self-centeredness, distancing you from real-life relationships and making it harder to cope with stress.

There is no specific amount of time spent on your phone, or the frequency you check for updates, or the number of messages you send or receive that indicates an addiction or overuse problem.

Spending a lot of time connected to your phone only becomes a problem when it absorbs so much of your time it causes you to neglect your face-to-face relationships, your work, school, hobbies, or other important things in your life. If you find yourself ignoring friends over lunch to read Facebook updates or compulsively checking your phone in while driving or during school lectures, then it’s time to reassess your smartphone use and strike a healthier balance in your life.

Warning signs of smartphone or internet overuse include:

Trouble completing tasks at work or home . Do you find laundry piling up and little food in the house for dinner because you’ve been busy chatting online, texting, or playing video games? Perhaps you find yourself working late more often because you can’t complete your work on time.

Isolation from family and friends . Is your social life suffering because of all the time you spend on your phone or other device? If you’re in a meeting or chatting with friends, do you lose track of what’s being said because you’re checking your phone? Have friends and family expressed concern about the amount of time you spend on your phone? Do you feel like no one in your “real” life—even your spouse—understands you like your online friends?

Concealing your smartphone use . Do you sneak off to a quiet place to use your phone? Do you hide your smartphone use or lie to your boss and family about the amount of time you spend online? Do you get irritated or cranky if your online time is interrupted?

Having a “fear of missing out” (or FOMO) . Do you hate to feel out of the loop or think you’re missing out on important news or information if you don’t check you phone regularly? Do you need to compulsively check social media because you’re anxious that others are having a better time, or leading a more exciting life than you? Do you get up at night to check your phone?

Feeling of dread, anxiety, or panic if you leave your smartphone at home , the battery runs down or the operating system crashes. Or do you feel phantom vibrations—you think your phone has vibrated but when you check, there are no new messages or updates?

Withdrawal symptoms from smartphone addiction

A common warning sign of smartphone or internet addiction is experiencing withdrawal symptoms when you try to cut back on your smartphone use. These may include:

Restlessness
Anger or irritability
Difficulty concentrating
Sleep problems
Craving access to your smartphone or other device

There are a number of steps you can take to get your smartphone and internet use under control. While you can initiate many of these measures yourself, an addiction is hard to beat on your own, especially when temptation is always within easy reach. It can be all too easy to slip back into old patterns of usage. Look for outside support, whether it’s from family, friends, or a professional therapist .

To help you identify your problem areas, keep a log of when and how much you use your smartphone for non-work or non-essential activities. There are specific apps that can help with this, enabling you to track the time you spend on your phone. Are there times of day that you use your phone more? Are there other things you could be doing instead? The more you understand your smartphone use, the easier it will be to curb your habits and regain control of your time.

Recognize the triggers that make you reach for your phone. Is it when you’re lonely or bored? If you are struggling with depression, stress, or anxiety, for example, your excessive smartphone use might be a way to self-soothe rocky moods . Instead, find healthier and more effective ways of managing your moods, such as practicing relaxation techniques.

Understand the difference between interacting in-person and online. Human beings are social creatures. We’re not meant to be isolated or to rely on technology for human interaction. Socially interacting with another person face-to-face—making eye contact, responding to body language—can make you feel calm, safe, and understood, and quickly put the brakes on stress . Interacting through text, email or messaging bypasses these nonverbal cues so won’t have the same effect on your emotional well-being. Besides, online friends can’t hug you when a crisis hits, visit you when you’re sick, or celebrate a happy occasion with you.

Build your coping skills. Perhaps tweeting, texting or blogging is your way of coping with stress or anger. Or maybe you have trouble relating to others and find it easier to communicate with people online. Building skills in these areas will help you weather the stresses and strains of daily life without relying on your smartphone.

Recognize any underlying problems that may support your compulsive behavior. Have you had problems with alcohol or drugs in the past? Does anything about your smartphone use remind you of how you used to drink or use drugs to numb or distract yourself?

Strengthen your support network. Set aside dedicated time each week for friends and family. If you are shy, there are ways to overcome social awkwardness and make lasting friends without relying on social media or the internet. To find people with similar interests, try reaching out to colleagues at work, joining a sports team or book club, enrolling in an education class, or volunteering for a good cause. You’ll be able to interact with others like you, let relationships develop naturally, and form friendships that will enhance your life and strengthen your health.

For most people, getting control over their smartphone and internet use isn’t a case of quitting cold turkey. Think of it more like going on a diet. Just as you still need to eat, you probably still need to use your phone for work, school, or to stay in touch with friends. Your goal should be to cut back to more healthy levels of use.

Set goals for when you can use your smartphone. For example, you might schedule use for certain times of day, or you could reward yourself with a certain amount of time on your phone once you’ve completed a homework assignment or finished a chore, for instance.
Turn off your phone at certain times of the day, such as when you’re driving, in a meeting, at the gym, having dinner, or playing with your kids. Don’t take your phone with you to the bathroom.
Don’t bring your phone or tablet to bed. The blue light emitted by the screens can disrupt your sleep if used within two hours of bedtime. Turn devices off and leave them in another room overnight to charge. Instead of reading eBooks on your phone or tablet at night, pick up a book. You’ll not only sleep better but research shows you’ll also remember more of what you’ve read.
Replace your smartphone use with healthier activities. If you are bored and lonely, resisting the urge to use your smartphone can be very difficult. Have a plan for other ways to fill the time, such as meditating , reading a book, or chatting with friends in person.
Play the “phone stack” game. Spending time with other smartphone addicts? Play the “phone stack” game. When you’re having lunch, dinner, or drinks together, have everyone place their smartphones face down on the table. Even as the phones buzz and beep, no one is allowed to grab their device. If someone can’t resist checking their phone, that person has to pick up the check for everyone.
Remove social media apps from your phone so you can only check Facebook, Twitter and the like from your computer. And remember: what you see of others on social media is rarely an accurate reflection of their lives—people exaggerate the positive aspects of their lives, brushing over the doubts and disappointments that we all experience. Spending less time comparing yourself unfavorably to these stylized representations can help to boost your mood and sense of self-worth.
Limit checks. If you compulsively check your phone every few minutes, wean yourself off by limiting your checks to once every 15 minutes. Then once every 30 minutes, then once an hour. If you need help, there are apps that can automatically limit when you’re able to access your phone.
Curb your fear of missing out. Accept that by limiting your smartphone use, you’re likely going to miss out on certain invitations, breaking news, or new gossip. There is so much information available on the internet, it’s almost impossible to stay on top of everything, anyway. Accepting this can be liberating and help break your reliance on technology.

If you need more help to curb your smartphone or internet use, there are now specialist treatment centers that offer digital detox programs to help you disconnect from digital media. Individual and group therapy can also give you a tremendous boost in controlling your technology use.

Cognitive-behavioral therapy provides step-by-step ways to stop compulsive behaviors and change your perceptions about your smartphone and the internet. Therapy can also help you learn healthier ways of coping with uncomfortable emotions—such as stress, anxiety, or depression—that may be fueling your smartphone use.

Marriage or couples counseling. If excessive use of internet pornography or online affairs is affecting your relationship, counseling can help you work through these challenging issues and reconnect with your partner.

Group support. Organizations such as Internet Tech Addiction Anonymous (ITAA) and On-Line Gamers Anonymous offer online support and face-to-face meetings to curb excessive technology use. Of course, you need real-life people to benefit fully from any addiction support group. Online support groups can be helpful in finding sources of assistance, but it’s easy to use them as an excuse to spend even more time on your smartphone. Sex Addicts Anonymous can be a place to try if you’re having trouble with cybersex addiction.

Any parent who’s tried to drag a child or teen away from a smartphone or tablet knows how challenging it can be to separate kids from social media, messaging apps, or online games and videos. Youngsters lack the maturity to curb their smartphone use on their own, but simply confiscating the device can often backfire, creating anxiety and withdrawal symptoms in your child. Instead, there are plenty of other ways to help your child find a healthier balance:

Be a good role model. Children have a strong impulse to imitate, so it’s important you manage your own smartphone and internet use. It’s no good asking your child to unplug at the dinner table while you’re staring at your own phone or tablet. Don’t let your own smartphone use distract from parent-child interactions.

Use apps to monitor and limit your child’s smartphone use. There are a number of apps available that can limit your child’s data usage or restrict texting and web browsing to certain times of the day. Other apps can eliminate messaging capabilities while in motion, so you can prevent your teen using a smartphone while driving.

Create “phone-free” zones. Restrict the use of smartphones or tablets to a common area of the house where you can keep an eye on your child’s activity and limit time online. Ban phones from the dinner table and bedrooms and insist they’re turned off after a certain time at night.

Encourage other interests and social activities. Get your child away from screens by exposing them to other hobbies and activities, such as team sports, Scouts, and after-school clubs. Spend time as a family unplugged.

Talk to your child about underlying issues. Compulsive smartphone use can be the sign of deeper problems. Is your child having problems fitting in? Has there been a recent major change, like a move or divorce, which is causing stress? Is your child suffering with other issues at school or home?

Get help. Teenagers often rebel against their parents , but if they hear the same information from a different authority figure, they may be more inclined to listen. Try a sports coach, doctor, or respected family friend. Don’t be afraid to seek professional counseling if you are concerned about your child’s smartphone use.

Support groups

On-Line Gamers Anonymous – Help and support for problems caused by excessive game playing. (OLGA)

Sex and Love Addicts Anonymous – 12-step programs for sexual addictions. (SLAA)

More Information

Risky Business: Internet Addiction - Help for recognizing and dealing with smartphone and internet addiction. (Mental Health America)
Internet Gaming - Symptoms of gaming disorder. (American Psychiatric Association)
Dopamine, Smartphones & You: A battle for your time - How using a smartphone can deliver a release of dopamine, reinforcing your behavior. (Harvard University)
Take Control - Things you can do right now to build a healthier relationship with your smartphone. (Center for Humane Technology)
Yu, S., & Sussman, S. (2020). Does Smartphone Addiction Fall on a Continuum of Addictive Behaviors? International Journal of Environmental Research and Public Health, 17(2), 422. Link
Conditions for Further Study. (2013). In Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association. Link
Internet Gaming. (n.d.). Retrieved August 2, 2021. Link
Sohn, S. Y., Rees, P., Wildridge, B., Kalk, N. J., & Carter, B. (2019). Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: A systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry, 19(1), 356. Link
Dopamine, Smartphones & You: A battle for your time. (2018, May 1). Science in the News. Link
Canale, N., Vieno, A., Doro, M., Rosa Mineo, E., Marino, C., & Billieux, J. (2019). Emotion-related impulsivity moderates the cognitive interference effect of smartphone availability on working memory. Scientific Reports, 9(1), 18519. Link
Twenge, Jean M., Thomas E. Joiner, Megan L. Rogers, and Gabrielle N. Martin. “Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates Among U.S. Adolescents After 2010 and Links to Increased New Media Screen Time.” Clinical Psychological Science 6, no. 1 (January 1, 2018): 3–17. Link
Lin, L. yi, Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., & Primack, B. A. (2016). Association between Social Media Use and Depression among U.S. Young Adults. Depression and Anxiety, 33(4), 323–331. Link
Kross, Ethan, Philippe Verduyn, Emre Demiralp, Jiyoung Park, David Seungjae Lee, Natalie Lin, Holly Shablack, John Jonides, and Oscar Ybarra. “Facebook Use Predicts Declines in Subjective Well-Being in Young Adults.” PLOS ONE 8, no. 8 (August 14, 2013): e69841. Link

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Smartphone Addiction Among the Young

More from our inbox:, cash still has a place in a digital world, i hear the birds, singing to me.

To the Editor:

Re “ The Smartphone Trap ,” by Jonathan Haidt and Jean M. Twenge (Opinion guest essay, Sunday Review, Aug. 1):

The rise of smartphone addiction among teenagers is undeniably real. However, the proposed solution of locking students’ phones up cold turkey (during school hours) may not be ideal.

My high school participated in a program that involved completely locking up students’ phones throughout the entire school day (in 2019-20). Through many conversations with my peers, I noticed that this solution — with the goal of helping students “practice the lost art of paying full attention to the people around them,” as your essay put it — produced unintended repercussions.

In fact, increased anxiety as a result of smartphone restriction often hindered students’ ability to fully engage with other students and teachers throughout the school day. Perhaps a smarter solution may include gradually weaning students off their smartphones, and increased education regarding responsible smartphone use.

Rushaad Mistry Foster City, Calif. The writer is a high school student.

Yes, face-to-face conversation among college students has declined during the time of smartphones. Fifteen years ago, I would enter a noisy college classroom to teach a class and have to draw the attention of the students, who were gabbing away with classmates. “It’s 9 o’clock; time to begin class,” I would say in a loud voice to end the student buzz.

Now I enter quiet college classrooms. The students are not speaking to each other; they have their faces buried in their cellphones. I urge them to keep their cellphones under wraps from the time that they enter the classroom and to speak to the students sitting near them. “The student sitting next to you might become your best friend, your spouse. The person might donate a kidney to you if you are in need.”

I try, but the allure of the smartphone usually triumphs.

James Tackach Narragansett, R.I. The writer is a professor of English at Roger Williams University.

Social media has many possible negative psychological and social effects. But perhaps the plunging happiness and self-esteem of teenage girls is due to another effect of smartphones: easy access to online pornography. Viewing degrading images of women in pornography can be traumatic, and the message is clear: Women are sex objects to be used for any purpose and disposed of at will.

The knowledge that the boys they know are using these images could lead to despair and cynicism among girls. The images may also encourage comparison of body type and a belief that being shaved and waxed as well as thin is necessary for attractiveness.

Anne Rettenberg San Rafael, Calif. The writer is a licensed clinical social worker.

Thank you for this all-important article. The issue of smartphones for children and teenagers does not receive the attention it needs; as your article points out, it is a serious threat to our youth.

The best practice of all is parental delay in adding full internet and social media to a young person’s phone, metering those out as the youth advances along the elementary, middle and high school years — along with weekly parental supervision of the phone.

Linda Bishop Jacksonville, Fla. The writer is a retired public-school teacher.

Re “ The United States Should Create a Digital Dollar ” (Opinion guest essay, July 26):

I couldn’t disagree more with Eswar Prasad’s views regarding the inevitable obsolescence of cash.

When power fails and towers topple (as in floods, fires, hurricanes and their aftermath), cash is king (“small bills, please”).

Mr. Prasad describes cash as being vulnerable to loss or theft. That is small change compared with what we’re seeing with digital currencies and credit card transactions.

He describes these transactions as only “relatively secure,” and while he acknowledges that electronic hacking “does pose a risk,” to say that it can be managed through more technology is a circular loop back to what makes digital transactions risky.

Bring on digital currency if you must. But leave cash in place for those of us who sensibly understand its place.

Kate Thurston Tardif Naples, Fla.

Eswar Prasad’s essay begins, “When was the last time you made a payment with dollar bills?”

The answer for me is about half an hour ago, when I bought The New York Times: $3 plus a dime, a penny and two nickels — the correct change for The Times and tax.

One morning I gave the clerk brand-new dollar bills that were stuck together, and I left the store. It was still dark. As I was about to get into my car, the clerk ran outside, calling to me, “You gave an extra dollar!”

How is digital currency going to show that kind of honesty, and from someone who may be living paycheck to paycheck?

Allen Berger Savannah, Ga.

Re “ Name That Songbird in One Click ,” by Margaret Renkl (Opinion guest essay, July 27):

The benefit of living alone as a near-nonagenarian is time for bird-watching. Thank you for reminding me of the songbird app. I look forward to knowing the identity of the avians cheering me on.

Joan May Maher Hudson, Ohio

Essay on Mobile Addiction in English for Children and Students

Table of Contents

Mobile addiction essay: Everyone these days is hooked to his/her mobile phone. While we may dismiss this as a common behaviour in the current times, the truth is that it has deep behavioural and social impacts. Mobile addiction is a real problem and a cause of great concern. It impacts our health, relationships as well as work. People suffering from mobile addiction suffer from nomophobia which is the fear of being without or unable to use your mobile phone for some reason or the other.

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Long and Short Essay on Mobile Addiction in English

Here are long and short essay on mobile addiction to help you with the topic in your exam or other competitions.

These mobile addiction essay have been written in simple language to convey the facts on mobile addiction.

After going through the essay you would be able to know what is mobile phone addiction, signs and symptoms of mobile phone addiction; impacts/effects of mobile phone addiction and treatment of mobile phone addiction etc.

Short Essay on Mobile Addiction 200 words

Mobile phones offer the freedom to instantly connect with just about anyone around the world. They enable us to find any information we require and are a great source of entertainment. While this invention was aimed at empowering us, sadly it is turning out to be something that is overpowering us. Most mobile users these days are suffering from mobile addiction.

One can do so much on a mobile phone. Our mobile phones enable us to indulge in gaming, gambling and online shopping. They connect us with people around the world, allow us to watch movies, click pictures, listen to music, surf the internet and enjoy various other activities. It is hard not to get addicted to this power house of entertainment.

However, it is essential not to fall prey to it. This is because its repercussions could be damaging. Mobile addiction causes several serious problems such as headache, weakened eyesight, sleep disorders, depression, social isolation, stress, aggressive behaviour, financial problems, ruined relationships and no or low professional growth.

Mobile phones have been created for our convenience. We must limit their usage to take charge of our lives. If you feel, you are getting addicted to your mobile phone then look for ways to get rid of it. You should also take it as your responsibility to help your loved ones get rid of this addiction.

Also Check: Essay on Addiction

Essay on Mobile Addiction 300 words – Signs and Symptoms

Mobile addiction is a growing concern. It is easy to get addicted to mobile phones but hard to overcome it. Numerous people around the world are addicted to their mobile phones. The behaviour pattern of mobile addicts is more or less the same. There are certain signs and symptoms that clearly show that a person is addicted to his/ her mobile phone.

Signs and Symptoms of Mobile Addiction

Here are some of the signs and symptoms of people suffering from mobile addiction:

Display Withdrawal Symptoms: Mobile phone addicts get anxious and angry if their mobile phone battery gets low or dies. They also display anxiety and appear extremely restless, on misplacing their mobile phone. They are almost on the verge of getting a panic attack in such a situation.
Hallucinate: Some mobile phone addicts even hear their mobile phone ring or vibrate even when it’s actually not the situation.
Prefer Connecting With People Online: Mobile addicts prefer connecting with people online rather than talking to those, they are surrounded with. They will be on their phone continually even during social gatherings, family dinners or outing with friends.
Check Mobile Phone Frequently: Mobile addicts check their mobile phones almost every minute even if there is nothing important to do. They simply scroll through the apps to check notifications or view who is online and indulge in other such useless activities on their mobile. They are so addicted to their mobile phones that they do not hesitate checking them even while driving, taking shower and in the middle of an important meeting.
Lose Sense of Time: Another sign of mobile addiction is a lost sense of time. A person who is addicted to mobile phone loses complete sense of time. He is often late to work and delays important tasks giving priority to his mobile phone.

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People addicted to mobile phones show all or some of the above mentioned symptoms. It is important to take these signs seriously and help your loved ones suffering from mobile addiction.

Essay on Solutions for Mobile Addiction 400 words

How to Get Away/Overcome from Mobile Addiction

It would not be wrong to say that humans have become a slave of the technology. We have particularly grown addicted to our mobile phones. Most people in the current times suffer from severe mobile addiction. It is as if their world revolves around their mobile phones and they cannot do without it even for an hour. It is important to overcome this addiction in order to lead a healthy, wealthy and peaceful life.

Here is how you can overcome mobile addiction:

As is the case with other types of addictions, you cannot overcome mobile addiction unless you do not want to seriously give up on it. Once, you determine, you wish to get over mobile addiction, you can do so by following the below mentioned tips:

Set Time for Mobile Use

Restrict your mobile usage by setting the number of hours you aim to spend on mobile each day. Assign a fixed amount of time for each activity such as social media, texting, gaming or watching videos. There are apps that help you calculate the time you spend on different apps. Use these apps to work this out.

Indulge in Other Activities

Involve yourself in activities such as painting, dancing, playing indoor/outdoor games, completing household tasks and the likes to stay occupied. This will lower your urge to check your cell phone frequently.

Help From Loved Ones

Your loved ones always have your back and will be happy to help you get rid of mobile addiction. Spend time talking to your parents, playing with your kid or helping your spouse with work rather than trying to connect with an unknown person online. You will soon notice how much more fun these activities are. Likewise, you may call your friends over to your place and indulge in various fun activities to get your mind off the mobile as you try to get rid of this addiction.

Professional Help

If you aren’t able to cope up with mobile addiction on your own and do not think your loved ones can help you much either then it is time to seek professional help. There are therapists who specialise in mobile addiction therapy. They offer individual as well as group therapy to help get rid of this addiction.

Mobile addiction can ruin our life if it is not stopped on time. Getting rid of this habit may be difficult but it is not impossible. With some effort and support from the loved ones, you can overcome this problem over the time. If this does not help, you should not hesitate to seek professional help.

Also Check: Essay on Technology Addiction

Essay on Impact of Mobile Addiction 500 words

Our mobile phone is meant to ease things for us. It helps us connect with our near and dear ones almost instantly. Communicating with our relatives and friends living in distant lands has become extremely easy with the introduction of mobile phones. A mobile phone with a high speed internet connection serves numerous purposes.

It helps us order food, shop online, look for just about any information online, read e-books, enjoy gaming and what not. But alas, while a mobile phone should be a value addition to our lives, it is turning out to be something that is degrading it. Mobile phones are becoming more and more addictive with the introduction of newer applications each day. Mobile addiction is taking a toll on our lives.

Impact of Mobile Addiction

More than half of the mobile users around the world are addicted to their mobile phones. Mobile addiction is impacting us on different levels.

Impulsive and Aggressive Behaviour

People addicted to mobile phones are known to show impulsive and aggressive behaviour. They keep checking their mobile phone every few minutes and cannot do without it. New messages and notifications give them a high. Lack of these can make them angry and depressed.

Anger and aggression is particularly seen among those who spend most time playing violent games on their mobile.

Decreased Attention Span

People addicted to cell phones aren’t able to concentrate on work for long. Too much screen time impacts the brain adversely and decreases the ability to focus. Besides, mobile addicts have a continuous urge to check their cell phones. Thus, they cannot focus on the work in hand.

Poor Eyesight and Headache

Mobile addicts often complain of headache. They develop migraine issues over the time. Viewing the screen for a long time also hurts the eyes and affects the eyesight.

Sleep Disorders and Depression

Mobile addicts use their mobile phones until late at night and often develop sleep disorders. The impact of sleep disorders is known to all. It can hamper our work and impact our health badly. Mobile addicts often cut ties from the real world. They are mostly busy connecting with people online, gaming and watching videos. Lack of human contact is the first step to moving towards depression.

Brain Cancer

Studies reveal that people who talk on their mobile phone for several of hours a day have a high chance of developing brain cancer. This is because mobile phones emit radio waves that damage the brain cells. However, many scientists and medical practitioners do not agree with this finding.

Continual use of mobile phones also impacts our nervous system adversely.

Phubbing is the term used to refer to the habit of constantly checking your mobile even when you are surrounded by people. Mobile addicts develop this habit and it is not good for their personal relationships. As they try to connect with people online, they distance themselves from their loved ones who crave their love and attention. Mobile addicts thus suffer from severe relationship issues.

As much as we neglect it, mobile addiction has become a big problem today. It is hampering our professional life and ruining our personal relationships. Mobile phones are causing more harm than good. People experiencing the problem of mobile addiction must make an effort to get rid of it and return to the real world.

Also Check: Essay on Addiction of Gadgets

Long Essay on Mobile Addiction 600 words – A Common Problem

Mobile addiction is growing by the day. With the introduction of newer and more attractive apps, people are spending more and more time on their mobile phones rather than concentrating on their real life. People have become delusional. They have created a new world for themselves with their mobile phones being central to their lives. It is sad to see how mobile addiction is robbing people of their real lives.

You may have heard about hydrophobia, acrophobia and claustrophobia but have you heard of nomophobia? This is a new kind of fear that is seen in huge number of humans. Nomophobia is “no mobile phone, phobia”. It is the fear of being without one’s mobile phone. It may seem funny to some, but it is an actual fear that grips more than half of the mobile users across the globe. The human race has grown so addicted to their mobile phones that they have developed this new type of fear. The problem is serious and needs attention.

People suffering from nomophobia show the following signs:

They get easily angered or irritated when they cannot access their phone.
They panic when they do not find their mobile phone.
They take their mobile phone everywhere they go including the washroom, dining table and other places where it should not be used.
They stress when the battery is low.
They check their mobile phones almost every minute.
They avoid places that do not have Wi-Fi connections.

Mobile Addiction among Teens

A mobile phone serves as an escape from the problems of real life. People of every age group suffer from mobile addiction. However, teenagers are most likely to develop this addiction.

Teenagers are in that phase of their life where they are discovering and exploring new things. They have numerous questions and their mobile phones have the answers. A mobile phone with an internet connection can answer almost any question they have.

They also have a lot to share but are often hesitant to talk about the same with their parents or teachers. This is because most parents these days are so engrossed in their work that they do not have time to talk to or listen to their kids. Secondly, many things they may want to discuss may be rather embarrassing. Their mobile phones can connect them with numerous people around the world. They make online friends and comfortably share their feelings with them.

Teenagers also like to brag about any new development in their life as it makes them feel superior to others. It is a way to gain popularity in school/ college and win over more friends. Their mobile phones enable them to do so by way of social media platforms.

Teens addicted to mobile phones are the worst. They cannot concentrate on their studies. Mobile addiction bars their ability to focus and lowers their ability to grasp things. Those addicted to mobile phones also have a higher risk of developing habits such as smoking, drinking and taking drugs. They also grow socially awkward as they are constantly on their mobile phone. So, their future is at stake.

Parents must ensure that they do not give smart phones to their teenage kids. It is time for them to concentrate on their studies and explore their interest in other useful activities. They should explore the world the right way and not by means of a mobile phone.

Mobile addiction is more serious than what we think. We must help our loved ones going through this problem. We can help them by talking to them about this problem without being judgemental. Express empathy and be open to their negative reactions. It is difficult to cope up with this problem, but the support from family and friends, can be really helpful in overcoming mobile addiction.

FAQs on Mobile Addiction

What are the symptoms of mobile addiction.

Symptoms include excessive screen time, feeling anxious without the phone, neglecting responsibilities, and constantly checking the phone even without notifications.

How does phone addiction affect your brain?

Phone addiction can reduce attention span, increase stress levels, disrupt sleep, and even alter brain areas linked to decision-making and emotional processing.

How can I reduce my phone usage?

To reduce phone usage, set screen time limits, use grayscale mode, keep phones out of the bedroom, designate tech-free times, and prioritize face-to-face interactions.

What are the bad effects of mobile phones?

Excessive mobile use can lead to eye strain, disrupted sleep, increased stress, decreased face-to-face social interaction, and physical ailments like text neck.

What is the healthy screen time?

Healthy screen time varies by age. For adults, it's advisable to limit recreational screen time to 2 hours daily, while ensuring regular breaks.

Is mobile addiction a serious problem?

Yes, mobile addiction is a growing concern as it can negatively impact mental and physical health, relationships, and overall well-being.

What is the main cause of mobile addiction?

The main cause is the instant gratification phones provide through social media, games, and constant connectivity, which can trigger dopamine release, reinforcing the habit.

What is mobile phone addiction?

Mobile phone addiction is an over-reliance on smartphones, leading to excessive screen time and the inability to reduce usage despite its negative consequences.

Why Do I Addict With Mobile Phone: The True Story Of Youth

The mobile phone helps us carry on with everyday life without the need for many separate devices. The current evolution of mobile phone technology has benefited various groups within communities. Malaysian Communication and Multimedia Commission annual report 2017 stated that 75.9% of the Malaysian population is smartphone users. Universities students are one of the groups rely on mobile phones. However, several studies showed that excessive mobile phone usage would affect their psychological aspects. This has driven the current study to determine the relationship between psychological factors and mobile phone addiction among Malaysian university students. Using the quantitative approach, this study had distributed questionnaires to 400 respondents, chosen at multi-stage cluster random sampling from four universities. The universities are grouped according to four zones; Universiti Utara Malaysia (UUM) for northern, Universiti Pendidikan Sultan Idris (UPSI) for central, Universiti Sains Islam Malaysia (USIM) for southern, and Universiti Sultan Zainal Abidin (UniSZA) for the east coast. The results of the study found that mobile phone addiction among Malaysian university students is at a moderate level. Simultaneously, the result of the Pearson’s correlation test shows a significant correlation between mobile phone addiction with three psychological factors among university students in Malaysia.

Keywords: Mobile Phone , Mobile Phone Addiction , Psychological Factor , Social problem , Youth , Youth Development

Introduction

In era of cutting-edge advances in science and technology, mobile phones or smartphones must be owned by the community and are not something strange to our everyday lives. Wherever we are, the phone will always be with us, especially for the younger generation today. The use of mobile phones also has implications that range for each user. As we know, the smartphone is considered a lifestyle trend, especially for adolescents, regardless of whether the upper or middle class. There is no doubt that the smartphone has affected the way people handle their everyday affairs. For example, the smartphone may make it easier for someone to get in touch with the family or with others, and thus, it can enhance and strengthen the ties between family members. The smartphone can also help us learn and know something related to education and so forth.

Mobile phone usage

Before we can identify the addiction to mobile phones, we need to know the pattern of cell phone ownership and usage among Malaysian as a general overview and university students as our primary focus. Handphone user survey made by the Malaysian Communication and Multimedia Commission ( 2018 ) shows that the percentage of smartphone ownership in Malaysia has increased by 2.4% from 74.0% in 2017 to 76.4% in 2018. From that number, the higher adoption rate (about 87%) of smartphone users are among younger people aged 20-34 years old. Furthermore, studies have been done by Khalid et al. ( 2016 ) shows that majority of students are smartphones user and have more than ten mobile applications (most of them not only download applications for use but also make comparisons which application is the best). The highest application usage is in the category for socialization purposes (WhatsApp, Telegram, and WeChat). This messaging application facilitates the sharing of information between users, and saves because users only need an Internet connection to connect to overhead calls ( Khalid et al., 2015 ).

Besides, the messaging service allows users to create groups for discussion within a specific community group. This kind of social interaction has been encouraging not only among students but also for most smartphone users. This study also identifies smartphone use paterns for learning purposes that are more likely to be used in communication and interaction between students. In line with developments in the 21st Century, much learning has been developed through discussion, and sharing of ideas and this may signal the degree to which students are prepared for the use of mobile materials for knowledge sharing and no longer rely entirely on lectures and lectures from lecturers ( Daud & Khalid, 2014 ; Khalid, 2014 ). This situation corresponds to the definition of Dependency Theory, which explains that the influence of media is determined by the relationship between the broader social system, the role of media in the system, and the relationship of the audience with the media. Saodah et al. ( 2003 ) give an idea of how society needs this mass media as if their daily tasks are incomplete if they do not get input from this media. In this study, it is clear that the mass media in question is the use of mobile phones.

Mobile Phone Addiction and Psychological Effect

The rapid advancement in technology has made many gadgets, a smartphone is one of them ( Nishad & Rana, 2016 ), and we are using a smartphone for many reasons. For example, people spend their time more likely on social media, do business emails, academic search, finding answers to questions, and playing games. All these activities are doing by people through a smartphone. In relations, statistical shows the smartphone usage increased day by day. Around the world, smartphones were used by 1.85 billion people in 2014, which is expected to be 2.32 billion in 2017 and 2.87 billion in 2020 ( Cha & Seo, 2018 ). As mentioned before, smartphone usage among Malaysian has increase year by year. However, excessive use of mobile phone usage will cause users to become addicted, and it will have a negative impact on their physiological health. Alavi et al. ( 2012 ) states that it becomes an addiction whenever a habit is converted into an obligation. According to Cha and Seo ( 2018 ), adolescents are at high risk of being smartphone addicts.

Furthermore, other research shows that excessive use of smartphone paired with a negative attitude and negative psychological effect such as a feeling of anxiety and if we increase the dependency on gadgets may increase the risk of anxiety and depression ( Jones, 2014 ; Rosen et al., 2013 ; Thomée et al., 2011 ). In the current situation, mobile phone usage during night hours was common among youngsters and reported that poor perceived health was shown due to staying up all night ( Schoeni et al., 2015 ). According to De-Sola Gutiérrez et al. ( 2016 ), sleep deficit, anxiety, stress, and depression, which are all associated with internet abuse, are the symptom of excessive mobile phone usage. When a person uses their cell phone most of the time, unable to cut back on cell phone usage, using cell phones as a solution to boredom, feeling anxiety or depression when your phone is out of your range, and make you losing your relationships.

Reinecke et al. ( 2017 ) investigated psychological health effects and stimulators of digital stress. Communication load was positively related to perceived stress and had an indirect impact on depression and anxiety too. Boumosleh & Jaalouk ( 2017 ) investigated whether anxiety and depression independently contributed to smartphone addiction. Their cross-sectional study proposed that depression and anxiety were also a positive predictor of smartphone addiction. Depression scores were a more powerful predictor as compared to anxiety. Researchers found an intensive increase in cell phone usage among teenagers and the symptoms of depression, suicide risk factors, and the suicide rate in 2012. Cell phone addiction is negatively correlated with academic performance ( Boumosleh and Jaalouk, 2018 ; Baert et al., 2020 ; Lepp et al., 2015 ; Ng et al., 2017 ).

Several studies conducted in Malaysia also show that addiction to mobile phones harms university students’ psychology. Results of a study conducted by Zulkefly & Baharudin ( 2009 ) about mobile phone addiction towards psychological health were significantly stated that any student who is addicted to the use of mobile phones would have lower self-esteem. Another study conducted by Ithnain et al. ( 2018 ) says that university students in Malaysia now tend to get addicted to mobile phones and are exposed to anxiety and depression. Furthermore, Ching et al. ( 2015 ) showed that 46.9% of Malaysian university students were addicted to mobile phones and began to rely on mobile phones in their daily activities. Although studies on the use and addiction of mobile phones are widely conducted globally, in Malaysia, studies related to mobile phone addiction needs to be further enhanced, especially the impact on user psychology.

Problem Statement

Regardless of age and gender, technology is overtaking our daily lives. Mobile phones, tablets, and computers are becoming our best friends. Without our mobile phone, we feel anxious and worried. We rely on our mobile phones for example; we can get directions using GPS navigation, get instant information over the internet, and take instant pictures that can be shared to social sites instantly, communicate and much more with smartphones’ power. Smartphone or mobile phone addiction is not a real illness. It is referring to the overusing of a smartphone. Some find it difficult to function without their phones by their side all time and every day. However, without us even realizing it, excessive consumption of mobile phones will affect the productivity of our daily lives and also their behavior. Thus, this artickes’ writing is intended to identify the relationship between psychological factors and mobile phone addiction among Malaysian university students.

Research Questions

Are university students in Malaysia excessively to the mobile phones?
Which psychological factors are able to influence by mobile phone usage among university students in Malaysia?

Purpose of the Study

To know the level of mobile phone addiction among university students in Malaysia.
To know the relationship between psychological factors and mobile phone addiction among university students in Malaysia.

Research Methods

This is a quantitative study whereby a developed questionnaire was employed to gain the required data. The questionnaire was developed based on the literature review and past studies. There are six sections in this questionnaire; smartphone usage, smartphone usage addiction scale, loneliness, shyness, perceived stress, and respondents’ background. The completed questionnaire was then pre-tested among 30 selected students from selected faculty in Universiti Putra Malaysia (UPM). The population of the study was the undergraduate students of 18 public universities in peninsular Malaysia. A multi-stage cluster random sampling was employed at the first stage, which is all the university (only the main campus) will be grouped according to four zones (northern, southern, central, and east coast). The southern zone comprises of states such as Johor, Malacca, and Negeri Sembilan (UTHM, USIM, UTeM, UTM). The central zone comprises states such as Selangor, Kuala Lumpur, and Perak (UPM, UKM, UIA, UPNM, UPSI). For east coast zone comprises states such as Pahang, Terengganu, and Kelantan (UMT, UnisZA, UMK, UMP). While northern zone comprises states such as Pulau Pinang, Kedah and Perlis (UUM, UNIMAP, USM). Then, for each zone, only one university was selected to represent that zone. Next, a faculty were randomly selected from each of the university, and at the last stage of sampling, a total of 100 students of each faculty were selected as the respondents. It makes 400 undergraduate students selected as the respondents for the actual data collection for this study. The questionnaire was distributed to the respondents and self-administer method were employed. The process of data collection is monitored by the research team to ensure that the data required can be gathered. After completing all collection activities, the data obtained will be analyzed using SPSS.

In table 01 shows the demographic profile of the respondents. From the table, four universities were involved in these studies with sum of 400 respondents. 65.5% of that number were male. The respondents are divided into 2 age category; students aged 20 years old and below, and students aged 21 years old and above. We can see that 66% of them fall into the category aged 21 years old and above. Furthermore, most of the respondents are Malay (79.3%), and the rest are Chinese, Indian, Sarawakian, Sabahan, and others. Besides, 59.8% of the respondents still in year 1 of their studies.

Next, table 02 below shows the level of mobile phone usage and mobile phone addiction among university students in Malaysia. For mobile phone usage, most of the respondents fall into moderate category with 63.8%, M= 3.465 and SD = 0.54899. Meanwhile, for mobile phone addiction, 46.8% of the respondents are at a moderate level. However, 36.8% of them is at a high level of mobile phone addiction.

A Pearson product-moment correlation was employed to investigate any relationship between mobile phone addiction and psychological factors. The psychological factors that had been selected were loneliness, shyness, and perceived stress. The summary table below shows a positive and significant relationship between mobile phone addiction and all psychological factors (loneliness,=0.000 and r=0.204; shyness, p=0.000 and r=0.207; and perceived stress, p=0.000 and r=0.288). As shown in table 03 below, the results of this study are in line with the findings from previous studies stating that addiction to mobile phones negatively impact user psychology.

A Pearson product-moment correlation again was employed to investigate any relationship between mobile phone usage and psychological factors. The psychological factors that had been selected were also loneliness, shyness, and perceived stress. The summary in table 04 below shows that only one psychological factor has a significant relationship between mobile phone usage (which is perceived stress p=0.034 and r=0.091). However, the other two psychological factors (loneliness and shyness) do not have a significant relationship with mobile phone usage among universities student in Malaysia.

There is no denying that mobile phones now provide many benefits and simplify daily affairs, however, if we are too engrossed in this mobile phone, it will actually 'haunt' us. In fact, many of us may not be aware that this mobile phone is now one of the causes of its users’ psychological health problems. Moreover, from the findings, mobile phone usage and mobile phone addiction among university students in Malaysia are at a moderate level. However, this trend is beginning to show a shift to higher and more severe levels. Which is this situation can eventually have a negative impact while further affecting their level of psychological health. Furthermore, more in-depth studies on the impact on mobile phone users’ psychological well-being of should be conducted to identify other effects that users may experience. Another recommendation is to implement health education and interventions related to mobile phone that are appropriate to deal with addiction and improve their mental well-being that can empower students to practice healthy behaviors.

Acknowledgments

Putra Grant from Universiti Putra Malaysia supported this research.

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Ramli, S. A., Ahmad Ghazali, A. H., & Mohamed Shafril, H. A. (2021). Why Do I Addict With Mobile Phone: The True Story Of Youth. In C. S. Mustaffa, M. K. Ahmad, N. Yusof, M. B. M. H. @. Othman, & N. Tugiman (Eds.), Breaking the Barriers, Inspiring Tomorrow, vol 110. European Proceedings of Social and Behavioural Sciences (pp. 349-356). European Publisher. https://doi.org/10.15405/epsbs.2021.06.02.45

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Essay on Mobile Phone: 100 Words, 300 Words, 500 Words

Updated on
Feb 21, 2024

Mobile Phones are portable electronic devices used to make calls, browse the internet, click pictures, and do several other tasks. However, the mobile phones discovered in the early 1970s were quite different from the compact and slim devices we use today. Cell phones were invented by John F. Mitchell and Martin Cooper of Motorola in 1973.

As modern humans, we all use mobile phones for our day-to-day functioning. At academic and higher education levels, students are given the task of writing an essay on mobile phones. An essay on mobile phones requires a comprehensive and detailed study of their history, major developments and the purposes it serve. In this article, we have provided essays on mobile phones for class 6,7,8.9, 10, and 12th standard students. Students can refer to these sample essays on mobile phones to write their own. Keep reading to find out essays on mobile phones and some fun facts about the device.

Table of Contents

1 Sample Essay on Mobile Phone (100 Words)
2 Sample Essay on Mobile Phone (300 words)
3 Sample Essay on Mobile Phone (500 words)
4 Essay on Mobile Phone: 5+ Facts About Smartphones

Sample Essay on Mobile Phone (100 Words)

Also Read: The Beginner’s Guide to Writing an Essay

Sample Essay on Mobile Phone (300 words)

Also Read: Essay on Importance of the Internet

Sample Essay on Mobile Phone (500 words)

Essay on mobile phone: 5+ facts about smartphones.

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The most expensive smartphone in the world is the Falcon Supernova iPhone 6 Pink Diamond. It is worth $48.5 million.
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The first phone greeting was “Ahoy-hoy, who’s calling please?”
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Also Read: Holi Essay: Free Sample Essays 100 To 500 Words In English

A mobile phone system gets its name from diving the service into small cells. Each of these cells has a base station with a useful range in the order of a kilometre/mile.

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Blessy George

Blessy George works as a Content Marketing Associate at Leverage Edu. She has completed her M.A. in Political Science and has experience working as an Intern with CashKaro. She has written extensively on studying abroad, English Test preparation, visas, and online courses. During her free time, she likes to read and write poetry, and songs.

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The Influence of Mobile Phone Addiction on Academic Performance Among Teenagers

2020, Jurnal Komunikasi: Malaysian Journal of Communication

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ABSTRACT Background: Smart phones have become an essential part for people of all ages worldwide. They have not only taken the place of mobile phones but also replaced personal computers and many other devices and gadgets due to their innumerable advantages. Keeping in view all these benefits the use of smart phone has the possibility of growing into an addictive behaviour. The phenomenon of smart phone addiction is more common among teenagers and university students. Therefore, the objective of this study is to assess smart phone addiction and its effect on academic performance of the students. Method: This cross sectional study was conducted on a sample of 120 students of Rehman College of Rehabilitation Sciences through Census Sampling technique during the months of February, March and April 2020. A validated version of SAS-SV questionnaire was used to collect data. Permission was obtained from head of institute and a consent form was also signed by students. Students having CGPA less than 2.00 and students of 1st semester were excluded from the study. Data was analysed using SPSS version: 20. Results: A total of 120 students were enrolled in this study among which 94 (78.3%) were females and 26(21.7%) were males. More than half of the participants 78(65%) were addicted to their smart phones while 42(35%) were not. Out of total population, 57(47.5%) females and 21(17.5%) males were addicted to their smart phones. Students within the age group of 21 years were most prevalent for smart phone addiction. Students having CGPA between 3.00 and 3.59 were most prevalent for smart phone addiction constituting 26.7% of the total population. Students of 2nd year had highest levels of smart phone addiction constituting 36.4% of the total population. A non-significant association existed between smart phone addiction and total score p= 0.986 as well as between smart phone addiction and academic performance, p= 0.815. In the same way, non significant association was found out between total score and year of study p=0.570. Conclusion: The current study results show that academic performance of the students is affected by smart phone addiction. Keywords: Academic Performance, Physical Therapy Students, Smart Phone Addiction

The Explorer Islamabad

Wireless communication is fast growing technology as it guarantees to access any individual in remote corner of the world. The usage of mobile phones has the potential to effect positively as well negatively on lives of people in the world. The Current study presents empirical investigation of the usage of mobile phones among university students in twin cities of Pakistan. Quantitative research design was employed and simple random sampling technique was used to extract the study sample from the whole population. A self-structured questionnaire was used as a tool for the data collection and a sample of 260 respondents from two universities of twin cities was extracted and then analyzed through (SPSS) statistical package for social sciences and presented in tabular form with description and interpretation. The study was hypothesis based and chi square was applied to test the hypothesis. The results of the current study revealed that addiction of mobile phones was negatively affecting the relationship of respondents with their families because they don’t tolerate any kind of interruption from their parents while using mobile phones. It was also concluded that mobile phones were intensively used by the respondents which kills their precious time and loss of their study. The present study suggested that there should be proper mechanism of guidance by the parents and teachers for the usage of cell phones.

Md. Moyazzem Hossain

As cell phone technology continues its rapid development, the device appears capable of contributing to student learning and improved academic performance. The recent rapid increase in cell phones has influenced multiple aspects of our daily lives, particularly those of Students. Therefore, the aims of the current study is to determine the influence of the mobile phone usage on academic performance among male and female students of Jahangirnagar University, Bangladesh. A face to face survey was conducted among 274 students which include 159 male students and 115 female students ranging from second year to fourth year from different departments of Jahangirnagar University, Bangladesh. Results depicts that gender, age and relationship with opposite sex have the significant positive effect on students’ academic performance. However, marital status, spending time on mobile phone, negative effect of mobile phone and application usage while studying has the negative effect on students’ ac...

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ORIGINAL RESEARCH article

Prevalence and related factors of sleep quality among chinese undergraduates in jiangsu province: multiple models' analysis.

$\r\nBin Hu&#x;$

1 Key Laboratory of Human Genetics and Environmental Medicine, School of Public Health, Xuzhou Medical University, Xuzhou, China
2 Department of Respiratory Diseases, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, Jiangsu, China
3 Department of Dermatology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, Jiangsu, China
4 Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
5 Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, China

Background and aims: In China, a significant number of undergraduates are experiencing poor sleep quality. This study was designed to investigate the prevalence of poor sleep quality and identify associated factors among undergraduates in Jiangsu Province, China.

Methods: A total of 8,457 participants were collected in 2022 using whole-group convenience sampling. The factors studied included basic demographics, family and social support, personal lifestyles, physical and mental health, mobile phone addiction index (MPAI), and the Connor-Davidson resilience scale (CD-RISC). The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Four models, including weighted multiple linear regression, binary logistic regression, weighted linear mixed model, and logistic regression with random effects, were applied to identify associated factors for sleep quality.

Results: Of the 8,457 participants analyzed, 26.64% (2,253) were classified into the poor sleep quality group with a PSQI score >7. No significant relationship was found between sleep quality and gender, native place, economic level of family, physical exercise, dormitory light, dormitory hygiene, and amativeness matter. Risk factors for sleep quality identified by the four models included lower CD-RISC, higher MPAI, fourth grade or above, smoking, drinking, greater academic pressure, greater employment pressure, roommate sleeping late, noisy dormitory, poorer physical health status, poorer mental health status, and psychological counseling.

Conclusions: These findings provide valuable insights for university administrators, enabling them to better understand the risk factors associated with poor sleep quality in undergraduates. By identifying these factors, administrators can provide targeted intervention measures and counseling programs to improve students' sleep quality.

Introduction

Sleep is a fundamental physiological process that is essential for our overall health. It is also important for our cognitive, emotional, and physical systems ( Baranwal et al., 2023 ). In recent years, sleep quality has declined among undergraduates and received attention from the public and academics ( Wang et al., 2016 ; Ahmed et al., 2020 ; Li Y. et al., 2020 ). Unfortunately, a significant number of undergraduates are experiencing poor sleep quality. In some provinces of China, the proportion of undergraduates with poor sleep quality has crossed more than 30.0%; for example, the proportions in Jilin, Guizhou, and Hong Kong were 33.8%, 53.7%, and 57.5%, respectively ( Suen et al., 2008 ; Li Y. et al., 2020 ; Zhou et al., 2022 ). Sleep quality among undergraduate students has also been poor in other countries such as Ethiopia (57.5%) and India (51.0%) ( Lemma et al., 2012 ; Ghrouz et al., 2019 ). Some studies focusing on medical students also found a high prevalence of poor sleep quality−52.4% in Greece during COVID-19 ( Eleftheriou et al., 2021 ), 76.0% in Saudi Arabia ( Almojali et al., 2017 ), and 27.8% in Inner Mongolia Medical University of China ( Wang et al., 2016 ). In Jiangsu Province, the status of sleep quality among undergraduates is still unknown, and it would be very interesting to investigate the prevalence of poor quality and identify the associated factors in this population.

Research has indicated that sleep deprivation can lead to metabolic disorders and negative effects, such as increased metabolite levels, which can lead to poor memory, poor concentration, lower academic performance, and emotional fluctuations ( Durmer and Dinges, 2005 ; Curcio et al., 2006 ; Basner et al., 2013 ; Davies et al., 2014 ; Lo et al., 2016 ; St-Onge, 2017 ; Gerhardsson et al., 2019 ; Vaccaro et al., 2020 ). In addition, poor sleep quality can also increase the risk of developing mental health problems such as anxiety and depression ( Almojali et al., 2017 ; Shao et al., 2020 ). Given the importance of sleep in maintaining wellbeing, it is essential to understand the factors that contribute to poor sleep quality among undergraduates. Additionally, it is also imperative to identify potential solutions to improve their sleep habits and reduce the negative effects.

Some studies have shown that sleep quality among Chinese undergraduates was a result of various factors. A cross-sectional study reported a few risk factors such as poor academic performance, interpersonal relationship, skipping breakfast, and higher grades ( Wang et al., 2016 ). Another study found that being a freshman, alcohol use, gambling, exercising for more than 30 min a week on <1 day, satisfaction with parental love, and harmonious relationship with classmates were risk factors, while no learning pressure, never having self-injurious behaviors, and harmonious family relationship were protective factors ( Li Y. et al., 2020 ). A gender-specific study in China identified some risk factors for poor sleep quality that were related to weak physical condition and smoking in males, while noisy dormitory, skipping breakfast, drinking coffee, playing games, bad physical condition, and severe academic stress led to poor sleep quality in females ( Zhou et al., 2022 ). A cluster randomized-controlled trial in China showed that good dormitory sleep environments could maintain good sleep quality ( Li et al., 2022 ). In addition, the use of electronic products was also related to poor sleep quality ( Demirci et al., 2015 ; Li L. et al., 2020 ). For example, using mobile phones, computers, and other electronic products at night could affect the regulation of the sleep clock, prolong the time to fall asleep, and shorten sleep time. Besides, there is a significant amount of similar research evaluating related factors for sleep quality ( Nyer et al., 2013 ; Bi et al., 2022 ; Peltz and Rogge, 2022 ; Xian et al., 2022 ). In summary, these factors that are associated with undergraduates' sleep quality can be classified into the following categories: demographics, personal lifestyle, family support, social support, dormitory environment, physical health, and mental health.

Because of the significant social and cultural diversity between different regions in China, previous findings may not fully represent the overall sleep characteristics of the Chinese population, especially undergraduates. Thus, our study not only aimed to examine the prevalence of poor sleep among undergraduates but also provide a comprehensive understanding of the factors that contribute to poor sleep quality among undergraduates in Jiangsu Province, China. The factors examined in this study also included mobile phone addiction and psychological resilience. By enhancing our understanding of these variables, this study may also help to develop effective strategies to improve sleep quality and ultimately promote undergraduates' life quality in China.

Special attention should be paid to the four statistical methods we utilized in association analysis. Except for the usual models of multiple linear regression and binary logistic regression, two mixed effect models were adopted such as linear mixed model and logistic random effect model. All these models are variations of the generalized linear mixed model ( Bolker et al., 2009 ; Stroup, 2013 ). The mixed model is also known as the hierarchical model and contains both fixed and random effects. The random effects model represents the variability between different conditions or blocks, and they are incorporated into the model to account for the correlation between observations. In the present study, the four regression models were conducted with standard diagnostics, ensuring more accurate parameters and p -values. Various studies have utilized regression models to identify risk factors associated with poor sleep quality, but few strictly performed regression diagnostics, which can potentially result in biased results.

Participants and explanatory variables

A whole-group convenience sampling was performed in universities of Jiangsu Province using an electronic questionnaire on the Wenjuanxing platform ( www.wjx.cn ) from October to November 2022. We distributed the link and QR code of the questionnaire to the undergraduates via university teachers and a WeChat group. The questionnaire contained survey instructions explaining the online survey's purpose and significance. Each undergraduate took the survey voluntarily and had the option to withdraw at any moment. The confidentiality of data and student personal information was guaranteed. To ensure the quality and accuracy of data collection, the questionnaire was pre-tested to validate question effectiveness and comprehensibility. The sample size was thoroughly calculated based on the expected prevalence rate of sleep disorders among college students, according to the sample size calculation formula: n = z α 2 × p q / d 2 . When the significance level is α = 0.05, z α = 1.96. Let p be the expected prevalence rate and q = 1− p . Based on the preliminary survey results of this study, the estimated detection rate of sleep disorders among undergraduates is p = 16%. The allowable error is d = 0.1 × p . The estimated sample size needed for the survey is 2017 individuals.

A total of 8,587 undergraduates attended the questionnaire voluntarily, among which 130 participants with incomplete information were excluded, leading to a response rate of 98.49%. So, the final sample consisted of 8,457 participants (5,917 females and 2,540 males). Participants were all students living on campuses and enrolled in 14 majors, including literature, history, philosophy, law, economics, management, education, science, engineering, agronomy, military, art, medicine, and others.

The questions in the questionnaire were organized into continuous, categorical, and ordinal variables (see Table 1 ). In categorical and ordinal variables, some values were merged to avoid extremely small sample sizes in some subgroups. Participants who never smoked were classified as not smoking and those who smoked occasionally or often were classified as smoking. Participants who had never drunk alcohol were classified as not drinking, and those who drank alcohol occasionally or often were classified as drinking.

Table 1 . The prevalence of poor sleep quality across basic demographic characteristics.

Pittsburgh Sleep Quality Index

The Pittsburgh Sleep Quality Index (PSQI) ( Buysse et al., 1989 ; Chen et al., 1999 ) was adopted to assess the sleep quality of undergraduates by translating it into a Chinese version. PSQI includes 19 self-rated questions, and only the first 18 entries are used to calculate scores. These questions can be grouped into seven dimensions that can evaluate sleep quality in the last month, including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The final PSQI score was calculated by summing the scores from these seven dimensions. The total score ranges from 0 to 21 (0–3 for each component), with higher values indicating poor sleep quality. A final PSQI score of more than 7 indicates poor sleep quality and a score of <7 represents good sleep quality. For the PSQI conducted in this study, Cronbach's alpha was 0.877.

Mobile phone addiction index

The mobile phone addiction index (MPAI) ( Bianchi and Phillips, 2005 ), which was already translated into Chinese and validated for the Chinese population ( Li L. et al., 2020 ), was used to evaluate mobile phone addiction. The MPAI consists of 17 items quantifying four dimensions, including the inability to control craving, withdrawal and escape, anxiety and feeling lost, and productivity loss. Scores range from 1 (not at all) to 5 (always) for each item. The higher total scores indicate a higher level of mobile phone addiction. The Cronbach's alpha for the MPAI of the present study was 0.925.

Connor-Davidson resilience scale

The present study employed the Connor-Davidson resilience scale (CD-RISC) ( Connor and Davidson, 2003 ) to measure the psychological resilience of undergraduates, which reflects how the participants felt over the last month. This scale has been employed in various populations and has also been translated into Chinese ( Xu et al., 2022 ). The CD-RISC consists of 25 items, and each item's score ranges from 0 to 4, representing not true at all, rarely true, sometimes true, usually true, and true almost all the time, respectively. The final CD-RISC score was obtained by summing each score of the items, which ranged from 0 to 100, with higher scores indicating greater resilience. The Cronbach's alpha for the CD-RISC of the present study was 0.969.

Statistical analysis

The software R version 4.3.0 was used for statistical analysis. Extreme values of continuous variables were identified by the interquartile range (IQR) method. The upper and lower fences were defined as Q 3+2 IQR and Q 1 − 2 IQR . Values below (resp., above) the lower (resp., upper) fence were replaced with the lower (resp., upper) fence. For continuous variables, their central and variation trends were described by mean and standard deviation (SD), i.e., x ¯ ± s , and the means between two (resp., or more) groups were compared by t -test (resp. ANOVA) when homoscedasticity was satisfied, otherwise, Welch's t -test (resp., Welch's ANOVA) was employed. A post-hoc test was performed using Tukey's method when homoscedasticity was satisfied; otherwise, the Games-Howell test was performed. In this study, homoscedasticity was measured using Levene's test. The categorical and ordinal variables were described by frequency and constituent ratio, and the differences between groups were compared by Chi-square test. The significant level was set as 0.05.

Statistical models

Multiple linear regression model.

The associated factors for undergraduate sleep quality were identified using a linear regression model with the continuous variable PSQI score as the dependent variable, other variables as explanatory variables, and ordinal variables as continuous. The standard regression diagnostics ( Kabacoff, 2015 ) were processed after the multiple linear regression model (MLR) was implemented with the following steps: (a) Samples containing extreme values, including outliers, high leverage points, and influential points were removed. The outliers were detected if the Bonferroni adjusted p -value of the corresponding absolute studentized residuals was significant; the high leverage points were identified via the hat statistics; the influential points were determined by Cook's distance. In our model, 497 samples were removed in this step. (b) The independence of residuals was checked using the Durbin–Watson's test. In our model, independence was satisfied. (c) Multicollinearity between explanatory variables was detected through variance inflation factor (VIF). In our model, the VIF values of BMI, weight (kg), and height (cm) were all more than 4, which indicated that multicollinearity exists. Hence, weight and height were removed since BMI contains information about both. (d) The normality and homoscedasticity of residuals were checked. Our detection indicated that homoscedasticity was not satisfied. Therefore, weighted MLR was utilized to solve the heteroscedasticity problem, with weights inversely proportional to the variance of the dependent variable. The absolute values of the residuals were regressed against the fitted values, and the resulting fitted values were squared to provide the desired estimate of the variance. (e) The linearity between dependent variables and explanatory variables was checked. The performance of linearity, homoscedasticity, normality, and extreme values after diagnostics is shown in Supplementary Figure S1 . Visual inspection did not reveal any obvious deviations from linearity or normality. It is noteworthy that non-normality is less likely to be a problem when other assumptions are met since Aitken's theorem shows that the regression coefficients obtained from weighted MLR are also the best linear unbiased estimator without the assumption of normality ( Hansen, 2022 ). The linear trend test for a continuous variable was performed by transforming it into a four-valued ordinal variable by quartiles in the weighted MLR model.

Binary logistic regression model

Odds ratios (ORs) of factors associated with poor sleep quality were performed using a binary logistic regression model (BLR). The dependent variable was set as good (coded as 0) and poor (coded as 1) sleep quality based on whether the PSQI score was <7, and other variables were treated in the same way as MLR. The diagnostics ( Kabacoff, 2015 ) were processed with the following steps: (a) Extreme values were detected by the same method as MLR and 408 samples were removed. (b) Multicollinearity was detected by VIF, and weight and height were also excluded. (c) The independence of observations was performed using Durbin–Watson's test, and this assumption was satisfied. (d) The linearity between the log odds and explanatory variables was checked by regressing the log odds against the explanatory variables. The performance of linearity and extreme values after diagnostics is shown in Supplementary Figure S2 . (e) Overdispersion and events per variable (EPV) were checked ( Peduzzi et al., 1996 ). Overdispersion was accessed by the Chi-square test, but it did not exist in our model. There were 28 explanatory variables, including dummy variables, 1,897 samples with good sleep quality, and 6,152 samples with poor sleep quality. Thus, the EPV principle was satisfied. The ratio of these two outcome events was ~1:3; therefore, it can be considered as approximately balanced data. The linear trend test was performed in the same way as the aforementioned method.

Linear mixed model

The linear mixed model (LMM) is an extension of the MLR to allow both fixed and random effects, and it is particularly used in hierarchical analysis ( Stroup, 2013 ). The package “lmerTest” was adopted to implement LMM. In LMM, the continuous variable PSQI served as the dependent variable. The candidate random effect terms were checked by the likelihood ratio test. As random effects, we had intercepts for grade, smoking, drinking, physical health status, mental health status, academic pressure, employment pressure, and psychological counseling, as well as random slopes for the effect of relationship with classmates among academic pressure and employment pressure. Other variables, excluding weight and height due to multicollinearity, were set as fixed effects. No outliers were detected, and the homoscedasticity was violated. Therefore, the weighted LMM was adopted, and the weights were determined in the same way as the weighted MLR. The independence was checked using Durbin–Watson's test, and the results were satisfied. The performance of linearity, homoscedasticity, normality, and extreme values after diagnostics is shown in Supplementary Figure S3 . Visual inspection did not reveal any obvious deviations from linearity or normality.

Logistic regression with random effects

The logistic regression with random effects model (LRRE) is a type of generalized linear mixed model. The dependent variable was set as BLR. We had random intercepts for grade, smoking, drinking, physical health status, mental health status, academic pressure, employment pressure, and psychological counseling, as well as random slopes for the effect of the relationship with classmates against employment pressure and academic pressure. Other variables, excluding weight and height due to multicollinearity, were set as fixed effects. A likelihood ratio test was performed to check the significance of random effects. No outliers were detected. Independency, linearity, and overdispersion were checked in the same way as BLR, and all assumptions were satisfied. The performance of linearity after diagnostics is shown in Supplementary Figure S4 .

Prevalence of poor sleep quality

The Cronbach's alpha for the questionnaire of this study was 0.911, which indicates the internal consistency of the questionnaire was good. The sample consisted of 69.97% male and 30.03% female. Out of the 8,457 undergraduates, 6,204 (73.36%) reported having good sleep quality while 2,253 (26.64%) experienced poor sleep quality, based on the PQSI threshold value of 7. The mean PSQI score of all participants was 5.64 ± 3.60. The prevalence of poor sleep quality in undergraduates is shown in Table 1 , and the PSQI score across different characteristics is shown in Supplementary Table S1 . There was no significant difference between male (5,917) and female (2,540) students in height, weight, BMI, medical major, and non-medical major in terms of PSQI score or sleep quality. Older students and those in higher grades had a higher prevalence of poor sleep quality compared with their counterparts who were younger and in lower grades. The prevalence of poor sleep quality among students from rural areas, only-child families, and fathers with low education levels was nearly 30% higher than among students from urban areas, having siblings and fathers with high education levels. Although the PSQI scores of the three levels of mothers' education were significantly different via Welch's ANOVA, the post hoc result did not show the difference, and the constituent ratios between students with good and poor sleep quality were not significantly different. Approximately 80% of the students with either good or poor sleep quality belonged to families with medium economic levels. Similarly, more than 70% of the students with good or poor sleep quality reported a monthly living expense between 1,000 to 2,000 CNY. In terms of dormitory and personal lifestyle, the constituent ratios between students with good and poor sleep quality were all significantly different, and the corresponding PSQI scores were also significantly different. The worse these parameters were, the higher the prevalence of poor sleep quality.

Undergraduates with poor sleep quality had higher PSQI scores across all seven components compared to those with good sleep quality. The mean PSQI score for undergraduates with poor sleep quality was 10.41, which was significantly higher than their counterparts with good quality sleep ( p < 0.001). The mean scores of CD-RISC and its three components for students with poor sleep quality were all significantly lower than those with good sleep quality ( p < 0.001). The MPAI situation was the opposite.

Results of weighted MLR and BLR

The Akaike Information Criterion (AIC) of MLR and weighted MLR were 38,170.2 and 38,103.3, respectively, which indicates that weighted MLR improved the bias caused by heteroscedasticity in MLR. Table 2 presents the factors associated with PSQI scores, which were identified by weighted MLR, and the risk factors for poor sleep quality identified through BLR. The variables age, MPAI, grade, smoking, drinking, academic pressure, employment pressure, relationship with classmates, roommate sleeping late, noisy dormitory, physical health status, mental health status, and psychological counseling exhibited significant positive associations with the PSQI scores. On the other hand, BMI, CD-RISC, medical major, and father's education level demonstrated significant negative correlations with the PSQI scores. Barring BMI and relationship with classmates, the association between all other factors and PSQI was linear.

Table 2 . Risk factors for sleep quality identified by weighted MLR and BLR.

The results of BLR showed that students with higher BMI (OR = 0.959, p < 0.001), CD-RISC (OR = 0.986, p < 0.001), and father's education level (OR = 0.777, p < 0.001), had a decreased risk of poor sleep quality. Students with higher MPAI (OR = 1.036, p < 0.001), higher grades (OR = 1.178, p = 0.003), higher mother's education level (OR = 1.214, p = 0.002), greater academic pressure (OR = 1.177, p = 0.012), greater employment pressure (OR = 1.226, p < 0.001), worse physical health status (OR = 1.331, p < 0.001), and worse mental health status (OR = 2.028, p < 0.001) had an increased risk of poor sleep quality. Students who majored in medicine (OR = 0.675, p < 0.001) and were the only child in the family (OR = 0.811, p = 0.002) had a lower risk of poor sleep quality than those who did not major in medicine and were not the only child in the family. Smoking (OR = 1.568, p < 0.001), drinking (OR = 1.255, p < 0.001), and receiving psychological counseling (OR = 1.383, p < 0.001) were significantly associated with poor sleep quality. Roommates sleeping late (OR = 1.141, p = 0.049) and noisy dormitory (OR = 1.543, p < 0.001) were shown to be risk factors for poor sleep quality. The OR of mental health status was the highest, followed by smoking, noisy dormitory, psychological counseling, physical health status, and drinking. The sorted ORs of significant factors for undergraduate poor sleep quality can be found in Figure 1 . Linear trend test showed that MPAI had both significant linear and quadratic trends with respect to poor sleep quality, but no substantial distinction was observed between the two trends on visual inspection (see Supplementary Figure S4 ). Besides, there was also no linear trend between grade and poor sleep quality ( p = 0.104). The receiver operating characteristic (ROC) curve of BLR can be found in Supplementary Figure S5 , and the area under the curve (AUC) was 0.776.

Figure 1 . Sorted ORs of significant factors for undergraduate poor sleep quality. The gray vertical solid line represents OR =1, and the short black horizontal solid line represents 95% CIs of ORs. Symbol * denotes p < 0.05, ** denotes p < 0.01, and *** denotes p < 0.001. (A) Comes from the results of BLR. (B) Comes results of LRRE.

Results of weighted LMM and LRRE

The AICs of LMM and weighted LMM were 43,857.1 and 43,554.2, respectively, which indicates that weighted LMM improved the bias caused by heteroscedasticity in LMM. Table 3 shows fixed and random effects of associated PSQI score factors identified by weighted LMM and related factors for poor sleep quality identified by LRRE. The intraclass correlation coefficients (ICCs) of the two models were 0.123 and 0.130, suggesting that 12.3% and 13.0% of the total variation in the responses were explained by subgroups. The fixed effects of age ( b = 0.142, p = 0.002) and MPAI ( b = 0.043, p < 0.001) exhibited a significant positive association with respect to PSQI scores, while the fixed effect of CD-RISC was the opposite ( b = −0.016, p < 0.001). Students with a roommate sleeping late ( b = 0.303, p < 0.001) and in a noisy dormitory ( b = 0.582, p < 0.001) had significantly higher PSQI scores at an average level compared to those without a roommate sleeping late and in a noisy dormitory. Except for age, all significant continuous factors displayed a linear trend with the PSQI score. However, an analysis of the trend graph between age and PSQI score revealed that the quadratic trend was approximately close to linearity (see Supplementary Figure S6 ). The random effects of weighted LMM explained 15.62% of the total variation. The detailed random effect values of weighted LMM can be found in Figure 2 . For random intercepts, being in the second and fourth grades, having poor mental and physical health status, receiving psychological counseling, smoking, and drinking had the effect of increasing PSQI scores. The random slopes of relationships with classmates varied across the three levels of employment pressure and academic pressure. However, the random effects of the relationship with classmates and employment pressure were not significant. In the subgroup of students without academic pressure, the intercept was −1.200, and the slope of the relationship with classmates was 0.812, suggesting that a poor relationship with classmates had a positive effect on increasing PSQI scores. In the subgroups of students with normal and great academic pressure, the intercepts were 0.496 and 0.704, respectively, indicating that these two subgroups had higher average PSQI scores compared to the subgroups of students without academic pressure. However, the slopes of relationship with classmates across the two subgroups were −0.458 and −0.353, demonstrating that a harmonious relationship with classmates had a positive effect on increasing the PSQI scores.

Table 3 . Fixed and random effects of factors for sleep quality by weighted LMM and LRRE.

Figure 2 . Random effects of weighted LMM. The red and blue solid dots represent negative and positive effects on PSQI scores, respectively. The short horizontal solid lines represent 95% CI.

For the fixed effect results of LRRE, higher BMI (OR = 0.983, p = 0.022) and CD-RISC (OR = 0.992, p < 0.001) had a decreased risk of poor sleep quality. On the contrary, students with higher MPAI (OR = 1.024, p < 0.001) and mother's education level (OR = 1.159, p = 0.007) had an increased risk of poor sleep quality. Being the only child was a protective factor of poor sleep quality compared to not being the only child (OR = 0.88, p = 0.034). All significant continuous factors had a linear trend with respect to poor sleep quality. A higher risk was also found for students with roommates sleeping late (OR = 1.149, p = 0.022) and noisy dormitories (OR = 1.405, p < 0.001). These results are consistent with previous BLR results. In fixed effects, noisy dormitory had the highest OR, followed by mother's education level and roommates sleeping late. The sorted ORs can be found in Figure 1 . The random effects explained 20.65% of the total variation when the dataset was fitted by LRRE and their ORs, which can be found in Figure 3 . It was found that the variability in students' relationships with classmates (random slopes) did not significantly differ across the three levels of employment pressure and academic pressure. This suggests that students' relationships with classmates were consistent regardless of the degree of pressure experienced. The effects of most random intercepts were consistent with the corresponding counterpart of weighted LMM. Being in the second and fourth grades, experiencing “great” and “not having” employment pressure, poor and normal levels of mental health status, poor physical health status, receiving psychological counseling, smoking, and drinking had a positive effect on poor sleep quality. The ROC curve of LRRE can be found in Supplementary Figure S7 , and the AUC was 0.720.

Figure 3 . ORs for random effects of LREE. The red and blue solid dots represent ORs that are <1 and >1, respectively. The short horizontal solid lines represent 95% CI.

This study investigated the prevalence and associated factors of sleep quality among undergraduates in Jiangsu Province, China. The results showed a mean PSQI score of 5.64 ± 3.60 and a prevalence of poor sleep quality of 26.64%, which is consistent with another study on medical college students in China, which showed the prevalence was 27.80% ( Wang et al., 2016 ) but lower than the results obtained in Ethiopia (55.8%) and Taiwan (33.80%) ( Kang and Chen, 2009 ; Lemma et al., 2012 ).

No significant association was found between gender and sleep quality through demographics and the four regression models. Similar results were reported in previous studies ( Lund et al., 2010 ; Wang et al., 2016 ; Li Y. et al., 2020 ). The study also found no significant correlation between sleep quality and native place, family economic level, physical exercise, dormitory light, dormitory hygiene, and amativeness matter through the four regression models. Previous studies ( Lund et al., 2010 ; Wang et al., 2016 ; Li Y. et al., 2020 ) also reported that there was no association between sleep quality and family economic level, physical exercise, and amativeness matter. Some studies showed that sleep quality might be influenced by native places ( Tang et al., 2017 ), but their cutoff values of PSQI score was not seven, and the population was not undergraduates. Our study found that there is no significant correlation between sleep quality and dormitory light brightness or dormitory hygiene; these factors were included in the cluster randomized-controlled trial described in research ( Li et al., 2022 ) to explore comprehensive environmental influences on sleep quality. The rationale for this inclusion is based on the hypothesis that environmental factors, beyond personal habits or demographic characteristics, have a significant impact on sleep quality. Although our findings and the intervention in the cited study did not demonstrate a direct impact of these factors on sleep quality, including them in the analysis will help to provide a holistic view of the environmental influences on sleep.

Since the dependent variable PSQI score in weighted MLR and weighted LMM was continuous, while the dependent variable in BLR and LRRE was dichotomous, the associated factors identified by the two types of models may differ. For example, age was significantly positively associated with PSQI score in weighted MLR and weighted LMM but was not significant in BLR and LRRE. Being the only child and mother's education level were significant in BLR and LRRE but were not in weighted MLR and weighted LMM. This may be because age can affect the PSQI score, but it has a limited impact on PSQI in the bounded range ( Basner et al., 2013 ; Demirci et al., 2015 ).

In the present study, the BLR and LRRE models identified significant associations between only child status and mother's education level with sleep quality. The effects of these factors appeared to be amplified in the dichotomous models (BLR and LRRE) than in the other models, suggesting that these variables may have a more sensitive response to categorical sleep quality measures. This phenomenon may reflect the complex relationship between these sociodemographic characteristics and individuals' daily behavioral patterns. Specifically, students who are the only child may receive more attention and resources within the family and also be subject to higher expectations and pressures from their parents, both of which may affect sleep quality.

Higher levels of maternal education may also imply better cognitive stimulation and family economic conditions, which indirectly affect the child's sleep status. On the other hand, these significant differences may be related to the family's socio-economic status and cultural background, which also affect undergraduates' performance in school or social activities and stress levels, and ultimately affect sleep quality. There was a difference in sleep quality between the only child and non-only child subgroups (see Table 1 ), but the difference in PSQI score was small (see Supplementary Table S1 ), resulting in the linear model not identifying it after adjusting for other factors. The same situation also applies to the mother's education level, in which the three levels had no difference in the PSQI score. BMI and the father's education levels were not significant in the weighted LMM but were significant in the other three models.

Relationship with classmates was significant in weighted MLR and had significant random slopes in weighted LMM but was not significant in BLR and LRRE. Some inconsistencies between weighted MLR and weighted LMM, BLR, and LRRE may be due to the different samples included. Three kinds of extreme values were removed from the weighted MLR and BLR, while LMM and LRRE had relatively few methods to detect extreme values. Weighted LMM and LRRE contained all samples since no extreme values were found in the diagnostic step.

The four regression models identified several common risk factors for sleep quality, including lower CD-RISC, higher MPAI, being in fourth grade or above, smoking, drinking, greater academic pressure, having no or great employment pressure, roommate sleeping late, noisy dormitory, poorer physical health status, poorer mental health status, and psychological counseling. Most of these results are consistent with previous studies. CD-RISC ( Bianchi and Phillips, 2005 ) measured the psychological resilience of undergraduates, and its negative association with CD-RISC was also reported by previous studies ( Li and Guo, 2023 ; Xie et al., 2023 ). A higher MPAI represents a higher level of mobile phone addiction. Mobile phone overuse may disrupt the sleep process and lead to depression. Furthermore, long-term exposure to blue light and electromagnetic fields emitted from the screen may affect melatonin levels and contribute to poor sleep quality ( Demirci et al., 2015 ). However, newer models of cell phones currently have the ability to turn off blue light, and this study did not investigate the condition with blue light turned off, so this finding may have limitations. A gender-specific study ( Zhou et al., 2022 ) found that smoking was a risk factor for poor sleep quality among males, and noisy dormitory and academic pressure were risk factors for poor sleep quality among females. A study on college students of Jilin Province ( Li Y. et al., 2020 ) found that drinking, academic pressure, and relationships with classmates were risk factors for poor sleep quality. A study on medical students ( Shao et al., 2020 ) showed that students with greater employment pressure had more anxiety symptoms, which may affect their sleep quality. A cluster randomized-controlled trial in China ( Li et al., 2022 ) showed that intervening with the sleep schedule of roommates can obtain a good sleep quality for them, suggesting that our findings of roommates sleeping late as a risk factor for poor sleep quality are consistent with it. However, there were some studies that considered low physical activity via the International Physical Activity Questionnaire-Short Form anxiety and depression scores via the Hospital Anxiety and Depression Scale as negative factors for sleep quality ( Ghrouz et al., 2019 ). Another study ( Nyer et al., 2013 ) found that students with depressive symptoms and sleep disturbance endorsed significantly more intense and frequent anxiety and poorer cognitive and physical functioning.

Being in a higher grade was a risk factor for sleep quality in weighted MLR and BLR, but only the random effects of being in the second grade and fourth grade or above were found to be positive in weighted LMM and GLMM, and the random effects of being in the first and third grades were negative. This may be due to the greater pressure faced by students in fourth grade or above, such as postgraduate entrance exams, employment, internships, and graduation thesis issues. Similarly, second grade was the year with the greatest learning pressure, and in addition to regular studies, they also needed to participate in various competitions and innovation training programs. The first grade was still in a transitional adaptation period from high school to college, while the third grade was a comfortable period after adapting to college life. The students in third grade were able to cope easily with previous pressure, and there was no new pressure similar to those in the fourth grade.

In the weighted LMM result, the slope of the relationship with classmates was positive, i.e., 0.812, in the subgroup of having no academic pressure, indicating that a poorer relationship with classmates meant a higher PSQI score. The corresponding slopes were negative, i.e., −0.458 and −0.353, in the subgroups of normal and great academic pressure, respectively, indicating that a poorer relationship with classmates meant a lower PSQI score. This may be because students who have good relationships with classmates need to spend more time dealing with communication or other issues between classmates, resulting in less rest time and high pressure. We recognize that this interpretation is speculative and based on observations from the current data set. Therefore, more comprehensive research is necessary to explore and test this hypothesis. Therefore, we call for further research to explore the complexity of the interplay between social stress, social relationships, and sleep quality.

In the weighted LMM result, the random intercept of having no employment pressure was 0.086, and its corresponding OR in the LRRE result was more than 1, which indicates that having no employment pressure had the effect of increasing the risk of poor sleep quality. This may be caused by the fact that the sample size of students with poor sleep quality and having no employment pressure was extremely low, i.e., 286. Thus, the model may not show the true effect of having no employment pressure. Another reason may be the random slopes of relationships with classmates, whose random effects were not significant.

The associated factors for sleep quality identified in various studies may differ, primarily due to several reasons. Firstly, the survey population and hierarchical structure may vary. Differences in population, region, and composition can yield diverse outcomes. Secondly, the included independent variables may differ; the same variable may have different values. Thirdly, the interaction between variables can lead to discrepancies in results. Furthermore, different models can yield different results. Even when analyzing data using the same model, different researchers may obtain distinct results due to differences in the operational methods of the model. The first two conditions are difficult to control, but we can try to avoid differences in results through standardized operations.

In the present study, four models were utilized to examine the factors associated with sleep quality. To ensure the robustness of the outcomes, we carried out rigorous diagnostics for all four models. After diagnostics, all four models obtained the best linear unbiased estimators (BLUE) or best linear unbiased predictions (BLUP) according to Gauss-Markov's theorem and Atkin's theorem ( Hansen, 2022 ). Regression diagnostics are an important step that has been overlooked by many researchers. Standard diagnostics ensure more accurate parameter estimation and p -values and ultimately improve the overall quality of the analysis. Without diagnostics, some biased results for regression models will be obtained and reported. In the weighted MLR (resp., BLR) model, we found that other levels will increase or decrease PSQI scores (resp., risk of poor sleep quality) relative to the reference level. However, we can obtain detailed effect values of each level through LMM and LRRE. To our knowledge, previous studies have not shown similar results.

Limitations

There are several limitations in the present study. Firstly, the present study selected as many candidate influencing factors as possible for sleep quality, such as family support, social support, personal lifestyle, physical health, mental health, mobile phone addiction, and psychological resilience. However, considering the length of the questionnaire, there were still some potential factors that were not included, such as coffee drinks, depression, and habits. Secondly, this study was cross-sectional, which precluded the establishment of definitive conclusions regarding the direction of causality between sleep quality and risk factors. Further, longitudinal studies are needed to investigate the causal relationships. Thirdly, all questionnaires were self-reported, highlighting the inherent limitations of self-reported measures. Lastly, there were no strict exclusion criteria in this study, which may have resulted in self-selection bias. This means that participants with anxiety and depression are characteristics that are likely to interact with the outcome under investigation, or alternatively, they may have been more likely to participate since the topic is relevant to them.

This study examined the prevalence of risk factors associated with poor sleep quality among undergraduates in Jiangsu Province, China. Our results showed a considerable prevalence of poor sleep quality among this group, with students in higher grades exhibiting a higher likelihood of experiencing poor sleep quality. The study also identified modifiable factors that correlate with poor sleep quality, including psychological resilience, mobile phone addiction, smoking, drinking, and poorer physical health. In response to the factors associated with poor sleep quality identified in our study, we advocate for university administrators to deploy systematic educational programs and interventions tailored to enhance sleep quality among students. These initiatives should directly target modifiable risk factors such as mental health status, academic and employment pressures, and suboptimal dormitory conditions. Specifically, proposed interventions include organizing workshops and seminars on sleep hygiene to educate students about the importance of good sleep practices and the physiological underpinnings of sleep. University administrators should also offer mental health services that provide counseling and stress management strategies. They should facilitate sessions on academic and time management skills to mitigate the impact of academic and employment pressures and improve dormitory living conditions through the establishment of quiet hours and better noise insulation to foster a more sleep-conducive environment. These recommendations illustrate a thoughtfully considered approach to ameliorate the sleep-related challenges faced by university students. University administrators can provide adequate psychological counseling for students to alleviate their pressures and set appropriate dormitory conventions to address dormitory-related issues.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving humans were approved by Ethics Committee of Xuzhou Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

BH: Methodology, Writing—review & editing. WS: Data curation, Funding acquisition, Writing—review & editing. YW: Writing—review & editing. QW: Data curation, Investigation, Writing—review & editing. JL: Writing—review & editing. XX: Writing—review & editing. YH: Writing—review & editing. LX: Data curation, Funding acquisition, Methodology, Writing—original draft. DY: Conceptualization, Writing—review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (grant number 12001470), the China Postdoctoral Science Foundation (grant number 2020M671607), and the Science and Technology Development Fund project of the Affiliated Hospital of Xuzhou Medical University (grant number XYFY202245). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1343186/full#supplementary-material

Supplementary Table S1. Pittsburgh Sleep Quality Index scores of undergraduates with different characteristics.

Supplementary Figure S1. The performance of linearity, homoscedasticity, extreme values, and normality after diagnostics for weighted multiple linear regression model.

Supplementary Figure S2. The performance of linearity and extreme values after diagnostics for binary logistic regression model.

Supplementary Figure S3. The performance of linearity, homoscedasticity, extreme values, and normality after diagnostics for the weighted linear mixed model.

Supplementary Figure S4. The performance of linearity after diagnostics for logistic regression with random effects model.

Supplementary Figure S5. The linear and quadratic trend between MPAI and PSQI score in weighted multiple linear regression.

Supplementary Figure S6. ROC curve of binary logistic regression model.

Supplementary Figure S7. The linear and quadratic trend between age and PSQI score in weighted linear mixed model.

Supplementary Figure S8. ROC curve of logistic regression with random effects model.

Abbreviations

PSQI, Pittsburgh sleep quality index; MPAI, mobile phone addiction index; CD-RISC, Connor-Davidson resilience scale; IQR, interquartile range; CI, confidence intervals; SD, standard deviation; ANOVA, analysis of variance; VIF, variance inflation factor; EPV, events per variable; BMI, body mass index; OR, odds ratios; MLR, multiple linear regression model; BLR, binary logistic regression model; LMM, linear mixed model; LRRE, logistic regression with random effects; AIC, Akaike Information Criterion; AUC, area under curve; ICC, intraclass correlation coefficient; BLUE, best linear unbiased estimator; BLUP, best linear unbiased prediction.

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Keywords: sleep quality, undergraduates, risk factors, Connor-Davidson resilience scale, mobile phone addiction index, Pittsburgh Sleep Quality Index

Citation: Hu B, Shen W, Wang Y, Wu Q, Li J, Xu X, Han Y, Xiao L and Yin D (2024) Prevalence and related factors of sleep quality among Chinese undergraduates in Jiangsu Province: multiple models' analysis. Front. Psychol. 15:1343186. doi: 10.3389/fpsyg.2024.1343186

Received: 23 November 2023; Accepted: 22 March 2024; Published: 10 April 2024.

Reviewed by:

Copyright © 2024 Hu, Shen, Wang, Wu, Li, Xu, Han, Xiao and Yin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Dehui Yin, yindh16@xzhmu.edu.cn ; Lishun Xiao, xiaolishun@xzhmu.edu.cn

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Addict Health
v.6(3-4); Summer-Autumn 2014

The Relationship between Mental Health and Addiction to Mobile Phones among University Students of Shahrekord, Iran

Zahra babadi-akashe.

1 Lecturer, Faculty Member, Department of Educational Sciences and Psychology, Payame Noor University of Shahreza, Isfahan, Iran

Bibi Eshrat Zamani

2 Associate Professor, Department of Educational Sciences, School of Educational Sciences and Psychology, University of Isfahan, Isfahan, Iran

Yasamin Abedini

3 Assistant Professor, Department of Educational Sciences, School of Educational Sciences and Psychology, University of Isfahan, Isfahan, Iran

Hojaetolah Akbari

4 Department of Educational Sciences, Payame Noor University of Farrokh Shahr, Shahrekord, Iran

Nasim Hedayati

5 Assistant Professor, Department of Pediatric Dentistry, School of Dentistry, Rafsanjan University of Medical Sciences, Rafsanjan, Iran

The risk of cell phone addiction is a social and psychological problem which has been proposed by psychologists, psychiatrists, and educational supervisors. The present study aimed to investigate the behavior of mobile phone addicts and mental health of university students of Shahrekord, Iran.

This study was an applied research survey for the purposes of this study. The study population ýconsisted of all the students of Payame Noor University, Islamic Azad University, and University of Medical Sciences. The study population consisted of 296 students who were randomly selected from the target population. To collect data, two types of questionnaires were used, the Symptom Checklist-90-R(SCL-90-R) questionnaire, and the 32-point scale questionnaire of behavior associated with ýmobile phone use (Hooper and Zhou, 2007). Data analysis was performed using SPSS software, statistical analysis, frequency distribution, mean, one-way ANOVA, chi-square, and LSD (Least significance difference).

The results showed that university students of Shahrekord, based on the six categories of mobile ýaddiction behaviors, were mostly placed in habitual behaviors (21.49%), addiction (21.49%), and intentional (21.49%) categories. By reviewing mental health indicators, it was found that students were affected with depressive disorder (17.30%), obsessive compulsive disorder (14.20%), and interpersonal sensitivity (13.80%). The results showed that there was a significant inverse relationship ýbetween mental health and habitual behaviors (r = -0.417), dependence (r = -0.317), addiction (r = -0.330), and incontinence (r = -0.309) in using mobile phone (P < 0.001).

Survey results showed that with increased and improved mental health, the student’s rate of cell phone addiction reduced.

Introduction

The current era can be known as a combination of information and communication. Today, in possession of advanced information and communication technology, we are able to establish connections and exchange information faster than before. 1 The most dominant type of information and communication technology is the mobile phone, the use of which in the past few years, due to social impact, has grown substantially. Mobile phone addiction, as a mental impairment resulting from modern technology, has come to the attention of psychologists, sociologists, and scholars of education. Troubled mobile phone use can be accounted a form of technological addiction.

Many mobile phone addicts are people with low self-esteem and poor social relationships; thus, they think they should be in constant contact with others. Mobile phone silence can lead to anxiety, irritability, sleep disturbances, shaking, insomnia, and digestive problems. 2 From the perspective of Thomee et al. problematic and overuse of mobile phones is associated with anxiety, insomnia, depression, psychological distress, and unhealthy lifestyle. 3 The emotional attachment to mobile phones for their users is in a way that makes them believe they cannot live without a cell phone. Researches have presented the negative impact of excessive use of mobile phones on physical and mental health of students. 4 Medical research on the effects of mobile phone indicates that this means of communication does not act in order to maintain the health of its users. For example, results of a number of studies show that mobile phone radiation causes changes in gene regulation, auditory and visual problems, increased pressure of acid on the cornea and lens tissue, headache, heat sensation in the ears, memory loss, and fatigue. 5 - 9 Studies also showed that prolonged use of cell phones cause brain tumors. 10 In terms of psychology, communication technology reduces social relations and the welfare of the individual due to loneliness, depression, and isolation. Beydokhti ýet al. found that among adults and young people, the use of information and communication technology can lead to social anxiety and sleep disorders. 11

From these contents it can be concluded that there is a relationship between addiction to mobile phones and physical and psychological health. In fact mental health includes behaving in harmony with the community, acceptance of social reality and the ability to cope with them, and satisfying one’s needs moderatly. 12 Mental health according to the World Health Organization is a health condition in which a person knows their own abilities, can cope with the normal stresses of life, is fruitful for the community, and is able to make decisions and collective participation. Therefore, mental health is the base for welfare and health for individuals and society.

Hooper ýand Zhou, psychologists from Staffordshire University, studied 106 people who had used ýmobile phones, and found that 16.00% of them have behavioral problems. Their research concluded that behavioral problems followed by the addiction to cell phone use causes stress. 12 Despite the importance of mobile phones in everyday life, research indicated that some people use this device uncontrollably and this has affected their personal lives. 13 Review of research literature on the subject indicated that excessive use of mobile phones is a form of technology addiction. Results of the study by Hooper and Zhou showed that the rate of mobile phone use among university students is very high. 12 There is a relatively high number of evidence for mandatory, voluntary, or dependent use of cell ýphones; however, habitual, compulsive, and addictive behaviors of mobile phone use are relatively ýless observed.

The findings of Shambare et al. showed that mobile phone use was mostly addictive, habitual, and dependent. 14 The study of Ahmed et al. showed that a small number, less than 18.50%, of Pakistani students displayed mobile-related addictive behaviors. In this study the targeted subjects used mobile phones under reasonable conditions, and thus, did not have the tendency for addictive behaviors in mobile phone use. 15 The results of several studies showed that addiction to text messages has a relationship with students’ social anxiety and nervousness, and personality traits of extraversion and neuroticism. Furthermore, the rate of addiction to text messages in different educational groups of students were different. 1 , 11 , 16

Moreover, other studies showed a positive correlation between depression and anxiety and the amount of sent text messages in a day, and loss of control and social anxiety. 17 , 18 Chen examined the relationship between depression and mobile phone addiction on 519 American students and concluded that there was a significant association between mobile phone addiction symptoms (distraction, withdrawal, and escape) and depression. In addition, he stated that women had significantly higher rates of mobile phone addiction symptoms compared to men. 19

Technology addiction in general and dependency on cell phones in particular are important for several reasons. Despite the advantages and necessity of technologies for human society, due to their stimulating factors, they results in excessive use and lead to addiction. Young people are more vulnerable to excessive phone use, and thus, become phone dependent. 20 Young people’s mental health, addiction to mobile phones, as a driving force, and an active community is major topics that are discussed in psychology and sociology. The present study addressed the question of whether there is any relationship between the amount and type of cell phone addiction and mental health status of university students in Shahrekord, Iran.

Due to the purpose of this study, this research was an applied research survey. The study population consisted of all students in different universities of Shahrekord (Islamic Azad University, Payame Noor University, and University of Medical Sciences). From among the target population, 296 subjects were randomly selected. Two types of questionnaires were used to collect data that include the Symptom Checklist-90-R (SCL-90-R) questionnaire, and 32-point scale questionnaire of behavior associated with mobile phone use (Hooper and Zhou, 2007). 12 The SCL-90-R questionnaire has been used in many researches in Iran and outside Iran, and has a high reliability. 12 , 21 - 23 The validity and reliability of the mobile phone use questionnaire in some researches was at a high level. 13 , 19 In order to analyze the collected data, SPSS for Windows (version 18, SPSS Inc., Chicago, IL, USA) was used. The Pearson correlation analysis, Student’s independent t-test, and chi-square test were used for data analysis. From the total of 296 students who participated in the study, 57.10% were men, 49.90% were female, 14.90% were single, 85.10% were married, 34.80% were living in a dormitory, and 65.20% lived elsewhere. Moreover, 50.30% studied in public Universities of Medical Sciences, 32.80% studied in Payame Noor University, and 16.90% enrolled in Islamic Azad University.

Based on the results of table 1 , the majority of students, according to the categories of addictive behaviors of mobile phone, were placed in the three categories of habitual behaviors (21.49%), voluntary behaviors (21.49%), and dependent behaviors (21.49%). Based on the results of table 2 , students, with regard to mental health, had higher rates of depression (17.30%), obsessive-compulsive disorder (OCD) (14.20%), and interpersonal sensitivity (13.80%). Results showed that with confidence interval of 0.99 and P < 0.001, there was a relationship between mental health and four out of six categories of mobile phone addiction (habitual behaviors, dependent behaviors, addictive behaviors, and involuntary behaviors). In fact, only at the level of voluntary and compulsive behaviors there was no significant relationship with students' mental health. The results showed that with confidence interval of 0.99 and P < 0.001, there was a significant negative relationship between mental health and general behavior of addiction to mobile phones. This means that as the rate of mobile addiction becomes less, the students’ mental health increases. Table 3 presented phone addictive behaviors that have a significant relationship with mental health. Based on these results, mental health of university students of Shahrekord did not differ according to demographic factors (gender, type of university, type of residence, education, and marital status).

Ranking mobile phone addiction in terms of defined behaviors (n = 296)

Ranking mental health in terms of study dimensions

OCD: Obsessive-compulsive disorder

The relationship between mobile phone addiction behavior and mental health

The results of this study showed that university students of Shahrekord, based on the six categories of mobile phone addictive behaviors, were mostly placed in categories of habitual, voluntary, and dependent behaviors with 20.30%. These finding were consistent with results of several studies. 12 , 17 - 20 As was noted in the findings section, there was a significant inverse relationship between mobile phone addiction and mental health. Findings show that the highest correlation was related to mental health and habitual behaviors. Habitual behaviors refer to behaviors that are formed from habit, without hesitation, thought, and mental awareness in order to achieve a particular purpose. 17 Students who suffer from lower mental health, usually when faced with a challenge, a problem, or an assignment or a specific purpose, feel helpless, frustrated, and powerless. As a result, to counteract these negative feelings, they turn to their previous habits involuntarily and automatically, such as mobile phone contacts, and with this they reduce their anxiety and worry due to their inefficiencies. It was observed that after habitual behaviors, addictions and dependent behaviors have the highest negative correlation with mental disorder. One of the most important causes of this serious relationship probably depends on the nature of addictive and dependent behaviors. Addictive behavior refers to a sudden and involuntary tendency to do a particular act or behavior, in the state of psychological imbalance, and the main factors that drive this conflict and psychological imbalance are irrational negative, inner thoughts. 12 , 21 - 23 Hence, it seems that students with lower mental health and psychological balance, are more vulnerable to addictive mobile phones use, because they try to reduce their internal tensions by talking to others. On the other hand, students who had average and high mental health levels also had some types of addiction to mobile phones (habitual addiction). This could be related to excessive use of mobile phones for long-distance calls to family members. Another factor is the lack of entertainment and addiction to entertainment and games that are available on mobile phones. Factors such as jealousy, personality characteristics, the presence or absence of metacognitive skills such as self-regulation skills, and financial considerations could be other factors for the usage or non-usage of cell phones. 21 - 23 Further research on the relationship between these factors and mobile phone addiction is recommended.

The results showed that between mental health and addictive behaviors toward mobile phones there was a significant inverse relationship in the categories of dependent, involuntary, and addiction behaviors. In other words, in higher mental health, human behavior is more rational, and the amount of cell phone addiction reduces. These findings were consistent with results from a number of studies such as Thomee et al., 3 Chen, 19 Billieux et al., 17 Park et al., 18 Hassanzadeh and Rezaei, 1 Golmohammadian and Yaseminejad, 24 and Seyed Ali. 25 Research results indicate that there is a relationship between addiction to mobile phone and mental health in dimensions of behavioral problems, anxiety, depression, and psychosis. In addition, the results showed that there was no relationship between the occurrence of behaviors of cell phone addiction and gender, type of residence, type of university, and the study filed. These findings were consistent with results of Koo and Park, 26 Pawlowska and Potembska, 27 Wei, 28 and Wilska, 29 and domestic research, including Zamani et al. 21 - 23 The investigation showed that there was a relationship between mobile phone addictive behavior and gender-related factors. Furthermore, there was a relationship between habitual behaviors of mobile phone use and marital status, addictive behaviors, and university. However, the results indicated that there was no significant relationship between mental health and gender, marital status, type of residence, university, and field of study.

Survey results showed that with increased and improved mental health, the rates of students’ addiction to mobile phones reduced. Hence, it is necessary to take more steps in developing recreational programs for students’ leisure time to maintain students’ mental health, and thereby decrease addiction to a variety of new digital media such as the Internet, chat rooms, computer games, and mobile phones. Therefore, it is necessary that the university authorities and higher education institutions develop training programs, and make efforts to maintain physical and mental health of students.

Acknowledgments

We would like to thank all students in different universities of Shahrekord for their collaboration in this research.

Conflicts of Interest

The Authors have no conflict of interest.

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Essay on Mobile Addiction: The mobile phone has become the central part of every human being's life. Everyone these days is pinned to their cell phones. While we may dismiss this as an expected behaviour in the current times, but the truth is it has profound behavioural and social impacts. Mobile addiction is a real quandary and a matter of ...
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Also, as W.K. Park points out, loneliness is "positively associated with mobile phone addiction" which shows that addicted persons tend to reduce their feelings of loneliness by using mobile devices, such as a cell phone (2005, p. 260). This kind of behavior would not only affect the teenager, but also the teenager's family members.
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Abstract. Background: Smartphones play a critical role in increasing human-machine interactions, with many advantages. However, the growing popularity of smartphone use has led to smartphone overuse and addiction. This review aims to systematically investigate the impact of smartphone addiction on health outcomes.
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Nomophobia: Fear tied to going without one's phone. Textaphrenia: Fear of the inability to receive or send text messages. Ringxiety: Feeling as though a notification has come through on your phone when it hasn't. Textiety: Feeling anxious about receiving and responding to text messages immediately. Some symptoms of phone addiction include:
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This increase in interest is particularly notable in the discussion on mobile phone addiction (Bianchi & Phillips, 2005; Billieux, Maurage, Lopez ... recent papers on the topic use alternative terms such as "problematic smartphone use" (Kuss, Kanjo, et al., 2018) or "self-reported dependence on mobile phones" (Lopez-Fernandez et al ...
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Globally, the prevalence of mobile phone addiction is varying from 2.4% to as high as 60.3% among adolescents and school-going children [ 2, 5 ]. India is one of the largest and fastest-growing markets for digital consumers, with 560 million Internet subscribers (nearly 41%) in 2018, second only to China.
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Smartphone addiction, sometimes colloquially known as "nomophobia" (fear of being without a mobile phone), is often fueled by an internet overuse problem or internet addiction disorder. After all, it's rarely the phone or tablet itself that creates the compulsion, but rather the games, apps, and online worlds it connects us to.
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This article is part of the Research Topic Significant Influencing Factors and Effective Interventions of Mobile Phone Addiction View all 9 articles. ... C., and Manolis, C. (2015). I need my smartphone: a hierarchical model of personality and cell-phone addiction. Pers. Individ. Differ. 79, 13-19. doi: 10.1016/j.paid.2015.01.049. CrossRef ...
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The high use of mobile phones by young people has been predominant among female users, as a way to keep interpersonal relations, as highlighted by De-Sola et al. (2016), who also declared its use is linked to personality traits like neurosis, extroversion, impulsiveness, self-esteem, self-image and identity.
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Ard Su. To the Editor: Re " The Smartphone Trap ," by Jonathan Haidt and Jean M. Twenge (Opinion guest essay, Sunday Review, Aug. 1): The rise of smartphone addiction among teenagers is ...
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phones and exper iencing distress on separation . multitude of studies have documented that mobile. phone addiction would bring about negative impacts. on physi cal and mental health as well as ...
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Results: The students in the study group demonstrated a 46.1% rate of problematic cell phone use. The students' Problematic Mobile Phone Use Scale total scores showed a significant correlation ...
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The study participants' demographic details are summarized in Table 1. About 95% of the study participants were using smart phones, with 81.7% of them having at least one mobile phone. Most of the study participants used mobile phone for more than 5 years. Around 64.3% of the study participants use mobile phone for an hour (approximately) and ...
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For mobile phone usage, most of the respondents fall into moderate category with 63.8%, M= 3.465 and SD = 0.54899. Meanwhile, for mobile phone addiction, 46.8% of the respondents are at a moderate level. However, 36.8% of them is at a high level of mobile phone addiction. Table 2 - Mobile Usage and Mobile Addiction See Full Size >
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The papers used for this review were retrieved from AIS (All Repositories), Elsevier, Wiley Online, Tailor and Francis and JSTOR databases using the phrase "Smartphone Addiction".
(PDF) The Influence of Mobile Phone Addiction on Academic Performance
Smartphone addiction is a new phenomenon when a smartphone becomes a necessity in our daily life. Smartphones bring many benefits when used for their basic intended purposes, but excessive use of smartphones might turn problematic and consequently, impairs well-being, negatively impacts academic performance, and leads to various psychological and physical complications among adolescents.
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One of the papers concluded that mobile dependency was better predicted by personality factors (such as low self-esteem and extraversion) than actual phone use . In one ... Another study in the same population showed that high mobile phone addiction was associated with an increase in incidence of poor sleep quality over time .
Teens are spending nearly 5 hours daily on social media. Here are the
41%. Percentage of teens with the highest social media use who rate their overall mental health as poor or very poor, compared with 23% of those with the lowest use. For example, 10% of the highest use group expressed suicidal intent or self-harm in the past 12 months compared with 5% of the lowest use group, and 17% of the highest users expressed poor body image compared with 6% of the lowest ...
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The mobile phone addiction index (MPAI) (Bianchi and Phillips, 2005), which was already translated into Chinese and validated for the Chinese population (Li L. et al., 2020), was used to evaluate mobile phone addiction. The MPAI consists of 17 items quantifying four dimensions, including the inability to control craving, withdrawal and escape ...
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Young people are more vulnerable to excessive phone use, and thus, become phone dependent.20 Young people's mental health, addiction to mobile phones, as a driving force, and an active community is major topics that are discussed in psychology and sociology. The present study addressed the question of whether there is any relationship between ...

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