thesis statement about using cell phone cause and effect

Environmental Effects of Cell Phones on Society Essay

Introduction, types of environmental pollution caused by cell phones, discussions, conclusions, works cited.

Cell phones or mobile phones, tablets, and other personal communication devices have become ubiquitous. During 2007-2016, more than seven billion cell phones have been sold. Depending on the user’s desire for changing products, and the amount of damage it can take, a cell phone has a life of 2.5-4 years, after which it is discarded (Statista). Some phones of premium brands such as Apple and Samsung are resold in the resale market, while other brands are scrapped, adding to the electronic or e-waste.

The amount of scrap salvaged, where workers manually extract copper, gold, and other metals from the motherboard and electrical circuit are, very low. The rest is discarded, melted, crushed, and disposed of in landfills. Discarded phones and the supply chain for cell phone parts manufacturing create substantial levels of pollution (Yin et al. 518).

About 41 million tons of e-waste is generated annually. A direct correlation between the GDP of a nation and the e-waste generated is established (Kumar et al. 39). This pollution in the form of hazardous waste, fumes of vehicles used for transportation, discarded plastic, dead lithium batteries, and other components, diffuse into the soil and water bodies. Cell phone towers generate harmful radiation, and toxins enter the food chain, and the environment causing long term harm (Logan). The thesis statement is excessive use of cell phones leads to health problems, and scrap metals, hazardous chemicals must be collected and recycled to yield profits, and for environment safety.

Electromagnetic Field Radiation

Electromagnetic Field Radiation (EMF) is generated by some electrical and electronic devices such as TVs, refrigerators, microwave ovens, transformers, cell phones, and cell phone towers. The amount of EMF generated, measured in watts per meter square (w/m²), by cell phones is in the range of 0.08- 4.439 w/m² for frequencies of 1800 MHz to 50 GHz. Cell towers that act as reception and transmission units radiate more than 100,000 w/m2. The safe level of exposure to EMF is 0.09 w/m², while higher doses of exposures are acceptable when speaking on the devices for a short duration (Gowd et al. 284).

The problem of EMF exposure is twofold. The first problem is the exposure caused by prolonged use of cell phones for more than an hour when people speak continuously on their devices. Danger also comes when users keep the phones in their shirt and pant pockets or keep them under the pillow when they sleep. Continuous exposure to EMF during sleep of 6-8 hours in the night is harmful. The second problem comes from cell towers erected in housing societies, in residential or office complexes.

Residents of buildings with cell towers are constantly exposed to severe EMF. The results of excessive EMF are headaches, memory loss, cardiovascular problems, low sperm counts and reduced sex drive, cancer of the brain and soft tissues, and birth defects of the fetus in the case of pregnant women. Household devices such as microwave ovens have sufficient lining and safety components that absorb EMF waves and minimal flux of waves is leaked to the environment. Cell phones are light and slim and the plastic casing does not absorb the waves. Cell towers are much more dangerous since they affect the health of many people in a large area (Gowd et al. 287).

Raw Materials used in Cell Phone Manufacturer

A major effect on the environment is the excess use of raw materials in cell phone manufacture. A cell phone has 40% of metal components, 40% plastics, about 20% trace metals, and ceramics. The raw materials used for these components are extracted from mines, processed, and then manufactured into sub-assemblies for the mobile phone. Power, water, and the fuel used in these items are high, considering the manufacture of a large number of mobile phones.

The mother-board or the circuit board has several embedded circuits made of metals such as lead, nickel, copper, beryllium, zinc, tantalum, and trace amounts of gold. The board is made of silica, crude oil is used for plastics, limestone and sand are used for fiberglass, and these materials are mined in large amounts causing damage to the environment. The Liquid Crystal Display or the touch-sensitive flat screen is made of materials such as silica, indium, mercury, glass, and plastic. The rechargeable battery is made of materials such as nickel-metal hydride, lithium-ion, nickel-cadmium, and these batteries contain lead, zinc, cadmium, metallic oxide, cobalt, nickel, and others.

These metals are mined as ores and then subjected to refining and processing, using large quantities of water and fuel. Plastic is made from crude oil derivatives and other chemicals. Many of these materials such as lead, nickel, mercury, and the chemicals are toxic. Cell phone factory workers are exposed directly to these materials, while discarded materials leach poisonous toxins in water bodies. These toxins are imbibed by fish, aquatic animals, insects, animals, and plants through the water-soil-plant pathway, and poison humans and animals (Kiddee et al. 1240).

Manufacturing of parts such as circuit boards, keypads, display screens, batteries, casing, and other components is energy and labor-intensive industry While many operations are automated, manufacturing is done with sophisticated machinery with a short life, high levels of power, and energy are used, and pollution in the form of water and airborne pollutants occurs. There is additional pollution and harm to the environment when coal is used to generate power. Coal plants produce toxic particulate matter that settles on plants, is suspended in the air and dissolves in water bodies. The toxins have a long-life and they continue to harm the environment even when dumped in landfills (Heacock et al. 559).

Transportation and Logistics

Many minerals used as raw materials are mined in Africa, China, and other regions. Extraction and refining plants of the minerals are located in India, China, Europe, and the US, while, manufacture of sub-assemblies and the complete product is done in China and India. Finished products are distributed across the world. Therefore, the logistics of the raw material from ore to final distribution centers cover the whole world.

The screen of mobile phones is a mixture of aluminum silica, indium, and tin. China exports 3000 tons of tin used for soldering. The battery is made of manganese, cobalt, and lithium. In 2014, Argentina, China, Australia, and Tibet produced 27,000 tons of Lithium. Electronic circuits of the cell phone and transmission of internal data are facilitated by silicon, antimony, gallium, indium, boron, phosphorous, and arsenic, all highly poisonous substances.

Congo exported 900,000 tons of copper in 2013. Micro-capacitors are made of palladium, platinum, niobium, tantalum, and Colton. Congo and Rwanda exported 2.4 million tons of these ores. Other products such as amplifiers, receivers, vibrators are powered by magnets made from gallium and arsenic, and South Africa is the highest exporters. China produced 80% of the global requirements of gallium and this metal is used in amplifiers, digital circuits, and in screens.

Tungsten is used in motors and China, Rwanda, Russia, Uganda, and Burundi, produced these metals, while East Africa produced 710 tons of this metal. China produces about 90% of the global requirement for neodymium, used in magnets, Cell phone casings are made from metals and plastics, with magnesium, and several petro-compounds used in the manufacture. Nations such as China, India, the US, and Brazil are the major exporters of magnesium. Underdeveloped economies such as Uganda, Rwanda, and others, derive their income from mineral exports (Olingo).

Low efficiency, high polluting, open-pit mines are constructed, and no thought is given to the number of toxic metals that are leached into the water bodies and soil, severely harming the environment. The cycle does not end here since the raw materials are shipped to China and India, where the ore is refined, and ingots of pure metals are produced. These items are then shipped to part manufacturers who process the parts to make components. The components are then shipped or airlifted to factories in China, India, the US, and South America, for further processing and assembly. The ready-to-market mobile phones are then airlifted to stores across the globe.

Therefore, a mobile phone has materials that traveled thousands of kilometers, damaging the environment along the supply chain (Ivanov et al. 54). The assessment is that large supply chains covering raw material, processing, manufacture, and shipping of finished components consume vast natural resources. The carbon footprint of the operations is substantial, causing damage to the environment.

Methods used for Collection, Processing, and Recycling of E-Waste

As noted in the introduction, e-waste generation is about 41.8 million tons in 2014, and by 2018, this figure is expected to reach 50 million tons. About 8% of this total weight is from cell phones. Advanced nations generate maximum waste. Old cell phones are reusable and they can be refurbished and reused. However, residents of advanced countries to replace their phones every 1-2 years, even though the old devices are operational.

Cell phone manufacturers create subassemblies that require replacement of the whole part costing a hundred dollars or more, even though a small resistor, costing a few dollars may be malfunctioning. It appears that these firms, parts dealers, and repair centers, make more profits when the whole sub-assembly is replaced. In some cases, for a mid-level phone, it costs slightly more to buy a new phone than to get the faulty device repaired, with no guarantees. In any case, the customer has to find a replacement phone until the device is repaired. Regulatory authorities appear powerless to stop these predatory tactics by device manufacturers, leading to increased e-waste generation (Tanskanen 1005).

E-waste provides opportunities for recyclers and salvagers. As noted in the previous sections, e-waste contains valuable metals that can be recovered and recycled for use in electronic products. An estimate shows that about $53.4 bn is present in discarded e-waste. Given the small number of precious metals present in each device, recyclers have to process more than 1000 tons to recover 100 grams of gold. The circuit board has metals such as steel, copper, aluminum, gold, silver, palladium, platinum, etc., with potential revenue of $23,500/ ton. However, these metals have different physical properties.

Extracting each metal requires different processes or equipment, adding to the costs (Sthiannopkao and Ming 1151). Besides, hazardous materials such as lead, arsenic, gallium, and other toxins are embedded in the parts, and any extraction process poses health risks to workers. Out of these metals, gold and palladium provide the maximum returns. Salvage and recycling can happen only when economies of scale can be applied to reduce costs. Plastic waste such as casings, covers, cannot be salvaged. Plastic parts can be shredded and used to make parts for domestic appliances and the automotive industry (Heacock et al. 556).

Salvaging and recycling materials can help to save the environment in many ways. The recovered metal can be used to make components and in the assembly of mobile phones. This will reduce the demand to a certain extent on mining and processing, since the recovered metal can be directly used, and wasteful mining activities will reduce. Power used in mining and processing will reduce, though some power will be needed to make the components that go into the cell phone. Recyclers and salvagers will make profits and scale up their operations to recycle more components. Overall, the environment will benefit from salvaging.

Some concerns are that melting and burning the circuit board and plastic insulations to recover the metal releases several toxins. These include antimony, cadmium, chromium, lead, mercury, phosphors, biphenyl ether, polychlorinated biphenyl, polybrominated hexavalent chromium, poly-brominated flame retardants in plastics, and ozone-depleting substances. Sufficient care must be taken to trap these chemicals and dispose of them safely, else, the negative impact on the environment will be severe (Kumar et al. 37).

Methods of collecting, segregating, and disposing of e-waste need some consideration and thought. People would not take excessive efforts to send discarded devices for disposal. Advanced nations such as Canada, Germany, the US, and others, have developed an easy and convenient method to collect e-waste. Some mobile phone manufacturers offer exchange offers, where old phones are taken in and new phones are sold at slightly lower prices.

Other methods are disposal in garbage areas, drop-off at stores, and designated spaces, where special bins are provided for users to drop their unwanted devices. Some people donate or resell, while others get their devices upgraded and repaired. Some countries have firms that offer recycling services for a small fee, while others take away old devices for free. These recycling agencies need to have licenses to practice, and they have to undergo audits.

Where e-waste is disposed of in landfills, a fee of up to $100/ ton may be charged. The recycled items are wrapped in plastic sheets and buried in the ground. This practice is not safe and it causes leaching of chemicals into the ground and water systems. Government policies are needed to encourage recycling and disposal in landfills or in garbage dumps that must be penalized (Kumar et al. 40).

The thesis statement proposed in the introduction section is discussed as follows. Cell phones and waste from cell phones cause some diseases and problems and damage the environment. Cell phones emit EMF and constant exposure can lead to diseases such as cancer, headaches, vision problems, weakening of cardiac muscles, and damage to soft tissues, and birth defects to the fetus. Cell phones have several toxic metals and chemicals such as arsenic, antimony, cadmium, chromium, lead, mercury, phosphorus, etc. Ingestion of these chemicals through the food cycle can lead to several ailments and diseases. Therefore, excessive use of cell phones, exposure to the cell tower, leads to a high dosage of EMF radiations that are harmful.

The safe collection, disposal, and recycling present several opportunities and challenges. Opportunities are seen in the form of recovered metals that can be reused to make components. Challenges are seen in developing viable business models to provide sufficient returns for investments made for scrap recovery operations. Users must be encouraged to dispose of their old devices at designated places, so that collection becomes easier. Unless economies of scale are applied and costs are reduced, recycling is not intensive, and the environment will suffer.

The paper researched the environmental problems caused by cell phones and their impact on society. With the widespread use of cell phones, the number of cell phones in the world is more than seven billion, with a large percentage disposed of. Cell phones and cell phone towers emit harmful EMF radiations and prolonged exposure to these emissions leads to problems such as headaches, cancer, reduced sperm count, loss of libido, cardiac problems, birth defects, and many other diseases.

Discarded cell phones are an environmental hazard since they leach harmful chemicals into water bodies and the ground. The supply chain used for mining, processing, manufacture of components and finished products, and their distribution, causes pollution. Many metals such as gold, silver, copper, arsenic, gallium, palladium, platinum, etc., and these can be recovered through the salvaging process.

However, recycling procedures emit dangerous gases with arsenic, lead, and gallium. Recycling will help to save the environment to some extent, provided the salvage operations are cost-effective, and economies of scale can be applied. Existing methods for the disposal of cell phones, their collection, recycling, and salvage must be more intensive. These procedures must be made more robust through government policies.

Gowd, Parandham, et al. “Determination of Invisible Environmental Pollution due to Cell Phones EMF Radiation and Projections for 2030.” Current World Environment , vol. 8, no. 2, 2013, pp. 283-290.

Heacock, Michelle, et al. “E-waste and Harm to Vulnerable Populations: A Growing Global Problem.” Environmental Health Perspectives , vol. 124, no. 5, 2016, pp. 550-561.

Ivanov, Dmitry, et al. Global Supply Chain and Operations Management: A Decision-Oriented Introduction to the Creation of Value . Springer, 2016.

Kiddee, Peeranart, et al. “Electronic Waste Management Approaches: An Overview.” Waste Management , vol. 33, no. 5, 2013, pp. 1237-1250.

Kumar, Amit, et al. “E-waste: An Overview on Generation, Collection, Legislation and Recycling Practices.” Resources, Conservation and Recycling , vol. 122, 2017, pp. 32-42.

Logan, Catalina. “ Effects of Cell Phones as an Environmental Hazard .” LiveStrong . 2015. Web.

Olingo, Allan. “ Minerals in your Mobile Phone. ” The East African . 2015. Web.

Statista. “ Number of Smartphones Sold to End Users Worldwide from 2007 to 2016 (in million units). ” Statista , 2017. Web.

Sthiannopkao, Suthipong, and Ming, Wong. “Handling E-Waste in Developed and Developing Countries: Initiatives, Practices, and Consequences.” Science of the Total Environment , vol. 463, no. 2013, 2013, pp. 1147-1153.

Tanskanen, Pia. “Management and Recycling of Electronic Waste.” Acta Materialia , vol. 61, no.3, 2013, pp. 1001-1011.

Yin, Jianfeng, et al. “Survey and Analysis of Consumers Behavior of Waste Mobile Phone Recycling in China.” Journal of Cleaner Production , vol. 65, no. 2014, 2014, pp. 517-525.

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Cause and Effect Thesis Statement

A cause and effect thesis statement comprises a sentence or a couple of sentences. It states the main idea، focusing on the cause and effect. relationship

What is Cause and Effect Thesis Statement

Table of Contents

A cause and effect thesis statement comprises a sentence or a couple of sentences. It states the main idea or argument of the essay and focuses on the cause and effect relationship between two or more events, actions, or phenomena. It also explains how one factor influences or leads to another.

It presents the overall purpose and direction of the essay . It indicates that the essay is going to examine the causes of a specific event or phenomenon and discuss the effects or consequences resulting from that event or phenomenon.

How to Write Cause and Effect Thesis Statement

Every thesis statement has its own specific tips for writing. Here are some steps to follow to write a cause and effect thesis stateme nt.

It must be kept in mind that these steps are only for illustrative purposes and not definition ones. Every person has his/her own individual approach to writing a thesis statement. You can adapt the steps and examples that suit your specific essay topic and content.

Parts of Cause and Effect Thesis Statement

Types of cause and effect thesis statements, examples of cause and effect thesis statement.

It must be noted that the revised and edited thesis statements aim to provide more specific and focused cause-and-effect relationships.

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• Yagelski, Robert P. Writing as a Way of Being: Writing Instruction, Nonduality, and the Crisis of Sustainability. Utah State University Press, 2022.

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ORIGINAL RESEARCH article

Attention or distraction the impact of mobile phone on users' psychological well-being.

• 1 Department of Science and Technology Communication and Policy, University of Science and Technology of China, Hefei, China
• 2 College of Media and International Culture, Zhejiang University, Hangzhou, China
• 3 Department of Technology Management, International Islamic University, Islamabad, Pakistan

Cumulative evidence has demonstrated that mobile phone distraction, in particular among emerging adults, is a growing problem. Considerable efforts have been made to contribute to the literature by proposing cognitive emotion pre-occupation which acts as an underlying mechanism through which mobile phone distraction results in a reduction in psychological well-being. The proposed model is supported by distraction-conflict theory which reveals that users, with high attention control, are better at coping with the negative consequences of mobile phone distraction. The data, consisting of 914 University students in China, was analyzed using statistical tools. The results support that mobile phone distraction has a significant positive relationship with cognitive emotional pre-occupation which negatively affects users' psychological well-being. Our findings also reveal that attention control moderated the mediation effect of cognitive emotional pre-occupation in association with mobile phone distraction and psychological well-being. The theoretical and practical implications are also discussed along with limitations and future research.

Introduction

Mobile phone technology has become a major part of people's daily life. People, especially youths use mobile technology for various purposes ( Soyemi Jumoke, 2015 ; Alalwan et al., 2018 ). Mobile phone manufacturers offer new features and functionalities that have compelled users to use them ( Zheng and Lee, 2016 ). The versatility of the mobile phone allows seamless integration of work, fun, social interaction, and enhances the quality of life in many ways ( Zhang and Adipat, 2005 ; David et al., 2015 ; Longstreet and Brooks, 2017 ). According to the report generated by the China Internet Network Information center in 2019, 98.6% of internet users in China had access to the internet via mobile devices in 2018—1.1% higher than a year earlier. People aged between 10 and 39 years accounted for 67.8% of all internet users in China, where students (25.4%) were the largest user group ( CNNIC, 2019 ). In China, young adolescents are very fond of using a mobile phone in their daily routine activities such as during working, driving, and studying making it their first priority ( Zhou, 2019 ), however, the negative consequences of the continuous usage of a mobile phone have been illustrated in recent studies. For example, the overuse of mobile phones has adverse effect on users' academic performance ( Thomée et al., 2011 ; Lepp et al., 2015 ; Anderson et al., 2017 ), and work performance ( Turel et al., 2011 ) and also cause technology driven consequences (e.g., distraction) ( Coursaris et al., 2012 ). The problematic use of a mobile phone has become a societal debate; therefore, it is important to investigate the negative consequences of mobile phone usage in China. One of the reasons for the negative consequences of mobile phone technology is distraction ( Sobhani and Farooq, 2018 ).

Mobile phone distraction (MD) is defined as the prevention of giving full attention to the nearest surroundings ( David et al., 2015 ). The cognitive demand related to phone calls, email, texting, playing games, browsing, and social networking sites on mobile phones grabs user's attention or moves their attention away from other things so that they are not be able to focus on work-related activities. The mobile phone limits the user's attention and to make appropriate timely decisions and ultimately affects their psychological well-being ( Salehan and Negahban, 2013 ). Psychological well-being is described as the overall psychological effectiveness of an individual ( Gechman and Wiener, 1975 ; Sekaran, 1985 ). It measures the hedonic or pleasant aspect of individual feelings ( Russell, 1980 ). Researchers have started analyzing the dark side of excessive mobile phone use on psychological well-being such a stress, depression, anxiety, and sleep disturbance ( Bianchi and Phillips, 2005 ; Thomée et al., 2011 ; Nawaz et al., 2018 ). Many studies have focused on exploring the nature, measurement, and dimensions of the excessive use of technology ( Chesley, 2005 ; Porter and Kakabadse, 2006 ; Thomée et al., 2007 ; Sahin and Çoklar, 2009 ; Choi and Lim, 2016 ). While many other research studies have investigated the cognitive and behavioral interconnections, particularly regarding negative consequences of mobile devices ( Thomée et al., 2011 ; Turel et al., 2011 ; Turel and Serenko, 2012 ; Salehan and Negahban, 2013 ; Luqman et al., 2017 ; Cao et al., 2018 ; Volkmer, 2019 ).

Recent research studies have analyzed the impact of mobile phone distraction on social media use at work ( Mark et al., 2018 ), during studying ( David et al., 2015 ) and also its impact on memory and cognition ( Craik, 2014 ). However, the negative consequences of mobile phone distraction have not been fully addressed in these previous studies. Due to an existing gap in previous research, it is important to study the negative consequences of mobile phone distraction.

This research study aims to examine how mobile phone distraction stimulates cognitive emotional pre-occupation which ultimately affect users' psychological well-being. Meanwhile, individuals' attentional control helps to enhance their psychological well-being ( Ellis et al., 2014 ). Attention control refers to an individual's ability to focus only on those stimuli relevant to the current goal, minimizing the extent to which bottom-up influences capture our attention ( Buschman and Miller, 2007 ). A few researchers have suggested that attention plays a critical role in reducing cognitive processing information by focusing and concentrating on the main objective ( Wolfe et al., 2004 ; Buschman and Miller, 2007 ). Therefore, this study examines how attention control moderates the association between mobile phone distraction, cognitive emotional pre-occupation, and psychological well-being.

This study involves four main objectives intended to make both theoretical and practical contributions to the existing literature. First, the study examines the impact of mobile phone distraction on users' psychological well-being using distraction-conflict theory. Second, the study examines how users' cognitive emotional pre-occupation mediates the relationships between mobile phone distraction and psychological well-being. Third, the current study analyzes the moderating effect of attention control on the association between mobile phone distraction and cognitive emotional pre-occupation of users. Finally, the study examines whether attention control moderate the mediating effect of cognitive emotional pre-occupation between mobile phone distraction and psychological well-being.

Theoretical Background and Hypothesis Development

Distraction-conflict theory.

According to Leung (2015) , a distraction is something that makes it hard for one to think or pay attention. It is a process by which an individual or group is distracted from the desired focus area, blocking, or reducing the desired information. Robert Baron's theory of distraction-conflict based on the idea that being aware of another object creates a conflict between attending to that object and attending to the task at hand ( Baron, 1986 ). Similarly, the distraction conflict model has three major steps (I) Others distract, (II) distraction causes attention to conflict, and (III) attention conflict elevates stress ( Nicholson et al., 2005 ). In the presence of others, there is a conflict between the object of attention and attending to the task that causes attention conflict ( Baron et al., 1978 ). Attention conflict refers to the situation in which the person feels a strong urge, desire, or obligation to pay attention to the distractor (i.e., mobile phone) during performing their tasks, especially when the distractor is attention-grabbing and difficult to ignore ( Baron, 1986 ). To be able to participate in more than one stimulus at a time, a person needs greater mental activity in the working memory of an individual ( Sweller, 1988 , 1994 ), known as a cognitive load ( Grieve et al., 2014 ). Increased cognitive load can have negative effects by decreasing the attention, precision, working memory, and effectiveness of the individual ( Coursaris et al., 2012 ) which can in turn increase stress ( Sanders and Baron, 1975 ). Previous studies on stress examined that stress induced by the use of technology affect user's psychological well-being ( Ayyagari et al., 2011 ; Thomée et al., 2011 ; Choi and Lim, 2016 ).

Distraction is due to a lack of attention; the absence of interest in the topic; and the great intensity, novelty or attraction of something other than the object of interest ( Craik, 2014 ). It comes from both internal and external sources ( Nicholson et al., 2005 ). External distractions include factors like visual triggers, social interactions, music, text messages, and telephone calls. While internal distractions include hunger, tiredness, illness, anxiety, and daydreaming. The interference of focus is supported by both external and internal distractions ( Schumm and Post, 1997 ). Distraction-conflict theory provides insight into the evaluation of social media as “other” technology that distracts people from their primary goal ( Leung, 2015 ). Negative consequences of distraction include effort difficulties and mental attention ( Baecker et al., 1995 ) and impaired task performance ( Cellier and Eyrolle, 1992 ; Suh et al., 1996 ).

Concerning mobile phones, its ubiquity and easy access makes it a potentially strong mechanism for distraction ( David et al., 2015 ). Mobile phone distractions can be initiated by sound (when a user gets a message or call) or by sight (when receiving a notification from social networking site posts, online notifications of friends and family available on social networking sites) ( Brooks, 2015 ). Users wonder what their friends and family are doing on social networking sites, scrolling and commenting on friend and family moments, sending videos and pictures, playing games, watching videos, online shopping, and listening to music only to engage themselves in mobile phone activities ( Wu et al., 2018 ). Therefore, the mobile phone has made distraction easier, due to their portability and the diversity of entertaining features. Even when users are doing work activities and studying ( Thomée et al., 2011 ; Zhou, 2019 ), their primary focus is distracted by mobile phone technology ( Coursaris et al., 2012 ). Therefore, the current study aims to test a proposed research model based on distraction-conflict theory to expand theoretical knowledge about whether and how mobile distraction, cognitive emotional pre-occupation and attention control affects users' psychological well-being.

Mobile Phone Distraction and Cognitive Emotional Pre-occupation

The use of mobile phone technology can lead to sacrificing other goals such as neglecting other commitments and a decrease in social activities with friends and family ( Lin, 2019 ). The increased use of mobile phone technology in the daily life developed user's checking habits whereby they constantly make a brief inspection of their mobile phone applications ( Porter and Kakabadse, 2006 ; Yang et al., 2016 ). It diverts the user's attention to non-work-related activities ( Ou and Davison, 2011 ; Rosen et al., 2013 ; Ziegler et al., 2018 ).

The diversity of mobile phone features and functions induce excessive usage behavior ( Oulasvirta et al., 2012 ) and users experience difficulty in controlling the time they spend on the device and are easily distracted ( Bianchi and Phillips, 2005 ). Such distraction stimulates cognitive emotional pre-occupation with behavior ( King et al., 2013 ). Cognitive emotional pre-occupation is defined as “obsessive thought patterns involving technology use” ( Caplan and High, 2006 ).

Pre-occupation with a behavior produces strong cravings to engage in the behavior which leads to problematic behavior ( Collins and Lapp, 1992 ). Users with excessive usage behavior, develop a strong link in their long-term memory and their behavioral tendencies are associated with their reactions ( Strack and Deutsch, 2004 ). The existing literature about addiction or pathologic use tends to consider cognitive emotional pre-occupation as one of the core symptoms of problematic technology use ( Nicholson et al., 2005 ). Cognitive emotional pre-occupation with mobile phone technology creates a strong willingness to use, which a mobile user may find difficult to endure and therefore, can act as a source for unplanned and even problematic use of the mobile phone ( Cao et al., 2018 ). With the use of a mobile phone, an increased level of pre-occupation develops strong thoughts and emotional attachments, and the users feel a powerful urge to use even in a dangerous situation, where it is banned such as when driving a vehicle ( Telemaque and Madueke, 2015 ; Turel and Bechara, 2016 ). Therefore, we hypothesized that

H1: Mobile phone distraction is positively related to cognitive emotional pre-occupation .

The diverse features of mobile phones increase the cognitive demand of users to use it. Such cognitive demand causes cognitive distraction. Cognitive distraction is defined as the user's difficulty to process two or more types of information at the same time ( David et al., 2015 ). Phone calls, texting, and social media networking sites may cause a lapse in attention and concentration.

Previous research found that on-going use of mobile phone technology causes psychological distress ( Chesley, 2005 ; Błachnio et al., 2013 ). Users expect enjoyment from the utilization of mobile phone technology but the loss of control on mobile phone usage affects cognitive limits and induces negative emotions. Previous research studies have found that mobile phone usage is negatively related to the concept of well-being, mood and anxiety disorder, fatigue, and mental health symptoms such as depression and sleep disturbance ( Thomée et al., 2007 , 2011 ; Dhir et al., 2018 ; Lin, 2019 ). Therefore, we hypothesized that

H2: Mobile phone distraction has a significant negative relationship with psychological well- being .

Cognitive Emotional Pre-occupation and Psychological Well-Being

Excessive use of a mobile phone leads to a reduction in the daily working routine, productivity, physical health, social relationships, and emotional well-being ( Horwood and Anglim, 2018 ). A recent study explored how the excessive use of a mobile phone induces stress ( Zheng and Lee, 2016 ). The continuous use, news and information, demands for attention from social networking sites, work activities and several forms of entertainment results in cognitive emotional pre-occupation ( Lee et al., 2014 ). Cognitive emotional pre-occupation develops clusters in the long-term memory of the users ( Strack and Deutsch, 2004 ). These clusters have strong impulses on behavior such as cognitive or emotional reactions ( Craik, 2014 ). The pre-occupation can be disturbing because, in the presence of such pre-occupying ideas and feelings, individuals find it hard to concentrate on other tasks ( Fillmore, 2001 ). These negative emotions weaken psychological well-being and eventually lead to disregarding essential elements of a user's life such as their family, education, and work ( Choi and Lim, 2016 ). Therefore, we hypothesized that

H3: Cognitive emotional pre-occupation is negatively related to psychological well-being .

Cognitive Emotional Pre-occupation as a Mediator

We expected that cognitive emotion pre-occupation performs a mediating role in the relationship of mobile phone distraction and psychological well-being for the following reasons. First, mobile phone distraction causes excessive use which generates emotional and cognitive pre-occupation with behaviors ( Cao et al., 2018 ). Such behaviors cause a strong desire to use a mobile phone to develop, which is difficult to resist ( Zheng and Lee, 2016 ). This increased use of the mobile phone causes strong thoughts and emotional attachment to develop, leading to depression, which ultimately causes their well-being to deteriorate ( Lee et al., 2014 ; Zhou, 2019 ). Second, previous studies have conceptualized that mobile phone users are extensively pre-occupied or “addicted” and overwhelmed with information, which reduces their cognitive capacity to manage the information effectively ( Eppler and Mengis, 2004 ). When the user's cognitive limit exceeds the optimum level of technology utilization it may result in negative consequences ( Ahuja et al., 2007 ). A previous study showed that mobile phone usage is negatively related to the concept of well-being that leads to interpersonal problems ( Griffiths, 2005 ). Third, a compulsive desire to use the mobile phone can result in negative emotions such as emotional exhaustion, fatigue, and anxiety which affects their health and social relationships ( Merrill and Liang, 2019 ). Such emotions reduce the psychological well-being of the users ( Dhir et al., 2018 ). Therefore, we hypothesized that

H4: Cognitive emotional pre-occupation mediates the relationship of mobile phone distraction and psychological well-being .

Attention Control as a Moderator

Mobile phones increase people's enjoyment and comfort by providing them with flexible access to information which can turn into excessive use of the mobile phone ( Yang et al., 2016 ). Such activities distract users from their routine work and enhances the cognitive and behavioral intentions of the users. However, due to attentional conflict, mobile phone distraction can have significant implications, ranging from short-term inconvenience (e.g., annoyance) to life-threatening circumstances such as motor accidents ( Turel and Bechara, 2016 ). According to Ellis et al. (2014) and Hu et al. (2017) the ability to control attention switching and maintaining the negative affective response effect is known as attention control (AC). Some researchers suggest that individual differences in working memory capacity represent a different attention control on the use of working memory resources ( Engle, 2002 ; Fukuda and Vogel, 2011 ). Attention control such as self- regulation ability, starting, maintaining concentration, and shifting internal and external attention to ensure flexibility is used to remain focused ( Chambers et al., 2008 ). According to Derakshan and Eysenck (2009) , attention control helps to increase processing efficiency and cognitive performance of an individual plays a critical role in decreasing data processing complexity and focusing on the concentrated goal. Therefore, this study uses attention control that helps to reduce the negative consequences of mobile phone distraction, because it may influence the capacity to neglect adverse cognitive and emotional consequences. Furthermore, evidence shows that distraction due to mobile phones has an impact on user's behavior ( Craik, 2014 ). According to Cao et al. (2018) cognitive emotional pre-occupation produces problematic behavior which can affect psychological well-being. Moreover, attention control helps to reduce depressive disorder ( Hu et al., 2017 ). Thus, the study suggested that mobile phone distraction influences user's cognitive emotional behavior and affects their psychological well-being. The indirect relationship weakens when users have high attention control. Therefore, we hypothesized that

H5: Attention control moderates the effect of mobile phone distraction and cognitive emotional pre-occupation the weaker the relationship with high attention control .

H6: Attention control moderates the mediating effect of cognitive emotional pre-occupation between mobile phone distraction and psychological well-being .

Figure 1 shows the proposed theoretical framework.

Figure 1 . Proposed research model.

Methodology

Sample and data collection.

To examine the reliability and validity of the construct, a pilot study was conducted before the data collection process. The questionnaire was distributed online to 50 volunteer students in a large University in China. We performed exploratory factor analysis to ensure the reliability and validity of the construct. On the basis of findings, two items were removed from the revised final questionnaire. Empirical data were collected online by sharing the link of the questionnaire amongst social groups of University students (WeChat, Weibo and QQ) and by sending invitations to students via University email. The targeted sample involved students from a large University in China. This sample is suitable considering that the younger generation make up the majority of active users as they constitute the main body of mobile phone users. Therefore, students are considered as an adequate source of data for this study. Compeau et al. (2012) validated that University students represent part of the population, and their characteristics are similar to population characteristics. According to Kuss et al. (2013) , students are more prone than others to present problematic online technology usage behavior. To ensure the quality of records we asked students to fill their student ID number in questionnaire so that repetition and redundancy of records will be removed. All participants were assured that their data will remain confidential and that it was collected for research purposes only. A convenience sampling technique was used to collect the data. A back-translation method was employed because the original questionnaire items were developed in English. Thus, the items were translated into Chinese by a Chinese translator for the data collection process and were subsequently converted back into English for further analysis ( Brislin, 1970 ). The sample size was calculated by using the Godden (2004) formula for an infinite population (recommended sample was 384). A total of 935 survey responses were collected. After outliers and incomplete responses were eliminated, a total of 914 responses were gathered for further analysis.

We adapted the questionnaire from the literature and some items were modified according to the context of the current study. All items were measured using a 5-point Likert scale ranging from 1 = strongly agree to 5= strongly disagree. The measurement items of all variables were described in Table 2 . The demographic variables such as age, gender, and frequency of use were measured as control variables.

Mobile Phone Distraction

Mobile phone distraction was assessed using a four-items construct and it was adapted from Davis et al. (2002) . The items represent the frequent use of a mobile phone while performing other activities. The Cronbach's alpha (CA) value is 0.95.

Cognitive Emotional Pre-occupation

Cognitive emotional pre-occupation was measured using a six-items scale and was adapted from Caplan and High (2006) and Zheng and Lee (2016) . The items represent the feeling of an urge and thoughts to use a mobile phone when not using it for some time. The CA value is 0.95.

Psychological Well-Being

Psychological well-being was measured using an eight-items scale that was adapted from Steinfield et al. (2008) and Choi and Lim (2016) . The items consist of positive and negative wording, negative items were reverse coded to measure the psychological well-being. The CA value is 0.96.

Attention Control

An eight-items scale was adapted from Farmer and Sundberg (1986) and Brooks (2015) to measure attention control. The items represent frequent shifting of attention during distraction and focusing on the main task. Some items were reverse coded to measure the positive effects of attention control. The CA value is 0.97.

Data Analysis and Results

For data analysis, we used IBM-SPSS 22, IBM-AMOS 23, and Process macro by Hayes.

First, we performed the descriptive analysis to measure the demographic data. The demographic data of 914 students were based on males (48.1%) and females (51.9%). The remaining demographic data of 914 respondents are given in Table 1 . Second, we performed exploratory factor analysis to measure the reliability and validity of the constructs. Third, we performed structural equation modeling (SEM) using IBM- AMOS 23 to find out the confirmatory factor analysis and model fit indices. Finally, we used Process macro in IBM-SPSS 22 to perform moderated-mediation analysis.

Table 1 . Descriptive statistics of respondent characteristics.

Validity and Reliability of the Measurement Items

Reliability pertains to the consistency of the construct, and validity pertains to how the constructs define the concept of the study ( Carmines and Zeller, 1979 ). This study performed the exploratory factor analysis (EFA) using a principle component analysis with varimax rotation and a suppressed value of <0.50 to measure the validity of the construct. The results of the principle component analysis produced four factors with an Eigen value >1 explaining 83.26 % of the total variance. All factor loadings on the expected factors are within the range of 0.81 to 0.93 (see Table 2 ) while the recommended values should exceed 0.7 to ensure construct validity ( Hair et al., 1998 ). To measure the reliability of the constructs, we used CA and composite reliability (CR) values. The values of CA and CR must exceed the threshold of 0.7 ( Anderson and Gerbing, 1988 ). Table 2 indicates that all CA and CR values exceed 0.7, thereby ensuring measurement reliability. We also checked the average variance extracted (AVE) for convergent validity. In our data, the average variance extracted values of constructs ranged from 0.74 to 0.80, greater than the minimum threshold of 0.5 as recommended by Fornell and Larcker (1981) which indicates that the items satisfied the convergent validity requirements.

Table 2 . Confirmatory factor analysis, AVE and composite reliability.

Discriminant validity is the square root of all AVE values greater than the off-diagonal correlations between the constructs. Table 3 shows that the value of the square root of AVE is greater than the correlation coefficient of the constructs, thereby indicating discriminant validity.

Table 3 . Correlations, mean, and standard deviation.

Furthermore, we used IBM AMOS 23 to conduct confirmatory factor analysis (CFA) for validating the measures. The value of CMIN/ df = 2.75, NFI = 0.98, TLI = 0.96, IFI = 0.98, CFI = 0.97, RMSEA = 0.05 indicated a valid model fit. The results indicated that the values are within the acceptable range as suggested by Hair et al. (1998) . Therefore, the results show a valid model fit.

We performed the Harman's one-factor test to evaluate the extent of common method bias ( Podsakoff et al., 2003 ) because all questions were answered by the same individual. In this test, the threat of common method bias is considered high if a single factor account for more than 50% of total variance ( Harman, 1976 ). The results reveal that none of the factors dominate the explanation of the variance, in which the most influential factor accounts for 36.9% of the variance. Moreover, other evidence of a common method bias includes high correlations ( r > 0.9) among variables ( Pavlou and El Sawy, 2006 ). Table 3 shows that unusually high correlation in the sample is non-existing.

Thus, the common method bias is not a serious concern in this study.

Structural Model

Structural equation modeling (SEM) was used to measure the model fit indices. The results of model fit indices show that the model was a good fit [χ 2 (666.752), df = 248, χ 2 / df =2.68, NFI = 0.96, IFI = 0.97, CFI = 0.97 and RMSEA = 0.06]. The proposed model is within the acceptable range that is defined by Anderson and Gerbing (1988) ; in particular, χ 2 / df < 5, NFI > 0.90, IFI > 0.90, CFI > 0.90 and RMSEA < 1.0.

Hypothesis Testing

This study used structural equation modeling to test the direct and mediation hypothesis. The results of direct and indirect effects are given in Table 4 . The relationship between mobile phone distraction and cognitive emotional pre-occupation (β = 0.29, p < 0.001) was significant, leading to the acceptance of hypothesis 1. The results indicate that the direct effect of mobile phone distraction and psychological well-being (without mediator) is significant (β = −0.23, p < 0.001), leading to the acceptance of hypothesis 2. The relationship between the cognitive emotional pre-occupation and psychological well-being (β = −0.35, p < 0.001) was also significant, indicating the acceptance of hypothesis 3. The path diagram of SEM is demonstrated in Figure 2 . We used the bootstrapping method with 5,000 bootstrap samples and a 95% confidence interval for indirect effect. The bootstrapping result of the indirect effect of mobile phone distraction on psychological well-being via cognitive emotional pre-occupation is also significant (β = −0.08, p < 0.01). Hence, cognitive emotional pre-occupation partially mediates the relationship between mobile phone distraction and psychological well-being, thereby accepting hypothesis 4. Regarding weak beta coefficient, previous studies have also identified weak beta value of indirect effect ( Qian et al., 2017 ; Liu and Li, 2018 ). Furthermore, we used ANOVA to check the significant differences of control variables (gender, age, and frequency to use). The control variables exhibit insignificant effects on psychological well-being. Therefore, we exclude the control variables for further analysis.

Table 4 . Bootstrap results for direct and indirect effects.

Figure 2 . SEM path diagram.

Moderated Mediation Analysis

The moderated-mediation results are described in Table 5 . The current study hypothesized a moderating role of attention control between mobile phone distraction and cognitive emotional pre-occupation. We used model 7 of the Process macro by Hayes (2013) in IBM-SPSS 22 to analyze the moderated mediation analysis. Interestingly, the results showed that the relationship between mobile phone distraction and cognitive emotional pre-occupation is highly significant when attention control is low (β = 0.44, p < 0.001) but becomes weakest and insignificant when attention control is high (β = 0.05, p > 0.05). Figure 3 shows the graphical presentation of moderating effect of attention control which describe that slop is becoming less positive as move from low to high attention control. Therefore, hypothesis 5 was supported and accepted.

Table 5 . Moderated mediation model of attention control, mobile phone distraction, cognitive emotional pre-occupation, and psychological well-being (model 7 process macro, n = 914).

Figure 3 . Moderating effect of attention control between the relationship of mobile phone distraction and cognitive emotional pre-occupation.

Furthermore, the conditional indirect effect is reflected in the index of a moderated mediation analysis and if zero does not fall between the lower and upper limit of the 95% confidence interval then the indirect effect is conditional on the level of the moderator ( Preacher and Hayes, 2008 ). The result of moderated mediation analysis [index = −0.0711, SE = 0.0103, CI = (−0.0927, −0.0516)] shows that attention control fully moderated the mediation effect of cognitive emotion pre-occupation between mobile phone distraction and psychological well-being, thereby accepting hypothesis 6.

On the basis of distraction conflict theory, the current study proposed a research model to examine the effects of mobile phone distraction on psychological well-being. Specifically, the current study has the following aims. First, to examine the effect of mobile phone distraction and cognitive emotional pre-occupation. Second, to investigate the impact of mobile phone distraction on psychological well-being. Third, to examine the mediating effect of cognitive emotional pre-occupation between mobile phone distraction and psychological well-being.

Finally, to study how attention control moderates the mediation effect of cognitive emotional pre-occupation between mobile phone distraction and psychological well-being. The key contribution of the current study is to examine mobile phone distraction in relation to attention control, cognitive emotional pre-occupation, and psychological well-being in China. The findings of the study support the proposed model and hypotheses and provides important theoretical and practical implications.

The current study resulted in several important findings. First, the current study contributes to the mobile phone distraction literature by identifying its consequences. Our result shows that mobile phone distraction exhibits a positive and significant relationship with cognitive emotional pre-occupation. Users have strong willingness to use mobile phone technology which causes strong emotional attachments to develop and users feel a powerful urge to use their mobile phone. Excessive use of mobile phone SNS is positively associated with cognitive emotional pre-occupation of Chinese students ( Cao et al., 2018 ). Similarly, Coursaris et al. (2012) and Longstreet and Brooks (2017) found that mobile phone distraction has an impact on the efficiency and effectiveness of users which in turn influence user's satisfaction and behavioral intention toward the usage of mobile phones. Moreover, Alalwan et al. (2018) and Leung (2015) revealed that mobile phone distraction has a positive relationship with perceived enjoyment and task performance.

Second, the excessive use of a mobile phone can result in lower psychological well-being. Our findings revealed that mobile phone distraction has a negative and significant association with psychological well-being. So much so that mobile phone usage limits the cognitive ability of the user so that they are not be able to focus on daily routine activities which leads to negative psychological well-being. Taiwanese students highly depend on mobile phone usage and perceive that being permanently connected to a mobile phone causes stress ( Lin, 2019 ). Turel and Bechara (2016) found that mobile phone usage during driving distract users which ultimately has negative outcomes (e.g., accidents). Similarly, Schwebel et al. (2012) found that mobile phone distraction (e.g., talking on the phone, texting, and listening to music) has a negative impact on pedestrian behaviors.

Third, many scholars have found that an increase in use of a mobile phone can result in psychological consequences (e.g., anxiety, depression, fatigue, exhaustion) ( Bianchi and Phillips, 2005 ; Thomée et al., 2011 ; Zheng and Lee, 2016 ). Excessive use of a mobile phone is positively related to mobile phone addiction, exerting a direct impact on psychological well-being in young Korean adults ( Choi and Lim, 2016 ; Cha and Seo, 2018 ). Tangmunkongvorakul et al. (2019) shows that excessive use of a mobile phone has a negative effect on user's psychological well-being.

Similarly, Sahin and Çoklar (2009 ) and Dhir et al. (2018) found that compulsive use of a mobile phone increases fatigue and stress levels, and ultimately effects users' psychological well-being. Moreover, Cao et al. (2018) found that excessive use of a mobile phone causes cognitive-emotional pre-occupation which in turn has a positive relationship with psychological strains (e.g., life invasion, techno-exhaustion, and privacy invasion). Our findings show that cognitive emotional pre-occupation has a negative and significant relationship with psychological well-being. This study predicts that the concentration demand of social networking sites, text messages, calls, and other mobile features grab user attention, influencing their negative emotional reactions and behaviors and ultimately lowering users' psychological well-being.

Fourth, our findings show that cognitive emotional pre-occupation partially mediate the relationship of mobile phone distraction and psychological well-being. Higher mobile phone use is associated with lower well-being ( Volkmer, 2019 ). Similarly, users with high levels of cognitive emotional pre-occupation with the internet will experience more negative outcomes ( Caplan and High, 2006 ). Therefore, users who spend more time online are more likely to exhibit an increase in depression and social separation.

Finally, the study identified an important variable—attention control—which helps users to cope with the negative impact of mobile phone distraction and help to avoid getting emotionally connected. Our finding indicates that users with low attention control, experience more cognitive attachment and face attention conflicts with the mobile phone, whereas users with high attention control do not experience such an attachment and are more focused on their goals. The results are in line with the study of Hu et al. (2017) who suggested that attention control helps to reduce depressive disorder. Moreover, Derakshan and Eysenck (2009) and Jung et al. (2019) found that attention control helps to increase cognitive performance and efficiency, and improves the decision making process of individuals.

Theoretical and Practical Implications

This study exhibits certain important theoretical implications. First, the current study contributes to the existing literature on mobile phone distraction by examining the underlying mechanism through which mobile phone distraction affects psychological well-being. The study theoretically expands the etiology of problematic mobile phone use and discusses its potential adverse effect. The current study extends the literature on distraction-conflict theory by emphasizing that mobile phone distraction negatively affects psychological well-being. It also validates the distraction conflict theory by examining its validity on the mobile phone distraction and cognitive emotional behavior. Cognitive emotional pre-occupation is a new phenomenon in the field of mobile phone distraction. Second, the current research aims to enhance the understanding association of mobile phone distraction with cognitive emotion pre-occupation and its impact on psychological well- being. Finally, the study complements previous studies on attention control and contributes to the literature by examining the moderating effect of attention control in the association between mobile phone distraction, cognitive emotional pre-occupation, and psychological well-being which previous studies have not examined.

The current study has some practical implications. First, to avoid the negative consequences of mobile phone distractions, users must reduce their usage and manage their behaviors accordingly to overcome psychological issues. Second, the findings also have implications on policies where institutions must educate students about the negative psychological consequences of excessive use of a mobile phone so that they can reduce their usage while performing their routine work. Finally, this study also suggests that users with high attention control are not affected by the negative consequences of mobile phone distraction. Therefore, users should be more focused on their goals and limit the usage of a mobile phone to avoid negative consequences.

Limitation and Future Research

The current study had certain limitations. First, data was collected from University students which was the best fit for our research study. It is an empirical question as to whether the findings can be generalized to other countries and cultures. Various cultural factors, values, and beliefs have an impact of individual psychological well-being ( Wissing and Temane, 2008 ; Grossi et al., 2012 ). Future research must focus on different target samples in other work settings or be conducted in a cross-cultural study of different countries to elucidate more interesting results. Particularly, researchers should focus on cultural factors such as gender, education and occupation to examine the effect of mobile phone distraction on psychological well-being. Second, the study focused on overall mobile phone distraction and was not specific to any mobile application such as social networking sites applications, mobile-gaming applications, etc. Future research must be focused on distraction caused by these applications to examine its effects on users' behavioral intentions. Third, the current study used control variables e.g., age, gender, and frequency of use, therefore, future research should use other control variables such as time and experience to find out more interesting results. Finally, the study considered the users' psychological well-being rather than focusing on specific psychological factors. Further investigation should extend this study to explore each factor of psychological well-being such as anxiety, sleep disorder and exhaustion, and should also examine its effect on physical and emotional well-being.

The current study was primarily focused on the implications of mobile phone distraction on psychological well-being. This study's greatest contribution was the finding that mobile phone distraction stimulates cognitive emotional pre-occupation with behavior and undermined user's psychological well-being. Moreover, users with high attention control, can easily manage their daily routine activities and ensure flexibility to remain focused. If the different factors proposed in the limitation of this study are included in future research, they could provide more interesting results of the negative functions of mobile phone usage.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This research was supported by National Science Foundation of China (NSFC No.71573241) and CAS-TWAS President's Fellowship Program.

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.

Acknowledgments

The authors would like to thank Dr. Muhammad Qaisar for his helpful comments and suggestions.

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Keywords: mobile phone, distraction, attention control, cognitive emotional pre-occupation, psychological well-being

Citation: Chu J, Qaisar S, Shah Z and Jalil A (2021) Attention or Distraction? The Impact of Mobile Phone on Users' Psychological Well-Being. Front. Psychol. 12:612127. doi: 10.3389/fpsyg.2021.612127

Received: 30 September 2020; Accepted: 05 February 2021; Published: 20 April 2021.

Reviewed by:

Copyright © 2021 Chu, Qaisar, Shah and Jalil. 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: Sara Qaisar, saraqaisar@mail.ustc.edu.cn

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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Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure

The purpose of this study was to carry out a review of observational studies that consider links between mobile phone use and mental health from a psychological or behavioral perspective. Systematic literature searches in PubMed and PsycINFO for articles published until 2017 were done. Exclusion criteria included: papers that considered radiofrequency fields, attention, safety, relational consequences, sexual behavior, cyberbullying, and reviews, qualitative, and case or experimental studies. A total of 4738 papers were screened by title and abstract, 404 were retrieved in full text, and 290 were included. Only 5% had any longitudinal design. Self-reporting was the dominating method of measurement. One third of the studies included children or youth. A majority of adult populations consisted of university students and/or self-selected participants. The main research results included associations between frequent mobile phone use and mental health outcomes, such as depressive symptoms and sleep problems. Mobile phone use at bedtime was associated with, e.g., shorter sleep duration and lower sleep quality. “Problematic use” (dependency) was associated with several negative outcomes. In conclusion, associations between mobile phone use and adverse mental health outcomes are found in studies that take a psychological or behavioral perspective on the exposure. However, more studies of high quality are needed in order to draw valid conclusions about the mechanisms and causal directions of associations.

1. Introduction

Mobile phones have over only a few decades revolutionized how we communicate, interact, search for information, work, do chores, and pass time. The development of the smartphone with its multitude of functions, increased memory capacity and speed, and constant connectedness to the internet, has increased the time spent using the phone, implying a near ubiquitous usage. This fast development with changed exposure patterns has raised questions about potential health effects of the exposure [ 1 , 2 ]. The mobile phone communicates through emission of radio signals, and the exposure to radiofrequency electromagnetic fields has been proposed to be a health risk. There are today few indications that radiofrequency electromagnetic fields associated with mobile phones have any major health effects [ 3 ]. The World Health Organization (WHO) is currently undertaking a health risk assessment of radiofrequency electromagnetic fields, to be published as a monograph in the Environmental Health Criteria Series [ 4 ]. However, in addition to physiological aspects of the exposure, there is a growing research literature that takes a psychological or behavioral perspective on potential health effects of mobile phone use. The purpose of this literature review was to supplement the work of the WHO expert group by carrying out a literature review of quantitative observational studies that consider links between mobile phone use and mental health from a psychological or behavioral perspective. A formal systematic critical review with quality assessment of the papers was not done due to the large amount of included studies. The report presents an overview of the studies and examples of the main results. It does not include a comprehensive account of all included papers.

2. Materials and Methods

Two skilled university librarians performed systematic literature searches in PubMed and PsycINFO on 2 May 2016, with supplemental searches on 19 March 2018. The final search strategies ( Table 1 ) aimed to identify scientific publications from 1993 to 31 December 2017 that included quantitative analyses of mobile phone use in relation to mental health outcomes and other psychological factors. Altogether, 4738 papers were identified, after automatic removal of duplicates. These were screened by title and abstract. Papers that considered radiofrequency electromagnetic fields (RF-EMF), attention or safety (while driving, working, or studying), consequences for relationships, sexual behavior (e.g., sexting), cyberbullying, as well as papers that were qualitative, case or experimental studies, literature reviews, or duplicates (not previously identified), were excluded. This left 404 articles to be retrieved in full text for evaluation. Another 114 papers were removed in accordance with the previously mentioned exclusion criteria, or if no mental health-related outcome could be distinguished, if mobile phone use could not be identified as a separate variable (e.g., was included in a composite variable such as “digital media” or “screen time”), if focused only on specific smartphone applications (e.g., Tinder, Facebook, camera) or phone loss scenarios, or were not in English. This left 290 studies [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 , 237 , 238 , 239 , 240 , 241 , 242 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 261 , 262 , 263 , 264 , 265 , 266 , 267 , 268 , 269 , 270 , 271 , 272 , 273 , 274 , 275 , 276 , 277 , 278 , 279 , 280 , 281 , 282 , 283 , 284 , 285 , 286 , 287 , 288 , 289 , 290 , 291 , 292 , 293 , 294 ] for closer scrutiny ( Appendix A . PRISMA Flow Chart).

Search strategies in PubMed and PsycINFO 2018-03-19.

The identified studies ( n = 290) mainly dealt with frequency or duration of mobile phone use in relation to mental health symptoms (such as depression, anxiety, and insomnia), mobile phone use and sleep habits, and “problematic mobile phone use” (dependency/addiction). The number of published papers greatly increased during the time-period, especially the last five years ( Table 2 ).

Number of included papers ( n = 290) by publication year.

1 Six papers were dated 2018 but had been published online previously and were categorized as 2017.

3.1. Study Designs and Populations

A massive majority of the retrieved studies had cross-sectional design. Only 14 studies, i.e., about 5% [ 26 , 65 , 95 , 123 , 132 , 144 , 148 , 156 , 184 , 249 , 268 , 269 , 274 , 286 ], were identified as having any form of longitudinal design, test-retest reliability studies excepted.

About one third of the studies were based on child or adolescent populations, mostly administered through schools. Of the more than 190 adult population studies, relatively few studies seemed to contain random or representative samples of adult populations. The majority were based on university or college student populations (>60%), or with students together with other groups (an additional 5%). Otherwise, participants were mainly recruited through advertisements, postings on websites (e.g., Mechanical Turk), mailing lists, or personal appeal, or were carried out in specific work places or health care units. Some papers lacked a description of the selection process of study participants altogether. The number of study participants varied from 40 to 120,115. Studies were performed on all continents.

3.2. Measurements

The vast majority of the studies were based on self-reported exposures and outcomes, mostly through pen-and-pencil or web questionnaires, but sometimes also through telephone or face-to-face interviews. For younger children, parental reports about the child’s mobile phone use and health outcomes were used. The quantity of mobile phone use was mainly given in frequency and duration of calls and text messaging. However, with an increase of studies about smartphone usage, frequency and time spent on different apps and functions, including general screen time, were also examined. Many studies also included, for example, the type of phone, number of phones, from what age one had used a mobile phone, presence of a phone in the bedroom, what time the phone was used (e.g., time slots over the day, evening/nighttime use), and the size of the phone bill. A majority of the studies included scales or measurements of excessive or problematic mobile phone use (dependency/addiction), discussed further below.

Twelve studies could be identified as using objective measures for the quantity of mobile phone use. Three studies (conducted in the same population) used operator data for a subgroup of the participants [ 84 , 237 , 248 ]. The remaining studies used an app that was installed on the participants’ phones to log usage [ 49 , 53 , 91 , 174 , 175 , 176 , 200 , 239 , 258 ]. Two studies included a procedure where participants responded to questions about activity, including mobile phone use, several times per day on a given signal [ 26 , 95 ].

Additional measurement methods for mental health variables included structured psychiatric interviews [ 49 , 126 , 177 , 196 , 197 ], actigraphy for sleep [ 83 , 205 ], and sleep diaries [ 5 , 83 , 144 , 205 ]. Two studies included magnetic resonance imaging of the participants’ brains [ 110 , 283 ]. Further measurement methods occurred (e.g., body composition measurements, pedometers for physical activity, etc.), but did not pertain to mental health or psychological outcomes.

3.3. Main Research Findings

This section presents summaries and examples of the main findings in the included papers. The results have been clustered into three sections: (a) frequency/duration of mobile phone use and mental health outcomes, (b) bedtime mobile phone use, and (c) problematic mobile phone use. The main findings of each section are summarized in Table 3 , Table 4 and Table 5 . Table 6 summarizes the psychological factors that were most commonly associated with mobile phone use (all aspects).

Frequency/duration of mobile phone use: summary of main results.

L = Longitudinal, CS = Cross-sectional, NA = Negative association. In crude, but not in adjusted, analyses: reference 53, 149. In subgroup of older women: reference 140.

Bedtime mobile phone use: summary of main results.

L = Longitudinal, CS = Cross-sectional.

Problematic mobile phone use: summary of main results.

L = Longitudinal, CS = Cross-sectional, NA = Negative association.

Summary of the psychological factors most commonly associated with mobile phone use (all aspects).

NA = Negative association.

3.3.1. Frequency/Duration of Mobile Phone Use and Mental Health Outcomes

Among the studies of children and adolescents, a longitudinal study with 126 US adolescents found that more time spent on mobile phone use at baseline was associated with increased depression, measured with Becks Depression Inventory for Primary care at the one-year follow-up, while controlling for baseline depression [ 26 ]. In another longitudinal study, adolescents who owned a smartphone compared to non-owners slept less and had more sleep problems at baseline. Following up after two years, there were no differences in sleep problems between smartphone owners, new owners, and non-owners, but those who had owned a smartphone since baseline, compared to those who still did not own a smartphone, had shorter sleep duration on weekdays [ 249 ]. Cross-sectional associations were seen between quantity of mobile phone use and depressive symptoms in a study with 2785 Japanese adolescents [ 113 ], a study with 1328 Spanish adolescents/young adults [ 244 ], and a study with 7292 Finnish adolescents [ 139 ]. Overall mobile phone use of more than 5 h per day among Japanese adolescents was not associated with depression after adjusting for confounders, while using the mobile phone for more than 2 h per day for social networking services or online chatting was [ 264 ]. In a large British study with 120,115 adolescents, smartphone use on the weekends was negatively associated with mental well-being, while the associations for weekday use was non-linear, in that only use above an extreme cut-off was negative for mental well-being [ 227 ]. In an Israeli study of 185 children, daily time spent on a smartphone was not associated with psychopathological outcomes [ 250 ]. Regarding sleep outcomes, a longitudinal study of Japanese adolescents found mobile phone use of 2 h per day to be associated with new insomnia onset at the two-year follow-up [ 274 ]. A cross-sectional German study with 7533 adolescents found associations between higher mobile phone use and sleep problems among the girls in the crude analysis, but these were not statistically significant when controlling for confounders [ 149 ]. In a study with 6247 Chinese schoolchildren, time spent on texting, playing games, or surfing the internet on the mobile phone was associated with later bedtimes, shorter sleep duration, difficulties initiating and maintaining sleep, and daytime tiredness [ 117 ]. Time spent on the mobile phone was associated with shorter sleep duration and tiredness also among Japanese adolescents [ 113 ], and with poor sleep quality and daytime sleepiness in adolescents in Hong Kong [ 187 ]. In a Finnish study, mobile phone use was associated with deteriorated sleep habits and daytime tiredness in 12–14 years old girls and boys, and in 16–18 years old girls [ 228 ].

Among the studies on adult populations, a prospective study with 1127 Swedish university students found frequent mobile phone use at baseline to be a risk factor for sleep problems and depressive symptoms at the one-year follow-up in the men, and prolonged stress in the women [ 268 ]. This study, however, did not account for any confounding factors. Another prospective cohort study with 4159 Swedish young adults which, besides sex, accounted for educational level, occupation, and relationship status, showed similar results: Frequent mobile phone use was a risk factor for new cases of sleep problems in men, and for depressive symptoms in both men and women at the one-year follow-up [ 269 ]. Among the cross-sectional studies, frequency and duration of mobile phone use, logged by an app on the participants’ phones, was associated with depressed mood [ 239 ]. In another app log study, smartphone screen time was associated with depressed mood, but only before adjusting for confounders [ 53 ]. Cross-sectional associations were further seen between the frequency of calls and texts and perceived stress, sleep problems, and depressive symptoms among Swedish young adults [ 269 ]. A study that focused on work-related mobile phone use found that intensive mobile phone use among employees who had been provided by with a smartphone by the employer was associated with more work–home interference, less relaxation, less psychological detachment from work, and more exhaustion [ 65 ]. In other studies, time spent on the mobile phone was associated with anxiety [ 162 ], while the number of texts was associated with anxiety [ 29 , 162 ] and depressed mood [ 29 ]. A Finnish study with 6121 working-age participants, which examined mental symptoms in relation to the use of new technology, found associations between mobile phone use and depression in females 51–60 years, only [ 140 ]. Furthermore, in a US study with 308 adults, smartphone use frequency was negatively associated with depressive symptoms [ 74 , 75 ], and a Chinese study with 514 adults found that higher mobile use for calls was associated with higher mental well-being and positive affect [ 37 ].

Regarding personality, in one study, in which an app registered incoming and outgoing calls and text messages over five weeks among 49 German university students, associations between the number of calls and extraversion were seen, while no clear associations were found for the number of text messages and personality variables [ 200 ]. Another app log study found that smartphone use for calls was negatively associated with social anxiousness and loneliness [ 91 ]. One study concluded that lonely persons preferred to make voice calls rather than text messaging, while socially anxious persons preferred to text [ 231 ]. In a longitudinal study, increased mobile phone use over time was associated with decreased self-esteem and coping ability [ 286 ]. However, a one-week diary study that measured modes of social interaction found that meaningful text-based communication had a positive effect on self-esteem, compared to face-to-face communication and mobile phone voice communication [ 95 ]. Other studies found associations between time spent on mobile calls and extraversion [ 34 ] and low agreeableness [ 34 , 73 ], while text messaging was associated with neuroticism [ 34 , 73 ], extraversion [ 34 ], low self-esteem [ 73 ], low agreeableness [ 34 ], and low conscientiousness [ 34 ]. Time spent on mobile game playing was associated with low agreeableness [ 220 , 253 ].

3.3.2. Bedtime Mobile Phone Use

At least 35 studies addressed mobile phone use in the evening or at night: i.e., prior to bedtime, in bed, after “lights out”, awakening at night because of the phone, or even just the presence of a phone in the bedroom. About two thirds of these studies were based on children or adolescent populations.

A longitudinal Australian study that included 1101 adolescents found cross-sectional associations between nighttime phone use, poor sleep behavior, and depressed mood [ 286 ], but in longitudinal analyses, changes in nighttime phone use was not directly associated with subsequent changes in depressed mood. However, changes in sleep behaviors acted as a mediator between night-time phone use and subsequent depressed mood [ 286 ]. Another longitudinal study found cross-sectional associations between nighttime awakenings by the phone and sleep problems, perceived stress, and depressive symptoms in young adults, but no statistically significant prospective associations were seen at the one-year follow-up [ 269 ]. A diary study of work-related smartphone use at night showed subsequent lower sleep quantity, which in turn was associated with greater fatigue the next morning and less engagement during the work day [ 148 ].

In cross-sectional studies with children, as well as with adults, bedtime mobile phone use (in the broad definition, above) was associated with later bedtimes [ 16 , 22 , 31 , 82 , 85 , 88 , 93 , 223 , 263 ], longer sleep onset latency [ 53 , 79 , 112 , 223 , 293 ], shorter sleep duration [ 14 , 15 , 22 , 36 , 71 , 82 , 86 , 148 , 161 , 202 , 210 ], insomnia or sleep problems [ 5 , 14 , 79 , 85 , 97 , 144 , 199 , 202 , 205 , 235 , 269 , 293 ], reduced sleep quality or sleep efficiency [ 5 , 32 , 53 , 71 , 79 , 82 , 83 , 167 , 202 , 205 ], and reduced daytime functioning or tiredness [ 79 , 86 , 93 , 112 , 121 , 202 , 223 , 242 , 248 , 277 , 293 ]. In one study, keeping the phone close, rather than placing the phone at a distance from the bed, was associated with less sleep problems [ 235 ].

Almost all of the referred studies used self-reported sleep outcomes. However, two studies examined sleep by actigraphy in relation to self-reported mobile phone use [ 83 , 205 ]. Receiving night-time notifications on the phone predicted global sleep problems, subjective poor sleep quality, and sleep disruptions [ 205 ], and media use in bed or being awakened by the mobile phone at night negatively affected sleep efficiency [ 83 ].

Besides sleep outcomes, “bedtime” mobile phone use was associated with reduced mental health, suicidal feelings and self-injury [ 210 ], depressive symptoms [ 161 , 242 , 269 , 286 ], anxiety and stress [ 242 ], low self-esteem [ 286 ], and reduced cognitive performance in one study [ 235 ], but not in another [ 248 ].

3.3.3. Problematic Mobile Phone Use

Approximately 70% of the papers in this literature review addressed what can be termed “excessive” or “problematic” mobile phone use. They explored health outcomes of excessive mobile phone use, predictors for excessive use, such as personality or other psychological factors, or were reliability and validity studies of scales. Research about overuse, excessive, dependent, addictive, problematic, or pathological mobile phone use has emerged in parallel with the increased mobile phone usage. The constructs are commonly referred to as behavioral addictions and are likened with other non-substance addictions such as gambling addiction. As such, it seems to be a case of impaired ability to regulate one’s mobile phone use and can be associated with general symptoms of dependency, such as tolerance, withdrawal, escape, craving, using the mobile phone even when it is unsafe or prohibited, or functional consequences, such as financial or relational problems [ 295 ] (review, not included). A relationship can be seen with the concept of internet addiction, which was proposed as a specific psychiatric disorder in the 1990s by Young [ 296 ], who applied Diagnostic and Statistical Manual of Mental Disorders (DSM)-criteria for pathological gambling to internet use. Other constructs that have emerged include nomophobia and phubbing. Nomophobia is an abbreviation of “no mobile phone phobia” and refers to a phobia of not having access to a mobile phone [ 297 ]. It includes four dimensions: not being able to communicate, losing connectedness, not being able to access information, and giving up convenience [ 298 ]. The term “phubbing” comes from merging the words “phone” and “snubbing” and refers to when an individual is looking at or attending to his or her phone while in a conversation with others [ 124 ]. Yet another construct is “ringxiety”, or “phantom ringing”, which refers to perceiving that the phone rings even when it does not [ 260 ].

Excessive or problematic mobile phone use is usually associated with a high quantity of mobile phone use, while a high quantity of use does not necessarily imply problematic use. 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 [ 108 ]. In one-month log data from 79 engineering students in Taiwan, a logarithm that combined frequency, duration, and frequency trend over time successfully predicted “smartphone addiction” [ 174 , 175 ]. Non-use patterns also predicted smartphone addiction [ 176 ]. Among functions that have been associated with excessive or problematic use are playing games [ 21 , 39 , 49 , 59 , 110 , 116 , 178 ] and the use of social networking sites (SNS) [ 33 , 39 , 49 , 116 , 183 , 209 , 224 , 285 , 288 ]. Another log data study showed that dependent participants, besides games and SNS, also used the phone more for web surfing, shopping, and entertainment, and less for talking and texting, than non-dependent participants [ 49 ].

A whole array of scales (>50) were used for examining problematic use in the papers. The great number is partly due to the fact that some scales existed in several versions, and that different names for what appear to be the same scales occurred, perhaps due to translations between languages. Several of the scales follow diagnosis criteria from the International Statistical Classification of Diseases and Related Health Problems (ICD) or DSM for pathological gambling or substance dependence, and some scales are direct adaptations of Young’s Internet Addiction Test [ 296 ], applied to mobile phones. Two of the most commonly referred to scales were the Mobile Phone Problem Use Scale (MPPUS) [ 25 ] and the Smartphone Addiction Scale (SAS) [ 146 ]. The MMPUS contains 27 items inspired from the addiction literature and covers areas such as tolerance, withdrawal, escape, craving, and negative consequences, giving a global score of problem use [ 25 ]. The SAS contains 48 items in six subscales: daily-life disturbance, positive anticipation, withdrawal, cyberspace-oriented relationship, overuse, and tolerance [ 146 ]. Several shortened versions of the scales were also used.

The prevalence of problematic mobile phone use varied greatly in the studies, which can be expected because the measures, definitions, and study populations varied. Most of the studies were cross-sectional. Among the exceptions was a longitudinal study with 1877 Korean adolescents that used three yearly measurements [ 123 ]. The study found bidirectional relationships between mobile phone addiction and depressive symptoms over time [ 123 ]; i.e., mobile phone addiction had an influence on depressive symptoms, and depressive symptoms influenced mobile phone addiction, over time. 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 [ 156 ]. In a Swedish study, subjective overuse of the mobile phone at baseline was a prospective risk factor for sleep disturbances at the one-year follow-up in female young adults [ 269 ].

In addition, cross-sectional associations were seen between excessive or problematic use and depression [ 7 , 18 , 39 , 42 , 62 , 80 , 89 , 90 , 94 , 98 , 100 , 105 , 123 , 130 , 131 , 168 , 180 , 184 , 185 , 189 , 214 , 244 , 251 , 256 , 267 , 269 , 282 , 290 ]. Conversely, in four studies, depression was negatively associated with problematic use [ 50 , 57 , 74 , 75 ]. Furthermore, associations were seen with anxiety [ 7 , 39 , 42 , 50 , 62 , 67 , 68 , 74 , 75 , 76 , 80 , 89 , 100 , 108 , 115 , 135 , 157 , 180 , 184 , 189 , 198 , 214 , 245 , 267 ] (but, a negative association between text message dependency and anxiety in Reference [ 185 ]), sleep problems or insomnia [ 7 , 32 , 115 , 269 ], reduced sleep quality [ 38 , 39 , 62 , 80 , 110 , 195 , 240 ], shorter sleep duration [ 110 , 130 , 179 , 289 ], eveningness [ 64 , 229 , 273 ], stress [ 18 , 46 , 89 , 105 , 106 , 116 , 131 , 143 , 180 , 243 , 269 , 280 , 285 ], lower general mental wellbeing [ 20 , 23 , 76 , 80 , 127 , 237 ], PTSD [ 55 , 56 ], suicidal thoughts [ 131 , 282 , 289 ], impulsivity or less self-control [ 27 , 28 , 29 , 30 , 33 , 46 , 56 , 67 , 68 , 102 , 110 , 116 , 119 , 120 , 130 , 137 , 166 , 233 , 234 , 256 , 283 , 288 , 292 ], attention deficit hyperactivity disorder (ADHD)-symptoms [ 252 ], productivity loss at work [ 72 ], and perceived phantom ringing [ 142 , 260 ]. Moreover, problematic use was associated with other behavioral addictions (e.g., internet addiction [ 12 , 19 , 43 , 45 , 50 , 52 , 63 , 100 , 105 , 118 , 127 , 145 , 146 , 154 , 178 , 186 , 198 , 217 , 236 , 266 ], shopping addiction [ 12 , 118 , 188 ], gambling addiction [ 78 , 245 ], and general addiction proneness [ 126 , 245 ]). Two studies examined participants with magnetic resonance imaging; when comparing mobile phone dependent subjects with non-dependent participants, differences in white matter integrity of the brain were seen [ 110 , 283 ].

Regarding psychological factors, several cross-sectional studies found associations between problematic mobile phone use and loneliness [ 24 , 91 , 98 , 129 , 133 , 158 , 270 , 279 ]. A longitudinal study with 288 participants 13–40 years of age examined causal relations between problematic use, loneliness, face-to-face-interaction, and the need for social assurance [ 132 ]. It found that loneliness predicted problematic use, while problematic use did not predict loneliness at the follow-up after four months. However, the authors concluded that loneliness increases problematic use, which in turn reduces face-to-face interactions and thus does not gratify increased needs for social assurance, and consequently, this process eventually leads to increased loneliness [ 132 ]. Other studies found associations with, e.g., shyness or social anxiousness [ 24 , 58 , 91 , 102 , 159 ], extraversion [ 12 , 13 , 18 , 25 , 46 , 64 , 81 , 108 , 255 , 256 , 261 ], fear of missing out [ 52 , 74 , 153 , 209 , 287 ], neuroticism [ 13 , 46 , 73 , 81 , 90 , 111 , 142 , 147 , 198 , 218 , 261 , 294 ], less self-esteem [ 13 , 25 , 100 , 108 , 256 , 281 , 289 , 291 ], low agreeableness [ 12 , 147 ], less openness [ 12 , 111 , 147 , 218 , 261 ], less conscientiousness [ 13 , 34 , 92 , 111 , 142 , 147 , 169 , 170 ], alexithymia [ 89 ], and less self-efficacy [ 99 ].

4. Discussion

The literature search showed that there is a vast—and increasing—amount of studies that explore links between mobile phone usage and mental health from a psychological or behavioral point of view. A high quantity of mobile phone use was associated with a wide range of mental health outcomes, such as depressive symptoms and sleep problems, in both children and adults. A relatively large proportion of the studies examined mobile phone use in relation to sleep habits; mobile phone use at bedtime or at night was associated with, e.g., shorter sleep and reduced quality of sleep. A dominating research field was excessive or problematic use, i.e., where intense mobile phone use is described as a behavioral addiction and/or pathological. A large amount of instruments to measure excessive or problematic use occurred, and problematic use was associated with several adverse outcomes, such as depression, anxiety, and sleep problems.

Only a few percent of the included studies had any form of longitudinal design. Cross-sectional studies limit the possibilities to draw valid conclusions about causal directions of associations. The found associations may thus be due to reversed causality, i.e., the outcome is causing what seems to be the risk factor, or the associations may be bi-directional or caused by common confounding factors not accounted for. For example, most of the studies on bedtime phone use and sleep variables were cross-sectional. In a longitudinal study with Canadian students [ 299 ] (not in the review due to the fact that mobile phone use was not analyzed separately), it was sleep problems that predicted media use and not the opposite. The researchers concluded that young adults used digital media to deal with sleep problems. Moreover, a study with 844 Belgian adults [ 300 ] (also not in the review) concluded that media, including mobile phones, was commonly used as a sleep aid.

Further, a majority of the papers were based on self-reporting, which implies that both exposures and outcomes may be subject to misclassification, recall difficulty, recall bias, and response-style bias. It is previously known that there is rather low agreement between self-reported mobile phone use for calling or texting compared to logged data (e.g., [ 301 ]), and this applies also to smartphone usage [ 297 ]. However, it seems that applications that log smartphone usage are becoming more available, and thus are increasingly used in research.

Strikingly, many of the studies on adult populations were done on university students or self-selected participants. This compromises generalizability of the results. Another observation was that in many studies, the found associations, although statistically significant, were small.

The current literature review focused on studies with mobile phone use as a specific entity. Broadening the search to include more general terms such as “screen time”, “media use”, “technology use”, or “social media”, would lead to a higher quantity of studies with results that probably could apply also to mobile phone usage. Several different technologies (such as computers, tablets, or other hand-held devices) are used for the same activities and in the same contexts, and results from studies that include other technologies are seen to show similar results. However, a broader definition of the exposure was outside the scope of this review.

Intense or frequent mobile phone usage is seen to be associated with a broad array of mental health related symptoms, behaviors, and psychological factors. Plausible behavioral and/or psychological mechanisms for the associations can be found in the review, such as impact on sleep habits, dependency/addiction issues, and individual personality traits. The extent to which mobile phone use interferes with the restorative functions of sleep can, of course, contribute to deteriorated health. Besides sleep being postponed, replaced, or disturbed by messages or calls at night, it is also conceivable that quantity as well as content of use can generate higher levels of psychological stress and physiological arousal. Higher levels of arousal can have a negative impact on sleep and recovery [ 302 ] and in other ways contribute to stress and ill health. In addition, there are studies [ 303 , 304 ] (not in the review) pointing to the fact that blue light emitted from screens may have an impact on melatonin levels and thus affect sleep and wakefulness.

It is also conceivable that the time spent on devices takes time from other activities and health-related behaviors, such as physical activity, supportive social interactions, or staying on task at work or school. In the current review, several relevant aspects were excluded in the literature search, for example, the impact of mobile phone use on attention, consequences for relationships, cyberbullying, cyber sexual behaviors, and physical health outcomes, all aspects likely to potentially have an impact on mental health. Furthermore, this report does not account for all factors analyzed in the included papers.

This review was done to supplement a systematic review of the potential health effects of exposure to radiofrequency electromagnetic fields (RF-EMF) from mobile phones. In light of this, it can be noted that there are several psychological and behavioral aspects that should be taken into consideration when assessing studies that examine health effects with RF-EMF exposure as the hypothesis. This is especially true given that many of the studies with an RF-EMF-perspective measure the exposures in the same manner as studies taking a psychological or behavioral perspective, i.e., with self-report.

5. Conclusions

Associations between mobile phone use and adverse mental health outcomes are found in studies that take a psychological or behavioral perspective on the exposure. However, more studies of high quality are needed—with longitudinal design, objective measurements, and well-defined study populations—in order to draw valid conclusions about mechanisms and causal directions of associations.

Acknowledgments

The author is grateful to Eva Hessman and Magnus Holmberg, research librarians at the University of Gothenburg Biomedical Library, for performing the literature searches and for giving valuable supervision on search strategies and management of search results.

This research received no external funding.

Conflicts of Interest

The author declares no conflict of interest.

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The impact of students’ cellphone-use and self-control on academic performance in traditional classroom

• Published: 01 February 2023
• Volume 24 , pages 591–598, ( 2023 )

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• Weifeng Ma 1 ,
• Xuefen Lin   ORCID: orcid.org/0000-0002-7528-3610 1 ,
• Jiao Lou 2 ,
• Yang Liu 1 ,
• Wei Tang 1 &
• Zongliang Bao 1  

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Cellphone-use in class has many negative effects on students' overall performance. By using a self-developed monitoring-app to record students' cellphone-use in class, this study attempts to explore the relationships of cellphone-use and self-control on academic performance. The subjects of this study are 207 freshmen who take part in advanced mathematics courses, and the research period lasts for 16 weeks. Two-factor ANOVA showed that cellphone-use duration and self-control had an interactive influence on students' academic performance. There was no statistical significance in the influence of cellphone-use frequency and self-control on academic performance. Simple slopes analysis revealed a negative relationship between cellphone-use duration and academic performance for those who were low on self-control, whereas there was no relationship between these constructs for those who were high on self-control. The results show that self-control plays a moderating role in the relationship between cellphone-use duration and academic performance. Self-control could weaken the influence of cellphone-use on academic performance. Furthermore, this study is helpful to better understand the way of cellphone-use affecting academic performance, and suggests appropriate intervention of cellphone-use to help poor self-controlled students achieve better academic performance.

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This study was supported by Teaching research project of Zhejiang University of science and technology (Grant No. 2022-jg38), Humanities and Social Science (Grant No. 17YJA88004), First class curriculum construction of educational department of Zhejiang Province, (Grant No. Zhejiang Education Office No. 195).

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Weifeng Ma, Xuefen Lin, Yang Liu, Wei Tang & Zongliang Bao

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Ma, W., Lin, X., Lou, J. et al. The impact of students’ cellphone-use and self-control on academic performance in traditional classroom. Asia Pacific Educ. Rev. 24 , 591–598 (2023). https://doi.org/10.1007/s12564-023-09824-6

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Received : 12 February 2022

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DOI : https://doi.org/10.1007/s12564-023-09824-6

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10.8 Cause and Effect

Learning objectives.

• Determine the purpose and structure of cause and effect in writing.
• Understand how to write a cause-and-effect essay.

The Purpose of Cause and Effect in Writing

It is often considered human nature to ask, “why?” and “how?” We want to know how our child got sick so we can better prevent it from happening in the future, or why our colleague a pay raise because we want one as well. We want to know how much money we will save over the long term if we buy a hybrid car. These examples identify only a few of the relationships we think about in our lives, but each shows the importance of understanding cause and effect.

A cause is something that produces an event or condition; an effect is what results from an event or condition. The purpose of the cause-and-effect essay is to determine how various phenomena relate in terms of origins and results. Sometimes the connection between cause and effect is clear, but often determining the exact relationship between the two is very difficult. For example, the following effects of a cold may be easily identifiable: a sore throat, runny nose, and a cough. But determining the cause of the sickness can be far more difficult. A number of causes are possible, and to complicate matters, these possible causes could have combined to cause the sickness. That is, more than one cause may be responsible for any given effect. Therefore, cause-and-effect discussions are often complicated and frequently lead to debates and arguments.

Use the complex nature of cause and effect to your advantage. Often it is not necessary, or even possible, to find the exact cause of an event or to name the exact effect. So, when formulating a thesis, you can claim one of a number of causes or effects to be the primary, or main, cause or effect. As soon as you claim that one cause or one effect is more crucial than the others, you have developed a thesis.

Consider the causes and effects in the following thesis statements. List a cause and effect for each one on your own sheet of paper.

• The growing childhood obesity epidemic is a result of technology.
• Much of the wildlife is dying because of the oil spill.
• The town continued programs that it could no longer afford, so it went bankrupt.
• More young people became politically active as use of the Internet spread throughout society.
• While many experts believed the rise in violence was due to the poor economy, it was really due to the summer-long heat wave.

Write three cause-and-effect thesis statements of your own for each of the following five broad topics.

• Health and nutrition

The Structure of a Cause-and-Effect Essay

The cause-and-effect essay opens with a general introduction to the topic, which then leads to a thesis that states the main cause, main effect, or various causes and effects of a condition or event.

The cause-and-effect essay can be organized in one of the following two primary ways:

• Start with the cause and then talk about the effects.
• Start with the effect and then talk about the causes.

For example, if your essay were on childhood obesity, you could start by talking about the effect of childhood obesity and then discuss the cause or you could start the same essay by talking about the cause of childhood obesity and then move to the effect.

Regardless of which structure you choose, be sure to explain each element of the essay fully and completely. Explaining complex relationships requires the full use of evidence, such as scientific studies, expert testimony, statistics, and anecdotes.

Because cause-and-effect essays determine how phenomena are linked, they make frequent use of certain words and phrases that denote such linkage. See Table 10.4 “Phrases of Causation” for examples of such terms.

Table 10.4 Phrases of Causation

The conclusion should wrap up the discussion and reinforce the thesis, leaving the reader with a clear understanding of the relationship that was analyzed.

Be careful of resorting to empty speculation. In writing, speculation amounts to unsubstantiated guessing. Writers are particularly prone to such trappings in cause-and-effect arguments due to the complex nature of finding links between phenomena. Be sure to have clear evidence to support the claims that you make.

Look at some of the cause-and-effect relationships from Note 10.83 “Exercise 2” . Outline the links you listed. Outline one using a cause-then-effect structure. Outline the other using the effect-then-cause structure.

Writing a Cause-and-Effect Essay

Choose an event or condition that you think has an interesting cause-and-effect relationship. Introduce your topic in an engaging way. End your introduction with a thesis that states the main cause, the main effect, or both.

Organize your essay by starting with either the cause-then-effect structure or the effect-then-cause structure. Within each section, you should clearly explain and support the causes and effects using a full range of evidence. If you are writing about multiple causes or multiple effects, you may choose to sequence either in terms of order of importance. In other words, order the causes from least to most important (or vice versa), or order the effects from least important to most important (or vice versa).

Use the phrases of causation when trying to forge connections between various events or conditions. This will help organize your ideas and orient the reader. End your essay with a conclusion that summarizes your main points and reinforces your thesis. See Chapter 15 “Readings: Examples of Essays” to read a sample cause-and-effect essay.

Choose one of the ideas you outlined in Note 10.85 “Exercise 3” and write a full cause-and-effect essay. Be sure to include an engaging introduction, a clear thesis, strong evidence and examples, and a thoughtful conclusion.

Key Takeaways

• The purpose of the cause-and-effect essay is to determine how various phenomena are related.
• The thesis states what the writer sees as the main cause, main effect, or various causes and effects of a condition or event.

The cause-and-effect essay can be organized in one of these two primary ways:

• Start with the cause and then talk about the effect.
• Start with the effect and then talk about the cause.
• Strong evidence is particularly important in the cause-and-effect essay due to the complexity of determining connections between phenomena.
• Phrases of causation are helpful in signaling links between various elements in the essay.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.