Next Article in Journal
Biomarkers of Exposure to Secondhand and Thirdhand Tobacco Smoke: Recent Advances and Future Perspectives
Previous Article in Journal
Health Consequences of an Armed Conflict in Zamboanga, Philippines Using a Syndromic Surveillance Database
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

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

Department of Psychology, University of Gothenburg, 405 30 Gothenburg, Sweden
Int. J. Environ. Res. Public Health 2018, 15(12), 2692;
Submission received: 5 November 2018 / Revised: 16 November 2018 / Accepted: 21 November 2018 / Published: 29 November 2018
(This article belongs to the Section Digital Health)


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).

3. Results

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).

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).

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.


This research received no external funding.


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.

Conflicts of Interest

The author declares no conflict of interest.

Appendix A

Ijerph 15 02692 i001


  1. World Health Organization. Electromagnetic Fields and Public Health: Mobile Phones. Available online: (accessed on 12 November 2018).
  2. US Food & Drug Administration (FDA). Health Issues: Do Cell Phones Pose a Health Hazard? Available online: (accessed on 12 November 2018).
  3. Swedish Radiation Safety Authority. Recent Research on EMF and Health Risk. Twelfth Report from SSM’s Scientific Council on Electromagnetic Fields. 2017. Available online: (accessed on 24 September 2018).
  4. World Health Organization. Available online: (accessed on 24 September 2018).
  5. Adams, S.K.; Kisler, T.S. Sleep quality as a mediator between technology-related sleep quality, depression, and anxiety. Cyberpsychol. Behav. Soc. Netw. 2013, 16, 25–30. [Google Scholar] [CrossRef] [PubMed]
  6. Aggarwal, M.; Grover, S.; Basu, D. Mobile phone use by resident doctors: Tendency to addiction-like behaviour. German J. Psychiatry 2012, 15, 50–55. [Google Scholar]
  7. Aker, S.; Sahin, M.K.; Sezgin, S.; Oguz, G. Psychosocial factors affecting Smartphone Addiction in university students. J. Addict. Nurs. 2017, 28, 215–219. [Google Scholar] [CrossRef] [PubMed]
  8. Alavi, S.S.; Maracy, M.R.; Jannatifard, F.; Ojaghi, R.; Rezapour, H. The psychometric properties of cellular phone dependency questionnaire in students of Isfahan: A pilot study. J. Educ. Health Promot. 2014, 3, 71. [Google Scholar] [CrossRef] [PubMed]
  9. Alavi, S.S.; Mohammadi, M.R.; Jannatifard, F.; Mohammadi Kalhori, S.; Sepahbodi, G.; BabaReisi, M.; Sajedi, S.; Farshchi, M.; KhodaKarami, R.; Hatami Kasvaee, V. Assessment of Semi-Structured Clinical Interview for Mobile Phone Addiction Disorder. Iran. J. Psychiatry 2016, 11, 115–119. [Google Scholar] [PubMed]
  10. Aljomaa, S.S.; Al.Qudah, M.F.; Albursan, I.S.; Bakhiet, S.F.; Abduljabbar, A.S. Smartphone addiction among university students in the light of some variables. Comput. Hum. Behav. 2016, 61, 155–164. [Google Scholar] [CrossRef]
  11. Alosaimi, F.D.; Alyahya, H.; Alshahwan, H.; Al Mahyijari, N.; Shaik, S.A. Smartphone addiction among university students in Riyadh, Saudi Arabia. Saudi Med. J. 2016, 37, 675–683. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Andreassen, C.S.; Griffiths, M.D.; Gjertsen, S.R.; Krossbakken, E.; Kvam, S.; Pallesen, S. The relationships between behavioral addictions and the five-factor model of personality. J. Behav. Addict. 2013, 2, 90–99. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Argumosa-Villar, L.; Boada-Grau, J.; Vigil-Colet, A. Exploratory investigation of theoretical predictors of nomophobia using the Mobile Phone Involvement Questionnaire (MPIQ). J. Adolesc. 2017, 56, 127–135. [Google Scholar] [CrossRef] [PubMed]
  14. Arora, T.; Broglia, E.; Thomas, G.N.; Taheri, S. Associations between specific technologies and adolescent sleep quantity, sleep quality, and parasomnias. Sleep Med. 2014, 15, 240–247. [Google Scholar] [CrossRef] [PubMed]
  15. Arora, T.; Hussain, S.; Hubert Lam, K.B.; Lily Yao, G.; Neil Thomas, G.; Taheri, S. Exploring the complex pathways among specific types of technology, self-reported sleep duration and body mass index in UK adolescents. Int. J. Obes. 2013, 37, 1254–1260. [Google Scholar] [CrossRef] [PubMed]
  16. Arrona-Palacios, A. High and low use of electronic media during nighttime before going to sleep: A comparative study between adolescents attending a morning or afternoon school shift. J. Adolesc. 2017, 61, 152–163. [Google Scholar] [CrossRef] [PubMed]
  17. Atwood, R.M.; Beckert, T.E.; Rhodes, M.R. Adolescent problematic digital behaviors associated with mobile devices. N. Am. J. Psychol. 2017, 19, 659–684. [Google Scholar]
  18. Augner, C.; Hacker, G.W. Associations between problematic mobile phone use and psychological parameters in young adults. Int. J. Public Health 2012, 57, 437–441. [Google Scholar] [CrossRef] [PubMed]
  19. Ayar, D.; Bektas, M.; Bektas, I.; Akdeniz Kudubes, A.; Selekoglu Ok, Y.; Sal Altan, S.; Celik, I. The Effect of Adolescents’ Internet Addiction on Smartphone Addiction. J. Addict. Nurs. 2017, 28, 210–214. [Google Scholar] [CrossRef] [PubMed]
  20. Babadi-Akashe, Z.; Zamani, B.E.; Abedini, Y.; Akbari, H.; Hedayati, N. The Relationship between Mental Health and Addiction to Mobile Phones among University Students of Shahrekord, Iran. Addict. Health 2014, 6, 93–99. [Google Scholar] [PubMed]
  21. Bae, S.M. The relationship between the type of smartphone use and smartphone dependence of Korean adolescents: National survey study. Child. Youth Serv. Rev. 2017, 81, 207–211. [Google Scholar] [CrossRef]
  22. Bartel, K.; Williamson, P.; van Maanen, A.; Cassoff, J.; Meijer, A.M.; Oort, F.; Knauper, B.; Gruber, R.; Gradisar, M. Protective and risk factors associated with adolescent sleep: Findings from Australia, Canada, and The Netherlands. Sleep Med. 2016, 26, 97–103. [Google Scholar] [CrossRef] [PubMed]
  23. Beranuy, M.; Oberst, U.; Carbonell, X.; Chamarro, A. Problematic internet and mobile phone use and clinical symptoms in college students: The role of emotional intelligence. Comput. Hum. Behav. 2009, 25, 1182–1187. [Google Scholar] [CrossRef]
  24. Bian, M.; Leung, L. Linking loneliness, shyness, smartphone addiction symptoms, and patterns of smartphone use to social capital. Soc. Sci. Comput. Rev. 2015, 33, 61–79. [Google Scholar] [CrossRef]
  25. Bianchi, A.; Phillips, J.G. Psychological predictors of problem mobile phone use. Cyberpsychol. Behav. 2005, 8, 39–51. [Google Scholar] [CrossRef] [PubMed]
  26. Bickham, D.S.; Hswen, Y.; Rich, M. Media use and depression: Exposure, household rules, and symptoms among young adolescents in the USA. Int. J. Public Health 2015, 60, 147–155. [Google Scholar] [CrossRef] [PubMed]
  27. Billieux, J.; Gay, P.; Rochat, L.; Van der Linden, M. The role of urgency and its underlying psychological mechanisms in problematic behaviours. Behav. Res. Ther. 2010, 48, 1085–1096. [Google Scholar] [CrossRef] [PubMed]
  28. Billieux, J.; van der Linden, M.; D’Acremont, M.; Ceschi, G.; Zermatten, A. Does impulsivity relate to perceived dependence on and actual use of the mobile phone? Appl. Cogn. Psychol. 2007, 21, 527–537. [Google Scholar] [CrossRef]
  29. Billieux, J.; Van Der Linden, M.; Rochat, L. The role of impulsivity in actual and problematic use of the mobile phone. Appl. Cogn. Psychol. 2008, 22, 1195–1210. [Google Scholar] [CrossRef]
  30. Bock, B.C.; Lantini, R.; Thind, H.; Walaska, K.; Rosen, R.K.; Fava, J.L.; Barnett, N.P.; Scott-Sheldon, L.A. The Mobile Phone Affinity Scale: Enhancement and Refinement. JMIR mHealth uHealth 2016, 4, e134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Brunborg, G.S.; Mentzoni, R.A.; Molde, H.; Myrseth, H.; Skouverøe, K.J.M.; Bjorvatn, B.; Pallesen, S. The relationship between media use in the bedroom, sleep habits and symptoms of insomnia. J. Sleep Res. 2011, 20, 569–575. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Bruni, O.; Sette, S.; Fontanesi, L.; Baiocco, R.; Laghi, F.; Baumgartner, E. Technology Use and Sleep Quality in Preadolescence and Adolescence. J. Clin. Sleep Med. 2015, 11, 1433–1441. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Burnell, K.; Kuther, T.L. Predictors of Mobile Phone and Social Networking Site Dependency in Adulthood. Cyberpsychol. Behav. Soc. Netw. 2016, 19, 621–627. [Google Scholar] [CrossRef] [PubMed]
  34. Butt, S.; Phillips, J.G. Personality and self reported mobile phone use. Comput. Hum. Behav. 2008, 24, 346–360. [Google Scholar] [CrossRef]
  35. Carbonell, X.; Chamarro, A.; Griffiths, M.; Oberst, U.; Cladellas, R.; Talarn, A. Problematic Internet and cell phone use in Spanish teenagers and young students. Anales de Psicología 2012, 28, 789–796. [Google Scholar]
  36. Chahal, H.; Fung, C.; Kuhle, S.; Veugelers, P.J. Availability and night-time use of electronic entertainment and communication devices are associated with short sleep duration and obesity among Canadian children. Pediatr. Obes. 2013, 8, 42–51. [Google Scholar] [CrossRef] [PubMed]
  37. Chan, M. Mobile phones and the good life: Examining the relationships among mobile use, social capital and subjective well-being. New Media Soc. 2015, 17, 96–113. [Google Scholar] [CrossRef]
  38. Chang, A.K.; Choi, J. Predictors of sleep quality among young adults in Korea: Gender differences. Issues Ment. Health Nurs. 2016, 37, 918–928. [Google Scholar] [CrossRef] [PubMed]
  39. Chen, B.; Liu, F.; Ding, S.; Ying, X.; Wang, L.; Wen, Y. Gender differences in factors associated with smartphone addiction: A cross-sectional study among medical college students. BMC Psychiatry 2017, 17, 341. [Google Scholar] [CrossRef] [PubMed]
  40. Chen, C.; Zhang, K.Z.K.; Gong, X.; Zhao, S.J.; Lee, M.K.O.; Liang, L. Understanding compulsive smartphone use: An empirical test of a flow-based model. Int. J. Inf. Manag. 2017, 37, 438–454. [Google Scholar] [CrossRef]
  41. Chen, C.; Zhang, K.Z.K.; Gong, X.; Zhao, S.J.; Lee, M.K.O.; Liang, L. Examining the effects of motives and gender differences on smartphone addiction. Comput. Hum. Behav. 2017, 75, 891–902. [Google Scholar] [CrossRef]
  42. Chen, L.; Yan, Z.; Tang, W.; Yang, F.; Xie, X.; He, J. Mobile phone addiction levels and negative emotions among Chinese young adults: The mediating role of interpersonal problems. Comput. Hum. Behav. 2016, 55, 856–866. [Google Scholar] [CrossRef]
  43. Ching, S.M.; Yee, A.; Ramachandran, V.; Sazlly Lim, S.M.; Wan Sulaiman, W.A.; Foo, Y.L.; Hoo, F.K. Validation of a Malay Version of the Smartphone Addiction Scale among Medical Students in Malaysia. PLoS ONE 2015, 10, e0139337. [Google Scholar] [CrossRef] [PubMed]
  44. Chiu, S.I. The relationship between life stress and smartphone addiction on Taiwanese university student: A mediation model of learning self-Efficacy and social self-Efficacy. Comput. Hum. Behav. 2014, 34, 49–57. [Google Scholar] [CrossRef]
  45. Chiu, S.I.; Hong, F.Y.; Chiu, S.L. An Analysis on the Correlation and Gender Difference between College Students’ Internet Addiction and Mobile Phone Addiction in Taiwan. ISRN Addict. 2013, 2013, 360607. [Google Scholar] [CrossRef] [PubMed]
  46. Cho, H.Y.; Kim, D.J.; Park, J.W. Stress and adult smartphone addiction: Mediation by self-control, neuroticism, and extraversion. Stress Health 2017, 33, 624–630. [Google Scholar] [CrossRef] [PubMed]
  47. Cho, K.S.; Lee, J.M. Influence of smartphone addiction proneness of young children on problematic behaviors and emotional intelligence: Mediating self-assessment effects of parents using smartphones. Comput. Hum. Behav. 2017, 66, 303–311. [Google Scholar] [CrossRef]
  48. Cho, S.; Lee, E. Development of a brief instrument to measure smartphone addiction among nursing students. Comput. Inform. Nurs. 2015, 33, 216–224. [Google Scholar] [CrossRef] [PubMed]
  49. Choi, J.; Rho, M.J.; Kim, Y.; Yook, I.H.; Yu, H.; Kim, D.J.; Choi, I.Y. Smartphone dependence classification using tensor factorization. PLoS ONE 2017, 12, e0177629. [Google Scholar] [CrossRef] [PubMed]
  50. Choi, S.W.; Kim, D.J.; Choi, J.S.; Ahn, H.; Choi, E.J.; Song, W.Y.; Kim, S.; Youn, H. Comparison of risk and protective factors associated with smartphone addiction and Internet addiction. J. Behav. Addict. 2015, 4, 308–314. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Choliz, M.; Pinto, L.; Phansalkar, S.S.; Corr, E.; Mujjahid, A.; Flores, C.; Barrientos, P.E. Development of a Brief Multicultural Version of the Test of Mobile Phone Dependence (TMDbrief) Questionnaire. Front. Psychol. 2016, 7, 650. [Google Scholar] [CrossRef] [PubMed]
  52. Chotpitayasunondh, V.; Douglas, K.M. How “phubbing” becomes the norm: The antecedents and consequences of snubbing via smartphone. Comput. Hum. Behav. 2016, 63, 9–18. [Google Scholar] [CrossRef]
  53. Christensen, M.A.; Bettencourt, L.; Kaye, L.; Moturu, S.T.; Nguyen, K.T.; Olgin, J.E.; Pletcher, M.J.; Marcus, G.M. Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep. PLoS ONE 2016, 11, e0165331. [Google Scholar] [CrossRef] [PubMed]
  54. Chung, N. Korean adolescent girls’ addictive use of mobile phones to maintain interpersonal solidarity. Soc. Behav. Personal. 2011, 39, 1349–1358. [Google Scholar] [CrossRef]
  55. Contractor, A.A.; Frankfurt, S.B.; Weiss, N.H.; Elhai, J.D. Latent-level relations between DSM-5 PTSD symptom clusters and problematic smartphone use. Comput. Hum. Behav. 2017, 72, 170–177. [Google Scholar] [CrossRef] [PubMed]
  56. Contractor, A.A.; Weiss, N.H.; Tull, M.T.; Elhai, J.D. PTSD’s relation with problematic smartphone use: Mediating role of impulsivity. Comput. Hum. Behav. 2017, 75, 177–183. [Google Scholar] [CrossRef]
  57. Csibi, S.; Griffiths, M.D.; Cook, B.; Demetrovics, Z.; Szabo, A. The psychometric properties of the Smartphone Application-Based Addiction Scale (SABAS). Int. J. Ment. Health Addict. 2018, 16, 393–403. [Google Scholar] [CrossRef] [PubMed]
  58. Darcin, A.E.; Kose, S.; Noyan, C.O.; Nurmedov, S.; Yılmaz, O.; Dilbaz, N. Smartphone addiction and its relationship with social anxiety and loneliness. Behav. Inf. Technol. 2016, 35, 520–525. [Google Scholar] [CrossRef]
  59. Das, A.; Sharma, M.K.; Thamilselvan, P.; Marimuthu, P. Technology Addiction among Treatment Seekers for Psychological Problems: Implication for Screening in Mental Health Setting. Indian J. Psychol. Med. 2017, 39, 21–27. [Google Scholar] [PubMed]
  60. Dasgupta, P.; Bhattacherjee, S.; Dasgupta, S.; Roy, J.K.; Mukherjee, A.; Biswas, R. Nomophobic behaviors among smartphone using medical and engineering students in two colleges of West Bengal. Indian J. Public Health 2017, 61, 199–204. [Google Scholar] [PubMed]
  61. Deleuze, J.; Rochat, L.; Romo, L.; Van der Linden, M.; Achab, S.; Thorens, G.; Khazaal, Y.; Zullino, D.; Maurage, P.; Rothen, S.; et al. Prevalence and characteristics of addictive behaviors in a community sample: A latent class analysis. Addict. Behav. Rep. 2015, 1, 49–56. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Demirci, K.; Akgonul, M.; Akpinar, A. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J. Behav. Addict. 2015, 4, 85–92. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Demirci, K.; Orhan, H.; Demirdas, A.; Akpinar, A.; Sert, H. Validity and reliability of the Turkish Version of the Smartphone Addiction Scale in a younger population. Klinik Psikofarmakoloji Bülteni/Bull. Clin. Psychopharmacol. 2014, 24, 226–234. [Google Scholar] [CrossRef] [Green Version]
  64. Demirhan, E.; Randler, C.; Horzum, M.B. Is problematic mobile phone use explained by chronotype and personality? Chronobiol. Int. 2016, 33, 821–831. [Google Scholar] [CrossRef] [PubMed]
  65. Derks, D.; Bakker, A.B. Smartphone use, work–home interference, and burnout: A diary study on the role of recovery. Appl. Psychol. Int. Rev. 2014, 63, 411–440. [Google Scholar] [CrossRef]
  66. De-Sola, J.; Talledo, H.; Rodriguez de Fonseca, F.; Rubio, G. Prevalence of problematic cell phone use in an adult population in Spain as assessed by the Mobile Phone Problem Use Scale (MPPUS). PLoS ONE 2017, 12, e0181184. [Google Scholar] [CrossRef] [PubMed]
  67. De-Sola, J.; Talledo, H.; Rubio, G.; de Fonseca, F.R. Development of a Mobile Phone Addiction Craving Scale and Its Validation in a Spanish Adult Population. Front. Psychiatry 2017, 8, 90. [Google Scholar] [CrossRef] [PubMed]
  68. De-Sola, J.; Talledo, H.; Rubio, G.; de Fonseca, F.R. Psychological Factors and Alcohol Use in Problematic Mobile Phone Use in the Spanish Population. Front. Psychiatry 2017, 8, 11. [Google Scholar] [CrossRef] [PubMed]
  69. Dixit, S.; Shukla, H.; Bhagwat, A.; Bindal, A.; Goyal, A.; Zaidi, A.K.; Shrivastava, A. A study to evaluate mobile phone dependence among students of a medical college and associated hospital of central India. Indian J. Community Med. 2010, 35, 339–341. [Google Scholar] [CrossRef] [PubMed]
  70. Dlodlo, N. Salient indicators of mobile instant messaging addiction with selected socio-demographic attributes among tertiary students in South Africa. S. Afr. J. Psychol. 2015, 45, 207–222. [Google Scholar] [CrossRef]
  71. Dube, N.; Khan, K.; Loehr, S.; Chu, Y.; Veugelers, P. The use of entertainment and communication technologies before sleep could affect sleep and weight status: A population-based study among children. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 97. [Google Scholar] [CrossRef] [PubMed]
  72. Duke, E.; Montag, C. Smartphone addiction, daily interruptions and self-reported productivity. Addict. Behav. Rep. 2017, 6, 90–95. [Google Scholar] [CrossRef] [PubMed]
  73. Ehrenberg, A.; Juckes, S.; White, K.M.; Walsh, S.P. Personality and self-esteem as predictors of young people’s technology use. Cyberpsychol. Behav. 2008, 11, 739–741. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Elhai, J.D.; Levine, J.C.; Dvorak, R.D.; Hall, B.J. Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use. Comput. Hum. Behav. 2016, 63, 509–516. [Google Scholar] [CrossRef]
  75. Elhai, J.D.; Levine, J.C.; Dvorak, R.D.; Hall, B.J. Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Comput. Hum. Behav. 2016, 69, 75–82. [Google Scholar] [CrossRef]
  76. Emelin, V.A.; Rasskazova, E.I.; Tkhostov, A.S. Technology-related transformations of imaginary body boundaries: Psychopathology of the everyday excessive Internet and mobile phone use. Psychol. Russ. State Art 2017, 10, 177–189. [Google Scholar]
  77. Emelin, V.A.; Tkhostov, A.S.; Rasskazova, E.I. Psychological adaptation in the info-communication society: The revised version of the Technology-Related Psychological Consequences Questionnaire. Psychol. Russ. State Art 2014, 7, 105–120. [Google Scholar] [CrossRef]
  78. Enwereuzor, I.K.; Ugwu, L.I.; Ugwu, D.I. Role of smartphone addiction in gambling passion and schoolwork engagement: A Dualistic Model of Passion approach. Asian J. Gambl. Issues Public Health 2016, 6, 9. [Google Scholar] [CrossRef] [PubMed]
  79. Exelmans, L.; Van den Bulck, J. Bedtime mobile phone use and sleep in adults. Soc. Sci. Med. 2016, 148, 93–101. [Google Scholar] [CrossRef] [PubMed]
  80. Eyvazlou, M.; Zarei, E.; Rahimi, A.; Abazari, M. Association between overuse of mobile phones on quality of sleep and general health among occupational health and safety students. Chronobiol. Int. 2016, 33, 293–300. [Google Scholar] [CrossRef] [PubMed]
  81. Ezoe, S.; Toda, M.; Yoshimura, K.; Naritomi, A.; Den, R.; Morimoto, K. Relationships of personality and lifestyle with mobile phone dependence among female nursing students. Soc. Behav. Personal. 2009, 37, 231–238. [Google Scholar] [CrossRef]
  82. Falbe, J.; Davison, K.K.; Franckle, R.L.; Ganter, C.; Gortmaker, S.L.; Smith, L.; Land, T.; Taveras, E.M. Sleep duration, restfulness, and screens in the sleep environment. Pediatrics 2015, 135, e367–e375. [Google Scholar] [CrossRef] [PubMed]
  83. Fobian, A.D.; Avis, K.; Schwebel, D.C. Impact of media use on adolescent sleep efficiency. J. Dev. Behav. Pediatr. 2016, 37, 9–14. [Google Scholar] [CrossRef] [PubMed]
  84. Foerster, M.; Roser, K.; Schoeni, A.; Roosli, M. Problematic mobile phone use in adolescents: Derivation of a short scale MPPUS-10. Int. J. Public Health 2015, 60, 277–286. [Google Scholar] [CrossRef] [PubMed]
  85. Fossum, I.N.; Nordnes, L.T.; Storemark, S.S.; Bjorvatn, B.; Pallesen, S. The association between use of electronic media in bed before going to sleep and insomnia symptoms, daytime sleepiness, morningness, and chronotype. Behav. Sleep Med. 2014, 12, 343–357. [Google Scholar] [CrossRef] [PubMed]
  86. Fuller, C.; Lehman, E.; Hicks, S.; Novick, M.B. Bedtime Use of Technology and Associated Sleep Problems in Children. Glob. Pediatr. Health 2017, 4. [Google Scholar] [CrossRef] [PubMed]
  87. Gallimberti, L.; Buja, A.; Chindamo, S.; Terraneo, A.; Marini, E.; Rabensteiner, A.; Vinelli, A.; Perez, L.J.; Baldo, V. Problematic cell phone use for text messaging and substance abuse in early adolescence (11- to 13-year-olds). Eur. J. Pediatr. 2016, 175, 355–364. [Google Scholar] [CrossRef] [PubMed]
  88. Gamble, A.L.; D’Rozario, A.L.; Bartlett, D.J.; Williams, S.; Bin, Y.S.; Grunstein, R.R.; Marshall, N.S. Adolescent sleep patterns and night-time technology use: Results of the Australian Broadcasting Corporation’s Big Sleep Survey. PLoS ONE 2014, 9, e111700. [Google Scholar] [CrossRef] [PubMed]
  89. Gao, T.; Li, J.; Zhang, H.; Gao, J.; Kong, Y.; Hu, Y.; Mei, S. The influence of alexithymia on mobile phone addiction: The role of depression, anxiety and stress. J. Affect. Disord. 2018, 225, 761–766. [Google Scholar] [CrossRef] [PubMed]
  90. Gao, T.; Xiang, Y.T.; Zhang, H.; Zhang, Z.; Mei, S. Neuroticism and quality of life: Multiple mediating effects of smartphone addiction and depression. Psychiatry Res. 2017, 258, 457–461. [Google Scholar] [CrossRef] [PubMed]
  91. Gao, Y.; Li, A.; Zhu, T.; Liu, X.; Liu, X. How smartphone usage correlates with social anxiety and loneliness. PeerJ 2016, 4, e2197. [Google Scholar] [CrossRef] [PubMed]
  92. Garcia-Oliva, C.; Piqueras, J.A. Experiential avoidance and technological addictions in adolescents. J. Behav. Addict. 2016, 5, 293–303. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Garmy, P.; Ward, T.M. Sleep habits and nighttime texting among adolescents. J. Sch. Nurs. 2017, 34, 121–127. [Google Scholar] [CrossRef] [PubMed]
  94. Ghasempour, A.; Mahmoodi-Aghdam, M. The Role of Depression and Attachment Styles in Predicting Students’ Addiction to Cell Phones. Addict. Health 2015, 7, 192–197. [Google Scholar] [PubMed]
  95. Gonzales, A.L. Text-based communication influences self-esteem more than face-to-face or cellphone communication. Comput. Hum. Behav. 2014, 39, 197–203. [Google Scholar] [CrossRef]
  96. Gonzalez-Cabrera, J.; Leon-Mejia, A.; Perez-Sancho, C.; Calvete, E. Adaptation of the Nomophobia Questionnaire (NMP-Q) to Spanish in a sample of adolescents. Actas Esp. Psiquiatr. 2017, 45, 137–144. [Google Scholar] [PubMed]
  97. Gradisar, M.; Wolfson, A.R.; Harvey, A.G.; Hale, L.; Rosenberg, R.; Czeisler, C.A. The sleep and technology use of Americans: Findings from the National Sleep Foundation’s 2011 Sleep in America poll. J. Clin. Sleep Med. 2013, 9, 1291–1299. [Google Scholar] [CrossRef] [PubMed]
  98. Güzeller, C.O.; Coşguner, T. Development of a Problematic Mobile Phone Use Scale for Turkish adolescents. Cyberpsychol. Behav. Soc. Netw. 2012, 15, 205–211. [Google Scholar] [CrossRef] [PubMed]
  99. Gökçearslan, Ş.; Mumcu, F.K.; Haşlaman, T.; Çevik, Y.D. Modelling smartphone addiction: The role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students. Comput. Hum. Behav. 2016, 63, 639–649. [Google Scholar] [CrossRef]
  100. Ha, J.H.; Chin, B.; Park, D.-H.; Ryu, S.-H.; Yu, J. Characteristics of excessive cellular phone use in Korean adolescents. Cyberpsychol. Behav. 2008, 11, 783–784. [Google Scholar] [CrossRef] [PubMed]
  101. Hadlington, L.J. Cognitive failures in daily life: Exploring the link with Internet addiction and problematic mobile phone use. Comput. Hum. Behav. 2015, 51, 75–81. [Google Scholar] [CrossRef]
  102. Han, L.; Geng, J.; Jou, M.; Gao, F.; Yang, H. Relationship between shyness and mobile phone addiction in Chinese young adults: Mediating roles of self-control and attachment anxiety. Comput. Hum. Behav. 2017, 76, 363–371. [Google Scholar] [CrossRef]
  103. Harada, T.; Morikuni, M.; Yoshii, S.; Yamashita, Y.; Takeuchi, H. Usage of mobile phone in the evening or at night makes Japanese students evening-typed and night sleep uncomfortable. Sleep Hypn. 2002, 4, 149–153. [Google Scholar]
  104. Harada, T.; Tanoue, A.; Takeuchi, H. Epidemiological studies on dreams, sleep habits and mental symptoms in students aged 18–25 years and the 24 hour a day commercialization of Japanese society (1). Sleep Biol. Rhythm. 2006, 4, 274–281. [Google Scholar] [CrossRef]
  105. Harwood, J.; Dooley, J.J.; Scott, A.J.; Joiner, R. Constantly connected—The effects of smart-devices on mental health. Comput. Hum. Behav. 2014, 34, 267–272. [Google Scholar] [CrossRef] [Green Version]
  106. Haug, S.; Castro, R.P.; Kwon, M.; Filler, A.; Kowatsch, T.; Schaub, M.P. Smartphone use and smartphone addiction among young people in Switzerland. J. Behav. Addict. 2015, 4, 299–307. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Hawi, N.S.; Samaha, M. To excel or not to excel: Strong evidence on the adverse effect of smartphone addiction on academic performance. Comput. Educ. 2016, 98, 81–89. [Google Scholar] [CrossRef]
  108. Hong, F.-Y.; Chiu, S.-I.; Huang, D.-H. A model of the relationship between psychological characteristics, mobile phone addiction and use of mobile phones by Taiwanese university female students. Comput. Hum. Behav. 2012, 28, 2152–2159. [Google Scholar] [CrossRef]
  109. Honkalampi, K.; Tanskanen, A.; Hintikka, J.; Haatainen, K.; Viinamäki, H. Does the cellular phone help to communicate when face-to-face contacts are difficult? Can. J. Psychiatry 2001, 46, 373. [Google Scholar] [CrossRef] [PubMed]
  110. Hu, Y.; Long, X.; Lyu, H.; Zhou, Y.; Chen, J. Alterations in White Matter Integrity in Young Adults with Smartphone Dependence. Front. Hum. Neurosci. 2017, 11, 532. [Google Scholar] [CrossRef] [PubMed]
  111. Hussain, Z.; Griffiths, M.D.; Sheffield, D. An investigation into problematic smartphone use: The role of narcissism, anxiety, and personality factors. J. Behav. Addict. 2017, 6, 378–386. [Google Scholar] [CrossRef] [PubMed]
  112. Hysing, M.; Pallesen, S.; Stormark, K.M.; Jakobsen, R.; Lundervold, A.J.; Sivertsen, B. Sleep and use of electronic devices in adolescence: Results from a large population-based study. BMJ Open 2015, 5, e006748. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Ikeda, K.; Nakamura, K. Association between mobile phone use and depressed mood in Japanese adolescents: A cross-sectional study. Environ. Health Prev. Med. 2014, 19, 187–193. [Google Scholar] [CrossRef] [PubMed]
  114. Itani, O.; Kaneita, Y.; Munezawa, T.; Ikeda, M.; Osaki, Y.; Higuchi, S.; Kanda, H.; Nakagome, S.; Suzuki, K.; et al. Anger and Impulsivity Among Japanese Adolescents: A Nationwide Representative Survey. J. Clin. Psychiatry 2016, 77, e860–e866. [Google Scholar] [CrossRef] [PubMed]
  115. Jenaro, C.; Flores, N.; Gómez-Vela, M.; González-Gil, F.; Caballo, C. Problematic internet and cell-phone use: Psychological behavioral, and health correlates. Addict. Res. Theory 2007, 15, 309–320. [Google Scholar] [CrossRef]
  116. Jeong, S.H.; Kim, H.; Yum, J.Y.; Hwang, Y. What type of content are smartphone users addicted to? SNS vs. Games. Comput. Hum. Behav. 2016, 54, 10–17. [Google Scholar] [CrossRef]
  117. Jiang, X.; Hardy, L.L.; Baur, L.A.; Ding, D.; Wang, L.; Shi, H. Sleep duration, schedule and quality among urban Chinese children and adolescents: Associations with routine after-school activities. PLoS ONE 2015, 10, e0115326. [Google Scholar] [CrossRef] [PubMed]
  118. Jiang, Z.; Shi, M. Prevalence and co-occurrence of compulsive buying, problematic Internet and mobile phone use in college students in Yantai, China: Relevance of self-traits. BMC Public Health 2016, 16, 1211. [Google Scholar] [CrossRef] [PubMed]
  119. Jiang, Z.; Zhao, X. Self-control and problematic mobile phone use in Chinese college students: The mediating role of mobile phone use patterns. BMC Psychiatry 2016, 16, 416. [Google Scholar] [CrossRef] [PubMed]
  120. Jiang, Z.; Zhao, X. Brain behavioral systems, self-control and problematic mobile phone use: The moderating role of gender and history of use. Personal. Individ. Differ. 2017, 106, 111–116. [Google Scholar] [CrossRef]
  121. Johansson, A.E.; Petrisko, M.A.; Chasens, E.R. Adolescent Sleep and the Impact of Technology Use Before Sleep on Daytime Function. J. Pediatr. Nurs. 2016, 31, 498–504. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  122. Jun, N.; Lee, A.; Baik, I. Associations of Caffeinated Beverage Consumption and Screen Time with Excessive Daytime Sleepiness in Korean High School Students. Clin. Nutr. Res. 2017, 6, 55–60. [Google Scholar] [CrossRef] [PubMed]
  123. Jun, S. The reciprocal longitudinal relationships between mobile phone addiction and depressive symptoms among Korean adolescents. Comput. Hum. Behav. 2016, 58, 179–186. [Google Scholar] [CrossRef]
  124. Karadağ, E.; Tosuntaş, Ş.B.; Erzen, E.; Duru, P.; Bostan, N.; Şahin, B.M.; Çulha, İ.; Babadağ, B. Determinants of phubbing, which is the sum of many virtual addictions: A structural equation model. J. Behav. Addict. 2015, 4, 60–74. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  125. Kawabe, K.; Horiuchi, F.; Ochi, M.; Oka, Y.; Ueno, S. Internet addiction: Prevalence and relation with mental states in adolescents. Psychiatry Clin. Neurosci. 2016, 70, 405–412. [Google Scholar] [CrossRef] [PubMed]
  126. Khoury, J.M.; de Freitas, A.A.C.; Roque, M.A.V.; Albuquerque, M.R.; das Neves, M.C.L.; Garcia, F.D. Assessment of the accuracy of a new tool for the screening of smartphone addiction. PLoS ONE 2017, 12, e0176924. [Google Scholar] [CrossRef] [PubMed]
  127. Kim, D.; Lee, Y.; Lee, J.; Nam, J.K.; Chung, Y. Development of Korean Smartphone Addiction Proneness Scale for youth. PLoS ONE 2014, 9, e97920. [Google Scholar] [CrossRef] [PubMed]
  128. Kim, D.; Nam, J.K.; Oh, J.; Kang, M.C. A latent profile analysis of the interplay between PC and smartphone in problematic Internet use. Comput. Hum. Behav. 2016, 56, 360–368. [Google Scholar] [CrossRef]
  129. Kim, E.; Cho, I.; Kim, E.J. Structural Equation Model of Smartphone Addiction Based on Adult Attachment Theory: Mediating Effects of Loneliness and Depression. Asian Nurs. Res. (Korean Soc. Nurs. Sci.) 2017, 11, 92–97. [Google Scholar] [CrossRef] [PubMed]
  130. Kim, E.Y.; Joo, S.W.; Han, S.J.; Kim, M.J.; Choi, S.Y. Depression, Impulse Control Disorder, and Life Style According to Smartphone Addiction. Stud. Health Technol. Inform. 2017, 245, 1272. [Google Scholar] [PubMed]
  131. Kim, H.J.; Min, J.Y.; Kim, H.J.; Min, K.B. Association between psychological and self-assessed health status and smartphone overuse among Korean college students. J. Ment. Health 2017, 1–6. [Google Scholar] [CrossRef] [PubMed]
  132. Kim, J.H. Longitudinal Associations among Psychological Issues and Problematic Use of Smartphones: A Two-Wave Cross-Lagged Study. J. Media Psychol. Theor. Methods Appl. 2017. [Google Scholar] [CrossRef]
  133. Kim, J.H. Smartphone-mediated communication vs. face-to-face interaction: Two routes to social support and problematic use of smartphone. Comput. Hum. Behav. 2017, 67, 282–291. [Google Scholar] [CrossRef]
  134. Kim, J.-H.; Seo, M.; David, P. Alleviating depression only to become problematic mobile phone users: Can face-to-face communication be the antidote? Comput. Hum. Behav. 2015, 51, 440–447. [Google Scholar] [CrossRef]
  135. Kim, R.; Lee, K.J.; Choi, Y.J. Mobile Phone Overuse among Elementary School Students in Korea: Factors Associated With Mobile Phone Use as a Behavior Addiction. J. Addict. Nurs. 2015, 26, 81–85. [Google Scholar] [CrossRef] [PubMed]
  136. Kim, S.E.; Kim, J.W.; Jee, Y.S. Relationship between smartphone addiction and physical activity in Chinese international students in Korea. J. Behav. Addict. 2015, 4, 200–205. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  137. Kim, Y.; Jeong, J.E.; Cho, H.; Jung, D.J.; Kwak, M.; Rho, M.J.; Yu, H.; Kim, D.J.; Choi, I.Y. Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control. PLoS ONE 2016, 11, e0159788. [Google Scholar] [CrossRef] [PubMed]
  138. King, A.L.; Valenca, A.M.; Silva, A.C.; Sancassiani, F.; Machado, S.; Nardi, A.E. “Nomophobia”: Impact of cell phone use interfering with symptoms and emotions of individuals with panic disorder compared with a control group. Clin. Pract. Epidemiol. Ment. Health 2014, 10, 28–35. [Google Scholar] [CrossRef] [PubMed]
  139. Koivusilta, L.K.; Lintonen, T.P.; Rimpela, A.H. Orientations in adolescent use of information and communication technology: A digital divide by sociodemographic background, educational career, and health. Scand. J. Public Health 2007, 35, 95–103. [Google Scholar] [CrossRef] [PubMed]
  140. Korpinen, L.; Paakkonen, R. Mental symptoms and the use of new technical equipment. Int. J. Occup. Saf. Ergon. 2009, 15, 385–400. [Google Scholar] [CrossRef] [PubMed]
  141. Korpinen, L.; Paakkonen, R. Self-reported depression and anxiety symptoms and usage of computers and mobile phones among working-age Finns. Int. J. Occup. Saf. Ergon. 2015, 21, 221–228. [Google Scholar] [CrossRef] [PubMed]
  142. Kruger, D.J.; Djerf, J.M. Bad vibrations? Cell phone dependency predicts phantom communication experiences. Comput. Hum. Behav. 2017, 70, 360–364. [Google Scholar] [CrossRef]
  143. Kuang-Tsan, C.; Fu-Yuan, H. Study on relationship among university students’ life stress, smart mobile phone addiction, and life satisfaction. J. Adult Dev. 2017, 24, 109–118. [Google Scholar] [CrossRef]
  144. Kubiszewski, V.; Fontaine, R.; Rusch, E.; Hazouard, E. Association between electronic media use and sleep habits: An eight-day follow-up study. Int. J. Adolesc. Youth 2014, 19, 395–407. [Google Scholar] [CrossRef]
  145. Kwon, M.; Kim, D.J.; Cho, H.; Yang, S. The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE 2013, 8, e83558. [Google Scholar] [CrossRef] [PubMed]
  146. Kwon, M.; Lee, J.Y.; Won, W.Y.; Park, J.W.; Min, J.A.; Hahn, C.; Gu, X.; Choi, J.H.; Kim, D.J. Development and validation of a smartphone addiction scale (SAS). PLoS ONE 2013, 8, e56936. [Google Scholar] [CrossRef] [PubMed]
  147. Lachmann, B.; Duke, É.; Sariyska, R.; Montag, C. Who’s Addicted to the Smartphone and/or the Internet? Psychol. Pop. Media Culture 2017. [Google Scholar] [CrossRef]
  148. Lanaj, K.; Johnson, R.E.; Barnes, C.M. Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organ. Behav. Hum. Decis. Process. 2014, 124, 11–23. [Google Scholar] [CrossRef]
  149. Lange, K.; Cohrs, S.; Skarupke, C.; Gorke, M.; Szagun, B.; Schlack, R. Electronic media use and insomnia complaints in German adolescents: Gender differences in use patterns and sleep problems. J. Neural Transm. 2017, 124, 79–87. [Google Scholar] [CrossRef] [PubMed]
  150. Lee, C.; Lee, S.J. Prevalence and predictors of smartphone addiction proneness among Korean adolescents. Child. Youth Serv. Rev. 2017, 77, 10–17. [Google Scholar] [CrossRef]
  151. Lee, E.B. Facebook use and texting among African American and Hispanic teenagers: An implication for academic performance. J. Black Stud. 2014, 45, 83–101. [Google Scholar] [CrossRef]
  152. Lee, E.B. Too much information: Heavy smartphone and Facebook utilization by African American young adults. J. Black Stud. 2015, 46, 44–61. [Google Scholar] [CrossRef]
  153. Lee, H.; Kim, J.W.; Choi, T.Y. Risk Factors for Smartphone Addiction in Korean Adolescents: Smartphone Use Patterns. J. Korean Med. Sci. 2017, 32, 1674–1679. [Google Scholar] [CrossRef] [PubMed]
  154. Lee, H.K.; Kim, J.H.; Fava, M.; Mischoulon, D.; Park, J.H.; Shim, E.J.; Lee, E.H.; Lee, J.H.; Jeon, H.J. Development and validation study of the Smartphone Overuse Screening Questionnaire. Psychiatry Res. 2017, 257, 352–357. [Google Scholar] [CrossRef] [PubMed]
  155. Lee, J. Does stress from cell phone use increase negative emotions at work? Soc. Behav. Personal. 2016, 44, 705–716. [Google Scholar] [CrossRef]
  156. Lee, J.E.; Jang, S.I.; Ju, Y.J.; Kim, W.; Lee, H.J.; Park, E.C. Relationship between Mobile Phone Addiction and the Incidence of Poor and Short Sleep among Korean Adolescents: A Longitudinal Study of the Korean Children & Youth Panel Survey. J. Korean Med. Sci. 2017, 32, 1166–1172. [Google Scholar] [PubMed]
  157. Lee, K.E.; Kim, S.H.; Ha, T.Y.; Yoo, Y.M.; Han, J.J.; Jung, J.H.; Jang, J.Y. Dependency on Smartphone Use and Its Association with Anxiety in Korea. Public Health Rep. 2016, 131, 411–419. [Google Scholar] [CrossRef] [PubMed]
  158. Lee, S.; Tam, C.L.; Chie, Q.T. Mobile phone usage preferences: The contributing factors of personality, social anxiety and loneliness. Soc. Indic. Res. 2014, 118, 1205–1228. [Google Scholar] [CrossRef]
  159. Lee, Y.K.; Chang, C.T.; Lin, Y.; Cheng, Z.H. The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. Comput. Hum. Behav. 2014, 31, 373–383. [Google Scholar] [CrossRef]
  160. Lee, Y.-K.; Chang, C.-T.; Cheng, Z.-H.; Lin, Y. Helpful-stressful cycle? Psychological links between type of mobile phone user and stress. Behav. Inf. Technol. 2016, 35, 75–86. [Google Scholar] [CrossRef]
  161. Lemola, S.; Perkinson-Gloor, N.; Brand, S.; Dewald-Kaufmann, J.F.; Grob, A. Adolescents’ electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. J. Youth Adolesc. 2015, 44, 405–418. [Google Scholar] [CrossRef] [PubMed]
  162. Lepp, A.; Barkley, J.E.; Karpinski, A.C. The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Comput. Hum. Behav. 2014, 31, 343–350. [Google Scholar] [CrossRef]
  163. Lepp, A.; Barkley, J.E.; Li, J. Motivations and experiential outcomes associated with leisure time cell phone use: Results from two independent studies. Leis. Sci. 2017, 39, 144–162. [Google Scholar] [CrossRef]
  164. Lepp, A.; Li, J.; Barkley, J.E.; Salehi-Esfahani, S. Exploring the relationships between college students’ cell phone use, personality and leisure. Comput. Hum. Behav. 2015, 43, 210–219. [Google Scholar] [CrossRef]
  165. Leung, C. Assessing mobile phone dependency and teens’ everyday life in Hong Kong. Aust. J. Psychol. 2017, 69, 29–38. [Google Scholar] [CrossRef]
  166. Leung, L.; Liang, J. Psychological traits, addiction symptoms, and feature usage as predictors of problematic smartphone use among university students in China. Int. J. Cyber Behav. Psychol. Learn. 2016, 6, 57–74. [Google Scholar] [CrossRef]
  167. Li, J.; Lepp, A.; Barkley, J.E. Locus of control and cell phone use: Implications for sleep quality, academic performance, and subjective well-being. Comput. Hum. Behav. 2015, 52, 450–457. [Google Scholar] [CrossRef]
  168. Li, M.; Jiang, X.; Ren, Y. Mediator Effects of Positive Emotions on Social Support and Depression among Adolescents Suffering from Mobile Phone Addiction. Psychiatr. Danub. 2017, 29, 207–213. [Google Scholar] [CrossRef] [PubMed]
  169. Lian, L. Alienation as mediator and moderator of the relationship between virtues and smartphone addiction among Chinese university students. Int. J. Ment. Health Addict. 2017, 16, 1208–1218. [Google Scholar] [CrossRef]
  170. Lian, L.; You, X. Specific virtues as predictors of Smartphone addiction among Chinese undergraduates. Curr. Psychol. 2017, 36, 376–384. [Google Scholar] [CrossRef]
  171. Lian, L.; You, X.; Huang, J.; Yang, R. Who overuses Smartphones? Roles of virtues and parenting style in smartphone addiction among Chinese college students. Comput. Hum. Behav. 2016, 65, 92–99. [Google Scholar] [CrossRef]
  172. Lin, Y.H.; Chang, L.R.; Lee, Y.H.; Tseng, H.W.; Kuo, T.B.; Chen, S.H. Development and validation of the Smartphone Addiction Inventory (SPAI). PLoS ONE 2014, 9, e98312. [Google Scholar] [CrossRef] [PubMed]
  173. Lin, Y.H.; Chiang, C.L.; Lin, P.H.; Chang, L.R.; Ko, C.H.; Lee, Y.H.; Lin, S.H. Proposed Diagnostic Criteria for Smartphone Addiction. PLoS ONE 2016, 11, e0163010. [Google Scholar] [CrossRef] [PubMed]
  174. Lin, Y.H.; Lin, P.H.; Chiang, C.L.; Lee, Y.H.; Yang, C.C.H.; Kuo, T.B.J.; Lin, S.H. Incorporation of Mobile Application (App) Measures into the Diagnosis of Smartphone Addiction. J. Clin. Psychiatry 2017, 78, 866–872. [Google Scholar] [CrossRef] [PubMed]
  175. Lin, Y.H.; Lin, Y.C.; Lee, Y.H.; Lin, P.H.; Lin, S.H.; Chang, L.R.; Tseng, H.W.; Yen, L.Y.; Yang, C.C.; Kuo, T.B. Time distortion associated with smartphone addiction: Identifying smartphone addiction via a mobile application (App). J. Psychiatr. Res. 2015, 65, 139–145. [Google Scholar] [CrossRef] [PubMed]
  176. Lin, Y.H.; Lin, Y.C.; Lin, S.H.; Lee, Y.H.; Lin, P.H.; Chiang, C.L.; Chang, L.R.; Yang, C.C.; Kuo, T.B. To use or not to use? Compulsive behavior and its role in smartphone addiction. Transl. Psychiatry 2017, 7, e1030. [Google Scholar] [CrossRef] [PubMed]
  177. Lin, Y.H.; Pan, Y.C.; Lin, S.H.; Chen, S.H. Development of short-form and screening cutoff point of the Smartphone Addiction Inventory (SPAI-SF). Int. J. Methods Psychiatr. Res. 2017, 26. [Google Scholar] [CrossRef] [PubMed]
  178. Liu, C.H.; Lin, S.H.; Pan, Y.C.; Lin, Y.H. Smartphone gaming and frequent use pattern associated with smartphone addiction. Medicine 2016, 95, e4068. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  179. Liu, Q.Q.; Zhou, Z.K.; Yang, X.J.; Kong, F.C.; Niu, G.F.; Fan, C.Y. Mobile phone addiction and sleep quality among Chinese adolescents: A moderated mediation model. Comput. Hum. Behav. 2017, 72, 108–114. [Google Scholar] [CrossRef]
  180. Long, J.; Liu, T.Q.; Liao, Y.H.; Qi, C.; He, H.Y.; Chen, S.B.; Billieux, J. Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates. BMC Psychiatry 2016, 16, 408. [Google Scholar] [CrossRef] [PubMed]
  181. Lopez-Fernandez, O. Short version of the Smartphone Addiction Scale adapted to Spanish and French: Towards a cross-cultural research in problematic mobile phone use. Addict. Behav. 2017, 64, 275–280. [Google Scholar] [CrossRef] [PubMed]
  182. Lopez-Fernandez, O.; Honrubia-Serrano, L.; Freixa-Blanxart, M.; Gibson, W. Prevalence of problematic mobile phone use in British adolescents. Cyberpsychol. Behav. Soc. Netw. 2014, 17, 91–98. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  183. Lopez-Fernandez, O.; Kuss, D.J.; Romo, L.; Morvan, Y.; Kern, L.; Graziani, P.; Rousseau, A.; Rumpf, H.J.; Bischof, A.; Gassler, A.K.; et al. Self-reported dependence on mobile phones in young adults: A European cross-cultural empirical survey. J. Behav. Addict. 2017, 6, 168–177. [Google Scholar] [CrossRef] [PubMed]
  184. Lu, X.; Katoh, T.; Chen, Z.; Nagata, T.; Kitamura, T. Text messaging: Are dependency and Excessive Use discretely different for Japanese university students? Psychiatry Res. 2014, 216, 255–262. [Google Scholar] [CrossRef] [PubMed]
  185. Lu, X.; Watanabe, J.; Liu, Q.; Uji, M.; Shono, M.; Kitamura, T. Internet and mobile phone text-messaging dependency: Factor structure and correlation with dysphoric mood among Japanese adults. Comput. Hum. Behav. 2011, 27, 1702–1709. [Google Scholar] [CrossRef]
  186. Mak, K.K.; Nam, J.K.; Kim, D.; Aum, N.; Choi, J.S.; Cheng, C.; Ko, H.C.; Watanabe, H. Cross-cultural adaptation and psychometric properties of the Korean Scale for Internet Addiction (K-Scale) in Japanese high school students. Psychiatry Res. 2017, 249, 343–348. [Google Scholar] [CrossRef] [PubMed]
  187. Mak, Y.W.; Wu, C.S.; Hui, D.W.; Lam, S.P.; Tse, H.Y.; Yu, W.Y.; Wong, H.T. Association between screen viewing duration and sleep duration, sleep quality, and excessive daytime sleepiness among adolescents in Hong Kong. Int. J. Environ. Res. Public Health 2014, 11, 11201–11219. [Google Scholar] [CrossRef] [PubMed]
  188. Martinotti, G.; Villella, C.; Di Thiene, D.; Di Nicola, M.; Bria, P.; Conte, G.; Cassano, M.; Petruccelli, F.; Corvasce, N.; Janiri, L.; et al. Problematic mobile phone use in adolescence: A cross-sectional study. J. Public Health 2011, 19, 545–551. [Google Scholar] [CrossRef]
  189. Matar Boumosleh, J.; Jaalouk, D. Depression, anxiety, and smartphone addiction in university students—A cross sectional study. PLoS ONE 2017, 12, e0182239. [Google Scholar] [CrossRef] [PubMed]
  190. Matsumoto, Y.; Uchimura, N.; Ishida, T.; Morimatsu, Y.; Mori, M.; Inoue, M.; Kushino, N.; Hoshiko, M.; Ishitake, T. The relationship of sleep complaints risk factors with sleep phase, quality, and quantity in Japanese workers. Sleep Biol. Rhythm. 2017, 15, 291–297. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  191. Mattila, A.K.; Luutonen, S.; Ylinen, M.; Salokangas, R.K.R.; Joukamaa, M. Alexithymia, human relationships, and mobile phone use. J. Nerv. Ment. Dis. 2010, 198, 722–727. [Google Scholar] [CrossRef] [PubMed]
  192. Mazaheri, M.A.; Karbasi, M. Validity and reliability of the Persian version of mobile phone addiction scale. J. Res. Med. Sci. 2014, 19, 139–144. [Google Scholar] [PubMed]
  193. McBride, J.; Derevensky, J. Internet gambling behavior in a sample of online gamblers. Int. J. Ment. Health Addict. 2009, 7, 149–167. [Google Scholar] [CrossRef]
  194. Merlo, L.J.; Stone, A.M.; Bibbey, A. Measuring Problematic Mobile Phone Use: Development and Preliminary Psychometric Properties of the PUMP Scale. J. Addict. 2013, 2013, 912807. [Google Scholar] [CrossRef] [PubMed]
  195. Mohammadbeigi, A.; Absari, R.; Valizadeh, F.; Saadati, M.; Sharifimoghadam, S.; Ahmadi, A.; Mokhtari, M.; Ansari, H. Sleep Quality in Medical Students; the Impact of Over-Use of Mobile Cell-Phone and Social Networks. J. Res. Health Sci. 2016, 16, 46–50. [Google Scholar] [PubMed]
  196. Mohammadi Kalhori, S.; Mohammadi, M.R.; Alavi, S.S.; Jannatifard, F.; Sepahbodi, G.; Baba Reisi, M.; Sajedi, S.; Farshchi, M.; KhodaKarami, R.; Hatami Kasvaee, V. Validation and Psychometric Properties of Mobile Phone Problematic Use Scale (MPPUS) in University Students of Tehran. Iran. J. Psychiatry 2015, 10, 25–31. [Google Scholar] [PubMed]
  197. Mohammadi, M.; Alavi, S.S.; Farokhzad, P.; Jannatifard, F.; Mohammadi Kalhori, S.; Sepahbodi, G.; Baba Reisi, M.; Sajedi, S.; Farshchi, M.; Khoda Karami, R.; et al. The Validity and Reliability of the Persian Version Test of Mobile Phone Dependency (TMD). Iran. J. Psychiatry 2015, 10, 265–272. [Google Scholar] [PubMed]
  198. Mok, J.Y.; Choi, S.W.; Kim, D.J.; Choi, J.S.; Lee, J.; Ahn, H.; Choi, E.J.; Song, W.Y. Latent class analysis on internet and smartphone addiction in college students. Neuropsychiatr. Dis. Treat. 2014, 10, 817–828. [Google Scholar] [PubMed] [Green Version]
  199. Monma, T.; Ando, A.; Asanuma, T.; Yoshitake, Y.; Yoshida, G.; Miyazawa, T.; Ebine, N.; Takeda, S.; Omi, N.; Satoh, M.; et al. Sleep disorder risk factors among student athletes. Sleep Med. 2018, 44, 76–81. [Google Scholar] [CrossRef] [PubMed]
  200. Montag, C.; Błaszkiewicz, K.; Lachmann, B.; Andone, I.; Sariyska, R.; Trendafilov, B.; Reuter, M.; Markowetz, A. Correlating personality and actual phone usage: Evidence from psychoinformatics. J. Individ. Differ. 2014, 35, 158–165. [Google Scholar] [CrossRef]
  201. Montag, C.; Sindermann, C.; Becker, B.; Panksepp, J. An affective neuroscience framework for the molecular study of Internet addiction. Front. Psychol. 2016, 7, 13. [Google Scholar] [CrossRef] [PubMed]
  202. Munezawa, T.; Kaneita, Y.; Osaki, Y.; Kanda, H.; Minowa, M.; Suzuki, K.; Higuchi, S.; Mori, J.; Yamamoto, R.; Ohida, T. The association between use of mobile phones after lights out and sleep disturbances among Japanese adolescents: A nationwide cross-sectional survey. Sleep 2011, 34, 1013–1020. [Google Scholar] [CrossRef] [PubMed]
  203. Munoz-Miralles, R.; Ortega-Gonzalez, R.; Lopez-Moron, M.R.; Batalla-Martinez, C.; Manresa, J.M.; Montella-Jordana, N.; Chamarro, A.; Carbonell, X.; Toran-Monserrat, P. The problematic use of Information and Communication Technologies (ICT) in adolescents by the cross sectional JOITIC study. BMC Pediatr. 2016, 16, 140. [Google Scholar] [CrossRef] [PubMed]
  204. Murdock, K.K.; Gorman, S.; Robbins, M. Co-rumination via cellphone moderates the association of perceived interpersonal stress and psychosocial well-being in emerging adults. J. Adolesc. 2015, 38, 27–37. [Google Scholar] [CrossRef] [PubMed]
  205. Murdock, K.K.; Horissian, M.; Crichlow-Ball, C. Emerging Adults’ Text Message Use and Sleep Characteristics: A Multimethod, Naturalistic Study. Behav. Sleep Med. 2017, 15, 228–241. [Google Scholar] [CrossRef] [PubMed]
  206. Nathan, N.; Zeitzer, J. A survey study of the association between mobile phone use and daytime sleepiness in California high school students. BMC Public Health 2013, 13, 840. [Google Scholar] [CrossRef] [PubMed]
  207. Nathanson, A.I.; Beyens, I. The relation between use of mobile electronic devices and bedtime resistance, sleep duration, and daytime sleepiness among preschoolers. Behav. Sleep Med. 2018, 16, 202–219. [Google Scholar] [CrossRef] [PubMed]
  208. Nikhita, C.S.; Jadhav, P.R.; Ajinkya, S.A. Prevalence of Mobile Phone Dependence in Secondary School Adolescents. J. Clin. Diagn. Res. 2015, 9, Vc06–Vc09. [Google Scholar] [CrossRef] [PubMed]
  209. Oberst, U.; Wegmann, E.; Stodt, B.; Brand, M.; Chamarro, A. Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out. J. Adolesc. 2017, 55, 51–60. [Google Scholar] [CrossRef] [PubMed]
  210. Oshima, N.; Nishida, A.; Shimodera, S.; Tochigi, M.; Ando, S.; Yamasaki, S.; Okazaki, Y.; Sasaki, T. The suicidal feelings, self-injury, and mobile phone use after lights out in adolescents. J. Pediatr. Psychol. 2012, 37, 1023–1030. [Google Scholar] [CrossRef] [PubMed]
  211. Paik, S.H.; Cho, H.; Chun, J.W.; Jeong, J.E.; Kim, D.J. Gaming Device Usage Patterns Predict Internet Gaming Disorder: Comparison across Different Gaming Device Usage Patterns. Int. J. Environ. Res. Public Health 2017, 14, 1512. [Google Scholar] [CrossRef] [PubMed]
  212. Paiva, T.; Gaspar, T.; Matos, M.G. Mutual relations between sleep deprivation, sleep stealers and risk behaviours in adolescents. Sleep Sci. 2016, 9, 7–13. [Google Scholar] [CrossRef] [PubMed]
  213. Pamuk, M.; Atli, A. Development of a Problematic Mobile Phone Use Scale for university students: Validity and reliability study. Düşünen Adam J. Psychiatry Neurol. Sci. 2016, 29, 49–59. [Google Scholar] [CrossRef]
  214. Panova, T.; Lleras, A. Avoidance or boredom: Negative mental health outcomes associated with use of Information and Communication Technologies depend on users’ motivations. Comput. Hum. Behav. 2016, 58, 249–258. [Google Scholar] [CrossRef]
  215. Park, N.; Kim, Y.C.; Shon, H.Y.; Shim, H. Factors influencing smartphone use and dependency in South Korea. Comput. Hum. Behav. 2013, 29, 1763–1770. [Google Scholar] [CrossRef]
  216. Park, N.; Lee, H. Social implications of smartphone use: Korean college students’ smartphone use and psychological well-being. Cyberpsychol. Behav. Soc. Netw. 2012, 15, 491–497. [Google Scholar] [CrossRef] [PubMed]
  217. Pavia, L.; Cavani, P.; Di Blasi, M.; Giordano, C. Smartphone Addiction Inventory (SPAI): Psychometric properties and confirmatory factor analysis. Comput. Hum. Behav. 2016, 63, 170–178. [Google Scholar] [CrossRef]
  218. Pearson, C.; Hussain, Z. Smartphone use, addiction, narcissism, and personality: A mixed methods investigation. Int. J. Cyber Behav. Psychol. Learn. 2015, 5, 17–32. [Google Scholar] [CrossRef]
  219. Peiro-Velert, C.; Valencia-Peris, A.; Gonzalez, L.M.; Garcia-Masso, X.; Serra-Ano, P.; Devis-Devis, J. Screen media usage, sleep time and academic performance in adolescents: Clustering a self-organizing maps analysis. PLoS ONE 2014, 9, e99478. [Google Scholar] [CrossRef] [PubMed]
  220. Phillips, J.G.; Butt, S.; Blaszczynski, A. Personality and self-reported use of mobile phones for games. Cyberpsychol. Behav. 2006, 9, 753–758. [Google Scholar] [CrossRef] [PubMed]
  221. Phillips, J.G.; Ogeil, R.P.; Blaszczynski, A. Electronic interests and behaviours associated with gambling problems. Int. J. Ment. Health Addict. 2012, 10, 585–596. [Google Scholar] [CrossRef]
  222. Phillips, J.G.; Sargeant, J.; Ogeil, R.P.; Chow, Y.-W.; Blaszczynski, A. Self-reported gambling problems and digital traces. Cyberpsychol. Behav. Soc. Netw. 2014, 17, 742–748. [Google Scholar] [CrossRef] [PubMed]
  223. Pieters, D.; De Valck, E.; Vandekerckhove, M.; Pirrera, S.; Wuyts, J.; Exadaktylos, V.; Haex, B.; Michiels, N.; Verbraecken, J.; Cluydts, R. Effects of pre-sleep media use on sleep/wake patterns and daytime functioning among adolescents: The moderating role of parental control. Behav. Sleep Med. 2014, 12, 427–443. [Google Scholar] [CrossRef] [PubMed]
  224. Piguet, C.; Berchtold, A.; Akre, C.; Suris, J.C. What keeps female problematic Internet users busy online? Eur. J. Pediatr. 2015, 174, 1053–1059. [Google Scholar] [CrossRef] [PubMed]
  225. Pourrazavi, S.; Allahverdipour, H.; Jafarabadi, M.A.; Matlabi, H. A socio-cognitive inquiry of excessive mobile phone use. Asian J. Psychiatry 2014, 10, 84–89. [Google Scholar] [CrossRef] [PubMed]
  226. Prasad, M.; Patthi, B.; Singla, A.; Gupta, R.; Saha, S.; Kumar, J.K.; Malhi, R.; Pandita, V. Nomophobia: A Cross-sectional Study to Assess Mobile Phone Usage Among Dental Students. J. Clin. Diagn. Res. 2017, 11, ZC34–ZC39. [Google Scholar] [CrossRef] [PubMed]
  227. Przybylski, A.K.; Weinstein, N. A Large-Scale Test of the Goldilocks Hypothesis. Psychol. Sci. 2017, 28, 204–215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  228. Punamaki, R.L.; Wallenius, M.; Nygard, C.H.; Saarni, L.; Rimpela, A. Use of information and communication technology (ICT) and perceived health in adolescence: The role of sleeping habits and waking-time tiredness. J. Adolesc. 2007, 30, 569–585. [Google Scholar] [CrossRef] [PubMed]
  229. Randler, C.; Wolfgang, L.; Matt, K.; Demirhan, E.; Horzum, M.B.; Besoluk, S. Smartphone addiction proneness in relation to sleep and morningness-eveningness in German adolescents. J. Behav. Addict. 2016, 5, 465–473. [Google Scholar] [CrossRef] [PubMed]
  230. Reed, D.D.; Becirevic, A.; Atchley, P.; Kaplan, B.A.; Liese, B.S. Validation of a novel delay discounting of text messaging questionnaire. Psychol. Rec. 2016, 66, 253–261. [Google Scholar] [CrossRef]
  231. Reid, D.J.; Reid, F.J. Text or talk? Social anxiety, loneliness, and divergent preferences for cell phone use. Cyberpsychol. Behav. 2007, 10, 424–435. [Google Scholar] [CrossRef] [PubMed]
  232. Roberts, J.A.; Petnji Yaya, L.H.; Manolis, C. The invisible addiction: Cell-phone activities and addiction among male and female college students. J. Behav. Addict. 2014, 3, 254–265. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  233. Roberts, J.A.; Pirog, S.F., 3rd. A preliminary investigation of materialism and impulsiveness as predictors of technological addictions among young adults. J. Behav. Addict. 2013, 2, 56–62. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  234. Roberts, J.A.; Pullig, C.; Manolis, C. I need my smartphone: A hierarchical model of personality and cell-phone addiction. Personal. Individ. Differ. 2015, 79, 13–19. [Google Scholar] [CrossRef]
  235. Rosen, L.; Carrier, L.M.; Miller, A.; Rokkum, J.; Ruiz, A. Sleeping with technology: Cognitive, affective, and technology usage predictors of sleep problems among college students. Sleep Health 2016, 2, 49–56. [Google Scholar] [CrossRef] [PubMed]
  236. Rosen, L.D.; Whaling, K.; Carrier, L.M.; Cheever, N.A.; Rokkum, J. The Media and Technology Usage and Attitudes Scale: An empirical investigation. Comput. Hum. Behav. 2013, 29, 2501–2511. [Google Scholar] [CrossRef] [Green Version]
  237. Roser, K.; Schoeni, A.; Foerster, M.; Roosli, M. Problematic mobile phone use of Swiss adolescents: Is it linked with mental health or behaviour? Int. J. Public Health 2016, 61, 307–315. [Google Scholar] [CrossRef] [PubMed]
  238. Rutland, J.B.; Sheets, T.; Young, T. Development of a scale to measure problem use of short message service: The SMS Problem Use Diagnostic Questionnaire. Cyberpsychol. Behav. 2007, 10, 841–843. [Google Scholar] [CrossRef] [PubMed]
  239. Saeb, S.; Zhang, M.; Karr, C.J.; Schueller, S.M.; Corden, M.E.; Kording, K.P.; Mohr, D.C. Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study. J. Med. Internet Res. 2015, 17, e175. [Google Scholar] [CrossRef] [PubMed]
  240. Sahin, S.; Ozdemir, K.; Unsal, A.; Temiz, N. Evaluation of mobile phone addiction level and sleep quality in university students. Pak. J. Med. Sci. 2013, 29, 913–918. [Google Scholar] [CrossRef] [PubMed]
  241. Salehan, M.; Negahban, A. Social networking on smartphones: When mobile phones become addictive. Comput. Hum. Behav. 2013, 29, 2632–2639. [Google Scholar] [CrossRef]
  242. Saling, L.L.; Haire, M. Are you awake? Mobile phone use after lights out. Comput. Hum. Behav. 2016, 64, 932–937. [Google Scholar] [CrossRef]
  243. Samaha, M.; Hawi, N.S. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Comput. Hum. Behav. 2016, 57, 321–325. [Google Scholar] [CrossRef]
  244. Sanchez-Martinez, M.; Otero, A. Factors associated with cell phone use in adolescents in the community of Madrid (Spain). Cyberpsychol. Behav. 2009, 12, 131–137. [Google Scholar] [CrossRef] [PubMed]
  245. Sapacz, M.; Rockman, G.; Clark, J. Are we addicted to our cell phones? Comput. Hum. Behav. 2016, 57, 153–159. [Google Scholar] [CrossRef]
  246. Sato, M.; Sekine, T. The usage of cell phones and the feeling to them in modern Japanese college students. J. Hum. Ergol. 2010, 39, 23–33. [Google Scholar]
  247. Savci, M.; Aysan, F. Technological addictions and social connectedness: Predictor effect of Internet addiction, social media addiction, digital game addiction and smartphone addiction on social connectedness. Düşünen Adam J. Psychiatry Neurol. Sci. 2017, 30, 202–216. [Google Scholar] [CrossRef]
  248. Schoeni, A.; Roser, K.; Roosli, M. Symptoms and Cognitive Functions in Adolescents in Relation to Mobile Phone Use during Night. PLoS ONE 2015, 10, e0133528. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  249. Schweizer, A.; Berchtold, A.; Barrense-Dias, Y.; Akre, C.; Suris, J.C. Adolescents with a smartphone sleep less than their peers. Eur. J. Pediatr. 2017, 176, 131–136. [Google Scholar] [CrossRef] [PubMed]
  250. Segev, A.; Mimouni-Bloch, A.; Ross, S.; Silman, Z.; Maoz, H.; Bloch, Y. Evaluating computer screen time and its possible link to psychopathology in the context of age: A cross-sectional study of parents and children. PLoS ONE 2015, 10, e0140542. [Google Scholar] [CrossRef] [PubMed]
  251. Seo, D.G.; Park, Y.; Kim, M.K.; Park, J. Mobile phone dependency and its impacts on adolescents’ social and academic behaviors. Comput. Hum. Behav. 2016, 63, 282–292. [Google Scholar] [CrossRef]
  252. Seo, M.; Kim, J.H.; David, P. Always connected or always distracted? Adhd symptoms and social assurance explain problematic use of mobile phone and multicommunicating. J. Comput. Mediat. Commun. 2015. [Google Scholar] [CrossRef]
  253. Seok, S.; DaCosta, B. Predicting video game behavior: An investigation of the relationship between personality and mobile game play. Games Culture 2015, 10, 481–501. [Google Scholar] [CrossRef]
  254. Sharma, M.K.; Rao, G.N.; Benegal, V.; Thennarasu, K.; Thomas, D. Technology Addiction Survey: An Emerging Concern for Raising Awareness and Promotion of Healthy Use of Technology. Indian J. Psychol. Med. 2017, 39, 495–499. [Google Scholar] [CrossRef] [PubMed]
  255. Siddiqui, K. Personality influences mobile phone usage. Interdiscip. J. Contemp. Res. Bus. 2011, 3, 554–563. [Google Scholar]
  256. Smetaniuk, P. A preliminary investigation into the prevalence and prediction of problematic cell phone use. J. Behav. Addict. 2014, 3, 41–53. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  257. Sohn, M.; Oh, H.; Lee, S.K.; Potenza, M.N. Suicidal Ideation and Related Factors among Korean High School Students: A Focus on Cyber Addiction and School Bullying. J. Sch. Nurs. 2017, 34, 310–318. [Google Scholar] [CrossRef] [PubMed]
  258. Stachl, C.; Hilbert, S.; Au, J.Q.; Buschek, D.; De Luca, A.; Bischl, B.; Hussmann, H.; Bühner, M. Personality traits predict smartphone usage. Eur. J. Personal. 2017, 31, 701–722. [Google Scholar] [CrossRef]
  259. Steelman, Z.R.; Soror, A.A. Why do you keep doing that? The biasing effects of mental states on IT continued usage intentions. Comput. Hum. Behav. 2017, 73, 209–223. [Google Scholar] [CrossRef]
  260. Subba, S.H.; Mandelia, C.; Pathak, V.; Reddy, D.; Goel, A.; Tayal, A.; Nair, S.; Nagaraj, K. Ringxiety and the Mobile Phone Usage Pattern among the Students of a Medical College in South India. J. Clin. Diagn. Res. 2013, 7, 205–209. [Google Scholar] [CrossRef] [PubMed]
  261. Takao, M. Problematic mobile phone use and big-five personality domains. Indian J. Community Med. 2014, 39, 111–113. [Google Scholar] [CrossRef] [PubMed]
  262. Takao, M.; Takahashi, S.; Kitamura, M. Addictive personality and problematic mobile phone use. Cyberpsychol. Behav. 2009, 12, 501–507. [Google Scholar] [CrossRef] [PubMed]
  263. Takeuchi, H.; Yamazaki, Y.; Oki, K.; Wada, K.; Noji, T.; Kawada, T.; Nakade, M.; Krejci, M.; Harada, T. Effects of chronotype and environmental factors upon sleep and mental health in Japanese students aged 18–40 yrs. Biol. Rhythm. Res. 2015, 46, 771–784. [Google Scholar] [CrossRef]
  264. Tamura, H.; Nishida, T.; Tsuji, A.; Sakakibara, H. Association between Excessive Use of Mobile Phone and Insomnia and Depression among Japanese Adolescents. Int. J. Environ. Res. Public Health 2017, 14, 701. [Google Scholar] [CrossRef] [PubMed]
  265. Tanis, M.; Beukeboom, C.J.; Hartmann, T.; Vermeulen, I.E. Phantom phone signals: An investigation into the prevalence and predictors of imagined cell phone signals. Comput. Hum. Behav. 2015, 51, 356–362. [Google Scholar] [CrossRef]
  266. Tao, S.; Wu, X.; Zhang, S.; Tong, S.; Hao, J.; Tao, F. Association of alcohol use with problematic mobile phone use and depressive symptoms among college students in Anhui, China. J. Public Health 2017, 25, 103–112. [Google Scholar] [CrossRef]
  267. Tao, S.; Wu, X.; Zhang, Y.; Zhang, S.; Tong, S.; Tao, F. Effects of Sleep Quality on the Association between Problematic Mobile Phone Use and Mental Health Symptoms in Chinese College Students. Int. J. Environ. Res. Public Health 2017, 14, 185. [Google Scholar] [CrossRef] [PubMed]
  268. Thomée, S.; Eklöf, M.; Gustafsson, E.; Nilsson, R.; Hagberg, M. Prevalence of perceived stress, symptoms of depression and sleep disturbances in relation to information and communication technology (ICT) use among young adults—An explorative prospective study. Comput. Hum. Behav. 2007, 23, 1300–1321. [Google Scholar] [CrossRef]
  269. Thomée, S.; Härenstam, A.; Hagberg, M. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults—A prospective cohort study. BMC Public Health 2011, 11, 66. [Google Scholar] [CrossRef] [PubMed]
  270. Titilope, A.O. Socio-psychological dimensions of mobile phone addiction and usage patterns amongst teenagers in higher institutions of learning in Kwara State. Int. J. Inf. Commun. Technol. Educ. 2014, 10, 1–3. [Google Scholar] [CrossRef]
  271. Toda, M.; Ezoe, S.; Takeshita, T. Mobile phone use and stress-coping strategies of medical students. Int. J. Cyber Behav. Psychol. Learn. 2014, 4, 41–46. [Google Scholar] [CrossRef]
  272. Toda, M.; Monden, K.; Kubo, K.; Morimoto, K. Mobile phone dependence and health-related lifestyle of university students. Soc. Behav. Personal. 2006, 34, 1277–1284. [Google Scholar] [CrossRef]
  273. Toda, M.; Nishio, N.; Ezoe, S.; Takeshita, T. Chronotype and smartphone use among Japanese medical students. Int. J. Cyber Behav. Psychol. Learn. 2015, 5, 75–80. [Google Scholar] [CrossRef]
  274. Tokiya, M.; Kaneita, Y.; Itani, O.; Jike, M.; Ohida, T. Predictors of insomnia onset in adolescents in Japan. Sleep Med. 2017, 38, 37–43. [Google Scholar] [CrossRef] [PubMed]
  275. Tsimtsiou, Z.; Haidich, A.B.; Drontsos, A.; Dantsi, F.; Sekeri, Z.; Drosos, E.; Trikilis, N.; Dardavesis, T.; Nanos, P.; Arvanitidou, M. Pathological Internet use, cyberbullying and mobile phone use in adolescence: A school-based study in Greece. Int. J. Adolesc. Med. Health 2017. [Google Scholar] [CrossRef] [PubMed]
  276. Walsh, S.P.; White, K.M.; McD Young, R. Needing to connect: The effect of self and others on young people’s involvement with their mobile phones. Aust. J. Psychol. 2010, 62, 194–203. [Google Scholar] [CrossRef] [Green Version]
  277. Van den Bulck, J. Text messaging as a cause of sleep interruption in adolescents, evidence from a cross-sectional study. J. Sleep Res. 2003, 12, 263. [Google Scholar] [CrossRef] [PubMed]
  278. Van Deursen, A.J.A.M.; Bolle, C.L.; Hegner, S.M.; Kommers, P.A.M. Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Comput. Hum. Behav. 2015, 45, 411–420. [Google Scholar] [CrossRef]
  279. Wang, H.-H.; Wang, M.-C.; Wu, S.-Q. Mobile phone addiction symptom profiles related to interpersonal relationship and loneliness for college students: A latent profile analysis. Chin. J. Clin. Psychol. 2015, 23, 881–885. [Google Scholar]
  280. Wang, J.L.; Wang, H.Z.; Gaskin, J.; Wang, L.H. The role of stress and motivation in problematic smartphone use among college students. Comput. Hum. Behav. 2015, 53, 181–188. [Google Scholar] [CrossRef]
  281. Wang, P.; Zhao, M.; Wang, X.; Xie, X.; Wang, Y.; Lei, L. Peer relationship and adolescent smartphone addiction: The mediating role of self-esteem and the moderating role of the need to belong. J. Behav. Addict. 2017, 6, 708–717. [Google Scholar] [CrossRef] [PubMed]
  282. Wang, P.W.; Liu, T.L.; Ko, C.H.; Lin, H.C.; Huang, M.F.; Yeh, Y.C.; Yen, C.F. Association between problematic cellular phone use and suicide: The moderating effect of family function and depression. Compr. Psychiatry 2014, 55, 342–348. [Google Scholar] [CrossRef] [PubMed]
  283. Wang, Y.; Zou, Z.; Song, H.; Xu, X.; Wang, H.; d’Oleire Uquillas, F.; Huang, X. Altered Gray Matter Volume and White Matter Integrity in College Students with Mobile Phone Dependence. Front. Psychol. 2016, 7, 597. [Google Scholar] [CrossRef] [PubMed]
  284. Warzecha, K.; Pawlak, A. Pathological use of mobile phones by secondary school students. Arch. Psychiatry Psychother. 2017, 19, 27–36. [Google Scholar] [CrossRef]
  285. Venkatesh, E.; Jemal, M.Y.; Samani, A.S. Smart phone usage and addiction among dental students in Saudi Arabia: A cross sectional study. Int. J. Adolesc. Med. Health 2017. [Google Scholar] [CrossRef] [PubMed]
  286. Vernon, L.; Modecki, K.L.; Barber, B.L. Mobile Phones in the Bedroom: Trajectories of Sleep Habits and Subsequent Adolescent Psychosocial Development. Child Dev. 2018, 89, 66–77. [Google Scholar] [CrossRef] [PubMed]
  287. Wolniewicz, C.A.; Tiamiyu, M.F.; Weeks, J.W.; Elhai, J.D. Problematic smartphone use and relations with negative affect, fear of missing out, and fear of negative and positive evaluation. Psychiatry Res. 2018, 262, 618–623. [Google Scholar] [CrossRef] [PubMed]
  288. Wu, A.M.; Cheung, V.I.; Ku, L.; Hung, E.P. Psychological risk factors of addiction to social networking sites among Chinese smartphone users. J. Behav. Addict. 2013, 2, 160–166. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  289. Yang, Y.S.; Yen, J.Y.; Ko, C.H.; Cheng, C.P.; Yen, C.F. The association between problematic cellular phone use and risky behaviors and low self-esteem among Taiwanese adolescents. BMC Public Health 2010, 10, 217. [Google Scholar] [CrossRef] [PubMed]
  290. Yen, C.-F.; Tang, T.-C.; Yen, J.-Y.; Lin, H.-C.; Huang, C.-F.; Liu, S.-C.; Ko, C.-H. Symptoms of problematic cellular phone use, functional impairment and its association with depression among adolescents in Southern Taiwan. J. Adolesc. 2009, 32, 863–873. [Google Scholar] [CrossRef] [PubMed]
  291. Yuchang, J.; Cuicui, S.; Junxiu, A.; Junyi, L. Attachment styles and smartphone addiction in Chinese college students: The mediating roles of dysfunctional attitudes and self-esteem. Int. J. Ment. Health Addict. 2017, 15, 1122–1134. [Google Scholar] [CrossRef]
  292. Yun, I.; Kim, S.G.; Kwon, S. Low self-control among South Korean adolescents: A test of Gottfredson and Hirschi’s Generality Hypothesis. Int. J. Offender Ther. Comp. Criminol. 2016, 60, 1185–1208. [Google Scholar] [CrossRef] [PubMed]
  293. Zarghami, M.; Khalilian, A.; Setareh, J.; Salehpour, G. The Impact of Using Cell Phones After Light-Out on Sleep Quality, Headache, Tiredness, and Distractibility Among Students of a University in North of Iran. Iran. J. Psychiatry Behav. Sci. 2015, 9, e2010. [Google Scholar] [CrossRef] [PubMed]
  294. Zhitomirsky-Geffet, M.; Blau, M. Cross-generational analysis of predictive factors of addictive behavior in smartphone usage. Comput. Hum. Behav. 2016, 64, 682–693. [Google Scholar] [CrossRef]
  295. Billieux, J. Problematic use of the mobile phone: A literature review and a pathways model. Curr. Psychiatry Rev. 2012, 8, 299–307. [Google Scholar] [CrossRef]
  296. Young, K.S. Internet addiction: The emergence of a new clinical disorder. Cyberpsychol. Behav. 1998, 1, 237–244. [Google Scholar] [CrossRef] [Green Version]
  297. Andrews, S.; Ellis, D.A.; Shaw, H.; Piwek, L. Beyond self-report: Tools to compare estimated and real-world smartphone use. PLoS ONE 2015, 10, e0139004. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  298. Yildirim, C.; Correia, A.P. Exploring the dimensions of nomophobia: Development and validation of a self-reported questionnaire. Comput. Hum. Behav. 2015, 49, 130–137. [Google Scholar] [CrossRef]
  299. Tavernier, R.; Willoughby, T. Sleep problems: Predictor or outcome of media use among emerging adults at university? J. Sleep Res. 2014, 23, 389–396. [Google Scholar] [CrossRef] [PubMed]
  300. Exelmans, L.; Van den Bulck, J. The Use of Media as a Sleep Aid in Adults. Behav. Sleep Med. 2014, 14, 121–133. [Google Scholar] [CrossRef] [PubMed]
  301. Boase, J.; Ling, R. Measuring Mobile Phone Use: Self-Report versus Log Data. J. Comput. Mediat. Commun. 2013, 18, 508–519. [Google Scholar] [CrossRef]
  302. Riemann, D.; Spiegelhalder, K.; Feige, B.; Voderholzer, U.; Berger, M.; Perlis, M.; Nissen, C. The hyperarousal model of insomnia: A review of the concept and its evidence. Sleep Med. Rev. 2010, 14, 19–31. [Google Scholar] [CrossRef] [PubMed]
  303. Chang, A.M.; Aeschbach, D.; Duffy, J.F.; Czeisler, C.A. Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proc. Natl. Acad. Sci. USA 2015, 112, 1232–1237. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  304. Wood, B.; Rea, M.S.; Plitnick, B.; Figueiro, M.G. Light level and duration of exposure determine the impact of self-luminous tablets on melatonin suppression. Appl. Ergon. 2013, 44, 237–240. [Google Scholar] [CrossRef] [PubMed]
Table 1. Search strategies in PubMed and PsycINFO 2018-03-19.
Table 1. Search strategies in PubMed and PsycINFO 2018-03-19.
DatabaseSearch Strings
PubMed“cell phones” [MeSH Terms] OR “mobile phone” [Text Word] OR “mobile telephone” [Text Word] OR “cell phone” [Text Word] OR “cellular phone” [Text Word] OR “cellular telephone” [Text Word] OR “mobile phones” [Text Word] OR “mobile telephones” [Text Word] OR “cellular phones” [Text Word] OR “cellular telephones” [Text Word] OR smartphone [MeSH Terms] OR smartphone [Text Word]
Stress [Title/Abstract] OR Depress* [Title/Abstract] OR Sleep* [Title/Abstract] OR Addict* [Title/Abstract] OR problem* [Title/Abstract] OR Mental [Title/Abstract] OR psychol* [Title/Abstract] OR psychi* [Title/Abstract] OR insomnia [Title/Abstract] OR compuls* [Title/Abstract] OR patholog* [Title/Abstract] OR dependen* [Title/Abstract] OR anxi* [Title/Abstract] OR symptom* [Title/Abstract]
(“1993/01/01” [PDat]: “2017/12/31” [PDat])
(English [lang] OR Norwegian [lang] OR Swedish [lang])
PsycINFOti (“cell phones” OR “mobile phone” OR “mobile telephone” OR “cell phone” OR “cellular phone” OR “cellular telephone” OR “mobile phones” OR “mobile telephones” OR “cellular phones” OR “cellular telephones” OR “smart phone” OR “smart phones” OR “smartphone” OR “smartphones”) OR ab(“cell phones” OR “mobile phone” OR “mobile telephone” OR “cell phone” OR “cellular phone” OR “cellular telephone” OR “mobile phones” OR “mobile telephones” OR “cellular phones” OR “cellular telephones” OR “smart phone” OR “smart phones” OR “smartphone” OR “smartphones”)
ti (Stress OR Depress* OR Sleep* OR Addict* OR problem* OR Mental OR psychol* OR psychi* OR insomnia OR compuls* OR patholog* OR dependen* OR anxi* OR symptom*) OR ab(Stress OR Depress* OR Sleep* OR Addict* OR problem* OR Mental OR psychol* OR psychi* OR insomnia OR compuls* OR patholog* OR dependen* OR anxi* OR symptom*)
Peer review,
Eng, No
Table 2. Number of included papers (n = 290) by publication year.
Table 2. Number of included papers (n = 290) by publication year.
20012002200320042005200620072008200920102011201220132014201520162017 1
1 Six papers were dated 2018 but had been published online previously and were categorized as 2017.
Table 3. Frequency/duration of mobile phone use: summary of main results.
Table 3. Frequency/duration of mobile phone use: summary of main results.
OutcomesStudy Designs and Citations
• Depression L: [26,268,269] CS: [29,53,113,139,140,239,244,264,269] NA: [74,75]
• Sleep problems, lower sleep qualityL: [268,269,274] CS: [117,149,187,269]
• Later bedtimes, shorter sleepCS: [103,113,117,228]
• Tiredness, reduced daytime functionCS: [65,113,117,187,228]
• Lower mental well-beingCS: [227] NA: [37]
• StressL: [268] CS: [269]
• AnxietyCS: [29,162]
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.
Table 4. Bedtime mobile phone use: summary of main results.
Table 4. Bedtime mobile phone use: summary of main results.
OutcomesStudy Designs and Citations
• Sleep problemsCS: [5,14,79,85,97,144,199,202,205,235,269,293]
• Lower sleep quality/efficiencyCS: [5,32,53,71,79,82,83,167,202,205]
• Longer sleep onset latencyCS: [53,79,112,223,293]
• Poor sleep behaviorL: [286] CS: [286]
• Later bedtimesCS: [16,22,31,82,85,88,93,223,263]
• Shorter sleepCS: [14,15,22,36,71,82,86,148,161,202,210]
• Tiredness, reduced daytime functionL: [148] CS: [79,86,93,112,121,202,223,235,242,248,277,293]
• DepressionCS: [161,210,242,269,286]
L = Longitudinal, CS = Cross-sectional.
Table 5. Problematic mobile phone use: summary of main results.
Table 5. Problematic mobile phone use: summary of main results.
OutcomesStudy Designs and Citations
• DepressionL: [123] (bidirectional)
CS: [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] NA: [50,57,74,75]
• AnxietyCS: [7,39,42,50,62,67,68,74,75,76,80,89,100,108,115,135,157,180,184,189,198,214,245,267] NA: [185]
• Sleep problemsL: [269] CS: [7,32,115,269]
• Lower sleep qualityL: [156] CS: [38,39,62,80,110,195,240]
• Shorter sleepCS: [110,130,179,289]
• StressCS: [18,46,89,105,106,116,131,143,180,243,269,280,285]
• Lower mental wellbeingCS: [20,23,76,80,127,237]
• Other behavioral addictionsCS: [12,19,43,45,50,52,63,78,100,105,118,126,127,145,146,154,178,186,188,198,217,236,245,266]
L = Longitudinal, CS = Cross-sectional, NA = Negative association.
Table 6. Summary of the psychological factors most commonly associated with mobile phone use (all aspects).
Table 6. Summary of the psychological factors most commonly associated with mobile phone use (all aspects).
Psychological FactorsCitations
• Impulsivity/less self-control[27,28,29,30,33,46,56,67,68,102,110,114,116,119,120,130,137,166,233,234,256,283,288,292]
• Extraversion[12,13,18,25,34,46,64,81,108,200,255,256,261]
• Neuroticism[13,34,46,73,81,90,111,142,147,198,218,261,294]
• Less self-esteem[13,25,73,100,108,256,281,286,289,291] NA: [95]
• Loneliness[24,91,98,129,132,133,158,231,270,279] NA: [91]
• Less conscientiousness[13,34,92,111,142,147,169,170]
• Low agreeableness[12,34,73,147,220,253]
• Social anxiety, shyness[24,58,91,102,159,231] NA: [91]
• Less openness[12,111,147,218,261]
• Fear of missing out[52,74,153,209,287]
NA = Negative association.

Share and Cite

MDPI and ACS Style

Thomée, S. Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure. Int. J. Environ. Res. Public Health 2018, 15, 2692.

AMA Style

Thomée S. Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure. International Journal of Environmental Research and Public Health. 2018; 15(12):2692.

Chicago/Turabian Style

Thomée, Sara. 2018. "Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure" International Journal of Environmental Research and Public Health 15, no. 12: 2692.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop