Ying Li, Guangxiao Li, Li Liu, Hui Wu. Correlations between mobile phone addiction and anxiety, depression, impulsivity, and poor sleep quality among college students: A systematic review and meta-analysis. J Behav Addict. 2020 Sep 8. doi: 10.1556/2006.2020.00057.
Background and aims: Mobile phone addiction (MPA) is frequently reported to be correlated with anxiety, depression, stress, impulsivity, and sleep quality among college students. However, to date, there is no consensus on the extent to which those factors are correlated with MPA among college students. We thus performed a meta-analysis to quantitatively synthesize the previous findings.
Methods: A systematic review and meta-analysis was conducted by searching PubMed, Embase, Cochrane Library, Wanfang, Chinese National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Chinese Biological Medicine (CBM) databases from inception to August 1, 2020. Pooled Pearson's correlation coefficients between MPA and anxiety, depression, impulsivity, and sleep quality were calculated by R software using random effects model.
Results: Forty studies involving a total of 33,650 college students were identified. Weak-to-moderate positive correlations were found between MPA and anxiety, depression, impulsivity and sleep quality (anxiety: summary r = 0.39, 95% CI = 0.34-0.45, P < 0.001, I2 = 84.9%; depression: summary r = 0.36, 95% CI = 0.32-0.40, P < 0.001, I2 = 84.2%; impulsivity: summary r = 0.38, 95% CI = 0.28-0.47, P < 0.001, I2 = 94.7%; sleep quality: summary r = 0.28, 95% CI = 0.22-0.33, P < 0.001, I2 = 85.6%). The pooled correlations revealed some discrepancies when stratified by some moderators. The robustness of our findings was further confirmed by sensitivity analyses.
O'Donnell S, Epstein LH. Smartphones are more reinforcing than food for students. Addict Behav. 2018 Oct 18;90:124-133. doi: 10.1016/j.addbeh.2018.10.018.
• College students engage in high frequency smartphone use despite consequences.
College students share a perception that smartphone ownership is beneficial; however smartphone use has been linked to increased anxiety (Jenaro, Flores, Gómez-Vela, González-Gil, & Caballo, 2007), social dysfunction (Jenaro et al., 2007) insomnia (Jenaro et al., 2007), low self-esteem (Bianchi & Phillips, 2005; Smetaniuk, 2014), emotional instability (Roberts, Pullig, & Manolis, 2015; Smetaniuk, 2014) and depression (Ezoe et al., 2009; Smetaniuk, 2014). Temporarily removing cell phones from high frequency cell phone users increased self-reported anxiety over a 75 min time period in comparison to less frequent users (Cheever et al., 2014). Despite the negative outcomes associated with problematic smartphone use, college students are highly motivated to use their smartphones.
Kates AW, Wu H, Chris LSC. The effects of mobile phone use on academic performance: A meta-analysis. Computers & Education. 127:107-112. Dec 2018.
• Study purpose is to further examine any relationships that may exist between mobile phone use and educational achievement.
Purpose Although the mobile phone has been conspicuously proliferated in the past decades, little is known about its influence; especially its effect on student learning and academic performance. Although there is a growing interest in mobile devices and their correlates and consequences for children, effects vary across related studies and the magnitude of the overall effect remains unclear. The purpose of this study is to further examine any relationships that may exist between mobile phone use and educational achievement.
Research design A meta-analysis of research conducted on the relationship between mobile phone use and student educational outcomes over a 10-year period (2008–2017) was conducted. The operational definition of cell phone use to guide the implementation of this study is: any measure of mobile phone use, whether considered normative or problematic, that quantifies the extent to which a person uses a phone, feels an emotional or other dependence on a phone, or categorizes the types of uses and situations in which use occurs. Studies examining use for the express purpose of educational improvement are not included, as the aim of this study is to ascertain the effects of normal smartphone use. The operational definition of academic achievement to guide the implementation of this study is: any measure that quantifies the extent to which a student or group of students is performing or feels he or she is performing to a satisfactory level, including but not limited to letter grades and test scores, knowledge and skill acquisition, and self-reported measures of academic ability or difficulty.
Findings The overall meta-analysis indicated that the average effect of mobile phone usage on student outcomes was r = −0.162 with a 95% confident interval of −0.196 to −0.128. The effect sizes of moderator variables (education level, region, study type, and whether the effect size was derived from a Beta coefficient, and mobile phone use construct) were analyzed. The results of this study and their implications for both research and practice are discussed.
The results of this study indicate that, overall, mobile phone use has a small negative effect (r = −0.16) on educational outcomes which is consistent with the previous literature (Lepp et al., 2015; Li et al., 2015). However, the results caution against coming to hasty conclusions based on these findings. The summary effect size is relatively small, even in the educational sphere. Hattie (2012), for example, conducted over 900 educational meta-analyses and found the largest summary effect for a classroom intervention to be a Cohen's d of 1.44. Taking this into account, it is not surprising that something so ubiquitous and increasingly integral to students' lives would have some influence on educational outcomes. Additionally, although the publication bias analysis suggests that these results are not greatly biased by a systematic exclusion of studies, it should be noted that the effects observed could be indicative of an association rather than causation. For example, those who are predisposed to overuse mobile devices may simply be less likely to achieve academically in the first place. That the summary effect is derived from studies involving experimental groups as well as cross-sectional studies, however, brings this possibility into question....
Despite the variability between studies, there appears to be a consistent negative, albeit small, effect on educational achievement. This suggests that avoidance of mobile phones in educational settings, or for those who are currently in school, could be beneficial for academic achievement.....
Kuss DJ, Kanjo E, Crook-Rumsey M, Kibowski F, Wang GY, Sumich A. Problematic mobile phone use and addiction across generations: the roles of psychopathological symptoms and smartphone use. J Technol Behav Sci. 2018;3(3):141-149. doi: 10.1007/s41347-017-0041-3.
Contemporary technological advances have led to a significant increase in using mobile technologies. Recent research has pointed to potential problems as a consequence of mobile overuse, including addiction, financial problems, dangerous use (i.e. whilst driving) and prohibited use (i.e. use in forbidden areas). The aim of this study is to extend previous findings regarding the predictive power of psychopathological symptoms (depression, anxiety and stress), mobile phone use (i.e. calls, SMS, time spent on the phone, as well as the engagement in specific smartphone activities) across Generations X and Y on problematic mobile phone use in a sample of 273 adults. Findings revealed prohibited use and dependence were predicted by calls/day, time on the phone and using social media. Only for dependent mobile phone use (rather than prohibited), stress appeared as significant. Using social media and anxiety significantly predicted belonging to Generation Y, with calls per day predicted belonging to Generation X. This finding suggests Generation Y are more likely to use asynchronous social media-based communication, whereas Generation X engage more in synchronous communication. The findings have implications for prevention and awareness-raising efforts of possibly problematic mobile phone use for educators, parents and individuals, particularly including dependence and prohibited use.
Kim J-H. Psychological issues and problematic use of smartphone: ADHD's moderating role in the associations among loneliness, need for social assurance, need for immediate connection, and problematic use of smartphone. Computers in Human Behavior. 80:390-398. Mar 2018. https://doi.org/10.1016/j.chb.2017.11.025
Going beyond looking at the direct association between psychological issues (loneliness and ADHD) and problematic use of media (smartphone), the present study examined the covert mechanism connecting the two. NSA (need for social assurance) and NIC (need for immediate connection) were selected as mediating steps between the two. A total of 615 U.S. American participants were recruited nationally for survey participation. Research findings suggest that individuals who are lonely would rely on smartphone hoping to be connected with and get assurance from others, but might end up struggling with problematic use of smartphone. Those with ADHD showed higher levels of loneliness, NSA, NIC, and problematic use of smartphone, and also showed stronger associations linking loneliness, NSA and NIC compared to those without ADHD. Face-to-face (FtF) interaction decreased the association between NSA and NIC for those with ADHD.
Carbonell X, Chamarro A, Oberst U, Rodrigo B, Prades M. Problematic Use of the Internet and Smartphones in University Students: 2006-2017. Intl J Environ Research Publ Health. 15(3). Article 475. Mar 2018.
Zou Z, Wang H, d'Oleire Uquillas F, Wang X, Ding J, Chen H. Definition of Substance and Non-substance Addiction. Adv Exp Med Biol. 2017;1010:21-41.
Results: Compared with normal users, participants who were addicted to smartphones were more likely to have experienced any accidents (OR = 1.90, 95% CI: 1.26-2.86), falling from height/slipping (OR = 2.08, 95% CI: 1.10-3.91), and bumps/collisions (OR = 1.83, 95% CI: 1.16-2.87). The proportion of participants who used their smartphones mainly for entertainment was significantly high in both the accident (38.76%) and smartphone addiction (36.40%) groups.
Discussion and conclusions: We suggest that smartphone addiction was significantly associated with total accident, falling/slipping, and bumps/collisions. This finding highlighted the need for increased awareness of the risk of accidents with smartphone addiction.
Han S, Kim KJ, Kim JH. Understanding Nomophobia: Structural Equation Modeling and Semantic Network Analysis of Smartphone Separation Anxiety. Cyberpsychol Behav Soc Netw. 2017 Jul;20(7):419-427. doi: 10.1089/cyber.2017.0113.
With widespread use of the smartphone, clinical evidence for smartphone addiction remains unclear. Against this background, we analyzed the effect of smartphone use patterns on smartphone addiction in Korean adolescents. A total of 370 middle school students participated. The severity of smartphone addiction was measured through clinical interviews and the Korean Smartphone Addiction Proneness Scale. As a result, 50 (13.5%) were in the smartphone addiction group and 320 (86.5%) were in the healthy group. To investigate the effect of smartphone use patterns on smartphone addiction, we performed self-report questionnaires that assessed the following items: smartphone functions mostly used, purpose of use, problematic use, and parental attitude regarding smartphone use. For smartphone functions mostly used, the addiction group showed significantly higher scores in "Online chat." For the purpose of use, the addiction group showed significantly higher "habitual use," "pleasure," "communication," "games," "stress relief," "ubiquitous trait," and "not to be left out." For problematic use, the addiction group showed significantly higher scores on "preoccupation," "tolerance," "lack of control," "withdrawal," "mood modification," "conflict," "lies," "excessive use," and "loss of interest." For parental attitude regarding children's smartphone use, the addiction group showed significantly higher scores in "parental punishment." Binary logistic regression analysis indicated that "female," "use for learning," "use for ubiquitous trait," "preoccupation," and "conflict" were significantly correlated with smartphone addiction. This study demonstrated that the risk factors for smartphone addiction were being female, preoccupation, conflict, and use for ubiquitous trait; the protective factor was use for learning. Future studies will be required to reveal the additional clinical evidence of the disease entity for smartphone addiction.
Kuss DJ, Griffiths MD. Social Networking Sites and Addiction: Ten Lessons Learned. Int J Environ Res Public Health. 2017 Mar 17;14(3). pii: E311. doi: 10.3390/ijerph14030311.
De-Sola Gutiérrez J, Rodríguez de Fonseca F, Rubio G. Cell-Phone Addiction: A Review. Front Psychiatry. 2016 Oct 24;7:175.
We present a review of the studies that have been published about addiction to cell phones. We analyze the concept of cell-phone addiction as well as its prevalence, study methodologies, psychological features, and associated psychiatric comorbidities. Research in this field has generally evolved from a global view of the cell phone as a device to its analysis via applications and contents. The diversity of criteria and methodological approaches that have been used is notable, as is a certain lack of conceptual delimitation that has resulted in a broad spread of prevalent data. There is a consensus about the existence of cell-phone addiction, but the delimitation and criteria used by various researchers vary. Cell-phone addiction shows a distinct user profile that differentiates it from Internet addiction. Without evidence pointing to the influence of cultural level and socioeconomic status, the pattern of abuse is greatest among young people, primarily females. Intercultural and geographical differences have not been sufficiently studied. The problematic use of cell phones has been associated with personality variables, such as extraversion, neuroticism, self-esteem, impulsivity, self-identity, and self-image. Similarly, sleep disturbance, anxiety, stress, and, to a lesser extent, depression, which are also associated with Internet abuse, have been associated with problematic cell-phone use. In addition, the present review reveals the coexistence relationship between problematic cell-phone use and substance use such as tobacco and alcohol.
There are increasing numbers of people who are now using smartphones. Consequently, there is a risk of addiction to certain web applications such as social networking sites (SNSs) which are easily accessible via smartphones. There is also the risk of an increase in narcissism amongst users of SNSs. The present study set out to investigate the relationship between smartphone use, narcissistic tendencies and personality as predictors of smartphone addiction. The study also aimed to investigate the distinction between addiction specificity and co-occurrence in smartphone addiction via qualitative data and discover why people continue to use smartphones in banned areas. A self-selected sample of 256 smartphone users (Mean age = 29.2, SD = 9.49) completed an online survey. The results revealed that 13.3% of the sample was classified as addicted to smartphones. Higher narcissism scores and neuroticism levels were linked to addiction. Three themes of; social relations, smartphone dependence and self-serving personalities emerged from the qualitative data. Interpretation of qualitative data supports addiction specificity of the smartphone. It is suggested smartphones encourage narcissism, even in non-narcissistic users. In turn, this increased use in banned areas. Future research needs to gather more in-depth qualitative data, addiction scale comparisons and comparison of use with and without SNS access. It is advised that prospective buyers of smartphones be pre-warned of the potential addictive properties of new technology.
Sussman CJ, Harper JM, Stahl JL, Weigel P. Internet and Video Game Addictions: Diagnosis, Epidemiology, and Neurobiology. Child Adolesc Psychiatric Clin N Am 27 (2018) 307–326. https://doi.org/10.1016/j.chc.2017.11.015.
- Proposed criteria for diagnosis of Internet gaming disorder and other digital technology addictions are analogous to those for substance use or gambling disorders.
- Diagnosis of Internet and video game addictions should include both screening tools and clinical interview for “red flags,” such as academic decline, sleep disruption, and changes in real-life activities and relationships.
- Epidemiologic studies, limited by variation in diagnostic methods, yield prevalence estimates ranging from less than 1.0% to 26.8%.
- Internet and video game addictions are associated with psychological and social comorbidities, such as depression, attention-deficit/hyperactivity disorder, alcohol use, anxiety,and poor psychosocial support.
- Neurobiological evidence suggests a dual processing model of digital technology addictions characterized by an imbalance between the reactive reward system and the reflective reward system
In spite of the lack of a consensus on diagnosis, and the resulting variations in epidemiologic, comorbidity, and neurobiological research, these studies provide overwhelming evidence of similarities between IVGA and SUD. Taken as a whole, the research presented here strongly suggests that IVGA is a clinically relevant and valid syndrome. Like other addictions, it is better understood when incorporating a neuro-biological perspective. Our field must successfully address IVGAs to meet the needs of a society that is increasingly enmeshed in digital technology. Research in this area should continue to accelerate, allowing clinicians to better screen for, diagnose, psycho-educate, and provide multimodal treatment for our patients with IVGA. Treatment of IVGAs is explored in David N. Greenfield’s article, “Treatment Considerations in Internet and Video Game Addiction: A Qualitative Discussion,” in this issue.
Significant limitations in the current body of research include the difficulty in determining causality among many epidemiologic correlations, the limited knowledge of brain changes occurring in IVGA, including whether they are reversible, and the absence of animal model studies. These weaknesses will likely continue to encourage challenges to the validity of IVGA from critics. Some argue that digital technology use is so pervasive that the diagnosis may overpathologize behavior that is normative and acceptable in our culture.105 On the other hand, modern society’s excessive engagement with technology risks falsely normalizing addictions to technology, in what may be a culture of “functional tech-oholics.” It seems difficult to walk down a public street without seeing multiple passersby engaged with smartphones, or to partake in a group conversation with no mention of digital media in some form. It seems evident that the human brain cannot evolve fast enough to adapt to the progress of digital technology, and that even the most powerful prefrontal cortex may be unable to resist the allure of instant stimulation in the ocean of digital screens that our world is becoming. Regardless of where we place the diagnostic cutoff for IVGA, our patients suffering the most profound dysfunction from their use of digital technology need better resources to recognize, understand, and treat their condition. If IVGA proves to be more abundant than a collection of a few extreme cases, it will be even more vital for our psychoeducational interventions to reach not only affected individuals, but their families and the communities as well. Ironically, social media and other forms of screen-based education may prove the best platforms for reaching out to those suffering IVGA without the insight, knowledge, and resources to address it.a This fact reminds us that learning more about the benefits of digital technology as well as its risks represents a challenge for modern providers and an opportunity for contemporary researchers.
OBJECTIVE: To perform a systematic review and meta-analysis of observational studies that investigated the putative association between internet addiction and suicidality.
DATA SOURCES: Major electronic databases (PubMed, Embase, ClinicalKey, Cochrane Library, ProQuest, Science Direct, and ClinicalTrials.gov) were searched using the following keywords (internet addiction OR internet gaming disorder OR internet use disorder OR pathological internet use OR compulsive internet use OR problematic internet use) AND (suicide OR depression) to identify observational studies from inception to October 31, 2017.
STUDY SELECTION: We included 23 cross-sectional studies (n = 270,596) and 2 prospective studies (n = 1,180) that investigated the relationship between suicide and internet addiction.
DATA EXTRACTION: We extracted the rates of suicidal ideation, planning, and attempts in individuals with internet addiction and controls.
RESULTS: The individuals with internet addiction had significantly higher rates of suicidal ideation (odds ratio [OR] = 2.952), planning (OR = 3.172), and attempts (OR = 2.811) and higher severity of suicidal ideation (Hedges g = 0.723). When restricted to adjusted ORs for demographic data and depression, the odds of suicidal ideation and attempts were still significantly higher in the individuals with internet addiction (ideation: pooled adjusted OR = 1.490; attempts: pooled adjusted OR = 1.559). In subgroup analysis, there was a significantly higher prevalence rate of suicidal ideation in children (age less than 18 years) than in adults (OR = 3.771 and OR = 1.955, respectively).
Fumero A, Marrero RJ, Voltes D, Peñate W. Personal and social factors involved in internet addiction among adolescents: A meta-analysis. Computers in Human Behavior. 86:387-400. Sep 2018. https://doi.org/10.1016/j.chb.2018.05.005
• Internet addiction (IA) was associated with psychosocial factors in adolescents.
Methods The search included cross-sectional, case-control and cohort studies which analyzed the relationship between IA and at least one of the following personal variables: (i) psychopathology, (ii) personality features and (iii) social difficulties, as well as (iv) self-esteem, (v) social skills and (vi) positive family functioning. These variables were classified as protective and promoting factors of the risk of developing IA.
Results A total of 28 studies with adequate methodological quality were identified in the primary medical, health and psychological literature databases up to November 2017. Of the 48,090 students included in the analysis, 6548 (13.62%) were identified as excessive Internet users. The results highlight that risk factors had a greater effect on IA than protective factors. Also, personal factors showed a greater link with IA than social factors.
Conclusions The data provide relevant information for those developing programs for the prevention of IA and the enhancement of protective factors.
Hunt MG, Marx R, Lipson C, Young J. No More FOMO: Limiting social media decreases loneliness and depression. Journal of Social and Clinical Psychology. 37(10): 751-768. 2018. https://doi.org/10.1521/jscp.2018.37.10.751
Discussion and conclusion: The implications of these results are further discussed in light of the existing evidence and debates regarding the status of technological addictions as primary and secondary disorders.