The effect of computer game playing habits of university students on their sleep states

Akçay, Duygu; Akçay, Bülent Devrim

doi:10.1111/ppc.12497

Cited by 12 publications

(18 citation statements)

References 32 publications

(55 reference statements)

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“…Prevalence rates of problematic gaming varied widely in the samples ranging from 1.2 to 73.9% (weighted mean = 16.0%), while three studies had comparison groups of similar sample size ( 41 , 71 , 76 ). The majority of samples ( n = 18) were recruited from primary- / high schools, while eight samples were recruited from college/universities ( 41 , 53 – 55 , 66 , 75 , 76 , 80 ), three were recruited from gaming communities ( 24 , 52 , 74 ), one was recruited from two pediatric lipid and obesity treatment clinics ( 77 ), one was recruited through social media ( 61 ), one was recruited in “non-working contexts” [e.g., pubs, sports associations, recreational places; ( 59 )], and two samples were recruited using random population sampling ( 56 , 79 ).…”

Section: Resultsmentioning

confidence: 99%

“…The majority of studies ( n = 32) investigated sleep using self-report questionnaires, while one study objectively measured sleep duration by employing Fitbit -actigraphy to register rest/activity cycles ( 77 ). Regarding self-report measures, 17 studies employed standardized sleep measurements where the most used instrument was the PSQI ( n = 8), while five studies employed assessments for insomnia ( 40 , 56 , 60 , 74 , 81 ), and two studies assessed for daytime sleepiness ( 53 , 70 ). The remaining studies assessed sleep-related outcomes using single/own-created items (see Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

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Problematic Gaming and Sleep: A Systematic Review and Meta-Analysis

et al. 2021

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Problematic gaming has been linked to poor sleep outcomes; however, these associations have not yet been synthesized quantitatively. This review employed a meta-analysis to investigate the relationship between problematic gaming and sleep-related outcomes. A search of Medline, Embase, Web of Science, PsycINFO, and Google Scholar identified a total of 763 studies, including 34 studies (n = 51,901 participants) eligible for inclusion. Papers were included if available in any European language, addressed problematic gaming, contained original data, and provided sufficient data for calculation of effect sizes. Two researchers independently extracted data using pre-defined fields including quality assessment. Sleep-related outcomes were meta-analyzed for sleep parameters that were reported by 5 or more papers. Significant overall effects were found for sleep duration (g = −0.238, 95% CI = −0.364, −0.112), poor sleep quality (OR = 2.02, 95% CI = 1.47, 2.78), daytime sleepiness (OR = 1.57, 95% CI = 1.00, 2.46) and sleep problems (OR = 2.60, 95% CI = 1.94, 3.47). Between-study heterogeneity was detected for all meta-analyses. Subgroup analyses showed a higher inverse effect size for adolescent samples compared to adult or non-specific age samples in terms of sleep duration. For daytime sleepiness, a larger effect size was found for studies based on single-item sleep measures compared to multi-item sleep measures. For sleep problems, the subgroup analysis showed the opposite with a higher effect size for studies based on single-item sleep measures than multi-item sleep measures. Across all sleep parameters, problematic gamers consistently reported a more adverse sleep status than non-problematic gamers.Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/; record ID: CRD42020158955.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Problematic Gaming and Sleep: A Systematic Review and Meta-Analysis

et al. 2021

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“…That is, the longer you play online games, the less likely you are to fall asleep. Akçay and Akçay ( 2020 ) found that as levels of game addiction increased, individuals experienced decreased sleep quality, increased severity of daytime sleepiness, and delayed waking times. The above reflects the impact of Problematic internet use (PIU)on sleep quality.…”

Section: Introductionmentioning

confidence: 99%

Online risky behavior and sleep quality among Chinese college students: The chain mediating role of rumination and anxiety

Qiu

Xie

et al. 2022

Curr Psychol

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This study explored the chain mediating effects of rumination and anxiety in the relationship between online risky behavior and sleep quality among Chinese college students. A sample of 1039 Chinese college students (Mage = 19.49, SD = 1.14, 53.32% males) were investigated with Online Risky Behavior Scale (ORBS), Pittsburgh Sleep Quality Index scale (PSQI), Ruminative Responses Scale (RRS) and Self-rating Anxiety Scale (SAS). The results showed that: (1) There is a significant positive correlation among online risky behavior, sleep quality, rumination and anxiety, and (2) Online risky behavior could not directly affect sleep quality, but it can affect sleep quality through the mediation of rumination and anxiety. The chain mediating effects includes three paths: The mediating role of rumination, the mediating role of anxiety, and the chain mediating role of rumination and anxiety after controlling for gender, grade, major, and network usage time. These findings contribute to a deeper understanding of how online risky behavior affects sleep quality and provide important practical guidance for improving sleep quality.

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“…The study characteristics are summarized in Table 2. Ten of the included twelve studies employed an observational study design (King and Delfabbro, 2009;Achab et al, 2011;Mario et al, 2014;Exelmans and Van den Bulck, 2015;Altintas et al, 2019;Evren et al, 2019;Akçay and Akçay, 2020;Ko et al, 2020;Rudolf et al, 2020;Wong et al, 2020), one study was a randomized crossover trial (Thomas et al, 2019) and another was a prospective cohort study (Thomée et al, 2015). All observational studies employed an online or paper-based survey approach, apart from two studies that also included face-to-face interviews (Exelmans and Van den Bulck, 2015;Ko et al, 2020), and one study that also performed clinical measurements in a controlled setting (Mario et al, 2014).…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Sample sizes of the included studies ranged from 9 to 1066 participants, with cohorts spanning over ten countries. Overall, the sex distribution of the studies was biased toward males and included: two studies with all-male populations (Mario et al, 2014;Thomas et al, 2019), six studies with populations that were majority male (range: 77 to 98% male) (King and Delfabbro, 2009;Achab et al, 2011;Thomée et al, 2015;Altintas et al, 2019;Ko et al, 2020;Rudolf et al, 2020) and four studies with populations that were majority female (range: 56 to 71%) (Exelmans and Van den Bulck, 2015;Evren et al, 2019;Akçay and Akçay, 2020;Wong et al, 2020). The cohorts used in six of the studies comprised gamers from the general population (King and Delfabbro, 2009;Achab et al, 2011;Exelmans and Van den Bulck, 2015;Thomée et al, 2015;Altintas et al, 2019;Rudolf et al, 2020) and one study cohort comprised an elite team of League of Legends esports players (Thomas et al, 2019).…”

Section: Study Characteristicsmentioning

confidence: 99%

Sleep in Habitual Adult Video Gamers: A Systematic Review

et al. 2021

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Video gaming is a popular, globally recognized phenomenon, played recreationally or competitively as esports. Gaming is a typically sedentary nighttime activity; therefore, the potential to impact sleep and health is high. Furthermore, there are limited studies on adult gamers, who represent the majority demographic in esports. This review examines evidence describing sleep in habitual adult gamers to understand the associated risk for cardiometabolic disease or the benefits to gaming performance. Three electronic databases (PubMed, Scopus, ISI Web of Science) were searched for peer-reviewed articles published between January 2000 – April 2020. Twelve studies reporting on sleep in habitual adult gamers were included. A narrative synthesis was employed to report results, owing to high levels of heterogeneity across the included studies. Gamers with higher gaming addiction scores were more likely to have shorter, poorer quality sleep and greater daytime sleepiness and insomnia scores than gamers with lower gaming addiction scores and non-gamers. In addition, high-volume gamers were more likely to have worsened sleep quantity and quality, with delayed sleep timing and increased prevalence of insomnia. Despite limitations in the design of the included studies, excessive gaming is broadly associated with worsened sleep parameters. Noteworthy is the lack of studies investigating cardiometabolic health in gamers. Future work should explore the relative contribution and associated risk that various games, genres, and timing of gaming activities have on sleep, physical and mental health, particularly in vulnerable gaming cohorts engaged with contemporary forms of gaming and esports.

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The effect of computer game playing habits of university students on their sleep states

Cited by 12 publications

References 32 publications

Problematic Gaming and Sleep: A Systematic Review and Meta-Analysis

Problematic Gaming and Sleep: A Systematic Review and Meta-Analysis

Online risky behavior and sleep quality among Chinese college students: The chain mediating role of rumination and anxiety

Sleep in Habitual Adult Video Gamers: A Systematic Review

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