Findings suggest that bedtime mobile phone use is negatively related to sleep outcomes in adults, too. It warrants continued scholarly attention as the functionalities of mobile phones evolve rapidly and exponentially.

Study Objectives: To investigate the prevalence of binge viewing, its association with sleep and examine arousal as an underlying mechanism of this association. Methods: Four hundred twenty-three adults (aged 18-25 years old, 61.9% female) completed an online survey assessing regular television viewing, binge viewing, sleep quality (Pittsburgh Sleep Quality Index), fatigue (Fatigue Assessment Scale), insomnia (Bergen Insomnia Scale), and pre-sleep arousal (Pre-Sleep Arousal Scale). Regression analyses were conducted. Mediation analysis was performed using PROCESS Macro. Results: There were 80.6% who identified themselves as a binge viewer. Among those who binge viewed (n = 341), 20.2% had binge viewed at least a few times a week during the past month. Among poor sleepers (Pittsburgh Sleep Quality Index > 5), 32.6% had a poor sleep quality associated with being a binge viewer. Higher binge viewing frequency was associated with a poorer sleep quality, increased fatigue and more symptoms of insomnia, whereas regular television viewing was not. Cognitive pre-sleep arousal fully mediated these relationships. Conclusions: New viewing styles such as binge viewing are increasingly prevalent and may pose a threat to sleep. Increased cognitive arousal functions as the mechanism explaining these effects. Measures of media exposure should take into account the user's level of engagement with media. Interventions aimed at (1) alerting viewers about excessive viewing duration and (2) reducing arousal before sleep may be useful ways to tackle sleep problems in binge viewers.

This study argues that going to bed may not be synonymous with going to sleep, and that this fragmentation of bedtime results in a two-step sleep displacement. We separated bedtime (i.e. going to bed) from shuteye time (i.e. attempting to go to sleep once in bed) and assessed the prevalence of electronic media use in both time slots. A convenience sample of 338 adults (aged 18-25 years, 67.6% women) participated in an online survey. Results indicated a gap of 39 min between bedtime and shuteye time, referred to as 'shuteye latency'. Respondents with a shuteye latency of, respectively, ≤30 min, ≤1 or >1 h, were 3.3, 6.1 and 9.3 times more likely to be rated as poor sleepers compared to those who went to sleep immediately after going to bed. Before bedtime, volume of electronic media use (17 h 55 min per week) was higher than non-media activities (14 h per week), whereas the opposite was true after bedtime (media = 3 h 41 min, non-media = 7 h 46 min). Shuteye latency was related exclusively to prebedtime media use. Findings confirmed the proposed fragmentation of bedtime. Sleep displacement should therefore be redefined as a two-step process, as respondents not only engage in the delay of bedtime, but also in the delay of shuteye time once in bed. Theoretical, methodological and practical implications are discussed.

SUMMARYMost literature on the relationship between video gaming and sleep disturbances has looked at children and adolescents. There is little research on such a relationship in adult samples. The aim of the current study was to investigate the association of video game volume with sleep quality in adults via face-to-face interviews using standardized questionnaires. Adults (n = 844, 56.2% women), aged 18-94 years old, participated in the study. Sleep quality was measured using the Pittsburgh Sleep Quality Index, and gaming volume was assessed by asking the hours of gaming on a regular weekday (Mon-Thurs), Friday and weekend day (Sat-Sun). Adjusting for gender, age, educational level, exercise and perceived stress, results of hierarchical regression analyses indicated that video gaming volume was a significant predictor of sleep quality (b = 0.145), fatigue (b = 0.109), insomnia (b = 0.120), bedtime (b = 0.100) and rise time (b = 0.168). Each additional hour of video gaming per day delayed bedtime by 6.9 min (95% confidence interval 2.0-11.9 min) and rise time by 13.8 min (95% confidence interval 7.8-19.7 min). Attributable risk for having poor sleep quality (Pittsburgh Sleep Quality Index > 5) due to gaming >1 h day was 30%. When examining the components of the Pittsburgh Sleep Quality Index using multinomial regression analysis (odds ratios with 95% confidence intervals), gaming volume significantly predicted sleep latency, sleep efficiency and use of sleep medication. In general, findings support the conclusion that gaming volume is negatively related to the overall sleep quality of adults, which might be due to underlying mechanisms of screen exposure and arousal.

There is ample evidence that media use displaces sleep, but little theory about the mechanism that explains this. We studied sleep displacement as a self-control issue: People postpone going to bed because they have trouble ending their media exposure. We therefore modeled television viewing (habitual viewing, deficient TV self-regulation, and viewing volume) as a mediator of the effect of trait self-control on bedtime procrastination. A random sample of 821 adults participated in face-to-face interviews using standardized questionnaires. Lower self-control was associated with more bedtime procrastination. This relationship was mediated by habitual viewing, which led to less bedtime procrastination, and deficient TV self-regulation, which led to more bedtime procrastination. Evening viewing volume was not a significant mediator. Our results support the idea that (1) self-regulatory failure over television viewing can partly explain the common struggle with bedtime, and (2) strong viewing habits seem to inhibit bedtime procrastination.

A sample of 844 adults, aged 18-94 years old, was queried about media habits and sleep behavior in face-to-face interviews with standardized questionnaires. A substantial proportion of this sample reported using books (39.8%), television (31.2%), music (26.0%), Internet (23.2%), and videogames (10.3%) as a sleep aid. The use of media as sleep aids was associated with increased fatigue and higher scores on the Pittsburgh Sleep Quality Index (PSQI), indicating poorer sleep quality. There was no relationship with sleep duration. Finally, results suggest that media use coincides with later bedtimes, but also later rise times, a process called time shifting.

This study expands knowledge on the effects of technology use on sleep by (1) focusing onsocial media use in an adult sample, (2) investigating the difference between overall andnighttime-specific social media use with regards to sleep, and (3) exploring a vulnerabilityperspective. For the latter, the moderating roles of gender, age, and habitual social mediachecking behavior were examined. A representative quota sample of 584 adults (18-96 yearsold) participated in an online survey. Results indicated that 2 out of 3 adults used socialmedia, and that use both shortly before and in bed was prevalent. Only nighttime use wasassociated with poorer sleep quality. Age and habitual checking behavior moderated thisassociation, identifying younger adults and those with strong checking habits as possiblevulnerable groups for poor sleep. The findings are interpreted in light of existing research onmedia habits and problematic (social) media use.

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