Mechanisms influencing older adolescents' bedtimes during videogaming: the roles of game difficulty and flow

A bidirectional model of sleep and technology use: A theoretical review of How much, for whom, and which mechanisms

The link between technology and sleep is more complex than originally thought. In this updated theoretical review, we propose a new model informed by the growing body of evidence in the area over the past 10 years. The main theoretical change is the addition of bi-directional links between the use of technology and sleep problems. We begin by reviewing the evidence to date for the originally proposed mechanisms of bright light, arousal, nighttime sleep disruptions, and sleep displacement. Then, in support of the new direction of effect (sleep problems preceding technology use), we propose two new mechanisms: technology before sleep might be used as a time filler and/or as an emotional regulation strategy to facilitate the sleep-onset process. Finally, we present potential moderators of the association between technology and sleep, in recognition of protective and vulnerability factors that may mitigate or exacerbate the effects of technology on sleep and vice versa. The goal of this theoretical review is to update the field, guide future public health messages, and to prompt new research into how much technology and sleep affect each other, for whom it may be problematic, and which mechanisms may explain their association.

Flow mediates the relationship between problematic smartphone use and satisfaction with life among college students

Smartphones, while useful, can also function as a source of distraction from daily life and may reduce life satisfaction by inhibiting concentration and interrupting attention to ongoing tasks, thoughts, and social interactions.

OBJECTIVES

The current study tested whether interruptions in flow mediate associations between problematic smartphone use (PSU) and life satisfaction among college students.

PARTICIPANTS

Two hundred and nine college student smartphone users screened in to the study.

METHODS

188 individuals (90%) completed baseline and 158 individuals (76%) completed one week of Ecological Momentary Assessment surveys and a one-week follow-up survey.

RESULTS

Flow mediates the relationship between PSU and satisfaction with life such that PSU was negatively associated with flow, flow was positively associated with satisfaction with life, and the indirect effect of PSU on satisfaction with life through flow was significant and negative.

CONCLUSION

These findings suggest that the lack of flow college students experience due to PSU contributes to lower life satisfaction.

What's "app"-ning to adolescent sleep? Links between device, app use, and sleep outcomes

This study investigated the associations between adolescent evening use of technology devices and apps, night time sleep, and daytime sleepiness. Participants were 711 adolescents aged 12-18 years old (46% Female, M_age = 15.1, SD = 1.2). Time spent using technology devices and apps in the hour before bed, and in bed before sleep onset, was self-reported. Participants additionally completed a questionnaire about their sleep on school nights and next day sleepiness. In the hour before bed, 30 min of phone use was associated with a 9-min delay in bedtimes. Thirty minutes spent using laptops, gaming consoles, and watching YouTube was associated with later lights out times of 9 min, ∼16 min and ∼11 min respectively, while watching TV was associated with a 9 min earlier lights out times. Using gaming consoles and watching YouTube were associated with greater odds of receiving insufficient sleep (≤7 h TST). In bed before sleep onset, 30 min spent using laptops, phones, iPad/tablets, and watching YouTube were linked with later lights out times of ∼7 min for phones and laptops, 9 min for iPad/tablets, and ∼13 min for YouTube. Watching Netflix was associated with greater daytime sleepiness. YouTube at this time point was associated with increased odds of sleeping ≤7 h on school nights. Adolescents are engaging with a wide range of technology devices and apps in the evenings. However, certain devices and apps (e.g., phones, laptops, gaming and YouTube) might lead to more negative sleep outcomes for adolescents on school nights compared to others.

Effectiveness of Digital Cognitive Behavior Therapy for the Treatment of Insomnia: Spillover Effects of dCBT

The effects of digital Cognitive Behavior Therapy for insomnia (dCBT-i) on sleep quality have been previously demonstrated but the spillover effects on fatigue, flow (a state of immersion in activities of interest), and cognitive flexibility remain unclear. The current study examined the effectiveness of dCBT-i. A total of 97 college students (20.96 ± 1.87 years, 73.1% female students) were randomly selected from a shortlist and divided into sleep intervention (n = 39), conventional education (n = 37), and healthy control (n = 21) groups. Task switching paradigm, Fatigue Severity Scale (FSS), Flow Experience Scale (FES), and the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) were measured pre- and post-intervention. Results show that the sleep quality of the intervention group improved, and fatigue was relieved. Participants in the sleep intervention group had increased flow experience scores post-intervention and improved cognitive flexibility. The control group’s sleep quality deteriorated and fatigue level increased. dCBT-i can not only achieve a significant improvement in sleep quality and reduce fatigue, but also improve learning abilities, quality of life, flow, and cognitive flexibility. Future research should pay attention to indicators such as work efficiency, sedative use, and the durability and stability of such effects.

Sleep’s role in the development and resolution of adolescent depression

Two adolescent mental health fields — sleep and depression — have advanced largely in parallel until about four years ago. Although sleep problems have been thought to be a symptom of adolescent depression, emerging evidence suggests that sleep difficulties arise before depression does. In this Review, we describe how the combination of adolescent sleep biology and psychology uniquely predispose adolescents to develop depression. We describe multiple pathways and contributors, including a delayed circadian rhythm, restricted sleep duration and greater opportunity for repetitive negative thinking while waiting for sleep. We match each contributor with evidence-based sleep interventions, including bright light therapy, exogenous melatonin and cognitive-behaviour therapy techniques. Such treatments improve sleep and alleviate depression symptoms, highlighting the utility of sleep treatment for comorbid disorders experienced by adolescents.

Sleep problems are both a symptom and precursor of adolescent depression. In this Review, Gradisar et al. describe how the combination of adolescent sleep biology and psychology predisposes adolescents to develop depression, and describe interventions that improve sleep and depression symptoms in this population.

A longitudinal investigation of sleep and technology use in early adolescence: does parental control of technology use protect adolescent sleep?

OBJECTIVE

A bi-directional relationship between technology use and adolescent sleep is likely, yet findings are mixed, and it is not known whether parental control of technology use can protect sleep. The current study examined bi-directionality between technology use on school nights and morning/eveningness, sleep duration and daytime sleepiness in early adolescents. We also examined whether time spent using technology mediated the relationship between parental control of technology and adolescent sleep.

METHODS

Adolescents and their primary caregiver (96% mothers) completed questionnaire measures of sleep, technology use and parental control across three, annual waves: Wave 1 (N = 528, M_age = 11.18, SD = 0.56, range = 10-12, 51% male), Wave 2 (N = 502, M_age = 12.19, SD = 0.53, 52% male) and Wave 3 (N = 478, M_age = 13.19, SD = 0.53, 52% male).

RESULTS

When examining the direct relationship between sleep and technology use, cross-lagged panel models showed that time spent using technology predicted shorter sleep duration and greater daytime sleepiness in adolescence, and evening diurnal preference and shorter sleep duration contributed to increased technology use over time. The relationship between technology use and sleep duration was bi-directional. Time spent using technology and adolescent sleep predicted, yet were not predicted by, parental control of technology use.

CONCLUSIONS

While normative changes in sleep (eg, increased eveningness) may promote increased technology use, technology use may further impinge upon sleep. Results suggest it may be pertinent to instead find creative ways in which adolescents themselves can mitigate their risk of inadequate sleep.