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

Abstract

A relationship between evening technology use and sleep has been established, and models suggest various mechanisms to explain this relationship. Recent updates to these models also suggest the influence of individual difference factors, such that the relationship between technology and sleep varies between young people. Flow is an experience of immersion and time distortion that could vary between adolescents when using technology. The aim of the present study was to investigate the effects of flow on the self-selected bedtimes of adolescents when videogaming. Seventeen older adolescent, experienced videogamers (age = 15.9 ± 0.83 years), played a new videogame on two school-night evenings in a sleep laboratory. Game difficulty was set to "hard" one evening (flow condition) and "easy" on the other evening (disrupted flow). Trait and state flow were measured, along with heart rate during videogaming, and bedtime measured objectively with real-time cameras. An interaction effect for heart rate indicated an elevated heart rate in the easy condition after 150 min of gaming (p < 0.02). No significant differences were found in bedtimes between the easy and hard conditions (p = 0.77). Adolescents high on trait flow played for longer and selected significantly later bedtimes than their low trait flow peers but only for the hard (flow) condition (12:22 AM vs. 10:53 PM, p = 0.004). Similarly, adolescents with high state flow went to bed significantly later than those low on state flow (12:24 PM vs. 10:52 PM, p = 0.001), again only in the hard condition. These findings suggest that individual and situational characteristics may amplify the effects of technology use on the "sleep" of adolescents and provides support for the displacement of bedtime hypothesis.

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

“…By contrast, tests of the displacement hypothesis have yielded more substantial effects (for example, delaying bedtime by 1.5 hours) 188 . However, bedtime delays only occur when there is an interaction between personality characteristics (such as risk-taking, flow state, self-control and bedtime procrastination) and the type of technology consumed before sleep 188 – 190 .…”

“…By contrast, tests of the displacement hypothesis have yielded more substantial effects (for example, delaying bedtime by 1.5 hours) 188 . However, bedtime delays only occur when there is an interaction between personality characteristics (such as risk-taking, flow state, self-control and bedtime procrastination) and the type of technology consumed before sleep 188 – 190 . Although empirical support is more substantive for the bedtime displacement mechanism, the emerging evidence suggests that technology use does not influence all adolescents in the same way.…”

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

“…Sleep problems (i.e., sleep onset difficulties, short sleep duration) tend to emerge in the second decade of life, therefore technology may also be used as a sleep aid. Given technology J o u r n a l P r e -p r o o f is designed to be engaging, it is possible that technology use in the evening may disrupt the normal build of up homeostatic sleep pressure (i.e., sleepiness) due to increased cognitive and physiological arousal (Cain & Gradisar, 2010), or that adolescents enter a state of flow (i.e., fully immersed and lacking a sense of time) causing them to inadvertently go to bed later and obtain less sleep (Smith, King, Richardson, Roane, & Gradisar, 2017). Further, screen light from technological devices may exacerbate delay circadian timing, via suppression of melatonin, and/or alter sleep architecture (Hale et al, 2018).…”

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

“…Kaida et al [ 37 ] found that sleepiness is significantly and negatively correlated with the flow experience. Smith et al [ 38 ] determined that personality traits, state of flow, and situational characteristics in older adolescents influence their bedtimes. Previously, research on the relationship between flow and insomnia was limited, and there were some shortcomings including small sample sizes (to our knowledge, the sample sizes were all less than 17), inadequate criteria for the diagnosis of insomnia [ 38 ], different measurement methods for flow [ 37 ], and the experience of flow not being a main variable in the results.…”

“…Smith et al [ 38 ] determined that personality traits, state of flow, and situational characteristics in older adolescents influence their bedtimes. Previously, research on the relationship between flow and insomnia was limited, and there were some shortcomings including small sample sizes (to our knowledge, the sample sizes were all less than 17), inadequate criteria for the diagnosis of insomnia [ 38 ], different measurement methods for flow [ 37 ], and the experience of flow not being a main variable in the results. In an attempt to overcome these limitations, our research explores whether dCBT-i’s improvement of sleep quality results in an increase in the experience of flow.…”