2017 - Mechanisms influencing older adolescents' bedtimes during videogaming
2017 - Mechanisms influencing older adolescents' bedtimes during videogaming
In this study, we asked a bunch of teenagers to fill out some questionnaires, come to our sleep lab, eat pizza, play a new videogame, and then sleep.
What they didn’t know is that the questionnaires were measuring a concept called ‘flow’. Flow is state of awareness where we get immersed in what we’re doing and we lose rack of time.
It just doesn’t happen during videogaming, but also other activites, like reading, gardening, knitting, etc.
And people differ in the amount of flow that they can experience.
And so this study showed that flow was a very important characteristic of teenagers, that determined when they chose to turn off their videogame - and go to sleep.
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.
