| Summary: | 碩士 === 國立政治大學 === 心理學研究所 === 100 === OBJECTIVE: College students tend to delay their sleep phase and have high prevalence of sleep problems, such as poor sleep quality and insufficient sleep. Many factors may be associated with the sleep patterns. First, delay sleep phase in college students may be affected by a natural tendency of delayed endogenous circadian phase in during puberty. Second, psychosocial and behavioral factors, such as late evening social events and computer use, may also contribute to these sleep patterns. Among these, computer use has been shown to be associated with poor sleep in previous studies. However, it’s unclear that what mechanisms through which computer use has an impact on sleep in college students. The goal of this study is to identify the underlying factors that mediate the effect of computer use to sleep. According to our pilot study in which college students were interviewed for their computer-use habits and sleep pattern, we hypothesize that mental flow, physical arousal and cognitive arousal are the factors mediating the impacts of computer use to sleep patterns characterize college students, including delayed sleep phase, longer sleep onset latency, insufficient sleep and poor sleep quality.
METHOD: Seventy-six college students who are habitual computer users (using computer at least one hour before sleep every day) participated in the study. They were required to complete a set of questionnaires everyday for one week, including the computer-use questionnaire, the Flow Scale, and the Pre-Sleep Arousal Scale. Hieratical Linear Model was conducted to analyze within-individual level (level one) and between- individual level (level two). In our study, within-individual levels were mental flow, physical arousal and cognitive arousal that mediated the impacts of computer use to sleep patterns when college students used computer before sleep every night. In addition, between- individual levels in our study were various circadian types and anxious trait between college students. They may moderate the impacts of mental flow, physical arousal and cognitive arousal to sleep patterns in college students.
RESULT: The results showed within-individual level that contents of computer using, including play on-line games, interpersonal interaction, and entertainment, could predict increased flow level. Higher flow level in turn predicted earlier bedtime, shorter sleep latency, more sleep duration and better sleep quality. In addition, physical arousal was not affected by computer use, but had a negative impact on sleep. Higher physical arousal level was able to predict later bedtime and shorter sleep duration. Computer-use time during the four hours prior to bedtime was associated with pre-sleep cognitive arousal. Cognitive arousal did not show significant association with any sleep variables, however. Furthermore, there was a positive relationship between cognitive arousal and physical arousal. In addition, because the results of between- individual levels showed that the mental flow, physical arousal and cognitive arousal completely explained sleep patterns, there was no need to add between- individual moderations.
CONCLUSION: Our study showed that flow level while engaging in computer use may have positive effect on sleep. However, playing on-line games before sleep, although may lead to higher flow level, were associated with later bedtime and shorter sleep duration. Also, the more time spending on computer before sleep, the higher the cognitive arousal. Higher cognitive arousal level may be associated with higher physical arousal level. And, higher physical arousal level lead to later bedtime and shorter sleep duration. The results suggested that in order to prevent the negative impacts of computer-use among college students, they should reduce computer using time and avoid on-line games before sleep. Future study can develop intervention program based on current findings to prevent college students from the negative impacts of computer.
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