大學生電腦使用對睡眠型態影響因素之探討

The impact of computer using on sleep in college students

宋鈺宸

電腦使用 ； 沈浸 ； 激發狀態 ； 睡眠 ； 大學生 ； computer using ； flow ； arousal ； sleep ； college students

政治大學心理學研究所學位論文

2011年

碩士

楊建銘

繁體中文

研究背景與目的：大學生睡眠型態呈現睡眠時相延遲、睡眠不足、睡眠品質不佳的狀況，造成身心健康與學業問題。此種睡眠型態，一方面受到生理發展的影響因素，形成內在日夜節律型態偏向夜貓型的情形，二方面為社會與心理的影響因素，隨著年齡增加，家長對於孩子生活監控程度降低，特別是邁入大學以後，生活自主權增加，大學生有更多的自由安排自己的生活與睡眠時間，而大學生生活時間的安排與規劃，影響著夜晚的睡眠。現今科技可日新月異，科技產品的使用，包括看電視、打電腦與使用手機，成為大學生生活中不可或缺的活動之一。其中，電腦與上網為休閒活動時重要的角色。過去研究發現大學生一天使用電腦約3至5小時以上，國外調查睡前活動的研究發現約42.4％的大學生睡前使用電腦，而睡前使用電腦使得就寢時間延遲，形成總睡眠時數減少，睡眠不足造成白天的疲倦感增加，除此之外也有可能影響入睡時間與睡眠品質，因此本研究目的希望找出電腦使用對於睡眠影響的因素，減少電腦使用對大學生睡眠作息造成的影響。本研究根據訪談的結果及過去的文獻彙整，假設電腦使用使得沈浸狀態（flow）與激發狀態（arousal）較高，進而影響睡眠，包括就寢時間較晚、入睡時間較長、睡眠品質不佳、總睡眠時數不足、週末較晚起床補眠的狀況。 研究方法：本研究為瞭解個體電腦使用的沈浸與激發狀態變化對睡眠的影響，採受試者內設計，以重複測量的方式進行研究，測量受試者一週使用電腦的型態與睡眠之關係。受試者需符合睡前4小時內使用電腦1小時以上的習慣，排除任何生理、心理、睡眠疾患與極端日夜型態者（circadian type），並排除使用非法或影響睡眠的藥物。研究共募集國立與私立大學共76名學生，研究一週間請受試者於睡前填寫電腦使用型態問卷與與沈浸量表、激發狀態量表與睡眠日誌。資料回收後進行階層線性分析。階層一分析個人內每天電腦使用的沈浸程度、生理激發程度與認知激發程度是否可預測各睡眠變項，階層二分析個人間的日夜節律型態與焦慮特質調節沉浸、生理激發與認知激發程度與睡眠變項的關係。 研究結果：本研究發現每人每天睡前4小時電腦使用的內容，包括遊戲類、人際互動類與娛樂活動類的沈浸程度皆比文書作業的沈浸程度來得高，就受試者內的比較而言，當晚上電腦使用的沈浸程度越高，當晚的就寢時間提早、入睡時間減少、總睡眠時數增加與提升睡眠品質。而睡前4小時電腦使用時間長度可預測認知激發程度，但認知激發並無法預測睡眠變項；另外，不論睡前電腦使用內容或總時間無法預測生理激發，但晚上電腦使用後的生理激發程度越高，當晚的就寢時間越晚且總睡眠時數越少。此外，認知激發與生理激發的關係為正相關。在階層二個人間調節變項的分析，由於沈浸程度對睡眠變項的預測，以及生理激發程度對就寢時間與總睡眠時數的預測，皆未有尚未解釋的部分，因此在研究模型中無需再加入調節變項。 研究討論：研究結果發現沈浸程度在睡前電腦使用對睡眠影響的過程中扮演正向角色，但若睡前從事虛擬角色的線上遊戲，雖然沈浸程度偏高，但就寢時間偏晚且總睡眠時數較少；此外睡前的電腦使用時間越長，認知激發越高，而認知激發與生理激發呈現正相關，因此有可能認知激發程度提高，使生理激發程度也越高，而生理激發程度越高，導致就寢時間較晚，總睡眠時數較少。建議睡前選擇電腦使用內容並控制使用時間，以減少電腦使用對睡眠的不良影響。

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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