The 13th Workshop on Visual Analytics in Healthcare (VAHC 2022)

Exploring the Sleep Patterns of Students in a Medical Sciences Baccalaureate Program using Self-Reported Data and Visual Analytics

Gargi Rajput, Andy Gao, Ching-Tzu Tsai, Jennifer R.V. Molano, Danny T.Y. Wu

Abstract: Poor sleep patterns have been commonly linked to college students. However, this study targets a gap in literature by exploring the sleep patterns in pre-medicine college students. Using self-report measures, student�s perception on their sleep quality and stress level was measured, along with their Pittsburgh Sleep Quality Index score. The collected data was analyzed via R-shiny visualization system. As a result, pre-med students turned out to sleep for an adequate duration but had poor sleep quality. They also experienced worse sleep than they had perceived. Hence, there is a need for self-monitoring amongst pre-meds to increase awareness and sleep hygiene.

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