Monitoring Sleep and Glucose Among University Students

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The primary aim of this proposed study is to obtain longitudinal information about the range of normal sleep, activity and meal schedules and metabolic rhythms among young healthy Chinese university students within a free-living setting. Monitoring free-living glucose rhythms, sleep and meal schedul...

The primary aim of this proposed study is to obtain longitudinal information about the range of normal sleep, activity and meal schedules and metabolic rhythms among young healthy Chinese university students within a free-living setting. Monitoring free-living glucose rhythms, sleep and meal schedules while controlling dietary intake may enable the provision of a useful characterisation of the range of normal sleep, meal and activity schedules and glucose rhythmicity in healthy populations to contribute to the identification of phenotypes at greater risk of metabolic disease. The secondary aim of this study relating to metabolism is to identify associations between glucose rhythms, health outcomes, and meal timings in relation to circadian phase from sleep-wake patterns. In terms of sleep monitoring, the primary goal is to validate passive WiFi sensing against measurement of sleep using a commercial sleep and activity tracker (Oura ring), smartphone touchscreen interactions (tappigraphy-based sleep estimation) and sleep diary logs in students who are residing in dormitories. Studying this sample affords a convenient, and privacy protecting way of obtaining WiFi data. This can contribute to establishing whether a combination of multiple data sources for sleep detection can improve accuracy of sleep detection,incorporating the influence of device usage in the peri-sleep period. The secondary goal of this sleep study is the triangulation of sleep detection techniques for long term sleep monitoring on university campus. The hope is to access a larger population of students to infer sleep behaviours and sleep health, and eventually, to develop interventions to improve population health using individualised sleep data. Moreover, these data can also enable the identification of changes in sleep patterns associated with closer proximity to school examination dates, when students are expected to experience increases in academic workloads and greater amounts of stress. Delays and more irregularities in sleep timings, and shorter sleep durations closer to exam dates are expected to be observed, and these are expected to affect higher glycemic variability and higher average glucose values in accord with the extent of sleep pattern alteration.

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