Monitoring Sleep and Glucose Among University Students

Study Overview
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Brief Summary
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This study aims to characterise associations between day-to-day sleep, activity, meal schedules, well-being and continuous glucose profiles in a cohort of free-living healthy, young adults. Multi-day data will be collected using wearables and smartphone-based measures in field settings.

Detailed Description
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There are two iterations of this study. In the first iteration (METWI1), wearables and smartphone-based measures are used to characterise free-living sleep, activity, meal schedules, well-being and continuous glucose profiles in a cohort of healthy, young Chinese university students for 4 weeks during the normal school term. While undergoing glucose monitoring (2 weeks), participants consume a standardised meal plan catered by the laboratory to reduce added variance from dietary intake. Examining relationships between sleep and behavioural characteristics and glucose profiles may contribute to the identification of phenotypes at higher risk of developing metabolic disorders. Data collected in this study may furthermore aid the identification of changes in sleep patterns associated with closer proximity to academic assessments, when students are predicted to experience increased academic workload and stress. Delays and more irregularity in sleep timing, shorter sleep durations and reduced sleep quality are expected closer to assessment dates. These in turn are predicted to result in higher glucose levels and glycemic variability. In the second iteration (METWI2), in addition to the above measures, participants undergo an oral glucose tolerance test following a night of moderate sleep restriction and baseline sleep (without sleep restriction). This allows us to examine effects of moderate, at-home sleep restriction on glucose tolerance and insulin sensitivity. In terms of sleep monitoring, we additionally aim 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.

Official Title
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Monitoring Sleep and Glucose Among University Students

Conditions
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Sleep

Intervention / Treatment
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* Behavioral: Baseline condition
* Behavioral: Sleep restriction condition


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Chinese NUS student residing on campus during semester Healthy No sleeping disorders/eating disorders/neurological illnesses BMI between 18.0 and 24.9 Exclusion Criteria: Smoker Pregnant Dietary restrictions Not able to collect meals and adhere to provided meal plan, or habitual meals/mealtimes from 3-day food diary deemed unsuitable for provided meal plan Moderate to severe depression/anxiety scores from BDI or BAI respectively Impaired glucose tolerance

Ages Eligible for Study
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Minimum Age: 21 Years
Maximum Age: 30 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Basic Science
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Sleep rectriction<br>Participants in METWI2 undergo both a baseline (unrestricted) sleep and sleep restriction condition. On the morning following each condition, participants complete an oral glucose tolerance test to measure changes in glucose and insulin following ingestion of a glucose load. | Behavioral: Baseline condition<br>* In the baseline sleep condition, participants are instructed not to restrict their sleep on the night before completing an oral glucose tolerance test (OGTT). Participants are prescribed sleep-wake timings based on their preferred and averaged sleep durations measured over 2 weeks using a wearable sleep tracker. Sleep timings and durations on the 3 nights before the OGTT are verified using a sleep diary and sleep tracker.<br>Behavioral: Sleep restriction condition<br>* In the sleep restriction condition, participants are prescribed sleep timings to restrict their sleep by 1 to 2 hours on the night before completing an oral glucose tolerance test (OGTT). Sleep timings and durations on the 3 nights before the OGTT are verified using a sleep diary and sleep tracker.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Intraindividual changes in sleep duration measured by wearable device (mins) across time | We hypothesise that participants will exhibit shorter sleep durations on nights in closer proximity to exam dates. | 4 weeks | 
| Intraindividual changes in sleep timing measured by wearable device (mins) across time | We hypothesise that participants will exhibit later sleep timings on nights in closer proximity to exam dates. | 4 weeks | 
| Intraindividual changes in sleep regularity measured by wearable device (mins) across time | We hypothesise that participants will exhibit less regular sleep timings on nights in closer proximity to exam dates. | 4 weeks | 
| Intraindividual changes in nap behaviour measured by self-report and wearable device (mins) across time | We hypothesise that participants will exhibit more polyphasic sleep schedules (more nap episodes) in closer proximity to exam dates. | 4 weeks | 
| Intraindividual changes in daily self-reported stress across time | We hypothesise that participants will report higher levels of stress in closer proximity to exam dates. | 4 weeks | 
| Intraindividual changes in daily self-reported sleep quality across time | We hypothesise that participants will report poorer sleep quality in closer proximity to exam dates. | 4 weeks | 
| Intraindividual changes in daily self-reported mood across time | We hypothesise that participants will report more negative mood reports in closer proximity to exam dates. | 4 weeks | 
| Intraindividual changes in glucose values across time | We hypothesise that mean daily, diurnal, nocturnal, and postprandial glucose values will be higher, and that 24-h glucose will be more variable in closer proximity to exam dates, and that these will be associated with the extent of sleep pattern alteration experienced by participants. | 4 weeks | 
| Intraindividual changes in average glucose across time | We hypothesise that average daily glucose values will be higher in closer proximity to exam dates, and that these changes will be associated with the extent of sleep pattern alteration experienced by participants. | 4 weeks | 
| Intraindividual changes in glycemic variability across time | We hypothesise that daily glucose values will be more variable in closer proximity to exam dates, and that these changes will be associated with the extent of sleep pattern alteration experienced by participants. | 4 weeks | 
| Intraindividual changes in postprandial glucose across time | We hypothesise that average postprandial change in glucose will be higher in closer proximity to exam dates, and that these changes will be associated with the extent of sleep pattern alteration experienced by participants. | 4 weeks | 
| Differences in average glucose between individuals with habitually different sleep profiles | We expect to observe higher daily average glucose among individuals who habitually obtain less sleep and have greater irregularity in sleeping patterns, compared to individuals who obtain more sleep and have more regular sleeping patterns | 4 weeks | 
| Differences in glycemic variability between individuals with habitually different sleep profiles | We expect to observe more variable daily glucose values among individuals who habitually obtain less sleep and have greater irregularity in sleeping patterns, compared to individuals who obtain more sleep and have more regular sleeping patterns | 4 weeks | 
| Accuracy of sleep detection using multiple data sources | We hypothesise that sleep detection accuracy will increase when wearable, WIFI and smartphone-based data sources are combined. | 4 weeks | 
| Effect of sleep restriction condition on glucose tolerance measured by oral glucose tolerance test. | We hypothesise that higher glucose tolerance will be observed in the sleep restriction condition compared to the baseline sleep condition. | 1 day | 
| Effect of sleep restriction condition on insulin resistance measured by oral glucose tolerance test. | We hypothesise that higher insulin resistance will be observed in the sleep restriction condition compared to the baseline sleep condition. | 1 day | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Glucose, Insulin