Validation and Feasibility of a Consumer-Grade Wearable Sleep Monitoring Device and an Exploratory Evaluation of Sleep Characteristics and Associated Factors Among Nurses: Observational Cohort Study.
Lim S, Aloweni F, Ong P, Ang S • JMIR nursing • 2026
Apple Watch-based sleep monitoring was feasible and showed preliminary agreement with actigraphy, while exploratory analyses found that nurses slept less than recommended and that BMI and age were independently associated with sleep outcomes.
Key Findings
Results
The Apple Watch Series 10 showed preliminary agreement with validated GENEActiv actigraphy for total sleep time, in-bed wake time, and sleep efficiency.
Intraclass correlation coefficient for total sleep time was 0.95
Intraclass correlation coefficient for in-bed wake time was 0.72
Intraclass correlation coefficient for sleep efficiency was 0.69
Phase 1 involved 5 nurses wearing both devices concurrently for 2 weeks
High wear compliance and minimal missing data were reported, supporting feasibility
Results
Nurses in the study slept less than the generally recommended amount, with a mean total sleep time of 381 minutes.
Mean total sleep time was 381 (SD 55) minutes, equivalent to approximately 6.35 hours
Mean sleep efficiency was 94.77% (SD 4.11%)
Data were collected from 50 nurses in phase 2 using wearable monitoring
Nurses worked either rotating or single shifts in a tertiary hospital in Singapore
Results
Higher BMI was independently associated with multiple poorer sleep outcomes in exploratory regression analyses.
Higher BMI was associated with shorter total sleep time (B = -3.76 min/kg/m²; P = .01)
Higher BMI was associated with reduced REM sleep (B = -1.18 min; P = .03)
Higher BMI was associated with shorter core sleep (B = -2.72 min; P = .02)
Higher BMI was associated with reduced time in bed (B = -4.05 min; P = .008)
Analyses were adjusted for age, sex, BMI, parental status, workplace, total length of service, and sleep hygiene
Results
Older age was independently associated with less deep sleep duration in exploratory analyses.
Unstandardized regression coefficient B = -1.00 min/year of age; P = .003
This association was found after adjustment for sex, BMI, parental status, workplace, total length of service, and sleep hygiene
The finding was identified as part of exploratory multiple linear regression analyses
Results
Shift nurses reported poorer sleep hygiene than non-shift nurses, but shift work status was not independently associated with sleep outcomes after adjustment.
Shift work status did not remain a statistically significant predictor of sleep parameters in adjusted multiple linear regression models
Sleep hygiene was measured using the Sleep Hygiene Index, completed via questionnaire in phase 2
The sample included nurses working both rotating and single shifts
This finding suggests that other factors (such as BMI and age) may be more influential than shift status alone
Methods
The study used a two-phase observational cohort design to assess both device feasibility and exploratory sleep associations among nurses.
Phase 1 (n = 5 nurses) assessed device agreement and feasibility over 2 weeks of concurrent wear
Phase 2 (n = 50 nurses) used the Apple Watch alongside demographic, work-related questionnaires, and the Sleep Hygiene Index
The study was conducted in a tertiary hospital in Singapore
Feasibility was determined through wear-time compliance and data completeness
Multiple linear regression was used for phase 2 analyses with adjustment for multiple covariates
What This Means
This research suggests that a consumer-grade smartwatch (Apple Watch Series 10) can be a practical and reasonably accurate tool for tracking sleep in healthcare workers, showing strong agreement with a validated research-grade device for key sleep measures like total sleep time. The study was conducted among nurses in Singapore and found that, on average, nurses slept only about 6.4 hours per night, which is below the commonly recommended 7–9 hours for adults. Nurses who worked shifts reported worse sleep hygiene habits than those on fixed schedules, though shift work itself did not emerge as a significant predictor of sleep outcomes once other factors were accounted for.
The exploratory analyses revealed that body mass index (BMI) and age were meaningfully linked to sleep quality and duration. Specifically, nurses with higher BMIs tended to sleep less overall, had less REM and core sleep, and spent less time in bed. Older nurses tended to get less deep (slow-wave) sleep. These associations held even after adjusting for a range of other factors including sex, parental status, and sleep hygiene scores.
This research suggests that wearable consumer devices like the Apple Watch could be viable tools for large-scale sleep monitoring studies in nursing and other healthcare populations, without requiring expensive specialized equipment. The findings also point to BMI and age as potentially important targets for workplace wellness strategies aimed at improving sleep health in nurses. The authors call for larger studies to confirm these preliminary findings and support the development of workplace programs to improve sleep opportunity and healthy sleep behaviors among healthcare workers.
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Lim S, Aloweni F, Ong P, Ang S. (2026). Validation and Feasibility of a Consumer-Grade Wearable Sleep Monitoring Device and an Exploratory Evaluation of Sleep Characteristics and Associated Factors Among Nurses: Observational Cohort Study.. JMIR nursing. https://doi.org/10.2196/96912