Predicting subjective sleep impairment and disturbance from wearable sleep data
Abstract
Introduction:
Wearables offer a scalable, passive and objective measure of sleep health. However, prior reported correlations (spearman) between subjective and wearable derived sleep measures have been modest (rS=0.3-0.46). We set out to determine if wearables adequately capture subjective feelings of sleep disturbance and impairment in a large, diverse ecologically valid sleep study.
Methods:
Subject data (n=2922, mean age= 45.4 (12.6), 74% female) came from the Digital Wellbeing Study: a joint study between the University of Oregon and Google to investigate how smartphone usage impacts well-being. Wearable (Fitbit) derived sleep metrics were summarized across the week prior to the administration of the PROMIS Sleep Disturbance (SD) and Sleep Related Impairment (SR) Short Form surveys. A series of stepwise OLS regressions were used to test the predictive power of each sleep metric over a baseline model of age and sex.
Results:
Sleep variables of total sleep time, resting heart rate, and the variability in total sleep time and restlessness (accelerometer based metric) improved both SI and SD above a baseline model (SIBaseline adjR2=0.087, SDBaseline adjR2=0.024). Deep (e.g. N3) minutes uniquely improved SI model fit, while longest wake length and total wake minutes improved SD fit. REM percent and normalized nightly heart rate did not improve model fit. The final model explained 12.9% of the variance of SI, and 8.4% of the variance of SD. The most predictive single sleep metric was the variability in total sleep time (adjR2=0.104) for SI, and total sleep time for SD (age & sex included). Fitbit’s composite “Sleep Score” was the single best predictor of SD when included in analysis (age and sex excluded).
Conclusion: As demonstrated in previous studies, wearable derived sleep metrics are modest predictors of perceived sleep disturbance or sleep related impairment. Composite metrics that include measures of sleep variability are recommended.
Support: This research was funded by Google Inc.
Wearables offer a scalable, passive and objective measure of sleep health. However, prior reported correlations (spearman) between subjective and wearable derived sleep measures have been modest (rS=0.3-0.46). We set out to determine if wearables adequately capture subjective feelings of sleep disturbance and impairment in a large, diverse ecologically valid sleep study.
Methods:
Subject data (n=2922, mean age= 45.4 (12.6), 74% female) came from the Digital Wellbeing Study: a joint study between the University of Oregon and Google to investigate how smartphone usage impacts well-being. Wearable (Fitbit) derived sleep metrics were summarized across the week prior to the administration of the PROMIS Sleep Disturbance (SD) and Sleep Related Impairment (SR) Short Form surveys. A series of stepwise OLS regressions were used to test the predictive power of each sleep metric over a baseline model of age and sex.
Results:
Sleep variables of total sleep time, resting heart rate, and the variability in total sleep time and restlessness (accelerometer based metric) improved both SI and SD above a baseline model (SIBaseline adjR2=0.087, SDBaseline adjR2=0.024). Deep (e.g. N3) minutes uniquely improved SI model fit, while longest wake length and total wake minutes improved SD fit. REM percent and normalized nightly heart rate did not improve model fit. The final model explained 12.9% of the variance of SI, and 8.4% of the variance of SD. The most predictive single sleep metric was the variability in total sleep time (adjR2=0.104) for SI, and total sleep time for SD (age & sex included). Fitbit’s composite “Sleep Score” was the single best predictor of SD when included in analysis (age and sex excluded).
Conclusion: As demonstrated in previous studies, wearable derived sleep metrics are modest predictors of perceived sleep disturbance or sleep related impairment. Composite metrics that include measures of sleep variability are recommended.
Support: This research was funded by Google Inc.
