ObjectivesTo determine whether longitudinal trajectories of nighttime sleep duration and daytime napping duration are related to subsequent multimorbidity risk. To explore whether daytime napping can compensate for negative effects of short nighttime sleep. MethodsThe current study included 5262 participants from China Health and Retirement Longitudinal Study. Self-reported nighttime sleep duration and daytime napping duration were collected from 2011 to 2015. The 4-year sleep duration trajectories were conducted by group-based trajectory modeling. The 14 medical conditions were defined by self-reported physician diagnoses. Multimorbidity was diagnosed as participants with 2 or more of the 14 chronic diseases after 2015. Associations between sleep trajectories and multimorbidity were assessed by Cox regression models. ResultsDuring 6.69 years of follow-up, we observed multimorbidity in 785 participants. Three nighttime sleep duration trajectories and three daytime napping duration trajectories were identified. Participants with persistent short nighttime sleep duration trajectory had the higher risk of multimorbidity (hazard ratio = 1.37, 95% confidence interval: 1.06-1.77), compared with those with persistent recommended nighttime sleep duration trajectory. Participants with persistent short nighttime sleep duration and persistent seldom daytime napping duration had the highest risk of multimorbidity (hazard ratio = 1.69, 95% confidence interval: 1.16-2.46). ConclusionsIn this study, persistent short nighttime sleep duration trajectory was associated with subsequent multimorbidity risk. Daytime napping could compensate for the risk of insufficient night sleep.

ObjectivesTo characterize and evaluate the estimation of oxygen saturation measured by a wrist-worn reflectance pulse oximeter during sleep. MethodsNinety-seven adults with sleep disturbances were enrolled. Oxygen saturation was simultaneously measured using a reflectance pulse oximeter (Galaxy Watch 4 [GW4], Samsung, South Korea) and a transmittance pulse oximeter (polysomnography) as a reference. The performance of the device was evaluated using the root mean squared error (RMSE) and coverage rate. Additionally, GW4-derived oxygen desaturation index (ODI) was compared with the apnea-hypopnea index (AHI) derived from polysomnography. ResultsThe GW4 had an overall RMSE of 2.3% and negligible bias of -0.2%. A Bland-Altman density plot showed good agreement between the GW4 and the reference pulse oximeter. RMSEs were 1.65 ± 0.57%, 1.76 ± 0.65%, 1.93 ± 0.54%, and 2.93 ± 1.71% for normal (n = 18), mild (n = 21), moderate (n = 23), and severe obstructive sleep apnea (n = 35), respectively. The data rejection rate was 26.5%, which was caused by fluctuations in contact pressure and the discarding of data less than 70% of saturation. A GW4-ODI ≥5/h had the highest ability to predict AHI ≥15/h with sensitivity, specificity, accuracy, and area under the curve of 89.7%, 64.1%, 79.4%, and 0.908, respectively. ConclusionsThis study evaluated the estimation of oxygen saturation by the GW4 during sleep. This device complies with both Food and Drug Administration and International Organization for Standardization standards. Further improvements in the algorithms of wearable devices are required to obtain more accurate and reliable information about oxygen saturation measurements.