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May 10, 2026SLEEP0 citations

0816 Identifying Time in Bed with Chest-Worn Accelerometry

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KCKeran ChamberlinLELacey EtzkornKJKening Jiang

Key Points

  • This research aims to determine if chest-worn accelerometers can accurately estimate time in bed without using sleep diaries.
  • Developed a classification method to estimate time in bed using data from chest-worn accelerometers.
  • Optimized the algorithm by maximizing the F1 score using minute-level data from 405 ARIC Study participants.
  • Analyzed the time in bed of 2,470 ARIC participants and examined associations with demographics and dementia.
  • Best algorithm achieved an F1 score of 0.843; time in bed estimated from chest-worn devices was 34.5 minutes shorter on average compared to hip-worn estimates.
  • Cumulative incidence ratio for dementia was 1.61 for the lowest tertile of time in bed compared to moderate levels.
  • Earlier in-bed midpoints and lower variability were marginally associated with reduced dementia incidence.

Abstract

Abstract Introduction Chest-worn accelerometers can capture both acceleration and posture, but their use in detecting and measuring time in bed (TIB) has been limited. We investigated whether TIB can be estimated from chest-worn accelerometers using both acceleration and posture data without using sleep diary information. Methods We developed a method to classify TIB using data from the Zio XT Patch (iRhythm, San Francisco, CA, USA) onboard accelerometer in the Atherosclerosis Risk in Communities (ARIC) Study. The method was optimized by maximizing its F1 score using minute level data relative to the in-bed status which was estimated from simultaneously collected hip-worn actigraphy data using a published method among ARIC participants (n=405, age 78.5±4.7 years). The method was then used to estimate whether a person was in or out of bed among all ARIC participants (n=2,470, age 79.2±4.6 years) and derived the average TIB, TIB midpoint, and the standard deviation of TIB midpoint across days. To evaluate contextual validity of the new TIB summaries, we examined their cross-sectional associations with demographic and clinical characteristics, and longitudinal associations with incident dementia. Results Participants contributed an average of 5.7 days (IQR: 5, 6.6) to the optimization process. The best algorithm had a F1 score of 0.843. Compared to the TIB estimated from the hip-worn device, TIB identified from the chest-worn device were on average 34.5 minutes shorter (95% CI: 28.4, 40.6), began 5.4 minutes earlier (95% CI: 0.3,10.5), and ended 36 minutes earlier (95% CI: 32.9,40.2). TIB was cross-sectionally associated with sex, race, comorbidities, and cognitive function. During a median 5.2 years (IQR: 4.5-5.7) of follow-up, 316 participants developed dementia. Compared to participants who spent a moderate amount of TIB (8.5 to 9.75 hours/day), the cumulative incidence ratio for dementia was 1.61 (95% CI: 1.15,2.27) for the lowest tertile and 1.37 (95% CI: 1.01,1.85) for the highest tertile of TIB. An earlier in-bed midpoint and lower day-to-day variability in TIB midpoint were marginally associated with a reduced dementia incidence. Conclusion This study suggests that chest-worn accelerometers provide reasonable and useful estimates of TIB, opening a new opportunity for sleep assessment in older adults. Support (if any)

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Cite This Study

Chamberlin et al. (2026) studied this question.

synapsesocial.com/papers/6a0020aec8f74e3340f9b8f4https://doi.org/10.1093/sleep/zsag091.0815
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