Quantification of Differences in Sleep Measurement by a Wrist-Worn Consumer Wearable Compared to Research-Grade Accelerometry and Sleep Diaries of Female Adults in Free-Living Conditions
Bibliographic record
Abstract
Cindy R Hu,1 Caitlin Delaney,2 Jorge E Chavarro,2– 4 Francine Laden,1– 3 Rachel Librett,4 Laura Katuska,4 Emily R Kaplan,5 Li Yi,4,6 Michael Rueschman,5 Joe Kossowsky,7 Jukka-Pekka Onnela,8 Brent A Coull,8 Susan Redline,3,5 Peter James,1,6,9 Jaime E Hart1,2 1Department of Environmental Health, Harvard T.H. Chan School of Public Health; Boston, Boston, MA, USA; 2Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School; Boston, Boston, MA, USA; 3Department of Epidemiology, Harvard T.H. Chan School of Public Health; Boston, Boston, MA, USA; 4Department of Nutrition, Harvard T.H. Chan School of Public Health; Boston, Boston, MA, USA; 5Division of Sleep of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital and Harvard Medical School; Boston, Boston, MA, USA; 6Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute; Boston, Boston, MA, USA; 7Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital and Harvard Medical School; Boston, Boston, MA, USA; 8Department of Biostatistics, Harvard T.H. Chan School of Public Health; Boston, Boston, MA, USA; 9Department of Public Health Sciences, University of California, Davis School of Medicine; Davis, Davis, CA, USACorrespondence: Cindy R Hu, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA, 02115, USA, Email cindyhu@g.harvard.eduPurpose: The objective of this study is to compare sleep measurements by a consumer-wearable with research-standard actigraphy coupled with sleep diaries in free-living female adults.Methods: Forty-seven females in the Nurses’ Health Study 3 (NHS3) participated in the Sleep and Physical Activity Validation Substudy (SPAVS), where they were asked to concurrently wear a consumer wearable (Fitbit Charge, Models 3 or 5) and a research-grade accelerometer (Actigraph, GT3X+ or Actisleep) on the same wrist and fill out a smartphone-based sleep diary for fourteen consecutive days. We compared measures of total sleep time (TST), time in bed (TIB), and sleep efficiency (SE) from the consumer wearable with actigraphy measures as our research-standard reference for TST and SE and self-reported sleep diary as our reference for TIB. We calculated mean absolute percent error (MAPE) and intra-class correlations (ICC), as well as Bland-Altman analyses to compute mean difference and limits of agreement.Results: For all three measures, the consumer wearable underestimated sleep parameters relative to research-standard actigraphy, with a mean bias of − 16.0 minutes and − 11.2 minutes for TST and TIB, respectively, and − 1.0% for SE. In terms of agreement, TST (MAPE = 11.18%; ICC = 0.79) and TIB (MAPE = 10.45%; ICC = 0.74) had similar MAPES and ICCs, while and SE (MAPE = 5.09%; ICC = 0.39) had a lower ICC.Conclusion: In the NHS3 SPAVS, the wearable sleep measurements modestly underestimated wrist actigraphy measures of TST, TIB, and SE from sleep over multiple days; within sleep measures assessed, TST and TIB had greater agreement with research-grade accelerometry than SE.Keywords: wearables, fitbit, sleep, actigraphy, accelerometer, women
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".