Sleep and Exercise in Emergency Medicine Residents: An Observational Pilot Study Exploring the Utility of Wearable Activity Monitors for Monitoring Wellness
Bibliographic record
Abstract
Introduction Burnout is well-documented in residents and emergency physicians. Wellness initiatives are becoming increasingly prevalent, but there is a lack of data supporting their efficacy. In some populations, a relationship between sleep, exercise, and wellness has been documented; however, this relationship has not been established in emergency medicine (EM) residents or physicians. We aim to determine whether a wearable activity monitor is a feasible method of evaluating exercise and sleep quality and quantity in emergency medicine residents and if these assessments are associated with greater perceived wellness. Methods Twenty EM residents from two training sites wore a wearable activity monitor (Fitbit ChargeTM, Fitbit, Inc., San Francisco, CA, USA) during a four-week EM rotation. The Fitbit recorded data on sleep quantity (minutes sleeping) and quality (sleep disruptions), as well as exercise quantity and quality (daily step count, daily active minutes performing activity of 3 - 6, and > 6 metabolic equivalents). Participants completed an end-of-rotation Perceived Wellness Survey (PWS), which provided information on six domains of personal wellness (psychological, emotional, social, physical, spiritual, and intellectual). PWS levels were compared between groups of subjects with higher or lower levels of activity and sleep (i.e., above and below the median subject-averaged values) using the Mann-Whitney U test. Other subject characteristics were similarly assessed for their association with PWS. When a possible confounding effect was seen, the data was stratified and reviewed using a scatterplot. Results Of the 28 eligible residents, 23 agreed to participate. Of these, 20 and 16 wore the device for at least 50% of the respective days and nights during the observation period. Two devices were lost. One PWS was not completed. There was no statistically significant correlation between resident perceived wellness survey scores, sleep interruptions, average daily sleep minutes, daily step count, or average daily active minutes for the sample overall. However, first-year residents and residents from years two to five reported different median PWS scores of 13.9 and 17.1, respectively. Further exploration by the training group suggested that step counts may correlate with wellness in participants in their first year of residency, while the quantity of sleep may have an association with wellness in participants in years two through five of their residency. Conclusion Using wearable activity monitor devices to capture sleep and exercise data among residents does not seem to be an effective approach. Our data does not support our hypothesis that overall resident wellness was associated with exercise and sleep quality and quantity as measured by such a device. These results are counterintuitive and may be complicated by several measurement-related limitations and the possibility that benefits depend on the stage of training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".