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This summary is machine-generated.

Wearable sleep trackers show measurement biases, especially in patients with mood disorders, impacting sleep offset and total sleep time (TST) accuracy. Demographic and seasonal factors also influence sleep tracking reliability.

Keywords:
EMAactigraphybed sensorsdepressionecological momentary assessmentsleep monitoringsleep quantificationsmartphone data

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Area of Science:

  • Sleep science and digital health
  • Psychiatric research utilizing wearable technology

Background:

  • Sleep is crucial for health and psychiatric disorder diagnosis.
  • Laboratory-based polysomnography is limited for long-term, naturalistic monitoring.

Purpose of the Study:

  • Assess sleep-tracking reliability using wearables, nearables, and ecological momentary assessment.
  • Examine measurement biases and factors influencing discrepancies across methods in healthy individuals and mood disorder patients.

Main Methods:

  • 14-day study in Finland with 169 participants (major depressive episode patients and controls).
  • Sleep patterns (onset, offset, total sleep time [TST]) collected via actigraphy, bed sensor, and smartphone data.
  • Alignment evaluated using Bland-Altman plots, Pearson correlation, and linear mixed models.

Main Results:

  • Patients showed greater sleep measure variability than controls.
  • Significant biases in sleep offset observed in patients across devices; TST underestimated by smartphone.
  • Older age, longer daylight, and specific demographics influenced alignment; bipolar/borderline personality disorder patients had lower TST alignment.

Conclusions:

  • Actigraphy, smartphone data, and bed sensors are feasible for naturalistic sleep tracking in patients.
  • Measurement biases, seasonal variations, and demographic factors significantly impact sleep tracking discrepancies.