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Association Between Sleep Quality and Cognitive Symptoms in Patients with Major Depressive Disorder
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Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and

Doljinsuren Enkhbayar1, Somin Oh1, Jinhee Lee2

  • 1Department of Biomedical Engineering, Mirae campus, Yonsei University, 1, Yeonsedae-gil, Heungeop-myeon, Wonju-si, Gangwan-do, 26493, Republic of Korea, 82 33 760 5270, 82 33 760 2919.

JMIR Mhealth and Uhealth
|July 15, 2026
PubMed
Summary

Artificial intelligence models using wearable actigraphy data show promise for passive depression screening. These AI models can identify depressive symptoms by analyzing daily activity and rest-activity patterns, aiding early detection.

Keywords:
actigraphyartificial intelligencedeep learningdepression screeningdepressive symptomspassive monitoringwearable devices

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Area of Science:

  • Digital health
  • Computational psychiatry
  • Biomedical engineering

Background:

  • Depressive symptoms are prevalent but often missed in routine care.
  • Scalable, passive screening methods are needed beyond self-reports.
  • Wearable actigraphy offers continuous monitoring of activity and rest-activity rhythms.

Purpose of the Study:

  • To develop and evaluate AI models for passive depression screening using wrist actigraphy data.
  • To assess the performance of deep learning architectures in classifying depressive symptoms.

Main Methods:

  • Actigraphy data from 1160 Hispanic/Latino adults were analyzed.
  • Deep learning models classified depressive symptom groups based on activity counts, light exposure, and wake status.
  • The 10-item Center for Epidemiologic Studies Depression scale (CESD-10) defined symptom groups.

Main Results:

  • Actigraphy revealed lower daytime activity and altered circadian rhythms with increased depressive symptoms.
  • A long short-term memory (LSTM) model showed the strongest discrimination (AUC 0.80).
  • Model performance improved with symptom severity, particularly for higher symptom groups (AUC 0.889).

Conclusions:

  • Actigraphy data can support AI-based classification of depressive symptoms.
  • AI models using actigraphy offer a scalable, passive, and noninvasive tool for early screening.
  • This approach can complement traditional assessments for depressive symptom detection.