research-article

Predicting Symptom Improvement During Depression Treatment Using Sleep Sensory Data

Authors:

Chinmaey Shende,

Soumyashree Sahoo,

Reynaldo Morillo,

Alexander Russell,

Bing WangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 7, Issue 3

Article No.: 121, Pages 1 - 21

https://doi.org/10.1145/3610932

Published: 27 September 2023 Publication History

Abstract

Depression is a serious mental illness. The current best guideline in depression treatment is closely monitoring patients and adjusting treatment as needed. Close monitoring of patients through physician-administered follow-ups or self-administered questionnaires, however, is difficult in clinical settings due to high cost, lack of trained professionals, and burden to the patients. Sensory data collected from mobile devices has been shown to provide a promising direction for long-term monitoring of depression symptoms. Most existing studies in this direction, however, focus on depression detection; the few studies that are on predicting changes in depression are not in clinical settings. In this paper, we investigate using one type of sensory data, sleep data, collected from wearables to predict improvement of depression symptoms over time after a patient initiates a new pharmacological treatment. We apply sleep trend filtering to noisy sleep sensory data to extract high-level sleep characteristics and develop a family of machine learning models that use simple sleep features (mean and variation of sleep duration) to predict symptom improvement. Our results show that using such simple sleep features can already lead to validation F1 score up to 0.68, indicating that using sensory data for predicting depression improvement during treatment is a promising direction.

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Cited By

Sahoo SShende CHossain MPatel PWang XMahmud MBi JKamath JRussell ASong DWang B(2024)Using Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)10.1109/CHASE60773.2024.00012(13-24)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1109/CHASE60773.2024.00012

Index Terms

Predicting Symptom Improvement During Depression Treatment Using Sleep Sensory Data
1. Information systems
  1. Information systems applications
    1. Mobile information processing systems

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 7, Issue 3

September 2023

1734 pages

EISSN:2474-9567

DOI:10.1145/3626192

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Published: 27 September 2023

Published in IMWUT Volume 7, Issue 3

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Cited By

Sahoo SShende CHossain MPatel PWang XMahmud MBi JKamath JRussell ASong DWang B(2024)Using Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)10.1109/CHASE60773.2024.00012(13-24)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1109/CHASE60773.2024.00012

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