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Cited 14 time in webofscience Cited 17 time in scopus
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Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study

Authors
Cho, Chul-HyunLee, TaekLee, Jung-BeenSeo, Ju YeonJee, Hee-JungSon, SerhimAn, HyongginKim, LeenLee, Heon-Jeong
Issue Date
Aug-2020
Publisher
JMIR Publications
Keywords
circadian rhythm; digital therapeutics; mood disorder; recurrence prevention; smartphone app; wearable activity tracker
Citation
JMIR Mental Health, v.7, no.8
Indexed
SCIE
SCOPUS
Journal Title
JMIR Mental Health
Volume
7
Number
8
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/32919
DOI
10.2196/21283
ISSN
2368-7959
Abstract
Background: Smartphones and wearable devices can be used to obtain diverse daily log data related to circadian rhythms. For patients with mood disorders, giving feedback via a smartphone app with appropriate behavioral correction guides could play an important therapeutic role in the real world. Objective: We aimed to evaluate the effectiveness of a smartphone app named Circadian Rhythm for Mood (CRM), which was developed to prevent mood episodes based on a machine learning algorithm that uses passive digital phenotype data of circadian rhythm behaviors obtained with a wearable activity tracker. The feedback intervention for the CRM app consisted of a trend report of mood prediction, H-score feedback with behavioral guidance, and an alert system triggered when trending toward a high-risk state. Methods: In total, 73 patients with a major mood disorder were recruited and allocated in a nonrandomized fashion into 2 groups: the CRM group (14 patients) and the non-CRM group (59 patients). After the data qualification process, 10 subjects in the CRM group and 33 subjects in the non-CRM group were evaluated over 12 months. Both groups were treated in a similar manner. Patients took their usual medications, wore a wrist-worn activity tracker, and checked their eMoodChart daily. Patients in the CRM group were provided with daily feedback on their mood prediction and health scores based on the algorithm. For the CRM group, warning alerts were given when irregular life patterns were observed. However, these alerts were not given to patients in the non-CRM group. Every 3 months, mood episodes that had occurred in the previous 3 months were assessed based on the completed daily eMoodChart for both groups. The clinical course and prognosis, including mood episodes, were evaluated via face-to-face interviews based on the completed daily eMoodChart. For a 1-year prospective period, the number and duration of mood episodes were compared between the CRM and non-CRM groups using a generalized linear model. Results: The CRM group had 96.7% fewer total depressive episodes (n/year; exp beta=0.033, P=.03), 99.5% shorter depressive episodes (total; exp (3=0.005, P<.001), 96.1% shorter manic or hypomanic episodes (exp beta=0.039, P<.001), 97.4% fewer total mood episodes (exp beta=0.026, P=.008), and 98.9% shorter mood episodes (total; exp beta=0.011, P<.001) than the non-CRM group. Positive changes in health behaviors due to the alerts and in wearable device adherence rates were observed in the CRM group. Conclusions: The CRM app with a wearable activity tracker was found to be effective in preventing and reducing the recurrence of mood disorders, improving prognosis, and promoting better health behaviors. Patients appeared to develop a regular habit of using the CRM app.
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1. Basic Science > Department of Biostatistics > 1. Journal Articles
2. Clinical Science > Department of Psychiatry > 1. Journal Articles

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