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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.
Cho, Chul-Hyun; Lee, Taek; Lee, Jung-Been; Seo, Ju Yeon; Jee, Hee-Jung; Son, Serhim; An, Hyonggin; Kim, Leen; Lee, Heon-Jeong.
Afiliación
  • Cho CH; Department of Psychiatry, College of Medicine, Chungnam National University, Daejeon, Republic of Korea.
  • Lee T; Department of Psychiatry, Chungnam National University Sejong Hospital, Sejong, Republic of Korea.
  • Lee JB; Korea University Chronobiology Institute, Seoul, Republic of Korea.
  • Seo JY; Department of Convergence Security Engineering, College of Knowledge-based Services Engineering, Sungshin Women's University, Seoul, Republic of Korea.
  • Jee HJ; Department of Computer Science, Korea University College of Information, Seoul, Republic of Korea.
  • Son S; Korea University Chronobiology Institute, Seoul, Republic of Korea.
  • An H; Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.
  • Kim L; Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.
  • Lee HJ; Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.
JMIR Ment Health ; 7(8): e21283, 2020 Aug 05.
Article en En | MEDLINE | ID: mdl-32755884
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 ß=0.033, P=.03), 99.5% shorter depressive episodes (total; exp ß=0.005, P<.001), 96.1% shorter manic or hypomanic episodes (exp ß=0.039, P<.001), 97.4% fewer total mood episodes (exp ß=0.026, P=.008), and 98.9% shorter mood episodes (total; exp ß=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. TRIAL REGISTRATION: ClinicalTrials.gov NCT03088657; https://clinicaltrials.gov/ct2/show/NCT03088657.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Observational_studies / Prognostic_studies / Qualitative_research Idioma: En Revista: JMIR Ment Health Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Observational_studies / Prognostic_studies / Qualitative_research Idioma: En Revista: JMIR Ment Health Año: 2020 Tipo del documento: Article
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