Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study (Preprint)
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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 β=0.033, P=.03), 99.5% shorter depressive episodes (total; exp β=0.005, P<.001), 96.1% shorter manic or hypomanic episodes (exp β=0.039, P<.001), 97.4% fewer total mood episodes (exp β=0.026, P=.008), and 98.9% shorter mood episodes (total; exp β=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. CLINICALTRIAL: ClinicalTrials.gov NCT03088657; https://clinicaltrials.gov/ct2/show/NCT03088657
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There were a large variety of study types among the 17 studies, including pilot randomised controlled trials, observational studies, model evaluations, and a qualitative study.
“There were a large variety of study types among the 17 studies, including pilot randomised controlled trials (RCTs) [28–30], observational studies [31–35], model evaluations [36,37], and a qualitative study [38].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [35]
12 of the 17 included studies had sample sizes smaller than 100.
“12 of the 17 included studies had sample sizes smaller than 100 [28–31,33–35,37–39,41,42].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [35]
The most common use of artificial intelligence in the mental health apps was prediction (of mood, stress, and risk) (8/17 studies).
“The most common uses of artificial intelligence in the mental health apps were prediction (of mood, stress, and risk) (8/17 studies) [31–37,41]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [35]
3 of the 17 studies focused on predicting mood in people with mood disorders.
“3 studies each focused on predicting mood in people with mood disorders [34,35,37]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [35]
One study found a significant reduction in amount and time of depressive, manic or hypomanic, and mood episodes in the app group (Circadian Rhythm for Mood) than the control group.
“one found a significant reduction in amount and time of depressive, manic or hypomanic, and mood episodes in the app group (Circadian Rhythm for Mood) than the control group [35].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [35]
7 of the 17 studies evaluated mental health as an outcome (rather than a model input).
“7 of the 17 studies evaluated mental health as an outcome (rather than a model input) [29,30,35,38,42–44].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [35]
Of the 12 studies that included an intervention period, only 3 (25%) had intervention periods longer than 1 year.
“of the 12 studies that included an intervention period, only 3 (25%) had intervention periods longer than 1 year [33–35].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [35]
The case-control study had no blinding of assessors.
“In the investigation classified as a case-control study [84], the main issues were related the lack of sample size justification, no blinding of assessors, and an unclear participant selection.”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [84]
The case-control study had unclear participant selection.
“In the investigation classified as a case-control study [84], the main issues were related the lack of sample size justification, no blinding of assessors, and an unclear participant selection.”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [84]
The case-control study lacked sample size justification.
“In the investigation classified as a case-control study [84], the main issues were related the lack of sample size justification, no blinding of assessors, and an unclear participant selection.”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [84]
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References 49 · Cited by 69
- Authors
- Cho, C.-H., Lee, T., Lee, J.-B., Seo, J. Y., Jee, H.-J., Son, S., An, H., Kim, L., & Lee, H.-J.
- Affiliations
- Chungnam National University (KR)
- Sungshin Women's University (KR)
- Korea University (KR)
- Corresponding author
- Lee, Heon-Jeong
- ORCID
- Venue
- JMIR Mental Health
- Publisher
- JMIR Publications Inc.
- Published
- 2020-06-28
- Volume
- 7
- Issue
- 8
- Pages
- e21283-e21283
- ISSN
- 2368-7959 (ISSN-L)
- PMC
- PMC7439135
- OpenAlex
- W3047382484
- Cited by
- 69
- References
- 49
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 13 · 2023: 18 · 2022: 6 · 2021: 6 · 2020: 1
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Mental Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation
- National Research Foundation of Korea (2019R1A2C2084158, 2017 M3A9F1031220)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- mood disorders, mobile application, wearable activity trackers, circadian rhythm, digital phenotyping, machine learning, mood prediction, behavior guidance, depressive episodes, real-world data, mental health monitoring, warning alerts
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