Evidence map›Paper›PMID 32755884›Full record

ArticleJMIR mental health2020

Effectiveness of a Smartphone App With a Wearable Activity Tracker in Preventing the Recurrence of Mood Disorders: Prospective Case-Control Study.

Chul-Hyun Cho, Taek Lee, Jung-Been Lee, Ju Yeon Seo, Hee-Jung Jee, Serhim Son, Hyonggin An, Leen Kim, Heon-Jeong Lee

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR mental health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03088657 (Mood Disorder Cohort Research Consortium), which is not on this map. Cited by 28 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 7 pooled it
5.7field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03088657 unknown statusnot on this map

Mood Disorder Cohort Research Consortium (MDCRC) in Korea

Typeobservational_patient_registrySponsorKorea University Anam HospitalRan2015 to 2022Enrolled500ConditionsMood Disorders, Bipolar Disorder, Major Depressive Disorder
3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 7 syntheses or guidelines pooled it, 63 citations in OpenAlex.

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  19. Telepsychiatry in the Treatment of Major Depressive Disorders.Advances in experimental medicine and biology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Chul-Hyun Cho *Department of Psychiatry, College of Medicine, Chungnam National University, Daejeon, Republic of Korea.ORCID https://orcid.org/0000-0002-1663-9680
Taek Lee *Department of Convergence Security Engineering, College of Knowledge-based Services Engineering, Sungshin Women's University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-2277-8211
Jung-Been LeeDepartment of Computer Science, Korea University College of Information, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-8208-0387
Ju Yeon SeoKorea University Chronobiology Institute, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-1312-761X
Hee-Jung JeeDepartment of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-0811-3053
Serhim SonDepartment of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-4539-4392
Hyonggin AnDepartment of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-0566-758X
Leen KimKorea University Chronobiology Institute, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-0400-8248
Heon-Jeong LeeKorea University Chronobiology Institute, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-9560-2383
Korea University · KRChungnam National University · KRSungshin Women's University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSmartphones 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.

objectiveWe 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.

methodsIn 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.

resultsThe 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.

conclusionsThe 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 registrationClinicalTrials.gov NCT03088657; https://clinicaltrials.gov/ct2/show/NCT03088657.

Indexed as

circadian rhythmdigital therapeuticsmood disorderrecurrence preventionsmartphone appwearable activity tracker

Identifiers

PMID32755884
PMCPMC7439135
OpenAlexW3047382484

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.