The utility of smartphone-based, ecological momentary assessment for depressive symptoms

•Patient satisfaction with EMA technology indicate positive results, via surveys•Detection of MDD via EMA was mostly conducted via machine learning techniques•5 of 7 studies were able to significantly predict depression from phone sensor data•2 of 3 studies were able to decrease depressive symptoms...

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Bibliographic Details
Published in:Journal of affective disorders 2020-09, Vol.274, p.602-609
Main Authors: Yim, Samantha J., Lui, Leanna M.W., Lee, Yena, Rosenblat, Joshua D., Ragguett, Renee-Marie, Park, Caroline, Subramaniapillai, Mehala, Cao, Bing, Zhou, Aileen, Rong, Carola, Lin, Kangguang, Ho, Roger C., Coles, Alexandria S., Majeed, Amna, Wong, Elizabeth R., Phan, Lee, Nasri, Flora, McIntyre, Roger S.
Format: Article
Language:English
Subjects:
Application
Depression
Depressive Disorder, Major - diagnosis
Depressive Disorder, Major - therapy
Ecological Momentary Assessment
Humans
Major Depressive Disorder
Passive Intervention
Smartphone
Text Messaging
Unobtrusive
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Summary:•Patient satisfaction with EMA technology indicate positive results, via surveys•Detection of MDD via EMA was mostly conducted via machine learning techniques•5 of 7 studies were able to significantly predict depression from phone sensor data•2 of 3 studies were able to decrease depressive symptoms via phone-based treatment Major Depressive Disorder (MDD) is a common and debilitating mood disorder. Individuals with MDD are often misdiagnosed or diagnosed in an untimely manner, exacerbating existing functional impairments. Ecological momentary assessment (EMA) involves the repeated sampling of an individual's symptoms within their natural environment and has been demonstrated to assist in illness assessment and characterization. Capturing data in this way would set the stage for improved treatment outcomes and serve as a complementary resource in the management and treatment of depressive symptoms. Online databases PubMed/MedLine and PsycINFO were searched using PRISMA guidelines and combinations of the following keywords: EMA, depression, smartphone app, diagnosing, symptoms, phone, app, ecological momentary assessment, momentary assessment, data mining, unobtrusive, passive data, GPS, sensor. A total of nineteen original articles were identified using our search parameters and ten articles met the inclusion criteria for full-text review. Among the ten relevant studies, three studies evaluated feasibility, seven evaluated detection, and three evaluated treatment of MDD. Limitations include that the design of all of the studies included in this review are non-randomized. It should be noted that most of the studies included were pilot studies and/or exploratory trials lacking a control group. Available evidence suggests that the use of passive smartphone-based applications may lead to improved management of depressive symptoms. This review aids the creation of new EMA applications, highlights the potential of EMA usage in clinical settings and drug development, emphasizes the importance for regulation of applications in the mental health field, and provides insight into future directions.
ISSN:0165-0327
1573-2517
DOI:10.1016/j.jad.2020.05.116