Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients
Open accessAbstract
BACKGROUND: Mobile mental-health trackers are mobile phone apps that gather self-reported mental-health ratings from users. They have received great attention from clinicians as tools to screen for depression in individual patients. While several apps that ask simple questions using face emoticons have been developed, there has been no study examining the validity of their screening performance. OBJECTIVE: In this study, we (1) evaluate the potential of a mobile mental-health tracker that uses three daily mental-health ratings (sleep satisfaction, mood, and anxiety) as indicators for depression, (2) discuss three approaches to data processing (ratio, average, and frequency) for generating indicator variables, and (3) examine the impact of adherence on reporting using a mobile mental-health tracker and accuracy in depression screening. METHODS: We analyzed 5792 sets of daily mental-health ratings collected from 78 breast cancer patients over a 48-week period. Using the Patient Health Questionnaire-9 (PHQ-9) as the measure of true depression status, we conducted a random-effect logistic panel regression and receiver operating characteristic (ROC) analysis to evaluate the screening performance of the mobile mental-health tracker. In addition, we classified patients into two subgroups based on their adherence level (higher adherence and lower adherence) using a k-means clustering algorithm and compared the screening accuracy between the two groups. RESULTS: With the ratio approach, the area under the ROC curve (AUC) is 0.8012, indicating that the performance of depression screening using daily mental-health ratings gathered via mobile mental-health trackers is comparable to the results of PHQ-9 tests. Also, the AUC is significantly higher (P=.002) for the higher adherence group (AUC=0.8524) than for the lower adherence group (AUC=0.7234). This result shows that adherence to self-reporting is associated with a higher accuracy of depression screening. CONCLUSIONS: Our results support the potential of a mobile mental-health tracker as a tool for screening for depression in practice. Also, this study provides clinicians with a guideline for generating indicator variables from daily mental-health ratings. Furthermore, our results provide empirical evidence for the critical role of adherence to self-reporting, which represents crucial information for both doctors and patients.
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In depression, mobile apps have tracked changes in patient-reported mood.
“In depression, mobile apps have tracked changes in patient-reported mood [27-29] and have been used as part of randomized controlled trials to evaluate treatment efficacy [30].”
From Wearable technology for high-frequency cognitive and mood assessment in major depressive disorder: longitudinal observational study (2019) · cites this paper as [27]
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- Authors
- Kim, J., Lim, S., Min, Y. H., Shin, Y.-W., Lee, B., Sohn, G., Jung, K. H., Lee, J.-H., Son, B. H., Ahn, S. H., Shin, S.-Y., & Lee, J. W.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Johns Hopkins University (US)
- Gachon University (KR)
- Asan Medical Center (KR)
- Corresponding author
- Lee, Jong Won
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2016-08-04
- Volume
- 18
- Issue
- 8
- Pages
- e216
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC4990716
- OpenAlex
- W2498898584
- Cited by
- 85
- References
- 51
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 10 · 2023: 6 · 2022: 14 · 2021: 9 · 2020: 10 · 2019: 11 · 2018: 7 · 2017: 10
- FWCI
- 14.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Gachon University (GCU-2015-0049)
- 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
- depression screening, mobile application, breast cancer patients, sleep satisfaction, mood, anxiety, PHQ-9, screening accuracy
- MeSH terms
- Smartphone, Breast Neoplasms, Depression, Female, Humans, Mass Screening, Middle Aged, ROC Curve, Telemedicine, Mobile Applications
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