Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review
Open accessAbstract
BACKGROUND: Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones. OBJECTIVE: We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes. METHODS: We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols. RESULTS: A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage. CONCLUSIONS: The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field.
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Systematic reviews have not found conclusive evidence in terms of direction or magnitude of self-monitoring effects.
“systematic reviews have not found conclusive evidence in terms of direction or magnitude (Chen et al., 2017; Dogan et al. 2017).”
From Effects of a Personalized Stress Management Intervention on Maternal Mental Health: A Randomized Clinical Trial. (2025) · cites this paper as Dogan et al. 2017
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Shin, J., & Bae, S. (2024) · Digital Health · doi:10.1177/20552076241261920 · Cited by 10 · see citing works
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Jordan, P. J., Troth, A. C., & Yan, H. (2024) · Australian Journal of Management · doi:10.1177/03128962241286258 · Cited by 15 · see citing works
Utilisation des objets connectés en recherche clinique
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Prévention du suicide et santé connectée
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So said, so done? The role of commitment in activity-based check-in discontinuance on APPs
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- Authors
- Dogan, E., Sander, C., Wagner, X., Hegerl, U., & Kohls, E.
- Affiliations
- Stiftung Deutsche Depressionshilfe (DE)
- Leipzig University (DE)
- Corresponding author
- Sander, Christian
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2017-07-24
- Volume
- 19
- Issue
- 7
- Pages
- e262
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5547249
- OpenAlex
- W2737034073
- Cited by
- 177
- References
- 95
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 15 · 2023: 18 · 2022: 36 · 2021: 23 · 2020: 21 · 2019: 27 · 2018: 18 · 2017: 2
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (13GW0162B)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- smartphone-based monitoring, mood disorders, heart rate variability, physical activity, voice characteristics, light exposure, location data, mood prediction, adherence, feasibility
- MeSH terms
- Smartphone, Female, Humans, Male, Mood Disorders
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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