The DOI matches its Crossref record; 192 of the 192 references it cites were found in a registry or an index.
Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms
Open accessAbstract
Introduction: Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has. Methods: We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals. Results: Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention. Discussion: We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed. Systematic Review Registration: Identifier: CRD42022357408.
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“Similarly, Hornstein et al. in their study noted that incorporating personalization strategies in digital mental health interventions specifically designed around patients’ symptoms and needs could lead to better management of depressive symptoms among healthcare workers [42].”
From Mental Health in Healthcare Workers Post-COVID-19: A Latin American Review and Insights into Personalized Management Strategies (2024) · cites this paper as [42]
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“While this study design does not permit definitive causal attribution to the personalization component specifically, the observed patterns are consistent with conceptual frameworks, suggesting that content alignment with users’ cognitive-emotional profiles enhances engagement [8,34].”
From Schema-Informed Digital Mental Health Intervention for Maladaptive Cognitive-Emotional Patterns: Randomized Controlled Trial (2025) · cites this paper as [8]
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Metadata
- Authors
- Hornstein, S., Zantvoort, K., Lueken, U., Funk, B., & Hilbert, K.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- Leuphana University of Lüneburg (DE)
- Corresponding author
- Hornstein, Silvan
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2023-05-22
- Volume
- 5
- Pages
- 1170002-1170002
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC10239832
- OpenAlex
- W4377294815
- Cited by
- 134
- References
- 187
- Citations by year
- 2026: 43 · 2025: 50 · 2024: 36 · 2023: 5
- FWCI
- 4.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- edoc Publication server (Humboldt University of Berlin) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Multilingual Matters (Channel View Publications) (submittedVersion, cc-by)
- edoc Publication server (Humboldt University of Berlin) (cc-by)
- PubData (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (491192747)
- Humboldt-Universität zu Berlin
- 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 › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- personalization, digital mental health interventions, depressive symptoms, depression, user choice, decision rules, machine learning, conceptual framework
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