Cited by 135Digital Mental Health InterventionsMobile Health and mHealth ApplicationsAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

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Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

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Hornstein, Silvan, Zantvoort, Kirsten, Lueken, Ulrike, Funk, Burkhardt, Hilbert, Kevin·2023·Frontiers in Digital Health

DOI 10.3389/fdgth.2023.1170002PMC PMC10239832

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Abstract

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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Figure 1 PRISMA flowchart of study inclusion.CC BY · PubMed Central

Figure 2 Personalization in comparison to the terms usage, customization, interactivity and group-based adaption.CC BY · PubMed Central

Figure 3 Dimensions of personalization.CC BY · PubMed Central

Figure 4 Mechanisms of personalization.CC BY · PubMed Central

Figure 5 Personalization mechanisms per dimension of the intervention.CC BY · PubMed Central

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“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.”
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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
ORCID: Hornstein, Silvan
Zantvoort, Kirsten
Lueken, Ulrike
Funk, Burkhardt
Hilbert, Kevin
Venue: Frontiers in Digital Health
Publisher: Frontiers Media SA
Published: 2023-05-22
Volume: 5
Pages: 1170002-1170002
ISSN: 2673-253X (ISSN-L)
DOI: 10.3389/fdgth.2023.1170002
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

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