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Review
. 2023 May 22:5:1170002.
doi: 10.3389/fdgth.2023.1170002. eCollection 2023.

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

Silvan Hornstein  1 , Kirsten Zantvoort  2 , Ulrike Lueken  1 , Burkhardt Funk  2 , Kevin Hilbert  1
Affiliations
Review

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

Silvan Hornstein et al. Front Digit Health. .

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.

Keywords: depression; digital mental health; personalization; precision care, iCBT, machine learning.

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Conflict of interest statement

Two of the authors declare no Competing Non-Financial Interests but the following Competing Financial Interests. SH is currently employed as Data Scientist by Elona Health, a digital mental health start-up building blended mental healthcare solutions for the German market. SH worked for Meru Health, a digital mental health company developing interventions, in the past. BF is a shareholder at HelloBetter, a digital mental health company developing digital interventions, and PersonalAIze, an AI consulting company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
PRISMA flowchart of study inclusion.
Figure 2
Figure 2
Personalization in comparison to the terms usage, customization, interactivity and group-based adaption.
Figure 3
Figure 3
Dimensions of personalization.
Figure 4
Figure 4
Mechanisms of personalization.
Figure 5
Figure 5
Personalization mechanisms per dimension of the intervention.
See this image and copyright information in PMC

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