Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
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.

PubMed Disclaimer

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

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Lim GY, Tam WW, Lu Y, Ho CS, Zhang MW, Ho RC. Prevalence of depression in the community from 30 countries between 1994 and 2014. Sci Rep. (2018) 8:2861. 10.1038/s41598-018-21243-x - DOI - PMC - PubMed
    1. Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M, et al. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry. (2018) 75:336. 10.1001/jamapsychiatry.2017.4602 - DOI - PMC - PubMed
    1. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJL, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLOS Med. (2013) 10:e1001547. 10.1371/journal.pmed.1001547 - DOI - PMC - PubMed
    1. Wang PS, Demler O, Kessler RC. Adequacy of treatment for serious mental illness in the United States. Am J Public Health. (2002) 92:92–8. 10.2105/ajph.92.1.92 - DOI - PMC - PubMed
    1. Singer S, Engesser D, Wirp B, Lang K, Paserat A, Kobes J, et al. Effects of a statutory reform on waiting times for outpatient psychotherapy: a multicentre cohort study. Couns Psychother Res. (2022) 22:982–97. 10.1002/capr.12581 - DOI