Tailoring in eHealth lifestyle interventions targeting people with cardiometabolic conditions and lower socioeconomic position: A scoping review

Sandra Van Mellaert, Martha S Kreuzberg, Iris ten Klooster, Bert-Jan F van Beijnum, Jan N van Rijn and Monique Tabak

PLOS Digital Health, 2026, vol. 5, issue 9, 1-31

Abstract: eHealth interventions can support healthy lifestyle change for preventing and managing cardiometabolic conditions. While most eHealth interventions are designed for the general population, these conditions are more prevalent in people from lower socioeconomic backgrounds. Tailoring interventions to the needs and characteristics of this population can increase adherence and engagement, thereby enhancing the overall intervention effectiveness. However, evidence on how to tailor eHealth interventions to users from low socioeconomic backgrounds remains limited. Therefore, this scoping review examines the tailoring approaches implemented within eHealth lifestyle interventions targeting people with cardiometabolic conditions and low socioeconomic position (SEP). We focus on what is being tailored, the tailoring variables, the algorithms used for tailoring, and how the tailoring approaches are evaluated. Using keywords related to low SEP, cardiometabolic conditions, eHealth interventions, lifestyle, and tailoring, we searched electronic databases including Scopus, Web of Science, PubMed, and PsycINFO. We identified 43 eligible articles, with 27 unique eHealth lifestyle interventions targeting primarily diet and exercise. The literature shows a variety of tailoring approaches, albeit with a trend towards tailoring to socioeconomic factors at the design stage. Most interventions (n = 26/27, 96%) used rule-based algorithms for tailoring, primarily through functions such as feedback selection (n = 17/27, 63%) or variable substitution (n = 16/27, 59%). Although evaluation of tailoring was missing from most studies (n = 15/27, 55%), the importance of sociocultural relevance, appropriate language, and health literacy-sensitive design was highlighted. These findings suggest that while tailoring is present in many interventions, the current approaches remain limited in the facilitating technology and dynamic adaptations to SEP-specific needs. Thus, future research should investigate the application of more advanced, but reproducible tailoring algorithms and rigorously evaluate the impact of different tailoring methods on intervention effectiveness. To synthesize our findings, we assembled a framework that encapsulates the key concepts from our review, in combination with envisioned future work.Author summary: eHealth interventions are increasingly used for lifestyle coaching to prevent or manage cardiometabolic diseases, such as diabetes. Tailoring these interventions can increase intervention adherence and eventually contribute to improved health outcomes for the users. People from low socioeconomic backgrounds are more vulnerable to cardiometabolic conditions and could especially benefit from such interventions. However, guidelines on how to tailor the lifestyle interventions to this group are lacking. Therefore, in this scoping review, we examined the tailoring approaches used within eHealth lifestyle interventions for this target group. We found that tailoring to socioeconomic factors mostly occurred at the design stage, using variables such as low literacy levels. The reviewed studies especially highlighted the importance of tailoring to the users’ sociocultural context, language and differing health literacy needs. We identified several gaps within the literature, including the limited use of non-rule-based algorithms and a general lack of evaluation of tailoring methods. Future research should further investigate the use of adaptive algorithms for tailoring and prioritize evaluation trial designs that can differentiate the impact of different tailoring methods. Based on our findings, we constructed a framework of key concepts identified in this review, together with envisioned future work, to guide tailoring in future interventions.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001713

DOI: 10.1371/journal.pdig.0001713

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