This review investigates persuasive design frameworks within eHealth, concentrating on methodologies, their prevalence in mental and behavioral health applications, and identifying current research gaps. An extensive search was conducted across 8 databases, focusing on English publications with full text available. The search prioritized primary research articles, post-2011 applications, and eHealth platforms emphasizing treatment or support. The inclusion process was iterative, involving multiple authors, and relied on detailed criteria to ensure the relevance and contemporaneity of selected works. The final review set comprised 161 articles, providing an overview of persuasive design frameworks in eHealth. The review highlights the state of the art in the domain, emphasizing the utilization and effectiveness of these frameworks in eHealth platforms. This review details the restricted adoption of persuasive design frameworks within the field of eHealth, particularly in the mental and behavioral sectors. Predominant gaps include the scarcity of comparative evaluations, the underrepresentation of tailored interventions, and the unclear influence of persuasive components on user experience. There is a notable requirement for further scrutiny and refinement of persuasive design frameworks. Addressing these concerns promises a more substantial foundation for persuasive design in eHealth, potentially enhancing user commitment and platform efficiency.

Objective: This systematic review and meta-analysis evaluates the available evidence on efficacy of social support strategies, as defined by the persuasive system design framework, in internet-based and mobile-based interventions (IMI) targeting mental health. Design: Systematic review and meta-analysis. Data sources: PubMed, Embase, PsycINFO and the Cochrane Central Register of Controlled Trials. Eligibility criteria for selecting studies: Randomised controlled trials comparing IMI implementing a social support strategy for mental health symptoms to various control conditions. Publications up to June 2023 (date of search 6 June 2023) were considered. Data extraction and synthesis: Two independent reviewers screened and extracted data in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Risk of bias was assessed with the Risk of Bias Tool V.2.0. Data were pooled based on a random-effects model. Results: After screening 6484 records, a total of 45 studies met our inclusion criteria. At 96%, social support was predominantly implemented through the strategy of social facilitation, by which users recognising others using the intervention (eg, discussion forum). IMI implementing social support strategies showed moderate effect sizes of Hedges’ g =−0.34 (95% CI −0.47 to −0.21, p<0.001) in comparison to different control conditions. Heterogeneity was considerable (I ² =73.6%; 95% CI 64.8 to 80.3). Subgroup analyses revealed a significant influence of targeted psychological condition (p<0.001), type of social support (p<0.001) and control condition (p<0.001). In k =11 component studies (ie, comparison to a disentangled version of the same intervention), IMI with social support strategies were not significantly more effective in comparison to the same IMI without social support (Hedges’ g =−0.08 (95% CI −0.22 to 0.05, p=0.19, I ² =0%). 64% (k=7) of component studies reported higher adherence rates in IMI with social support. Conclusions: Based on a small number of component studies, implementing social support strategies in IMI that target mental health symptoms has no significant incremental benefit on effectiveness. To draw more robust conclusions, the potential of other social support strategies besides social facilitation should be exploited in future component studies. PROSPERO registration number: CRD42020222810.

The proliferation of online eHealth has made it much easier for users to access healthcare services and interventions from the comfort of their own homes. This study looks at how well one such platform—eSano—performs in terms of user experience when delivering mindfulness interventions. In order to assess usability and user experience, several tools such as eye-tracking technology, think-aloud sessions, a system usability scale questionnaire, an application questionnaire, and post-experiment interviews were employed. Participants were evaluated while they accessed the first module of the mindfulness intervention provided by eSano to measure their interaction with the app, and their level of engagement, and to obtain feedback on both the intervention and its overall usability. The results revealed that although users generally rated their experience with the app positively in terms of overall satisfaction, according to data collected through the system usability scale questionnaire, participants rated the first module of the mindfulness intervention as below average. Additionally, eye-tracking data showed that some users skipped long text blocks in favor of answering questions quickly while others spent more than half their time reading them. Henceforth, recommendations were put forward to improve both the usability and persuasiveness of the app—such as incorporating shorter text blocks and more engaging interactive elements—in order to raise adherence rates. Overall findings from this study provide valuable insights into how users interact with the eSano's participant app which can be used as guidelines for the future development of more effective and user-friendly platforms. Moreover, considering these potential improvements will help foster more positive experiences that promote regular engagement with these types of apps; taking into account emotional states and needs that vary across different age groups and abilities.

The challenge of sustaining user engagement in eHealth interventions is a pressing issue with significant implications for the effectiveness of these digital health tools. This study investigates user engagement in a cognitive-behavioral therapy-based eHealth intervention for procrastination, using a dataset from a randomized controlled trial of 233 university students. Various machine learning models, including Decision Tree, Gradient Boosting, Logistic Regression, Random Forest, and Support Vector Machines, were employed to predict patterns of user engagement. The study adopted a two-phase analytical approach. In the first phase, all features of the dataset were included, revealing ‘total_minutes’—the total time participants spent on the intervention and the eHealth platform—as the most significant predictor of engagement. This finding emphasizes the intuitive notion that early time spent on the platform and the intervention is a strong indicator of later user engagement. However, to gain a deeper understanding of engagement beyond this predominant metric, the second phase of the analysis excluded ‘total_minutes’. This approach allowed for the exploration of the roles and interdependencies of other engagement indicators, such as ‘number_intervention_answersheets’—the number of completed lessons, ‘logins_first_4_weeks’—login frequency, and ‘number_diary_answersheets’—the number of completed diaries. The results from this phase highlighted the multifaceted nature of engagement, showing that while ‘total_minutes’ is strongly correlated with engagement, indicating that more engaged participants tend to spend more time on the intervention, the comprehensive engagement profile also depends on additional aspects like lesson completions and frequency of platform interactions.

Adherence to digital health interventions remains a persistent challenge, limiting their effectiveness and motivating the need for scalable strategies that promote sustained engagement. This randomized controlled trial investigated whether user adherence could be enhanced by two specific persuasive design strategies: a gamification approach focused on visualizing skill progression, and a social support approach based on automated interaction between supportive partners. University students were recruited and assigned to a control, gamification, or social support group. The control group received the standard intervention without persuasive strategies, whereas the gamification group utilized skill-based progression systems and infographics. In the social support group, participants were paired into teams. Participants were encouraged to motivate teammates to complete daily diary entries, with mutual completion triggering a congratulatory email. Average module completions were: control ( $$\varvec{M = 2.52}$$ M = 2.52 , SD = 1.92), gamification ( $$\varvec{M = 2.69}$$ M = 2.69 , SD = 1.89), and social support ( $$\varvec{M = 2.76}$$ M = 2.76 , SD = 2.03). Neither the gamification components nor the automated peer support system improved adherence compared to the control. Although the peer support strategy in the social support group recorded more diary entries, this did not translate to improved module adherence. User experience ratings were comparable in all groups, indicating that the strategies did not affect usability. The smaller-than-planned sample size and higher attrition rates warrant cautious interpretation of these findings. These results contribute to healthcare informatics by demonstrating the limitations of generic persuasive strategies and highlighting the need for more adaptive, context-aware engagement mechanisms in digital interventions.