Feasibility and effects of an eHealth intervention to support patients with a low socioeconomic position during their waiting period preceding cardiac rehabilitation 127 6 pointing toward potential intervention effects. First, the qualitative findings suggest that the participants felt that the intervention contributed to their feelings of certainty and guidance. The interview results suggested improved expectations for future CR, better information, and guidance during the waiting period. These insights hint at the intervention enhancing participant readiness and motivation for CR. Second, the finding that the length of the waiting period was negatively associated with the change in feelings of certainty and guidance in the control group but not in the intervention group suggests that the intervention could serve as an emotional buffer for patients facing longer waiting periods. Qualitative feedback further supports this, with participants reporting that the intervention helped to set expectations and provide information regarding their rehabilitation journey. Although it did not directly improve certainty and guidance, the intervention might have fostered a sense of readiness for rehabilitation by giving information and early engagement with the program. In future versions of the intervention, its content should be focused more directly on improving the patient’s feeling of certainty and guidance. Lastly, although the difference in dropout rates between the intervention and control group was not significant, the 10% dropout rate in the control group is consistent with the general dropout rate in CR (Brouwers et al., 2021). The absence of dropouts in the intervention group could suggest that the intervention may have boosted participant’s commitment to CR. This should, however, be confirmed in a sufficiently powered trial. Our study found participants preferred more personally relevant content and additional depth and detail in information. This suggest that the intervention’s one-sizefits-all approach may not meet varying needs for content depth and relevance. This desire for personalized content also aligns with previous research findings (Tenbult-Van Limpt et al., 2022; Yates et al., 2018). Personalized information, as opposed to generic information, has demonstrated a greater positive impact on wellbeing (Doets et al., 2019), health plan decision making (Kaufmann et al., 2018), and lifestyle behavior (Tong et al., 2021). Within CR, several studies have shown to be effective that employed dynamic personalization techniques, such as using initial screenings (van den Brekel-Dijkstra et al., 2016) or artificial intelligence algorithms to adapt the content and delivery in real-time based on user’s interactions and responses (Aharon et al., 2022; Doets et al., 2019). Future research could explore developing personas or patient profiles reflecting diverse content needs based on health concerns, condition severity, and motivation (Vosbergen et al., 2015). These profiles would guide the creation of tailored pathways for pre-cardiac rehabilitation content, accommodating different patient types during their waiting period. Pathways may vary by exercise difficulty aligned with disease severity and information delivery adjusted to individual knowledge and health literacy levels.
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