Background: Self-help eHealth interventions provide automated support to change health behaviors without any further human assistance. The main advantage of self-help eHealth interventions is that they have the potential to lower the workload of health care professionals. However, one disadvantage is that they generally have a lower uptake. Possibly, the absence of a relationship with a health care professional (referred to as the working alliance) could lead to negative expectations that hinder the uptake of self-help interventions. The Unified Theory of Acceptance and Use of Technology (UTAUT) identifies which expectations predict use intention. As there has been no previous research exploring how expectations affect the adoption of both self-help and human-supported eHealth interventions, this study is the first to investigate the impact of expectations on the uptake of both kinds of eHealth interventions. Objective: This study investigated the intention to use a self-help eHealth intervention compared to a human-supported eHealth intervention and the expectations that moderate this relationship. Methods: A total of 146 participants were randomly assigned to 1 of 2 conditions (human-supported or self-help eHealth interventions). Participants evaluated screenshots of a human-supported or self-help app–based stress intervention. We measured intention to use the intervention-expected working alliance and the UTAUT constructs: performance expectancy, effort expectancy, and social influence. Results: Use intention did not differ significantly between the 2 conditions (t ₁₄₂=–1.133; P=.26). Performance expectancy (F _1,140=69.269; P<.001), effort expectancy (F _1,140=3.961; P=.049), social influence (F _1,140=90.025; P<.001), and expected working alliance (F _1,140=26.435; P<.001) were positively related to use intention regardless of condition. The interaction analysis showed that performance expectancy (F _1,140=4.363; P=.04) and effort expectancy (F _1,140=4.102; P=.045) more strongly influenced use intention in the self-help condition compared to the human-supported condition. Conclusions: As we found no difference in use intention, our results suggest that we could expect an equal uptake of self-help eHealth interventions and human-supported ones. However, attention should be paid to people who have doubts about the intervention’s helpfulness or ease of use. For those people, providing additional human support would be beneficial to ensure uptake. Screening user expectations could help health care professionals optimize self-help eHealth intervention uptake in practice. Trial Registration: OSF Registries osf.io/n47cz; https://osf.io/n47cz

Health disparities between socio-economic classes are growing. While eHealth has been proposed as a potential solution, it often disproportionately benefits people with a higher socio-economic position (SEP) thereby exacerbating health disparities. We aimed to develop an eHealth intervention that supports specifically people with a low SEP during their waiting period between hospital referral and start of cardiac rehabilitation (CR). We followed a participatory design process in which we used a variety of participatory and inclusive design tools and techniques to identify needs of the target group and develop the intervention. 15 cardiac patients with a low SEP participated in the development (n = 8) and evaluation (n = 7) of the intervention. We found that patients with a low SEP require certainty and guidance during their waiting period. To address this, healthcare providers guide them through this transitional phase using multimedia resources in the eHealth intervention. This includes introductions of the healthcare providers and information about their roles, peer experiences, practical tips and daily updates on the remaining waiting time. The evaluation yielded high scores on usability (4.4 out of 5), experience (4.1 out of 5) and perceived effect on certainty and guidance (4.0 out of 5). This study explored how eHealth can bridge gaps between hospitals and CR and between high and low SEP. It emphasizes the significance of utilizing participatory and inclusive approaches to develop feasible and acceptable eHealth interventions for those with low SEP. Currently, follow-up evaluative research is conducted to determine the proof-of-principle of the intervention.