eHealth lifestyle interventions without human support (self-help interventions) are generally less effective, as they suffer from lower adherence levels. To solve this, we investigated whether (1) using a text-based conversational agent (TCA) and applying human cues contribute to a working alliance with the TCA, and whether (2) adding human cues and establishing a positive working alliance increase intervention adherence. Participants (N = 121) followed a TCA-supported app-based physical activity intervention. We manipulated two types of human cues: visual (ie, message appearance) and relational (ie, message content). We employed a 2 (visual cues: yes, no) x 2 (relational cues: yes, no) between-subjects design, resulting in four experimental groups: (1) visual and relational cues, (2) visual cues only, (3) relational cues only, or (4) no human cues. We measured the working alliance with the Working Alliance Inventory Short Revised form and intervention adherence as the number of days participants responded to the TCA's messages. Contrary to expectations, the working alliance was unaffected by using human cues. Working alliance was positively related to adherence (t(78) = 3.606, p = .001). Furthermore, groups who received visual cues showed lower adherence levels compared to those who received relational cues only or no cues (U = 1140.5, z = −3.520, p < .001). We replicated the finding that establishing a working alliance contributes to intervention adherence, independently of the use of human cues in a TCA. However, we were unable to show that adding human cues impacted the working alliance and increased adherence. The results indicate that adding visual cues to a TCA may even negatively affect adherence, possibly because it may create confusion concerning the true nature of the coach, which may prompt unrealistic expectations.@en

Aims Little is known about the impact of daily physical activity timing (here referred to as 'chronoactivity') on cardiovascular disease (CVD) risk. We aimed to examined the associations between chronoactivity and multiple CVD outcomes in the UK Biobank. Methods and results physical activity data were collected in the UK-Biobank through triaxial accelerometer over a 7-day measurement period. We used K-means clustering to create clusters of participants with similar chronoactivity irrespective of the mean daily intensity of the physical activity. Multivariable-adjusted Cox-proportional hazard models were used to estimate hazard ratios (HRs) comparing the different clusters adjusted for age and sex (model 1), and baseline cardiovascular risk factors (model 2). Additional stratified analyses were done by sex, mean activity level, and self-reported sleep chronotype. We included 86 657 individuals (58% female, mean age: 61.6 [SD: 7.8] years, mean BMI: 26.6 [4.5] kg/m2). Over a follow-up period of 6 years, 3707 incident CVD events were reported. Overall, participants with a tendency of late morning physical activity had a lower risk of incident coronary artery disease (HR: 0.84, 95%CI: 0.77, 0.92) and stroke (HR: 0.83, 95%CI: 0.70, 0.98) compared to participants with a midday pattern of physical activity. These effects were more pronounced in women (P-value for interaction=0.001). We did not find evidence favouring effect modification by total activity level and sleep chronotype. Conclusion Irrespective of total physical activity, morning physical activity was associated with lower risks of incident cardiovascular diseases, highlighting the potential importance of chronoactivity in CVD prevention.

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Background: Cardiovascular disease is one of the leading causes of disability worldwide. eHealth has the possibility to improve health outcomes and quality of life adjunct to regular care. “The Myocardial Infarction Box” (MI Box) is an at home monitoring system using smartphone-enabled health monitoring devices. However, like many eHealth interventions, the MI Box had variable adherence This variable adherence could be due to a misalignment with the needs of the users and the intervention. Through co-creation, this study aims to develop design strategies to enhance the effectiveness of the MI Box in the long term: to increase adherence in the first year and support MI patients in adopting a healthy lifestyle. Methods: Six iterative co-creation session were conducted with various stakeholders. One with designers and with healthcare professionals. Four sessions with users. In these sessions, the patient journey and patient personas were discussed to identify needs and opportunities for enhancements to the intervention. Furthermore, design strategies for enhancing the intervention, aiming both to optimize adherence and to stimulate a healthy lifestyle, were discussed. Findings: This resulted in themes such as personalized feedback, understanding the data, and regaining confidence in one’s own body to be key aspects to creating a more meaningful and personally relevant intervention. Discussion: Involving the stakeholders in co-creation sessions provides invaluable insights into the userexperiences and opportunities for personalization. Adapting the intervention to these needs is the next step to increase engagement with the intervention and thereby increase adherence. @en

Objective eHealth is a useful tool to deliver lifestyle interventions for patients with cardiometabolic diseases. However, there are inconsistent findings about whether these eHealth interventions should be supported by a human professional, or whether self-help interventions are equally effective. Methods Databases were searched between January 1995 and October 2021 for randomized controlled trials on cardiometabolic diseases (cardiovascular disease, chronic kidney disease, type 1 and 2 diabetes mellitus) and eHealth lifestyle interventions. A multilevel meta-analysis was used to pool clinical and behavioral health outcomes. Moderator analyses assessed the effect of intervention type (self-help versus human-supported), dose of human support (minor versus major part of intervention), and delivery mode of human support (remote versus blended). One hundred seven articles fulfilled eligibility criteria and 102 unique (N = 20,781) studies were included. Results The analysis showed a positive effect of eHealth lifestyle interventions on clinical and behavioral health outcomes (p <.001). However, these effects were not moderated by intervention type (p =.169), dose (p =.698), or delivery mode of human support (p =.557). Conclusions This shows that self-help eHealth interventions are equally effective as human-supported ones in improving health outcomes among patients with cardiometabolic disease. Future studies could investigate whether higher-quality eHealth interventions compensate for a lack of human support. Meta-analysis registration: PROSPERO CRD42021269263.

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Background: Cardiovascular diseases (CVDs) pose a significant health threat and reduce both people's life expectancy and quality of life. Healthy living is a key component in the effective prevention and treatment of CVD. However, health care professionals (HCPs) experience difficulties in supporting lifestyle changes among their patients. eHealth can provide a solution to these barriers. Objective: This study aims to provide insights into the factors HCPs find important in the support of patients with CVD in the uptake of and adherence to a healthy lifestyle and the perceived facilitators of and barriers to using eHealth to provide lifestyle support to patients with CVD. Methods: In-depth interviews were conducted with 16 Dutch HCPs specializing in lifestyle support in cardiac care. Results: We identified 13 themes, of which the first 12 concerned lifestyle support in general and were related to intervention, patient, or health care. Throughout these themes, the use of eHealth reoccurred as a potential facilitator of or solution to barriers to lifestyle support. Our final theme specifically concerned barriers to the adoption and usability of eHealth. Conclusions: HCPs do recognize the potential advantages of eHealth while experiencing barriers to using digital tools. Incorporating their needs and values in the development of lifestyle support programs, especially eHealth, could increase their use and lead to a more widespread adoption of eHealth into health care.

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Background: Healthy living is key in the prevention and rehabilitation of cardiovascular disease (CVD). Yet, supporting and maintaining a healthy lifestyle is exceptionally difficult and people differ in their needs regarding optimal support for healthy lifestyle interventions. Objective: The goals of this study were threefold: to uncover stakeholders’ needs and preferences, to translate these to core values, and develop eHealth technology based on these core values. Our primary research question is: What type of eHealth application to support healthy living among people with (a high risk of) CVD would provide the greatest benefit for all stakeholders? Methods: User-centered design principles from the CeHRes roadmap for eHealth development were followed to guide the uncovering of important stakeholder values. Data were synthesized from various qualitative studies (i.e., literature studies, interviews, think-aloud sessions, focus groups) and usability tests (i.e., heuristic evaluation, cognitive walkthrough, think aloud study). We also developed an innovative application evaluation tool to perform a competitor analysis on 33 eHealth applications. Finally, to make sure to take into account all end-users needs and preferences in eHealth technology development, we created personas and a customer journey. Results: We uncovered 10 universal values to which eHealth-based initiatives to support healthy living in the context of CVD prevention and rehabilitation should adhere to (e.g., providing social support, stimulating intrinsic motivation, offering continuity of care). These values were translated to 14 desired core attributes and then prototype designs. Interestingly, we found that the primary attribute of good eHealth technology was not a single intervention principle, but rather that the technology should be in the form of a digital platform disseminating various interventions, i.e., a ‘one-stop-shop’. Conclusion: Various stakeholders in the field of cardiovascular prevention and rehabilitation may benefit most from utilizing one personalized eHealth platform that integrates a variety of evidence-based interventions, rather than a new tool. Instead of a one-size-fits-all approach, this digital platform should aid the matchmaking between patients and specific interventions based on personal characteristics and preferences.

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