Background: A healthy lifestyle is crucial in mitigating cardiovascular disease risk. Numerous tools for cardiovascular risk behaviors have been developed that people can use for self-assessment purposes. However, the validity of these tools is insufficiently understood in the context of self-assessment. This systematic review examines the validity of self-assessment tools for cardiovascular risk behaviors, including lack of physical activity (PA), tobacco smoking, excessive alcohol consumption, unhealthy diet, and chronic psychological stress. Methods: The PubMed, Ovid Embase, and the Cochrane Library databases were searched. Studies investigating the validity of tools in the context of self-assessment (i.e., without active involvement of a healthcare professional) were included. We investigated criterion validity (i.e., comparison to a gold standard), convergent validity (comparison to similar measures), face and content validity, and reliability. Results: Thirty-one unique articles reporting on 37 separate validation studies were included, which examined a total of 49 distinct self-assessment tools (with tools for PA (n = 40), nutritional intake (n = 7), psychological stress (n = 1), and multiple domains (n = 1)). No validation studies were found for self-assessment of tobacco smoking or alcohol consumption. All wearable PA intensity assessment–energy expenditure studies demonstrated weak validity, both in laboratory and free-living conditions. Criterion validity was examined for only two nutritional intake tools, showing weak to moderate validity. For psychological stress and tools measuring multiple domains, only convergent validity was examined. Discussion: Behavioral self-assessment tools are predominantly focused on PA and nutritional intake, with limited evidence for good validity. There is a pressing need for developing and validating comprehensive and accurate self-assessment tools.

Active and healthy ageing depends on maintaining physical and cognitive activity, but it is still challenging to motivate older adults to participate in regular training. This paper describes the iterative design and evaluation of a digital platform for increasing older adults' motivation to perform physical and cognitive exercises. The digital solution was designed and evaluated in four iterations with a total of 13 older adults. The first stage focused on identifying effective communication methods, including different formats of instructional delivery and feedback, as well as tone. The second stage explored the combination of physical activity with cognitively stimulating activities, such as brain games, sport, and hobbies, to find the most motivating combinations. The final stage developed the prototype further by integrating motivational elements into one coherent design, emphasizing clarity, guidance, and user agency. The final evaluation reviewed the overall design, including the importance of adaptive systems that dynamically adjust the difficulty level to align with users' physical and cognitive abilities to increase motivation. This study contributes to the growing field of participatory design within digital health interventions, aligning with best practices that emphasize the need for dynamic user involvement in all stages of development.

People with a physically demanding job have an unhealthy disbalance in occupational and leisure-time physical activity (PA). We aimed to understand which contextual factors influence this disbalance, and explore opportunities for lifestyle interventions that could restore this disbalance. We applied a contextmapping study with six production workers from a Dutch coating department. Participants filled in a sensitizing booklet with PA-related activities, and were interviewed afterwards. Participants reported reasons for (not) being active in leisure-time using an experience sampling method. Our results indicate that main reasons for being inactive during leisure time were their believes that occupational PA is enough for a healthy lifestyle, and the need to rest after work. Results show that lifestyle interventions should tackle workers inadequate risk perception and over-exhaustion to empower them to shift their PA behavior in a healthier direction. This indicates the need for a holistic approach targeting both home and working environments.

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.

BACKGROUND: People with a low socioeconomic position (SEP) are less likely to benefit from eHealth interventions, exacerbating social health inequalities. Professionals developing eHealth interventions for this group face numerous challenges. A comprehensive guide to support these professionals in their work could mitigate these inequalities. OBJECTIVE: We aimed to develop a web-based guide to support professionals in the development, adaptation, evaluation, and implementation of eHealth interventions for people with a low SEP. METHODS: This study consisted of 2 phases. The first phase involved a secondary analysis of 2 previous qualitative and quantitative studies. In this phase, we synthesized insights from the previous studies to develop the guide's content and information structure. In the second phase, we used a participatory design process. This process included iterative development and evaluation of the guide's design with 11 professionals who had experience with both eHealth and the target group. We used test versions (prototypes) and think-aloud testing combined with semistructured interviews and a questionnaire to identify design requirements and develop and adapt the guide accordingly. RESULTS: The secondary analysis resulted in a framework of recommendations for developing the guide, which was categorized under 5 themes: development, reach, adherence, evaluation, and implementation. The participatory design process resulted in 16 requirements on system, content, and service aspects for the design of the guide. For the system category, the guide was required to have an open navigation strategy leading to more specific information and short pages with visual elements. Content requirements included providing comprehensible information, scientific evidence, a user perspective, information on practical applications, and a personal and informal tone of voice. Service requirements involved improving suitability for different professionals, ensuring long-term viability, and a focus on implementation. Based on these requirements, we developed the final version of "the inclusive eHealth guide." CONCLUSIONS: The inclusive eHealth guide provides a practical, user-centric tool for professionals aiming to develop, adapt, evaluate, and implement eHealth interventions for people with a low SEP, with the aim of reducing health disparities in this population. Future research should investigate its suitability for different end-user goals, its external validity, its applicability in specific contexts, and its real-world impact on social health inequality.

Importance: Cardiac telerehabilitation (CTR) has been found to be a safe and beneficial alternative to traditional center-based cardiac rehabilitation (CR) and might be associated with higher participation rates by reducing barriers to CR use. However, implementation of CTR interventions remains low, which may be owing to a lack of cost-effectiveness analyses of data from large-scale randomized clinical trials. Objective: To assess the cost-effectiveness of CTR with relapse prevention compared with center-based CR among patients with coronary artery disease. Design, Setting, and Participants: This economic evaluation performed a cost-utility analysis of data from the SmartCare-CAD (Effects of Cardiac Telerehabilitation in Patients With Coronary Artery Disease Using a Personalized Patient-Centred ICT Platform) randomized clinical trial. The cost-effectiveness and utility of 3 months of cardiac telerehabilitation followed by 9 months of relapse prevention were compared with the cost-effectiveness of traditional center-based cardiac rehabilitation. The analysis included 300 patients with stable coronary artery disease who received care at a CR center serving 2 general hospitals in the Netherlands between May 23, 2016, and July 26, 2018. All patients were entering phase 2 of outpatient CR and were followed up for 1 year (until August 14, 2019). Data were analyzed from September 21, 2020, to September 24, 2021. Intervention: After baseline measurements were obtained, participants were randomly assigned on a 1:1 ratio to receive CTR (intervention group) or center-based CR (control group) using computerized block randomization. After 6 supervised center-based training sessions, patients in the intervention group continued training at home using a heart rate monitor and accelerometer. Patients uploaded heart rate and physical activity data and discussed their progress during a weekly video consultation with their physical therapist. After 3 months, weekly coaching was concluded, and on-demand coaching was initiated for relapse prevention; patients were instructed to continue using their wearable sensors and were contacted in cases of nonadherence to the intervention or reduced exercise or physical activity volumes. Main Outcomes and Measures: Quality-adjusted life-years were assessed using the EuroQol 5-Dimension 5-Level survey (EQ-5D-5L) and the EuroQol Visual Analogue Scale (EQ-VAS), and cardiac-associated health care costs and non-health care costs were measured by health care consumption, productivity, and informal care questionnaires (the Medical Consumption Questionnaire, the Productivity Cost Questionnaire, and the Valuation of Informal Care Questionnaire) designed by the Institute for Medical Technology Assessment. Costs were converted to 2020 price levels (in euros) using the Dutch consumer price index (to convert to US dollars, euro values were multiplied by 1.142, which was the mean exchange rate in 2020). Results: Among 300 patients (266 men [88.7%]), the mean (SD) age was 60.7 (9.5) years. The quality of life among patients receiving CTR vs center-based CR was comparable during the study according to the results of both utility measures (mean difference on EQ-5D-5L: -0.004; P =.82; mean difference on EQ-VAS: -0.001; P =.92). Intervention costs were significantly higher for CTR (mean [SE], 224 [4] [$256 ($4)]) compared with center-based CR (mean [SE], 156 [5] [$178 ($6)]; P <.001); however, no difference in overall cardiac health care costs was observed between CTR (mean [SE], 4787 [503] [$5467 ($574)] and center-based CR (mean [SE], 5507 [659] [$6289 ($753)]; P =.36). From a societal perspective, CTR was associated with lower costs compared with center-based CR (mean [SE], 20 495 [ 2751] [$23 405 ($3142)] vs 24 381 [3613] [$27 843 ($4126)], respectively), although this difference was not statistically significant (-3887 [-$4439]; P =.34). Conclusions and Relevance: In this economic evaluation, a CTR intervention with relapse prevention was likely to be cost-effective compared with center-based CR, suggesting that CTR maybe used as an alternative intervention for the treatment of patients with coronary artery disease. These results add to the evidence base in favor of CTR and may increase the implementation of CTR interventions in clinical practice..

Background: Despite proven clinical benefits, only a minority of patients complete outpatient cardiac rehabilitation (CR) after acute myocardial infarction (AMI). The main purpose of this study was to evaluate to what extent and at which time patients drop out of CR, and to assess which patient-related characteristics can predict dropout. Methods: In a retrospective cohort study, we selected patients who had been hospitalised with an AMI in our centre in 2015 or 2016. Patients were selected pseudonymously based on reimbursement codes in the electronic health record. We extracted baseline characteristics and data on CR referral, enrolment and completion for each patient. Multivariable logistic regression was used to assess which characteristics predicted referral and dropout. Results: The 666 patients included were predominantly male (66%), with a mean age of 69.0 years. Of the 640 eligible patients, 201 (31%) were not referred for CR. Enrolment after referral was 94%. Nonreferral was independently associated with older age, female sex, traveling distance, non-ST-elevation myocardial infarction (NSTEMI; as compared with STEMI), no coronary revascularisation and prior manifestations of coronary artery disease. Of the 414 enrolled patients, 24% did not complete their CR programmes (i.e. dropped out). Older age and worse exercise capacity at baseline were independently associated with dropout. The ability of the multiple regression models to predict nonreferral and noncompletion was good to fair, with an area under the receiver operating characteristic curves of 0.86 and 0.71, respectively. Conclusion: The main reason for not participating in or not completing CR after AMI was nonreferral. To optimise CR utilisation, improvement of referral rates should be prioritised.