Background: Financial incentives are a promising tool to help people increase their physical activity, but they are expensive to provide. Deposit contracts are a type of financial incentive in which participants pledge their own money. However, low uptake is a crucial obstacle to the large-scale implementation of deposit contracts. Therefore, we investigated whether (1) matching the deposit 1:1 (doubling what is deposited) and (2) allowing for customizable deposit amounts increased the uptake and short term effectiveness of a deposit contract for physical activity. Methods: In this randomized controlled trial, 137 healthy students (age M = 21.6 years) downloaded a smartphone app that provided them with a tailored step goal and then randomized them to one of four experimental conditions. The deposit contract required either a €10 fixed deposit or a customizable deposit with any amount between €1 and €20 upfront. Furthermore, the deposit was either not matched or 1:1 matched (doubled) with a reward provided by the experiment. During 20 intervention days, daily feedback on goal progress and incentive earnings was provided by the app. We investigated effects on the uptake (measured as agreeing to participate and paying the deposit) and effectiveness of behavioral adoption (measured as participant days goal achieved). Findings: Overall, the uptake of deposit contracts was 83.2%, and participants (n = 113) achieved 14.9 out of 20 daily step goals. A binary logistic regression showed that uptake odds were 4.08 times higher when a deposit was matched (p = .010) compared to when it was not matched. Furthermore, uptake odds were 3.53 times higher when a deposit was customizable (p = .022) compared to when it was fixed. Two-way ANCOVA showed that matching (p = .752) and customization (p = .143) did not impact intervention effectiveness. However, we did find a marginally significant interaction effect of deposit matching X deposit customization (p = .063, ηp2 = 0.032). Customization decreased effectiveness when deposits were not matched (p = .033, ηp2 = 0.089), but had no effect when deposits were matched (p = .776, ηp2 = 0.001). Conclusions: We provide the first experimental evidence that both matching and customization increase the uptake of a deposit contract for physical activity. We recommend considering both matching and customization to overcome lack of uptake, with a preference for customization since matching a deposit imposes significant additional costs. However, since we found indications that customizable deposits might reduce effectiveness (when the deposits are not matched), we urge for more research on the effectiveness of customizable deposit contracts. Finally, future research should investigate which participant characteristics are predictive of deposit contract uptake and effectiveness. Pre-registration: OSF Registries, https://osf.io/cgq48.

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Background: Type 2 diabetes (T2D) tremendously affects patient health and health care globally. Changing lifestyle behaviors can help curb the burden of T2D. However, health behavior change is a complex interplay of medical, behavioral, and psychological factors. Personalized lifestyle advice and promotion of self-management can help patients change their health behavior and improve glucose regulation. Digital tools are effective in areas of self-management and have great potential to support patient self-management due to low costs, 24/7 availability, and the option of dynamic automated feedback. To develop successful eHealth solutions, it is important to include stakeholders throughout the development and use a structured approach to guide the development team in planning, coordinating, and executing the development process. Objective: The aim of this study is to develop an integrated, eHealth-supported, educational care pathway for patients with T2D. Methods: The educational care pathway was developed using the first 3 phases of the Center for eHealth and Wellbeing Research roadmap: the contextual inquiry, the value specification, and the design phase. Following this roadmap, we used a scoping review about diabetes self-management education and eHealth, past experiences of eHealth practices in our hospital, focus groups with health care professionals (HCPs), and a patient panel to develop a prototype of an educational care pathway. This care pathway is called the Diabetes Box (Leiden University Medical Center) and consists of personalized education, digital educational material, self-measurements of glucose, blood pressure, activity, and sleep, and a smartphone app to bring it all together. Results: The scoping review highlights the importance of self-management education and the potential of telemonitoring and mobile apps for blood glucose regulation in patients with T2D. Focus groups with HCPs revealed the importance of including all relevant lifestyle factors, using a tailored approach, and using digital consultations. The contextual inquiry led to a set of values that stakeholders found important to include in the educational care pathway. All values were specified in biweekly meetings with key stakeholders, and a prototype was designed. This prototype was evaluated in a patient panel that revealed an overall positive impression of the care pathway but stressed that the number of apps should be restricted to one, that there should be no delay in glucose value visualization, and that insulin use should be incorporated into the app. Both patients and HCPs stressed the importance of direct automated feedback in the Diabetes Box. Conclusions: After developing the Diabetes Box prototype using the Center for eHealth and Wellbeing Research roadmap, all stakeholders believe that the concept of the Diabetes Box is useful and feasible and that direct automated feedback and education on stress and sleep are essential. A pilot study is planned to assess feasibility, acceptability, and usefulness in more detail.

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This research explores the dynamic nature of family involvement in remote patient management for cardiovascular disease and its impact on lifestyle behaviour changes. Through an interview study with patients and family members, we categorise family involvement into three types: Inform, Integrate, and Influence, highlighting the dynamic and heterogeneous nature of family involvement across different phases and activities. Overall, we emphasise the need for personalised and adaptable interventions to cater to the diversity of families and propose a modular approach to remote monitoring design.@en

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

Background: eHealth interventions have the potential to enhance health outcomes for people with a low socioeconomic position (SEP), but lack of knowledge on how to effectively meet the specific needs of this population can limit their effectiveness and widen the digital divide. Our study aimed to address this issue by developing an online guide for professionals (e.g., researchers and health professionals) to support the development and adaptation of eHealth interventions for people with low SEP. Methods: During the initial phase, we conducted two studies: (1) a Delphi study with professionals, identifying barriers to developing, evaluating, and implementing eHealth interventions (top-down), and (2) a communitybased study with people from low SEP, exploring their perspectives on health, healthcare, and eHealth (bottom-up). In the second phase, an iterative participatory process was used to develop the online guide, involving 11 professionals in think-aloud sessions and interviews to assess content and design acceptance. The presentation focuses on outcomes of this second phase. Findings: Professionals found the online guide to be user-friendly and helpful, particularly the recommendations on barriers, facilitators, and user scenarios. However, they requested more practical examples and engaging content. Discussion: This guide is an important contribution to addressing the knowledge gap in eHealth interventions for people with a low SEP and provides valuable information for professionals, whether they are new to the field or have experience. Future research should focus on enhancing the guide's usability and flexibility for its diverse audience, including healthcare practitioners, and other eHealth professionals working with this population. @en

Background: Gamification and deposit contracts (a financial incentive in which participants pledge their own money) can enhance effectiveness of mobile behavior change interventions. However, to assess their potential for improving population health, research should investigate implementation of gamified deposit contracts outside the research setting. Therefore, we analyzed data from StepBet, a smartphone application originally developed by WayBetter, Inc. Objective: To perform a naturalistic evaluation of StepBet gamified deposit contracts, for whom they work best, and under which conditions they are most effective to help increase physical activity. Methods: WayBetter provided data of StepBet participants that participated in a stepcount challenge between 2015 and 2020 (N = 72,974). StepBet challenges were offered on the StepBet smartphone application. The modal challenge consisted of a $40 deposit made prior to a 6-week challenge period during which participants needed to reach daily and weekly step goals in order to regain their deposit. Participants who met their goals also received additional earnings which were paid out from the money lost by those who failed their challenge. Challenge step goals were tailored on a 90-day historic step count retrieval that was also used as the baseline comparison for this study. Primary outcomes were increase in step count (continuous) and challenge success (dichotomous). Results: Overall, average daily step counts increased by 31.2 % (2423 steps, SD = 3462) from 7774 steps (SD = 3112) at baseline to 10,197 steps (SD = 4162) during the challenge. The average challenge success rate was 73 %. Those who succeeded in their challenge (n = 53,281) increased their step count by 44.0 % (3465 steps, SD = 3013), while those who failed their challenge (n = 19,693) decreased their step count by −5.3 % (−398 steps, SD = 3013). Challenges started as a New Year's resolution were slightly more successful (77.7 %) than those started during the rest of the year (72.6 %). Discussion: In a real-world setting, and among a large and diverse sample, participating in a gamified deposit contract challenge was associated with a large increase in step counts. A majority of challenges were successful and succeeding in a challenge was associated with a large and clinically relevant increase in step counts. Based on these findings, we recommend implementing gamified deposit contracts for physical activity where possible. An interesting avenue for future research is to explore possible setback effects among people who fail a challenge, and how setbacks can be mitigated. Pre-registration: Open Science Framework (doi:10.17605/OSF.IO/D237C).

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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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The authors regret that the Standard Deviation (SD) for those who failed their challenge (n = 19,693) was erroneously reported in the Abstract (page 1) and Table 2 of the Results section (page 6) as 3013 steps. The correct Standard Deviation that should have been reported there is 2993 steps. Furthermore, in the Results section under header 3.3 Exploratory Analyses (page 6) we erroneously state that exploratory analyses were performed on a subsample of 29,001 participants. The correct number that should have been reported there is 29,002 participants. The authors would like to apologise for any inconvenience caused.

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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

Background and aims: Adopting healthier diets can drastically improve societal health. Our environment plays a crucial role in daily dietary choices and hospitals in particular can stimulate patients to adopt healthier eating habits. Unfortunately, no robust clinically applicable cuing tools exist to help guide in-hospital dietary interventions. The purpose of this study was to identify patient-related barriers and facilitators to adopting healthier dietary choices. Methods and results: This cross-sectional observational study was conducted on the cardiology ward of a university medical center between June 2020 and January 2021. Of the 594 patients asked and the 312 completed surveys on healthy eating intentions, 285 responses were considered for analysis. Notably, the majority of respondents were male (68.8%), with an average hospital stay of 3.3 days. The results indicate that cardiac patients attribute significantly greater influence on their dietary behavior to doctors compared to other caregivers, including dieticians (X² = 37.09, df = 9, p < 0.001). Also, younger patients (below 70 years of age) were more inclined to plan changing dietary behavior than older patients. Most mentioned facilitators for adopting a healthier diet were more information/counseling, help in preparing food, support from family and friends, and more emphasis from a doctor. Conclusion: The study highlights the importance of involving doctors in formulating dietary policies and patient-directed interventions within hospital settings. It also sheds light on the barriers and facilitators for promoting healthier dietary behaviors among patients during their hospitalization.

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Promoting health behaviors and preventing chronic diseases through a healthy lifestyle among those with a low socioeconomic status (SES) remain major challenges. eHealth interventions are a promising approach to change unhealthy behaviors in this target group. Objective: This review aims to identify key components, barriers, and facilitators in the development, reach, use, evaluation, and implementation of eHealth lifestyle interventions for people with a low SES. This review provides an overview for researchers and eHealth developers, and can assist in the development of eHealth interventions for people with a low SES. Methods: We performed a scoping review based on Arksey and O'Malley's framework. A systematic search was conducted on PubMed, MEDLINE (Ovid), Embase, Web of Science, and the Cochrane Library, using terms related to a combination of the following key constructs: eHealth, lifestyle, low SES, development, reach, use, evaluation, and implementation. There were no restrictions on the date of publication for articles retrieved upon searching the databases. Results: The search identified 1323 studies, of which 42 met our inclusion criteria. An update of the search led to the inclusion of 17 additional studies. eHealth lifestyle interventions for people with a low SES were often delivered via internet-based methods (eg, websites, email, Facebook, and smartphone apps) and offline methods, such as texting. A minority of the interventions combined eHealth lifestyle interventions with face-to-face or telephone coaching, or wearables (blended care). We identified the use of different behavioral components (eg, social support) and technological components (eg, multimedia) in eHealth lifestyle interventions. Facilitators in the development included iterative design, working with different disciplines, and resonating intervention content with users. Facilitators for intervention reach were use of a personal approach and social network, reminders, and self-monitoring. Nevertheless, barriers, such as technological challenges for developers and limited financial resources, may hinder intervention development. Furthermore, passive recruitment was a barrier to intervention reach. Technical difficulties and the use of self-monitoring devices were common barriers for users of eHealth interventions. Only limited data on barriers and facilitators for intervention implementation and evaluation were available. Conclusions: While we found large variations among studies regarding key intervention components, and barriers and facilitators, certain factors may be beneficial in building and using eHealth interventions and reaching people with a low SES. Barriers and facilitators offer promising elements that eHealth developers can use as a toolbox to connect eHealth with low SES individuals. Our findings suggest that one-size-fits-all eHealth interventions may be less suitable for people with a low SES. Future research should investigate how to customize eHealth lifestyle interventions to meet the needs of different low SES groups, and should identify the components that enhance their reach, use, and effectiveness.

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Background: A promising new approach to support lifestyle changes in patients with cardiovascular disease (CVD) is the use of financial incentives. Although financial incentives have proven to be effective, their implementation remains controversial, and ethical objections have been raised. It is unknown whether health care professionals (HCPs) involved in CVD care find it acceptable to provide financial incentives to patients with CVD as support for lifestyle change. Objective: This study aims to investigate HCPs’ perspectives on using financial incentives to support healthy living for patients with CVD. More specifically, we aim to provide insight into attitudes toward using financial incentives as well as obstacles and facilitators of implementing financial incentives in current CVD care. Methods: A total of 16 semistructured, in-depth, face-to-face interviews were conducted with Dutch HCPs involved in supporting patients with CVD with lifestyle changes. The topics discussed were attitudes toward an incentive system, obstacles to using an incentive system, and possible solutions to facilitate the use of an incentive system. Results: HCPs perceived an incentive system for healthy living for patients with CVD as possibly effective and showed generally high acceptance. However, there were concerns related to focusing too much on the extrinsic aspects of lifestyle change, disengagement when rewards are insignificant, paternalization and threatening autonomy, and low digital literacy in the target group. According to HCPs, solutions to mitigate these concerns included emphasizing intrinsic aspects of healthy living while giving extrinsic rewards, integrating social aspects to increase engagement, supporting autonomy by allowing freedom of choice in rewards, and aiming for a target group that can work with the necessary technology. Conclusions: This study mapped perspectives of Dutch HCPs and showed that attitudes are predominantly positive, provided that contextual factors, design, and target groups are accurately considered. Concerns about digital literacy in the target group are novel findings that warrant further investigation. Follow-up research is needed to validate these insights among patients with CVD.

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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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