Participatory Development of an Integrated, eHealth-Supported, Educational Care Pathway (Diabetes Box) for People With Type 2 Diabetes: Development and Usability Study.

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.

Use Intention and User Expectations of Human-Supported and Self-Help eHealth Interventions: Internet-Based Randomized Controlled Trial.

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 (t142=-1.133; P=.26). Performance expectancy (F1,140=69.269; P<.001), effort expectancy (F1,140=3.961; P=.049), social influence (F1,140=90.025; P<.001), and expected working alliance (F1,140=26.435; P<.001) were positively related to use intention regardless of condition. The interaction analysis showed that performance expectancy (F1,140=4.363; P=.04) and effort expectancy (F1,140=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.

Human cues in eHealth to promote lifestyle change: An experimental field study to examine adherence to self-help interventions.

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.

How to Use Costs in Value-Based Healthcare: Learning from Real-life Examples.

BACKGROUND: Healthcare organizations measure costs for business operations but do not routinely incorporate costs in decision-making on the value of care. AIM: Provide guidance on how to use costs in value-based healthcare (VBHC) delivery at different levels of the healthcare system. SETTING AND PARTICIPANTS: Integrated practice units (IPUs) for diabetes mellitus (DM) and for acute myocardial infarction (AMI) at the Leiden University Medical Center and a collaboration of seven breast cancer IPUs of the Santeon group, all in the Netherlands. PROGRAM DESCRIPTION AND EVALUATION: VBHC aims to optimize care delivery to the patient by understanding how costs relate to outcomes. At the level of shared decision-making between patient and clinician, yearly check-up consultations for DM type I were analyzed for patient-relevant costs. In benchmarking among providers, quantities of cost drivers for breast cancer care were assessed in scorecards. In continuous learning, cost-effectiveness analysis was compared with radar chart analysis to assess the value of telemonitoring in outpatient follow-up. DISCUSSION: Costs vary among providers in healthcare, but also between provider and patient. The joint analysis of outcomes and costs using appropriate methods helps identify and optimize the aspects of care that drive desired outcomes and value.

Identifying barriers and facilitators to adopting healthier dietary choices in clinical care: a cross-sectional observational study.

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.