Aim: Remote patient management (RPM) effectively aids cardiovascular risk management, but its large-scale implementation remains challenging. Panel management may facilitate implementation by using comprehensive data to identify patients at risk of cardiovascular diseases and tailor interventions. This study evaluated the implementation strategies and clinical outcomes of a multi-component RPM intervention ‘Connect@Heart’. Methods: We conducted a mixed-methods study over six months in four primary care practices in the Netherlands, evaluating two patient groups: (i) patients with a BMI < 25 received a blood pressure monitor alone (BP Box), and (ii) patients with a BMI > 25 or cardiovascular disease received a combination of a BP monitor, a scale, and an activity tracker (Lifestyle Box). Baseline and six-month follow-up assessments were performed using linear mixed-effects models, and implementation outcomes were evaluated using the RE-AIM framework. Results: Our approach achieved high enrolment, with 189 out of 200 initially interested patients (94%) participating. The intervention was associated with a significant reduction in BP levels within both groups (BP Box systolic BP from 139 ± 21 mmHg at baseline to 132 ± 18 mmHg at follow-up, p < 0.001 and Lifestyle Box 142 ± 16 mmHg to 131 ± 14 mmHg at follow-up, p < 0.001), especially for those with uncontrolled hypertension. After six months, 66% of patients performed measurements weekly. All participating practices continued using the intervention post-study. Conclusion: This study demonstrates that proactively identifying patient panels at risk for CVD and tailoring multi-component RPM interventions to patient panels are promising implementation strategies for reaching favourable clinical outcomes at scale.

Background: Coronary artery disease (CAD) comprises one of the leading causes of morbidity and mortality both in the European population and globally. All established clinical risk stratification scores and models require blood lipids and physical measurements. The latest reports of the European Commission suggest that attracting health professionals to collect these data can be challenging, both from a logistic and cost perspective, which limits the usefulness of established models and makes them unsuitable for population-wide screening in resource-limited settings, i.e., rural areas. Therefore, the aim of this study was to develop and externally validate a questionnaire-based risk stratification model on a population scale at minimal cost, i.e., the Questionnaire-Based Evaluation for Estimating Coronary Artery Disease (QUES-CAD) to stratify the 10-year incidence of coronary artery disease. Methods: Cox proportional hazards (CoxPH) and Cox gradient boosting (CoxGBT) models were trained with 10-fold cross-validation using combinations of ten questionnaire variables on the White population of the UK Biobank (n = 448,818) and internally validated the models in all ethnic minorities (n = 27,433). The Lifelines cohort was employed as an independent external validation population (n = 97,770). Additionally, we compared QUES-CAD's performance, containing only questionnaire variables, to clinically established risk prediction tools, i.e., Framingham Coronary Heart Disease Risk Score, American College of Cardiology/American Heart Association pooled cohort equation, World Health Organization cardiovascular disease risk charts, and Systematic Coronary Risk Estimation 2 (SCORE2). We conducted partial log-likelihood ratio (PLR) tests and C-index comparisons between QUES-CAD and established clinical prediction models. Findings: In the external validation set, QUES-CAD exhibited C-index values of CoxPH: 0.692 (95% Confidence Interval [CI]: 0.673–0.71) and CoxGBT: 0.699 (95% CI: 0.681–0.717) for the male population and CoxPH: 0.771 (95% CI: 0.748–0.794) and CoxGBT: 0.759 (95% CI: 0.736–0.783) for the female population. The addition of measurement-based variables and variables that require a prior medical examination (i.e., insulin use, number of treatments/medications taken, prevalent cardiovascular disease [other than CAD, and stroke diagnosed by a doctor]) and the further addition of biomarkers/other measurements (i.e., high-density lipoprotein [HDL] cholesterol, total cholesterol, and glycated haemoglobin) did not significantly improve QUES-CAD's performance in most instances. C-index comparisons and PLR tests showed that QUES-CAD performs and fits the data at least as well as the clinical prediction models. Interpretation: QUES-CAD performs comparably to established clinical prediction models and enables a population-wide identification of high-risk individuals for CAD. The model developed and validated herein relies solely on ten questionnaire variables, overcoming the limitations of existing models that depend on physical measurements or biomarkers. Funding: University Medical Center Groningen.

Aims Digital health technologies are considered promising innovations to reduce healthcare’s environmental footprint. However, this assumption remains largely unstudied. We compared the environmental impact of telemonitoring and care on site (CoS) in post-myocardial infarction (MI) follow-up and explored how it influenced patients’ and healthcare professionals’ (HPs) perceptions of using telemonitoring. Methods and results We conducted a mixed-method study; a standardized life cycle assessment, and qualitative interviews and focus groups. We studied the environmental impact of resource use per patient for 1-year post-MI follow-up in a Dutch academic hospital, as CoS or partially via telemonitoring. We used the Environmental Footprint 3.1 method. Qualitative data were analysed using Thematic Analysis. The environmental impact of telemonitoring was larger than CoS for all impact categories, including global warming (+480%) and mineral/metal resource use (+4390%). Production of telemonitoring devices contributed most of the environmental burden (89%). Telemonitoring and CoS achieved parity in most impact categories at 65 km one-way patient car commute. Healthcare professionals and patients did not consider the environmental impact in their preference for telemonitoring, as the patient’s individual health was their primary concern—especially after a cardiac event. However, patients and HPs were generally positive towards sustainable healthcare and willing to use telemonitoring more sustainably. Conclusion Telemonitoring had a substantially bigger environmental impact than CoS in the studied setting. Patient commute distance, reuse of devices, and tailored use of devices should be considered when implementing telemonitoring for clinical follow-up. Patients and HPs supported these solutions to enhance sustainability-informed cardiovascular care as the default option.

Background: Remote patient management (RPM) using smartphone-enabled health monitoring devices (SHMDs) can be an effective, value-added part of cardiovascular care. However, cardiac patients’ adherence to RPM is variable. Personas are fictional representations of users with common behaviors, needs, and motivation and can thereby help guide tailoring of interventions to be meaningful and possibly more effective. Personas can be used to understand the needs of the patient group and guide tailoring toward more personalized and effective eHealth intervention. Objective: The aim of this study was to develop data-driven personas for patients with myocardial infarction (MI) based on both quantitative and qualitative results. Methods: This study used a mixed methods design involving (1) database analysis of patients with MI (N=261) SHMD usage data (blood pressure [BP], weight, step count) over the course of a one-year care track and (2) semistructured interviews with patients with MI (N=16) currently using SHMDs. Overall, 12-month adherence rates were calculated based on the number of weeks patients performed the prescribed home measurements with the SHMDs. Results: A cluster analysis was conducted on the self-monitoring data resulting in four distinctive usage patterns labeled as stiff starting (low adherent in first 6 weeks: 13%, 34/261 of users), temporary persisting (decreasing adherence: 24%, 62/261), loyally persisting (continuously adherent: 26%, 68/261), and negligent quitting (nonadherent: 37%, 97/261). Health outcomes (BP, step count, and weight) were analyzed based on these patterns. More adherent usage patterns show better controlled BP when compared to less adherent usage patterns, suggesting that adherence is associated with health outcomes. Patient experiences regarding adherence or nonadherence to the RPM relating to the four distinctive usage patterns were uncovered by means of semistructured interviews, providing insight into adherence factors most relevant for each of the clusters. Thus, 4 distinct personas were developed by data collection (database analysis and semistructured interviews), persona segmentation, and persona creation, named Tamara, Sam, Peter, and Kim. Conclusions: This study identified 4 personas regarding adherence experiences and usage patterns of patients within an RPM care track. Adherent usage patterns were characterized by improved BP and step count. These personas can guide future tailoring of eHealth interventions to maximize patient adherence.

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.

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.

Background: Lifestyle support is essential in preventing and treating cardiovascular diseases (CVD), and eHealth may be an easy and affordable solution to provide this support. However, CVD patients vary in their ability and interest to use eHealth. This study investigates demographic characteristics determining CVD patients' online and offline lifestyle support preferences. Methods: We used a cross-sectional study design. 659 CVD patients (Harteraad panel) completed our questionnaire. We assessed demographic characteristics and preferred lifestyle support type (coach, eHealth, family/friends, self-supportive). Results: Respondents mostly preferred being self-supportive (n = 179, 27.2%), and a coach in a group or individually (n = 145, 22.0%; n = 139, 21.1%). An app/internet to work independently (n = 89, 13.5%) or being in touch with other CVD patients (n = 44, 6.7%) was least preferred. Men were more likely to prefer being supported by family/friends (p = .016) or self-supportive (p < .001), while women preferred a coach individually or via an app/internet (p < .001). Older patients mostly preferred self-support (p = .001). Patients with low social support were more likely to prefer being coached individually (p < .001), but not support from family/friends (p = .002). Conclusion: Men and older patients are more interested in being self-supportive, and patients with lower levels of social support could need extra support outside their social network. eHealth could provide a solution, but attention should be paid to spike interest for digital interventions among certain groups.