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.

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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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Objective The objective of this study was to investigate whether placebo effect induced by pharmacological conditioning with intranasal insulin can affect glucose, insulin, C-peptide, hunger, and memory in patients with diabetes type 2 and healthy controls. Methods Placebo effect was induced by pharmacological conditioning. Thirty-two older patients (mean age = 68.3 years) with diabetes type 2 and age-and sex-matched thirty-two healthy older adults (mean age = 67.8 years) were randomly assigned to a conditioned or a control group. On day 1, conditioned group received six administrations of intranasal insulin with a conditioned stimulus (CS; smell of rosewood oil), whereas the control group received a placebo with the CS. On day 2, both groups received a placebo spray with the CS. Glucose, insulin, and C-peptide were repeatedly measured in blood. Hunger and memory were assessed with validated measures. Results Intranasal insulin stabilized dropping glucose levels in patients (B = 0.03, SE = 0.02, p =.027) and healthy men (B = 0.046, SE = 0.02, p =.021), and decreased C-peptide levels in healthy controls (B = 0.01, SE = 0.001, p =.008). Conditioning also prevented the drop of glucose levels but only in men (both healthy and patients; B = 0.001, SE = 0.0003, p =.024). Conditioning significantly decreased hunger in healthy participants (B = 0.31, SE = 0.09, p <.001). No effects were found on other measures. Conclusions Placebo effect induced by conditioning with intranasal insulin modifies blood glucose levels and decreases hunger in older adults, but its effects depend on health status and sex. Insulin conditioning might be beneficial for groups suffering from intensive hunger but seems not be particularly suitable for blood glucose reduction. Trial Registration Netherlands Trial Register, NL7783 (https://www.trialregister.nl/trial/7783).

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Hybrid e-health support was given to 11 insulin-dependent type 2 diabetes mellitus (DM2) patients, with electronic support plus a multi-disciplinary health support team. Challenges were low ICT and health literacy. After 50 weeks, attractiveness and feasibility of the intervention were perceived as high: recommendation 9.5 out of 10 and satisfaction 9.6 out of 10. Technology acceptance model (TAM) surveys showed high usefulness and feasibility. Acceptance and health behaviours were reinforced by the prolonged health results: aerobic and strength capacity levels were improved at 50 weeks, plus health related quality of life (plus biometric benefits and medication reductions, reported elsewhere). Regarding e-health theory, we conclude that iterative skill growth cycles are beneficial for long-term adoption and e-relationships. Next, the design analysis shows opportunities for additional affective and social support, on top of the strong benefits already apparent from the direct progress feedback loops used within the health coach processes.

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We report on the 50 weeks follow up results from a healthy lifestyle pilot (High Intensity Nutrition, Training & coaching), conducted with 11 insulin - dependent Type 2 Diabetes Mellites (DM2) patients. Hybrid eH ealth support was given, with electronic support plus a multi - disciplinary health support team. Regarding the pilot goal of long term healthy lifestyle adoption in senior DM2 patients, challenges were: low ICT - and health literacy. This exploratory design analysis formulates design lessons based on 50 weeks follow up. The first 12 weeks contained intensive face - to - face and eSupported coaching. After that, patient self - management and eTools were key. After 50 weeks, attractiveness and feasibility of the in tervention were perceived as high: recommendation 9,5 out of 10 and satisfaction 9,6 out of 10. TAM (Technology Acceptance Model) surveys showed high usefulness and feasibility. Acceptance and health behaviours were reinforced by the prolonged health resu lts: Aerobic and strength capacity levels were improved at 50 weeks, plus Health Related Quality of Life (and biometric benefits and medication reductions, reported elsewhere). We draw three types of conclusions. First, patients’ health literacy and qualit y of life improved strongly, which both supported healthy behaviours, even after 50 weeks. Second, regarding eHealth theory, iterative growth cycles are beneficial for long term adoption and e - relationships. Third, a design analysis was conducted regarding long term service mix efficacy in relation to key requirements for designing ICT - enabled lifestyle interventions. Several suggestions for long term lifestyle eSupport are given. @en

Advanced diabetes-type-2 patients often have high insulin resistance. Over the years their insulin medication rises, which further increases their insulin resistance and glucose management problems. A HINTc (High Intensity Nutrition, Training & coaching) pilot study was conducted with 11 insulin-dependent patients. Hybrid eHealth support was given, with electronic support plus a multi-disciplinary health support team. Based on preliminary 12 week results, attractiveness and feasibility of the intervention were high: recommendation 9,0 out of 10 and satisfaction 9,1 out of 10. TAM (Technology Acceptance Model) surveys showed high usefulness, feasibility and intentions for future use. Acceptance and health behaviours were also reinforced by the rapid results (average 9% weight loss, 20% lower fasting glucose and 71% lower insulin medication, plus a 46% increase on the Quality of Life Physical Health dimension). Our analysis supports three types of conclusions. First, patients’ health literacy and quality of life improved strongly, both supporting healthier behaviours. Second, a virtuous cycle was started, helping patients reverse diabetes-2 progression. Third, a design analysis was conducted regarding service mix efficacy in relation to key requirements for designing ICT-enabled lifestyle interventions.@en