Evidence-based healthcare innovation
Risk stratification and population health
Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities: a model development and validation study
This study developed simple yet powerful questionnaire-based models to predict both the current presence and future risk of type 2 diabetes across diverse ethnic groups. Using data from over 630,000 individuals, the models achieved high accuracy, with AUCs up to 0.917 for prevalence and 0.873 for 8-year incidence. Impressively, they performed well not only in the Caucasian UK Biobank population but also across five other ethnicities and in an external Dutch cohort. The models outperformed two established non-lab clinical tools and correctly reclassified over 3,000 additional diabetes cases. While adding blood biomarkers slightly improved performance, the inclusion of physical measurements added little value, highlighting the strength of the questionnaire-only approach. These scalable, low-cost tools hold great promise for early diabetes detection and large-scale risk screening across diverse populations.
Simplifying coronary artery disease risk stratification: development and validation of a questionnaire-based alternative comparable to clinical risk tools
This study developed and validated QUES-CAD, a questionnaire-based model to predict 10-year coronary artery disease (CAD) risk without needing physical exams or lab tests. Using data from the UK Biobank and being external validated in the Lifelines cohort, the model showed strong predictive performance, with C-index values comparable to established clinical risk tools. QUES-CAD performed well in both men and women, and adding traditional medical measurements or biomarkers did not significantly improve its accuracy. The model consists of only ten questionnaire items, making it low-cost, easily accessible and scalable. It is especially useful for large-scale screening in resource-limited settings, such as rural areas or areas with limited resources. QUES-CAD offers a promising alternative to traditional models for identifying individuals at high risk for CAD.
The necessity of incorporating non-genetic risk factors into polygenic risk score models
The growing public interest in DNA presents a big opportunity for care providers and policymakers to engage people in preventive health actions. Yet, many of the commercially available genetic risk reports are of limited value, as they fail to consider other vital (non-genetic) factors that influence a person’s health risks.
In our new research paper, modelled with genetic and lifestyle data from 500,000 individuals, we observe how genetic risk predictions become vastly more accurate when non-genetic risk factors are added to an individual’s DNA analysis. Suppliers can obtain data on such risk factors by asking users to fill out a short online questionnaire.
Development and validation of decision rules models to stratify coronary artery disease, diabetes, and hypertension risk in preventive care: cohort study of returning UK Biobank participants.
A common problem across the healthcare industry is that patient risk prediction models fail to distinguish between clinical risk – which requires medical or pharmacological interventions – and pre-clinical risk, for which lifestyle interventions are most effective.
In this study, we demonstrated that Ancora’s methodology outperforms commonly used clinical risk models among 60,782 UK Biobank participants.
Behavioural change and outcomes
A digitally-enabled lifestyle intervention delivers positive changes in nutrition, activity, sleep and stress management health behaviors
We surveyed 64 volunteers who participated in Ancora’s digitally-enabled, preventive 16-week program. Participants were asked to report the days in a typical week before and after the program in which they practiced 11 different health behaviors.
We found that 60% of participants reported an improvement in dietary health behaviors, with an average increase from 5 to 6 days consciously choosing/preparing healthy meals, and an increase of 1.5 days per week limiting unhealthy foods and controlling portion sizes. Similarly, 60% of participants reported an increase in days performing low-to-moderate intensity activity, with an average increase of 2 days per week. Participants also reported getting 1 extra day of better and/or more sleep, with 28% reporting an improvement; similarly, 47% of individuals reported using stress management techniques more often. Lastly, 47% also reported the intervention motivated them to search for and consume content about health and wellbeing.
In conclusion, individuals participating in this behavioral change program significantly improved several essential health behaviors across nutrition, physical activity, sleep and stress management.
A Digitally Enabled Combined Lifestyle Intervention for Weight Loss: Pilot Study in a Dutch General Population Cohort
This study evaluated the feasibility and effectiveness of a digitally enabled combined lifestyle intervention for weight loss (CLI-WL) in the Netherlands. Fifty-three participants with overweight or obesity and cardiometabolic risk followed a 16-week coaching program supported by a digital app. At a median of 10 months post-intervention, participants achieved an average weight loss of 4.2%, with those classified as having obesity losing 5.6% on average. Significant improvements were also observed in cholesterol levels and blood pressure. Participants highlighted the structured action plans and frequent digital coaching contact as key strengths. The intervention was well-received and appears to be a feasible, scalable alternative to traditional offline CLI-WLs.
Changes in Blood Lipid Levels After a Digitally Enabled Cardiometabolic Preventive Health Program: Pre-Post Study in an Adult Dutch General Population Cohort
In this research paper, we analyze aggregate data from participants enrolled in one of our preventive health programs to study the association between lifestyle changes and improvements in lipid levels. In the participants who did a remeasurement after the completion of the program, lipid levels improved over time, including in participants who crossed clinical thresholds.
A pilot study on the prevalence of micronutrient imbalances in a Dutch general population cohort and the effects of a digital lifestyle program
In this study we explored the incidence of vitamin and mineral imbalances in a general population cohort of Dutch adults, and evaluated the effect of a digital, lifestyle program on the nutritional status and nutrition health behaviors of these individuals. We identified at least one nutritional imbalance in more than 86% of participants. After intervention, a total of 80% improved and normalized B6, 67% improved folate, 70% improved B12, and 86% improved vitamin D. These preliminary results show nutritional imbalances can be sustainably corrected with adequate behavioral support complemented with supplementation where needed.
Effect of a Digitally-Enabled, Preventive Health Program on Blood Pressure in an Adult, Dutch General Population Cohort: An Observational Pilot Study
Digital, lifestyle-based interventions are a promising alternative to standard care for hypertension prevention and management. In this study we assessed the prevalence of elevated and high blood pressure in a Dutch general population cohort undergoing a health screening, and observed the effects of a subsequent self-initiated, digitally-enabled lifestyle program on blood pressure regulation. Participants with elevated SBP and DBP at baseline showed a mean decrease of 7.2 mmHg and 5.4 mmHg, respectively. Additionally, 70% and 72.5% of participants showed an improvement in systolic and diastolic blood pressure at remeasurement.
Remote patient management
The effect and implementation of the COVID Box, a remote patient monitoring system for patients with a COVID-19 infection in primary care
This study evaluated the impact of the COVID Box, a remote patient monitoring system, on managing COVID-19 patients in primary care. The study aimed to determine whether this system could reduce hospital admissions and improve patient outcomes. The findings suggest that implementing the COVID Box in primary care settings may help decrease the need for hospital admissions among patients with COVID-19 and other respiratory conditions, thereby alleviating pressure on healthcare facilities and enhancing patient care.
Home monitoring reduced short-stay admissions in suspected COVID-19 patients: COVID-box project
This study discusses the implementation of home monitoring for patients with suspected COVID-19. The study found that 92% of patients could be monitored at home, with only 5% requiring hospital admission. This suggests that home monitoring can effectively reduce short hospital stays for suspected COVID-19 patients.
The Box-eHealth in the outpatient Clinic follow-up of patients with accurate myocardial infarction: Cost-utility analysis
This study highlights the potential of eHealth and remote management tools to enhance care and reduce costs for patients recovering from a heart attack. With the rise of smartphone-compatible devices, remote monitoring has become a promising solution to improve patient outcomes while easing the burden on healthcare systems. In this randomized trial, patients with acute myocardial infarction were assigned to either standard follow-up care or an eHealth intervention that included home-based monitoring devices and fewer in-hospital visits. Patients using remote monitoring tools like a BP monitor, step counter, and ECG device needed fewer in-person visits, saving an average of €471 per patient compared to standard care. The intervention group also showed a significantly higher quality of life after one year. Participants in the eHealth group reported improved health outcomes with fewer clinic visits, suggesting greater convenience and satisfaction. These promising results support the idea that digital health can offer both clinical and economic benefits.
Effect of Smartphone-Enabled Health Monitoring Devices vs Regular Follow-up on Blood Pressure Control Among Patients After Myocardial Infarction: A Randomized Clinical Trial
This randomized clinical trial investigated whether smart technology could improve blood pressure (BP) regulation in patients after a myocardial infarction. Two hundred patients were randomly assigned to receive either regular care or a smart technology–based intervention, which included smartphone-compatible devices and fewer in-person visits. After one year, BP control in the remotely managed group was slightly better comapred to the standard care group. Patient satisfaction was high, with >90% of patients being remotely managed being specifically satisfied with the digital aspects of the intervention. Measurement adherence varied, with 63% of participants sending data for over 80% of the study weeks. Overall, the intervention was found to be feasible and well accepted, with further steps being taken to refine patient selection and adherence.
Mobile health vs. standard care after cardiac surgery: results of The Box 2.0 study
Postoperative atrial fibrillation (POAF) is a common complication of cardiac surgery, yet difficult to detect in ambulatory patients. This study investigated the effect of a mobile health intervention on POAF detection after cardiac surgery. 730 adult patients who underwent cardiac surgery at a tertiary care hospital in The Netherlands were included. The mHealth intervention consisted of blood pressure, temperature, weight, and electrocardiogram (ECG) monitoring. In the intervention group, 61 (16.7%) patients were diagnosed with POAF vs. 25 (6.8%) patients in the control group [adjusted RR 2.15; 95% CI: 1.55–3.97]. De novo atrial fibrillation was found in 13 patients using mHealth (6.5%) vs. 4 control group patients (1.8%) [adjusted RR 3.94, 95% CI: 1.50–11.27). This observational cohort study shows that follow-up with mHealth devices could increase POAF detection in postcardiac surgery. Also, this study observed a reduced number of unplanned ED visits in the intervention arm.
Continuum of Care: Positioning of the Virtual Hospital
Patients with multiple chronic conditions often experience reduced life expectancy and fragmented care across various healthcare providers. This study introduces a Virtual Care Center (VCC) that integrates primary, secondary, and tertiary care through remote monitoring, video consultations, and automated data analysis. Patients with three or more chronic diseases receive monitoring devices and are followed by nurses, under supervision of GPs and specialists. The study’s main outcome is the change in hospital admissions compared to the year prior to enrollment, with secondary outcomes including mortality, quality of life, and patient satisfaction. This intervention aims to create a continuous, patient-centered care model using eHealth technology.
Upcoming publications
Health Behaviors, Mental Wellbeing, and Blood Pressure Outcomes After a Digitally-Enabled Preventative Health Program: A Pre-Post Study in a Dutch Working Population
The increasing burden of chronic diseases in aging populations calls for effective and scalable interventions that promote healthier lifestyles. We retrospectively analyzed data from participants enrolled between January 2022 and July 2023 in a digitally-enabled intervention aimed at promoting health behaviors across nutrition, physical activity, sleep, and mental wellbeing.
Prospective Prediction of Health-Adjusted Life Expectancy: Development and Validation of a Machine Learning-based Methodological Framework
Traditional public health metrics like QALYs, DALYs, and HALE have been essential for evaluating disease burden but fall short in assessing individual health trajectories and forecasting future healthspan. With chronic diseases driving a growing global health and economic burden, there is an urgent need for tools that can prospectively and objectively assess individual health status to inform targeted prevention. This study introduces and validates a novel, scalable methodology to predict individual HALE using data from the UK Biobank and Lifelines, offering a precision health framework to evaluate interventions and close the healthspan-lifespan gap.