Apps, Databases or other Digital Health.
All ARC OxTV projects using apps, databases or other digital health means.
Featured project
Utilisation of Digital Health for Infertility
Applied Digital Health Apps, Databases, and Digital Health Evaluation
This research evaluates the impact of digital health technologies on infertility care, examining how fertility trackers and "Femtech" influence patient experiences and healthcare pathways, with an emphasis on reproductive justice and ethical considerations.
A Feasibility Trial of the MyPregnancyCare App
This feasibility trial explores a new approach to managing high blood pressure (hypertension) in pregnancy. It will test the real-world effects of women using our My Pregnancy Care app, developed in another ARC project.
A focused exploration of digital inclusion and exclusion for specific community groups accessing and using the NHS App
This project examines digital inclusion and exclusion among specific community groups using the NHS App, aiming to understand and address barriers faced by minoritised and vulnerable populations to ensure equitable access to healthcare services.
Activity Changes in MULtiple long-term conditions To Identify Decline (MULTI)
The MULTI project explores how monitoring activity changes in older adults with multiple long-term conditions can help detect early signs of health decline, using technologies like smartphone apps and fitness watches to create an innovative self-management intervention.
Adults Regulating Their weight Everyday with Mobile Internet Support (ARTEMIS): A randomised controlled trial
ARTEMIS is a randomised controlled trial that investigates the effectiveness of a mobile application designed to help adults living with obesity manage their weight.
Audit-based Education to Implement NICE Do-Not-Do Recommendations in People with Cardiometabolic Multimorbiditory (MONITORY); a cluster randomised trial
This study aims to improve medication prescribing in GP surgeries using an Audit-Based Education (ABE) programme to enhance adherence to NICE recommendations, focusing on reducing inappropriate prescriptions for patients with cardiometabolic conditions.
BLOod Test Trend for cancEr Detection (BLOTTED): An Observational and Prediction Model Development Study Using English Primary Care Electronic Health Records Data
This research assesses combinations of blood tests and their changes over time to enhance cancer diagnosis efficiency, potentially leading to earlier detection, more successful treatments, and improved survival rates while reducing unnecessary testing.
Development of the MyPregnancyCare app
The My Pregnancy Care project aims to develop a comprehensive digital app to support women with hypertensive (high blood pressure) disorders during pregnancy, a condition affecting roughly 10% of pregnancies.
Digital Guided Self-Help for Binge Eating Disorder: Devising an Effectiveness Study and Testing its Acceptability and Feasibility
Exploring a digital self-help programme for Binge Eating Disorder, this research aims to offer personalised treatment through technology, enhancing accessibility and effectiveness, while prioritising patient and public involvement.
Empowering New Mums: Taking Charge of Your Blood Pressure After Pregnancy (SNAP2)
The SNAP2 project helps new mothers manage blood pressure after pregnancy to reduce long-term cardiovascular risks through self-management techniques, emphasizing health equity and involving a diverse group of women in its research and trials.
Human-Centred AI Design to Develop Digital Health Artificial Intelligence for Multiple Long-Term Conditions (Multimorbidity)
The project focuses on improving healthcare for individuals with multiple long-term conditions by integrating Human-Centred AI Design into digital health tools, aiming to co-create AI-driven technologies with patients and professionals to ensure safe, effective, and understandable healthcare solutions.
Identification and Case-Ascertainment of Self-Harm in Electronic Health Records (EHR): Development of Machine Learning-Led Tool Using CRIS Data
This project leverages machine learning and natural language processing to enhance the identification of self-harm cases in Electronic Health Records, aiming to improve mental health care delivery and reduce health inequalities.
Natural Experiments by Interrupted Time Series Analysis for the NHS
Exploring the simplification of Interrupted Time Series Analysis (ITSA) to enhance its application in healthcare research, aiming to provide clearer insights into the effects of healthcare interventions.
Regulatory Frameworks for Evaluation of AI/ML-Enabled Digital Health Technologies in Healthcare Institutions
This project examines the regulatory frameworks governing AI/ML-enabled digital health technologies, aiming to enhance safety in healthcare by improving oversight and reporting mechanisms for AI-driven clinical decision support tools.
REthiNking Approaches to Excess Weight in AdoLescents (RENEWAL)
Exploring innovative weight management strategies for adolescents, this study examines mobile apps and gathers insights from young people and their parents, aiming to develop tailored, effective solutions for healthier futures.
The OSCAR Study: Optimising Structured Medication Reviews
OSCAR focuses on optimising Structured Medication Reviews in England, investigating their implementation and impact on patient safety and treatment effectiveness, aiming to enhance care quality for those with long-term conditions.
The Role of Digital Measures in Clinical Trials: Exploring the use of digital biomarkers, outcomes, and endpoints in studies of interventions for serious mental illness
This research explores the use of digital health technologies like smartphones and wearables in clinical trials for serious mental illnesses, aiming to improve patient-generated data collection, enhance trial designs, and ultimately provide more precise and personalised care.
The role of lifestyle interventions in high-risk pregnancies
This project explores lifestyle interventions for high-risk pregnancies, particularly focusing on the DAPHNY App to support women with hypertension, aiming to promote healthy behaviours and enhance prenatal care.
The SHIP Study: A Service Evaluation and Mixed Methods Implementation Research Study of Hypertension Plus
The SHIP Study evaluates the Hypertension Plus system for self-monitoring high blood pressure, assessing its integration into UK primary care to improve patient outcomes and inform future health policy.
Understanding Attitudes to Genetic Risk for Cardiovascular Disease
This project investigates how polygenic risk scores for cardiovascular disease affect individuals' healthcare decisions, aiming to refine the use of genetic risk information in primary care and improve prevention and management strategies.
Using Behavioural Insights to Improve Effectiveness of Digital Weight Loss Interventions
Researchers from the University of Oxford and Second Nature, a digital weight management service provider, are collaborating to develop and test new features to improve digital weight loss programmes using behavioural science insights.