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The home of collaborative applied health and social care research in Oxford and the Thames Valley.
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.
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.
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.
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)
- Apps, Databases, and Digital Health
- Methods and Tools
- Novel Methods to Aid and Evaluate Implementation
- Social Care
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.
Understanding Attitudes to Genetic Risk for Cardiovascular Disease
- Apps, Databases, and Digital Health
- Evaluation
- Methods and Tools
- Novel Methods to Aid and Evaluate Implementation
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.
Natural Experiments by Interrupted Time Series Analysis for the NHS
- Apps, Databases, and Digital Health
- Evaluation
- Methods and Tools
- Novel Methods to Aid and Evaluate Implementation
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.
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.
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.
Using Behavioural Insights to Improve Effectiveness of Digital Weight Loss Interventions
- Apps, Databases, and Digital Health
- Changing Behaviours for Better Health and Preventing Disease
- Self-managament
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.
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.
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.
Activity Changes in MULtiple long-term conditions To Identify Decline (MULTI)
- Apps, Databases, and Digital Health
- Helping Patients to Manage Their Own Conditions
- Self-managament
- Social Care
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.
OSI-GROWS: Improving Support for Child Anxiety through Online Intervention
The OSI-GROWS project evaluates the use of the Online Support and Intervention (OSI) programme to support children with anxiety within NHS-backed mental health services. By partnering with organisations, OSI-GROWS integrates OSI into routine clinical care, providing structured online modules, therapist guidance, and engaging tools to help families and clinicians manage childhood anxiety effectively and collaboratively.
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.
Utilisation of Digital Health for Infertility
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.
The role of lifestyle interventions in high-risk pregnancies
- Apps, Databases, and Digital Health
- Helping Patients to Manage Their Own Conditions
- Self-managament
- Weight, Health, and Behaviour
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 OSCAR Study: Optimising Structured Medication Reviews
- Apps, Databases, and Digital Health
- Evaluation
- Helping Patients to Manage Their Own Conditions
- Implementation
- Self-managament
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 SHIP Study: A Service Evaluation and Mixed Methods Implementation Research Study of Hypertension Plus
- Apps, Databases, and Digital Health
- Evaluation
- Helping Patients to Manage Their Own Conditions
- Implementation
- Self-managament
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.
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.