Outcome measures in clinical trials are invariably collected as infrequent snapshots of retrospective, self-reported symptoms during brief face-to-face clinic visits. Patients attempt to describe complex and varying symptoms and effects on their life. Clinically assessed outcomes lack precision leading to poor reproducibility, capture only a momentary state in fluctuating diseases and are hampered by inter-and intra-person variability of individual symptom progression. No objective laboratory or imaging biomarker exists that reflects wellbeing or quality of life in older people who live with multiple conditions and frailty.
Life space mobility (LSM) is a measure of enacted mobility behaviour encompassing the interaction between intrinsic capabilities of the person and the demands of the extrinsic environment (1). This measure captures multiple negative impacts of living with health condition with physical and cognitive sequalae and thus reflects a final ‘common pathway’ (2,3). Reduction in life space has important direct and indirect consequences including social withdrawal and longitudinally it may act a surrogate ‘digital biomarker’ for risk of adverse outcomes including hospital admission, falls, cognitive decline frailty and mortality (2–4). We have published a comprehensive review (5) to lay the foundation for this PhD programme of work.
Aims and objectives
LSM measured using GPS and accelerometry will be acceptable to older people, be associated with quantification of frailty and cognitive impairment and predict meaningful outcomes.
To use non-invasive digital measures to capture data remotely in clinical trials to develop better outcome measures.
a) use GPS and accelerometry to facilitate objective LSM quantification using wearable sensors with complementary data on physical activity behaviours
b) determine mobility and physical activity behaviour metrics among people who act as carers and explore the agreement of LSM with their quality of life, depression, social isolation, carer strain, and health.
This mixed-methods PhD project aims to integrate digital surrogates of mobility and physical activity behaviours in clinical trials. Commercially available GPS sensors and research-grade accelerometers will be used to deeply phenotype patients and carers. The student will gain experience in clinical trial data collection with a focus on remote delivery and digital health. Methodologically the PhD studentship will be aligned to benefit from the PHS short course programme, which will inform quantitative and qualitative data analysis. Data processing pipelines will be developed (in R/Python) to generate digital health outcomes including those which approximate conventional measures of LSM. Complementary data from accelerometry will be processed to provide detailed data on physical activity behaviours, sleep (duration/quality) and diurnal rhythm. Digital health measures will be stratified across sex, age groups, health and sociocultural and economic measures. Subsequently relationships between these digital outcomes and physical, mental, and social health will be described. Further statistical analysis will establish a minimal set of “digital biomarkers” that are associated with key clinical outcomes including quality of life, hospitalisation, morbidity, and mortality. Acceptability and usability of digital devices to people with dementia, carers and healthcare professionals will be evaluated using in-depth interviews.
How to apply for this project
This project will be based in Bristol Medical School - Population Health Sciences in the Faculty of Health Sciences at the University of Bristol.
Please visit the Faculty of Health Sciences website for details of how to apply