Title: Using data science to understand & improve pain in mental illness informing the development and testing of a feasibility trial
Funder: Department of Health and Social Care

Abstract: Research questions Leveraging unparalleled opportunities across multiple electronic health records (EHRs), I will address the following research priorities: How does chronic pain interact with mental health conditions (MHC) to affect future wellbeing (emotional, cognitive, functional), social outcomes (e.g. employment) and healthcare usage? What are the key patient and care system factors accounting for MHC patients' reduced access/update and use of physiotherapy/pain management services? What is the engagement/effectiveness of routine physiotherapy for improving pain-related symptomatology and functioning across MHCs? Based on 1-3 and stakeholder workshops, using new NIHR/MRC guidelines, develop & conduct a feasibility trial, of a physiotherapy intervention for people with MHC-pain.Background Pain and MHCs are leading causes of years lived with disability. Approximately 50% of people with chronic pain have a MHC, yet MHC-pain treatment research/guidelines are lacking. My pilot study of 450 people with severe mental illness (SMI) found that 33% had pain, but none were accessing physiotherapy, and PPI work confirmed inadequate access.Critical gaps include: 1) the data science evidence base for the long-term outcomes and effective management of pain within MHC is sparse/inadequate; 2) no interventions targeting pain co-occurring with MHCs. Specific aims: Determine bidirectional relationship between MHCs and pain, and use EHRs to ascertain underuse, effectiveness, and long-term outcomes of pain in MHCs. Systematically review personal and care-related factors that can be modulated to resolve pain within MHCs/SMI. Based on 1-2, conduct national stakeholder events to gain consensus on a self-management protocol to improve pain and mental health in MHCs/SMI. Undertake a feasibility trial to evaluate the acceptability and fidelity of a physiotherapy-led pain management intervention in people with SMI.Methods & timelines Aim 1- Identify MHC cohorts and compare profiles, uptake to pain services (including physiotherapy) and outcomes across linked local primary care (Lambeth Datanet (LDN) together with the Clinical Record Interactive Search (CRIS) resource containing de-identified mental healthcare EHR data from the South London and Maudsley Trust (SLAM) (>450,000 MHC patients) (Timeline=02/2022-10/2022). Nationally I will re-evaluate this using CPRD linked to Hospital Episode Statistics (HES) (Timeline=9/2022-5/2023). Finally, I will compare outcomes (e.g. pain symptoms, function) in depression/anxiety cases (17-18% based on pilot data) in a large London musculoskeletal department versus controls (n=8,000 per year) (Timeline=9/2023-4/2024). Aim 2- Systematically review the literature investigating non-pharmacological pain interventions in a) primary studies in MHCs and b) meta-overview of previous systematic reviews in other populations to identify personal and care system-related components that can be inform the intervention (Timeline=6/2023-6/2024). Aim 3-Employ nominal group techniques within three national stakeholder/PPI meetings to refine a pain management intervention based on evidence from 1 and 2 (Timeline=3/2024-9/2024).Aim 4- Assess feasibility, acceptability, fidelity & safety of a physiotherapy pain management intervention informed by 1-3 & within new NIHR/MRC complex health intervention development framework, with 50-60 people with MHCs/SMI-pain across two NHS Trusts compared with usual care (Timeline=1/2025-6/2026).Impact/dissemination Publish & disseminate literature for academic, patient, clinical, and public audiences. Establish parameters required for sample size calculation & protocol for multisite RCT investigating pain and MHCs/SMI. Inform (inter)national policy.

Lay abstract: Background Chronic pain and mental illness are common and have a high impact. I have previously shown that pain occurs in large numbers with mental illness; however, these people are often excluded from pain research and have difficulty accessing pain services. Physiotherapists lack confidence in working with mental illness, yet have a central role in pain management in the NHS. My PPI work has found that people with mental illness have to 'fight' to get help for their pain; this is unacceptable. Aims and objectives My aim is to investigate the relationship between mental illness and pain, treatment received, and outcomes experienced (e.g. worse physical or mental health, or social outcomes such as unable to work). I will use this information together with stakeholder consultation (e.g. patients, carers, charities) to develop a new treatment pathway and test this in the NHS for people with mental illness and pain. Design and Methods I will achieve my aims through several linked projects.First, I will understand the nature and relationship between mental illness and pain. I will investigate experiences of people with mental illness and pain using anonymous General Practice (GP) data in a borough of south London (approximately 790,000 people) linked to local mental healthcare records (450,000). Internationally, this is a unique linkage between GP-data and secondary mental healthcare and can help me understand the mental health impact and profiles of those with pain. Pilot data suggests chronic pain levels are twice as high in people with severe mental illness (SMI) than the general population; however only 9% and 1% of people with SMI were referred to physiotherapy or pain clinics respectively, compared to 20% and 5% in the general population.Second, I will investigate the interrelationship and outcomes of pain and mental illness and what may affect these using a larger GP dataset to obtain a more national picture. My pilot national data suggests 30% with SMI have chronic pain versus 10% in general population. Third, I will compare the number of sessions, discharges and outcomes (e.g. improved function, reduced pain) in a large London physiotherapy department that sees around 8,000 new patients primarily for pain each year. Of these 16% and 17% have anxiety or depression respectively; this work will focus on investigating if people with depression/anxiety have equal uptake and outcomes to physiotherapy pain services.Fourth, I will search previous research into non-drug pain treatments for people with mental illness, and non-drug treatments in other populations and consider what approaches could be useful. Using information from earlier stages, I will hold three national stakeholder meetings to discuss these findings with patients, carers, researchers, clinicians and policy makers to decide collectively on the best non-drug treatment to improve pain and mental health in people that experience both symptoms.In a final step, I will test the new intervention in a feasibility randomised study considering engagement and potential benefit in 50-60 people with pain and mental illness compared to usual care.Patient, carer & charity involvement Throughout the project I will work alongside patients and carers to guide the study, interpret results and produce meaningful summaries, as well as with charity partners Rethink and Arthritis and Musculoskeletal Association. I will use links to NHS England to maximise policy impact.BenefitsThis research will shed new light on the impact of mental illness and pain and help clinicians identify/manage this better. This award will also provide essential opportunities to accelerate my role as an international academic leader in mental health and Data Science. Despite its (inter)national importance, to my knowledge there are no other senior academic physiotherapists working in this area.

Research activity codes: 7.1 INDIVIDUAL CARE NEEDS
Health categories: MENTAL HEALTH