Identifying chronic pain subgroups in the UK biobank for persona development: A clustering analysis

Purpose: To conduct a preliminary clustering analysis using the UK Biobank to (1) identify distinct chronic pain clusters based on age, sex, and number of pain sites; (2) assess the associations between chronic pain clusters and health-related outcomes; and (3) outline future directions for developing chronic pain personas to inform targeted digital health interventions. Methods: Participants were selected from a 2019 chronic pain survey. The domains included demographics, pain, daily functioning, and emotional health. The clustering analysis employed the k-prototype algorithm. Cluster characteristics were summarised and quantified using multinomial logistic regression. Preliminary data personas were described. Results: 89,853 people with chronic pain were analysed (60.4% female, mean age 66.5 years). Five clusters were identified: Fibromyalgia-like pain (FP, 11.2%), multisite pain (MP, 17.9%), younger with regional pain (21.9%), middle age with regional pain (MRP, 25.5%), and elderly with regional pain (ERP, 23.5%). FP was associated with more severe health-related outcomes, characterised by greater depression, fatigue, and difficulties with daily activities and social relationships. Sleep, mobility, and usual activities were commonly affected at mild and moderate levels across all clusters. Fatigue and depression varied, with FP and MP experiencing greater impacts. ERP and MRP were associated with a lower likelihood of adverse health-related outcomes. Conclusion: All chronic pain clusters identified from the UK Biobank showed common challenges in sleep, mobility and daily functioning; the impacts of fatigue and depression varied between clusters. The next step involves engaging key stakeholders to create, refine, and validate these personas to inform the development of targeted digital health interventions.