Identifying chronic pain subgroups in the UK biobank for persona development: a clustering analysis
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.
Chronic pain, UK Biobank, clustering analysis, digital health interventions, persona
Hsu, Ting-Chen Chloe
ecc0b802-b3b5-4fb3-8db6-ef0a44dc9938
Whelan, Pauline
cae5309a-474f-49a6-b064-60d105cf3cf9
Armitage, Christopher J.
88a48bb4-9c40-4d5c-944c-05a5768c6219
McBeth, John
98012716-66ba-480b-9e43-ac53b51dce61
14 May 2025
Hsu, Ting-Chen Chloe
ecc0b802-b3b5-4fb3-8db6-ef0a44dc9938
Whelan, Pauline
cae5309a-474f-49a6-b064-60d105cf3cf9
Armitage, Christopher J.
88a48bb4-9c40-4d5c-944c-05a5768c6219
McBeth, John
98012716-66ba-480b-9e43-ac53b51dce61
Hsu, Ting-Chen Chloe, Whelan, Pauline, Armitage, Christopher J. and McBeth, John
(2025)
Identifying chronic pain subgroups in the UK biobank for persona development: a clustering analysis.
Digital Health, 11, [20552076251333497].
(doi:10.1177/20552076251333497).
Abstract
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.
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hsu-et-al-2025-identifying-chronic-pain-subgroups-in-the-uk-biobank-for-persona-development-a-clustering-analysis
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Accepted/In Press date: 20 March 2025
Published date: 14 May 2025
Keywords:
Chronic pain, UK Biobank, clustering analysis, digital health interventions, persona
Identifiers
Local EPrints ID: 502453
URI: http://eprints.soton.ac.uk/id/eprint/502453
ISSN: 2055-2076
PURE UUID: 5d87635b-0012-4748-8ee3-b105c875d39b
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Date deposited: 26 Jun 2025 17:02
Last modified: 22 Aug 2025 02:43
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Contributors
Author:
Ting-Chen Chloe Hsu
Author:
Pauline Whelan
Author:
Christopher J. Armitage
Author:
John McBeth
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