Unsupervised machine learning models reveal two distinct post-operative physical activity profiles among joint arthroplasty patients: a United Kingdom biobank cohort study
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Objective
To identify and characterise distinct post-operative physical activity profiles in joint arthroplasty patients.
Methods
This cohort study utilised wrist-worn accelerometer data from the UK Biobank, linked to hospital records, to identify patients who underwent primary unilateral hip or knee arthroplasty. Daily step counts from 4 to 12 months post-operatively were extracted using validated algorithms. Principal component analysis (PCA) was applied to demographic and clinical variables to reduce dimensionality, followed by clustering using k-means and Partitioning Around Medoids (PAM). The optimal number of clusters was determined using the elbow method and silhouette score. Clustering validity was assessed using the Rand Index and Adjusted Rand Index.
Results
237 patients were included, the majority of whom were female, with a mean age of 66 years. Based on the elbow plot and the highest average silhouette width, a two-cluster solution was deemed optimal, consistently emerging across both clustering methods as distinct high-and low-performing physical activity profiles. High performers had significantly higher daily step counts (mean > 10,000 vs. < 6,000, P < 0.001), were younger, had a lower body mass index, fewer comorbidities, and were more likely to have undergone total hip replacement. Sociodemographic factors such as higher educational attainment and lower deprivation index were also associated with the high-performing group. The clustering methods demonstrated a weak-but-positive agreement (ARI = 0.224).
Conclusion
Unsupervised learning of accelerometer-derived physical activity data revealed two clinically meaningful recovery profiles following joint arthroplasty. These findings underscore the multifactorial nature of post-operative recovery and support the development of personalised rehabilitation strategies to improve outcomes in lower limb arthroplasty patients.
To identify and characterise distinct post-operative physical activity profiles in joint arthroplasty patients.
Methods
This cohort study utilised wrist-worn accelerometer data from the UK Biobank, linked to hospital records, to identify patients who underwent primary unilateral hip or knee arthroplasty. Daily step counts from 4 to 12 months post-operatively were extracted using validated algorithms. Principal component analysis (PCA) was applied to demographic and clinical variables to reduce dimensionality, followed by clustering using k-means and Partitioning Around Medoids (PAM). The optimal number of clusters was determined using the elbow method and silhouette score. Clustering validity was assessed using the Rand Index and Adjusted Rand Index.
Results
237 patients were included, the majority of whom were female, with a mean age of 66 years. Based on the elbow plot and the highest average silhouette width, a two-cluster solution was deemed optimal, consistently emerging across both clustering methods as distinct high-and low-performing physical activity profiles. High performers had significantly higher daily step counts (mean > 10,000 vs. < 6,000, P < 0.001), were younger, had a lower body mass index, fewer comorbidities, and were more likely to have undergone total hip replacement. Sociodemographic factors such as higher educational attainment and lower deprivation index were also associated with the high-performing group. The clustering methods demonstrated a weak-but-positive agreement (ARI = 0.224).
Conclusion
Unsupervised learning of accelerometer-derived physical activity data revealed two clinically meaningful recovery profiles following joint arthroplasty. These findings underscore the multifactorial nature of post-operative recovery and support the development of personalised rehabilitation strategies to improve outcomes in lower limb arthroplasty patients.
Date Issued
2025-12-01
Date Acceptance
2025-10-01
Citation
Arthroplasty, 2025, 7 (1)
ISSN
2524-7948
Publisher
BMC
Journal / Book Title
Arthroplasty
Volume
7
Issue
1
Copyright Statement
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1186/s42836-025-00339-6
Publication Status
Published
Article Number
ARTN 57
Date Publish Online
2025-11-12