Unsupervised machine learning models reveal two distinct post-operative physical activity profiles among joint arthroplasty patients: a United Kingdom biobank cohort study.

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

SUBMITTER: Musbahi O 

PROVIDER: S-EPMC12606873 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature