Currently submitted to: Journal of Medical Internet Research
Date Submitted: Jan 23, 2026
Open Peer Review Period: Jan 25, 2026 - Mar 22, 2026
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Development of a novel musculoskeletal hypothesis in the ADVANCE cohort: application of sparse Group Factor Analysis methodology
ABSTRACT
Background:
Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly following trauma, remain incompletely understood. Machine learning could be applied to identify previously unknown patterns in large-scale multimodal datasets.
Objective:
Test the ability of a new sparse Group Factor Analysis method to uncover hidden patterns in large-scale multi-modal datasets and generate testable, clinically relevant hypotheses.
Methods:
This study applies sparse Group Factor Analysis, a hierarchical unsupervised machine learning method, to the ADVANCE cohort—a longitudinal dataset of 1445 UK Afghanistan War servicemen—to identify latent structures in multimodal clinical data. Study 1 validated the approach by rediscovering known group-level patterns between combat-injured and non-injured participants, including poorer outcomes in pain, mobility, and bone health among those with lower limb loss. Study 2 explored the Injured, non-amputee subgroup without prespecified labels to identify new hypothesis-generating clusters that could subsequently be tested using standard hypothesis testing methods.
Results:
A subgroup of 125 individuals with worse musculoskeletal outcomes was uncovered. This group had greater body mass, higher injury severity, and a higher prevalence of head injury. These findings led to a novel hypothesis: that head injury, including potential traumatic brain injury, is associated with long-term musculoskeletal deterioration. This hypothesis is supported by literature in both athletic and military populations and will be tested in follow-up analyses.
Conclusions:
Our findings demonstrate how sparse Group Factor Analysis, combined with clinical insight, can uncover hidden patterns in large-scale datasets and generate testable, clinically relevant hypotheses that inform prevention, treatment, and rehabilitation strategies.
Citation
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