Journal of medical Internet research · 2026
Novel Musculoskeletal Hypotheses in the Armed Services Trauma and Rehabilitation Outcome (ADVANCE) Cohort: Development and Application of Sparse Group Factor Analysis Methodology.
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Watson Fraje C E, Ferreira Fabio S, Kadirvelu Balasundaram, Bennett Alex N, Faisal Aldo A, Graham Neil, Kemp Harriet, Cullinan Paul, Boos Christopher, Fear Nicola T, Bull Anthony M J
Аннотация
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: This study aims to test the ability of a new sparse group factor analysis method to uncover hidden patterns in large-scale multimodal datasets and generate testable, clinically relevant hypotheses.
METHODS: This study applies sparse group factor analysis, a hierarchical unsupervised machine learning method, to the Armed Services Trauma and Rehabilitation Outcome (ADVANCE) cohort to identify latent structures in multimodal clinical data. ADVANCE is a prospective longitudinal dataset of 1145 UK military personnel and veterans who served in Afghanistan. Half the cohort sustained combat injuries, and the remainder were frequency matched on deployment, service, rank, role, age, and ethnicity. Study 1 validated the approach by rediscovering known group-level patterns between combat-injured and noninjured participants, including poorer outcomes in pain, mobility, and bone health among those with lower limb loss. Study 2 explored the injured, nonamputee subgroup without prespecified labels to identify new hypothesis-generating clusters that could subsequently be tested using standard hypothesis-testing methods.
RESULTS: The ADVANCE cohort was 34.1 (SD 5.4) years old and 8.3 (SD 2.1) years postinjury or 7.7 (SD 1.9) years since matched deployment. A subgroup of 125 individuals with worse musculoskeletal outcomes was uncovered. This group had greater body mass (mean 92.6, SD 14.7 kg vs mean 88.0, SD 13.4 kg; P=.002), higher injury severity (median 12, IQR 5-22 vs median 9, IQR 4-14; P=.002), and reduced health-related quality of life with 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.
OBJECTIVE: This study aims to test the ability of a new sparse group factor analysis method to uncover hidden patterns in large-scale multimodal datasets and generate testable, clinically relevant hypotheses.
METHODS: This study applies sparse group factor analysis, a hierarchical unsupervised machine learning method, to the Armed Services Trauma and Rehabilitation Outcome (ADVANCE) cohort to identify latent structures in multimodal clinical data. ADVANCE is a prospective longitudinal dataset of 1145 UK military personnel and veterans who served in Afghanistan. Half the cohort sustained combat injuries, and the remainder were frequency matched on deployment, service, rank, role, age, and ethnicity. Study 1 validated the approach by rediscovering known group-level patterns between combat-injured and noninjured participants, including poorer outcomes in pain, mobility, and bone health among those with lower limb loss. Study 2 explored the injured, nonamputee subgroup without prespecified labels to identify new hypothesis-generating clusters that could subsequently be tested using standard hypothesis-testing methods.
RESULTS: The ADVANCE cohort was 34.1 (SD 5.4) years old and 8.3 (SD 2.1) years postinjury or 7.7 (SD 1.9) years since matched deployment. A subgroup of 125 individuals with worse musculoskeletal outcomes was uncovered. This group had greater body mass (mean 92.6, SD 14.7 kg vs mean 88.0, SD 13.4 kg; P=.002), higher injury severity (median 12, IQR 5-22 vs median 9, IQR 4-14; P=.002), and reduced health-related quality of life with 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.