Developing machine learning-enhanced WHODAS 2.0 short forms for persons with dementia

Topic: Risk, prevention and care in population studies

Both ML short forms showed high internal consistency (α = 0.96 for ML-WHODAS-16 and 0.93 for ML-WHODAS-10) and excellent concurrent validity with the full WHODAS-32 (r = 0.98 for both). Compared with the Standard-12, they showed lower error, negligible Bland-Altman bias, and met predefined equivalence criteria, including ±0.5 points. Anchor-based Responsiveness was broadly comparable to the Disability Severity Grade (anchor) (r = 0.66-0.67; standardized response mean = 0.37-0.40). Anchor-based minimal clinically important differences were 9.26 for ML-WHODAS-16 and 9.95 for ML-WHODAS-10. The ML-WHODAS-16 and ML-WHODAS-10 substantially reduced assessment burden while maintaining scores that closely reflected those of the full WHODAS-32, particularly for group-level assessment and longitudinal monitoring. These findings support their use as practical, low-burden alternatives in dementia disability assessment. However, external validation, validation against harder clinical outcomes, formal non-inferiority testing, and clinically anchored longitudinal thresholds remain needed before individual-level interchangeability can be inferred.

Key findings

• Both ML short forms showed high internal consistency (α = 0.96 for ML-WHODAS-16 and 0.93 for ML-WHODAS-10) and excellent concurrent validity with the full WHODAS-32 (r = 0.98 for both).
• Compared with the Standard-12, they showed lower error, negligible Bland-Altman bias, and met predefined equivalence criteria, including ±0.5 points.
• Anchor-based Responsiveness was broadly comparable to the Disability Severity Grade (anchor) (r = 0.66-0.67; standardized response mean = 0.37-0.40).
• Anchor-based minimal clinically important differences were 9.26 for ML-WHODAS-16 and 9.95 for ML-WHODAS-10.
• The ML-WHODAS-16 and ML-WHODAS-10 substantially reduced assessment burden while maintaining scores that closely reflected those of the full WHODAS-32, particularly for group-level assessment and longitudinal monitoring.

Why it matters

This study developed shorter machine learning versions of a disability assessment questionnaire for people with dementia that closely reflect the full version while reducing assessment burden. It concerns measurement of disability, not the treatment, cure or prevention of Alzheimer's disease, and the authors note external validation is still needed.

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Study details

• Study designlongitudinal study
• Participants31,952
• Evidence strengthModerate

Read the original

• DOI: 10.1016/j.jfma.2026.04.085
• PubMed: 42069499
• Original source →
• Free full text: Journal of the Formosan Medical Association →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{lin2026-42069499,
  author  = {Lin, Gong‐Hong and Wang, Yi‐Ching and Lee, Shih‐Chieh and Chi, Wen‐Chou and Liu, Jen-Hsuan and Hsieh, Ching‐Lin},
  title   = {{Developing machine learning-enhanced WHODAS 2.0 short forms for persons with dementia}},
  journal = {Journal of the Formosan Medical Association},
  year    = {2026},
  doi     = {10.1016/j.jfma.2026.04.085},
  pmid    = {42069499},
  url     = {https://doi.org/10.1016/j.jfma.2026.04.085}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

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