{
  "abstract": "BackgroundGait propulsion (anterior ground reaction force) is commonly impaired after stroke. Force plates and wearable sensors can quantify propulsion but are not routinely available in clinical settings. Walking speed alone does not adequately capture the heterogeneity of propulsion deficits.\n\nObjectiveTo determine whether standardized clinical measures can estimate post- stroke propulsion metrics and classify propulsion impairment.\n\nMethodsForty individuals post-stroke participated: 33 for model development and cross-validation (11 with repeated observations for longitudinal validation) and 7 for independent-cohort validation. Clinical measures included the 6-Minute Walk Test (6MWT), comfortable walking speed (CWS), Functional Gait Assessment (FGA), Fugl- Meyer Assessment of the Lower Extremity (FMA-LE), Short Physical Performance Battery (SPPB), Timed Up and Go test (TUG), and stroke chronicity. Regression models were developed to estimate three propulsion metrics: paretic propulsion peak and impulse, and interlimb propulsion symmetry. Model performance was evaluated using R2 and RMSE. Propulsion impairment was classified from symmetry estimates using a prespecified cutoff of 31.5%, with classification accuracy reported.\n\nResultsModels using the 6MWT, SPPB, TUG, and chronicity estimated paretic propulsion peak (R2 = 0.77, RMSE = 3.00 %body weight) and impulse (R2 = 0.81, RMSE = 0.65 %body weight{middle dot}%stance phase). FMA-LE, SPPB, TUG, and chronicity estimated propulsion symmetry (R2 = 0.75, RMSE = 8.56%) and classified propulsion impairment with 84.8% accuracy. Classification accuracy was 91.7% in longitudinal validation and 85.7% in independent-cohort validation.\n\nConclusionsA multidomain clinical assessment framework may support practical and accurate estimation and classification of post-stroke propulsion impairment in clinical settings where specialized instrumentation is unavailable.",
  "abstract_shown": true,
  "affiliations": [
    "Boston University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "J. Fei"
    },
    {
      "name": "R. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "L. Awad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/091wyfa6syb13.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.30.26364361",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26364361v1.full",
  "id": "091wyfa6syb13",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26364361v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "rehabilitation medicine and physical therapy"
    ],
    [
      "openalex",
      "2742|Stroke Rehabilitation and Recovery"
    ]
  ],
  "openalex": "W7215955624",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26364361v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/091wyfa6syb13.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:55Z",
      "native_id": "10.64898/2026.09.30.26364361",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26364361v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:rehabilitation medicine and physical therapy",
      "subfield": "general"
    }
  ],
  "title": "Beyond Walking Speed: Estimating and Classifying Post-Stroke Gait Propulsion From Standardized Clinical Measures",
  "title_text": "Beyond Walking Speed: Estimating and Classifying Post-Stroke Gait Propulsion From Standardized Clinical Measures",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:55Z",
  "url": "https://research.draconitic.com/paper/091wyfa6syb13",
  "venue": "medRxiv",
  "versions": []
}