{
  "abstract": "Introduction\n\nEstimating how physiotherapy (PT) intensity influences functional independence is central to personalized care planning for older adults. However, most existing causal inference approaches treat PT as a binary intervention, which requires collapsing a naturally continuous measure of treatment intensity and limits the ability to characterize nuanced dose-response relationships.Methods\n\nTo address this limitation, we use continuous causal inference to estimate individualized continuous dose-response curves for PT. This approach enables granular assessment of treatment effects across therapy intensities and supports the identification of optimal treatment doses. The model is trained on Canadian Community Care data and externally validated using an independent Canadian Home Care cohort.Results\n\nAcross factual, proxy counterfactual, and external validation settings, our approach generally achieved lower prediction error on activities of daily living outcomes, as measured by root mean squared error (RMSE), than standard baselines. In particular, we attained factual and proxy counterfactual RMSEs of 3.79 and 3.89, respectively, compared with 4.09 and 6.49 for linear regression, while also demonstrating strong external generalization.Conclusion\n\nContinuous causal modeling of individualized dose-response relationships provides a principled approach to estimating physiotherapy intensity effects on functional independence, with direct implications for personalized rehabilitation planning in older adults.",
  "abstract_shown": true,
  "affiliations": [
    "Vrije Universiteit Amsterdam, Department of Computer Science, Amsterdam, Netherlands.",
    "Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of General Practice, Amsterdam, Netherlands.",
    "Amsterdam Public Health, Aging & Later Life, Amsterdam, Netherlands.",
    "School of Physiotherapy, Faculty of Health, Dalhousie University, Halifax, NS, Canada."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Louk van Remmerden"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Eline Kooijmans"
    },
    {
      "affs": [
        0
      ],
      "name": "Shujian Yu"
    },
    {
      "affs": [
        1
      ],
      "name": "Karlijn Joling"
    },
    {
      "affs": [
        1
      ],
      "name": "Emiel Hoogendijk"
    },
    {
      "affs": [
        3
      ],
      "name": "Caitlin McArthur"
    },
    {
      "affs": [
        0
      ],
      "name": "Mark Hoogendoorn"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Hein van Hout"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cw0f14yb2zr8v.bib",
  "cited_by": 0,
  "doi": "10.3389/fdgth.2026.1793661",
  "fulltext_url": "https://europepmc.org/articles/PMC13748423",
  "id": "cw0f14yb2zr8v",
  "issns": [
    "2673-253X"
  ],
  "landing_url": "https://europepmc.org/article/MED/43011093",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7223917922",
  "pdf_url": "https://europepmc.org/articles/PMC13748423?pdf=render",
  "pmcid": "PMC13748423",
  "pmid": "43011093",
  "published": "2026-10-27",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/cw0f14yb2zr8v.ris",
  "sources": [
    {
      "first_seen": "2026-11-12T15:25:13Z",
      "native_id": "MED:43011093",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/43011093"
    },
    {
      "first_seen": "2026-11-13T12:26:16Z",
      "native_id": "PMC13748423",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fdgth.2026.1793661"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Advanced Causal Inference Techniques",
      "subfield": "statistics"
    }
  ],
  "title": "Personalized physiotherapy effect modeling with continuous causal machine learning.",
  "title_text": "Personalized physiotherapy effect modeling with continuous causal machine learning.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-14T03:20:07Z",
  "url": "https://research.draconitic.com/paper/cw0f14yb2zr8v",
  "venue": "Frontiers in digital health",
  "versions": []
}