{
  "abstract": "Background: Knee pain is common in runners and may precede injury; clinical labels are sparse, but repeated symptom monitoring is practical for training decisions. The purpose of this paper is to develop a probabilistic, interpretable model that predicts a continuous knee pain severity score in professional distance runners using Gaussian Process Regression (GPR), and to quantify the relative influence of routinely measurable training and anthropometric variables.\n\nMethods: Fifty professional male distance runners aged 20–30 years were recruited from professional athletics in Ankara, Turkey. Six predictors were used: resistance training frequency, weekly running distance, training surface, running technique (foot-strike category), history of prior knee problems, and body mass index (BMI). The outcome was a continuous knee pain severity score normalized to 0–1 from a self-reported numerical rating scale. A GPR model with a radial basis function kernel was trained using a train-test split and evaluated with mean squared error (MSE).\n\nResults: Prediction uncertainty was quantified using 95% credible intervals, and sensitivity analysis were performed to assess feature influence. The GPR model achieved low prediction error on the held-out test set [MSE = (0.02)] and stable performance under cross-validation [mean MSE = (0.028)].\n\nConclusion: Sensitivity analysis showed pronounced GPR response patterns for resistance training frequency and BMI, with smaller response patterns for weekly running distance and prior knee problems/injuries. Training surface and running technique showed comparatively limited sensitivity. GPR effectively models self-reported pain with uncertainty quantification and is scalable for symptom-based risk assessment, though not intended for clinical diagnosis.",
  "abstract_shown": true,
  "affiliations": [
    "Pasinler Atatürk Regional Boarding Middle School, Erzurum Provincial Directorate of National Education, Ministry of National Education (Turkey)",
    "Department of Coaching Education, Faculty of Sports Sciences, Istanbul Rumeli University",
    "Department of Coaching Education, Faculty of Sports Sciences, Fırat University",
    "Department of Sport Management, Faculty of Sports Sciences, Hitit University",
    "Çorum Provincial Directorate of National Education, Ministry of National Education (Turkey)",
    "Department of Coaching Education, Faculty of Sport Sciences, Istanbul Esenyurt University",
    "Department of Movement and Training Sciences, Faculty of Sports Sciences, Düzce University",
    "Department of Exercise and Sport Sciences, Faculty of Sport Sciences, Avrasya University",
    "Red and River Digital Publishing and Trade Limited Company",
    "Department of Exercise and Sports Sciences for Individuals with Disabilities, Faculty of Sports Sciences, Istanbul Gelişim University",
    "Department of Sports Injuries and Corrective Exercises, Faculty of Sports Sciences, Shahid Bahonar University of Kerman",
    "HERC – Health, Exercise and Research Center",
    "Department of Physical Education, Technical and Vocational University (TVU)",
    "Institute of Human Movement Science, Sport and Health, University of Graz"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yunus Sadır"
    },
    {
      "affs": [
        1
      ],
      "name": "Mehmet İnan"
    },
    {
      "affs": [
        2
      ],
      "name": "Mustafa Karadağ"
    },
    {
      "affs": [
        3
      ],
      "name": "Levent Ceylan"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Tülay Ceylan"
    },
    {
      "affs": [
        6
      ],
      "name": "Heidar Sajedi"
    },
    {
      "affs": [
        7,
        8
      ],
      "name": "Faik Öz"
    },
    {
      "affs": [
        9
      ],
      "name": "Yunus Şahinler"
    },
    {
      "affs": [
        10,
        11
      ],
      "name": "Mohammad Alimoradi"
    },
    {
      "affs": [
        12
      ],
      "name": "Mohammad Alghosi"
    },
    {
      "affs": [
        13
      ],
      "name": "Andreas Konrad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ekxkeqkce1f4.bib",
  "cited_by": 0,
  "doi": "10.3389/fpubh.2026.1973629",
  "fulltext_url": null,
  "id": "4ekxkeqkce1f4",
  "issns": [
    "2296-2565"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1973629/full",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2204|Lower Extremity Biomechanics and Pathologies"
    ]
  ],
  "openalex": "W7223854567",
  "pdf_url": "https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1973629/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/4ekxkeqkce1f4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:24Z",
      "native_id": "10.3389/fpubh.2026.1973629",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1973629/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2296-2565",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Predicting knee pain severity in professional distance runners using gaussian process regression",
  "title_text": "Predicting knee pain severity in professional distance runners using gaussian process regression",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:24Z",
  "url": "https://research.draconitic.com/paper/4ekxkeqkce1f4",
  "venue": "Frontiers in Public Health",
  "versions": []
}