Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study
Abstract BackgroundKnee osteoarthritis (OA) is a common chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits of exercise therapy and patient education in managing OA pain and functional limitations, these strategies are often unde...
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JMIR Publications
2025-03-01
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| Series: | JMIR Rehabilitation and Assistive Technologies |
| Online Access: | https://rehab.jmir.org/2025/1/e60162 |
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| author | Mahdie Rafiei Supratim Das Mohammad Bakhtiari Ewa Maria Roos Søren T Skou Dorte T Grønne Jan Baumbach Linda Baumbach |
| author_facet | Mahdie Rafiei Supratim Das Mohammad Bakhtiari Ewa Maria Roos Søren T Skou Dorte T Grønne Jan Baumbach Linda Baumbach |
| author_sort | Mahdie Rafiei |
| collection | DOAJ |
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Abstract
BackgroundKnee osteoarthritis (OA) is a common chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits of exercise therapy and patient education in managing OA pain and functional limitations, these strategies are often underused. To motivate and enhance patient engagement, personalized outcome prediction models can be used. However, the accuracy of existing models in predicting changes in knee pain outcomes remains insufficiently examined.
ObjectiveThis study aims to validate existing models and introduce a concise personalized model predicting changes in knee pain from before to after participating in a supervised patient education and exercise therapy program (GLA:D) among patients with knee OA.
MethodsOur prediction models leverage self-reported patient information and functional measures. To refine the number of variables, we evaluated the variable importance and applied clinical reasoning. We trained random forest regression models and compared the rate of true predictions of our models with those using average values. In supplementary analyses, we additionally considered recently added variables to the GLA:D registry.
ResultsWe evaluated the performance of a full, continuous, and concise model including all 34 variables, all 11 continuous variables, and the 6 most predictive variables, respectively. All three models performed similarly and were comparable to the existing model, with R2
ConclusionsOur concise personalized prediction model provides more often accurate predictions for changes in knee pain after the GLA:D program than using average pain improvement values. Neither the increase in sample size nor the inclusion of additional variables improved previous models. Based on current knowledge and available data, no better predictions are possible. Guidance is needed on when a model’s performance is good enough for clinical practice use. |
| format | Article |
| id | doaj-art-5ff57afebccf48c1a1bdd45cf67c613b |
| institution | DOAJ |
| issn | 2369-2529 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Rehabilitation and Assistive Technologies |
| spelling | doaj-art-5ff57afebccf48c1a1bdd45cf67c613b2025-08-20T02:48:29ZengJMIR PublicationsJMIR Rehabilitation and Assistive Technologies2369-25292025-03-0112e60162e6016210.2196/60162Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model StudyMahdie Rafieihttp://orcid.org/0009-0007-4807-3590Supratim Dashttp://orcid.org/0009-0009-7197-0120Mohammad Bakhtiarihttp://orcid.org/0000-0002-4169-9669Ewa Maria Rooshttp://orcid.org/0000-0001-5425-2199Søren T Skouhttp://orcid.org/0000-0003-4336-7059Dorte T Grønnehttp://orcid.org/0000-0002-8235-1175Jan Baumbachhttp://orcid.org/0000-0002-0282-0462Linda Baumbachhttp://orcid.org/0000-0001-8105-3494 Abstract BackgroundKnee osteoarthritis (OA) is a common chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits of exercise therapy and patient education in managing OA pain and functional limitations, these strategies are often underused. To motivate and enhance patient engagement, personalized outcome prediction models can be used. However, the accuracy of existing models in predicting changes in knee pain outcomes remains insufficiently examined. ObjectiveThis study aims to validate existing models and introduce a concise personalized model predicting changes in knee pain from before to after participating in a supervised patient education and exercise therapy program (GLA:D) among patients with knee OA. MethodsOur prediction models leverage self-reported patient information and functional measures. To refine the number of variables, we evaluated the variable importance and applied clinical reasoning. We trained random forest regression models and compared the rate of true predictions of our models with those using average values. In supplementary analyses, we additionally considered recently added variables to the GLA:D registry. ResultsWe evaluated the performance of a full, continuous, and concise model including all 34 variables, all 11 continuous variables, and the 6 most predictive variables, respectively. All three models performed similarly and were comparable to the existing model, with R2 ConclusionsOur concise personalized prediction model provides more often accurate predictions for changes in knee pain after the GLA:D program than using average pain improvement values. Neither the increase in sample size nor the inclusion of additional variables improved previous models. Based on current knowledge and available data, no better predictions are possible. Guidance is needed on when a model’s performance is good enough for clinical practice use.https://rehab.jmir.org/2025/1/e60162 |
| spellingShingle | Mahdie Rafiei Supratim Das Mohammad Bakhtiari Ewa Maria Roos Søren T Skou Dorte T Grønne Jan Baumbach Linda Baumbach Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study JMIR Rehabilitation and Assistive Technologies |
| title | Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study |
| title_full | Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study |
| title_fullStr | Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study |
| title_full_unstemmed | Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study |
| title_short | Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study |
| title_sort | personalized predictions for changes in knee pain among patients with osteoarthritis participating in supervised exercise and education prognostic model study |
| url | https://rehab.jmir.org/2025/1/e60162 |
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