{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:32:35Z","timestamp":1772299955110,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009500","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000}}],"reference-count":48,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DGE1745016 and DGE2140739"],"award-info":[{"award-number":["DGE1745016 and DGE2140739"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients. Moreover, customized interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a regression model that uses minimal clinical data\u2014a set of six features easily obtained in the clinic\u2014to predict the extent of first peak KAM reduction after toe-in gait retraining. For such a model to generalize, the training data must be large and variable. Given the lack of large public datasets that contain different gaits for the same patient, we generated this dataset synthetically. Insights learned from a ground-truth dataset with both baseline and toe-in gait trials (N = 12) enabled the creation of a large (N = 138) synthetic dataset for training the predictive model. On a test set of data collected by a separate research group (N = 15), the first peak KAM reduction was predicted with a mean absolute error of 0.134% body weight * height (%BW*HT). This error is smaller than the standard deviation of the first peak KAM during baseline walking averaged across test subjects (0.306%BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and provides clinicians with a new tool to predict the outcome of patient-specific gait retraining without requiring gait lab instrumentation.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009500","type":"journal-article","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T13:30:46Z","timestamp":1652707846000},"page":"e1009500","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Predicting knee adduction moment response to gait retraining with minimal clinical data"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6897-1933","authenticated-orcid":true,"given":"Nataliya","family":"Rokhmanova","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5004-0313","authenticated-orcid":true,"given":"Katherine J.","family":"Kuchenbecker","sequence":"additional","affiliation":[]},{"given":"Peter B.","family":"Shull","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6154-9115","authenticated-orcid":true,"given":"Reed","family":"Ferber","sequence":"additional","affiliation":[]},{"given":"Eni","family":"Halilaj","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"pcbi.1009500.ref001","doi-asserted-by":"crossref","first-page":"100587","DOI":"10.1016\/j.eclinm.2020.100587","article-title":"Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies","volume":"29","author":"A Cui","year":"2020","journal-title":"EClinicalMedicine"},{"key":"pcbi.1009500.ref002","article-title":"Musculoskeletal dynamics: locomotion and clinical applications","author":"TP Andriacchi","year":"1997","journal-title":"Basic orthopaedic biomechanics"},{"issue":"Suppl 1","key":"pcbi.1009500.ref003","doi-asserted-by":"crossref","first-page":"95","DOI":"10.2106\/JBJS.H.01408","article-title":"Gait mechanics influence healthy cartilage morphology and osteoarthritis of the knee","volume":"91","author":"TP Andriacchi","year":"2009","journal-title":"The Journal of Bone and Joint Surgery. 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