{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T23:39:04Z","timestamp":1791934744191,"version":"4.3.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Mitacs Acceleration-Emovi Inc.","award":["IT26235"],"award-info":[{"award-number":["IT26235"]}]},{"name":"Mitacs Acceleration-Emovi Inc.","award":["IT26235"],"award-info":[{"award-number":["IT26235"]}]},{"name":"CRSNG-Prompt","award":["RDCPJ 544278-19"],"award-info":[{"award-number":["RDCPJ 544278-19"]}]},{"name":"CRSNG-Prompt","award":["RDCPJ 544278-19"],"award-info":[{"award-number":["RDCPJ 544278-19"]}]},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"crossref","award":["950-231214"],"award-info":[{"award-number":["950-231214"]}],"id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Orthop Surg Res"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>\n                           <jats:bold>Background:<\/jats:bold>\n                        <\/jats:title>\n                <jats:p>Characterizing the condition of patients suffering from knee osteoarthritis is complex due to multiple associations between clinical, functional, and structural parameters. While significant variability exists within this population, especially in candidates for total knee arthroplasty, there is increasing interest in knee kinematics among orthopedic surgeons aiming for more personalized approaches to achieve better outcomes and satisfaction. The primary objective of this study was to identify distinct kinematic phenotypes in total knee arthroplasty candidates and to compare different methods for the identification of these phenotypes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>\n                           <jats:bold>Methods:<\/jats:bold>\n                        <\/jats:title>\n                <jats:p>Three-dimensional kinematic data obtained from a Knee Kinesiography exam during treadmill walking in the clinic were used. Various aspects of the clustering process were evaluated and compared to achieve optimal clustering, including data preparation, transformation, and representation methods.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>\n                           <jats:bold>Results:<\/jats:bold>\n                        <\/jats:title>\n                <jats:p>A K-Means clustering algorithm, performed using Euclidean distance, combined with principal component analysis applied on data transformed by standardization, was the optimal approach. Two unique kinematic phenotypes were identified among 80 total knee arthroplasty candidates. The two distinct phenotypes divided patients who significantly differed both in terms of knee kinematic representation and clinical outcomes, including a notable variation in 63.3% of frontal plane features and 81.8% of transverse plane features across 77.33% of the gait cycle, as well as differences in the Pain Catastrophizing Scale, highlighting the impact of these kinematic variations on patient pain and function.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>\n                           <jats:bold>Conclusion:<\/jats:bold>\n                        <\/jats:title>\n                <jats:p>Results from this study provide valuable insights for clinicians to develop personalized treatment approaches based on patients\u2019 phenotype affiliation, ultimately helping to improve total knee arthroplasty outcomes.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13018-024-04990-8","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T12:03:03Z","timestamp":1723636983000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Unveiling distinct kinematic profiles among total knee arthroplasty candidates through clustering technique"],"prefix":"10.1186","volume":"19","author":[{"given":"Lina","family":"Abou-Abbas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Hagemeister","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youssef","family":"Ouakrim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alix","family":"Cagnin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Laundry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glen","family":"Richardson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Dunbar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neila","family":"Mezghani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"issue":"9","key":"4990_CR1","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/j.joca.2013.03.018","volume":"21","author":"T Neogi","year":"2013","unstructured":"Neogi T. The epidemiology and impact of pain in osteoarthritis. Osteoarthritis Cartilage. 2013;21(9):1145\u201353.","journal-title":"Osteoarthritis Cartilage"},{"issue":"1","key":"4990_CR2","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1186\/s12891-022-05845-1","volume":"23","author":"F Bensalma","year":"2022","unstructured":"Bensalma F, Hagemeister N, Cagnin A, Ouakrim Y, Bureau NJ, Choini\u00e8re M, Mezghani N. Biomechanical markers associations with pain, symptoms, and disability compared to radiographic severity in knee osteoarthritis patients: a secondary analysis from a cluster randomized controlled trial. BMC Musculoskelet Disord. 2022;23(1):896.","journal-title":"BMC Musculoskelet Disord"},{"issue":"7","key":"4990_CR3","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/S0021-9290(01)00036-7","volume":"34","author":"KR Kaufman","year":"2001","unstructured":"Kaufman KR, Hughes C, Morrey BF, Morrey M, An K-N. Gait characteristics of patients with knee osteoarthritis. J Biomech. 2001;34(7):907\u201315.","journal-title":"J Biomech"},{"issue":"7","key":"4990_CR4","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1053\/joca.2002.0797","volume":"10","author":"A Baliunas","year":"2002","unstructured":"Baliunas A, Hurwitz D, Ryals A, Karrar A, Case J, Block J, Andriacchi T. Increased knee joint loads during walking are present in subjects with knee osteoarthritis. Osteoarthritis Cartilage. 2002;10(7):573\u20139.","journal-title":"Osteoarthritis Cartilage"},{"issue":"3","key":"4990_CR5","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1109\/TBME.2007.905388","volume":"55","author":"N Mezghani","year":"2008","unstructured":"Mezghani N, Husse S, Boivin K, Turcot K, Aissaoui R, Hagemeister N, Guise JA. Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier. IEEE Trans Biomed Eng. 2008;55(3):1230\u20132.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"24","key":"4990_CR6","doi-asserted-by":"publisher","first-page":"12054","DOI":"10.3390\/app112412054","volume":"11","author":"N Mezghani","year":"2021","unstructured":"Mezghani N, Soltana R, Ouakrim Y, Cagnin A, Fuentes A, Hagemeister N, Vendittoli P-A. Healthy knee kinematic phenotypes identification based on a clustering data analysis. Appl Sci. 2021;11(24):12054.","journal-title":"Appl Sci"},{"key":"4990_CR7","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.jbiomech.2016.12.022","volume":"52","author":"N Mezghani","year":"2017","unstructured":"Mezghani N, Ouakrim Y, Fuentes A, Mitiche A, Hagemeister N, Vendittoli P-A, De Guise JA. Mechanical biomarkers of medial compartment knee osteoarthritis diagnosis and severity grading: Discovery phase. J Biomech. 2017;52:106\u201312.","journal-title":"J Biomech"},{"issue":"7","key":"4990_CR8","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/s00167-005-0005-6","volume":"14","author":"Z Bejek","year":"2006","unstructured":"Bejek Z, Par\u00f3czai R, Illy\u00e9s \u00c1, Kiss RM. The influence of walking speed on gait parameters in healthy people and in patients with osteoarthritis. Knee Surg Sports Traumatol Arthrosc. 2006;14(7):612\u201322.","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"issue":"4","key":"4990_CR9","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1080\/10255842.2022.2066973","volume":"26","author":"F Bensalma","year":"2023","unstructured":"Bensalma F, Mezghani N, Cagnin A, Fuente A, Lenoir L, Hagemeister N. Multimodal data analysis of knee osteoarthritis assessment: factors selection for conservative care decision making. Comput Methods Biomech Biomed Eng. 2023;26(4):450\u20139.","journal-title":"Comput Methods Biomech Biomed Eng"},{"key":"4990_CR10","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1007\/s00402-018-2873-5","volume":"138","author":"C Planckaert","year":"2018","unstructured":"Planckaert C, Larose G, Ranger P, Lacelle M, Fuentes A, Hagemeister N. Total knee arthroplasty with unexplained pain: new insights from kinematics. Arch Orthop Trauma Surg. 2018;138:553\u201361.","journal-title":"Arch Orthop Trauma Surg"},{"key":"4990_CR11","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s11999-009-1119-9","volume":"468","author":"RB Bourne","year":"2010","unstructured":"Bourne RB, Chesworth BM, Davis AM, Mahomed NN, Charron KD. Patient satisfaction after total knee arthroplasty: who is satisfied and who is not? Clin Orthopaedics Related Res. 2010;468:57\u201363.","journal-title":"Clin Orthopaedics Related Res"},{"issue":"6","key":"4990_CR12","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1111\/os.12826","volume":"12","author":"Z-X Gao","year":"2020","unstructured":"Gao Z-X, Long N-J, Zhang S-Y, Yu W, Dai Y-X, Xiao C. Comparison of kinematic alignment and mechanical alignment in total knee arthroplasty: a meta-analysis of randomized controlled clinical trials. Orthop Surg. 2020;12(6):1567\u201378.","journal-title":"Orthop Surg"},{"key":"4990_CR13","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1007\/s00167-019-05584-2","volume":"28","author":"Z Luo","year":"2020","unstructured":"Luo Z, Zhou K, Peng L, Shang Q, Pei F, Zhou Z. Similar results with kinematic and mechanical alignment applied in total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. 2020;28:1720\u201335.","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"key":"4990_CR14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13075-020-2143-0","volume":"22","author":"W Van Spil","year":"2020","unstructured":"Van Spil W, Bierma-Zeinstra S, Deveza L, Arden N, Bay-Jensen A-C, Kraus VB, Carlesso L, Christensen R, Van Der Esch M, Kent P, et al. A consensus-based framework for conducting and reporting osteoarthritis phenotype research. Arthritis Res Ther. 2020;22:1\u20137.","journal-title":"Arthritis Res Ther"},{"issue":"3","key":"4990_CR15","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.joca.2019.11.007","volume":"28","author":"W Van Spil","year":"2020","unstructured":"Van Spil W, Szilagyi I. Osteoarthritis year in review 2019: biomarkers (biochemical markers). Osteoarthritis Cartilage. 2020;28(3):296\u2013315.","journal-title":"Osteoarthritis Cartilage"},{"issue":"10","key":"4990_CR16","doi-asserted-by":"publisher","first-page":"0205174","DOI":"10.1371\/journal.pone.0205174","volume":"13","author":"G Kuntze","year":"2018","unstructured":"Kuntze G, Nettel-Aguirre A, Ursulak G, Robu I, Bowal N, Goldstein S, Emery CA. Multi-joint gait clustering for children and youth with diplegic cerebral palsy. PLoS One. 2018;13(10):0205174.","journal-title":"PLoS One"},{"issue":"1","key":"4990_CR17","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1080\/00325481.2019.1665457","volume":"132","author":"A Cagnin","year":"2020","unstructured":"Cagnin A, Choini\u00e8re M, Bureau NJ, Durand M, Mezghani N, Gaudreault N, Hagemeister N. A multi-arm cluster randomized clinical trial of the use of knee kinesiography in the management of osteoarthritis patients in a primary care setting. Postgrad Med. 2020;132(1):91\u2013101.","journal-title":"Postgrad Med"},{"issue":"1","key":"4990_CR18","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1302\/0301-620X.80B1.0800063","volume":"80","author":"J Dawson","year":"1998","unstructured":"Dawson J, Fitzpatrick R. Questionnaire on the perceptions of patients about total knee replacement. J Bone Joint Surg Br. 1998;80(1):63\u20139.","journal-title":"J Bone Joint Surg Br"},{"issue":"4","key":"4990_CR19","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1037\/1040-3590.7.4.524","volume":"7","author":"MJ Sullivan","year":"1995","unstructured":"Sullivan MJ, Bishop SR, Pivik J. The pain catastrophizing scale: development and validation. Psychol Assess. 1995;7(4):524.","journal-title":"Psychol Assess"},{"issue":"4","key":"4990_CR20","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi H, Williams LJ. Principal component analysis. Wiley Interdis Rev Comput Stat. 2010;2(4):433\u201359.","journal-title":"Wiley Interdis Rev Comput Stat"},{"issue":"03","key":"4990_CR21","doi-asserted-by":"publisher","first-page":"1350026","DOI":"10.1142\/S0219519413500267","volume":"13","author":"N Mezghani","year":"2013","unstructured":"Mezghani N, Fuentes A, Gaudreault N, Mitiche A, Aissaoui R, Hagmeister N, De Guise JA. Identification of knee frontal plane kinematic patterns in normal gait by principal component analysis. J Mechan Med Biol. 2013;13(03):1350026.","journal-title":"J Mechan Med Biol"},{"issue":"3","key":"4990_CR22","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.clinbiomech.2010.10.004","volume":"26","author":"N Gaudreault","year":"2011","unstructured":"Gaudreault N, Mezghani N, Turcot K, Hagemeister N, Boivin K, De Guise JA. Effects of physiotherapy treatment on knee osteoarthritis gait data using principal component analysis. Clin Biomech. 2011;26(3):284\u201391.","journal-title":"Clin Biomech"},{"key":"4990_CR23","doi-asserted-by":"publisher","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","volume":"8","author":"KP Sinaga","year":"2020","unstructured":"Sinaga KP, Yang M-S. Unsupervised k-means clustering algorithm. IEEE Access. 2020;8:80716\u201327.","journal-title":"IEEE Access"},{"key":"4990_CR24","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/978-3-540-74048-3_4","volume-title":"Dynamic time warping. Information retrieval for music and motion","author":"M M\u00fcller","year":"2007","unstructured":"M\u00fcller M. Dynamic time warping. Information retrieval for music and motion. Berlin: Springer; 2007. p. 69\u201384."},{"issue":"3","key":"4990_CR25","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1016\/j.patcog.2010.09.013","volume":"44","author":"F Petitjean","year":"2011","unstructured":"Petitjean F, Ketterlin A, Gan\u00e7arski P. A global averaging method for dynamic time warping, with applications to clustering. Pattern Recogn. 2011;44(3):678\u201393.","journal-title":"Pattern Recogn"},{"issue":"2","key":"4990_CR26","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.joca.2021.10.011","volume":"30","author":"ET Petersen","year":"2022","unstructured":"Petersen ET, Rytter S, Koppens D, Dalsgaard J, Hansen TB, Larsen NE, Andersen MS, Stilling M. Patients with knee osteoarthritis can be divided into subgroups based on tibiofemoral joint kinematics of gait-an exploratory and dynamic radiostereometric study. Osteoarthritis Cartilage. 2022;30(2):249\u201359.","journal-title":"Osteoarthritis Cartilage"},{"issue":"6","key":"4990_CR27","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1308\/rcsann.2019.0016","volume":"101","author":"A Agarwal","year":"2019","unstructured":"Agarwal A, Miller S, Hadden W, Johnston L, Wang W, Arnold G, Abboud R. Comparison of gait kinematics in total and unicondylar knee replacement surgery. Ann Royal Coll Surg Engl. 2019;101(6):391\u20138.","journal-title":"Ann Royal Coll Surg Engl"},{"issue":"2","key":"4990_CR28","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.joca.2012.11.008","volume":"21","author":"D Kumar","year":"2013","unstructured":"Kumar D, Manal KT, Rudolph KS. Knee joint loading during gait in healthy controls and individuals with knee osteoarthritis. Osteoarthritis Cartilage. 2013;21(2):298\u2013305.","journal-title":"Osteoarthritis Cartilage"},{"issue":"5","key":"4990_CR29","first-page":"331","volume":"8","author":"VR Patel","year":"2011","unstructured":"Patel VR, Mehta RG. Impact of outlier removal and normalization approach in modified k-means clustering algorithm. Int J Comput Sci Issues (IJCSI). 2011;8(5):331.","journal-title":"Int J Comput Sci Issues (IJCSI)"},{"key":"4990_CR30","unstructured":"Dudek A, Walesiak M. The choice of variable normalization method in cluster analysis 2020"},{"key":"4990_CR31","first-page":"168","volume":"4","author":"NK Visalakshi","year":"2009","unstructured":"Visalakshi NK, Kuttiyannan DT. Impact of normalization in distributed k-means clustering. Int J Soft Comput. 2009;4:168\u201372.","journal-title":"Int J Soft Comput"},{"key":"4990_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinbiomech.2021.105447","volume":"88","author":"G Leporace","year":"2021","unstructured":"Leporace G, Gonzalez F, Metsavaht L, Motta M, Carpes FP, Chahla J, Luzo M. Are there different gait profiles in patients with advanced knee osteoarthritis? a machine learning approach. Clin Biomech. 2021;88: 105447.","journal-title":"Clin Biomech"},{"key":"4990_CR33","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.knee.2023.08.002","volume":"44","author":"FF Gonzalez","year":"2023","unstructured":"Gonzalez FF, Leporace G, Franciozi C, Cockrane M, Metsavaht L, Carpes FP, Chahla J, Luzo M. Clinical and radiographic characterization of three-dimensional gait profiles of patients with knee osteoarthritis. Knee. 2023;44:211\u20139.","journal-title":"Knee"}],"container-title":["Journal of Orthopaedic Surgery and Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13018-024-04990-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13018-024-04990-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13018-024-04990-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T12:07:53Z","timestamp":1723637273000},"score":1,"resource":{"primary":{"URL":"https:\/\/josr-online.biomedcentral.com\/articles\/10.1186\/s13018-024-04990-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,14]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["4990"],"URL":"https:\/\/doi.org\/10.1186\/s13018-024-04990-8","relation":{},"ISSN":["1749-799X"],"issn-type":[{"value":"1749-799X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,14]]},"assertion":[{"value":"11 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Research Ethics Boards of the \u00c9cole de technologie sup\u00e9rieure and the Centre hospitalier de l\u2019Universit\u00e9 de Montr\u00e9al (CE20.096). All subjects signed informed consent prior to participating.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"A.C. and P.L. are paid employees of Emovi which commercializes the Knee Kinesiography exam used to capture kinematic data in this study; N.H. and N.M. report research support from Emovi; Other authors have no Conflict of interest to declare.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing\u00a0 interests"}}],"article-number":"479"}}