{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T14:18:11Z","timestamp":1778077091924,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["WORK: A Journal of Prevention, Assessment &amp; Rehabilitation"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Hand grip and torque strength are critical in occupational tasks, particularly in industries requiring manual tool handling. Ergonomic designs tailored to workers\u2019 anthropometric characteristics can help reduce Musculoskeletal Disorders (MSDs), a leading cause of workplace absenteeism. While several studies have examined anthropometric influences on grip strength, predictive models integrating machine learning remain limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to identify the most influential anthropometric variables in predicting hand grip and torque strength among economically active individuals, using machine learning models to enhance force estimation for ergonomic applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A cross-sectional study was conducted with 382 participants (194 women, 188 men), aged 15\u201365 years. Three predictive models-Linear Regression (LR), Random Forest (RF), and AdaBoost (AB)-were evaluated based on Mean Absolute Error (MAE), Mean Squared Error (MSE), and Explained Variance Score (EVS). Feature importance was analyzed using Mean Decrease in Impurity (MDI) for RF and AB and coefficient magnitude for LR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LR was the best model in four cases, while RF and AB performed better in nonlinear relationships. In women, grip and torque strength depended on hand and wrist dimensions, whereas in men, variables such as palmar length and lateral reach played a more significant role.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Anthropometric characteristics significantly influence manual strength, highlighting the need for sex-specific ergonomic adaptations. Machine learning models improve predictive accuracy, supporting the design of interventions to reduce MSD risks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10519815251400749","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T16:38:28Z","timestamp":1765903108000},"page":"128-140","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of anthropometric variables influencing the prediction of manual strength using advanced models to prevent occupational injuries in the economically active population"],"prefix":"10.1177","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5763-7139","authenticated-orcid":false,"given":"Mayra","family":"Pacheco-Card\u00edn","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Universidad Aut\u00f3noma de Ciudad Ju\u00e1rez, Ciudad Ju\u00e1rez, Mexico"},{"name":"Department of Industrial Engineering, Tecnol\u00f3gico Nacional de M\u00e9xico - Campus M\u00e9rida, M\u00e9rida, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8612-5132","authenticated-orcid":false,"given":"Juan Luis","family":"Hern\u00e1ndez-Arellano","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Universidad Aut\u00f3noma de Ciudad Ju\u00e1rez, Ciudad Ju\u00e1rez, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5832-6623","authenticated-orcid":false,"given":"Jos\u00e9-Manuel","family":"Mej\u00eda-Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Autonomous University of Ciudad Juarez, Ciudad Juarez, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/10803548.2021.1896139"},{"key":"e_1_3_3_3_2","first-page":"1171","article-title":"Ergonomic design and evaluation of a hand tool handle to reduce risk of work-related musculoskeletal disorders","volume":"66","author":"Abeysekera JDA","year":"2023","unstructured":"Abeysekera JDA, Suwannarat P, Masilamani S, et al. Ergonomic design and evaluation of a hand tool handle to reduce risk of work-related musculoskeletal disorders. Ergonomics 2023; 66: 1171\u20131186.","journal-title":"Ergonomics"},{"key":"e_1_3_3_4_2","first-page":"483","article-title":"Effect of a novel adaptive handle design on the ergonomic performance of manual cutting tasks","volume":"79","author":"Hassan E","year":"2024","unstructured":"Hassan E, Marei NH, Abdelrahman AA, et al. Effect of a novel adaptive handle design on the ergonomic performance of manual cutting tasks. Work 2024; 79: 483\u2013494.","journal-title":"Work"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12891-024-07914-z"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1201\/9780429298199"},{"key":"e_1_3_3_7_2","volume-title":"Statistical report 2021 \u2013 Chapter VII: Occupational health","author":"Mexican Institute of Social Security","year":"2021","unstructured":"Mexican Institute of Social Security. 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