{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T05:31:25Z","timestamp":1793770285753,"version":"4.6.1"},"reference-count":48,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Expertise Centre Tech for Future in the Netherlands","award":["TFF150101 SHIFT"],"award-info":[{"award-number":["TFF150101 SHIFT"]}]},{"name":"Saxion University of Applied Sciences","id":[{"id":"https:\/\/ror.org\/005t9n460","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["023.011.076"],"award-info":[{"award-number":["023.011.076"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["WORK: A Journal of Prevention, Assessment &amp; Rehabilitation"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Occupational Health Check-ups (OHCs) have evolved from identifying work-related health risks to supporting sustainable employability through individualized lifestyle advice, but translating multidimensional demographic, occupational, and health characteristics into effective, personalized recommendations remains complex and lacks validated decision-support methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Aims of this study are to: 1) develop a Machine Learning Decision Support Tool (ML-DST) based on workers\u2019 demographic, occupational, and health characteristics to support personalized advice about lifestyle changes to improve sustainable employability, and 2) evaluate performance of the ML-DST.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Real-world OHC data from a population-based cohort (2010\u20132020) were used to develop ML-DST models providing lifestyle advice (physical activity, smoking, alcohol, nutrition, relaxation, or no change) to improve workability and vitality. Paired baseline and 1\u20134-year follow-up data were used to train and evaluate 12 models. Per model, three rule-based algorithms were applied. Performance was considered useful when both True Positive Rate (TPR) and macroF1 score were \u22650.7. The best performing algorithm is proposed per model for inclusion in the final ML-DST.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 20,440 individual OHCs, 3412 paired sets were included. Best TPRs ranged from 0.51 to 0.71. One model achieved TPR and macroF1 scores \u22650.7, supporting nutritional behavior change to improve vitality; however, its profiles were not conceptually meaningful. The other 11 models did not achieve useful quantitative performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Training algorithms for an ML-DST to support advice about lifestyle changes to improve long-term sustainable employability using data from regular Occupational Health Check-ups did not lead to useful model performance for improving workability and vitality.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10519815261490878","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T15:56:35Z","timestamp":1791906995000},"page":"902-916","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of machine learning for personalized advice about workers\u2019 lifestyle changes to improve sustainable employability: Development and evaluation of a decision support tool"],"prefix":"10.1177","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5074-2422","authenticated-orcid":false,"given":"Marianne WMC.","family":"Six Dijkstra","sequence":"first","affiliation":[{"name":"University of Groningen"},{"name":"Saxion University of Applied Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4154-1711","authenticated-orcid":false,"given":"Remko","family":"Soer","sequence":"additional","affiliation":[{"name":"University of Groningen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2368-4970","authenticated-orcid":false,"given":"Etto L.","family":"Salomons","sequence":"additional","affiliation":[{"name":"Saxion University of Applied Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-4947","authenticated-orcid":false,"given":"Andr\u00e9 H.J.","family":"Bieleman","sequence":"additional","affiliation":[{"name":"Saxion University of Applied Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4058-3437","authenticated-orcid":false,"given":"Michiel F.","family":"Reneman","sequence":"additional","affiliation":[{"name":"University of Groningen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2427-6277","authenticated-orcid":false,"given":"Douglas P.","family":"Gross","sequence":"additional","affiliation":[{"name":"University of Alberta"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(23)00868-1"},{"key":"e_1_3_3_3_2","volume-title":"Priorities for occupational safety and health research in Europe : 2013-2020","author":"Sas K","year":"2013","unstructured":"Sas K, Suarez A, EAfSaHa W. Priorities for occupational safety and health research in Europe : 2013-2020. Publications Office of the European Union, 2013."},{"key":"e_1_3_3_4_2","volume-title":"Regional office for Europe. healthy, prosperous lives for all: The European health equity status report: Executive summary","author":"World Health Organization","year":"2019","unstructured":"World Health Organization. Regional office for Europe. healthy, prosperous lives for all: The European health equity status report: Executive summary. World Health Organization. Regional Office for Europe, 2019, Contract No.: WHO\/EURO:2019-3536-43295-60680."},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-06490-3"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.5271\/sjweh.3444"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1136\/oemed-2019-105961"},{"key":"e_1_3_3_8_2","unstructured":"Perosh. 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