What this means
Researchers tried to build a computer tool that suggests lifestyle changes to help workers stay employed. Most versions did not work well enough to use.
Plain-English summary written by an AI model from the abstract. The original is below.
- Who it was tested in
- 3412 paired health check-ups of workers from a population group, 2010-2020
- Kind of research
- A study
The science
- Original title
- Application of machine learning for personalized advice about workers' lifestyle changes to improve sustainable employability: Development and evaluation of a decision support tool.
- Authors
- Six Dijkstra MW, Soer R, Salomons EL et al.
- Journal
- Work (Reading, Mass.)
- Published
- Oct 13, 2026
- Study type
- Study
- PMID
- 42867901 ↗
Abstract
BackgroundOccupational 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.ObjectiveAims of this study are to: 1) develop a Machine Learning Decision Support Tool (ML-DST) based on workers' demographic, occupational, and health characteristics to support personalized advice about lifestyle changes to improve sustainable employability, and 2) evaluate performance of the ML-DST.MethodsReal-world OHC data from a population-based cohort (2010-2020) 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 1-4-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 ≥0.7. The best performing algorithm is proposed per model for inclusion in the final ML-DST.ResultsOf 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 ≥0.7, supporting nutritional behavior change to improve vitality; however, its profiles were not conceptually meaningful. The other 11 models did not achieve useful quantitative performance.ConclusionTraining 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.
Six Dijkstra MW, Soer R, Salomons EL et al. Application of machine learning for personalized advice about workers' lifestyle changes to improve sustainable employability: Development and evaluation of a decision support tool. Work (Reading, Mass.). 2026. doi:10.1177/10519815261490878. PMID: 42867901. https://pubmed.ncbi.nlm.nih.gov/42867901/