A Systematic Analysis of Biological, Sociodemographic, Psychosocial, and Lifestyle Factors Contributing to Work Ability Across the Working Life Span: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: As employees age, their physical and mental abilities decline and work ability decreases, enhancing the risk for long-term sick leave or even premature retirement. However, the relative impact of biological and environmental determinants on work ability with increasing age is poorly understood in terms of their complexity. OBJECTIVE: Previous research has shown relationships between work ability and job and individual resources, as well as specific demographic and lifestyle-related variables. However, other potentially important predictors of work ability remain unexplored, such as personality traits and biological determinants, including cardiovascular, metabolic, immunological, and cognitive abilities or psychosocial factors. Our aim was to systematically evaluate a wide range of factors to extract the most crucial predictors of low and high work ability across the working life span. METHODS: As part of the Dortmund Vital Study, 494 participants from different occupational sectors, aged between 20 and 69 years, completed the Work Ability Index (WAI) assessing employee's mental and physical resources. A total of 30 sociodemographic variables were grouped into 4 categories (social relationships, nutrition and stimulants, education and lifestyle, and work related), and 80 biological and environmental variables were grouped into 8 domains (anthropometric, cardiovascular, metabolic, immunologic, personality, cognitive, stress related, and quality of life) and have been related to the WAI. RESULTS: Using the analyses, we extracted important sociodemographic factors influencing work ability, such as education, social activities, or sleep quality, and identified age-dependent and age-independent determinants of work ability. Regression models explained up to 52% of the WAI variance. Negative predictors of work ability were chronological and immunological age, immunological inefficiency, BMI, neuroticism, psychosocial stress, emotional exhaustion, demands from work, daily cognitive failures, subclinical depression, and burnout symptoms. Positive predictors were maximum heart rate during ergometry, normal blood pressure, hemoglobin and monocyte concentration, weekly physical activity, commitment to the company, pressure to succeed, and good quality of life. CONCLUSIONS: The identified biological and environmental risk factors allowed us to evaluate work ability in its complexity. Policy makers, employers, and occupational safety and health personnel should consider the modifiable risk factors we identified to promote healthy aging at work through focused physical, dietary, cognitive, and stress-reduced preventive programs, in addition to well-balanced working conditions. This may also increase the quality of life, commitment to the job, and motivation to succeed, which are important factors to maintain or even enhance work ability in the aging workforce and to prevent early retirement. TRIAL REGISTRATION: ClinicalTrials.gov NCT05155397; https://clinicaltrials.gov/ct2/show/NCT05155397. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/32352.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".