A Systematic Analysis of Biological, Sociodemographic, Psychosocial, and Lifestyle Factors Contributing to Work Ability Across the Working Life Span: Cross-sectional Study

**Background:** As the workforce ages, work ability declines, increasing the risk of sick leave and early retirement. Work ability is a multifactorial construct reflecting the balance between job demands and individual resources. While prior research has examined demographic, lifestyle, and psychosocial predictors, the simultaneous contribution of biological parameters (cardiovascular, metabolic, immunological, cognitive) has been underexplored.

**Methods:** This cross-sectional analysis used baseline data from the Dortmund Vital Study (ClinicalTrials.gov NCT05155397). A total of 494 currently employed participants (317/494, 64.2% female; mean age 42.7, SD 12.7; range 20–69 years) completed the Work Ability Index (WAI). The sample was divided into terciles: young adults (n=177; mean 28.2 years), middle-aged adults (n=182; mean 45.3 years), and older adults (n=134; mean 58.1 years). Thirty sociodemographic variables were grouped into 4 categories (social relationships, nutrition and stimulants, education and lifestyle, work-related), and 80 biological/environmental variables were grouped into 8 domains (anthropometric, cardiovascular, metabolic, immunologic, personality, cognitive, stress-related, quality of life). Pearson correlations and standard multiple linear regression analyses were performed for each domain separately, followed by a final backward elimination regression model including the strongest predictors.

**Key Results:** The mean WAI score was 39.16 (SD not clearly reported for total), indicating good work ability. WAI decreased significantly with age (F₂,₄₉₃=15.5, P=.001). Sociodemographic factors associated with higher WAI included more close friends (P=.048), more frequent friendship meetings (P=.002), higher education (P<.001), frequent foreign language use (P<.001), good sleep quality (P<.001), and flexible (vs. repetitive) job characteristics (P=.03). Having a family pet was associated with lower WAI (P=.002). Watching >3 hours of television per day was associated with lower WAI (P<.001).

In domain-specific regression models, the following predictors were significant (all P<.05): BMI (negative), maximum heart rate during ergometry (positive), hemoglobin concentration (positive), weekly physical activity (positive), immunological age (negative), CD4/CD8 ratio (negative), monocyte count (positive), cognitive failures in daily life (negative), neuroticism (negative), depressive symptoms (negative), burnout symptoms (negative), emotional exhaustion (negative), psychosocial stress (negative), job demands (negative), commitment (positive), pressure to succeed (positive), and physical and global quality of life (positive). The personality domain explained 34.3% of WAI variance; the occupational stress domain explained 48.0%; and the quality of life domain explained 51.8%.

The final regression model (R²=0.525; adjusted R²=0.516; F₁₃,₃₇₆=51.05, P<.001; n=377) identified 8 essential predictors: age (β=−0.256, P=.001), maximum heart rate (β=0.089, P=.02), hemoglobin (β=0.099, P=.009), depressive symptoms (β=−0.204, P=.001), burnout symptoms (β=−0.106, P=.02), commitment (β=0.096, P=.01), pressure to succeed (β=0.088, P=.02), and physical quality of life (β=0.457, P=.001).

**Clinical Implications:** Work ability is a complex construct influenced by modifiable and non-modifiable factors. Chronological age, metabolic status, and personality traits (e.g., neuroticism) are relatively stable, but cardiovascular fitness, hemoglobin levels, psychosocial stress, depressive and burnout symptoms, sleep quality, and social engagement are potentially modifiable. The findings support the development of targeted workplace interventions—including physical activity programs, stress management training, cognitive training, and improvements in psychosocial working conditions—to maintain or enhance work ability and prevent early retirement in an aging workforce. The authors recommend that policy makers, employers, and occupational safety and health personnel use these modifiable risk factors to design preventive programs and promote healthy aging at work.