A Systematic Analysis of Biological, Sociodemographic, Psychosocial, and Lifestyle Factors Contributing to Work Ability Across the Working Life Span: Cross-sectional Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,032 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,009 |
| Études des sciences et des technologies | 0,007 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».