The association between core functions of algorithmic management and work-related health problems: indications for effects beyond well-known psychosocial stressors - Andreas Müller; Ivaneta Danailova; Michael Ertel; Harald Gündel; Meike Heming; Anne Kemter; Louisa Scheepers; Carsten Röttgen (Frontiers in Public Health, 2026)
Introduction Algorithmic management (AM) marks a transformation in work organization as managerial decisions increasingly rely on data-driven algorithms. While research suggests that AM may affect employee health, empirical evidence remains fragmented. This study examines whether associations between AM and work-related health impairments generalize across occupations. Methods Data from the cross-sectional Flash Eurobarometer “OSH Pulse” ( N = 27,242 workers) were analyzed using cross-classified mixed-effects logistic regression. Exposure to AM—algorithmic monitoring, task allocation, and performance evaluation—was examined in relation to self-reported health problems (stress, depression or anxiety; musculoskeletal problems; headaches or eyestrain; accidents or injuries; overall fatigue), controlling for demographic variables and psychosocial work stressors. Results Findings show that with each additional AM function the likelihood of reported health problems increased modestly but constantly (OR = 1.06–1.11). Among the single AM functions examined, monitoring appeared to have most consistent associations with workers health problems. Discussion Findings suggest that AM may represent an additional risk factor for worker health accross different occupations. However, results should be interpreted cautiously due to non-established screening measures and an overrepresentation of tertiary-educated workers.