Algorithmic management is associated with psychological distress, musculoskeletal pain, and occupational accidents: a cross-sectional study in logistics

OBJECTIVE: Algorithmic Management (AM) is increasingly shaping work environments across various sectors, influencing how tasks are assigned and monitored. While concerns have been raised regarding its potential impact on worker health, empirical evidence remains limited. This study examines the association between level of AM exposure and adverse health outcomes among logistics workers. METHODS: This cross-sectional study used an online survey, targeting logistics workers in Sweden. AM exposure was measured using an 11-item scale capturing aspects such as task allocation, surveillance, and performance monitoring. Health outcomes included psychological distress, musculoskeletal pain, headaches, sleep disturbances, and occupational accidents. RESULTS: Higher AM exposure was associated with increased prevalence of psychological distress (PR 2·12, 95% CI 1·49-3·02), occupational accidents (PR 1·92, 95% CI 1·22-3·01), headaches (PR 1·68, 95%CI 1·09-2·58), and musculoskeletal pain (PR 1·54, 95% CI 1·23-1·92). Stratified analyses revealed stronger associations for drivers, particularly regarding psychological distress, headaches, and sleep disturbances, while warehouse workers exhibited less consistent patterns. CONCLUSIONS: These findings highlight AM as a potential occupational health hazard, particularly when involving high levels of automated oversight and direction. While AM can enhance efficiency, its impact on worker well-being and public health warrants further attention and potentially mitigation strategies to inform policies that balance technological advancements with worker health protection.