The hazards of skipping meals and losing sleep for ride-hailing drivers-evidence based on quasi-experimental and experience sampling methodology.

The rapid growth of ride-hailing services, driven by big data and platform algorithms, has intensified physiological stressors among drivers, particularly during peak mealtime periods, where hunger and sleep deprivation ("skipping meals and losing sleep") have become prevalent occupational hazards. While prior research has separately documented the adverse effects of hunger and sleep loss on self-control, their synergistic impact on driving safety remains underexplored, especially in ecologically valid settings. Guided by the self-control strength model, this study investigated how hunger and sleep deprivation interact to influence dangerous driving behaviours among ride-hailing drivers, examining the mediating role of self-control resource depletion and the moderating effects of sleep deprivation and work identity perception (contractor vs. worker). Using a mixed-methods approach, Study 1 (N = 70 students) employed a 2 × 2 mixed quasi-experiment to establish baseline effects, revealing that hunger directly depleted self-control resources (measured via Stroop task), with sleep deprivation exacerbating this relationship. Study 2 extended these findings to 153 active ride-hailing drivers using a 2 × 2 between-subjects quasi-experiment, confirming that hungry drivers exhibited greater self-control resource depletion than fed drivers, particularly under sleep deprivation. Study 3 utilized experience sampling with 76 drivers (651 data points) to test cross-level mechanisms, demonstrating that hunger indirectly increased dangerous driving behaviors through self-control depletion, with sleep deprivation and worker identity perception amplifying these effects. These findings highlight the compounding risks of physiological stressors in ride-hailing work, emphasizing the need for interventions targeting both hunger management and sleep hygiene. The study contributes to accident prevention research by integrating ecological validity with theoretical rigor, offering actionable insights for drivers and platform operators to mitigate fatigue-related safety risks.