The association between workloads and health hazard among delivery riders in China

Abstract

Introduction: Drawing on the extended job demands-resources model, this study aims to identify the primary occupational health risks affecting delivery riders and explore mechanisms between workload and illness.

Methods: A respondent-driven sampling method was employed to minimize data bias. A total of 1,092 riders in Beijing, Shanghai, and Jinan participated in the survey. Logit regression analysis was conducted to assess the associations and mechanisms were also analyzed.

Results: A significant positive relationship was observed between the number of daily deliveries and daily working hours with reported illness. This association was more significant among riders who are primary family breadwinners or who work part-time.

Conclusion: Excessive workload negatively affects the health of delivery riders. Overwork may heighten riders' risk perception, which can ultimately lead to illness. However, this relationship can be mitigated if delivery platforms implementing measures to reduce work-related pressure. A key practical implication of this study is the urgent need for platform companies to assume greater responsibility in labor protection, particularly in curbing the tendency toward overwork.

Keywords: delivery riders; gig workers; job demands; occupational health; risk management.

Figure 1

Proposed conceptual framework.

Figure 2

Proportion of job-related disease types. GE, gastro enteropathy; CS, cervical spondylosis; LS, lumbar spondylosis; RTD, respiratory tract diseases; AR, arthritis; PD, prostate disease; SS, sunstroke; SD, skin diseases; HD, heart disease; OD, other diseases.

Figure 3

Association between workload and illness among different subgroups. This figure plots point estimates and 95% confidence intervals of DD’s impact on the probability of getting illness between different groups. These coefficients are derived from Logit estimates of Equation 1 after it has been fully interacted with the moderator variables of interest. The moderator variable in Panel (A) is whether the rider is his family’s main financial supporter. The moderator variable in Panel (B) is riders’ job type—full-time or part-time. The 95% intervals are constructed using robust standard errors.