Residents’ health behaviors are crucial factors in the prevention of diseases. A city’s digitalization degree refers to the extent to which digital infrastructure and products can empower local industries and residents. Existing studies about the influence of digital technologies and products on health behaviors are mainly individual-level studies. Extant city-level studies primarily investigate the influence of cities’ digitalization degrees on economic and environmental factors. There is a lack of city-level study examining the impact of cities’ digitalization degrees on residents’ health behaviors. This study constructs a panel dataset comprising the data of 289 prefecture-level or above cities in China in the period from 2020 to 2023. Regression models are employed to examine the relationship between cities’ digitalization degrees and residents’ health behaviors. Based on geographic locations, cities are sorted as “eastern or middle cities” and “western cities”. According to the concentration level of economic and social resources, cities are sorted into “central cities” and “non-central cities”. Based on these two categorizations, the heterogeneity of the main effect is also investigated. Furthermore, the average slopes and the dummy variable indicating whether a city is selected by the “Broadband China Strategy” are utilized as instrumental variables to address the endogeneity caused by omitted variables. In the basic regression model, cities’ digitalization degrees show significantly positive relationship with residents’ health behaviors (β 1 =0.178, 95% CI 0.042, 0.315, p=0.011; β 2 =0.841, 95% CI 0.458, 1.225, p<0.001). The result is robust in the regression models using instrumental variables. Heterogeneity analysis indicates that for “eastern and middle cities”, there is a significantly positive relationship between cities’ digitalization degrees and residents’ health behaviors (β 1 =0.251, 95% CI 0.105, 0.396, p=0.001; β 2 =0.986, 95% CI 0.512, 1.459, p<0.001), while this relationship is not significant for “western cities” (β 1 =0.032, 95% CI -0.176, 0.242, p=0.759; β 2 =0.500, 95% CI -0.138, 1.139, p=0.123). For “non-central cities”, there is a significantly positive relationship between cities’ digitalization degrees and residents’ health behaviors (β 1 =0.285, 95% CI 0.107, 0.463, p=0.002; β 2 =1.097, 95% CI 0.670, 1.524, p<0.001), while this relationship is not significant for “central cities” (β 1 =0.014, 95% CI -0.184, 0.213, p=0.885; β 2 =0.146, 95% CI -0.593, 0.886, p=0.690). Digitalization of cities can promote residents’ health behaviors for three potential reasons. First, cities’ digitalization degrees improve local residents’ accessibility to health resources. Second, cities’ digitalization degrees strengthen local residents’ capacity regarding health behaviors. Third, cities’ digitalization degrees enhance local residents’ purchasing power for health resources. • Cities’ digitalization degrees show significantly positive relationship with residents’ health behaviors. • The positive relationship between cities’ digitalization degrees and residents’ health behaviors is significant for cities in the east or middle region of China, but is not significant for cities in the west region of China. • The positive relationship between cities’ digitalization degrees and residents’ health behaviors is significant for non-central cities, but is not significant for central cities.
Wang et al. (Wed,) studied this question.