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A growing number of information technology systems and services have occurred to change users' attitudes or behaviors or both in the rapid development of mobile social media platforms. It is a new topic in the field of health communication whether the digitization and socialization of individual exercise behavior can stimulate health behavior. WeRun is a typical platform for the digitization and socialization of individual exercise. Based on 689 WeRun users' questionnaires, this study first repairs the missing and abnormal data by BP neural network. Then, the decision tree is used to evaluate the relationship between the perceived social support and exercise behavior under different intervention conditions, and detects the heterogeneous intervention effects for different pre-intervention profiles. In addition, this study further discusses the performances of social support features of persuasion technology in the WeRun. The data-driven method used in this study is beneficial to reducing self-selection bias and evaluate the intervention effect. The decision tree does not require decision-makers to have much expertise or to make parameter hypotheses while evaluating the intervention effect, and the results are more direct and intelligible. The results show that the decision tree can detect the heterogeneous intervention effect. In some cases, there is not a perfectly positive correlation between the degree of perceived social support and the number of average daily steps, and the relationship with friends has a great impact on the user's perceived social support. In addition, it also reveals the relationship between social comparison and perceived social support, and their interaction on exercise behavior. Finally, this study provides practical suggestions for the design and operation of e-health social network platform. The platforms are supposed to take corresponding persuasive strategies according to the various characteristics of users, so as to improve the continuous attention and participation of users.

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PurposeThis study aims to analyze the relevance of the city spatial structure for smart city innovation from the perspective of agglomeration externalities, and discusses whether there is heterogeneity in innovation across different geographical areas and population scales of cities.Design/methodology/approachThe authors construct the centralization and concentration indexes to conceptualize the city spatial structure of 286 cities (prefecture-level) in China based on the LandScan Global Population Dataset from 2001 to 2016. A fixed-effects panel data model is employed to analyze the relationship between the spatial structure and the innovation ability of smart cities; the results were validated through robustness tests and heterogeneity analyses.FindingsThe study found that the more concentrated and more evenly the distribution of urban population, namely the more city spatial structure tends to be weak-monocentricity, the higher the level of innovation in smart cities. The relevance of the weak-monocentricity structure and smart city innovation varies significantly depending on their geographical location and the size of the city. This result is more applicable to cities in the eastern and central regions, as well as to cities with smaller populations.Originality/valueThe adjustment and optimization of the city spatial structure is important for enhancing smart city construction. Unlike previous studies, which mostly use a single dimension of “the proportion of population in sub-centres to the population of all central areas” to measure city spatial structure, the authors employed the spatial centralization and spatial concentration. It is hoped that this study can guide smart city construction from the perspective of the development model of city spatial structure.