With the proliferation of artificial intelligence in the sports industry, AI-powered fitness apps that offer personalized fitness services are becoming increasingly prevalent. The personalized workout plan, a popular feature of AI-powered fitness apps, is being added to a growing number of fitness apps and embraced by a rapidly expanding user base. Personalized workout plans are tailored to exercisers’ fitness goals, demographics, body features, preferences, and skill levels. Although this feature is expected to enhance exerciser engagement and improve their satisfaction, scholarly works investigating the factors that drive and hinder exercisers from adopting it remain scarce. Based on the technology affordance theory, this research sheds light on the affordances of the feature that affect exercisers’ adoption. Method: To investigate the factors that drive or hinder exercisers from adopting the personalized workout plan feature, we first conducted 30 semi-structured interviews via telephone. The participants were recruited from the social media platforms, who have previously used the feature in Keep or Huawei Health, two popular AI-powered fitness apps in China. Then, we performed content analysis of the interview records. Specifically, two researchers manually coded the interview records using NVivo 14. After that, they discussed their results of the coding and determined the factors that drive or hinder exercisers from adopting it. Our analysis reveals three factors that drive exercisers to adopt the feature include (1) the ability to create scientific and systematic personalized workout plans, (2) the ability to combine the workout plan and workout tracking data, and (3) the provision of diversified incentives to motivate long-term adherence. Meanwhile, our analysis shows that (1) the limited assessment on the fitness profile of exercisers, (2) the inability to make dynamic adjustments during plan execution, and (3) the concerns on data security and privacy are the three factors that hinder exercisers from adopting the feature. The findings of this research provide fresh insights into the factors that drive or hinder exercisers from adopting the personalized workout plan feature. Theoretically, this research contributes to the literature on fitness apps by illuminating exerciser experience with the emerging feature. It also adds to the human-AI interaction literature by examining the interaction between exercisers and AI-powered fitness apps in the context of digital fitness. Practically, this research offers guidance to providers of AI-powered fitness apps on how to improve the personalized workout plan feature.
Jiao et al. (Thu,) studied this question.