Anticipating the Impact of Artificial Intelligence on Diet and Exercise Applications: A Foresight Study Applying the Futures Wheel (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Artificial intelligence (AI) has entered the wellness space through applications and wearables. These technologies can collect real-time data, infer lifestyle patterns and dynamically suggest nutrition and exercise recommendations. Generative AI personalizes diet and activity information, encouraging behavior change. </sec> <sec> <title>OBJECTIVE</title> The objective of this study is to systematically explore the potential medium- to long-term consequences of AI integration in diet and exercise applications from an end-user perspective. We applied a foresight methodology to systematically explore consequences associated with AI-driven tracking, personalization, and coaching in consumer wellness technologies. </sec> <sec> <title>METHODS</title> We use a futures methodology—the Futures Wheel—to anticipate the consequences of AI use in diet and exercise applications. We defined the central trend as the growing adoption of AI in diet and exercise tracking, coaching, and personalization. Brainstorming sessions, literature review, and expert insight were used to identify and organize first- and second-order effects. </sec> <sec> <title>RESULTS</title> We identified seven first-order consequences: (1) Personalization of Nutrition and Fitness Plans, (2) 24/7 Health Coaching, (3) Integration with Smart Technology, (4) Increased Privacy and Surveillance Concerns, (5) Data-Driven Risk Profiling and Moral Hazard, (6) Incorporation into Organizational Processes, and (7) Acceleration of Health Inequalities Driven by the Digital Divide. Second-order consequences included potential improvements in health outcomes and health literacy, as well as risks of privacy erosion, algorithmic bias, behavior-linked underwriting models, de-skilling of health and fitness professionals, and shifts in food and exercise culture toward more individualized and potentially isolating practices. Cross-cutting patterns highlighted recurring trade-offs between personalization and surveillance, scalability and user agency, and optimization and equity. </sec> <sec> <title>CONCLUSIONS</title> Wellness practice will expand along with AI’s ability to personalize recommendations, automate behaviors and engage users. AI wellness popularization is promising for chronic disease prevention and health optimization. The Futures Wheel reveals that the depth of adaptation will be determined by the implementation of changes at the levels of technology, user behavior, infrastructure, and legal/ethical framework. The analysis reveals that Futures Wheel is an appropriate foresight method to investigate user-facing technological changes as these domains present a large number of scenarios. </sec>

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