From Screens to Sprints: The Role of Artificial Intelligence in Mitigating Sedentary Behaviour and Promoting Physical Activity among Late Adolescents (Aged 16–21)

Abstract

Physical activity significantly reduces and digital sedentary behavior rapidly increases as one move through the transition from late adolescent to early adulthood (ages 16–21). While technology is often considered as the primary cause of physical inactivity, new developments in artificial intelligence (AI) bring about a fundamental shift by encouraging movement using the very platforms that teenagers use on a regular basis. The effectiveness of AI-driven interventions, such as adaptive gamification, predictive wearable analytics, and generative AI (GAI) health coaching, is examined in this research. The study investigates how AI personalizes health goals to improve exercise self-efficacy and sustained engagement through a systematic analysis of current health-tech applications. Studies show that by providing \"Just-In-Time Adaptive Interventions\" (JITAIs), AI-integrated systems significantly outperform static workouts. The study indicates that while AI is a powerful tool for changing behavior, its effectiveness depends on protecting teenage autonomy and health information literacy. It also discusses key challenges such as algorithmic bias and data privacy.

Introduction

This text discusses how AI-based fitness systems can help address the growing problem of physical inactivity among adolescents and young adults aged 16–21.

It begins by highlighting that most teenagers do not meet recommended daily physical activity levels, largely due to increased screen time and sedentary lifestyles. Ironically, the same digital devices that contribute to inactivity are now being used to promote healthier behavior through AI-driven interventions.

The paper explains how modern AI technologies improve fitness engagement through:

• Conversational AI coaches that provide personalized, non-judgmental guidance.
• Predictive analytics that anticipate periods of inactivity and send timely reminders.
• Adaptive gamification systems that adjust workout difficulty and provide rewards to maintain motivation.

The study is motivated by the need to understand whether these AI systems actually improve exercise habits, reduce sedentary time, and how they impact user behavior, especially in relation to privacy concerns.

The literature review shows that AI systems aligned with Self-Determination Theory are more effective because they support autonomy, competence, and motivation. Research also shows that AI chatbots reduce “gym anxiety,” predictive models can successfully interrupt long sitting periods, and gamification increases engagement through social competition and rewards.

The methodology uses a mixed-method approach involving surveys, interviews, and app usage data from around 200 participants aged 16–21.

Key findings include:

• AI-driven fitness apps significantly increase physical activity compared to traditional tracking apps.
• Users of AI systems show higher engagement, more frequent movement breaks, and better retention rates.
• Most users feel more supported by AI systems, but a significant portion experience notification fatigue.

The discussion highlights that AI works best when it supports user autonomy rather than controlling behavior. Gamification and social incentives are highly effective in reducing inactivity. However, the system raises ethical concerns about privacy, since effective personalization requires access to sensitive biometric and behavioral data.

Conclusion

A. Summary of Findings According to the research\'s results, artificial intelligence is potentially a breakthrough in the battle against the sedentary lifestyle that the majority of individuals in the 16–21 age range live. AI addresses the particular psychological and lifestyle challenges of late adolescence by shifting from reactive tracking to proactive, adaptive intervention. Predictive analytics and generative AI coaching have been found to have a measurable impact on increasing regular physical activity and reducing prolonged sitting. B. Recommendations for Upcoming Research Even though the first results are promising, additional research should focus on: • Long-term Adherence: Exploring whether AI is capable of keeping motivation for years compared to just a few months. • The Digital Divide: Making AI health tools affordable for teenagers from lower socioeconomic groups who may not be able to buy expensive wearables. • Algorithmic Transparency: Developing \"Explainable AI\" (XAI) in order to help users understand the reasoning behind an AI\'s suggestion for a specific medical intervention. C. Concluding Remark Health interventions must meet the 16–21 age group where they live—on their gadgets—as they continue to adopt a \"digital-first\" lifestyle. AI gives the cognitive framework necessary to make physical effort consistent, personalized, and engaging in a society that is growing increasingly sedentary, but it does not replace the need for physical effort.

Copyright

Copyright © 2026 Shubham N Shinde, Amruta Tribhuvan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.