Exploring Smartphone-Based Artificial Intelligence Systems for Physical Activity in Urban Areas: A Scoping Review with a Focus on Psychosocial Theory, Inclusivity, Ethics, and Sustainability

Abstract

Over half of the global population resides in urban areas and a vast majority of adults worldwide own smartphones, which increasingly become pervasive sensing and intervention platforms, positioning human–computer interaction at the center of everyday behavior change (Harari & Gosling, 2023; Pawar, 2025; Ritchie et al., 2025). Building on this opportunity (An et al., 2023; Gao et al., 2024; Wang et al., 2023), we propose a scoping review that maps how digital health via smartphone (i.e., mobile health) uses artificial intelligence (AI) systems to assess and promote physical activity (PA) in urban areas (see Figure 1). The review will pay explicit attention to psychosocial theory integration, inclusivity and ethical considerations as well as sustainability aspects of AI systems. We will synthesize literature that spans mobile sensing, machine learning assessment, and AI-enabled interventions, while emphasizing design considerations for responsible, human-centered real-world deployment. The aims are to (1) chart the landscape of AI-enabled systems on smartphones as mobile health assessment (e.g., passive sensing, computer vision, natural language processing) and intervention (e.g., just-in-time adaptive interventions, on-device/in-cloud causal reasoning); (2) identify how psychosocial theory (e.g., Integrated Behavior-Change Model, Social-Cognitive Theory, Behavior Change Techniques) is operationalized in algorithms, interfaces, and user journeys; (3) reflect on surface equity, ethical considerations, accessibility practices (e.g., language, ability, context constraints), and existing gaps; and (4) articulate sustainability levers (e.g., energy-aware modelling, on-device vs. in-cloud computing). Following the PRISMA-ScR framework, we will implement a transparent, reproducible protocol with the following eligibility choices (Peters et al., 2022; Tricco et al., 2018): (a) Population: adults (18+), Concept: smartphone-based AI systems for PA - assessment and intervention - plus system development processes - psychosocial theory, inclusivity, ethics, and sustainability -, Context: urban areas globally; (b) Source databases spanning health and technology research (e.g., PubMed, PsycINFO, SportDiscus, Web of Science, Scopus, IEEE Xplore, ACM Digital Library) plus targeted citation searching on seed references (Hirt et al., 2024); (c) Timeframe includes studies from the first AI deployed on smartphones onward (i.e., Siri in 2011; Bosch, 2018) in English and German; (d) Data charting extracts methodological variables (e.g., design, sample, context) and outcome domains aligned with our conceptual framework: Technology (i.e., AI-type; on-device vs. cloud; sensing modalities), PA outcomes (e.g., exercise, daily activity, sedentary behavior disruption, active transport), psychosocial theory factors (e.g., intention, habit), inclusivity and ethics (e.g., accessibility, subgroup tailoring), sustainability (e.g., energy/climate considerations, nature-linked activity design), and system idea/development process (e.g., assessment or intervention, research vs. business, co-creation). Analysis will be descriptive (e.g., frequencies, evidence maps) with a conceptual synthesis of AI system designs and disseminations as mobile health via smartphones in urban areas. This review thus covers mobile health in applied contexts (i.e., PA promotion and behavior maintenance through AI via smartphones as everyday technology) and provides a rigorous map of current mobile health research, populations which are targeted, and conditions under which mobile health is implemented. A forward-looking agenda for informed, inclusive, ethical, and sustainable human–computer interaction in mobile health research and practice for the AI era concludes the review.

References

An, R., Shen, J., Wang, J., & Yang, Y. (2023). A scoping review of methodologies for applying artificial intelligence to physical activity interventions. Journal of Sport and Health Science. https://doi.org/10.1016/j.jshs.2023.09.010

Bosch. (2018, January 30). Die Geschichte der Künstlichen Intelligenz: Von Turing bis Watson: Die Entwicklung der denkenden Systeme. Künstliche Intelligenz. https://www.bosch.com/de/stories/geschichte-der-kuenstlichen-intelligenz/

Gao, N., Yu, Z., Xu, Y., Yu, C., Wang, Y., Salim, F. D., & Shi, Y. (2024). Leveraging Large Language Models for Generating Mobile Sensing Strategies in Human Behavior Modeling. Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, 729–735. https://doi.org/10.1145/3675094.3678423

Harari, G. M., & Gosling, S. D. (2023). Understanding behaviours in context using mobile sensing. Nature Reviews Psychology, 2(12), 767–779. https://doi.org/10.1038/s44159-023-00235-3

Hirt, J., Nordhausen, T., Fuerst, T., Ewald, H., & Appenzeller-Herzog, C. (2024). Guidance on terminology, application, and reporting of citation searching: The TARCiS statement. BMJ, 385, e078384. https://doi.org/10.1136/bmj-2023-078384

Pawar, P. (2025, January 2). Smartphone Statistics By Country, Demographics, Usage and Time Spent. Coolest Gadgets. https://coolest-gadgets.com/smartphone-statistics/

Peters, M. D. J., Godfrey, C., McInerney, P., Khalil, H., Larsen, P., Marnie, C., Pollock, D., Tricco, A. C., & Munn, Z. (2022). Best practice guidance and reporting items for the development of scoping review protocols. JBI Evidence Synthesis, 20(4), 953–968. https://doi.org/10.11124/JBIES-21-00242

Ritchie, H., Samborska, V., & Roser, M. (2025, March). Urbanization—The world population is moving to cities. Why is urbanization happening and what are the consequences? [Our World in Data]. Urbanization. https://ourworldindata.org/urbanization

Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850

Wang, T., Du, Y., Gong, Y., Choo, K.-K. R., & Guo, Y. (2023). Applications of Federated Learning in Mobile Health: Scoping Review. Journal of Medical Internet Research, 25, e43006. https://doi.org/10.2196/43006