personalization; senior citizen; health; fitness

Ahmad, N. A., Ludin, A. F. M., Shahar, S., Noah, S. A. M., & Tohit, N. M. (2020). Willingness, perceived barriers and motivators in adopting mobile applications for health-related interventions among older adults: a scoping review protocol. BMJ open, 10(3), e033870. https://doi.org/10.1136/bmjopen-2019-033870 Alturki, R., & Gay, V. (2019). Augmented and virtual reality in mobile fitness applications: a survey. Applications of intelligent technologies in healthcare, 67-75. https://doi.org/10.1007/978-3-030-19182-3_8 Andronie, M., Lăzăroiu, G., Ștefănescu, R., Ionescu, L., & Cocoșatu, M. (2021). Neuromanagement decision-making and cognitive algorithmic processes in the technological adoption of mobile commerce apps. Oeconomia Copernicana, 12(4), 1033-1062. https://doi.org/10.24136/oc.2021.033 Anthony Berauk, V. L., Murugiah, M. K., Soh, Y. C., Chuan Sheng, Y., Wong, T. W., & Ming, L. C. (2018). Mobile health applications for caring of older people: review and comparison. Therapeutic innovation & regulatory science, 52(3), 374-382. https://doi.org/10.1177/2168479017714854 Banshal, S. K., Verma, M. K., & Yuvaraj, M. (2022). Quantifying global digital journalism research: a bibliometric landscape. Library Hi Tech, 40(5), 1337-1358. https://doi.org/10.1108/LHT-10-2021-0415 Benlahcene, A., & Ramdani, A. (2020). The process of qualitative interview: Practical insights for novice researchers. European Proceedings of Social and Behavioural Sciences. https://doi.org/10.15405/epsbs.2020.11.02.45 Cho, H., Chi, C., & Chiu, W. (2020). Understanding sustained usage of health and fitness apps: Incorporating the technology acceptance model with the investment model. Technology in Society, 63, 101429. https://doi.org/10.1016/j.techsoc.2020.101429 Chopra, K. (2019). Indian shopper motivation to use artificial intelligence: Generating Vroom’s expectancy theory of motivation using grounded theory approach. International Journal of Retail & Distribution Management, 47(3), 331-347. https://doi.org/10.1108/IJRDM-03-2018-0056. Chin, S. O., Keum, C., Woo, J. et al, (2016). Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity. Scientific Reports, 6, 34563. https://doi.org/10.1038/srep34563. Chung, A. E., Griffin, A. C., Selezneva, D., & Gotz, D. (2018). Health and fitness apps for hands-free voice-activated assistants: content analysis. JMIR mHealth and uHealth, 6(9), e9705. https://doi.org/10.2196/mhealth.9705 Davidavičienė, V., Raudeliūnienė, J., & Viršilaitė, R. (2021). Evaluation of user experience in augmented reality mobile applications. Journal of business economics and management, 22(2), 467-481. https://doi.org/10.3846/jbem.2021.14112 de Moraes Lopes, M. H. B., Ferreira, D. D., Ferreira, A. C. B. H., da Silva, G. R., Caetano, A. S., & Braz, V. N. (2020). Use of artificial intelligence in precision nutrition and fitness. In Artificial Intelligence in Precision Health (pp. 465-496). Academic Press. https://doi.org/10.1016/B978-0-12-817133-2.00027-5 Depper, A., & Howe, P. D. (2017). Are we fit yet? English adolescent girls’ experiences of health and fitness apps. Health Sociology Review, 26(1), 98-112. https://doi.org/10.1080/14461242.2016.1196599 Dixit, M. S., Shad, M., Tyagi, A., Qadir, A., & Baloni, M. D. (2021). Adaptive & Personalized Fitness App. Engineering, Science, 2021. https://doi.org/10.1016/j.esj.2021.09.014 Dorgo, S., King, G. A., & Brickey, G. D. (2009). The application of peer mentoring to improve fitness in older adults. Journal of Aging and Physical Activity, 17(3), 344-361. https://doi.org/10.1123/japa.17.3.344 Elguera Paez, L., & Zapata Del Río, C. (2019). Elderly users and their main challenges usability with mobile applications: a systematic review. In Design, User Experience, and Usability. Design Philosophy and Theory: 8th International Conference, DUXU 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part I 21 (pp. 423-438). Springer International Publishing. https://doi.org/10.1007/978-3-030-23570-1_32 Emrouznejad, A., Abbasi, S., & Sıcakyüz, Ç. (2023). Supply chain risk management: A content analysis-based review of existing and emerging topics. Supply Chain Analytics, 3, 100031. https://doi.org/10.1016/j.sca.2022.100031 Fieraru, M., Zanfir, M., Pirlea, S. C., Olaru, V., & Sminchisescu, C. (2021). Aifit: Automatic 3D human-interpretable feedback models for fitness training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9919-9928). https://doi.org/10.1109/CVPR46437.2021.00979 Flores, A., Hall, B., Carter, L., Lanum, M., Narahari, R., & Goodman, G. (2021, November). Verum fitness: An ai powered mobile fitness safety and improvement application. In 2021 IEEE 33rd international Conference on tools with Artificial Intelligence (ICTAI) (pp. 980-984). IEEE. https://doi.org/10.1109/ICTAI52525.2021.00156 Ghanvatkar, S., Kankanhalli, A., & Rajan, V. (2019). User models for personalized physical activity interventions: scoping review. JMIR mHealth and uHealth, 7(1), e11098. https://doi.org/10.2196/11098 Grundy, Q. (2022). A review of the quality and impact of mobile health apps. Annual review of public health, 43, 117-134. https://doi.org/10.1146/annurev-publhealth-052020-121216 Jin, D., Halvari, H., Maehle, N., & Olafsen, A. H. (2022). Self-tracking behaviour in physical activity: A systematic review of drivers and outcomes of fitness tracking. Behaviour & Information Technology, 41(2), 242-261. https://doi.org/10.1080/0144929X.2020.1823160 Kari, T., Sell, A., Makkonen, M., Wallin, S., Walden, P., Carlsson, C., & Carlsson, J. (2020). Implementing a digital wellness application into use–challenges and solutions among aged people. In Human Aspects of IT for the Aged Population. Healthy and Active Aging: 6th International Conference, ITAP 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II 22 (pp. 310-328). Springer International Publishing. https://doi.org/10.1007/978-3-030-50232-2_23 Khaghani-Far, I., Nikitina, S., Baez, M., Taran, E. A., & Casati, F. (2016). Fitness applications for home-based training. IEEE Pervasive Computing, 15(4), 56-65. https://doi.org/10.1109/MPRV.2016.72 Kim, M. (2021). Conceptualization of e-servicescapes in the fitness applications and wearable devices context: Multi-dimensions, consumer satisfaction, and behavioral intention. Journal of Retailing and Consumer Services, 61, 102562. https://doi.org/10.1016/j.jretconser.2021.102562 Lee, J. C., & Lin, R. (2023). The continuous usage of artificial intelligence (AI)-powered mobile fitness applications: the goal-setting theory perspective. Industrial Management & Data Systems, 123(6), 1840-1860. https://doi.org/10.1108/IMDS-10-2022-0594 Lee, J. C., Gao, Z., & Xiong, L. (2024). Impact of artificial intelligence-enabled service quality on user consumption value and continuous intention to use mobile fitness applications: Evidence from China. Information Development. https://doi.org/10.1177/02666669241269666 Licorish, S. A., Owen, H. E., Savarimuthu, B. T. R., & Patel, P. (2022). Investigating Expectation Violations in Mobile Apps. arXiv preprint arXiv:2201.02269. https://doi.org/10.48550/arXiv.2201.02269 Martschukat, J. (2021). The age of fitness: How the body came to symbolize success and achievement. John Wiley & Sons. https://doi.org/10.1002/9781119026912 Meng, X. (2021, June). Fitness app Application Research Based on big data and algorithm. In Journal of Physics: Conference Series (Vol. 1952, No. 3, p. 032041). IOP Publishing. https://doi.org/10.1088/1742-6596/1952/3/032041 Mittal, S., & Patel, S. (2024). Face Age Estimation Using Siamese and Age Regression Network. International Journal of Computing and Digital Systems, 15(1), 1-14. https://doi.org/10.12785/ijcds/150106 Nyrup, R., Chu, C. H., & Falco, E. (2023). Digital ageism, algorithmic bias, and feminist critical theory. In Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines (pp. 309-320). https://doi.org/10.4324/9781003284747-25 Priya, B. H., Vamsi, B., Reddy, A. A., Radhika, M., Hariharan, S., & Kekreja, V. (2024, June). Speech enabled personal workout assistant recommendation system. In 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET) (pp. 1-6). IEEE. https://doi.org/10.1109/ICICET57646.2024.00001 Qian, K., Zhang, Z., Yamamoto, Y., & Schuller, B. W. (2021). Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring. IEEE Signal Processing Magazine, 38(4), 78-88. https://doi.org/10.1109/MSP.2021.3072810 Sathya, A., Vignesh, A., Akash, M., & Gokulakrishnan, S. (2024, April). Fitness Guide: A Holistic Approach for Personalized Health and Wellness Recommendation System. In 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) (pp. 01-06). IEEE. https://doi.org/10.1109/ADICS.2024.00001 Shandilya, E., & Fan, M. (2022, October). Understanding older adults’ perceptions and challenges in using AI-enabled everyday technologies. In Proceedings of the Tenth International Symposium of Chinese CHI (pp. 105-116). https://doi.org/10.1145/3565698.3565774 Serafini, F., & Reid, S. F. (2023). Multimodal content analysis: expanding analytical approaches to content analysis. Visual Communication, 22(4), 623-649. https://doi.org/10.1177/14703572231170811 Siebert, A., Gopaldas, A., Lindridge, A., & Simões, C. (2020). Customer experience journeys: Loyalty loops versus involvement spirals. Journal of Marketing, 84(4), 45-66. https://doi.org/10.1177/0022242920911092 Stocchi, L., Michaelidou, N., Pourazad, N., & Micevski, M. (2018). The rules of engagement: How to motivate consumers to engage with branded mobile apps. Journal of Marketing Management, 34(13-14), 1196-1226. https://doi.org/10.1080/0267257X.2018.1533912 Stone, M. (2024). Essential Balance: Energy Cleaning. Ahzuria Publishing. (DOI not available) Wang, S., Sporrel, K., van Hoof, H., Simons, M., de Boer, R. D., Ettema, D., & Kröse, B. (2021). Reinforcement learning to send reminders at right moments in smartphone exercise application: A feasibility study. International Journal of Environmental Research and Public Health, 18(11), 6059. https://doi.org/10.3390/ijerph18116059 Weiss, T., & Strahringer, S. (2021). Development and Demonstrational Instantiation of a Method for the Structured Content Analysis of Smartphone Apps. Complex Systems Informatics & Modeling Quarterly, (28). https://doi.org/10.7250/csimq.2021-28.03 Whelan, E., & Clohessy, T. (2021). How the social dimension of fitness apps can enhance and undermine wellbeing: A dual model of passion perspective. Information Technology & People, 34(1), 68-92. https://doi.org/10.1108/ITP-08-2019-0408 Yadav, R., Giri, A., & Chatterjee, S. (2022). Understanding the users’ motivation and barriers in adopting healthcare apps: A mixed-method approach using behavioral reasoning theory. Technological Forecasting and Social Change, 183, 121932. https://doi.org/10.1016/j.techfore.2022.121932 Yang, Y., & Koenigstorfer, J. (2021). Determinants of fitness app usage and moderating impacts of education-, motivation-, and gamification-related app features on physical activity intentions: cross-sectional survey study. Journal of medical Internet research, 23(7), e26063. https://doi.org/10.2196/26063 Zhang, B., Ying, L., Khan, M. A., Ali, M., Barykin, S., & Jahanzeb, A. (2023). Sustainable digital marketing: Factors of adoption of m-technologies by older adults in the Chinese market. Sustainability, 15(3), 1972. https://doi.org/10.3390/su15031972 Zhu, J., Dallal, D. H., Gray, R. C., Villareale, J., Ontañón, S., Forman, E. M., & Arigo, D. (2021). Personalization paradox in behavior change apps: lessons from a social comparison-based personalized app for physical activity. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1-21. https://doi.org/10.1145/3449128 Ziden, A. A., & Joo, O. C. (2020). Exploring digital onboarding for organizations: A concept paper. International Journal of Innovation, Creativity and Change, 13(9), 734-750. https://www.ijicc.net/images/Vol_13/Iss_9/13957_Ziden_2020_E_R.pdf