The Evolution and Adaptation of Personal Physical Fitness

In this paper we explore possible negative drawbacks in the use of wearable sensors, i.e., wearable devices used to detect different kinds of activity, e.g., from step and calories counting to heart rate and sleep monitoring. These technologies, which in the latter years witnessed a rapid development in terms of accuracy and diffusion, are now available on different platforms at reasonable prices and can lead to an healthier behavior in people using them. Nevertheless, we will try to investigate possibly harming behaviors related to these devices. We will provide different scenarios in which wearable sensors, in connection with social media, data mining, or other technologies, could prove harmful for their users.

Eleanor Afful, 2025

The paper discusses the socioeconomic benefits of AI-driven wearable accessories adoption in Ghana's health sector, focusing on gym center members, and highlights the benefits of wearables, including user-friendliness, privacy, data security, and long battery life. The study emphasizes the need for awareness, education, and voluntary use of wearables to improve remote health monitoring processes and provide evidence-based medical services. Also, the socioeconomic impact of wearable technology with smartphones integrated with wearable accessories and embedded sensors such as the Internet of Things (IoT) is crucial to providing predictive analysis suggesting future research should address resource constraints and remote patient monitoring systems. Content related to thematic perspective aid to identify critical factors considering, integrated research framework with capability theory and aspects of constructs from the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), Unified Theory of Acceptance and Use of Technology (UTAUT). A case study aid to explore easy-to-use of AIdriven wearable accessories, which prepares citizens for technology-driven healthcare systems to provide affordable healthcare solutions with the innovativeness of wearable accessories on wearers. .

International Journal of Human Movement and Sports Sciences, 2025

Artificial intelligence (AI) has proved its potential in various educational settings. However, its application in exercise-based learning remains unexplored. It is vital as some countries (e.g., the Philippines) have a high prevalence of physical inactivity, and most youth do not meet the recommended levels for maintaining health. Thus, this study investigates how incorporating AI-driven applications into tertiary physical education classes affects students' physical fitness performance. A non-randomized two-group design was utilized to compare pre- and post-test scores. Standardized tests measured cardiovascular endurance (CVE), upper body (UBS), core (CS), and lower body strength (LBS). An 8-week intervention with AI-driven exercise was implemented. Post-test scores showed that the AI-enhanced group (AG) performed significantly better than the traditional group (TG), with CVE at p<0.01 and UBS, CS, and LBS at ≤0.001. The TG showed improvement only in CVE and LBS p≤0.01. Conversely, the AG showed significant improvements in CVE, UBS, and LBS (p < 0.001) and CS (p < 0.01). These mean that using AI-driven applications during exercise has a favourable effect on students' physical fitness. Further research is recommended to encompass a variety of populations and educational settings while addressing AI literacy and ethical concerns to maximize its potential in promoting lifelong physical activity participation for overall fitness improvement.

2020

In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises and find a strong link of exercise to respondent socioeconomic status, as well as the value of loyalty.

IEEE Access

Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete's behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it suggests modifications in the athlete's behavior. The athlete's team is integrated into SmartFit, a software framework for supporting trainers and coaches in monitoring and manage athletes' fitness activity and results. Through IoT sensors embedded in wearable devices and applications for manual logging (e.g. mood, food income), SmartFit continuously captures measurements, initially treated as the dynamic data describing the current physical twins' status. Dynamic data allows adapting each DT's status and triggering the DT's predictions and suggestions. The analyzed measurements are stored as the historical data, further processed by the DT to update (increase) its knowledge and ability to provide reliable predictions. Results show that, thanks to the team of DTs, SmartFit computes trustable predictions of the physical twins' conditions and produces understandable suggestions which can be used by trainers to trigger optimization actions in the athletes' behavior. Though applied in the sport context, SmartFit can be easily adapted to other monitoring tasks. INDEX TERMS Counterfactual explanations, digital twins, Internet of Things, machine learning, smart health, sociotechnical design, wearables.

Simulation & Gaming, 2020

Background Exergames—which combine physical activity with elements of virtual interaction (e.g., “cyber-cycling”)—come in a wide range of styles and with an array of structural and environmental characteristics that are likely to influence a user’s experience, including their overall enjoyment. Aim The aim of the current study was thus to compare adolescents’ experience of flow and enjoyment across two cyber-cycling sessions that featured two unique styles of cycling exergames. Method Eighty-eight participants aged 11-13 participated in two cyber-cycling sessions spaced one week apart. Participants engaged in two unique conditions, one virtual game featuring the collection of coins to earn points and one a virtual simulation of an outdoor trail ride. The order of condition delivery was randomly selected. Results Participants reported experiencing greater flow and enjoyment when the cyber-cycling session was centered on a virtual gaming experience. Conclusion Adolescents find greater...

ACM Transactions on Interactive Intelligent Systems, 2020

Our research aims to develop interactive, social agents that can coach people to learn new tasks, skills, and habits. In this article, we focus on coaching sedentary, overweight individuals (i.e., “trainees”) to exercise regularly. We employ adaptive goal setting in which the intelligent health coach generates, tracks, and revises personalized exercise goals for a trainee. The goals become incrementally more difficult as the trainee progresses through the training program. Our approach is model-based—the coach maintains a parameterized model of the trainee’s aerobic capability that drives its expectation of the trainee’s performance. The model is continually revised based on trainee-coach interactions. The coach is embodied in a smartphone application, N utri W alking , which serves as a medium for coach-trainee interaction. We adopt a task-centric evaluation approach for studying the utility of the proposed algorithm in promoting regular aerobic exercise. We show that our approach ...

2022

Despite the indisputable personal and societal benefits of regular physical activity, a large portion of the population does not follow the recommended guidelines, harming their health and wellness. The World Health Organization has called upon governments, practitioners, and researchers to accelerate action to address the global prevalence of physical inactivity. To this end, an emerging wave of research in ubiquitous computing has been exploring the potential of interactive self-tracking technology in encouraging positive health behavior change. Numerous findings indicate the benefits of personalization and inclusive design regarding increasing the motivational appeal and overall effectiveness of behavior change systems, with the ultimate goal of empowering and facilitating people to achieve their goals. However, most interventions still adopt a "one-size-fits-all" approach to their design, assuming equal effectiveness for all system features in spite of individual and collective user differences. To this end, we analyze a corpus of 12 years of research in self-tracking technology for health behavior change, focusing on physical activity, to identify those design elements that have proven most effective in inciting desirable behavior across diverse population segments. We then provide actionable recommendations for designing and evaluating behavior change self-tracking technology based on age, gender, occupation, fitness, and health condition. Finally, we engage in a critical commentary on the diversity of the domain and discuss ethical concerns surrounding tailored interventions and directions for moving forward. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; HCI design and evaluation methods; HCI theory, concepts and models; • Applied computing → Consumer health.

Frontiers in Public Health, 2022

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization - UMAP '19, 2019

Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification.