Smart Devices for Health and Wellness Applied to Tele-Exercise: An Overview of New Trends and Technologies Such as IoT and AI
Abstract
:1. Introduction
2. Emerging Technologies Related to Tele-Exercise
2.1. Internet of Things (IoT)
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- Effectiveness: the article notes that many of the eHealth technologies reviewed have been shown to be effective in promoting physical activity and improving health-related outcomes;
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- Broad reach: eHealth solutions have the potential to reach a wide range of users in different geographic areas, overcoming geographic barriers;
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- Personalization: eHealth interventions can be tailored to individual needs and preferences, helping to improve adherence and motivation;
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- Real-time monitoring: eHealth technologies can provide immediate feedback and monitor progress over time, which can help users maintain motivation and adapt their physical activity programs.
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- Heterogeneity of interventions: the article points out that there is a considerable variety of eHealth interventions, which makes it difficult to determine what the key success factors are and how they can be applied more broadly;
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- Long-term adherence: many studies included in the systematic review have a limited duration, making it difficult to assess the long-term effectiveness of eHealth interventions and user adherence over time;
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- Digital inequalities: people with limited access to digital technologies or limited digital skills may not benefit from eHealth interventions to the same extent as more experienced users;
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- Privacy and data security issues: the collection and storage of users’ personal and sensitive data may raise concerns about privacy and data security, which may affect users’ trust in eHealth technologies.
2.2. Artificial Intelligence (AI)
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- Improved diagnosis and treatment of diseases;
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- Personalization of medicine and treatment for individual patients;
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- Reduced workload for health care providers and improved efficiency;
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- Potential for discovering new therapeutic approaches and new drugs;
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- Use of AI can help identify and prevent health problems before they become serious;
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- Improved communication and collaboration among different members of the healthcare team;
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- Accurate monitoring of physical activities and health parameters;
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- Providing personalized feedback and artificial intelligence-based advice to improve fitness and health;
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- Increased motivation and adherence to physical activity;
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- Prevention and management of lifestyle-related chronic diseases;
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- AI can help identify behavior patterns and provide early interventions to improve health and well-being;
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- The use of wearable devices and AI-based fitness systems can encourage a healthier and more active lifestyle;
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- Integration of AI into existing clinical practice and interoperability with healthcare systems;
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- Managing large volumes of data and ensuring data quality;
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- Ensuring fairness and reducing algorithmic bias in the treatment of patients;
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- Difficulties in ensuring the adoption and acceptance of AI by patients and health professionals;
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- Improving human-machine interaction to make systems more intuitive and user-friendly;
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- Personalize fitness systems to suit individual needs and preferences;
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- Maintain long-term user interest and engagement;
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- Scalability and adaptability of AI-based fitness systems to different populations and contexts.
2.3. Virtual Reality (VR) and Augmented Reality (AR)
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- Provide a safe and controlled training environment for learning and perfecting skills;
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- Enhance learning through repetition and focused practice;
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- Improve performance analysis and feedback to athletes;
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- Increase athlete engagement and motivation during training;
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- Virtual reality can help develop mental and cognitive skills, such as decision-making and situational awareness;
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- The use of virtual reality can facilitate collaboration and training among athletes and coaches from different geographical locations;
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- Addressing technical limitations, such as latency and graphics resolution;
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- Ensuring the validity and transferability of skills learned in virtual reality to the real world;
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- Reduce costs and increase the accessibility of virtual reality technology;
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- Adapting and customizing virtual reality systems for different sports and skill levels;
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- Managing potential side effects, such as motion sickness, while using virtual reality.
2.4. Blockchain and Data Security
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- Improve the data security of users;
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- Ensure privacy protection and comply with relevant regulations;
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- Making registration procedures easier and more secure for users.
2.5. Mobile Applications and Tele-Exercise Platforms
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- Improvement of exercise-related habits;
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- Reducing sedentary lifestyle and its negative effects;
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- Facilitating access to tele-exercise programs for different types of users;
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- Increase participants within a young age range;
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- Increase the customization of exercise programs;
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- Ensure optimal performance of the various proposed tele-exercise programs.
2.6. Wearable Technology
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- Simplicity and immediacy in device use;
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- Increase in user satisfaction levels;
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- Monitoring the physical condition of participants in tele-exercise sessions;
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- Improve appeal to older age groups.
2.7. Big Data and Predictive Analysis
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- Cross-utilization of available data to prevent the occurrence of health risks;
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- Data analysis for the implementation of individual tele-exercise programs;
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- Monitoring the behavior of individual user subjects;
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- Lack of currently developed predictive models;
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- Implementation of a specific analysis algorithm.
2.8. Social Fitness and Gamification
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- Use of rewards and gratification as stimuli to increase participation;
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- Finding a functional gamification methodology for all users;
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- Limit the possibility of course dropout/interruption as a result of defeats in the gamification activity.
2.9. Biotechnology and Advanced Health Monitoring
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- 360-degree, real-time analysis of the subject’s health condition;
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- Ability to adapt exercises to the practitioner’s state of health, in a manner directly dependent on the practitioner’s physical condition;
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- Correct setting of instruments if not performed by an experienced hand;
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- Facilitate their use by the user who is “unfamiliar” with technology.
2.10. Robotics and Automation
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- Implementation of robot instructors customized to the needs of the individual user;
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- Improve the quality of training and exercise activities;
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- Reduction of possible failure and malfunction of devices used in tele-exercise;
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- Decreasing manufacturing costs;
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- Avoid the possible reduction of jobs for “traditional” trainers;
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- Keeping users’ sensitive data safe.
3. Benefits of Tele-Exercise and New Emerging Technologies
3.1. Accurate and Personalized Performance Analysis and Health Monitoring
3.2. Real-Time Feedback and Suggestions to Improve Training and Wellness
3.3. Increased User Motivation and Engagement
3.4. Opportunities for Early Identification of Health Problems and Their Prevention
4. Challenges and Issues in Tele-Exercise and Emerging Technologies
4.1. Privacy and Data Security Concerns
4.2. Disparities in Technology Availability
4.3. Overcoming Cultural Barriers and Distrust of Emerging Technologies
5. Opportunities for Future Research
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- Development of more advanced and personalized AI algorithms to analyze data collected from IoT devices and provide user-specific feedback and recommendations;
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- Integration of smart devices with other emerging technologies, such as virtual and augmented reality, to create more immersive and immersive workout experiences;
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- Studies on the long-term impact of smart devices and tele-exercise use on users’ health and well-being, as well as the prevention and treatment of chronic diseases;
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- Investigation of barriers to the adoption of smart devices and tele-exercise services, with the goal of improving access and equity in the use of these technologies.
Investigation of Barriers to the Adoption of Smart Devices and Tele-Exercise Services
6. International Perspectives and Future Solutions
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- Sharing best practices and global standards. This can help ensure that these technologies are used effectively and safely and that the benefits are shared more equitably globally;
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- Development of partnerships and collaborations. These partnerships can foster knowledge exchange, access to financial and technological resources, and the creation of support networks for the implementation of tele-exercise projects in different regions of the world;
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- Promotion of innovations through research and development. International cooperation can stimulate research and development of new technologies, for example, by jointly funding research projects, establishing centers of excellence, and promoting training and exchange initiatives among researchers and practitioners in the field.
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- Development of more advanced and accurate devices and sensors. They can improve the quality and reliability of data collected by smart devices, enabling better personalization of tele-exercise experiences and more accurate monitoring of users’ health and well-being;
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- Developments in device and platform integration can enable users to easily access and use data collected by smart devices across different platforms and tele-exercise services. This can improve the user experience and ensure that health and wellness information is easily accessible and usable;
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- Trials on the effectiveness and impact on individual health and well-being are critical to understanding how these technologies can best be used to promote global health and well-being. Studies should examine the short- and long-term impact of the use of smart devices and tele-exercise services on users’ physical and mental health, as well as their habits and behaviors. In addition, research should investigate the effectiveness of these technologies in preventing and treating chronic diseases and lifestyle-related health conditions.
7. The Ideal Tele-Exercise App
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Stroud, N.; Minahan, C.; Sabapathy, S. The perceived benefits and barriers to exercise participation in persons with multiple sclerosis. Disabil. Rehabil. 2009, 31, 2216–2222. [Google Scholar] [CrossRef]
- Thorpe, O.; Johnston, K.; Kumar, S. Barriers and enablers to physical activity participation in patients with COPD. J. Cardiopulm. Rehabil. Prev. 2012, 32, 359–369. [Google Scholar] [CrossRef]
- Sechrist, K.R.; Walker, S.N.; Pender, N.J. Development and psychometric evaluation of the exercise benefits/barriers scale. Res. Nurs. Health 1987, 10, 357–365. [Google Scholar] [CrossRef]
- Stavrou, V.T.; Astara, K.; Ioannidis, P.; Vavougios, G.D.; Daniil, Z.; Gourgoulianis, K.I. Tele-Exercise in Non-Hospitalized versus Hospitalized Post-COVID-19 Patients. Sports 2022, 10, 179. [Google Scholar] [CrossRef]
- Costa, R.R.G.; Dorneles, J.R.; Veloso, J.H.; Gonçalves, C.W.; Neto, F.R. Synchronous and asynchronous tele-exercise during the coronavirus disease 2019 pandemic: Comparisons of implementation and training load in individuals with spinal cord injury. J. Telemed. Telecare 2023, 29, 308–317. [Google Scholar] [CrossRef]
- Ho, V.; Merchant, R.A. The Acceptability of Digital Technology and Tele-Exercise in the Age of COVID-19: Cross-sectional Study. JMIR Aging 2022, 5, e33165. [Google Scholar] [CrossRef]
- Browne, J.D.; Boland, D.M.; Baum, J.T.; Ikemiya, K.; Harris, Q.; Phillips, M.; Neufeld, E.V.; Gomez, D.; Goldman, P.; Dolezal, B.A. Lifestyle Modification Using a Wearable Biometric Ring and Guided Feedback Improve Sleep and Exercise Behaviors: A 12-Month Randomized, Placebo-Controlled Study. Front. Physiol. 2021, 12, 777874. [Google Scholar] [CrossRef]
- Sitges, C.; Terrasa, J.L.; García-Dopico, N.; Segur-Ferrer, J.; Velasco-Roldán, O.; Crespí-Palmer, J.; González-Roldán, A.M.; Montoya, P. An Educational and Exercise Mobile Phone-Based Intervention to Elicit Electrophysiological Changes and to Improve Psychological Functioning in Adults with Nonspecific Chronic Low Back Pain (BackFit App): Nonrandomized Clinical Trial. JMIR mHealth uHealth 2022, 10, e29171. [Google Scholar] [CrossRef]
- Wilke, J.; Mohr, L.; Yuki, G.; Bhundoo, A.K.; Jiménez-Pavón, D.; Laiño, F.; Murphy, N.; Novak, B.; Nuccio, S.; Ortega-Gómez, S.; et al. Train at home, but not alone: A randomised controlled multicentre trial assessing the effects of live-streamed tele-exercise during COVID-19-related lockdowns. Br. J. Sports Med. 2022, 56, 667–675. [Google Scholar] [CrossRef]
- Ozaslan, B.; Patek, S.D.; Breton, M.D. Impact of Daily Physical Activity as Measured by Commonly Available Wearables on Mealtime Glucose Control in Type 1 Diabetes. Diabetes Technol. Ther. 2020, 22, 742–748. [Google Scholar] [CrossRef]
- Gell, N.; Hoffman, E.; Patel, K. Technology Support Challenges and Recommendations for Adapting an Evidence-Based Exercise Program for Remote Delivery to Older Adults: Exploratory Mixed Methods Study. JMIR Aging 2021, 4, e27645. [Google Scholar] [CrossRef]
- Novatchkov, H.; Baca, A. Artificial intelligence in sports on the example of weight training. J. Sports Sci. Med. 2013, 12, 27–37. [Google Scholar]
- Baca, A.; Dabnichki, P.; Heller, M.; Kornfeind, P. Ubiquitous computing in sports: A review and analysis. J. Sports Sci. 2009, 27, 1335–1346. [Google Scholar] [CrossRef]
- Baca, A.; Kornfeind, P.; Preuschl, E.; Bichler, S.; Tampier, M.; Novatchkov, H. A server-based Mobile Coaching system. Sensors 2010, 10, 10640–10662. [Google Scholar] [CrossRef] [Green Version]
- Andrioti, A.; Papadopetraki, A.; Maridaki, M.; Philippou, A. The Effect of a Home-Based Tele-Exercise Training Program on the Quality of Life and Physical Performance in Breast Cancer Survivors. Sports 2023, 11, 102. [Google Scholar] [CrossRef]
- Yoo, I.; Kong, H.J.; Joo, H.; Choi, Y.; Kim, S.W.; Lee, K.E.; Hong, J. User Experience of Augmented Reality Glasses-based Tele-Exercise in Elderly Women. Healthc. Inform. Res. 2023, 29, 161–167. [Google Scholar] [CrossRef] [PubMed]
- Haley, J.A.; Rhind, D.J.A.; Maidment, D.W. Applying the behaviour change wheel to assess the theoretical underpinning of a novel smartphone application to increase physical activity in adults with spinal cord injuries. Mhealth 2023, 17, 9–10. [Google Scholar] [CrossRef]
- Najafi, P.; Hadizadeh, M.; Cheong, J.P.G.; Mohafez, H.; Abdullah, S.; Poursadeghfard, M. Effects of Tele-Pilates and Tele-Yoga on Biochemicals, Physical, and Psychological Parameters of Females with Multiple Sclerosis. J. Clin. Med. 2023, 12, 1585. [Google Scholar] [CrossRef]
- Divecha, A.A.; Bialek, A.; Kumar, D.S.; Garn, R.M.; Currie, L.E.J.; Campos, T.; Friel, K.M. Effects of a 12-week, seated, virtual, home-based tele-exercise programme compared with a prerecorded video-based exercise programme in people with chronic neurological impairments: Protocol for a randomised controlled trial. BMJ Open 2023, 13, e065032. [Google Scholar] [CrossRef]
- Kim, Y.; Mehta, T.; Tracy, T.; Young, H.J.; Pekmezi, D.W.; Rimmer, J.H.; Niranjan, S.J. A qualitative evaluation of a clinic versus home exercise rehabilitation program for adults with multiple sclerosis: The tele-exercise and multiple sclerosis (TEAMS) study. Disabil. Health J. 2022, 21, 101437. [Google Scholar] [CrossRef]
- Stonsaovapak, C.; Sangveraphunsiri, V.; Jitpugdee, W.; Piravej, K. Telerehabilitation in Older Thai Community-Dwelling Adults. Life 2022, 12, 2029. [Google Scholar] [CrossRef]
- Gell, N.M.; Dittus, K.; Caefer, J.; Martin, A.; Bae, M.; Patel, K.V. Remotely delivered exercise to older rural cancer survivors: A randomized controlled pilot trial. J. Cancer Surviv. 2022, 14, 1–10. [Google Scholar] [CrossRef]
- Li, S.; Li, Y.; Liang, Q.; Yang, W.J.; Zi, R.; Wu, X.; Du, C.; Jiang, Y. Effects of tele- exercise rehabilitation intervention on women at high risk of osteoporotic fractures: Study protocol for a randomised controlled trial. BMJ Open. 2022, 12, e064328. [Google Scholar] [CrossRef]
- Chiang, S.L.; Shen, C.L.; Lee, M.S.; Lin, C.H.; Lin, C.H. Effectiveness of a 12-week tele-exercise training program on cardiorespiratory fitness and heart rate recovery in patients with cardiometabolic multimorbidity. Worldviews Evid. Based Nurs. 2022, 4. [Google Scholar] [CrossRef]
- Ryan, A.S.; Serra, M.C.; Gray, V.L. Editorial: Exercise and aging with musculoskeletal conditions. Front. Rehabil. Sci. 2022, 3, 902241. [Google Scholar] [CrossRef]
- Amorese, A.J.; Ryan, A.S. Home-Based Tele-Exercise in Musculoskeletal Conditions and Chronic Disease: A Literature Review. Front. Rehabil. Sci. 2022, 3, 811465. [Google Scholar] [CrossRef]
- Tracy, T.F.; Young, H.J.; Lai, B.; Layton, B.; Stokes, D.; Fry, M.; Mehta, T.; Riser, E.S.; Rimmer, J. Supporting successful recruitment in a randomized control trial comparing clinic and home-based exercise among adults with multiple sclerosis. Res. Involv. Engagem. 2022, 8, 35. [Google Scholar] [CrossRef] [PubMed]
- Alpozgen, A.Z.; Kardes, K.; Acikbas, E.; Demirhan, F.; Sagir, K.; Avcil, E. The effectiveness of synchronous tele-exercise to maintain the physical fitness, quality of life, and mood of older people—A randomized and controlled study. Eur. Geriatr. Med. 2022, 13, 1177–1185. [Google Scholar] [CrossRef] [PubMed]
- Schneider, V.; Kale, D.; Herbec, A.; Beard, E.; Fisher, A.; Shahab, L. UK Adults’ Exercise Locations, Use of Digital Programs, and Associations with Physical Activity During the COVID-19 Pandemic: Longitudinal Analysis of Data from the Health Behaviours During the COVID-19 Pandemic Study. JMIR Form. Res. 2022, 6, e35021. [Google Scholar] [CrossRef]
- Tejera, C.; Guerrero, D.B. “Salud digital”: Tele-ejercicio en obesidad, ¿qué nos puede aportar? [“Digital health”: Tele-exercise in obesity, what can we expect?]. Nutr. Hosp. 2022, 39, 245–246. [Google Scholar] [PubMed]
- Stavrou, V.T.; Tourlakopoulos, K.N.; Daniil, Z.; Gourgoulianis, K.I. Respiratory Muscle Strength: New Technology for Easy Assessment. Cureus 2021, 13, e14803. [Google Scholar] [CrossRef]
- Zasadzka, E.; Trzmiel, T.; Pieczyńska, A.; Hojan, K. Modern Technologies in the Rehabilitation of Patients with Multiple Sclerosis and Their Potential Application in Times of COVID-19. Medicina 2021, 57, 549. [Google Scholar]
- Lotfi, A.; Langensiepen, C.; Yahaya, S.W. Socially Assistive Robotics: Robot Exercise Trainer for Older Adults. Technologies 2018, 6, 32. [Google Scholar] [CrossRef] [Green Version]
- Zignoli, A.; Fornasiero, A.; Rota, P.; Muollo, V.; Peyré-Tartaruga, L.A.; Low, D.A.; Fontana, F.Y.; Besson, D.; Pühringer, M.; Ring-Dimitriou, S.; et al. Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests. Eur. J. Sport. Sci. 2020, 22, 425–435. [Google Scholar] [CrossRef] [PubMed]
- Patel, K.V.; Hoffman, E.V.; Phelan, E.A.; Gell, N.M. Remotely Delivered Exercise to Rural Older Adults with Knee Osteoarthritis: A Pilot Study. ACROR 2022, 4, 735–744. [Google Scholar]
- Sun, C.; Chrysikou, E.; Savvopoulou, E.; Hernandez-Garcia, E.; Fatah gen. Schieck, A. Healthcare Built Environment and Telemedicine Practice for Social and Environmental Sustainability. Sustainability 2023, 15, 2697. [Google Scholar] [CrossRef]
- Vandoni, M.; Carnevale Pellino, V.; Gatti, A.; Lucini, D.; Mannarino, S.; Larizza, C.; Rossi, V.; Tranfaglia, V.; Pirazzi, A.; Biino, V.; et al. Effects of an Online Supervised Exercise Training in Children with Obesity during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2022, 19, 9421. [Google Scholar] [CrossRef] [PubMed]
- Jassil, F.C.; Richards, R.; Carnemolla, A.; Lewis, N.; Montagut-Pino, G.; Kingett, H.; Doyle, J.; Kirk, A.; Brown, A.; Chaiyasoot, K.; et al. Patients’ views and experiences of live supervised tele-exercise classes following bariatric surgery during the COVID-19 pandemic: The BARI-LIFESTYLE qualitative study. Clin. Obes. 2022, 12, e12499. [Google Scholar] [CrossRef]
- Calcaterra, V.; Verduci, E.; Vandoni, M.; Rossi, V.; Di Profio, E.; Pellino, V.C.; Tranfaglia, V.; Pascuzzi, M.C.; Borsani, B.; Bosetti, A.; et al. Telehealth: A Useful Tool for the Management of Nutrition and Exercise Programs in Pediatric Obesity in the COVID-19 Era. Nutrients 2021, 13, 3689. [Google Scholar] [CrossRef] [PubMed]
- Vandoni, M.; Codella, R.; Pippi, R.; Carnevale Pellino, V.; Lovecchio, N.; Marin, L.; Silvestri, D.; Gatti, A.; Magenes, V.C.; Regalbuto, C.; et al. Combatting Sedentary Behaviors by Delivering Remote Physical Exercise in Children and Adolescents with Obesity in the COVID-19 Era: A Narrative Review. Nutrients 2021, 13, 4459. [Google Scholar]
- Gülü, M.; Yagin, F.H.; Gocer, I.; Yapici, H.; Ayyildiz, E.; Clemente, F.M.; Ardigò, L.P.; Zadeh, A.K.; Prieto-González, P.; Nobari, H. Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction. Front. Psychol. 2023, 14, 1097145. [Google Scholar] [CrossRef]
- Zimatore, G.; Gallotta, M.C.; Innocenti, L.; Bonavolontà, V.; Ciasca, G.; De Spirito, M.; Guidetti, L.; Baldari, C. Recurrence Quantification Analysis of Heart Rate Variability during Continuous Incremental Exercise Test in Obese Subjects. Chaos 2020, 30, 033135. [Google Scholar] [CrossRef] [PubMed]
- Domínguez-Muñoz, A.; Carlos-Vivas, J.; Barrios-Fernandez, S.; Adsuar, J.C.; Morenas-Martín, J.; Garcia-Gordillo, M.A.; Domínguez-Muñoz, F.J. Pedagogical Proposal of Tele- Exercise Based on “Square Stepping Exercise” in Preschoolers: Study Protocol. Int. J. Environ. Res. Public Health 2021, 18, 8649. [Google Scholar] [CrossRef] [PubMed]
- Chen, J.J.; Cooper, D.M.; Haddad, F.; Sladkey, A.; Nussbaum, E.; Radom-Aizik, S. Tele-Exercise as a Promising Tool to Promote Exercise in Children with Cystic Fibrosis. Front. Public Health 2018, 6, 269. [Google Scholar] [CrossRef]
- Rodríguez, M.Á.; Crespo, I.; Valle, M.D.; Olmedillas, H. Home-Based Vigorous Tele-Exercise in People with Parkinson’s Disease: Feasibility Beyond Complexity. J. Parkinsons Dis. 2021, 11, 843–845. [Google Scholar] [CrossRef] [PubMed]
- Marasco, I.; Niro, G.; Demir, S.M.; Marzano, L.; Fachechi, L.; Rizzi, F.; Demarchi, D.; Motto Ros, P.; D’Orazio, A.; Grande, M.; et al. Wearable Heart Rate Monitoring Device Communicating in 5G ISM Band for IoHT. Bioengineering 2023, 10, 113. [Google Scholar] [CrossRef] [PubMed]
- Concheiro-Moscoso, P.; Groba, B.; Alvarez-Estevez, D.; Miranda-Duro, M.D.C.; Pousada, T.; Nieto-Riveiro, L.; Mejuto-Muiño, F.J.; Pereira, J. Quality of Sleep Data Validation from the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study. J. Med. Internet Res. 2023, 25, e42073. [Google Scholar] [CrossRef]
- Paulauskaite-Taraseviciene, A.; Siaulys, J.; Sutiene, K.; Petravicius, T.; Navickas, S.; Oliandra, M.; Rapalis, A.; Balciunas, J. Geriatric Care Management System Powered by the IoT and Computer Vision Techniques. Healthcare 2023, 11, 1152. [Google Scholar]
- Thorne, C.S.; Gatt, A.; DeRaffaele, C.; Bazena, A.; Formosa, C. Innovative single-sensor, in-shoe pressure and temperature monitoring device: A dynamic laboratory validation study. Gait Posture 2023, 100, 70–74. [Google Scholar] [CrossRef]
- Direito, A.; Carraça, E.; Rawstorn, J.; Whittaker, R.; Maddison, R. The effectiveness of eHealth interventions for physical activity promotion: A systematic review. J. Med. Internet Res. 2021, 23, e22380. [Google Scholar]
- He, J.; Baxter, S.L.; Xu, J.; Xu, J.; Zhou, X.; Zhang, K. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019, 25, 30–36. [Google Scholar] [CrossRef] [PubMed]
- Liu, H.; Liu, Y.; Wang, X.; Chen, S.; Yin, B. AI-based personal fitness system with wearable devices: A survey. J. Ambient. Intell. Humaniz. Comput. 2022, 13, 621–635. [Google Scholar]
- CAROL. Available online: https://carolbike.com/ (accessed on 7 June 2023).
- Jrny. Available online: https://www.jrny.com/ (accessed on 7 June 2023).
- TEMPO. Available online: https://tempo.fit/move (accessed on 7 June 2023).
- FITURE. Available online: https://www.fiture.com/us (accessed on 7 June 2023).
- YogìFì. Available online: https://yogifi.fit/?v=cd32106bcb6d (accessed on 7 June 2023).
- OXEFIT. Available online: https://www.oxefit.com/ (accessed on 7 June 2023).
- Vinolo Gil, M.J.; Gonzalez-Medina, G.; Lucena-Anton, D.; Perez-Cabezas, V.; Ruiz-Molinero, M.D.C.; Martín-Valero, R. Augmented Reality in Physical Therapy: Systematic Review and Meta-analysis. JMIR Serious Games 2021, 9, e30985. [Google Scholar] [CrossRef] [PubMed]
- Nekar, D.M.; Kang, H.Y.; Yu, J.H. Improvements of Physical Activity Performance and Motivation in Adult Men through Augmented Reality Approach: A Randomized Controlled Trial. J. Environ. Public Health 2022, 2022, 3050424. [Google Scholar] [CrossRef]
- Farrow, M.; Lutteroth, C.; Rouse, P.C. Virtual reality in sports coaching, skill acquisition and application to rugby. Comput. Hum. Behav. 2019, 99, 56–67. [Google Scholar]
- Kim, J.; Park, Y.J. The potential of virtual and augmented reality in exercise and sport psychology research. Curr. Opin. Psychol. 2021, 41, 43–47. [Google Scholar]
- Casino, F.; Dasaklis, T.K.; Patsakis, C. A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telemat. Inform. 2019, 36, 55–81. [Google Scholar]
- Silva, A.G.; Simões, P.; Queirós, A.P.; Rocha, N.; Rodrigues, M. Effectiveness of Mobile Applications Running on Smartphones to Promote Physical Activity: A Systematic Review with Meta-Analysis. Int. J. Environ. Res. Public Health 2020, 17, 2251. [Google Scholar] [CrossRef] [Green Version]
- He, Z.; Wu, H.; Yu, F.; Fu, J.; Sun, S.; Huang, T.; Wang, R.; Chen, D.; Zhao, G.; Quan, M. Effects of Smartphone-Based Interventions on Physical Activity in Children and Adolescents: Systematic Review and Meta-analysis. JMU 2021, 9, e22601. [Google Scholar]
- Vieira, W.O.; Ostolin, T.L.V.D.P.; Simões, M.D.S.M.P.; Proença, N.L.; Dourado, V.Z. Profile of adults users of smartphone applications for monitoring the level of physical activity and associated factors: A cross-sectional study. Front. Public Health 2022, 10, 966470. [Google Scholar]
- Kruse, C.S.; Krowski, N.; Rodriguez, B.; Tran, L.; Vela, J.; Brooks, M. Telehealth and patient satisfaction: A systematic review and narrative analysis. BMJ Open 2017, 7, e016242. [Google Scholar] [CrossRef] [PubMed]
- Bellomo, R.G.; Paolucci, T.; Saggino, A.; Pezzi, L.; Bramanti, A.; Cimino, V.; Tommasi, M.; Saggini, R. The WeReha Project for an Innovative Home-Based Exercise Training in Chronic Stroke Patients: A Clinical Study. J. Cent. Nerv. Syst. Dis. 2020, 13, 12. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Li, X. The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning. Front. Public Health 2022, 10, 917053. [Google Scholar] [CrossRef] [PubMed]
- Ristevski, B.; Chen, M. Big Data Analytics in Medicine and Healthcare. J. Integr. Bioinform. 2018, 15, 20170030. [Google Scholar] [CrossRef]
- Arora, C.; Razavian, M. Ethics of Gamification in Health and Fitness-Tracking. Int. J. Environ. Res. Public Health 2021, 18, 11052. [Google Scholar] [CrossRef]
- Esmaeilzadeh, P. The Influence of Gamification and Information Technology Identity on Postadoption Behaviors of Health and Fitness App Users: Empirical Study in the United States. JMIR Serious Games 2021, 9, e28282. [Google Scholar] [CrossRef]
- Olivas Martinez, G.; Orso, V.; Bettelli, A.; Gamberini, L. Exploiting Mobile Gamification to Foster Physical Activity: A Remotely-Managed Field Study. Sensors 2023, 23, 2598. [Google Scholar] [CrossRef]
- Cotton, V.; Patel, M.S. Gamification Use and Design in Popular Health and Fitness Mobile Applications. Am. J. Health Promot. 2019, 33, 448–451. [Google Scholar] [CrossRef]
- De Cola, M.C.; De Luca, R.; Bramanti, A.; Bertè, F.; Bramanti, P.; Calabrò, R.S. Tele-health services for the elderly: A novel southern Italy family needs-oriented model. J. Telemed. Telecare 2016, 22, 356–362. [Google Scholar] [CrossRef]
- De Luca, R.; Bramanti, A.; De Cola, M.C.; Trifiletti, A.; Tomasello, P.; Torrisi, M.; Reitano, S.; Leo, A.; Bramanti, P.; Calabrò, R.S. Tele-health-care in the elderly living in nursing home: The first Sicilian multimodal approach. Aging Clin. Exp. Res. 2016, 28, 753–759. [Google Scholar] [CrossRef] [PubMed]
- Mascret, N.; Temprado, J.J. Acceptance of a Mobile Telepresence Robot, before Use, to Remotely Supervise Older Adults’ Adapted Physical Activity. Int. J. Environ. Res. Public Health 2023, 20, 3012. [Google Scholar]
- Kim, K.T.; Choi, Y.; Cho, J.H.; Lee, S. Feasibility and Usability of a Robot-Assisted Complex Upper and Lower Limb Rehabilitation System in Patients with Stroke: A Pilot Study. Ann. Rehabil. Med. 2023, 47, 108–117. [Google Scholar] [CrossRef] [PubMed]
- Prakashan, D.; P R, R.; Gandhi, S. A Systematic Review on the Advanced Techniques of Wearable Point-of-Care Devices and Their Futuristic Applications. Diagnostics 2023, 13, 916. [Google Scholar] [CrossRef]
- Venkatachalam, P.; Ray, S. How do context-aware artificial intelligence algorithms used in fitness recommender systems? A literature review and research agenda. Int. J. Inf. Manag. 2022, 2, 100139. [Google Scholar]
- Mouatt, B.; Smith, A.E.; Mellow, M.L.; Parfitt, G.; Smith, R.T.; Stanton, T.R. The Use of Virtual Reality to Influence Motivation, Affect, Enjoyment, and Engagement During Exercise: A Scoping Review. Front. Virtual Real. 2020, 1, 564664. [Google Scholar] [CrossRef]
- Anikwe, C.V.; Nweke, H.F.; Ikegwu, A.C.; Egwuonwu, C.A.; Onu, F.U.; Alo, U.R.; The, Y.W. The Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect. Expert Syst. App. 2022, 202, 117362. [Google Scholar] [CrossRef]
- Zimatore, G.; Serantoni, C.; Gallotta, M.C.; Guidetti, L.; Maulucci, G.; De Spirito, M. Automatic Detection of Aerobic Threshold through Recurrence Quantification Analysis of Heart Rate Time Series. Int. J. Environ. Res. Public Health 2023, 20, 1998. [Google Scholar] [CrossRef]
- Zimatore, G.; Falcioni, L.; Gallotta, M.C.; Bonavolontà, V.; Campanella, M.; De Spirito, M.; Guidetti, L.; Baldari, C. Recurrence Quantification Analysis of Heart Rate Variability to Detect Both Ventilatory Thresholds. PLoS ONE 2021, 16, e0249504. [Google Scholar]
- Mourot, L.; Tordi, N.; Bouhaddi, M.; Teffaha, D.; Monpere, C.; Regnard, I. Heart rate variability to assess ventilatory thresholds: Reliable in cardiac disease? Eur. J. Prev. Cardiol. 2012, 19, 1272. [Google Scholar]
- Evenson, K.R.; Goto, M.M.; Furberg, R.D. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int. J. Behav. Nutr. Phys. Act. 2015, 12, 159. [Google Scholar]
- Barker, H. Global economic inequality and health. Med. Confl. Surviv. 2020, 36, 368–374. [Google Scholar] [CrossRef]
- Du, Y.; Zhou, Q.; Cheng, W.; Zhang, Z.; Hoelzer, S.; Liang, Y.; Xue, H.; Ma, X.; Sylvia, S.; Tian, J.; et al. Factors Influencing Adoption and Use of Telemedicine Services in Rural Areas of China: Mixed Methods Study. JMIR Public Health Surveill. 2022, 8, e40771. [Google Scholar] [CrossRef]
- Richardson, S.; Lawrence, K.; Schoenthaler, A.M.; Mann, D. A framework for digital health equity. NPJ Digit. Med. 2022, 5, 119. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Z.; Henley, T.; Schiaffino, M.; Wiese, J.; Sachs, D.; Migliaccio, J.; Huh-Yoo, J. Older adults’ perceptions of community-based telehealth wellness programs: A qualitative study. Inform. Health Soc. Care 2022, 47, 361–372. [Google Scholar] [CrossRef]
- Ruegsegger, G.N.; Booth, F.W. Health Benefits of Exercise. Cold Spring Harb. Perspect. Med. 2018, 2, a029694. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, Y.; Ashokan, K. Physical Exercise: An Overview of Benefits from Psychological Level to Genetics and Beyond. Front. Physiol. 2021, 12, 731858. [Google Scholar] [CrossRef] [PubMed]
- Murdoch, B. Privacy and artificial intelligence: Challenges for protecting health information in a new era. BMC Med. Ethics 2021, 22, 122. [Google Scholar] [CrossRef]
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Fabbrizio, A.; Fucarino, A.; Cantoia, M.; De Giorgio, A.; Garrido, N.D.; Iuliano, E.; Reis, V.M.; Sausa, M.; Vilaça-Alves, J.; Zimatore, G.; et al. Smart Devices for Health and Wellness Applied to Tele-Exercise: An Overview of New Trends and Technologies Such as IoT and AI. Healthcare 2023, 11, 1805. https://doi.org/10.3390/healthcare11121805
Fabbrizio A, Fucarino A, Cantoia M, De Giorgio A, Garrido ND, Iuliano E, Reis VM, Sausa M, Vilaça-Alves J, Zimatore G, et al. Smart Devices for Health and Wellness Applied to Tele-Exercise: An Overview of New Trends and Technologies Such as IoT and AI. Healthcare. 2023; 11(12):1805. https://doi.org/10.3390/healthcare11121805
Chicago/Turabian StyleFabbrizio, Antonio, Alberto Fucarino, Manuela Cantoia, Andrea De Giorgio, Nuno D. Garrido, Enzo Iuliano, Victor Machado Reis, Martina Sausa, José Vilaça-Alves, Giovanna Zimatore, and et al. 2023. "Smart Devices for Health and Wellness Applied to Tele-Exercise: An Overview of New Trends and Technologies Such as IoT and AI" Healthcare 11, no. 12: 1805. https://doi.org/10.3390/healthcare11121805
APA StyleFabbrizio, A., Fucarino, A., Cantoia, M., De Giorgio, A., Garrido, N. D., Iuliano, E., Reis, V. M., Sausa, M., Vilaça-Alves, J., Zimatore, G., Baldari, C., & Macaluso, F. (2023). Smart Devices for Health and Wellness Applied to Tele-Exercise: An Overview of New Trends and Technologies Such as IoT and AI. Healthcare, 11(12), 1805. https://doi.org/10.3390/healthcare11121805