An Exergame-Integrated IoT-Based Ergometer System Delivers Personalized Training Programs for Older Adults and Enhances Physical Fitness: A Pilot Randomized Controlled Trial

Introduction: Regular physical exercise is believed to counteract the adverse physiological consequences of aging. However, smart fitness equipment specifically designed for older adults is quite rare. Here we designed an exergame-integrated internet of things (IoT)-based ergometer system (EIoT-ergo) that delivers personalized exercise prescriptions for older adults. First, physical fitness was evaluated using the Senior Fitness Test (SFT) application. Then, radio frequency identification (RFID) triggered the EIoT-ergo to deliver the corresponding exercise session based on the individual level of physical fitness. The exercise intensity during each workout was measured to generate the next exercise session. Further, EIoT-ergo provides an exergame to help users control and maintain their optimal cadence while engaging in exercise. Methods: This was a randomized controlled trial with 1:1 randomization. Participants were older adults, 50+ years of age (N = 35), who are active in their community. Participants in the EIoT-ergo group received a 12-week personalized exercise program delivered by EIoT-ergo for 30 min per session, with 2 sessions per week. Participants in the control group continued with their usual activities. A senior’s fitness test and a health questionnaire were assessed at baseline and at a 13-week reassessment. The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) was used to evaluate the satisfaction of EIoT-ergo. Results: Compared with the control group, the EIoT-ergo group showed significant improvements in muscle strength (time-by-group interaction, sit-to-stand: β = 5.013, p < 0.001), flexibility (back stretch: β = 4.008, p = 0.005; and sit-and-reach: β = 4.730, p = 0.04), and aerobic endurance (2-min step: β = 9.262, p = 0.03). The body composition was also improved in the EIoT-ergo group (body mass index: β = −0.737, p < 0.001; and skeletal muscle index: β = 0.268, p = 0.03). Satisfaction with EIoT-ergo was shown in QUEST, with an average score of 4.4 ± 0.32 (5 for very satisfied). The percentage maximum heart rate in each session also indicated that EIoT-ergo can gradually build up the exercise intensity of users. Conclusions: EIoT-ergo was developed to provide personal identification, exergames, intelligent exercise prescriptions, and remote monitoring, as well as to significantly enhance the physical fitness of the elderly individuals under study.