Supplementary Material for: 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.

引言：规律的体育锻炼被认为可对抗衰老引发的不良生理效应。然而，专为老年人研发的智能健身器材仍较为稀缺。本研究设计了一款集成运动游戏的物联网（Internet of Things, IoT）功率车系统（EIoT-ergo），可为老年人提供个性化运动处方。首先，通过老年体能测试（Senior Fitness Test, SFT）应用程序评估受试者的体能水平；随后，射频识别（Radio Frequency Identification, RFID）技术触发EIoT-ergo系统，基于个体体能状况推送对应运动课程；每次训练过程中的运动强度将被监测，用于生成下一次的运动方案。此外，EIoT-ergo系统还配备了运动游戏，可帮助使用者在运动过程中控制并维持最佳步频。
方法：本研究采用1:1随机分组的随机对照试验设计。受试者为50岁及以上、活跃于社区的老年人（N=35）。EIoT-ergo组受试者接受由EIoT-ergo系统支持的12周个性化运动方案，每次训练30分钟，每周2次；对照组受试者则维持日常活动水平。在基线阶段与13周随访阶段，分别对受试者开展老年体能测试与健康问卷调查。采用魁北克辅助技术用户满意度评估量表（Quebec User Evaluation of Satisfaction with Assistive Technology, QUEST）评估受试者对EIoT-ergo系统的满意度。
结果：与对照组相比，EIoT-ergo组在肌肉力量（组间-时间交互效应：坐位站起测试：β=5.013，p<0.001）、柔韧性指标（背部伸展测试：β=4.008，p=0.005；坐位体前屈测试：β=4.730，p=0.04）与有氧耐力指标（2分钟台阶测试：β=9.262，p=0.03）方面均出现显著改善。EIoT-ergo组的身体成分也得到优化：体质量指数：β=-0.737，p<0.001；骨骼肌质量指数：β=0.268，p=0.03。QUEST量表结果显示受试者对EIoT-ergo系统的满意度良好，平均得分为4.4±0.32（满分5分，5分代表非常满意）。各训练阶段的最大心率占比数据同样证实，EIoT-ergo系统可逐步提升使用者的运动强度。
结论：本研究开发的EIoT-ergo系统具备个人身份识别、运动游戏、智能运动处方与远程监测等功能，且可显著提升本次研究中老年受试者的体能水平。