文献类型：期刊文献

英文题名：Comparing the Effects of AI-Assisted and Traditional Exercise on Physical Health Outcomes in Older Adults: A Systematic Review and Meta-Analysis

作者：Fan, Sijing[1];Tan, Xin[2,3];Zheng, Hongyun[1];Cui, Yicong[4];Du, Xiaotong[1];Huang, Boqiao[5];Ren, Jingzhan[6];Ye, Xinming[1];Fang, Wen[1]

机构：[1]East China Univ Sci & Technol, Sch Sports Sci & Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Fudan Univ, Coll Smart Mat & Future Energy, 220 Handan Rd, Shanghai 200433, Peoples R China;[3]Hunan Normal Univ, Coll Engn & Design, 36 Lushan Rd, Changsha 410081, Peoples R China;[4]Tsinghua Univ, Div Sports Sci & Phys Educ, 30 Shuangqing Rd, Beijing 100084, Peoples R China;[5]Jilin Univ, China Japan Union Hosp, 2699 Qianjin St, Changchun 130012, Peoples R China;[6]East China Univ Sci & Technol, Sch Business, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2025

卷号：13

期号：23

外文期刊名：HEALTHCARE

收录：;WOS:【SSCI(收录号:WOS:001634373800001)，SCI-EXPANDED(收录号:WOS:001634373800001)】；

语种：英文

外文关键词：artificial intelligence; network meta-analysis; older adults; physical function; cognitive function; quality of life

摘要：Objective: Exercise is widely recognized as an effective non-pharmacological intervention to maintain health in older adults. With advances in artificial intelligence (AI), AI-assisted exercise has emerged as a novel rehabilitation approach, yet its comparative effectiveness against traditional and software-assisted programs remains unclear. This study aimed to evaluate and rank the relative effectiveness of these interventions on multiple physical and psychological outcomes using a network meta-analysis (NMA). Methods: Following the PRISMA-NMA guidelines, we systematically searched PubMed, Embase, Cochrane Library, Web of Science, and Scopus up to June 2025. Eligible studies were randomized controlled trials (RCTs) involving adults >= 60 years comparing AI-assisted, software-assisted, and conventional upper/lower limb rehabilitation. Six outcomes were analyzed: gait, balance, range of motion (ROM), muscle strength, cognitive function, and quality of life (QOL). Stata 17.0 was used to conduct the NMA, calculating the standardized mean differences (SMDs) and SUCRA rankings, with assessments of heterogeneity and risk of bias. Results: Seventy RCTs with 808 participants were included. All active interventions outperformed the placebo. AI-assisted programs showed the strongest effects on gait (SMD = 1.33) and balance (SMD = 0.76), while software-assisted interventions ranked highest for ROM (SMD = 0.69) and QOL (SMD = 1.06). Both AI and software interventions improved cognition and muscle strength. Heterogeneity was low (I2 <= 38.5%). Subgroup analysis indicated that AI-based methods were superior to traditional rehabilitation, although differences among novel interventions were not statistically significant. Conclusions: AI-assisted exercise is highly effective for gait and balance, while software-assisted approaches excel in ROM and QOL. These interventions hold promise for community and home-based rehabilitation. Future studies should investigate integrated "AI + traditional" models and incorporate biomechanical and neurophysiological indicators to optimize personalized care.