Evidence map›Paper›PMID 41373216›Full record

ReviewHealthcare (Basel, Switzerland)2025

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

Sijing Fan, Xin Tan, Hongyun Zheng, Yicong Cui, Xiaotong Du, Boqiao Huang, Jingzhan Ren, Xinming Ye, Wen Fang

Erratum issuedAbstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Sijing FanSchool of Sports, Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai 200237, China.
Xin TanCollege of Smart Materials and Future Energy, Fudan University, 220 Handan Road, Yangpu District, Shanghai 200433, China.
Hongyun ZhengSchool of Sports, Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai 200237, China.
Yicong CuiDivision of Sports Science and Physical Education, Tsinghua University, No. 30 Shuangqing Road, Haidian District, Beijing 100084, China.
Xiaotong DuSchool of Sports, Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai 200237, China.
Boqiao HuangChina Japan Union Hospital, Jilin University, No. 2699 Qianjin Street, Changchun 130012, China.
Jingzhan RenSchool of Business, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai 200237, China.
Xinming YeSchool of Sports, Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai 200237, China.ORCID 0000-0002-2138-2701
Wen FangSchool of Sports, Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai 200237, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveExercise 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).

methodsFollowing 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.

resultsSeventy 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 (I

conclusionsAI-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.

Indexed as

artificial intelligencecognitive functionnetwork meta-analysisolder adultsphysical functionquality of life

Identifiers

PMID41373216
PMCPMC12692026

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.