Evidence map›Paper›PMID 39453567›Full record

Trial reportEuropean geriatric medicine2025

Investigating the efficacy of AI-enhanced telerehabilitation in sarcopenic older individuals.

Meiqi Wei, Deyu Meng, Shichun He, Zongnan Lv, Hongzhi Guo, Guang Yang, Ziheng Wang

Abstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in European geriatric medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 5 pooled it
–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

11 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Article
  8. Article
  9. Effectiveness of digital health exercise interventions on muscle function and physical performance in older adults with possible, confirmed, or severe sarcopenia: a systematic review and meta-analysis.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2026
    Review
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Meiqi WeiDivision of Computational Biology, Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China.
Deyu MengDivision of Computational Biology, Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China.
Shichun HeDivision of Computational Biology, Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China.
Zongnan LvDepartment of Adolescent Physical Health, Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China.
Hongzhi GuoAI Group, Intelligent Lancet LLC, Sacramento, CA, 95816, USA.
Guang YangDivision of Computational Biology, Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China.
Ziheng WangDivision of Computational Biology, Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China. wangzh654@nenu.edu.cn.

Funding

Fundamental Research Funds for the Central Universities 135222026
6 · The paper itself

Abstract

objectiveThis study explores the effectiveness of 3D pose estimation technology in Yi Jin Jing (a traditional Chinese exercise) interventions for sarcopenic older individuals.

designA randomized controlled trial involving 93 participants (mean age: 71.64 ± 7.09 years; 41 males and 52 females) divided into three groups: a face-to-face offline traditional training group (OFFG), a general remote online training group (ONG), and an AI-based online remote training group (AIONG).

methodsParticipants in each group underwent their respective training programs. The effectiveness of the interventions was measured using Appendicular Skeletal Muscle Mass Index, Grip Strength, 6-meter Walking Speed, Timed-Up-and-Go Test, and Quality of Life assessments.

resultsSignificant improvements were observed across all groups in ASMI, Grip Strength, 6-meter Walking Speed, TUGT, and QoL. However, there were no statistically significant differences between the groups in terms of the magnitude of these improvements. AIONG showed outcomes comparable to OFFG and ONG methods.

conclusionsAI-based telerehabilitation with 3D pose estimation is a viable and effective alternative for remote exercise interventions. It offers precise guidance and enhances the quality of rehabilitation training, demonstrating outcomes comparable to traditional and general online methods.

Indexed as

Exercise TherapySarcopeniaTelerehabilitationAgedAged, 80 and overFemaleHand StrengthHumansMaleMiddle AgedQuality of LifeTreatment OutcomeWalking SpeedChinese traditional exerciseHuman pose estimation technologyRemote rehabilitationSarcopeniaYi Jin Jing

Identifiers

PMID39453567

What OpenQuestion holds

Textmetadata
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.