Evidence map›Paper›PMID 40402551›Full record

Trial reportJMIR mHealth and uHealth2025

Clinical Efficacy of Multimodal Exercise Telerehabilitation Based on AI for Chronic Nonspecific Low Back Pain: Randomized Controlled Trial.

Chongwu Xiao, Yijin Zhao, Gege Li, Zhuodong Zhang, Siyu Liu, Weichao Fan, Jinjing Hu, Qiuru Yao, Chengduan Yang, Jihua Zou and 2 more

Erratum issuedAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR mHealth and uHealth, 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 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Chongwu Xiao *Center for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0009-0004-1944-1153
Yijin Zhao *Center for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0000-0001-9353-5378
Gege Li *Center for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0009-0002-3983-4988
Zhuodong ZhangCenter for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0009-0006-0693-4682
Siyu LiuSchool of Sport Medicine and Physical Therapy, Beijing Sport University, Beijing, China.ORCID 0009-0000-2529-9190
Weichao FanCenter for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0009-0007-4548-4888
Jinjing HuCenter for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0009-0000-7165-1263
Qiuru YaoCenter for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0000-0002-2598-7724
Chengduan YangCenter for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0009-0006-3429-6905
Jihua ZouCenter for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0000-0003-0129-2271
Qing ZengCenter for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0000-0002-1182-1598
Guozhi HuangCenter for Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, No.253, Industrial Avenue Middle Guangzhou, Guangzhou, 510280, China, 86 19543576136.ORCID 0000-0002-5889-0603

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Exercise therapy is strongly recommended as a treatment for chronic nonspecific low back pain (CNSLBP). However, therapist-guided exercise therapy requires significant medical resources. Ordinary digital telerehabilitation affects efficacy due to a lack of guidance and dynamic support. Artificial intelligence (AI)-assisted interactive health promotion systems may solve these problems. Objective: We aimed to explore whether AI-assisted multimodal exercise telerehabilitation is superior to conventional telerehabilitation in the treatment of people with CNSLBP. Methods: This study was a prospective, double-arm, open-label, randomized clinical controlled trial. People with CNSLBP were randomly allocated to either the AI or video group, receiving AI-assisted multimodal exercise therapy or conventional video guidance, respectively, via a WeChat application add-in. The multimodal exercise consisted of deep core muscle, flexibility, Mackenzie, and breathing exercises. The exercises were performed for 30-45 minutes per session, 3 times a week, for 4 weeks. Participants underwent face-to-face assessment at baseline and week 4, and web-based assessment at weeks 2 and 8. The primary outcome was the change in Numerical Rating Scale (NRS) relative to baseline at week 4. Secondary outcomes included changes in the Roland-Morris Disability Questionnaire (RMDQ), Oswestry Disability Index (ODI), Pain Castastrophizing Scale (PCS), Timed Up-and-Go (TUG) test, and thickness of the transverse abdominus (TrA) and multifidus (MF) muscles relative to baseline at week 4. Generalized estimating equation and covariance were used to examine the efficacy of the interventions. Results: A total of 38 participants (19 participants per group) were recruited. Eighteen participants in the AI group and 16 participants in the video group completed and were included in the final analysis. There was a significant difference in NRS at week 4 between the AI group and video group (most severe NRS: -3.00 vs -1.50; adjusted mean difference -1.08, 95% CI -1.68 to -0.49; P<.001; mean NRS: -2.61 vs -1.62; adjusted mean difference -0.67, 95% CI -1.19 to -0.15; P=.01). The difference in most severe NRS persisted until week 8 (-3.06 vs -1.69; adjusted mean difference -0.95, 95% CI -1.73 to -0.18; P=.02). Compared with the video group at week 4, the AI group showed significant improvement in secondary outcomes, including RMDQ, PCS, and core muscle thickness of left TrA, right TrA, left MF, and right MF. Conclusions: We showed that 4 weeks of telerehabilitation based on AI-assisted multimodal exercise has better therapeutic effects compared to conventional exercise telerehabilitation in people with CNSLBP. This study provides guidance for developing effective real-time home-based exercise therapies for people with CNSLBP, which may help reduce economic and human resource costs associated with treatment.

Indexed as

Artificial IntelligenceExercise TherapyLow Back PainAdultChronic PainFemaleHumansMaleMiddle AgedPain MeasurementProspective StudiesTelerehabilitationTreatment Outcomeartificial intelligenceexerciselow back paintelerehabilitation

Identifiers

PMID40402551
PMCPMC12121543

What OpenQuestion holds

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Registered trials

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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.