Evidence map›Paper›PMID 36514174›Full record

ArticleTrials2022

Effects of Internet of Things-based power cycling and neuromuscular training on pain and walking ability in elderly patients with KOA: protocol for a randomized controlled trial.

Yujia Zhang, Suhang Xie, Xiaoyi Wang, Kangping Song, Lin Wang, Ruishi Zhang, Yuan Feng, Chengqi He

Open access · goldAbstract readClinical Trial Protocol
In one paragraph

Article in Trials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
1.2field-weighted citation impact, top 20% of its field
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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Exercise for osteoarthritis of the knee.The Cochrane database of systematic reviews · 2024
    Pooled it
  3. Review
  4. Review
  5. 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

8 authors at 1 institution in 1 country.

Yujia Zhang *Rehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Suhang Xie *Rehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Xiaoyi WangRehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Kangping SongRehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Lin WangRehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Ruishi ZhangRehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Yuan FengRehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Chengqi HeRehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China. hxkfhcq2015@126.com.ORCID http://orcid.org/0000-0002-5349-0571
Sichuan University · CN

Funding

National Natural Science Foundation 81972146the Department of Science and Technology of Sichuan Province 2020YJ0210West China Hospital of Sichuan University ZYGD18018
6 · The paper itself

Abstract

backgroundOsteoarthritis (OA) is a common and highly disabling disease that imposes a heavy burden on individuals and society. Although physical therapy is recommended as an important method to relieve OA symptoms, patients cannot continue treatment after returning home. Research on Internet telerehabilitation for knee osteoarthritis (KOA) can reduce pain and improve patient quality of life, and Internet of Things (IoT)-based telerehabilitation is a new form of delivering rehabilitation. This study will evaluate the effect of telerehabilitation via IoT, as a medium to deliver exercises, on pain and walking in patients with KOA.

methodsThis study is a single-blind randomized controlled trial. We will recruit 42 middle-aged and elderly patients with KOA aged ≥ 50 years and randomly divided into power cycling group, neuromuscular exercise group, and control group, and intervention will last for 12 weeks. Outcome measures will be taken at baseline and 4 weeks, 8 weeks, and 12 weeks post-intervention. The pre- and posttreatment differences in knee pain and physical function between participants undergoing power cycling and neuromuscular training and those in the control group will be determined by each scale. The effectiveness will be assessed by the Western Ontario and McMaster Universities Osteoarthritis Index Score (WOMAC) and an 11-point numerical pain rating scale. Walking function and quality of life will be assessed by the timed up and go and walk test, 6-min walk test, and quality of life health status questionnaires. DISCUSSION: The findings from this trial will establish the feasibility and effectiveness of IoT-based power cycling and neuromuscular training on elderly patients with KOA in the community. As a result, this trial may help provide experimental evidence for finding a better exercise method suitable for elderly patients with KOA in the community. TRAIL REGISTRATION: Chinese Clinical Trials Registry ChiCTR2200058924. Prospectively registered on 6 May 2022.

Indexed as

Internet of ThingsOsteoarthritis, KneeAgedExercise TherapyHumansMiddle AgedPainQuality of LifeRandomized Controlled Trials as TopicSingle-Blind MethodTreatment OutcomeWalkingInternet of ThingsKnee osteoarthritisNeuromuscular exercisePower cyclingTelerehabilitation

Identifiers

PMID36514174
PMCPMC9745721
OpenAlexW4311387054

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.