Evidence map›Paper›PMID 30053843›Full record

Trial reportBMC musculoskeletal disorders2018

Construction of an adherence rating scale for exercise therapy for patients with knee osteoarthritis.

Jianji Wang, Long Yang, Qingjun Li, Zhanyu Wu, Yu Sun, Qiang Zou, Xuanze Li, Zhe Xu, Chuan Ye

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC musculoskeletal disorders, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 19% 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
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

9 authors at 2 institutions in 1 country.

Jianji WangDepartment of Orthopedic Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Long YangDepartment of Orthopedic Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Qingjun LiDepartment of Orthopedic Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Zhanyu WuDepartment of Orthopedic Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Yu SunDepartment of Orthopedic Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Qiang ZouDepartment of Orthopedic Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Xuanze LiDepartment of Orthopedic Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Zhe XuDepartment of Orthopedic Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Chuan YeDepartment of Orthopedic Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China. yechuanchina@hotmail.com.
Affiliated Hospital of Guizhou Medical University · CNGuiyang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundKnee osteoarthritis (KOA) is one of the most common chronic diseases in the elderly and is the primary cause of the loss of motor function and disability in this population. Exercise therapy is a core, basic and matureand treatment method of treating patients with KOA. Exercise therapy is "strongly recommended" or "recommended" in the diagnosis and treatment guidelines of osteoarthritis in many countries, and most scholars advocate exercise therapy as the preferred rehabilitation method for KOA patients. However, poor long-term adherence is a serious problem affecting the therapeutic effect of this mature treatment. The objective of this study was to construct a concise and practical adherence rating scale (ARS) based on the exercise therapy adherence prediction model in patients with knee osteoarthritis.

methodsA binary logistic regression model was established, with the adherence of 218 cases of KOA patients as the dependent variable. The patients' general information, exercise habits, knowledge, attitude, and exercise therapy were independent variables. The regression coefficients were assigned to various variables in the model, and the ARS was constructed accordingly. Receiver operating characteristic curves and curve fitting were used to analyse the effect of the ARS in predicting the adherence and to determine the goodness of fit for the adherence. The external validity of the ARS was examined in a randomized controlled trial.

resultsThe construction of the adherence model and the ARS included the following variables: age (1 point), education level (1 point), degree of social support (2 points), exercise habits (3 points), knowledge of KOA prevention and treatment (2 points), degree of care needed to treat the disease (1 point), familiarity with exercise therapy (4 points) and treatment confidence (3 points). The critical value of the total score of the ARS was 6.50, with a sensitivity of 87.20% and a specificity of 76.34%.

conclusionsA KOA exercise therapy adherence model and a simple and practical ARS were constructed. The ARS has good internal validity and external validity and can be used to evaluate the adherence to exercise therapy in patients with KOA.

Indexed as

Exercise TherapyPatient ComplianceAgedAged, 80 and overBiomechanical PhenomenaChinaFemaleHabitsHealth Knowledge, Attitudes, PracticeHumansKnee JointMaleMiddle AgedOsteoarthritis, KneePatient Education as TopicReproducibility of ResultsAdherenceExercise therapyKOALogistic regression analysisRandomized controlled trial

Identifiers

PMID30053843
PMCPMC6064120
OpenAlexW2883696080

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.