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Record W3033433477 · doi:10.1186/s13075-020-02222-y

The effect of transtheoretical model-lead intervention for knee osteoarthritis in older adults: a cluster randomized trial

2020· article· en· W3033433477 on OpenAlexaboutno aff
Limin Wang, Hongbo Chen, Lu Han, Yun-Lin Wang, Congying Liu, Xu Dong, Jieru Chen, Nan Liu, Fang Yu, Qiaoqin Wan, Shaomei Shang

Bibliographic record

VenueArthritis Research & Therapy · 2020
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicinePhysical therapyOsteoarthritisWOMACTranstheoretical modelRandomized controlled trialPsychological interventionTimed Up and Go testPhysical medicine and rehabilitationIntervention (counseling)Balance (ability)Internal medicineAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Knee osteoarthritis (KOA) is a common joint disease in people over 60 years old. Exercise therapy is one of the most effective non-pharmacological treatments for KOA, but low exercise adherence needs to be improved. The present study aimed to evaluate the effect of the transtheoretical model-lead home exercise intervention (TTM-HEI) program on exercise adherence, KOA symptoms, and knee function in older adults with KOA. METHODS: A two-arm, superiority, assessor-blinded, cluster randomized trial was conducted. Community-dwelling older adults with KOA were recruited from 14 community centers in Beijing, China, via print and social media advertisements from April to October 2018. The present study lasted 48 weeks, with an intervention duration of 0-24 weeks and follow-up time of 24-48 weeks. The intervention was a two-stage and 24-week TTM-based exercise program, and the control group underwent a same-length exercise program guidance without any exercise adherence interventions. The primary outcome was exercise adherence to the prescribed home exercise program and was measured using an 11-point numerical (0 = not at all through and 10 = completely as instructed) self-rating scale at weeks 4, 12, 24, 36, and 48 after the program started. KOA symptoms (pain intensity and joint stiffness) were measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and knee function (lower limb muscle strength and balance) was measured using the Five-Times-Sit-to-Stand Test (FTSST) and the Timed Up and Go Test (TUG) at baseline, week 24, and week 48. Latent growth model (GLM), repeated measures ANOVA, and independent t test were the main statistical tests used. RESULTS: A total of 189 older adults (intervention group: n = 103, control group: n = 86) were enrolled. Differences of any outcome measures at baseline were not significant between groups. The growth rate of exercise adherence in the intervention group increased 2.175 units compared with that in the control group (unstandardized coefficient of slope on group B2 = 2.175, p < 0.001), and the intervention program maintained participants' exercise adherence with 5.56 (SD = 1.00) compared with 3.16 (SD = 1.31) in the control group at week 48. In addition, the TTM-HEI program showed significant effects on relieving KOA symptoms and improving knee function. CONCLUSION: Over time, TTM-HEI could improve participants' exercise adherence, KOA symptoms, and knee function. TRIAL REGISTRATION: This study was approved by the ethics committee (IRB00001052-17066) in July 2017 and was registered at the Chinese Clinical Trial Registry (website: www.chictr.org.cn, registry number: ChiCTR1800015458).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.335
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations39
Published2020
Admission routes1
Has abstractyes

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