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Record W2802595572 · doi:10.1371/journal.pone.0195653

Efficacy of a biomechanically-based yoga exercise program in knee osteoarthritis: A randomized controlled trial

2018· article· en· W2802595572 on OpenAlexafffundabout
Alexander B. Kuntz, Jaclyn N. Chopp‐Hurley, Elora C. Brenneman, Sarah Karampatos, Emily G. Wiebenga, Jonathan D. Adachi, Michael D. Noseworthy, Monica R. Maly

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of WaterlooMcMaster University
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsMedicineOsteoarthritisPhysical therapyRandomized controlled trialPsychological interventionQuality of life (healthcare)Knee painRheumatologyPhysical medicine and rehabilitationInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Certain exercises could overload the osteoarthritic knee. We developed an exercise program from yoga postures with a minimal knee adduction moment for knee osteoarthritis. The purpose was to compare the effectiveness of this biomechanically-based yoga exercise (YE), with traditional exercise (TE), and a no-exercise attention-equivalent control (NE) for improving pain, self-reported physical function and mobility performance in women with knee osteoarthritis. DESIGN: Single-blind, three-arm randomized controlled trial. SETTING: Community in Southwestern Ontario, Canada. PARTICIPANTS: A convenience sample of 31 women with symptomatic knee osteoarthritis was recruited through rheumatology, orthopaedic and physiotherapy clinics, newspapers and word-of-mouth. INTERVENTIONS: Participants were stratified by disease severity and randomly allocated to one of three 12-week, supervised interventions. YE included biomechanically-based yoga exercises; TE included traditional leg strengthening on machines; and NE included meditation with no exercise. Participants were asked to attend three 1-hour group classes/sessions each week. MEASUREMENTS: Primary outcomes were pain, self-reported physical function and mobility performance. Secondary outcomes were knee strength, depression, and health-related quality of life. All were assessed by a blinded assessor at baseline and immediately following the intervention. RESULTS: The YE group demonstrated greater improvements in KOOS pain (mean difference of 22.9 [95% CI, 6.9 to 38.8; p = 0.003]), intermittent pain (mean difference of -19.6 [95% CI, -34.8 to -4.4; p = 0.009]) and self-reported physical function (mean difference of 17.2 [95% CI, 5.2 to 29.2; p = 0.003]) compared to NE. Improvements in these outcomes were similar between YE and TE. However, TE demonstrated a greater improvement in knee flexor strength compared to YE (mean difference of 0.1 [95% CI, 0.1 to 0.2]. Improvements from baseline to follow-up were present in quality of life score for YE and knee flexor strength for TE, while both also demonstrated improvements in mobility. No improvement in any outcome was present in NE. CONCLUSIONS: The biomechanically-based yoga exercise program produced clinically meaningful improvements in pain, self-reported physical function and mobility in women with clinical knee OA compared to no exercise. While not statistically significant, improvements in these outcomes were larger than those elicited from the traditional exercise-based program. Though this may suggest that the yoga program may be more efficacious for knee OA, future research studying a larger sample is required. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02370667).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations69
Published2018
Admission routes3
Has abstractyes

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