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Record W4416455847 · doi:10.37341/jkf.v9i2.454

Kinesiotape Versus Home-Based Exercise for Reducing Pain and Disability in Elderly Osteoarthritis

2025· article· W4416455847 on OpenAlexaboutno aff
Putu Ayu Sita Saraswati, Made Hendra Satria Nugraha

Bibliographic record

VenueJurnal Keterapian Fisik · 2025
Typearticle
Language
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersUniversitas Udayana
KeywordsOsteoarthritisWOMACVisual analogue scaleRandomized controlled trialQuality of life (healthcare)Knee painIntervention (counseling)

Abstract

fetched live from OpenAlex

Background: Osteoarthritis (OA) in the knee causes various health problems, including pain and limited movement that reduces productivity and quality of life, causing disability. This study aims to determine the comparative effectiveness between home-based exercise and kinesiotape in lowering pain and disability in the elderly with knee osteoarthritis. Methods: It was an experimental study with a randomized pre-test and post-test control group design. There were 30 participants divided into 2 groups: Group 1 with Kinesiotape and Group 2 with Home-Based Exercise. The intervention was given 3 times per week for 4 weeks. Disability was measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and pain was measured by the Visual Analogue Scale (VAS). Results: The p-value for WOMAC and VAS scores before and after intervention is <0.001 in kinesiotape and Home-Based exercise interventions, which states that there is a significant improvement in lowering disability. Comparative analysis between groups also showed p< 0.001. Conclusion: Kinesiotape and home-based exercises are both effective in reducing knee pain and disability in elderly with knee OA. However, home-based exercises have proven to be more effective than kinesiotape. Home-based exercises are recommended as the primary intervention to decrease pain and disability in elderly individuals with genetic knee OA.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.270
Teacher spread0.257 · 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.

Study designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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