Transcranial Direct Current Stimulation Combined With Meditation for Older Adults With Knee Osteoarthritis
Bibliographic record
Abstract
Abstract Osteoarthritis (OA) of the knee is one of the most common causes of pain in older adults. Recent evidence suggests that knee OA pain is characterized by alterations in central pain processing in the brain. Two nonpharmacological pain treatments, transcranial direct current stimulation (tDCS) and mindfulness-based meditation (MBM), have been shown to improve pain-related brain function in older adults with knee OA. Because tDCS promotes neuroplasticity, it may potentiate the effect of MBM that also stimulates adaptive changes in the brain. However, no studies have examined whether tDCS combined with MBM can reduce OA symptoms in older adults with knee OA. Thus, the purpose of this study was to examine the preliminary efficacy of tDCS combined with MBM in older adults with knee OA. Thirty participants with knee OA were randomly assigned to receive 10 daily sessions of home-based 2 mA tDCS combined with active MBM for 20 minutes (n=15) or sham tDCS combined with sham MBM (n=15). We measured OA-related clinical symptoms using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Participants (60% female) had a mean age of 59 years. Active tDCS combined with active MBM significantly reduced scores on the WOAMC (Cohen’s d = 0.83, P = 0.02). Participants tolerated tDCS combined with MBM well without serious adverse effects. Our findings demonstrate promising clinical efficacy of home-based tDCS combined with MBM for older adults with knee OA. Future studies with larger-scale randomized controlled trials with follow-up assessments are needed to validate our findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".