Home‐Based, Remotely Supervised Transcranial Direct Current Stimulation Improves the Overall Pain Experience of Older Adults With Knee Osteoarthritis
Bibliographic record
Abstract
Objective: Chronic pain in knee osteoarthritis (OA) is a multidimensional phenomenon requiring thorough assessment and appropriate treatment. We assessed the impact of home‐based, remotely supervised transcranial direct current stimulation (tDCS) on the overall pain experience of older adults with knee OA by simultaneously examining its effects on multiple pain domains—pain intensity, pain interference, and pain catastrophizing—using multigroup latent transition analysis (LTA). Methods: This secondary analysis of a randomized clinical trial involved 120 participants with knee OA pain, randomly assigned in a 1:1 ratio to receive 15 daily sessions of 2‐mA tDCS or sham tDCS (20 min per session) over three weeks, with real‐time remote supervision. Pain intensity was measured using the Numeric Rating Scale (NRS) and the pain subscale of the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) Index. Pain interference was measured using the WOMAC functional scale. Pain catastrophizing was assessed using the Pain Catastrophizing Scale (PCS). All the measures were assessed at baseline and at the end of each week (weeks 1, 2, and 3), after the participants had completed five tDCS sessions per week. Multigroup LTA enabled the simultaneous measurement of multiple pain domains and analysis of their changes as a function of intervention exposure by modeling the transition probabilities of latent classes and comparing these changes between the groups. Results: Based on the NRS, WOMAC, and PCS scores, three latent categories were identified: “high pain (all scores high),” “moderate pain (all scores moderate),” and “low pain (all scores low).” Active group participants with “moderate pain” at baseline had a 24.2% probability of transitioning to “low pain” after Week 1, whereas sham group participants remained stagnant during this interval. Notably, 37.6% of active group participants with “high pain” at Week 1 transitioned to “moderate pain,” while 35.8% of those with “moderate pain” at Week 1 transitioned to “low pain” by Week 2 (after an additional five sessions). Nevertheless, no noticeable changes were observed in the sham group during this period. No pronounced intervention effects were noted by Week 3. Conclusions: Simultaneously modeling pain‐related measures enriches our understanding of the efficacy of tDCS in improving the overall pain experience among older adults with knee OA. Trial Registration: ClinicalTrials.gov identifier: NCT04016272
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".