Relieving Chronic Musculoskeletal Pain in Older Adults Using Transcranial Direct Current Stimulation: Effects on Pain Intensity, Quality, and Pain-Related Outcomes
Bibliographic record
Abstract
Introduction Chronic pain is a significant health problem and is particularly prevalent amongst the elderly. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that has been proposed to reduce chronic pain. The aim of this study was to evaluate and compare the efficacy of active and sham tDCS in reducing pain in older individuals living with chronic musculoskeletal pain. Materials and Methods Twenty-four older individuals (mean age: 68 ± 7 years) suffering from chronic musculoskeletal pain were randomized to receive either anodal tDCS over the contralateral motor cortex (2 mA, 20 min; n = 12) or sham tDCS (20 min; n = 12) for five consecutive days. Pain logbooks were used to measure pain intensity. Questionnaires (McGill Pain Questionnaire, Brief Pain Inventory, Beck Depression Inventory [BDI], Beck Anxiety Inventory, Pain Catastrophizing Scale [PCS], and Margolis Pain Drawing and Scoring System [MPDSS]) were also used to assess pain in its globality. Results Analysis of pain logbooks revealed that active tDCS led to a reduction in daily average pain intensity (all p ≤ 0.04), while sham tDCS did not produce any change (p = 0.15). Between-group comparisons for change in pain intensity reduction between active and sham tDCS showed a trend during treatment (p = 0.08) which was significant at the follow-up period (p = 0.02). Active tDCS also improved scores of all questionnaires (all p ≤ 0.02), while sham tDCS only reduced MPDSS scores (p = 0.04). Between-group comparisons for the pain-related outcomes showed significant differences for BDI et PCS after the last tDCS session. Conclusions These results suggest that anodal tDCS applied over the primary motor cortex is an effective modality to decrease pain in older individuals. tDCS can also improve other key outcomes, such as physical and emotional functioning, and catastrophic thinking.
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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) | 0.001 | 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".