Can we improve pain and sleep in elderly individuals with transcranial direct current stimulation? – Results from a randomized controlled pilot study
Bibliographic record
Abstract
Background: The prevalence of chronic pain and sleep disturbances substantially increases with age. Pharmacotherapy remains the primary treatment option for these health issues. However, side effects and drug interactions are difficult to control in elderly individuals. Aims: The objective of this study was to assess the feasibility of conducting a randomized sham-controlled trial and to collect preliminary data on the efficacy of transcranial direct current stimulation (tDCS) to reduce pain and improve sleep in older adults suffering from chronic pain. Methods: Fourteen elderly individuals (mean age 71±7 years) suffering from chronic pain and sleep complaints were randomized to receive either anodal tDCS, applied over the primary motor cortex (2 mA, 20 minutes), or sham tDCS, for 5 consecutive days. Pain was measured with visual analog scales, pain logbooks and questionnaires, while sleep was assessed with actigraphy, sleep diaries and questionnaires. Results: There were no missing data for pain and sleep measures, except for actigraphy, that generated several missing data. Blinding was maintained throughout the study, for both the evaluator and participants. Active but not sham tDCS significantly reduced pain ( P <0.05). No change was observed in sleep parameters, in both the active and sham tDCS groups (all P ≥0.18). Conclusion: The present study provides guidelines for the implementation of future tDCS studies in larger populations of elderly individuals. M1 anodal tDCS in this population appears to be effective to reduce pain, but not to improve sleep. Keywords: transcranial direct current stimulation, tDCS, pain, sleep, elderly, actigraphy, aging
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".