Prolonged Reduction in Shoulder Strength after Transcutaneous Electrical Nerve Stimulation Treatment of Exercise‐Induced Acute Muscle Pain
Bibliographic record
Abstract
OBJECTIVES: Transcutaneous electrical nerve stimulation (TENS) is commonly used for reducing musculoskeletal pain to improve function. However, peripheral nerve stimulation using TENS can alter muscle motor output. Few studies examine motor outcomes following TENS in a human pain model. Therefore, this study investigated the influence of TENS sensory stimulation primarily on motor output (strength) and secondarily on pain and disability following exercise-induced delayed-onset muscle soreness (DOMS). METHODS: Thirty-six participants were randomized to a TENS treatment, TENS placebo, or control group after completing a standardized DOMS protocol. Measures included shoulder strength, pain, mechanical pain sensitivity, and disability. TENS treatment and TENS placebo groups received 90 minutes of active or sham treatment 24, 48, and 72 hours post-DOMS. All participants were assessed daily. RESULTS: A repeated measures analysis of variance and post-hoc analysis indicated that, compared to the control group, strength remained reduced in the TENS treatment group (48 hours post-DOMS, P < 0.05) and TENS placebo group (48 hours post-DOMS, P < 0.05; 72 hours post-DOMS, P < 0.05). A mixed-linear modeling analysis was conducted to examine the strength (motor) change. Randomization group explained 5.6% of between-subject strength variance (P < 0.05). Independent of randomization group, pain explained 8.9% of within-subject strength variance and disability explained 3.3% of between-subject strength variance (both P < 0.05). DISCUSSION: While active and placebo TENS resulted in prolonged strength inhibition, the results were nonsignificant for pain. Results indicated that higher pain and higher disability were independently related to decreased strength. Regardless of the impact on pain, TENS, or even the perception of TENS, may act as a nocebo for motor output.
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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 | 0.001 |
| 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) | 0.002 | 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".