Near-Infrared Light Therapy to Attenuate Strength Loss After Strenuous Resistance Exercise
Bibliographic record
Abstract
CONTEXT: Near-infrared (NIR) light therapy is purported to act as an ergogenic aid by enhancing the contractile function of skeletal muscle. Improving muscle function is a new avenue for research in the area of laser therapy; however, very few researchers have examined the ergogenic effects of NIR light therapy and the influence it may have on the recovery process during rehabilitation. OBJECTIVE: To evaluate the ergogenic effect of NIR light therapy on skeletal muscle function. DESIGN: Crossover study. SETTING: Controlled laboratory. PATIENTS OR OTHER PARTICIPANTS: Thirty-nine healthy men (n = 21) and women (n = 18; age = 20.0 ± 0.2 years, height = 169 ± 2 cm, mass = 68.4 ± 1.8 kg, body mass index = 23.8 ± 0.4 kg/m(2)). INTERVENTION(S): Each participant received active and sham treatments on the biceps brachii muscle on 2 separate days. The order of treatment was randomized. A class 4 laser with a cumulative dose of 360 J was used for the active treatment. After receiving the treatment on each day, participants completed an elbow-flexion resistance-exercise protocol. MAIN OUTCOME MEASURE(S): The dependent variables were elbow range of motion, muscle point tenderness, and strength (peak torque). Analysis of variance with repeated measures was used to assess changes in these measures between treatments at baseline and at follow-up, 48 hours postexercise. Additionally, immediate strength loss postexercise was compared between treatments using a paired t test. RESULTS: Preexercise to postexercise strength loss for the active laser treatment, although small, was less than with the sham treatment (P = .05). CONCLUSIONS: Applied to skeletal muscle before resistance exercise, NIR light therapy effectively attenuated strength loss. Therefore, NIR light therapy may be a beneficial, noninvasive modality for improving muscle function during rehabilitation after musculoskeletal injury. However, future studies using higher treatment doses are warranted.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".