Effectiveness of Photobiomodulation to Treat Motor and Non-Motor Symptoms of Parkinson’s Disease: A Randomised Clinical Trial with Extended Treatment
Bibliographic record
Abstract
Background/Objective: Few treatment options improve symptoms and the quality of life of Parkinson’s disease (PD); more treatment choices are needed. This study examined the effectiveness of photobiomodulation therapy (PBMt) combined with exercise to improve PD symptoms and quality of life. Methods: Participants were randomised into Active (n = 32) or Sham (n = 31) PBMt groups. Stage 1 was an 8-week double-blind, randomised, placebo-controlled trial using either active or sham PBMt to the head, back of the neck and abdomen three times weekly at home, followed by a 4-week washout. Stage 2 was 8 weeks of active PBMt for all participants. In Stage 3, participants chose to continue active PBMt treatment (‘continuers’) or receive no PBMt treatment (‘non-continuers’) for up to 48 weeks. Participants continued vigorous exercise throughout the study. Participants were assessed on enrolment and after each stage. The primary outcome measure was timed up-and-go, with a range of secondary motor and non-motor outcomes, including UPDRS. Results: There was no significant difference between the Active and Sham Groups after Stages 1 or 2, apart from minimal increase in MoCA score/cognition (Sham Group) in Stage 1. After Stage 3, continuers showed a significant improvement in the primary outcome measure compared to non-continuers. Anxiety and the motor experiences of daily living (MDS-UPDRS Part II) were also significantly improved, while other outcomes approached significance, including MDS-UPDRS Total score (p = 0.062). Conclusions: As the largest study to date, results add increasing weight to previous clinical trials and highlight potential for at-home, scalable treatment as adjunctive therapy alongside medication and exercise.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".