Negative expectations and measurable movement mechanics: a scoping review of the nocebo effect on motor performance
Bibliographic record
Abstract
Introduction: The nocebo effect, where negative responses can occur as a result of negative expectations, has gained increasing attention in motor control research, with growing evidence highlighting its impact on both athletic and everyday movements. However, the specific methodologies used to induce nocebo effects on motor outcomes remain unexplored. This scoping review aimed to address three key questions: (1) What experimental protocols have been developed and used to elicit nocebo effects in motor performance in healthy individuals? (2) How are these effects assessed and measured? (3) What are the observed effects on motor outcomes? Methods: A scoping review was conducted following the PRISMA framework, searching PubMed, EBSCO, EMBASE, Cochrane, Web of Science, Scopus, and Google Scholar from inception to March 2025. Eighteen studies were included. Results and discussion: Verbal instruction was the most common induction method (94.4 %), often combined with conditioning or visual cues. Motor tasks assessed gross skills, such as sprinting and cycling), with limited attention given to fine motor control. Outcomes were more frequently centered on performance measures (e.g., strength, endurance), with less emphasis on movement quality (e.g., coordination). Nocebo effects were observed in half of the studies impairing motor performance, including reduced force production, diminished endurance, disrupted postural stability and slower movement speed. The findings highlight methodological diversity in induction protocols and measurement methods. Future research should expand participant diversity, investigate fine motor tasks, and further explore the interplay between induction methods and motor outcomes.
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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 | 0.023 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".