Head, Face, and Neck Cooling for Performance: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Cooling the head, face, and neck can have strong perceptual effects that contribute to improved performance. This systematic review aimed to determine the effect of cooling strategies targeting the head, face, and neck on physical and cognitive performance, determine any associated physiological and perceptual responses, synthesize adverse events, and provide practical applications. METHODS: We conducted a systematic review and multilevel meta-analysis, adhering to PRISMA guidelines. Studies that investigated the effect of cooling strategies targeting the head, face, or neck on a physical or cognitive task using a controlled trial design were included. RESULTS: Sixty-three studies were identified, involving 618 participants (86.6% male). Cooling strategies included water-perfused devices (18.7%), phase-change neck collars (17.3%), fanning/cold air (14.7%), phase-change headwear (13.3%), ice/gel packs (13.3%), cold towels (5.3%), menthol application (4.0%), water spraying/dousing (4.0%), or a combination of strategies (9.3%). The effect of cooling on both self-paced and fixed-intensity exercise tasks was inconclusive; the 95% CI of the pooled effect was compatible with no effect and medium beneficial effects but not harmful effects. We were unable to pool cognitive data. Cooling reduced the skin temperature at the target site and improved thermal sensation and comfort. Effects on heart rate and core and mean skin temperatures were negligible. Adverse events were rare, and no intervention subgroup was superior. CONCLUSION: We recommend that athletes experiment with a range of head-, face-, and neck-cooling strategies, including using different doses and timings, to determine the optimal strategy for their individual and sport context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".