Efficacy of Hot Yoga as a Heat Stress Technique for Enhancing Plasma Volume and Cardiovascular Performance in Elite Female Field Hockey Players
Bibliographic record
Abstract
Perrotta, AS, White, MD, Koehle, MS, Taunton, JE, and Warburton, DER. Efficacy of hot yoga as a heat stress technique for enhancing plasma volume and cardiovascular performance in elite female field hockey players. J Strength Cond Res 32(10): 2878-2887, 2018-This investigation examined the efficacy of hot yoga as an alternative heat stress technique for enhancing plasma volume percentage (PV%) and cardiovascular performance. Ten international caliber female field hockey players completed six 60-minute hot yoga sessions using permissive dehydration over 6 days, followed by a 6-day national team camp. Changes in PV% were examined throughout the intervention and postintervention period. A graded maximal exercise test was performed in a thermoneutral environment (23.2 ± 1.0° C) 24 hours before and 24 hours after intervention. Six days of hot yoga initiated a moderate state of hypovolemia (PV% = -3.5%, 90% confidence limit [CL] [-6.9 to -0.13]), trivial improvements in maximal aerobic power (V[Combining Dot Above]O2max) (effect size [ES] = 0.06, 90% CL [-0.16 to 0.28]), and run time to exhaustion (ES = 0.11, 90% CL [-0.07 to 0.29]). Small meaningful improvements were observed in running speed (km·h) at ventilatory threshold (VT1) (ES = 0.34, 90% CL [-0.08 to 0.76]), VT2 (ES = 0.53, 90% CL [-0.05 to 1.1]), along with adaptations in the respiratory exchange ratio during high-intensity exercise (ES = -0.25, 90% CL [-0.62 to 0.12]). A large plasma volume expansion transpired 72 hours after intervention (PV% = 5.0%, 90% CL [1.3-8.7]) that contracted to a small expansion after 6 days (PV% = 1.6%, 90% CL [-1.0 to 4.2]). This investigation provides practitioners an alternative heat stress technique conducive for team sport, involving minimal exercise stress that can preserve maximal cardiovascular performance over periodized rest weeks within the yearly training plan. Furthermore, improvements in submaximal performance and a delayed hypervolemic response may provide a performance-enhancing effect when entering a 6-day competition period.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".