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Record W7033010347

Optimization of post-exercise recovery beverages’ composition

2019· article· en· W7033010347 on OpenAlexaboutno aff

Bibliographic record

VenueUnicam Scientific Publications (University of Camerino) · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSubterranean biodiversity and taxonomy
Canadian institutionsnot available
Fundersnot available
KeywordsDiafiltrationLimitingProtein isoformMyoglobinuria
DOInot available

Abstract

fetched live from OpenAlex

Fluid intake and adequate hydration are essential and critical during and after training sessions and competition events. Reductions in body water content will prejudice performance with exercise performance significantly impaired when 2% or more of body weight is lost through sweat [1-3]. Guidelines states that in the post exercise (within 30 minutes to 1 hour after the end of the physical exercise), effective rehydration requires the intake of a volume of fluid equal to, at least, the 150% of the difference between body weight before and after the training [4] but a lack is still present regarding specific guidelines related to beverage formulations, strategies and volumes.
\nIndeed, in addition to water, sweat contains variable amounts of sodium, with lesser amounts of potassium, calcium, and magnesium so it is important that athletes can benefit from intake of an appropriate amount of a well-formulated drink [5-9]. 
\nOur prevoius studies underlined the potential role of skimmed milk as post recovery drink possibly linked to its specific characteristics [10-12]. In particular, milk has naturally high concentrations of electrolytes, that should aid in fluid recovery following exercise [4, 13].
\nMoreover, it contains casein and whey proteins in a ratio of 3:1 which provides for slower digestion and absorption of these proteins resulting in sustained elevations of blood amino acid concentrations.
\nIn this regard, the aim of this study was to further evaluate the role of milk as post-recovery beverage. In particular, skimmed milk was tested in different volumes (no diluted skimmed milk, skimmed milk diluted 1:2 and 1:3) in order to better evaluate its role and to optimize volume. Moreover, specific nutrients (i.e. Casein, Sodium) and their possible combinations (Casein + Sodium, Whey proteins + Sodium) were taken into account in different concentrations to investigate possible role of specific nutrients. 
\n30 athletes, both male and female, aged between 19 and 47, took part to the study. Each strategy was tested for one week with an intake equal to 500ml of the specific drink with the addiction of an amount of water equal to the volume needed to reach the 150% of loss body weight during the exercise.
\nA specific questionnaire was performed to ask about type of activity performed, the intensity of the activity, the urine colour, the thirst sensation after waking up in the morning; eventual cramps. Anthropometric measurement and Bioimpedence Analysis (both mono-frequency and multi-frequency) were performed to assess body composition and hydration in term of total body water, intra-cellular water and extra-cellular water.
\nFollowing guidelines indications, through the use of 150% of water intake in the post exercise, a variability among subjects were observed with cases also of worsening of hydration status underlining the importance of beverage characteristics as several elements might affect fluid balance: the macronutrient content, the electrolyte (i.e. sodium and potassium) [5-9].
\nAmong the different strategies tested, skimmed milk resulted again the best one, able both to improve total body water in terms of intra-cellular water and body composition more than single milk nutrients. This underlines the importance of further investigate milk as post-exercise recovery beverage in terms not only of nutrients content but also nutrients synergism.
\nEven if data obtained by this study are limited by the restricted number of athletes, obtained results can be considered a step ahead to better evaluate the role of milk as recovery beverages, to define its applicability and to study new hydration protocols in order to improve the present knowledge on athlete’s hydration and recovery. 
\n
\nReferences
\n
\n[1]\tMaughan RJ. Investigating the associations between hydration and exercise performance: methodology and limitations. Nutr Rev. 2012;70(suppl_2):S128-S131.
\n[2]\tMaughan RJ, Shirreffs SM, Leiper JB. Errors in the estimation of hydration status from changes in body mass. J Sports Sci. 2007;25(7):797-804.
\n[3]\tGoulet ED, et al. Pre-exercise hyperhydration delays dehydration and improves endurance capacity during 2 h of cycling in a temperate climate. J Physiol Anthropol. 2008;27(5):263-71.
\n[4]\tShirreffs SM, Watson P, Maughan RJ. Milk as an effective post-exercise rehydration drink. Br J Nutr. 2007; 98(1):173-180.
\n[5]\tThomas DT, Erdman KA, Burke LM. Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and Athletic Performance. J Acad Nutr Diet. 2016;116(3):501-528.
\n[6]\tBrancaccio P, et al. Supplementation of Acqua Lete(R) (Bicarbonate Calcic Mineral Water) improves hydration status in athletes after short term anaerobic exercise. J Int Soc Sports Nutr. 2012; 9(1):35.
\n[7]\tMaughan RJ, et al. A randomized trial to assess the potential of different beverages to affect hydration status: development of a beverage hydration index. Am J Clin Nutr. 2016;103(3):717-23.
\n[8]\tKalman DS, et al. Comparison of coconut water and a carbohydrate-electrolyte sport drink on measures of hydration and physical performance in exercise-trained men. J Int Soc Sports Nutr. 2012;9(1):1.
\n[9]\tOliver S, et al. Development of a hydration index: a randomized trial to assess the potential of different beverages to affect hydration status. Nutr Hosp. 2015;32 Suppl 2:10264.
\n[10]\tVici G, Albertini F, Quintavalle A, Belli L, Polzonetti V. Milk as recovery drink after exercise: a case study. Alimenti Funzionali e Nutraceutici per la Salute. Camerino, 28th June 2016
\n[11]\tVici G, Camilletti D, Cesanelli L, Belli L, Polzonetti V. Effects of different nutritional strategies in post exercise recovery. Cibo e Nutraceutici: direzione salute. Camerino, 10th July 2018
\n[12]\tVici G, Camilletti D, Mozzoni A, Cesanelli L, Belli L, Polzonetti V. Effects of specific re-hydration protocols after exercise in non-elite and elite athletes. 3rd Sport Nutrition International Conference. Bologna, 30th November 2018
\n[13]\tRoy BD. Milk: the new sports drink? A Review. J Int Soc Sports Nutr. 2008;5:15.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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.

Opus teacher head0.015
GPT teacher head0.163
Teacher spread0.148 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
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