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Enregistrement W2971098437 · doi:10.28985/jsc.v8i2.480

Reliability of acute training responses elicited by exhaustive work intervals prescribed with the delta concept

2019· article· en· W2971098437 sur OpenAlexaboutno aff
Arthur Henrique Bossi

Notice bibliographique

RevueJournal Of Science & Cycling · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueSports Performance and Training
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésReliability (semiconductor)DeltaWork (physics)MathematicsStatisticsEngineering

Résumé

récupéré en direct d'OpenAlex

Reliability of acute training responses elicited by exhaustive work intervals prescribed with the delta concept  Arthur H. Bossi 1 , Wouter P. Timmerman 1 , Louis Passfield 1,2 , James G. Hopker 1 1 School of Sport and Exercise Sciences, University of Kent, Chatham, Kent, England 2 Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada  Correspondence: Arthur Henrique Bossi School of Sport and Exercise Sciences University of Kent at Medway Medway Building Chatham Maritime Chatham, Kent ME4 4AG England asnb3@kent.ac.uk +447398944056       Background: Biomolecular research has suggested that the chronic adaptation to an exercise programme is modulated by the extent individual training sessions produce homeostatic stress. Hence, training intensity must be carefully prescribed to ensure the expected stimulus for adaptation is provided. To this end, the delta concept (%I”) has been proposed as an intensity prescription method to minimising inter-individual variability of physiological and perceptual responses—expressed by: WI‡ prescribed = WI‡ GET + [(WI‡ VI‡O2max – WI‡ GET ) · %I”]  where WI‡ prescribed is the set work rate, WI‡ GET is the work rate associated with the gas exchange threshold, WI‡ VI‡O2max is the work rate associated with the maximal oxygen uptake and %I” is the targeted intensity. Surprisingly, the inter-individual variability of acute training responses has not been investigated during high-intensity interval training (HIIT), despite HIIT being commonly performed in the laboratory and field settings. Moreover, the intra-individual variability of acute HIIT responses has not been established in cycling, which is vital to understand whether the anticipated training stimulus is achieved whenever a session is performed.  Purpose: We explored the levels of inter- and intra-individual variability of acute training responses elicited by exhaustive work intervals prescribed with %I”.  Methods: Eighteen male and four female cyclists [age: 36 ± 12 years, height: 178 ± 10 cm, body mass: 75.2 ± 13.7 kg, VI‡O 2max : 52 ± 5 ml·kg -1 ·min -1 , peak power output (PPO): 4.72 ± 0.48 W·kg -1 ] volunteered for this study. They performed a ramp test in the first visit to determine VI‡O 2max , PPO, WI‡ GET and WI‡ VI‡O2max . The next four visits consisted of a standardised 21-min warm-up and a HIIT session performed to exhaustion [i.e. 4-min work intervals at 70%I” (WI‡ prescribed = 4.00 ± 0.43 W·kg -1 , 84.7 ± 0.4 %PPO), interspersed with 2-min active recovery at 0.2·WI‡ prescribed ]. Breath-by-breath gas exchanges and heart rate (HR) were continuously measured, with ratings of perceived exertion (RPE) and blood lactate concentration ([La]) obtained after each work interval and at exhaustion. Time at >90%VI‡O 2max was quantified as absolute values and as a percentage of the time to exhaustion. One-way repeated measures analysis of variance was used to test for systematic changes between HIIT sessions. Statistical significance was set at P ≤ 0.1. Reliability estimates [typical error (TE), coefficient of variation (CV), and intraclass correlation coefficient (ICC)] were obtained through Hopkins spreadsheet and are reported with 90% confidence limits. Based on the acquired data, we used G*Power software to perform sample size estimations for a two-tailed matched paired t-test, with alpha error probability set at 0.05 and power at 0.80.  Results: Time to exhaustion, absolute and relative time at >90%VI‡O 2max , peak HR, peak RPE, and peak [La] were not different between HIIT sessions (all F ≤ 2.10, P ≥ 0.13, I· 2 p ≤ 0.09). Dependent variables and their reliability estimates are reported in Table 1. Sample size estimations are reported in Table 2.  Table 1. Reliability data [with 90% confidence limits] for the dependent variables.  mean ± SD TE ICC intra-individual CV (%) inter-individual CV (%)  Time to exhaustion (s) 1219 ± 618 244 [210-294] 0.86 [0.76-0.92] 31.0 67.0  Absolute time at >90%VI‡O 2max (s) 502 ± 366 137 [118-165] 0.87 [0.79-0.93] 67.0 139.3  Relative time at >90%VI‡O 2max (%) 57.0 ± 22.0 14.2 [12.2-17.1] 0.61 [0.43-0.77] 63.7 109.2  Peak HR (b·min -1 ) 179 ± 11 2 [1.7-2.4] 0.97 [0.94-0.98] 1.2 6.2  Peak RPE 19.6 ± 0.8 0.3 [0.3-0.4] 0.85 [0.75-0.92] 1.9 4.7  Peak [La] (mmol·L -1 ) 14.3 ± 2.6 2.0 [1.7-2.4] 0.45 [0.25-0.65] 15.0 20.4  SD, standard deviation; TE, typical error; ICC, intraclass correlation coefficient; CV, coefficient of variation; HR, heart rate; RPE, ratings of perceived exertion; [La], blood lactate concentration  Table 2. Required sample size to detect baseline changes for a given variable analysed with two-tailed matched paired t-test, with alpha error probability set at 0.05 and power at 0.80.  2% 5% 10% 20% 30%  Time to exhaustion (s) 1411 228 59 17 9  Absolute time at >90%VI‡O 2max (s) 2854 458 116 31 15  Relative time at >90%VI‡O 2max (%) 2421 389 99 27 13  Peak HR (b·min -1 ) 8 4 3 3 2  Peak RPE 14 5 3 3 2  Peak [La] (mmol·L -1 ) 724 119 32 10 6  HR, heart rate; RPE, ratings of perceived exertion; [La], blood lactate concentration  Discussion: Although participants consistently achieved peak values of HR, RPE, and to a lesser extent [La], there was substantial inter-individual variability in time to exhaustion, and both absolute and relative time at >90%VI‡O 2max . Importantly, inter-individual variability was much higher than intra-individual, suggesting a greater day-to-day consistency would still produce marked heterogeneity between participants. This raises questions over the validity of %I” to normalise acute HIIT responses. The levels of intra-individual variability also cast doubt on the assumption that a similar stimulus for adaptation is triggered every time a standard HIIT session is performed. Besides the effect on sample size estimations, achievable only if studies aim to detect large changes, this result also suggests athletes may not need to overly adhere to the prescribed power output during HIIT.  Conclusions: In contrast to previous suggestions based on continuous exercise, %I” does not produce consistent inter- and intra-individual acute HIIT responses. Future studies should consider alternative methods for training intensity normalisation. Whether day-to-day consistency in training stimulus is a pre-requisite for optimal adaptation following HIIT is another question that merits investigation.  Acknowledgements: A.H.B. is a CNPq (Conselho Nacional de Desenvolvimento CientA­fico e TecnolA³gico) scholarship holder [200700/2015-4]. Â

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,311
Écart entre enseignants0,286 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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