Assessing VO₂max Trainability: The Role of Standard Deviation in Individual Response to Aerobic Exercise
Notice bibliographique
Résumé
Background: Despite extensive research on the effects of exercise training on VO2max, current literature still lacks to be more conclusive regarding the optimal protocols for maximizing these effects. Variability in study designs, participant populations, and measurement techniques has contributed to inconsistencies in findings, leaving important questions unanswered regarding the most effective exercise strategies. This systematic review aimed to assess whether significant inter-individual differences exist in VO₂max trainability across different aerobic training protocols, using non-exercising comparator groups. Methods: A comprehensive literature search was conducted using Covidence, a web-based platform for systematic reviews to screen studies from three databases: EMBASE, PubMed, and SCOPUS. The search focused on two key concepts VO₂max and aerobic exercise training. Studies were included if they met the following criteria: involved human participants, implemented supervised and standardized training protocols, measured absolute or relative VO₂max, included a non-exercising control group, reported VO₂max change scores for both groups, and provided standard deviation (SD) of change. The standard deviation of individual response (SDIR) was calculated to evaluate variability in trainability across studies. Results: Out of 32,968 screened studies, 24 met the inclusion criteria and were analyzed. The findings indicated that: (1) most observed variation in VO₂max change scores can be attributed to measurement error, (2) estimating SDIR from a single study lacks precision due to typically small sample sizes, and (3) meta-analysis of SDIR across studies does not provide compelling evidence for meaningful inter-individual differences in VO₂max response. Discussion: While past research has debated whether some people respond better to aerobic training than others, our findings indicate that much of the variation in VO₂max improvements is due to measurement error rather than true biological differences. One major challenge in studying VO₂max trainability is the small sample sizes in most studies. When only a few participants are included in a study, it becomes difficult to tell whether differences in VO₂max improvements are real or just due to random variation. Even when data is combined across multiple studies in a meta-analysis, there is still no strong evidence that meaningful individual differences in VO₂max response exist. Another factor that complicates this research is the inconsistency in training programs, participant characteristics, and VO₂max measurement methods. Differences in exercise intensity, duration, and frequency, as well as variations in how VO₂max is tested, make it harder to compare results across studies. If future research aims to better understand VO₂max trainability, it will need more standardized training protocols and measurement techniques. These findings also challenge the idea that some people are naturally “high responders” or “low responders” to aerobic exercise. Instead of focusing on predicting individual responses, training programs should prioritize general strategies that help most people improve their VO₂max. If true individual differences in trainability exist, they are difficult to detect with the methods currently used. Conclusion: The meta-analysis does not support the existence of strong inter-individual variability in VO₂max trainability within single interventions. Consequently, the likelihood of identifying clinically significant predictors of VO₂max response appears to be low.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».