A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models.
1RM estimation
1RM prediction
linear regression
load-velocity profiling
maximal strength
Journal
Sports (Basel, Switzerland)
ISSN: 2075-4663
Titre abrégé: Sports (Basel)
Pays: Switzerland
ID NLM: 101722684
Informations de publication
Date de publication:
22 Jun 2021
22 Jun 2021
Historique:
received:
23
05
2021
revised:
16
06
2021
accepted:
17
06
2021
entrez:
2
7
2021
pubmed:
3
7
2021
medline:
3
7
2021
Statut:
epublish
Résumé
The study aim was to compare different predictive models in one repetition maximum (1RM) estimation from load-velocity profile (LVP) data. Fourteen strength-trained men underwent initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Profiles were constructed via a combined method (jump squat (0 load, 30-60% 1RM) + back squat (70-100% 1RM)) or back squat only (0 load, 30-100% 1RM) in 10% increments. Quadratic and linear regression modeling was applied to the data to estimate 80% 1RM (kg) using 80% 1RM mean velocity identified in LVP one as the reference point, with load (kg), then extrapolated to predict 1RM. The 1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (
Identifiants
pubmed: 34206534
pii: sports9070088
doi: 10.3390/sports9070088
pmc: PMC8309813
pii:
doi:
Types de publication
Journal Article
Langues
eng
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