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Using Powerlifting Athletes to Determine Strength Adaptations Across Ages in Males and Females: A Longitudinal Growth Modelling Approach

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Abstract

Introduction

Several retrospective studies of strength sport athletes have reported strength adaptations over months to years; however, such adaptations are not linear.

Methods

We explored changes in strength over time in a large, retrospective sample of powerlifting (PL) athletes. Specifically, we examined the rate and magnitude of strength adaptation based on age category and weight class for PL competition total, and the squat, bench press, and deadlift, respectively. Mixed effects growth modelling was performed for each operationalised performance outcome (squat, bench press, deadlift, and total) as the dependent variables, with outcomes presented on both the raw, untransformed time scale and on the common logarithmic scale. Additionally, the fitted values were rescaled as a percentage.

Results

Collectively, the greatest strength gains were in the earliest phase of PL participation (~ 7.5–12.5% increase in the first year, and up to an ~ 20% increase after 10 years). Females tended to display faster progression, possibly because of lower baseline strength. Additionally, female Masters 3 and 4 athletes (> 59 years) still displayed ~ 2.5–5.0% strength improvement, but a slight strength loss was observed in Masters 4 (> 69 years) males (~ 0.35%/year).

Conclusion

Although directly applicable to PL, these findings provide population-level support for the role of consistent and continued strength training to improve strength across the age span and, importantly, to mitigate, or at least largely attenuate age-related declines in strength compared to established general population norms. This information should be used to encourage participation in strength sports, resistance training more generally, and to support future public health messaging.

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Notes

  1. Though, notably, partial pooling effects enable the estimation of effects for even those with missing data in mixed effects models (Gelman and Hill, 2006).

  2. Note, although for the visualisations shown in the results, we utilise the weight categories as reference values for extracting predicted values from the models, bodyweight was included in the models as a continuous time-varying covariate. The bodyweight recorded on the day of the competition was used, and as such, this allowed for it to vary within participants over time such that we could interpret the general effect of bodyweight. We assumed it to be an exogenous time-varying covariate, however, being only associated with past values of itself and not the outcome per se.

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Authors and Affiliations

  1. School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia

    Christopher Latella

  2. Neurophysiology Research Laboratory, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia

    Christopher Latella

  3. School of Health, University of the Sunshine Coast, Petrie, Queensland, Australia

    Daniel van den Hoek

  4. Department of Exercise Science and Recreation, Applied Muscle Development Laboratory, CUNY Lehman College, Bronx, NY, USA

    Milo Wolf & Patroklos Androulakis-Korakakis

  5. Department of Sport and Health, Solent University, Southampton, UK

    James P. Fisher & James Steele

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Correspondence to Christopher Latella.

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Latella, C., van den Hoek, D., Wolf, M. et al. Using Powerlifting Athletes to Determine Strength Adaptations Across Ages in Males and Females: A Longitudinal Growth Modelling Approach. Sports Med 54, 753–774 (2024). https://doi.org/10.1007/s40279-023-01962-6

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