A mathematical model of the effects of resistance exercise-induced muscle hypertrophy on body composition

Torres, Marcella; Trexler, Eric T.; Smith‐Ryan, Abbie E.; Reynolds, Angela

doi:10.1007/s00421-017-3787-6

Cited by 7 publications

(7 citation statements)

References 53 publications

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“…Nevertheless, the work by Morton et al [6] focuses on the effect of protein intake on muscle gain, without addressing the effect of training without protein supplementation; while the model created by Torres et al is a theoretical model focused on fat loss and lean mass gain in obese people, not determining the effect of the different training parameters. In fact, the authors declare that further models may be developed and redefined to include parameters they had not addressed [7]. Therefore, it seems that a really integrative model, taking into account all the variables that influence muscle hypertrophy, is currently lacking.…”

Section: Introductionmentioning

confidence: 99%

“…In this respect, muscle hypertrophy in humans is influenced by numerous variables, from those inherent to exercise such as mechanical tension, metabolic stress, muscle damage, training volume and intensity [8,9]; to those being specific to the individual (age, sex, previous training status, etc.) [10], or those related to energy balance and protein metabolism [6,7]. Consequently, any study aiming to analyse and estimate muscle hypertrophy in humans must precisely address all confounding factors that may completely alter the final results.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned above, only two studies have attempted to estimate the changes in muscle mass after a training intervention [6,7]. However, none of them handle the effect of these confounding factors that we have mentioned.…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Review with Meta-Analysis of the Effect of Resistance Training on Whole-Body Muscle Growth in Healthy Adult Males

Benito

Cupeiro

Ramos-Campo

et al. 2020

IJERPH

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We performed a systematic review and meta-analysis to study all published clinical trial interventions, determined the magnitude of whole-body hypertrophy in humans (healthy males) and observed the individual responsibility of each variable in muscle growth after resistance training (RT). Searches were conducted in PubMed, Web of Science and the Cochrane Library from database inception until 10 May 2018 for original articles assessing the effects of RT on muscle size after interventions of more than 2 weeks of duration. Specifically, we obtain the variables fat-free mass (FMM), lean muscle mass (LMM) and skeletal muscle mass (SMM). The effects on outcomes were expressed as mean differences (MD) and a random-effects meta-analysis and meta-regressions determined covariates (age, weight, height, durations in weeks…) to explore the moderate effect related to the participants and characteristics of training. One hundred and eleven studies (158 groups, 1927 participants) reported on the effects of RT for muscle mass. RT significantly increased muscle mass (FFM+LMM+SMM; Δ1.53 kg; 95% CI [1.30, 1.76], p < 0.001; I2 = 0%, p = 1.00). Considering the overall effects of the meta-regression, and taking into account the participants’ characteristics, none of the studied covariates explained any effect on changes in muscle mass. Regarding the training characteristics, the only significant variable that explained the variance of the hypertrophy was the sets per workout, showing a significant negative interaction (MD; estimate: 1.85, 95% CI [1.45, 2.25], p < 0.001; moderator: -0.03 95% CI [−0.05, −0.001] p = 0.04). In conclusion, RT has a significant effect on the improvement of hypertrophy (~1.5 kg). The excessive sets per workout affects negatively the muscle mass gain.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Systematic Review with Meta-Analysis of the Effect of Resistance Training on Whole-Body Muscle Growth in Healthy Adult Males

Benito

Cupeiro

Ramos-Campo

et al. 2020

IJERPH

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“…Previous studies have indicated that older individuals with sarcopenia who receive RET interventions with training durations of 8–24 weeks can achieve greater changes in appendicular LM (MD = 0.45–1.20 kg) [ 15 , 48 , 49 , 50 ] and AMI (MD = 0.17–1.16 kg/m 2 ) [ 48 , 49 , 50 ] compared with their untrained peers, regardless of the RET protocol. The training period required to achieve evident LM gains in response to RET was estimated as 8–12 weeks for older individuals [ 51 ]. Following previous results, a 12-week RET program was employed in the present study for overweight and obese older women who had received TKR; the EG made greater gains in appendicular LM (MD = 1.09 kg) and AMI (MD = 0.24 kg/m 2 ) than did the CG.…”

Section: Discussionmentioning

confidence: 99%

Effects of Elastic Resistance Exercise on Postoperative Outcomes Linked to the ICF Core Sets for Osteoarthritis after Total Knee Replacement in Overweight and Obese Older Women with Sarcopenia Risk: A Randomized Controlled Trial

Liao

Chiu

et al. 2020

JCM

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(1) Background: Knee osteoarthritis (KOA) and aging are associated with high sarcopenia risk; sarcopenia may further affect outcomes after total knee replacement (TKR). Elastic resistance exercise training (RET) limits muscle attenuation in older adults. We aimed to identify the effects of post-TKR elastic RET on lean mass (LM) and functional outcomes in overweight and obese older women with KOA by using the brief International Classification of Functioning, Disability and Health Core Set for osteoarthritis (Brief-ICF-OA). (2) Methods: Eligible women aged ≥60 years who had received unilateral primary TKR were randomly divided into an experimental group (EG), which received postoperative RET twice weekly for 12 weeks, and a control group (CG), which received standard care. The primary and secondary outcome measures were LM and physical capacity, respectively, and were linked to the Brief-ICF-OA. The assessment time points were 2 weeks prior to surgery (T0) and postoperative at 1 month (T1; before RET) and 4 months (T2; upon completion of RET) of follow-up. An independent t test with an intention-to-treat analysis was conducted to determine the between-group differences in changes of outcome measures at T1 and T2 from T0. (3) Results: Forty patients (age: 70.9 ± 7.3 years) were randomly assigned to the EG (n = 20) or CG (n = 20). At T2, the EG exhibited significantly greater improvements in leg LM (mean difference (MD) = 0.86 kg, p = 0.004) and gait speed (MD = 0.26 m/s, p = 0.005) compared with the CG. Furthermore, the EG generally obtained significantly higher odds ratios than the CG for treatment success for most Brief-ICF-OA categories (all p < 0.001). Conclusions: Early intervention of elastic RET after TKR yielded positive postoperative outcomes based on the Brief-ICF-OA. The findings of this study may facilitate clinical decision-making regarding the optimal post-TKR rehabilitation strategy for older women with KOA.

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“…Torres et al [44] extended an energy balance model to account for the hypertrophic effects of resistance training and used the model for simulation studies. Moreover, the model was fit to data from elderly subjects following a resistance training routine.…”

Section: Existing Modelsmentioning

confidence: 99%

A mathematical model-based approach to optimize loading schemes of isometric resistance training sessions

Herold

Sommer

2020

Preprint

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Individualized resistance training (RT) is necessary to optimize training results. A model-based optimization of loading schemes could provide valuable impulses for practitioners and complement the predominant manual program design by customizing the loading schemes to the trainee and the training goals. We compile a literature overview of model-based approaches used to simulate or optimize the response to single RT sessions or to longterm RT plans in terms of strength, power, muscle mass, or local muscular endurance by varying the loading scheme. To the best of our knowledge, contributions employing a predictive model to algorithmically optimize loading schemes for different training goals are nonexistent in the literature. Thus, we propose to set up optimal control problems as follows. For the underlying dynamics, we use a phenomenological model of the time course of maximum voluntary isometric contraction force. Then, we provide mathematical formulations of key performance indicators for loading schemes identified in sport science and use those as objective functionals or constraints. We then solve those optimal control problems using previously obtained parameter estimates for the elbow flexors. We discuss our choice of training goals, analyze the structure of the computed solutions, and give evidence of their real-life feasibility. The proposed optimization methodology is independent from the underlying model and can be transferred to more elaborate physiological models once suitable ones become available.

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A mathematical model of the effects of resistance exercise-induced muscle hypertrophy on body composition

Abstract: The model is able to reproduce the time course of change in LM in response to RE and can be used to generate a simulated cohort for in silico clinical studies. Simulations suggest that the additional LM generated by RE may shift the body composition to a healthier state.

Cited by 7 publications

References 53 publications

A Systematic Review with Meta-Analysis of the Effect of Resistance Training on Whole-Body Muscle Growth in Healthy Adult Males

A Systematic Review with Meta-Analysis of the Effect of Resistance Training on Whole-Body Muscle Growth in Healthy Adult Males

Effects of Elastic Resistance Exercise on Postoperative Outcomes Linked to the ICF Core Sets for Osteoarthritis after Total Knee Replacement in Overweight and Obese Older Women with Sarcopenia Risk: A Randomized Controlled Trial

A mathematical model-based approach to optimize loading schemes of isometric resistance training sessions

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