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11 pages, 1058 KiB

Open AccessEditor’s ChoiceArticle

Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting

by Katja Oberhofer, Raphael Erni, Mark Sayers, Dominik Huber, Fabian Lüthy and Silvio Lorenzetti

Sports 2021, 9(9), 118; https://doi.org/10.3390/sports9090118 - 27 Aug 2021

Cited by 5 | Viewed by 5375

Abstract

The goal of this study was to assess the validity, reliability and accuracy of a smartwatch-based workout analysis application in exercise recognition, repetition count and One Repetition Maximum (1RM) prediction in the strength training-specific setting. Thirty recreationally trained athletes performed four consecutive sets [...] Read more.

The goal of this study was to assess the validity, reliability and accuracy of a smartwatch-based workout analysis application in exercise recognition, repetition count and One Repetition Maximum (1RM) prediction in the strength training-specific setting. Thirty recreationally trained athletes performed four consecutive sets of barbell deadlift, barbell bench press and barbell back squat exercises with increasing loads from 60% to 80% of their estimated 1RM with maximum lift velocity. Data was measured using an Apple Watch Sport and instantaneously analyzed using an iOS workout analysis application called StrengthControl. The accuracies in exercise recognition and repetition count, as well as the reliability in predicting 1RM, were statistically analyzed and compared. The correct strength exercise was recognised in 88.4% of all the performed sets (N = 363) with accurate repetition count for the barbell back squat (p = 0.68) and the barbell deadlift (p = 0.09); however, repetition count for the barbell bench press was poor (p = 0.01). Only 8.9% of attempts to predict 1RM using the StrengthControl app were successful, with failed attempts being due to technical difficulties and time lag in data transfer. Using data from a linear position transducer instead, significantly different 1RM estimates were obtained when analysing repetition to failure versus load-velocity relationships. The present results provide new perspectives on the applicability of smartwatch-based strength training monitoring to improve athlete performance. Full article

(This article belongs to the Special Issue New Perspectives in Resistance Training)

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12 pages, 1180 KiB

Open AccessArticle

A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models

by Steve W. Thompson, David Rogerson, Alan Ruddock, Leon Greig, Harry F. Dorrell and Andrew Barnes

Sports 2021, 9(7), 88; https://doi.org/10.3390/sports9070088 - 22 Jun 2021

Cited by 20 | Viewed by 8798

Abstract

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 [...] Read more.

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 (g/

η_{p}^{2}

), Pearson correlation coefficients (r), paired t-tests, standard error of the estimate (SEE), and limits of agreement (LOA). p < 0.05. All models reported systematic bias < 10 kg, r > 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (p = 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (p < 0.001;

η_{p}^{2}

= 0.90) and back squat (p = 0.004,

η_{p}^{2}

= 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001,

η_{p}^{2}

= 0.67–0.80), but not quadratic (p = 0.632–0.929,

η_{p}^{2}

= 0.001–0.18). Quadratic modeling employing the combined method rendered the greatest predictive validity. Practitioners should therefore utilize this method when looking to predict daily 1RMs as a means of load autoregulation. Full article

(This article belongs to the Special Issue New Perspectives in Resistance Training)

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16 pages, 950 KiB

Open AccessEditor’s ChoiceArticle

“Is It Overtraining or Just Work Ethic?”: Coaches’ Perceptions of Overtraining in High-Performance Strength Sports

by Lee Bell, Alan Ruddock, Tom Maden-Wilkinson, Dave Hembrough and David Rogerson

Sports 2021, 9(6), 85; https://doi.org/10.3390/sports9060085 - 7 Jun 2021

Cited by 10 | Viewed by 10834

Abstract

Optimal physical performance is achieved through the careful manipulation of training and recovery. Short-term increases in training demand can induce functional overreaching (FOR) that can lead to improved physical capabilities, whereas nonfunctional overreaching (NFOR) or the overtraining syndrome (OTS) occur when high training-demand [...] Read more.

Optimal physical performance is achieved through the careful manipulation of training and recovery. Short-term increases in training demand can induce functional overreaching (FOR) that can lead to improved physical capabilities, whereas nonfunctional overreaching (NFOR) or the overtraining syndrome (OTS) occur when high training-demand is applied for extensive periods with limited recovery. To date, little is known about the OTS in strength sports, particularly from the perspective of the strength sport coach. Fourteen high-performance strength sport coaches from a range of strength sports (weightlifting; n = 5, powerlifting; n = 4, sprinting; n = 2, throws; n = 2, jumps; n = 1) participated in semistructured interviews (mean duration 57; SD = 10 min) to discuss their experiences of the OTS. Reflexive thematic analysis resulted in the identification of four higher order themes: definitions, symptoms, recovery and experiences and observations. Additional subthemes were created to facilitate organisation and presentation of data, and to aid both cohesiveness of reporting and publicising of results. Participants provided varied and sometimes dichotomous perceptions of the OTS and proposed a multifactorial profile of diagnostic symptoms. Prevalence of OTS within strength sports was considered low, with the majority of participants not observing or experiencing long-term reductions in performance with their athletes. Full article

(This article belongs to the Special Issue New Perspectives in Resistance Training)

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8 pages, 237 KiB

Open AccessArticle

Use of Machine-Learning and Load–Velocity Profiling to Estimate 1-Repetition Maximums for Two Variations of the Bench-Press Exercise

by Carlos Balsalobre-Fernández and Kristof Kipp

Sports 2021, 9(3), 39; https://doi.org/10.3390/sports9030039 - 16 Mar 2021

Cited by 8 | Viewed by 4482

Abstract

The purpose of the current study was to compare the ability of five different methods to estimate eccentric–concentric and concentric-only bench-press 1RM from load–velocity profile data. Smith machine bench-press tests were performed in an eccentric–concentric (n = 192) and a concentric-only manner [...] Read more.

The purpose of the current study was to compare the ability of five different methods to estimate eccentric–concentric and concentric-only bench-press 1RM from load–velocity profile data. Smith machine bench-press tests were performed in an eccentric–concentric (n = 192) and a concentric-only manner (n = 176) while mean concentric velocity was registered using a linear position transducer. Load–velocity profiles were derived from incremental submaximal load (40–80% 1RM) tests. Five different methods were used to calculate 1RM using the slope, intercept, and velocity at 1RM (minimum velocity threshold—MVT) from the load–velocity profiles: calculation with individual MVT, calculation with group average MVT, multilinear regression without MVT, regularized regression without MVT, and an artificial neural network without MVT. Mean average errors for all methods ranged from 2.7 to 3.3 kg. Calculations with individual or group MVT resulted in significant overprediction of eccentric–concentric 1RM (individual MVT: difference = 0.76 kg, p = 0.020, d = 0.17; group MVT: difference = 0.72 kg, p = 0.023, d = 0.17). The multilinear and regularized regression both resulted in the lowest errors and highest correlations. The results demonstrated that bench-press 1RM can be accurately estimated from load–velocity data derived from submaximal loads and without MVT. In addition, results showed that multilinear regression can be used to estimate bench-press 1RM. Collectively, the findings and resulting equations should be helpful for strength and conditioning coaches as they would help estimating 1RM without MVT data. Full article

(This article belongs to the Special Issue New Perspectives in Resistance Training)

12 pages, 760 KiB

Open AccessArticle

Using Velocity to Predict the Maximum Dynamic Strength in the Power Clean

by G. Gregory Haff, Amador Garcia-Ramos and Lachlan P. James

Sports 2020, 8(9), 129; https://doi.org/10.3390/sports8090129 - 18 Sep 2020

Cited by 17 | Viewed by 4536

Abstract

The primary aim of the present study was to examine the commonly performed training exercise for athlete preparation. Twenty-two recreationally trained males (age: 26.3 ± 4.1 y, height: 1.80 ± 0.07 m; body mass (BM): 87.01 ± 13.75 kg, 1-repetitoon maximum(1-RM)/BM: 0.90 ± [...] Read more.

The primary aim of the present study was to examine the commonly performed training exercise for athlete preparation. Twenty-two recreationally trained males (age: 26.3 ± 4.1 y, height: 1.80 ± 0.07 m; body mass (BM): 87.01 ± 13.75 kg, 1-repetitoon maximum(1-RM)/BM: 0.90 ± 0.19 kg) participated in the present study. All subjects had their 1-RM power clean tested with standard procedures. On a separate testing day, subjects performed three repetitions at 30% and 45%, and two repetitions at 70% and 80% of their 1-RM power clean. During all trials during both sessions, peak velocity (PV) and mean velocity (MV) were measured with the use of a GymAware device. There were no significant differences between the actual and estimated 1-RM power clean (p = 0.37, ES = −0.11) when the load-PV profile was utilized. There was a large typical error (TE) present for the load-PV- and load-MV-estimated 1-RM values. Additionally, the raw TE exceeded the smallest worthwhile change for both load-PV and load-MV profile results. Based upon the results of this study, the load-velocity profile is not an acceptable tool for monitoring power clean strength. Full article

(This article belongs to the Special Issue New Perspectives in Resistance Training)

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19 pages, 2558 KiB

Open AccessArticle

The Reliability and Validity of Current Technologies for Measuring Barbell Velocity in the Free-Weight Back Squat and Power Clean

by Steve W. Thompson, David Rogerson, Harry F. Dorrell, Alan Ruddock and Andrew Barnes

Sports 2020, 8(7), 94; https://doi.org/10.3390/sports8070094 - 30 Jun 2020

Cited by 58 | Viewed by 11009

Abstract

This study investigated the inter-day and intra-device reliability, and criterion validity of six devices for measuring barbell velocity in the free-weight back squat and power clean. In total, 10 competitive weightlifters completed an initial one repetition maximum (1RM) assessment followed by three load-velocity [...] Read more.

This study investigated the inter-day and intra-device reliability, and criterion validity of six devices for measuring barbell velocity in the free-weight back squat and power clean. In total, 10 competitive weightlifters completed an initial one repetition maximum (1RM) assessment followed by three load-velocity profiles (40–100% 1RM) in both exercises on four separate occasions. Mean and peak velocity was measured simultaneously on each device and compared to 3D motion capture for all repetitions. Reliability was assessed via coefficient of variation (CV) and typical error (TE). Least products regression (LPR) (R²) and limits of agreement (LOA) assessed the validity of the devices. The Gymaware was the most reliable for both exercises (CV < 10%; TE < 0.11 m·s⁻¹, except 100% 1RM (mean velocity) and 90‒100% 1RM (peak velocity)), with MyLift and PUSH following a similar trend. Poorer reliability was observed for Beast Sensor and Bar Sensei (CV = 5.1–119.9%; TE = 0.08–0.48 m·s⁻¹). The Gymaware was the most valid device, with small systematic bias and no proportional or fixed bias evident across both exercises (R² > 0.42–0.99 LOA = −0.03–0.03 m·s⁻¹). Comparable validity data was observed for MyLift in the back squat. Both PUSH devices produced some fixed and proportional bias, with Beast Sensor and Bar Sensei being the least valid devices across both exercises (R² > 0.00–0.96, LOA = −0.36–0.46 m·s⁻¹). Linear position transducers and smartphone applications could be used to obtain velocity-based data, with inertial measurement units demonstrating poorer reliability and validity. Full article

(This article belongs to the Special Issue New Perspectives in Resistance Training)

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14 pages, 1572 KiB

Open AccessArticle

Individual Sprint Force-Velocity Profile Adaptations to In-Season Assisted and Resisted Velocity-Based Training in Professional Rugby

by Johan Lahti, Pedro Jiménez-Reyes, Matt R. Cross, Pierre Samozino, Patrick Chassaing, Benjamin Simond-Cote, Juha P. Ahtiainen and Jean-Benoit Morin

Sports 2020, 8(5), 74; https://doi.org/10.3390/sports8050074 - 25 May 2020

Cited by 53 | Viewed by 15476

Abstract

We tested the hypothesis that the degree of adaptation to highly focused sprint training at opposite ends of the sprint Force-Velocity (FV) spectrum would be associated with initial sprint FV profile in rugby athletes. Training-induced changes in sprint FV profiles were computed before [...] Read more.

We tested the hypothesis that the degree of adaptation to highly focused sprint training at opposite ends of the sprint Force-Velocity (FV) spectrum would be associated with initial sprint FV profile in rugby athletes. Training-induced changes in sprint FV profiles were computed before and after an eight-week in-season resisted or assisted sprint training protocol, including a three-week taper. Professional male rugby players (age: 18.9 ± 1.0 years; body height: 1.9 ± 0.0 m; body mass: 88.3 ± 10.0 kg) were divided into two groups based on their initial sprint FV profiles: 1) Heavy sled training (RESISTED, N = 9, velocity loss 70–80%), and 2) assisted acceleration training (ASSISTED, N = 12, velocity increase 5–10%). A total of 16 athletes were able to finish all required measurements and sessions. According to the hypothesis, a significant correlation was found between initial sprint FV profile and relative change in sprint FV profile (RESISTED: r = −0.95, p < 0.01, ASSISTED: r = −0.79, p < 0.01). This study showed that initial FV properties influence the degree of mechanical response when training at different ends of the FV spectrum. Practitioners should consider utilizing the sprint FV profile to improve the individual effectiveness of resisted and assisted sprint training programs in high-level rugby athletes. Full article

(This article belongs to the Special Issue New Perspectives in Resistance Training)

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8 pages, 516 KiB

Open AccessArticle

A Cluster Set Protocol in the Half Squat Exercise Reduces Mechanical Fatigue and Lactate Concentrations in Comparison with a Traditional Set Configuration

by Daniel Varela-Olalla, Alejandro Romero-Caballero, Juan Del Campo-Vecino and Carlos Balsalobre-Fernández

Sports 2020, 8(4), 45; https://doi.org/10.3390/sports8040045 - 4 Apr 2020

Cited by 7 | Viewed by 4967

Abstract

Splitting sets into clusters has been shown to maintain performance during resistance training. This study compared the acute fatigue produced by a traditional (TSC) versus a cluster (CSC) set configuration in the smith machine half squat exercise. Fifteen males performed a single bout [...] Read more.

Splitting sets into clusters has been shown to maintain performance during resistance training. This study compared the acute fatigue produced by a traditional (TSC) versus a cluster (CSC) set configuration in the smith machine half squat exercise. Fifteen males performed a single bout of TSC and CSC separated by 72–96 h. In the TSC, participants performed as many repetitions as possible until reaching a 20% drop in barbell velocity (MPV), while in the CSC, they performed the same number of repetitions with 15 seconds inter-repetition rest. Effects of both protocols in MPV, countermovement jump height (CMJ), and blood lactate (BLa) were measured. Significant differences between protocols were found for MPV of the last repetition (0.4 vs 0.5 m/s TSC and CSC) and BLa (6.8 mmol/L vs 3.2 mmol/L TSC and CSC). Significant drop of velocity from the first to the last repetition of the set (19.9%), decrease in CMJ height (35.4 vs 32.6 cm), and increase in BLa (2.1 vs 6.8 mmol/L) pre–post-exercise was observed just for the TSC protocol. The results of the present study showed that CSC reduces the lactate response and mechanical fatigue produced by a single set on the half squat exercise in comparison with TSC. Full article

(This article belongs to the Special Issue New Perspectives in Resistance Training)

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Review

Jump to: Research

14 pages, 2338 KiB

Open AccessEditor’s ChoiceReview

The Implementation of Velocity-Based Training Paradigm for Team Sports: Framework, Technologies, Practical Recommendations and Challenges

by Carlos Balsalobre-Fernández and Lorena Torres-Ronda

Sports 2021, 9(4), 47; https://doi.org/10.3390/sports9040047 - 30 Mar 2021

Cited by 27 | Viewed by 17152

Abstract

While velocity-based training is currently a very popular paradigm to designing and monitoring resistance training programs, its implementation remains a challenge in team sports, where there are still some confusion and misinterpretations of its applications. In addition, in contexts with large squads, it [...] Read more.

While velocity-based training is currently a very popular paradigm to designing and monitoring resistance training programs, its implementation remains a challenge in team sports, where there are still some confusion and misinterpretations of its applications. In addition, in contexts with large squads, it is paramount to understand how to best use movement velocity in different exercises in a useful and time-efficient way. This manuscript aims to provide clarifications on the velocity-based training paradigm, movement velocity tracking technologies, assessment procedures and practical recommendations for its application during resistance training sessions, with the purpose of increasing performance, managing fatigue and preventing injuries. Guidelines to combine velocity metrics with subjective scales to prescribe training loads are presented, as well as methods to estimate 1-Repetition Maximum (1RM) on a daily basis using individual load–velocity profiles. Additionally, monitoring strategies to detect and evaluate changes in performance over time are discussed. Finally, limitations regarding the use of velocity of execution tracking devices and metrics such as “muscle power” are commented upon. Full article

(This article belongs to the Special Issue New Perspectives in Resistance Training)

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