Background
Optimal prescription of resistance exercise load (kg) is essential for the development of maximal strength. Two methods are commonly used in practice with no clear consensus on the most ...effective approach for the improvement of maximal strength.
Objective
The primary aim of this review was to compare the effectiveness of percentage 1RM (% 1RM) and repetition maximum targets (RM) as load prescription methods for the development of maximal strength.
Methods
Electronic database searches of MEDLINE, SPORTDiscus, Scopus, and CINAHL Complete were conducted in accordance with PRISMA guidelines. Studies were eligible for inclusion if a direct measure of maximal strength was used, a non-training control group was a comparator, the training intervention was > 4 weeks in duration and was replicable, and participants were defined as healthy and between the ages of 18–40. Methodological quality of the studies was evaluated using a modified Downs and Black checklist. Percentage change (%) and 95% confidence intervals (CI) for all strength-based training groups were calculated. Statistical significance (
p
< 0.05) was reported from each study.
Results
Twenty-two studies comprising a total of 761 participants (585 males and 176 females) were found to meet the inclusion criteria. 12 studies were returned for % 1RM, with 10 for RM. All studies showed statistically significant improvements in maximal strength in the training groups (31.3 ± 21.9%; 95% CI 33.1–29.5%). The mean quality rating for all studies was 17.7 ± 2.3. Four studies achieved a good methodological rating, with the remainder classified as moderate.
Conclusions
Both % 1RM and RM are effective tools for improving maximal strength. % 1RM appears to be a better prescriptive method than RM potentially due to a more sophisticated management of residual fatigue. However, large heterogeneity was present within this data. Lower body and multi-joint exercises appear to be more appropriate for developing maximal strength. Greater consensus is required in defining optimal training prescriptions, physiological adaptations, and training status.
Thompson, SW, Lake, JP, Rogerson, D, Ruddock, A, and Barnes, A. Kinetics and kinematics of the free-weight back squat and loaded jump squat. J Strength Cond Res 37(1): 1-8, 2023-The aim of this study ...was to compare kinetics and kinematics of 2 lower-body free-weight exercises, calculated from concentric and propulsion subphases, across multiple loads. Sixteen strength-trained men performed back squat 1 repetition maximum (1RM) tests (visit 1), followed by 2 incremental back squat and jump squat protocols (visit 2) (loads = 0% and 30-60%, back squat 1RM). Concentric phase and propulsion phase force-time-displacement characteristics were derived from force plate data and compared using analysis of variance and Hedges' g effect sizes. Intrasession reliability was calculated using intraclass correlation coefficient (ICC) and coefficient of variation (CV). All dependent variables met acceptable reliability (ICC >0.7; CV < 10%). Statistically significant 3-way interactions (load × phase × exercise) and 2-way main effects (phase × exercise) were observed for mean force, velocity (30-60% 1RM), power, work, displacement, and duration (0%, 30-50% 1RM) ( p < 0.05). A significant 2-way interaction (load × exercise) was observed for impulse ( p < 0.001). Jump squat velocity ( g = 0.94-3.80), impulse ( g = 1.98-3.21), power ( g = 0.84-2.93), and work ( g = 1.09-3.56) were significantly larger across concentric and propulsion phases, as well as mean propulsion force ( g = 0.30-1.06) performed over all loads ( p < 0.001). No statistically significant differences were observed for mean concentric force. Statistically longer durations ( g = 0.38-1.54) and larger displacements ( g = 2.03-4.40) were evident for all loads and both subphases ( p < 0.05). Ballistic, lower-body exercise produces greater kinetic and kinematic outputs than nonballistic equivalents, irrespective of phase determination. Practitioners should therefore use ballistic methods when prescribing or testing lower-body exercises to maximize athlete's force-time-displacement characteristics.
Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has unique capabilities in the area of high-resolution mass spectrometric imaging of biological samples. The technique offers parallel ...detection of native and non-native molecules at physiological concentrations with potentially submicrometer spatial resolution. Recent advances in SIMS technology have been focused on generating new ion sources that can in turn be used to eject more intact molecular and biological characteristic species from a sample. The introduction of polyatomic ion beams, particularly C60, for TOF-SIMS analysis has created a whole new application of molecular depth profiling and 3D molecular imaging. However, such analyses, particularly at high lateral resolution, are severely hampered by the accompanying mass spectrometry associated with current TOF-SIMS instruments. Hence, we have developed an instrument that overcomes many of the drawbacks of current TOF-SIMS spectrometers by removing the need to pulse the primary ion beam. The instrument samples the secondary ions using a buncher that feeds into a specially designed time-of-flight analyzer. We have validated this new instrumental concept by analyzing a number of biological samples generating 2D and 3D images showing molecular localization on a subcellular scale, over a practical time frame, while maintaining high mass resolution. We also demonstrate large area mapping and the MS/MS capability of the instrument.
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) (R
) 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
, 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
). The Gymaware was the most valid device, with small systematic bias and no proportional or fixed bias evident across both exercises (R
> 0.42-0.99 LOA = -0.03-0.03 m·s
). 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 (R
> 0.00-0.96, LOA = -0.36‒0.46 m·s
). Linear position transducers and smartphone applications could be used to obtain velocity-based data, with inertial measurement units demonstrating poorer reliability and validity.
Understanding tissue structure and function requires tools that quantify the expression of multiple proteins while preserving spatial information. Here, we describe MIBI-TOF (multiplexed ion beam ...imaging by time of flight), an instrument that uses bright ion sources and orthogonal time-of-flight mass spectrometry to image metal-tagged antibodies at subcellular resolution in clinical tissue sections. We demonstrate quantitative, full periodic table coverage across a five-log dynamic range, imaging 36 labeled antibodies simultaneously with histochemical stains and endogenous elements. We image fields of view up to 800 μm × 800 μm at resolutions down to 260 nm with sensitivities approaching single-molecule detection. We leverage these properties to interrogate intrapatient heterogeneity in tumor organization in triple-negative breast cancer, revealing regional variability in tumor cell phenotypes in contrast to a structured immune response. Given its versatility and sample back-compatibility, MIBI-TOF is positioned to leverage existing annotated, archival tissue cohorts to explore emerging questions in cancer, immunology, and neurobiology.
To compare kinetic and kinematic data from 3 different velocity-based training sessions and a 1-repetition-maximum (1RM)-percent-based training (PBT) session using full-depth, free-weight back squats ...with maximal concentric effort.
Fifteen strength-trained men performed 4 randomized resistance-training sessions 96 h apart: PBT session involved 5 sets of 5 repetitions using 80% 1RM; load-velocity profile (LVP) session contained 5 sets of 5 repetitions with a load that could be adjusted to achieve a target velocity established from an individualized LVP equation at 80% 1RM; fixed sets 20% velocity loss threshold (FS
) session consisted of 5 sets at 80% 1RM, but sets were terminated once the mean velocity (MV) dropped below 20% of the threshold velocity or when 5 repetitions were completed per set; and variable sets 20% velocity loss threshold session comprised 25 repetitions in total, but participants performed as many repetitions in a set as possible until the 20% velocity loss threshold was exceeded.
When averaged across all repetitions, MV and peak velocity (PV) were significantly (P < .05) faster during the LVP (MV effect size ES = 1.05; PV ES = 1.12) and FS
(MV ES = 0.81; PV ES = 0.98) sessions compared with PBT. Mean time under tension (TUT) and concentric TUT were significantly less during the LVP sessions compared with PBT. The FS
sessions had significantly less repetitions, total TUT, and concentric TUT than PBT. No significant differences were found for all other measurements between any of the sessions.
Velocity-based training permits faster velocities and avoids additional unnecessary mechanical stress but maintains similar measures of force and power output compared with strength-oriented PBT in a single training session.
This paper describes a transformation technique aimed at solving the peak-to-average power ratio (PAPR) problem associated with OFDM (orthogonal frequency-division multiplexing). Constant envelope ...OFDM (CE-OFDM) transforms the OFDM signal, by way of phase modulation, to a signal designed for efficient power amplification. At the receiver, the inverse transformation - phase demodulation - is applied prior to the conventional OFDM demodulator. The performance of CE-OFDM is analyzed in additive white Gaussian noise (AWGN) and fading channels. CE-OFDM is shown to achieve good performance in dense multipath with the use of cyclic prefix transmission in conjunction with a frequency- domain equalizer (FDE). By way of computer simulation and hardware realization, CE-OFDM is shown to compare favorably to conventional OFDM.
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 (
/ηp2), Pearson correlation coefficients (
), paired
-tests, standard error of the estimate (SEE), and limits of agreement (LOA).
< 0.05. All models reported systematic bias < 10 kg,
> 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (
= 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (
< 0.001; ηp2 = 0.90) and back squat (
= 0.004, ηp2 = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (
< 0.001, ηp2 = 0.67-0.80), but not quadratic (
= 0.632-0.929, ηp2 = 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.
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