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LeMense, AT, Malone, GT, Kinderman, MA, Fedewa, MV, and Winchester, LJ. Validity of using the load-velocity relationship to estimate 1 repetition maximum in the back squat exercise: a systematic review and meta-analysis. J Strength Cond Res 38(3): 612–619, 2024—The one repetition maximum (1RM) test is commonly used to assess muscular strength. However, 1RM testing can be time consuming, physically taxing, and may be difficult to perform in athletics team settings with practice and competition schedules. Alternatively, 1RM can be estimated from bar or movement velocity at submaximal loads using the minimum velocity threshold (MVT) method based on the load-velocity relationship. Despite its potential utility, this method's validity has yielded inconsistent results. The purpose of this systematic review and meta-analysis was to assess the validity of estimated 1RM from bar velocity in the back squat exercise. A systematic search of 3 electronic databases was conducted using combinations of the following keywords: "velocity-based training," "load-velocity profiling," "mean velocity," "mean propulsive velocity," "peak velocity," "maximal strength," "1RM," "estimation," "prediction," "back squat," and "regression." The search identified 372 unique articles, with 4 studies included in the final analysis. Significance was defined as a p level less than 0.05. A total of 27 effects from 71 subjects between the ages of 17–25 years were analyzed; 85.2% of effects were obtained from male subjects. Measured 1RMs ranged from 86.5 to 153.1 kg, whereas estimated 1RMs ranged from 88.6 to 171.6 kg. Using a 3-level random effects model, 1RM back squat was overestimated when derived from bar velocity using the MVT method (effect sizes [ES] = 0.5304, 95% CI: 0.1878–0.8730, p = 0.0038). The MVT method is not a viable option for estimating 1RM in the free weight back squat. Strength and conditioning professionals should exercise caution when estimating 1RM from the load-velocity relationship. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study was to compare a single two-dimensional image processing system (IMAGE) to underwater weighing (UWW) for measuring body volume (BV) and subsequently estimating body fat percentage (%Fat), fat mass (FM), and fat-free mass (FFM) via a 3-compartment (3C) model. Methods: A sample of participants age 18–39 yr was recruited for this study (n = 67, 47.8% female). BV was measured with UWW and predicted via the IMAGE software. The BV estimates from UWW (3CUWW) and IMAGE (3CIMAGE) were separately combined with constant total body water and body mass values for 3C model calculation of %Fat, FM, and FFM. Results: BV obtained from the IMAGE was 67.76 ± 12.19 and 67.72 ± 12.04 L from UWW, which was not significantly different (P = 0.578) and very largely correlated (r = 0.99, P < 0.001). When converted to %Fat (3CUWW = 21.01% ± 7.30%, 3CIMAGE = 21.08% ± 7.04%, P = 0.775), FM (3CUWW = 14.68 ± 5.15 kg, 3CIMAGE = 14.78 ± 5.08 kg, P = 0.578), and FFM (3CUWW = 57.00 ± 13.20 kg, 3CIMAGE = 56.90 ± 12.84 kg, P = 0.578) with the 3C model, no significant mean differences and very large correlations (r values ranged from 0.96 to 0.99) were observed. In addition, the standard error of estimate, total error, and 95% limits of agreement for all three metrics were small and considered acceptable. Conclusions: An IMAGE system provides valid estimates of BV that accurately estimates body composition in a 3C model. [ABSTRACT FROM AUTHOR]

The purpose of this study was to quantify the effect of an overload microcycle and taper on bench press velocity and to determine if the load-velocity relationship could accurately predict 1-repetition maximum (1RM). Twelve male powerlifters participated in resistance training structured into an introduction microcycle, overload microcycle (PostOL), and taper (PostTP). At the end of each microcycle, subjects completed a bench press for 1RM assessment consisting of warm-up sets at 40, 55, 70, and 85% of a previously established 1RM. The mean concentric velocity (MCV) was recorded during each warm-up set. A predicted 1RM (p1RM) was calculated using an individualized load-velocity profile (LVP). The average MCV decreased after PostOL (0.66 ± 0.07 m·s-1) compared with baseline (BL) (p = 0.003; 0.60 ± 0.11 m·s-1) but increased after PostTP (0.67 ± 0.09 m·s-1). One-repetition maximum increased from PostOL (146.7 ± 19.8 kg) to PostTP (p = 0.002; 156.1 ± 21.0 kg), with no differences observed between other test sessions (p > 0.05). Bland-Altman analysis indicated that p1RM was consistently higher than measured 1RM (3.4-7.8 kg), and the limits of agreement were extremely wide. However, very large to near perfect correlations (r = 0.89 to 0.96) were observed between p1RM and 1RM during BL, PostOL, and PostTP. The load-velocity relationship established from submaximal sets did not accurately predict 1RM, but MCV was affected by changes in weekly training loads. Velocity-based measurements seem to be more sensitive to changes in training loads than maximal strength. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this investigation was to observe the association in the time course in recovery between multiple heart rate variability (HRV) metrics and neuromuscular performance, as assessed by mean bar velocity (BVM) in the back squat, over a 72-h period after an exhaustive back squat protocol. Methods: Eight resistance-trained males completed five laboratory visits within a 7-d period. The first visit involved short-term HRV recordings followed by a familiarization of BVM procedures and a one-repetition maximum test of the back squat. Forty-eight hours later, participants returned to the laboratory for prestimulus measurements, immediately followed by a back squat protocol (8 sets of 10 repetitions at 70% of one-repetition maximum with a 2-min rest). The HRV and the BVM measurements were replicated at 0.5, 24, 48, and 72 h after squat protocol. A multivariate profile analysis and repeated-measures correlation between recovery scores [(new/prestimulus) × 100] for each HRV metric and BVM was computed. Results: All log-transformed (ln) HRV metrics, except low frequency (lnLF) (P = 0.051), had a significant interaction with BVM over time (P < 0.05), indicating that recovery scores in BVM and HRV were not parallel. In addition, recovery scores in all HRV metrics significantly differed from BVM (P < 0.05) in at least one time point across the 72-h period. Furthermore, repeated-measures correlation analysis indicated a lack of intraindividual association (P > 0.05) between the change in BVM and all HRV measurements over time. Conclusion: The time course in recovery in HRV measurements after an exhaustive bout of lower-body resistance exercise was not associated with neuromuscular performance recovery. [ABSTRACT FROM AUTHOR]

Flatt, AA, Esco, MR, Allen, JR, Robinson, JB, Bragg, A, Keith, CM, Fedewa, MV, and Earley, RL. Cardiac-autonomic responses to in-season training among Division-1 college football players. J Strength Cond Res 34(6): 1649–1656, 2020—Despite having to endure a rigorous in-season training schedule, research evaluating daily physiological recovery status markers among American football players is limited. The purpose of this study was to determine whether recovery of cardiac-autonomic activity to resting values occurs between consecutive-day, in-season training sessions among college football players. Subjects (n = 29) were divided into groups based on position: receivers and defensive backs (SKILL, n = 10); running backs, linebackers, and tight-ends (MID-SKILL, n = 11) and linemen (LINEMEN, n = 8). Resting heart rate (RHR) and the natural logarithm of the root mean square of successive differences multiplied by 20 (LnRMSSD) were acquired at rest in the seated position before Tuesday and Wednesday training sessions and repeated over 3 weeks during the first month of the competitive season. A position X time interaction was observed for LnRMSSD (p = 0.04), but not for the RHR (p = 0.33). No differences in LnRMSSD between days was observed for SKILL (Tuesday = 82.8 6 9.3, Wednesday = 81.9 6 8.7, p . 0.05). Small reductions in LnRMSSD were observed for MID-SKILL (Tuesday = 79.2 ± 9.4, Wednesday = 76.2 ± 9.5, p ≤ 0.05) and LINEMEN (Tuesday = 79.4 ± 10.5, Wednesday = 74.5 ± 11.5, p ≤ 0.05). The individually averaged changes in LnRMSSD from Tuesday to Wednesday were related to PlayerLoad (r = 0.46, p = 0.02) and body mass (r = 20.39, p = 0.04). Cardiac-parasympathetic activity did not return to resting values for LINEMEN or MID-SKILL before the next training session. Larger reductions in LnRMSSD tended to occur in players with greater body mass despite having performed lower workloads, although some individual variability was observed. These findings may have implications for how coaches and support staff address training and recovery interventions for players demonstrating inadequate cardiovascular recovery between sessions. [ABSTRACT FROM AUTHOR]

The electromyographical threshold (EMGT) has been previously validated as a means to predict the work rate at which lactate threshold (LT) occurs. The reliability of these measures has yet to be examined after a bout of exercise. The purpose was to determine the agreement between electromyography (EMG) and LT after a 30-minute bout of steady-state aerobic exercise. Participants completed 2 graded exercise tests (GXT) on a cycle ergometer separated by 30 minutes of steady-state exercise. Blood lactate was measured the last 45 seconds of each stage during both GXTs, whereas EMG of the vastus lateralis was monitored continuously. Individual agreement demonstrated that pre-exercise and post-exercise LT occurred at the same work rate in 2 of 10 participants, whereas EMGT occurred at the same work rate in 6 of 10 participants. Results showed no mean difference between work rates for LT or EMG threshold for the pre-exercise GXT, but LT was significantly lower (p < 0.01) than EMGT during the post-exercise GXT. Post-GXT LT work rates were also determined to be significantly lower than pre-GXT LT (p = 0.034), whereas no differences existed in EMG thresholds. Although both LT and EMGT testing may display similar properties, they are not interchangeable. The physiological responses to increasing exercise intensity between La and EMG signaling seem to be associated, and their interaction may not be cause-effect. Because of poor individual agreement, caution should be used when determining LT through the use of EMG. Further research is needed to determine the ability of these 2 metrics to prescribe training intensities. [ABSTRACT FROM AUTHOR]

The primary aim of this systematic review and meta-analysis was to quantify the magnitude of the effect of postactivation potentiation (PAP) on explosive vertical power while accounting for the nesting of multiple effects within each study. This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Statement (PRISMA). Original research articles published by January 18, 2018, were located using an electronic search of 4 databases and yielded 759 original publications. Data were extracted and independently coded by 2 authors (W.C.D. and D.V.T.). The standardized mean effect size (ES) was calculated by subtracting the pre-treatment mean from the post-treatment mean and dividing by the pooled SD, adjusting for small sample bias. Multilevel random-effects model was used to aggregate a mean ES and 95% confidence interval (CI) for PAP on vertical jump performance. The cumulative results of 179 effects gathered from 36 studies indicate that PAP does not improve vertical jump performance (ES = 0.08, 95% CI -0.04 to 0.21, p = 0.197), with moderate heterogeneity. Moderator analysis indicated that rest intervals between 3 and 7 minutes provided favorable performance outcomes (ES = 0.18, 95% CI 0.05 to 0.31, p = 0.007). Conversely, rest intervals less than 3 minutes (ES = -0.15, 95% CI -0.31 to 0.01, p = 0.052) or performing isometric contractions (ES = -0.52, 95% CI -0.89 to -0.14, p = 0.007) may be detrimental to performance. Meta-regression indicated that rest interval was the only moderator significantly associated with ES (β = -0.04, 95% CI -0.57 to -0.02, R = 14.31%, p < 0.001). When appropriate PAP guidelines are followed, an increase in vertical jump performance may be achieved. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare body mass index (BMI)-based body fat percentage (BF%) equations and skinfolds with a 4-compartment (4C) model in men and women. One hundred thirty adults (63 women and 67 men) volunteered to participate (age = 23 ± 5 years). BMI was calculated as weight (kg) divided by height squared (m²). BF% was predicted with the BMI-based equations of Jackson et al. (BMIJA), Deurenberg et al. (BMIDE), Gallagher et al. (BMIGA), Zanovec et al. (BMIZA), Womersley and Durnin (BMIWO), and from 7-site skinfolds using the generalized skinfold equation of Jackson et al. (SF7JP). The 4C model BF% was the criterion and derived from underwater weighing for body volume, dual-energy X-ray absorptiometry for bone mineral content, and bioimpedance spectroscopy for total body water. The constant error (CE) was not significantly different for BMIZA compared with the 4C model (p = 0.74, CE = -0.2%). However, BMIJA, BMIDE, BMIGA, and BMIWO produced significantly higher mean values than the 4C model (all p < 0.001, CEs = 1.8-3.2%), whereas SF7JP was significantly lower (p < 0.001, CE = -4.8%). The standard error of estimate ranged from 3.4 (SF7JP) to 6.4% (BMIJA) while the total error varied from 6.0 (SF7JP) to 7.3% (BMIJA). The 95% limits of agreement were the smallest for SF7JP (±7.2%) and widest for BMIJA (±13.5%). Although the BMI-based equations produced similar group mean values as the 4C model, SF7JP produced the smallest individual errors. Therefore, SF7JP is recommended over the BMI-based equations, but practitioners should consider the associated CE. [ABSTRACT FROM AUTHOR]

The article offers information importance of behavioral economics principles such as short-term preference, loss aversion and commitment in understanding and working with clients, and it also mention about behavioral economic principle nudging in helping clients on beneficial decision-making.

The purpose of this study was to compare underwater weighing (UWW) and 4-compartment (4C) model body fat percentage (BF%) for predicted vs. simultaneously measured residual lung volume (RLV). Forty-seven women and 33 men (age = 22 ± 5 years) had UWW and 4C model BF% determined using Boren et al. (RLVBOREN), Goldman and Becklake (RLVGB), and Miller et al. (RLVMILLER) RLV prediction equations. Criterion UWW BF% included body density (BD) values with simultaneous RLV. Criterion 4C model BF% included BD through UWW with simultaneous RLV, total body water through bioimpedance spectroscopy, and bone mineral content through dual-energy x-ray absorptiometry. The standard error of estimate (SEE) for UWW and 4C model BF% determined through RLV prediction equations varied from 2.0 to 2.6% and from 1.3 to 1.5%, respectively. The constant error (CE) was significantly different for UWW BF% when using RLVBOREN, RLVGB, and RLVMILLER (all p < 0.016; CE = 0.7, -2.0, 1.0%, respectively). However, the CEs for RLVBOREN and RLVMILLER were not significant in the 4C model (p = 0.73 and 0.11; CE = 0.1 and 0.2%, respectively), whereas RLVGB remained significantly different (p < 0.001; CE = -1.5%). The 95% limits of agreement were less than ±5.2% for UWW BF% and less than ±3.1% for the 4C model when using the 3 RLV equations. When used in a 4C model, the RLV equations yielded a smaller CE, SEE, and 95% limits of agreement than UWW BF% results. However, because of the range of individual error shown in the current study, caution should be employed when using the 4C model as a criterion method with predicted RLV. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare body fat percentage (BF%) and fat-free mass (FFM) values from bioelectrical impedance analysis (BIA) equations to values determined from a 4-compartment (4C) model. Eighty-two adults (42 men and 40 women) volunteered to participate (age = 23 ± 5 years). Body fat percentage and FFM were estimated from previously developed BIA equations by Chumlea et al. (BIACH), Deurenberg et al. (BIADE), Kyle et al. (BIAKYLE), and Sun et al. (BIASUN). Four-compartment model body composition was derived from underwater weighing for body density, dual-energy X-ray absorptiometry for bone mineral content, and bioimpedance spectroscopy for total body water. The standard error of estimate (SEE) for group BF% and FFM ranged from 3.0 to 3.8% and 2.1 to 2.7 kg, respectively. The constant error (CE) was significantly higher and lower for BF% and FFM (p < 0.001), respectively, for 3 BIA equations (BIACH, CE = 3.1% and -2.2 kg; BIADE, CE = 3.7% and -2.9 kg; BIAKYLE, CE = 2.3% and -1.9 kg), but was not significant for BF% (p = 0.702) and FFM (p = 0.677) for BIASUN (CE = -0.1% and 0.1 kg). The 95% limits of agreement were narrowest for BIACH (±5.9%; ±4.2 kg) and largest for BIADE (±7.4%; ±6.2 kg). The significant CE yielded by BIACH, BIADE, and BIAKYLE indicates these equations tend to overpredict group BF% and underestimate group FFM. However, all BIA equations produced low SEEs and fairly narrow limits of agreement. When the use of a 4C model is not available, practitioners might consider using one of the selected BIA equations, but should consider the associated CE. [ABSTRACT FROM AUTHOR]