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Countermovement jumps (CMJ) are frequently used in strength and conditioning as a key performance indicator. Force platforms are often considered the standard for measuring ground reaction forces (GRF) and deriving subsequent performance metrics. Although force platforms are often the standard, they may not always fall within an allotted budget. Jump mats are another option for CMJ testing that are cheaper than the criterion force plates. Although cheaper, jump mats only measure flight time (FT) and jump height (JH), whereas the criterion measures GRF. Therefore, this study looks at the reliability and validity of the Just Jump System compared to the criterion reference ForceDecks force plates during a CMJ with a self-selected depth. This study examined the agreement between the metrics: jump mat FT to force plate FT, jump mat JH to force plate JH, jump mat JH derived by FT to force plate JH derived by impulse. Twelve participants (male [n=7] and female [n=5] with an average age of 24 years [SD 3], average body mass of 83.7 kilograms [SD 17.5], average height of 176.2 centimeters [SD 11], and an average shoe thickness of 1.8 centimeters [SD .5]) completed a standardized warmup including a familiarization protocol. Following, the participant completed 5 sets of 5 repetitions of CMJ. Each repetition was followed by a 30 second rest period. Each set of 5 jumps was followed by two minutes of rest. Force plate data was analyzed in the proprietary ForceDecks software, and then exported to excel. Jump mat data was manually recorded on a data collection sheet which was then manually entered into excel. From excel, an interclass correlation coefficient (ICC) and the coefficient of variation (CV) for reliability were calculated. The ICC was reported as .96 and the CV was reported as 9.16%. These results indicated that the Just Jump mats have excellent reliability. For validity, means and standard deviations, a Pearson’s correlation, coefficient of determination, scatter plots, and a Bland-Altman 95% limits of agreement (LOA) were utilized. The means and standard deviations reported by the jump mat were flight time (ms) 541.37 (SD 49.64), jump height (cm) 36.74 (SD 6.79). The means and standard deviations reported by the force platforms were flight time (ms) 446.87 (SD 52.60), jump height derived by flight time (cm) 24.82 (SD 5.91), and jump height derived by impulse (cm) 24.69 (SD 5.83). The Pearson’s correlation indicated a very strong positive relationship with all metrics where force plate FT and jump mat FT (r = 0.991), force plate JH derived by FT and jump mat JH (r= 0.995), force plate JH derived by impulse and jump mat JH (r = 0.974). The Bland-Altman 95% limits of agreement (LOA) analysis between force plate FT and jump mat FT showed a mean bias ± LOA of 1.80 (LOA 14.77 ms). The Bland-Altman 95% LOA analysis between force plate JH derived by FT and jump mat JH showed a mean bias ± LOA of 2.02 (LOA 2.14 cm). The Bland-Altman 95% LOA analysis between force plate JH derived by impulse and jump mat JH showed a mean bias ± LOA of 2.07 (LOA 3.37 cm). The Just Jump mat overestimates FT, and thus, overestimates JH. Although reliable, the Just Jump mats are not valid in comparison to the criterion force plates.

An acute increase in maximum strength, power, or speed following a conditioning contraction known as a postactivation performance enhancement has been previously determined to be better performed when the initial exercise is of the same movement pattern. However, no research has been performed studying the effects of a hex-bar farmer’s walk on subsequent sprinting speeds. Therefore, this research examined the use of different loads of a hex-bar farmer’s walk completed at 20-m and their effect on subsequent 20-m sprinting performance. Through a randomized and counterbalanced design, resistance and running trained men and women (n = 12) performed five 20-m sprints (with 10-m splits) at baseline, 4, 8, 12, and 16-minutes after a bodyweight control (C), light farmer’s walk (LFW), and heavy farmer’s walk (HFW), utilizing 70% and 80% users hex-bar deadlift 1-RM respectively. Mean sprint velocities over 10-m and 20-m distances were similar at baseline. At 20-m, sprint velocity significantly increased during the LFW condition at 8 minutes (M = 6.03, SE = 0.14, p = 0.025), 12 minutes (M = 6.05, SE = 0.15, p = 0.016), and 16 minutes (M = 6.03, SE = 0.14, p = 0.011) when compared with C (M = 5.96, SE = 0.14, t(11) = -2.59, r = 0.98); (M = 5.97, SE = 0.15, t(11) = -2.85, r = 0.98); (M = 5.94, SE = 0.14, t(11) = -3.06, r = 0.98, p < 0.05). At 10-m, sprint velocity significantly increased during the LFW condition at 8 minutes (M = 5.10, SE = 0.12, p = 0.010), when compared with C (M = 5.01, SE = 0.12, t(11) = -3.08, p <0.05, r = 0.97). No change to sprinting velocity was witnessed across either of the C conditions or HFW conditions. These results help to substantiate the use of a load at near-maximal capacities during the warm-up preceding sprinting to acutely increase muscular force.

The purpose of this study was to examine the influential factors involved in Division III college football recruiting. Furthermore, both Division III college football players and coaches rated a variety of factors to determine if any discrepancies exist between what players and coaches rate as influential during the college-selection process. Statement of ProblemThere is a considerable amount of research for college football recruiting and general college sports recruiting. However, most of the data in the current literature consists of studies that examined recruiting and the different choice factors from the perspectives of student-athletes choosing among Division I institutions. None of these studies include the perspectives of college coaches and what they determine to be influential factors for players during recruiting. Sources of DataAn anonymous 6-point Likert Scale survey was created using a list of 27 pre-determined choice factors and then distributed via email to 246 Division III college football head coaches across the United States. In the emails, coaches were asked to participate in the study by sharing the attached survey with all members of their current football staff and player roster. For data analysis, a Mann-Whitney U test was done using SPSS software to compare the responses of the two groups. Conclusion ReachedThe players’ and coaches’ ratings of eight of the 27 choice factors were significantly different. Furthermore, the players appeared to place more value in academic-related factors whereas the coaches seemed to value athletic-related ones. This may indicate that a disconnect exists between what NCAA Division III college football players and coaches find important in the college-selection process.

Background: Health related quality of life (HRQOL), a measure of perceived quality of health is significantly related to current and future health. Handgrip strength (HGS) asymmetry is an aspect of muscle function that can be measured using handheld dynamometry. While several studies have examined relationships between HGS asymmetry and HRQOL, few have used nationally-representative data, and none have used Canadian data. The aim of this study, therefore, was to examine the association between HGS asymmetry and HRQOL in a nationally-representative sample of Canadian adults.Methods: A secondary analysis of cross-sectional data from cycles 5 and 6 (2016–17 and 2018–19) of the Canadian Health Measure Surveys (CHMS) dataset was performed for adults (aged 18–79 years). HGS asymmetry was calculated as the ratio between the maximum HGS scores for the strongest and weakest hands. HRQOL was measured using the Health Utility Index. Crude and covariate-adjusted logistic regression models were used to quantify the relationships between HGS asymmetry and HRQOL.Results: This study showed that HGS asymmetry was significantly associated with poor HRQOL in Canadian adults. Relative to individuals without asymmetry, adults with ≥21% asymmetry had 1.80 (95%CI: 1.26–2.56) greater odds for poor overall HRQOL after adjustment for covariates. In addition, adults with ≥21% HGS asymmetry had 3.29 (95%CI: 1.37–7.91) greater odds for poor mobility.Conclusions: These findings may be important for clinical screening and population health surveillance. We recommend that HGS asymmetry be included as a standard part of clinical practice and continue to be used in national health surveillance systems.Keywords: Hand grip strength, Grip strength, Hand grip asymmetry, Health related quality of life (HRQOL), Quality of life (QOL), Well-being, Health Utility Index (HUI), Canadian, Canada

\"Publication of a research article can be a defining moment in a researcher's career. However, the steps involved in turning an initial research question into a published article can be a long and arduous journey. To aid in this process, Professional Writing in Kinesiology and Sports Medicine was developed to serve as a comprehensive writing guide for research professionals and students who are looking to improve their academic writing skills. Dr. Mark Knoblauch and his contributors developed Professional Writing in Kinesiology and Sports Medicine to focus around the area of manuscript development and presentation, while also including chapters that outline the foundational concepts of professional writing, developing a research grant, and the journal selection process. Each chapter is written by content experts who bring a wealth of experience not only from their own academic writing but also from having spent countless hours helping students become better, more effective writers. Many books have been written that focus on development of the research manuscript itself, but what sets Professional Writing in Kinesiology and Sports Medicine apart is that it includes so much more to aid writers in their process. Professional Writing in Kinesiology and Sports Medicine is ideal for anyone looking to publish their articles including undergraduate and graduate students as well as faculty and professionals\"-- Provided by publisher.

Fifteen participants ages 18 – 40 volunteered for this study. Inclusion criteria consisted of scoring 28 or higher on the motor imagery questionnaire- revised second version. Pre and post maximal contractions were performed during each intervention, kinesthetic motor imagery, visual motor imagery, and a control. Kinesthetic motor imagery: listening to an audio of what to imagine. Visual motor imagery: watching a video of what to image and a control: resting for 5 minutes. Results of two-way (3 x 2) Repeated Measures ANOVA [condition (control, kinesthetic, visual) x time (pre x post)] showed no significant interaction between the condition and the time. These results show that mental imagery does not have an acute effect on maximal torque.