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Keith V. Osai, Travis E. Dorsch and Shawn D. Whiteman

Organized youth sport is a relatively common family context in which sibling dynamics are not well understood. The present study was designed to address two contrasting mechanisms of socialization—modeling and differentiation—in examining older siblings’ influence on younger siblings’ sport participation. American youth (N = 221) age 10–15 years (M = 12.38, SD = 1.01) who were active sport participants completed an online survey measuring individual and family demographics, sibling relationship qualities, and parent–child relationship dimensions. The participants reported on their most proximal older siblings, all of whom were within 4 years of age. The analyses suggest that sibling differentiation dynamics decreased the likelihood of playing the same primary sport as an older sibling for (a) the same biological sex, close in age to siblings; (b) the same biological sex, further in age from siblings; and (c) mixed biological sex, wide in age from siblings. The “Discussion” section highlights the practical value of understanding the impact of sibling influence processes on the individual, sibling dyad, and family system.

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Jessica Gorzelitz, Chloe Farber, Ronald Gangnon and Lisa Cadmus-Bertram

Background: The evidence base regarding validity of wearable fitness trackers for assessment and/or modification of physical activity behavior is evolving. Accurate assessment of moderate- to vigorous-intensity physical activity (MVPA) is important for measuring adherence to physical activity guidelines in the United States and abroad. Therefore, this systematic review synthesizes the state of the validation literature regarding wearable trackers and MVPA. Methods: A systematic search of the PubMed, Scopus, SPORTDiscus, and Cochrane Library databases was conducted through October 2019 (PROSPERO registration number: CRD42018103808). Studies were eligible if they reported on the validity of MVPA and used devices from Fitbit, Apple, or Garmin released in 2012 or later or available on the market at the time of review. A meta-analysis was conducted on the correlation measures comparing wearables with the ActiGraph. Results: Twenty-two studies met the inclusion criteria; all used a Fitbit device; one included a Garmin model and no Apple-device studies were found. Moderate to high correlations (.7–.9) were found between MVPA from the wearable tracker versus criterion measure (ActiGraph n = 14). Considerable heterogeneity was seen with respect to the specific definition of MVPA for the criterion device, the statistical techniques used to assess validity, and the correlations between wearable trackers and ActiGraph across studies. Conclusions: There is a need for standardization of validation methods and reporting outcomes in individual studies to allow for comparability across the evidence base. Despite the different methods utilized within studies, nearly all concluded that wearable trackers are valid for measuring MVPA.

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Kayla J. Nuss, Nicholas A. Hulett, Alden Erickson, Eric Burton, Kyle Carr, Lauren Mooney, Jacob Anderson, Ashley Comstock, Ethan J. Schlemer, Lucas J. Archambault and Kaigang Li

Objective: To validate and compare the accuracy of energy expenditure (EE) and step counts measured by ActiGraph accelerometers (ACT) at dominant and nondominant wrist and hip sites. Methods: Thirty young adults (15 females, age 22.93 ± 3.30 years) wore four ActiGraph wGT3X accelerometers while walking and running on a treadmill for 7 min at seven different speeds (1.7, 2.5, 3.4, 4.2, 5.0, 5.5, and 6.0 mph). The EE from each ACT was calculated using the Freedson Adult equation, and the “worn on the wrist” option was selected for the wrist data. Indirect calorimetry and manually counted steps were used as criterion measures. Mean absolute percentage error and two one-sided test procedures for equivalence were used for the analyses. Results: All ACTs underestimated the EE with mean absolute percentage errors over 30% for wrist placement and over 20% for hip placement. The wrist-worn ACTs underestimated the step count with mean absolute percentage errors above 30% for both dominant and nondominant placements. The hip-worn ACTs accurately assessed steps for the whole sample and for women and men (p < .001 to .05 for two one-sided tests procedures), but not at speeds slower than 2.0 mph. Conclusion: Neither hip nor wrist placements assess EE accurately. More algorithms and methods to derive EE estimates from wrist-worn ACTs must be developed and validated. For step counts, both dominant and nondominant hip placements, but not wrist placements, lead to accurate results for both men and women.

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Ryan Eckert, Jennifer Huberty, Heidi Kosiorek, Shannon Clark-Sienkiewicz, Linda Larkey and Ruben Mesa

Introduction: The delivery of online interventions in cancer patients/survivors has increased. The measurement of participation in online interventions is important to consider, namely, the challenges of the remote assessment of activity. The purpose of this study was to report the measures used to assess intervention compliance and other physical activity participation in two online yoga studies, the relationship between the multimethod measures used, and the ability of cancer patients to complete these measures. Methods: The methods described are of two online yoga studies (feasibility and pilot). Cancer patients were asked to participate in 60 min/week of online yoga for 12 weeks, complete a weekly yoga log, wear a Fitbit daily for 12 weeks, and complete a weekly physical activity log. Finally, Clicky®, a web analytics software, was used to track online yoga participation. Results: Eighty-four people participated across both studies, with 63/84 participating in online yoga, averaging 57.5 ± 33.2 min/week of self-reported yoga participation compared to 41.4 ± 26.1 min/week of Clicky® yoga participation (Lin concordance = 0.28). All 84 participants averaged 95.5 ± 111.8 min/week of self-reported moderate/vigorous physical activity compared with 98.1 ± 115.9 min/week of Fitbit-determined moderate/vigorous physical activity (Lin concordance = 0.33). Across both studies, 82.9% of the yoga logs were completed, the Fitbit was worn on 75.2% of the days, and 78.7% of the physical activity logs were completed. Conclusions: Weak relationships between self-report and objective measures were demonstrated, but the compliance rates were above 75% for the study measures. Future research is needed, investigating the intricacies of self-report physical activity participation in remote interventions and the validation of a gold standard measurement for online interventions.

Open access

Fahim A. Salim, Fasih Haider, Dees Postma, Robby van Delden, Dennis Reidsma, Saturnino Luz and Bert-Jan van Beijnum

Automatic tagging of video recordings of sports matches and training sessions can be helpful to coaches and players and provide access to structured data at a scale that would be unfeasible if one were to rely on manual tagging. Recognition of different actions forms an essential part of sports video tagging. In this paper, the authors employ machine learning techniques to automatically recognize specific types of volleyball actions (i.e., underhand serve, overhead pass, serve, forearm pass, one hand pass, smash, and block which are manually annotated) during matches and training sessions (uncontrolled, in the wild data) based on motion data captured by inertial measurement unit sensors strapped on the wrists of eight female volleyball players. Analysis of the results suggests that all sensors in the inertial measurement unit (i.e., magnetometer, accelerometer, barometer, and gyroscope) contribute unique information in the classification of volleyball actions types. The authors demonstrate that while the accelerometer feature set provides better results than other sensors, overall (i.e., gyroscope, magnetometer, and barometer) feature fusion of the accelerometer, magnetometer, and gyroscope provides the bests results (unweighted average recall = 67.87%, unweighted average precision = 68.68%, and κ = .727), well above the chance level of 14.28%. Interestingly, it is also demonstrated that the dominant hand (unweighted average recall = 61.45%, unweighted average precision = 65.41%, and κ = .652) provides better results than the nondominant (unweighted average recall = 45.56%, unweighted average precision = 55.45, and κ = .553) hand. Apart from machine learning models, this paper also discusses a modular architecture for a system to automatically supplement video recording by detecting events of interests in volleyball matches and training sessions and to provide tailored and interactive multimodal feedback by utilizing an HTML5/JavaScript application. A proof of concept prototype developed based on this architecture is also described.

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Bryson Carrier, Andrew Creer, Lauren R. Williams, Timothy M. Holmes, Brayden D. Jolley, Siri Dahl, Elizabeth Weber and Tyler Standifird

The purpose of this study was to determine the validity of the Garmin fēnix® 3 HR fitness tracker. Methods: A total of 34 healthy recreational runners participated in biomechanical or metabolic testing. Biomechanics participants completed three running conditions (flat, incline, and decline) at a self-selected running pace, on an instrumented treadmill while running biomechanics were tracked using a motion capture system. Variables extracted were compared with data collected by the Garmin fēnix 3 HR (worn on the wrist) that was paired with a chest heart rate monitor and a Garmin Foot Pod (worn on the shoe). Metabolic testing involved two separate tests; a graded exercise test to exhaustion utilizing a metabolic cart and treadmill, and a 15-min submaximal outdoor track session while wearing the Garmin. 2 × 3 analysis of variances with post hoc t tests, mean absolute percentage errors, Pearson’s correlation (R), and a t test were used to determine validity. Results: The fēnix kinematics had a mean absolute percentage errors of 9.44%, 0.21%, 26.38%, and 5.77% for stride length, run cadence, vertical oscillation, and ground contact time, respectively. The fēnix overestimated (p < .05) VO2max with a mean absolute percentage error of 8.05% and an R value of .917. Conclusion: The Garmin fēnix 3 HR appears to produce a valid measure of run cadence and ground contact time during running, while it overestimated vertical oscillation in every condition (p < .05) and should be used with caution when determining stride length. The fēnix appears to produce a valid VO2max estimate and may be used when more accurate methods are not available.

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Thomas Mullen, Craig Twist and Jamie Highton

Purpose: To examine responses to a simulated rugby league protocol designed to include more stochastic commands, and therefore require greater vigilance, than traditional team-sport simulation protocols. Methods: Eleven male university rugby players completed 2 trials (randomized and control [CON]) of a rugby league movement simulation protocol, separated by 7 to 10 d. The CON trial consisted of 48 repeated ∼115-s cycles of activity. The stochastic simulation (STOCH) was matched for the number and types of activity performed every 5.45 min in CON but included no repeated cycles of activity. Movement using GPS, heart rate, rating of perceived exertion, and Stroop test performance was assessed throughout. Maximum voluntary contraction peak torque, voluntary activation (in percentage), and global task load were assessed after exercise. Results: The mean mental demand of STOCH was higher than CON (effect size [ES] = 0.56; ±0.69). Mean sprint speed was higher in STOCH (22.5 [1.4] vs 21.6 [1.6] km·h−1, ES = 0.50; ±0.55), which was accompanied by a higher rating of perceived exertion (14.3 [1.0] vs 13.0 [1.4], ES = 0.87; ±0.67) and a greater number of errors in the Stroop test (10.3 [2.5] vs 9.3 [1.4] errors; ES = 0.65; ±0.83). Maximum voluntary contraction peak torque (CON = −48.4 [31.6] N·m and STOCH = −39.6 [36.6] N·m) and voluntary activation (CON = −8.3% [4.8%] and STOCH = −6.0% [4.1%]) was similarly reduced in both trials. Conclusions: Providing more stochastic commands, which requires greater vigilance, might alter performance and associated physiological, perceptual, and cognitive responses to team-sport simulations.

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Jonpaul Nevin and Paul M. Smith

Purpose: The aim of this study was to investigate the relationship between selected anthropometric, physiological, and upper-body strength measures and 15-km handcycling time-trial (TT) performance. Methods: Thirteen trained H3/H4 male handcyclists performed a 15-km TT, graded exercise test, 15-second all-out sprint, and 1-repetition-maximum assessment of bench press and prone bench pull strength. Relationship between all variables was assessed using a Pearson correlation coefficient matrix with mean TT velocity representing the principal performance outcome. Results: Power at a fixed blood lactate concentration of 4 mmol·L−1 (r = .927; P < .01) showed an extremely large correlation with TT performance, whereas relative V˙O2peak (peak oxygen uptake) (r = .879; P < .01), power-to-mass ratio (r = .879; P < .01), peak aerobic power (r = .851; P < .01), gross mechanical efficiency (r = 733; P < .01), relative prone bench pull strength (r = .770; P = .03) relative bench press strength (r = .703; P = .11), and maximum anaerobic power (r = .678; P = .15) all demonstrated a very large correlation with performance outcomes. Conclusion: Findings of this study indicate that power at a fixed blood lactate concentration of 4 mmol·L−1, relative V˙O2peak, power-to-mass ratio, peak aerobic power, gross mechanical efficiency, relative upper-body strength, and maximum anaerobic power are all significant determinants of 15-km TT performance in H3/H4 handcyclists.

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Omid Kazemi, Amir Letafatkar and Paulo H. Marchetti

Context: Several studies report static-stretch-induced deficits and dynamic-stretch performance improvement after intervention. Purpose: To investigate the muscle activation of the forehand and backhand in table tennis players after experiencing static- and dynamic-stretching protocols. Methods: A total of 24 elite male table tennis players (age 22.7 [3.46] y, height 1.78 [0.03] m) were tested before and 0, 10, 20, and 30 min after the 3 conditions (dynamic stretch, static stretch, and no stretch). The MEGA ME6000 (Mega Electronics, Kuopio, Finland) was used to capture the surface EMG data of the anterior deltoid, middle deltoid, posterior deltoid, biceps, and triceps muscles. Muscle activation data of the pretest were compared with posttest 0, 10, 20, and 30 min. These data were also compared between 3 different conditions (dynamic stretch, static stretch, and no stretch). Results: A 2-way repeated-measures analysis of variance indicated significant differences in the forehand and backhand, and Bonferroni test as a post hoc comparison revealed significant differences between the pretest and posttests in several muscles (P < .05). Furthermore, there were significant differences in the posttest between the 3 conditions (P < .05). Conclusions: In general, there was a short-term effect of static- and dynamic-stretching protocols on glenohumeral-joint muscle activation in elite table tennis players. The static and dynamic stretching presented a decrease and increase, respectively, in muscle activation up to 30 min after stretching. In conclusion, the additive and subtractive effects of dynamic- and static-stretching protocols on muscle activation seem to persist after 30 min.

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Justin J. Merrigan, James J. Tufano, Michael Falzone and Margaret T. Jones

Purpose: To identify acute effects of a single accentuated eccentric loading (AEL) repetition on subsequent back-squat kinetics and kinematics with different concentric loads. Methods: Resistance-trained men (N = 21) participated in a counterbalanced crossover design and completed 4 protocols (sets × repetitions at eccentric/concentric) as follows: AEL65, 3 × 5 at 120%/65% 1-repetition maximum (1-RM); AEL80, 3 × 3 at 120%/80% 1-RM; TRA65, 3 × 5 at 65%/65% 1-RM; and TRA80, 3 × 3 at 80%/80% 1-RM. During AEL, weight releasers disengaged from the barbell after the eccentric phase of the first repetition and remained off for the remaining repetitions. All repetitions were performed on a force plate with linear position transducers attached to the barbell, from which eccentric and concentric peak and mean velocity, force, and power were derived. Results: Eccentric peak velocity (−0.076 [0.124] m·s−1; P = .01), concentric peak force (187.8 [284.4] N; P = .01), eccentric mean power (−145.2 [62.0] W; P = .03), and eccentric peak power (−328.6 [93.7] W; P < .01) during AEL65 were significantly greater than TRA65. When collapsed across repetitions, AEL65 resulted in slower eccentric velocity and power during repetition 1 but faster eccentric and concentric velocity and power in subsequent repetitions (P ≤ .04). When comparing AEL80 with TRA80, concentric peak force (133.8 [56.9] N; P = .03), eccentric mean power (−83.57 [38.0] W; P = .04), and eccentric peak power (−242.84 [67.3] W; P < .01) were enhanced. Conclusions: Including a single supramaximal eccentric phase of 120% 1-RM increased subsequent velocity and power with concentric loads of 65% 1-RM, but not 80% 1-RM. Therefore, AEL is sensitive to the magnitude of concentric loads, which requires a large relative difference to the eccentric load, and weight releasers may not need to be reloaded to induce performance enhancement.