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Sarah Deck, Brianna DeSantis, Despina Kouali, and Craig Hall

In team sports, it has been found that team mistakes were reported as a stressor by both males and females, and at every playing level (e.g., club, university, national). The purpose of this study was to examine the impact of partners’ play on performance, emotions, and coping of doubles racquet sport athletes. Seventeen one-on-one semistructured interviews were conducted over the course of 6 months. Inductive and deductive analysis produced the main themes of overall impact on performance (i.e., positive, negative, or no impact), negative emotions (i.e., anger), positive emotions (i.e., excitement), emotion-focused coping (i.e., acceptance), and problem-focused coping (i.e., team strategy). These athletes acknowledge that how their partner plays significantly affects not only their emotions but also their own play and their choice of coping strategies. Future research should try to understand which forms of coping reduce the impact of partners’ play.

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Pedro L. Valenzuela, Guillermo Sánchez-Martínez, Elaia Torrontegi, Javier Vázquez-Carrión, Zigor Montalvo, and G. Gregory Haff

Purpose: To analyze the differences in the force–velocity (F–v) profile assessed under unconstrained (ie, using free weights) and constrained (ie, on a Smith machine) vertical jumps, as well as to determine the between-day reliability. Methods: A total of 23 trained participants (18 [1] y) performed an incremental load squat jump test (with ∼35%, 45%, 60%, and 70% of the subjects’ body mass) on 2 different days using free weights and a Smith machine. Nine of these participants repeated the tests on 2 other days for an exploratory analysis of between-day reliability. F–v variables (ie, maximum theoretical force [F 0], velocity [v 0], and power, and the imbalance between the actual and the theoretically optimal F–v profile) were computed from jump height. Results: A poor agreement was observed between the F–v variables assessed under constrained and unconstrained conditions (intraclass correlation coefficient [ICC] < .50 for all). The height attained during each single jump performed under both constrained and unconstrained conditions showed an acceptable reliability (coefficient of variation < 10%, ICC > .70). The F–v variables computed under constrained conditions showed an overall good agreement (ICC = .75–.95 for all variables) and no significant differences between days (P > .05), but a high variability for v 0, the imbalance between the actual and the theoretically optimal F–v profile, and maximal theoretical power (coefficient of variation = 17.0%–27.4%). No between-day differences were observed for any F–v variable assessed under unconstrained conditions (P > .05), but all of the variables presented a low between-day reliability (coefficient of variation > 10% and ICC < .70 for all). Conclusions: F–v variables differed meaningfully when obtained from constrained and unconstrained loaded jumps, and most importantly seemed to present a low between-day reliability.

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Scott J. Strath, Taylor W. Rowley, Chi C. Cho, Allison Hyngstrom, Ann M. Swartz, Kevin G. Keenan, Julian Martinez, and John W. Staudenmayer

Purpose: To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions. Methods: 118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson’s disease (n = 17), and stroke (n = 18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit. Results: All activities’ accelerometer counts per minute (CPM) were 29.5–72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, −0.49 (−0.71, −0.27) for arthritis, −0.38 (−0.53, −0.22) for healthy, −0.44 (−0.68, −0.20) for MS, −0.34 (−0.58, −0.09) for Parkinson’s, and −0.30 (−0.54, −0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, −0.37 METs (−0.52, −0.23); Group 2, −0.44 METs (−0.60, −0.28); and Group 3, −0.33 METs (−0.55, −0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition. Conclusion: Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.

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Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan A. Carlson, Andrea Z. LaCroix, Sheri J. Hartman, Dori E. Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

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Stacey Alvarez-Alvarado and Gershon Tenenbaum

Inquiry of the psychological states during the exercise experience failed to fully account for the role of motivation to adhere and the disposition of exertion tolerance (ET). The current study expands the scope of the integrated cognitive–perceptual–affective framework by measuring the motivation to sustain effort in two physical tasks and accounting for ET. Thirty male participants performed cycling and isometric handgrip tasks to assess the progression of the rating of perceived exertion, attentional focus, affective responses, and motivation to adhere, along with an incremental workload. The ET was determined by a handgrip task time to voluntary exhaustion. The findings indicated significant time effects and linear trends for perceived exertion, attentional focus, affect, and perceived arousal but not motivation to adhere during the handgrip and cycling tasks. The ET played a key role in the integrity of the model, particularly in perceptual, attentional, and affective responses. The intended model serves to stimulate new research into adaptation mechanisms.

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Bronwyn Clark, Elisabeth Winker, Matthew Ahmadi, and Stewart Trost

Accurate measurement of time spent sitting, standing, and stepping is important in studies seeking to evaluate interventions to reduce sedentary behavior. In this study, the authors evaluated the agreement in classification of these activities from three algorithms applied to thigh-worn ActiGraph accelerometers using predictions from the widely used activPAL device as a criterion. Participants (n = 29, 72% female, age 23–68 years) wore the activPAL3 micro (processed by PAL software, version 7.2.32) and the ActiGraph GT9X accelerometer on the right front thigh concurrently for working hours on one full workday (7.2 ± 1.2 hr). ActiGraph output was classified via the three test algorithms: ActiGraph’s ActiLife software (inclinometer); an open source method; and, a machine-learning algorithm reported in the literature (Acti4). Performance at an instance level was evaluated by computing classification accuracy (F scores) for 15-s windows. The F scores showed high accuracy relative to the criterion for identifying sitting (96.7–97.1) and were 84.7–85.1 for identifying standing and 78.1–80.6 for identifying stepping. The four methods agreed strongly in total time spent sitting, standing, and stepping, with intraclass correlation coefficients of .96 (95% confidence interval [.92, .96]), .92 (95% confidence interval [.81, .96]), and .87 (95% confidence interval [.53, .95]) but sometimes overestimated sitting time and underestimated standing time relative to activPAL. These algorithms for identifying sitting, standing, and stepping from thigh-worn accelerometers provide estimates that are very similar to those obtained using the activPAL.

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Shanie A.L. Jayasinghe, Rui Wang, Rani Gebara, Subir Biswas, and Rajiv Ranganathan

Impairment of arm movements poststroke often results in the use of compensatory trunk movements to complete motor tasks. These compensatory movements have been mostly observed in tightly controlled conditions, with very few studies examining them in more naturalistic settings. In this study, the authors quantified the presence of compensatory movements during a set of continuous reaching and manipulation tasks performed with both the paretic and nonparetic arm (in 9 chronic stroke survivors) or the dominant arm (in 20 neurologically unimpaired control participants). Kinematic data were collected using motion capture to assess trunk and elbow movement. The authors found that trunk displacement and rotation were significantly higher when using the paretic versus nonparetic arm (P = .03). In contrast, elbow angular displacement was significantly lower in the paretic versus nonparetic arm (P = .01). The reaching tasks required significantly higher trunk compensation and elbow movement than the manipulation tasks. These results reflect increased reliance on compensatory trunk movements poststroke, even in everyday functional tasks, which may be a target for home rehabilitation programs. This study provides a novel contribution to the rehabilitation literature by examining the presence of compensatory movements in naturalistic reaching and manipulation tasks.

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Jim McKay, Keith Davids, Sam Robertson, and Carl T. Woods

This is an exciting era for applied research in high-performance sporting environments. Specifically, there are growing calls for researchers to work with coaches to produce “real-world” case examples that offer first-hand experiences into the application of theory. While ecological dynamics has emerged as a guiding theoretical framework for learning and performance in sport, there is a caveat to its use in the field. Namely, there is a general paucity of applied research that details how expert coaches have brought life to its theoretical contentions in practice. In light of this, the current paper offers a unique insight into how a professional Rugby union organization set out to ground their preparation for competitive performance within an ecological dynamics framework. More directly, this paper details how the Queensland Reds designed and integrated a set of attacking game principles that afforded players with opportunities in practice to search, discover, and exploit their actions. While this paper offers insight specific to Rugby union, its learnings are transferrable to coaches in other sports looking to situate their practice design within an ecological dynamics framework.

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Katie Potter, Robert T. Marcotte, Greg J. Petrucci, Caitlin Rajala, Deborah E. Linder, and Laura B. Balzer

Given high rates of obesity and chronic disease in both people and dogs, it is important to understand how dogs and dog owners influence each other’s health, including physical activity (PA) levels. Research suggests that dog owners who walk their dogs are more likely to meet PA guidelines than those who do not, but few studies have investigated dog walking intensity or its contribution to dog owners’ total moderate-to-vigorous PA using accelerometry. Furthermore, no studies have examined the contribution of dog walking to dogs’ total PA or the relationship between dog and dog owner PA using accelerometers on dogs. The authors used accelerometers on 33 dog owner–dog pairs to investigate (a) the intensity of dog walking behavior, (b) the contribution of dog walking to dog owners’ overall moderate-to-vigorous PA and dogs’ overall PA, and (c) the correlation between dog and dog owner PA. Dog owners wore an ActiGraph accelerometer and logged all dog walking for 7 days; dogs wore a Fitbark activity monitor. On average, 64.1% (95% confidence interval [55.2, 73.1]) of daily dog walking was moderate to vigorous intensity, and dog walking accounted for 51.2% (95% confidence interval [44.1, 58.3]) of dog owners’ daily moderate-to-vigorous PA. Dog walking accounted for 41.2% (95% confidence interval [36.0, 46.4]) of dogs’ daily PA. Dog owners’ daily steps were moderately correlated (r = .54) with dogs’ daily activity points. These findings demonstrate the interdependence of dog and dog owner PA and can inform interventions that leverage the dog–owner bond to promote PA and health in both species.

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David Morley, Andrew Miller, James Rudd, Johann Issartel, Jackie Goodway, Donna O’Connor, Stephen Harvey, Paul Ogilvie, and Thomas van Rossum

Coaches can provide an appropriate environment for children to develop a range of movement skills, but there is a dearth of research exploring the creation of appropriate resources to support the coach in developing and assessing children’s Complex Movement Skills. There is also a lack of research around coaches’ perceived feasibility of the limited resources in this area. Therefore, the purpose of this study was to design and then assess the feasibility of a Movement-Oriented Games-Based Assessment (MOGBA) for children aged 8–12 years, to be used by coaches within “Made to Play” programs. Thirteen coaches from across the United States and the United Kingdom used pilot materials to assess the feasibility of MOGBA over a 9-week period. Individual, paired, and focus group interviews were structured and data were thematically analyzed using Bowen et al.’s feasibility framework. Findings suggested that MOGBA provided a welcomed and much needed enhancement to their programs, with effective use of technology-enhanced coaching. Coaching involved notions of pedagogy and assessment, with issues emerging around class size and complexity of assessment. Coaches often used MOGBA covertly and flavored the resource to the sport being delivered, and this revealed to coaches the capability of children not viewed before.