Browse

You are looking at 11 - 20 of 16,761 items for :

  • Sport and Exercise Science/Kinesiology x
  • All content x
Clear All
Restricted access

Kim Gammage, Alyson Crozier, Alison Ede, Christopher Hill, Sean Locke, Eric Martin, Desi McEwan, Kathleen Mellano, Eva Pila, Matthew Stork, and Svenja Wolf

Restricted access

Marcelo Gonçalves Duarte, Glauber Carvalho Nobre, Thábata Viviane Brandão Gomes, and Rodolfo Novelino Benda

Background: Studies related to the motor performance of children have suggested an interaction between organisms and the environment. Although motor development seems to be similar among people, the behavior is specific to the context that people are part of. Thus, the aim of this study was to compare the fundamental motor skill performance between indigenous (IN) and nonindigenous children. Methods: One hundred and thirteen children (43 IN and 70 nonindigenous children) between 8 and 10 years of age underwent the Test of Gross Motor Development—2. Results: A multivariate analysis showed a significant group main effect on both locomotor (p < .01) and object control (p < .01) performance with large and medium effect sizes (ηp2 values = .57–.40, respectively). The IN showed the highest scores for galloping, hopping, leaping, jumping, sliding, striking a stationary ball, stationary dribbling, catching a ball, kicking, and overhand throwing (p < .01) with small to large effect sizes (ηp2 values = .05–.50). Conclusion: The IN presented the highest levels of performance in fundamental motor skills compared with those of nonindigenous children. Most likely, IN have more opportunities for motor development in the environmental context (i.e., villages) where they live.

Restricted access

Arthur Alves Dos Santos, James Sorce, Alexandra Schonning, and Grant Bevill

This study evaluated the performance of 6 commercially available hard hat designs—differentiated by shell design, number of suspension points, and suspension tightening system—in regard to their ability to attenuate accelerations during vertical impacts to the head. Tests were conducted with impactor materials of steel, wood, and lead shot (resembling commonly seen materials in a construction site), weighing 1.8 and 3.6 kg and dropped from 1.83 m onto a Hybrid III head/neck assembly. All hard hats appreciably reduced head acceleration to the unprotected condition. However, neither the addition of extra suspension points nor variations in suspension tightening mechanism appreciably influenced performance. Therefore, these results indicate that additional features available in current hard hat designs do not improve protective capacity as related to head acceleration metrics.

Restricted access

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.

Restricted access

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.

Restricted access

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.

Restricted access

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.

Restricted access

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.