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Takeshi Kokubo, Yuta Komano, Ryohei Tsuji, Daisuke Fujiwara, Toshio Fujii and Osamu Kanauchi

test”. After exercise, mice were immediately moved to cages where locomotor activity was measured for 18 hr. In Experiment 2, mice were divided into two groups (control and LC-Plasma [ n  = 15/group]) and fed either the control diet or the LC-Plasma diet for 4 weeks. During this period, mice were

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Mathieu Lacome, Ben M. Simpson, Yannick Cholley and Martin Buchheit

floaters and regular players are presented in Figure  1 . Overall, locomotor activity and MechL demands were likely-to-most likely lower (moderate to large magnitude) in floaters compared with regular players, whereas differences in HR responses were unclear to possibly higher (small) in floaters. Floaters

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Leslie A. Pruitt, Nancy W. Glynn, Abby C. King, Jack M. Guralnik, Erin K. Aiken, Gary Miller and William L. Haskell

The authors explored using the ActiGraph accelerometer to differentiate activity levels between participants in a physical activity (PA, n = 54) or “successful aging” (SA) program (n = 52). The relationship between a PA questionnaire for older adults (CHAMPS) and accelerometry variables was also determined. Individualized accelerometry-count thresholds (ThreshIND) measured during a 400-m walk were used to identify “meaningful activity.” Participants then wore the ActiGraph for 7 days. Results indicated more activity bouts/day ≥10 min above ThreshIND in the PA group than in the SA group (1.1 ± 2.0 vs 0.5 ± 0.8, p = .05) and more activity counts/day above ThreshIND for the PA group (28,101 ± 27,521) than for the SA group (17,234 ± 15,620, p = .02). Correlations between activity counts/hr and CHAMPS ranged from .27 to .42, p < .01. The ActiGraph and ThreshIND might be useful for differentiating PA levels in older adults at risk for mobility disability.

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Steve Barrett

’ external loads. 2 – 4 Locomotor activities such as total distance covered (TDC), high-speed running distance covered (HSR), or sprinting distance covered (SP 4 ) are common external load metrics used by practitioners. More recently, accelerometers have been utilized to monitor the external load of soccer

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Jin H. Yan

Empirical evidence from this study supports the hypothesis that Tai Chi practice can improve senior citizens’ dynamic balance control and rapid-aiming arm movement performance. Of 38 senior citizens, 28 (M = 78.8 years. SD = 2.1) chose to practice the 24-form simplified Tai Chi. The remaining 10 seniors (M = 79.2 years. SD = 1.9) selected a locomotor activity (walking or jogging). Dynamic balance tests and ballistic-aiming arm movements were conducted for all participants at the beginning, middle (4th week), and end of the 8-week exercise program. The Tai Chi participants improved their time on balance more than did their counterparts who performed locomotor activities. In addition, Tai Chi practice improved arm movement smoothness to a greater extent than the locomotor activities. However, no changes in arm movement speed were observed in either group. The results suggest that Tai Chi practice may help senior citizens improve dynamic balance control and gain smoothness in rapid-aiming arm movements.

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Paul S. Bradley and Jason D. Vescovi

There is no methodological standardization of velocity thresholds for the quantification of distances covered in various locomotor activities for women’s soccer matches, especially for high-speed running and sprinting. Applying velocity thresholds used for motion analysis of men’s soccer has likely created skewed observations about high-intensity movement demands for the women’s game because these thresholds do not accurately reflect the capabilities of elite female players. Subsequently, a cohesive view of the locomotor characteristics of women’s soccer does not yet exist. The aim of this commentary is to provide suggestions for standardizing high-speed running and sprint velocity thresholds specific to women’s soccer. The authors also comment on using generic vs individualized thresholds, as well as age-related considerations, to establish velocity thresholds.

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Amelia Mays Woods, Kim Graber and David Daum

The benefits of recess can be reaped by all students regardless of socioeconomic status, race, or gender and at relatively little cost. The purpose of this study was to examine physical activity (PA) variables related to the recess PA patterns of third and fourth grade children and the social preferences and individuals influencing their PA (friends and parents). Data were collected on students (N = 115) utilizing the System of Observing Children’s Activity and Relationships during Play (SOCARP) instrument. In addition, each child was interviewed during the recess period in which SOCARP was completed. Results found that boys spent significantly more time being very active (t (95.64) = 3.252, d = .62, p < .008) than girls and preferred sport activities (t = (73.62) 5.64, d = 1.14, p < .0125) in large groups (t (69.34) = 4.036, d = .83, p < .0125). Meanwhile, girls preferred locomotor activities (t (113) = 3.19, d = .60, p < .0125), sedentary activities (t (113) = 2.829, d = .53, p < .0125) and smaller groups (t (112.63) = 4.259, d = .79, p < .0125). All 115 participants indicated that they wanted to spend time with their friends during recess.

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James J. Malone, Ric Lovell, Matthew C. Varley and Aaron J. Coutts

Athlete-tracking devices that include global positioning system (GPS) and microelectrical mechanical system (MEMS) components are now commonplace in sport research and practice. These devices provide large amounts of data that are used to inform decision making on athlete training and performance. However, the data obtained from these devices are often provided without clear explanation of how these metrics are obtained. At present, there is no clear consensus regarding how these data should be handled and reported in a sport context. Therefore, the aim of this review was to examine the factors that affect the data produced by these athlete-tracking devices and to provide guidelines for collecting, processing, and reporting of data. Many factors including device sampling rate, positioning and fitting of devices, satellite signal, and data-filtering methods can affect the measures obtained from GPS and MEMS devices. Therefore researchers are encouraged to report device brand/model, sampling frequency, number of satellites, horizontal dilution of precision, and software/firmware versions in any published research. In addition, details of inclusion/exclusion criteria for data obtained from these devices are also recommended. Considerations for the application of speed zones to evaluate the magnitude and distribution of different locomotor activities recorded by GPS are also presented, alongside recommendations for both industry practice and future research directions. Through a standard approach to data collection and procedure reporting, researchers and practitioners will be able to make more confident comparisons from their data, which will improve the understanding and impact these devices can have on athlete performance.

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Darcy M. Brown, Dan B. Dwyer, Samuel J. Robertson and Paul B. Gastin

The purpose of this study was to assess the validity of a global positioning system (GPS) tracking system to estimate energy expenditure (EE) during exercise and field-sport locomotor movements. Twenty-seven participants each completed a 90-min exercise session on an outdoor synthetic futsal pitch. During the exercise session, they wore a 5-Hz GPS unit interpolated to 15 Hz and a portable gas analyzer that acted as the criterion measure of EE. The exercise session was composed of alternating 5-minute exercise bouts of randomized walking, jogging, running, or a field-sport circuit (×3) followed by 10 min of recovery. One-way analysis of variance showed significant (P < .01) and very large underestimations between GPS metabolic power– derived EE and oxygen-consumption (VO2) -derived EE for all field-sport circuits (% difference ≈ –44%). No differences in EE were observed for the jog (7.8%) and run (4.8%), whereas very large overestimations were found for the walk (43.0%). The GPS metabolic power EE over the entire 90-min session was significantly lower (P < .01) than the VO2 EE, resulting in a moderate underestimation overall (–19%). The results of this study suggest that a GPS tracking system using the metabolic power model of EE does not accurately estimate EE in field-sport movements or over an exercise session consisting of mixed locomotor activities interspersed with recovery periods; however, is it able to provide a reasonably accurate estimation of EE during continuous jogging and running.

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Mohamed Ali Nabli, Nidhal Ben Abdelkrim, Imed Jabri, Tahar Batikh, Carlo Castagna and Karim Chamari

Purpose:

To examine the relation between game performance, physiological responses, and field-test results in Tunisian basketball referees.

Methods:

Computerized time–motion analysis, heart rate (HR), and blood lactate concentration [La] were measured in 15 referees during 8 competitive games (under-19-y-old Tunisian league). Referees also performed a repeated-sprint test (RSA), Yo-Yo Intermittent Recovery Test level 1 (YYIRTL1), agility T-test, and 30-m sprint with 10-m lap time. Computerized video analysis determined the time spent in 5 locomotor activities (standing, walking, jogging, running, and sprint), then grouped in high-, moderate-, and low-intensity activities (HIAs, MIAs, and LIAs, respectively).

Results:

YYIRTL1 performance correlated with (1) total distance covered during the 4th quarter (r = .52, P = .04) and (2) distance covered in LIA during all game periods (P < .05). Both distance covered and time spent in MIA during the 1st quarter were negatively correlated with the YYIRTL1 performance (r = –.53, P = .035; r = –.67, P = .004, respectively). A negative correlation was found between distance covered at HIA during the 2nd half (3rd quarter + 4th quarter) and fatigue index of the RSA test (r = –.54, P = .029). Mean HR (expressed as %HRpeak) during all game periods was correlated with YYIRTL1 performance (.61 ≤ r < .67, P < .01).

Conclusions:

This study showed that (1) the YYIRTL1 performance is a moderate predictor of game physical performance in U-19 basketball referees and (2) referees’ RSA correlates with the amount of HIA performed during the 2nd half, which represents the ability to keep up with play.