training group, with an average duration of each training session of 120.80 minutes (SD = 32.37). The classification of competitive athletes was based on performance level, thereby complying with the recommendations by Heidari et al 13 and Swann et al. 41 Athletes competing in the first to fourth German
Jahan Heidari, Johanna Belz, Monika Hasenbring, Jens Kleinert, Claudia Levenig and Michael Kellmann
Morteza Sadeghi, Gholamali Ghasemi and Mohammadtaghi Karimi
between T1 and T12 (American Spinal Cord Association classification A = 6, B = 6, C = 2, and D = 2) were randomly assigned to 2 groups (ie, rebound and control) in this study. Table 1 shows the characteristics of the subjects having participated in this study. The rebound group received rebound therapy
Theo Ouvrard, Alain Groslambert, Gilles Ravier, Sidney Grosprêtre, Philippe Gimenez and Frederic Grappe
(ranged from 0.7% for the final general classification to 2.2% for the time trial stages 39 ). This sizeable impact on performance clearly suggests that it may no longer be possible for any professional cyclist to win a Grand Tour without using this strategy and explains why the best teams always employ
Lesley Fishwick and Diane Hayes
Traditional involvement patterns in leisure-time physical activities may have changed with demographic shifts in American society. We analyzed a community survey of 401 Illinois adults to determine involvement in recreational activities by gender, age, race, and social class. Regression analyses reveal differences in participation in individual and team activities. These differences by demographic classification are explained by structural and normative influences.
Keith P. Gennuso, Kathryn Zalewski, Susan E. Cashin and Scott J. Strath
To examine the effectiveness of the American College of Sports Medicine (ACSM) and the American Heart Association (AHA) resistance training (RT) guidelines to improve physical function and functional classification in older adults with reduced physical abilities.
Twenty-five at-risk older adults were randomized to a control (CON = 13) or 8-week resistance training intervention arm (RT = 12). Progressive RT included 8 exercises for 1 set of 10 repetitions at a perceived exertion of 5–6 performed twice a week. Individuals were assessed for physical function and functional classification change (low, moderate or high) by the short physical performance battery (SPPB) and muscle strength measures.
Postintervention, significant differences were found between groups for SPPB—Chair Stand [F(1,22) = 9.14, P < .01, η = .29] and SPPB—Total Score [F(1,22) = 7.40, P < .05, η = .25]. Functional classification was improved as a result of the intervention with 83% of participants in the RT group improving from low to moderate functioning or moderate to high functioning. Strength significantly improved on all exercises in the RT compared with the CON group.
A RT program congruent with the current ASCM and AHA guidelines is effective to improve overall physical function, functional classification, and muscle strength for older adults with reduced physical abilities.
Xanne Janssen, Dylan P. Cliff, John J. Reilly, Trina Hinkley, Rachel A. Jones, Marijka Batterham, Ulf Ekelund, Soren Brage and Anthony D. Okely
This study examined the classification accuracy of the activPAL, including total time spent sedentary and total number of breaks in sedentary behavior (SB) in 4- to 6-year-old children. Forty children aged 4–6 years (5.3 ± 1.0 years) completed a ~150-min laboratory protocol involving sedentary, light, and moderate- to vigorous-intensity activities. Posture was coded as sit/lie, stand, walk, or other using direct observation. Posture was classified using the activPAL software. Classification accuracy was evaluated using sensitivity, specificity and area under the receiver operating characteristic curve (ROC-AUC). Time spent in each posture and total number of breaks in SB were compared using paired sample t-tests. The activPAL showed good classification accuracy for sitting (ROC-AUC = 0.84) and fair classification accuracy for standing and walking (0.76 and 0.73, respectively). Time spent in sit/lie and stand was overestimated by 5.9% (95% CI = 0.6−11.1%) and 14.8% (11.6−17.9%), respectively; walking was underestimated by 10.0% (−12.9−7.0%). Total number of breaks in SB were significantly overestimated (55 ± 27 over the course of the protocol; p < .01). The activPAL performed well when classifying postures in young children. However, the activPAL has difficulty classifying other postures, such as kneeling. In addition, when predicting time spent in different postures and total number of breaks in SB the activPAL appeared not to be accurate.
Lachlan E. Garrick, Bryce C. Alexander, Anthony G. Schache, Marcus G. Pandy, Kay M. Crossley and Natalie J. Collins
as the test limb for all single-leg squat testing. Classification of Single-Leg Squat Performance Participants were barefoot and wore appropriate clothing to allow adequate visualization of anatomical landmarks on the trunk, pelvis, and lower limb. To facilitate classification of single-leg squat
Joseph P. Winnick and Francis X. Short
In order to compare their physical fitness, the UNIQUE Physical Fitness Test was administered to 203 retarded and nonretarded subjects with cerebral palsy from both segregated and integrated settings throughout the United States. The test was administered to subjects between the ages of 10 and 17 by professional persons prepared as field testers. Subjects were free from multiple handicapping conditions other than mild mental retardation and cerebral palsy. Regardless of intellectual classification, older subjects significantly exceeded the performance of younger subjects on dominant grip strength. Regardless of intellectual classification, older subjects significantly exceeded the scores of younger subjects on the softball throw and flexed arm hang. No significant differences between retarded and nonretarded subjects at the .01 level of significance were found on any of the test items on the UNIQUE test. The factor structures of both retarded and nonretarded groups were identical with regard to the items that loaded on specific physical fitness factors.
Jennifer Ryan, Michael Walsh and John Gormley
This study investigated the ability of published cut points for the RT3 accelerometer to differentiate between levels of physical activity intensity in children with cerebral palsy (CP). Oxygen consumption (metabolic equivalents; METs) and RT3 data (counts/min) were measured during rest and 5 walking trials. METs and corresponding counts/min were classified as sedentary, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) according to MET thresholds. Counts were also classified according to published cut points. A published cut point exhibited an excellent ability to classify sedentary activity (sensitivity = 89.5%, specificity = 100.0%). Classification accuracy decreased when published cut points were used to classify LPA (sensitivity = 88.9%, specificity = 79.6%) and MVPA (sensitivity = 70%, specificity = 95–97%). Derivation of a new cut point improved classification of both LPA and MVPA. Applying published cut points to RT3 accelerometer data collected in children with CP may result in misclassification of LPA and MVPA.
Collin Webster, Diana Mîndrilă and Glenn Weaver
Affective learning is a major focus of the national K-12 physical education (PE) content standards (National Association for Sport and Physical Education [NASPE, 2004]). Understanding how students might fit into different affective learning subgroups would help extend affective learning theory in PE and suggest possible intervention strategies for teachers wanting to increase students’ affective learning. The present study used cluster analysis (CA) and latent profile analysis (LPA) to develop a two-level affective learning-based typology of high school students in compulsory PE from an instructional communication perspective. The optimal classification system had ten clusters and four latent profiles. A comparison of students’ class and cluster memberships showed that the two classification procedures yielded convergent results, thus suggesting distinct affective learning profiles. Students’ demographic and biographical characteristics, including gender, race, body mass index, organized sport participation, and free time physical activity, were helpful in further characterizing each profile.