functional test has been accepted for clinical use. Thus, LPHC instability was defined based on a specific classification system derived from previous studies. We classified participants as “unstable” if they displayed knee valgus greater than 15° at 45° knee flexion in the descending phase of the squat. 19
Gabrielle G. Gilmer, Jessica K. Washington, Jeffrey R. Dugas, James R. Andrews and Gretchen D. Oliver
Hannah Horris, Barton E. Anderson, R. Curtis Bay and Kellie C. Huxel Bliven
tests separately by test position. Table 1 Indicators of Breathing Pattern Classification by Breathing Test and Test Position Test position Breathing test Indicators of breathing pattern classification Breathing pattern Supine Seated Standing Half kneeling Hi-lo Lack of abdominal excursion Dysfunctional
Bradley J. Cardinal, Hermann-J. Engels and Weimo Zhu
The Transtheoreticai Model of behavior change was applied to a sample of 669 preadolescents (M age = 8.2) to determine whether stages of exercise could be observed. Associations between stage of exercise classification and demographic, fitness, and cognitive variables were examined. Stage of exercise classifications, on the basis of the Children’s Stage of Exercise Algorithm, were as follows: maintenance (50.8%), action (36.5%), preparation (3.1%), contemplation (4.9%), and precontemplation (4.6%). Stage of exercise was significantly related to gender, age, and grade level. Controlling for these differences, the relationship between exercise beliefs and stage of exercise was significant.
Jahan Heidari, Johanna Belz, Monika Hasenbring, Jens Kleinert, Claudia Levenig and Michael Kellmann
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
Swati M. Surkar, Rashelle M. Hoffman, Brenda Davies, Regina Harbourne and Max J. Kurz
exclusion criteria to qualify for the study. The inclusion criteria for children with HCP were 1) children diagnosed with hemiplegia due to brain damage between the age of 3–10 years; 2) children with HCP with the Manual Ability Classification System (MACS) level II (handles most of the objects but with
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
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.
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.
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.