, & Pfeiffer, 2016 ). However, it remains to be determined whether there is a correlation between accelerometer output and oxygen consumption (VO 2 , ml·min −1 ·kg −1 ) during high-speed running. It has been proposed that the plateau phenomenon is caused by the built-in filtering of the accelerometer signals
Katja Krustrup Pedersen, Esben Lykke Skovgaard, Ryan Larsen, Mikkel Stengaard, Søren Sørensen and Kristian Overgaard
Erin Strutz, Raymond Browning, Stephanie Smith, Barbara Lohse and Leslie Cunningham-Sabo
in one group will precipitate PA changes in the other group. Thus, for such interventions to be successful, a significant positive correlation between parent and child PA must exist. Previous explorations that have examined the correlation between parent and child PA levels using direct observation
Stefan Koehn, Alan J. Pearce and Tony Morris
The main purpose of the study was to examine crucial parts of Vealey’s (2001) integrated framework hypothesizing that sport confidence is a mediating variable between sources of sport confidence (including achievement, self-regulation, and social climate) and athletes’ affect in competition. The sample consisted of 386 athletes, who completed the Sources of Sport Confidence Questionnaire, Trait Sport Confidence Inventory, and Dispositional Flow Scale-2. Canonical correlation analysis revealed a confidence-achievement dimension underlying flow. Bias-corrected bootstrap confidence intervals in AMOS 20.0 were used in examining mediation effects between source domains and dispositional flow. Results showed that sport confidence partially mediated the relationship between achievement and self-regulation domains and flow, whereas no significant mediation was found for social climate. On a subscale level, full mediation models emerged for achievement and flow dimensions of challenge–skills balance, clear goals, and concentration on the task at hand.
Weimo Zhu, Zorica Nedovic-Budic, Robert B. Olshansky, Jed Marti, Yong Gao, Youngsik Park, Edward McAuley and Wojciech Chodzko-Zajko
To introduce Agent-Based Model (ABM) to physical activity (PA) research and, using data from a study of neighborhood walkability and walking behavior, to illustrate parameters for an ABM of walking behavior.
The concept, brief history, mechanism, major components, key steps, advantages, and limitations of ABM were first introduced. For illustration, 10 participants (age in years: mean = 68, SD = 8) were recruited from a walkable and a nonwalkable neighborhood. They wore AMP 331 triaxial accelerometers and GeoLogger GPA tracking devices for 21 days. Data were analyzed using conventional statistics and highresolution geographic image analysis, which focused on a) path length, b) path duration, c) number of GPS reporting points, and d) interaction between distances and time.
Average steps by subjects ranged from 1810−10,453 steps per day (mean = 6899, SD = 3823). No statistical difference in walking behavior was found between neighborhoods (Walkable = 6710 ± 2781, Nonwalkable = 7096 ± 4674). Three environment parameters (ie, sidewalk, crosswalk, and path) were identified for future ABM simulation.
ABM should provide a better understanding of PA behavior’s interaction with the environment, as illustrated using a real-life example. PA field should take advantage of ABM in future research.
Cristiane Petra Miculis, Wagner De Campos and Margaret Cristina da Silva Boguszewski
The aim of this study was to correlate glycemic control (GC) and variables of physical activity levels (PAL) in children with type 1 diabetes mellitus (T1DM).
Fifty children and adolescents with T1DM were selected. Personal and medical data for the patients were collected. Physical evaluations of body weight and sexual maturation were undertaken. Bouchard’s questionnaire was applied to evaluate PAL as well as for time spent on physical activities.
Sixty-four percent of the subjects were sexually mature. Differences were observed between females and males in insulin dose, duration of light physical activity, and sleeping time (P < .05). Ninety percent presented poor GC and 80% had a low PAL. Fasting blood glucose (FBG) was significantly correlated with PAL, with sedentary time, and with sleeping time. Glycated hemoglobin (HbA1c) was significantly correlated with sedentary time and sleeping time. Among the three groups of PAL (insufficient × moderate × active) there were differences in HbA1c (%), FBG (mg/dL), duration of disease (years), and insulin dose (UI/kg/day) (P < 0.001).
GC was significantly correlated with PAL. Among the three groups of physical activity level, the most active group was seen to have the best GC.
Christopher J. Nightingale, Sidney N. Mitchell and Stephen A. Butterfield
in senior citizens. The purpose of this study was to examine correlations between the TUG test and various indicators of balance utilizing the OptoGait (Microgate USA, Mahopec, NY) photoelectric system. The TUG test is used to assess mobility and balance. Specifically, the patient is asked to rise
Ece Acar, Tamer Çankaya and Serkan Öner
skills by decreasing the force released by these muscles. In other words, the decrease in muscle mass may lead to the loss of muscle strength, which, in turn, may lead to a decrease in muscle functions. However, the correlations between these factors are not yet fully discovered. Although there are
Michael J. LaMonte, I-Min Lee, Eileen Rillamas-Sun, John Bellettiere, Kelly R. Evenson, David M. Buchner, Chongzhi Di, Cora E. Lewis, Dori E. Rosenberg, Marcia L. Stefanick and Andrea Z. LaCroix
( LaCroix et al., 2017 ) (1–6, 7–9, 10–12; higher score is better). Analyses on accelerometer measures were adjusted for differences in awake wear time of the device. In a main set of analyses, we calculated Spearman correlations between accelerometer measures of PA or SB and their corresponding
Khaya Morris-Binelli, Sean Müller and Peter Fadde
baseball batters (of single-A minor league level) and whether their scores were related to game batting statistics. They found a significant positive correlation between overall pitch type anticipation at the front-foot landing temporal occlusion (pre-ball release information) and base-on-balls (how often
Hitoshi Koda, Yoshihiro Kai, Shin Murata, Hironori Osugi, Kunihiko Anami, Takahiko Fukumoto and Hidetaka Imagita
walking by using Pearson’s correlation coefficient. Moreover, regarding the muscles recognized to have significant correlation with the body sway, defining the quartile of subjects with the greatest body sway as “increased sway,” we used the receiver operating characteristic curve to calculate the