Search Results

Background : Multiple activity monitors are utilized for the estimation of moderate- to vigorous-intensity physical activity in youth. Due to differing methodological approaches, results are not comparable when developing thresholds for the determination of moderate- to vigorous-intensity physical activity. This study aimed to develop and validate count-to-activity thresholds for 1.5, 3, and 6 metabolic equivalents of task in five of the most commonly used activity monitors in adolescent research. Methods : Fifty-two participants (mean age = 16.1 [0.78] years) selected and performed activities of daily living while wearing a COSMED K4b2 and five activity monitors; ActiGraph GT1M, ActiGraph wGT3X-BT, activPAL3 micro, activPAL, and GENEActiv. Receiver-operating-characteristic analysis was used to examine the area under the curve and to define count-to-activity thresholds for the vertical axis (all monitors) and the sum of the vector magnitude (ActiGraph wGT3X-BT and activPAL3 micro) for 15 s (all monitors) and 60 s (ActiGraph monitors) epochs. Results : All developed count-to-activity thresholds demonstrated high levels of sensitivity and specificity. When cross-validated in an independent group (N = 20), high levels of sensitivity and specificity generally remained (≥73.1%, intensity and monitor dependent). Conclusions : This study provides researchers with the opportunity to analyze and cross-compare data from different studies that have not employed the same motion sensors.

Background: Although levels of physical activity (PA) have been researched, no information on how university students organize their PA across different life domains is available. The purpose of this study is to explore if and how students organize their PA across transport and recreational domains, and to identify the psychosocial factors related to these patterns. Methods: Students from 31 Irish universities completed a supervised online survey measuring participant characteristics, psychosocial factors, and PA. Two-step cluster analysis was used to identify specific PA patterns in students. Binary logistic regressions identified factors associated with cluster membership while controlling for age, sex, household income, and perceived travel time to a university. Results: Analysis was performed on 6951 students (50.7% male; 21.51 [5.55] y). One Low Active cluster emerged. Four clusters containing a form of PA emerged including Active Commuters, Active in University, Active Outside University, and High Active. Increases in motivation and planning improved the likelihood of students being categorized in a cluster containing PA. Conclusion: One size does not fit all when it comes to students PA engagement, with 5 patterns identified. Health professionals are advised to incorporate strategies for increasing students’ motivation, action planning, and coping planning into future PA promotion efforts.