Development and Evaluation of a Novel Computer-Based Tool for Assessing Physical Activity Levels in Schoolchildren

2009 ◽  
Vol 21 (4) ◽  
pp. 506-519 ◽  
Author(s):  
Sally A. McLure ◽  
John J. Reilly ◽  
Sean Crooks ◽  
Carolyn D. Summerbell

A novel computer tool (peas@tees), designed to assess habitual physical activity levels in children aged 9 and 10 years, was evaluated. Study 1 investigated agreement between peas@tees and accelerometry in 157 children. Bland-Altman limits of agreement (LOA) revealed peas@tees underestimated physical activity levels compared with accelerometry (bias −21 min; 95% LOA -146–105). Study 2 investigated stability of peas@tees in a separate sample of 42 children. Intraclass correlation coefficient was 0.75 (95% CI 0.62–0.84). Computer tools are promising as a cheap, feasible, and useful method to monitor children’s habitual levels of physical activity at the group level.

2018 ◽  
Vol 7 (9) ◽  
pp. 268 ◽  
Author(s):  
Jungyun Hwang ◽  
Austin Fernandez ◽  
Amy Lu

We assessed the agreement of two ActiGraph activity monitors (wGT3X vs. GT9X) placed at the hip and the wrist and determined an appropriate epoch length for physical activity levels in an exergaming setting. Forty-seven young adults played a 30-min exergame while wearing wGT3X and GT9X on both hip and wrist placement sites and a heart rate sensor below the chest. Intraclass correlation coefficient indicated that intermonitor agreement in steps and activity counts was excellent on the hip and good on the wrist. Bland-Altman plots indicated good intermonitor agreement in the steps and activity counts on both placement sites but a significant intermonitor difference was detected in steps on the wrist. Time spent in sedentary and physical activity intensity levels varied across six epoch lengths and depended on the placement sites, whereas time spent from a 1-s epoch of the hip-worn monitors most accurately matched the relative exercise intensity by heart rate. Hip placement site was associated with better step-counting accuracy for both activity monitors and more valid estimation of physical activity levels. A 1-s epoch was the most appropriate epoch length to detect short bursts of intense physical activity and may be the best choice for data processing and analysis in exergaming studies examining intermittent physical activities.


2018 ◽  
Author(s):  
Taotao Wang ◽  
Mengyuan Ren ◽  
Ying Shen ◽  
Xiaorou Zhu ◽  
Xing Zhang ◽  
...  

BACKGROUND Physical inactivity is a risk factor for chronic noncommunicable diseases. Insufficient physical activity has become an important public health problem worldwide. As mobile apps have rapidly developed, physical activity apps have the potential to improve the level of physical activity among populations. OBJECTIVE This study aimed to evaluate the effect of physical activity apps on levels of physical activity among college students. METHODS A Web-based questionnaire was used to survey college students in Beijing from December 27, 2017, to January 5, 2018. According to a previous survey, 43% of college students using physical activity apps and 36% of those who never used such apps achieved the physical activity recommendations. In this study, the sample size was calculated to be 500. The questionnaire consisted of 5 parts: the use of physical activity apps, sports habits, social support, self-efficacy, and social demographic information. Structural equation modeling was used to test the relationships between the use of physical activity apps, self-efficacy, social support, and level of physical activity. RESULTS Of the 1245 participants, 384 college students (30.8%) used physical activity apps (in the past month). Of these 384 students, 191 (49.7%) gained new friends via the app. College students who were using physical activity apps had a higher level of physical activity and higher scores for social support and self-efficacy (<italic>P</italic>&lt;.001) than those who did not use such apps. The use of physical activity apps significantly affected the mediating effect of physical activity level through social support (beta=.126; <italic>P</italic>&lt;.001) and self-efficacy (beta=.294; <italic>P</italic>&lt;.001). Gender played an important role in app use, self-efficacy, and physical activity in the mediation model: male users spent more time on physical activity and had higher self-efficacy scores (<italic>P</italic>&lt;.001). CONCLUSIONS This study focused on college students in Beijing and found that the use of physical activity apps is associated with higher physical activity levels among these students. This effect is mainly through the mediation effect of social support and self-efficacy, rather than the direct effect of physical activity apps. The use of physical activity apps is associated with a higher social support level and higher self-efficacy score. Furthermore, a high social support level and high self-efficacy score are associated with higher physical activity levels.


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