Reliability of Using Motion Sensors to Measure Children's Physical Activity Levels in Exergaming.
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ABSTRACT:
SUBMITTER: Zeng N
PROVIDER: S-EPMC5977139 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
Publications
Reliability of Using Motion Sensors to Measure Children's Physical Activity Levels in Exergaming.
Zeng Nan N Gao Xingyuan X Liu Yuanlong Y Lee Jung Eun JE Gao Zan Z
Journal of clinical medicine 20180502 5
<h4>Objectives</h4>This study examined the reliability of two objective measurement tools in assessing children’s physical activity (PA) levels in an exergaming setting.<h4>Methods</h4>A total of 377 children (190 girls, <i>M<sub>age</sub></i> = 8.39, <i>SD</i> = 1.55) attended the 30-min exergaming class every other day for 18 weeks. Children’s PA levels were concurrently measured by NL-1000 pedometer and ActiGraph GT3X accelerometer, while children’s steps per min and time en ...[more]
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Project description:BackgroundSocial media technology such as Twitter allows users to share their thoughts, feelings, and opinions online. The growing body of social media data is becoming a central part of infodemiology research as these data can be combined with other public health datasets (eg, physical activity levels) to provide real-time monitoring of psychological and behavior outcomes that inform health behaviors. Currently, it is unclear whether Twitter data can be used to monitor physical activity levels.ObjectiveThe aim of this study was to establish the feasibility of using Twitter data to monitor physical activity levels by assessing whether the frequency and sentiment of physical activity-related tweets were associated with physical activity levels across the United States.MethodsTweets were collected from Twitter's application programming interface (API) between January 10, 2017 and January 2, 2018. We used Twitter's garden hose method of collecting tweets, which provided a random sample of approximately 1% of all tweets with location metadata falling within the United States. Geotagged tweets were filtered. A list of physical activity-related hashtags was collected and used to further classify these geolocated tweets. Twitter data were merged with physical activity data collected as part of the Behavioral Risk Factor Surveillance System. Multiple linear regression models were fit to assess the relationship between physical activity-related tweets and physical activity levels by county while controlling for population and socioeconomic status measures.ResultsDuring the study period, 442,959,789 unique tweets were collected, of which 64,005,336 (14.44%) were geotagged with latitude and longitude coordinates. Aggregated data were obtained for a total of 3138 counties in the United States. The mean county-level percentage of physically active individuals was 74.05% (SD 5.2) and 75.30% (SD 4.96) after adjusting for age. The model showed that the percentage of physical activity-related tweets was significantly associated with physical activity levels (beta=.11; SE 0.2; P<.001) and age-adjusted physical activity (beta=.10; SE 0.20; P<.001) on a county level while adjusting for both Gini index and education level. However, the overall explained variance of the model was low (R2=.11). The sentiment of the physical activity-related tweets was not a significant predictor of physical activity level and age-adjusted physical activity on a county level after including the Gini index and education level in the model (P>.05).ConclusionsSocial media data may be a valuable tool for public health organizations to monitor physical activity levels, as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (eg, surveys and observational studies). Consequently, this tool may have the potential to help public health organizations better mobilize and target physical activity interventions.
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