@文章{info:doi/10.2196/12394，作者=“Liu, Sam and Chen, Brian and Kuo, Alex”，标题=“利用Twitter数据监测身体活动水平:信息流行病学研究”，期刊=“J Med Internet Res”，年=“2019”，月=“Jun”，日=“03”，卷=“21”，数=“6”，页=“e12394”，关键词=“身体活动;社交媒体;互联网;Twitter消息;人口监测;背景:Twitter等社交媒体技术允许用户在线分享他们的想法、感受和观点。越来越多的社交媒体数据正在成为信息流行病学研究的核心部分，因为这些数据可以与其他公共卫生数据集(如身体活动水平)结合，以提供心理和行为结果的实时监测，为健康行为提供信息。目前，尚不清楚Twitter数据能否用于监测身体活动水平。目的:本研究的目的是通过评估与身体活动相关的推文的频率和情绪是否与美国各地的身体活动水平相关，建立使用推特数据监测身体活动水平的可行性。方法:收集2017年1月10日至2018年1月2日Twitter应用程序编程接口(API)中的推文。 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. Results: During 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). Conclusions: Social 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. ", issn="1438-8871", doi="10.2196/12394", url="//www.mybigtv.com/2019/6/e12394/", url="https://doi.org/10.2196/12394", url="http://www.ncbi.nlm.nih.gov/pubmed/31162126" }