National University of Ireland Galway, Galway, Ireland.
Transl Behav Med. 2013 Sep;3(3):304-11. doi: 10.1007/s13142-013-0209-0.
The purpose of this project was to design and test data collection and management tools that can be used to study the use of mobile fitness applications and social networking within the context of physical activity. This project was conducted over a 6-month period and involved collecting publically shared Twitter data from five mobile fitness apps (Nike+, RunKeeper, MyFitnessPal, Endomondo, and dailymile). During that time, over 2.8 million tweets were collected, processed, and categorized using an online tweet collection application and a customized JavaScript. Using the grounded theory, a classification model was developed to categorize and understand the types of information being shared by application users. Our data show that by tracking mobile fitness app hashtags, a wealth of information can be gathered to include but not limited to daily use patterns, exercise frequency, location-based workouts, and overall workout sentiment.
本项目旨在设计和测试数据收集和管理工具，用于研究移动健身应用程序和社交网络在体育活动背景下的使用情况。该项目历时 6 个月，涉及从五个移动健身应用程序（Nike+、RunKeeper、MyFitnessPal、Endomondo 和 dailymile）中收集公开分享的 Twitter 数据。在此期间，使用在线推文收集应用程序和定制的 JavaScript 收集、处理和分类了超过 280 万条推文。使用扎根理论，开发了一种分类模型来对应用程序用户分享的信息类型进行分类和理解。我们的数据表明，通过跟踪移动健身应用程序的标签，可以收集大量信息，包括但不限于日常使用模式、锻炼频率、基于位置的锻炼以及整体锻炼情绪。
