A new study led by Boston University School of Public Health (BUSPH) researchers and published in BMJ Open Sport & Exercise Medicine used machine learning to find and comb through exercise-related tweets from across the United States, unpacking regional and gender differences in exercise types and intensity levels. By analyzing the language of the tweets, this method was also able to show how different populations feel about different kinds of exercise.
"In most cases, lower-income communities tend to lack access to resources that encourage a healthy lifestyle," says study senior author Dr. Elaine Nsoesie, assistant professor of global health at BUSPH. "By understanding differences in how people are exercising across different communities, we can design interventions that target the specific needs of those communities."
Future - Media - Data - Interventions - Policies
In the future, social media and other digital data could help create interventions and policies informed not just by the habits of these communities, but also by what they think of different physical activities, says study lead author Dr....
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