Using machine learning technology new study finds exercise-related tweets from across the United States, unwrapping regional and gender differences in exercise types and intensity levels, reveals a new study. The findings of the study are published in the journal BMJ Open Sport & Exercise Medicine.
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 senior study 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."
The researchers used a set of AI models to find and analyze 1,382,284 relevant tweets by 481,146 Twitter users in 2,900 US counties (and get rid of false positives, like references to The Walking Dead or watching sports, or using the expression "running late"). Finally, the researchers compared tweets by men and women, and from four different regions of the country: the Northeast, the South, the Midwest, and the West.
The top exercise terms were "walk," "dance," "golf," "workout," "run," "pool," "hike," "yoga," "swim," and "bowl." Walking was the most popular activity overall, but other activities varied by gender and region. Women in the West did more intensive exercise than in any other region, while the Midwest had the most intensive exercise among men. Men did slightly more intensive exercise than women overall, and the South had the biggest gender gap in exercise intensity.