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Twitter, AI used to track people's physical activities
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IANS | 19 Jul, 2019
Using Artificial Intelligence (AI) and data from various social media
sites, researchers tracked people's physical activities, from bowling to
crossfit, in a bid to inform future efforts to tackle health
disparities.
Published in the journal BMJ Open Sport &
Exercise Medicine, the researchers used machine learning to find and
comb through exercise-related tweets from across the US, unpacking
regional and gender differences in exercise types and intensity levels.
By
analysing 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," said study author Elaine Nsoesie,
Assistant Professor at the Boston University in the US.
"By
understanding differences in how people are exercising across different
communities, we can design interventions that target the specific needs
of those communities," Nsoesie said.
For the study, the
researchers used a set of AI models to find and analyse 1,382,284
relevant tweets by 481,146 Twitter users in 2,900 US counties.
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.
According to the findings, 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 South had the biggest gender
gap in exercise intensity, said the study.
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