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Researchers use social media and AI to gain insight into people's exercise habits
POSTED 23 Jul 2019 . BY Tom Walker
The researchers used a set of artificial intelligence models to find and analyse more than 1.38m relevant tweets by 481,146 individual Twitter users
Boston University School of Public Health (BUSPH) has combined social media posts and machine learning to chart entire populations' exercise habits.

BUSPH researchers combed through exercise-related tweets from across the US and analysed the language of the tweets to uncover how different populations feel about different kinds of exercise.

The researchers used a set of artificial intelligence models to find and analyse more than 1.38m relevant tweets by 481,146 individual Twitter users in 2,900 US counties.

By doing so, researchers were able to unpack regional and gender differences in exercise types and intensity levels.

According to Dr. Elaine Nsoesie, assistant professor of global health at BUSPH. the research unearthed insight that could be used to help make populations more active.

"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.

"The study showed that, in most cases, lower-income communities tend to lack access to resources that encourage a healthy lifestyle."

Dr. Nina Cesare, study senior author and postdoctoral associate in global health at BUSPH, added: "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.

"We believe this work provides a step in the right direction."

The top exercise terms were walking, dance, golf, workout, run, pool, hike, yoga and swim.

Walking was the most popular activity overall, but other activities varied by gender and region.

Findings included that women in the West of US 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.

The research was published in BMJ Open Sport & Exercise Medicine. To read the full article, click here.
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NEWS
Researchers use social media and AI to gain insight into people's exercise habits
POSTED 23 Jul 2019 . BY Tom Walker
The researchers used a set of artificial intelligence models to find and analyse more than 1.38m relevant tweets by 481,146 individual Twitter users
Boston University School of Public Health (BUSPH) has combined social media posts and machine learning to chart entire populations' exercise habits.

BUSPH researchers combed through exercise-related tweets from across the US and analysed the language of the tweets to uncover how different populations feel about different kinds of exercise.

The researchers used a set of artificial intelligence models to find and analyse more than 1.38m relevant tweets by 481,146 individual Twitter users in 2,900 US counties.

By doing so, researchers were able to unpack regional and gender differences in exercise types and intensity levels.

According to Dr. Elaine Nsoesie, assistant professor of global health at BUSPH. the research unearthed insight that could be used to help make populations more active.

"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.

"The study showed that, in most cases, lower-income communities tend to lack access to resources that encourage a healthy lifestyle."

Dr. Nina Cesare, study senior author and postdoctoral associate in global health at BUSPH, added: "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.

"We believe this work provides a step in the right direction."

The top exercise terms were walking, dance, golf, workout, run, pool, hike, yoga and swim.

Walking was the most popular activity overall, but other activities varied by gender and region.

Findings included that women in the West of US 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.

The research was published in BMJ Open Sport & Exercise Medicine. To read the full article, click here.
RELATED STORIES
The Lancet: social media preventing teenagers being physically active – and affecting their mental health


Teenagers' 'incessant' use of social media is radically reducing the time they spend sleeping and exercising – and could have a detrimental effect on mental health and wellbeing.
Study: fitness boosts brainpower in adults


Physical fitness has been associated with better brain structure and brain functioning in adults.
FEATURE: Software: Social media


How software is helping spas drive new business through social media
FEATURE: People profile: Em Sheldon


The social media influencer explains how operators can tap into influencer marketing
MORE NEWS
David Rockwell creates immersive magic destination, The Hand and The Eye
A US$50 million (£44.2 million, €51.2 million) transformation of Chicago's historic McCormick Mansion has created a new destination that combines live magic, immersive theatre, dining and private membership under one roof.
Montana Heritage Center opens with immersive exhibits and US$107 million investment
The Montana Historical Society has officially celebrated the opening of its new Montana Heritage Center, a US$107 million (£79 million, €92 million) destination that combines immersive storytelling with cutting-edge audiovisual technology to bring the sta
Universal launches new theme park model with Kids Resort
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San Antonio Zoo reports $283 million economic impact as expansion plans progress
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