Sticking to an exercise routine is a challenge many people face. But a research team is using machine learning to uncover what keeps individuals committed to their workouts.
Sticking to an exercise routine is a challenge many people face. But a University of Mississippi research team is using machine learning to uncover what keeps individuals committed to their workouts.
The Office of Disease Prevention and Health Promotion, part of the U.S. Department of Health and Human Services, suggests that adults should aim for at least 150 minutes of moderate exercise, or 75 minutes of vigorous exercise, each week as part of a healthy lifestyle. "We aimed to use machine learning to predict whether people follow physical activity guidelines based on questionnaire data, and find the best combination of variables for accurate predictions," said Choe, the study's lead author."Demographic variables such as gender, age, race, educational status, marital status and income, along with anthropometric measures like BMI and waist circumference, were considered.
According to Choe, these factors are especially important for understanding who is more likely to stay active and socially connected, and they could help guide future health recommendations. The researchers say machine learning gives them more freedom to study the data. Older methods expect things to follow a straight-line pattern, and they don't work well when some pieces of information are too similar."One limitation of our study was using subjectively measured physical activity data, where participants recalled their activity from memory," Choe said.
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