{"id":148827,"date":"2025-04-29T10:56:47","date_gmt":"2025-04-29T15:56:47","guid":{"rendered":"https:\/\/www.thelocalvoice.net\/oxford\/?p=148827"},"modified":"2025-04-29T10:56:52","modified_gmt":"2025-04-29T15:56:52","slug":"researchers-use-machine-learning-to-predict-exercise-adherence","status":"publish","type":"post","link":"https:\/\/www.thelocalvoice.net\/oxford\/researchers-use-machine-learning-to-predict-exercise-adherence\/","title":{"rendered":"Researchers Use Machine Learning to Predict Exercise Adherence"},"content":{"rendered":"\n

Method helps team reduce error and improve predictions of physical activity commitment<\/em><\/h2>\n\n\n\n

By Jordan Karnbach<\/em><\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

The team \u2013 Seungbak Lee<\/strong> and Ju-Pil Choe<\/strong>, both doctoral students in physical education, and Minsoo Kang<\/strong>,\u00a0professor of sport analytics in the\u00a0Department of Health, Exercise Science and Recreation Management<\/a>\u00a0<\/strong>\u2013\u00a0hopes\u00a0to predict whether a person is meeting physical activity guidelines based on their body measurements, demographics, and lifestyle.<\/p>\n\n\n\n

They have examined data from about 30,000 surveys. To quickly sort through such a huge data set, they’ve turned to machine learning, a way of using computers to identify patterns and make predictions based on the information.<\/p>\n\n\n\n

The group’s results,\u00a0published in the Nature Portfolio journal Scientific Reports<\/em><\/a>\u00a0are timely, Kang said. “Physical activity adherence to the guidelines is a public health concern because of its relationship to disease prevention and overall health patterns,” he said. “We wanted to use advanced data analytic techniques, like machine learning, to predict this behavior.”<\/p>\n\n\n\n

The\u00a0Office of Disease Prevention and Health Promotion<\/strong>, part of the\u00a0U.S. Department of Health and Human Services<\/strong>,\u00a0suggests that\u00a0adults should aim for at least 150 minutes of moderate exercise<\/a>, or 75 minutes of vigorous exercise, each week as part of a healthy lifestyle.<\/p>\n\n\n\n

Research<\/a>\u00a0shows that the average American spends just two hours per week on physical activity \u2013 half of the four hours recommended by the Centers for Disease Control and Prevention<\/strong>.<\/p>\n\n\n\n

Lee, Choe, and Kang used public data from the National Health and Nutrition Examination Survey<\/strong>, a government-sponsored survey, covering 2009-18.<\/p>\n\n\n\n

“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.”<\/p>\n\n\n\n

The researchers also considered lifestyle factors including alcohol consumption, smoking, employment, sleep patterns, and sedentary behavior to understand their impact on a person’s physical activity, he said.<\/p>\n\n\n\n

The results showed that three key factors \u2013 how much time someone spends sitting, their gender, and their education level \u2013 showed up consistently in all the top-performing models that predict exercise habits, even though each model identified different variables as important.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

“I expected that factors like gender, BMI, race or age would be important for our prediction model, but I was surprised by how significant educational status was,” he said. “While factors like gender, BMI and age are more innate to the body, educational status is an external factor.”<\/p>\n\n\n\n

During the analysis, the researchers excluded data from people with certain diseases and responses missing physical activity data. That culled the relevant data to 11,683 participants.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Machine learning doesn’t have those limits, so it can find patterns with greater flexibility.<\/p>\n\n\n\n

“One limitation of our study was using subjectively measured physical activity data, where participants recalled their activity from memory,” Choe said. “People tend to overestimate their physical activity when using questionnaires, so more accurate, objective data would improve the study’s reliability.”<\/p>\n\n\n\n

Because of this, the researchers say they could use a similar method for future research in this area, but explore different factors, including dietary supplements use, using more machine learning algorithms or relying on objective data instead of self-reported information.<\/p>\n\n\n\n

That could help trainers and fitness consultants produce workout regimens that people can actually stick with for the long haul.<\/p>\n\n\n\n

\"\"<\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"

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