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AI Reveals What Keeps People Committed to Exercise – Neuroscience News

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AI Reveals What Keeps People Committed to Exercise – Neuroscience News

Summary: A new study has used machine learning to identify the key predictors of physical activity adherence, analyzing data from nearly 12,000 individuals. The research found that time spent sitting, gender, and education level were the strongest indicators of whether someone met weekly exercise guidelines.

By training models on lifestyle, demographic, and health survey data, researchers could predict exercise habits more flexibly than traditional approaches. These insights could inform more effective fitness recommendations and public health strategies tailored to individual needs.

Key Facts:

  • Top Predictors: Sedentary time, gender, and education level were the most consistent predictors of exercise adherence.
  • Study Scope: Researchers used machine learning on data from 11,683 participants in a national health survey.
  • Potential Impact: Findings could improve personalized workout plans and inform health policy.

Source: University of Mississippi

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 team – Seungbak Lee and Ju-Pil Choe, both doctoral students in physical education, and Minsoo Kang, professor of sport analytics in the Department of Health, Exercise Science and Recreation Management – hopes to predict whether a person is meeting physical activity guidelines based on their body measurements, demographics and lifestyle.

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Machine learning doesn’t have those limits, so it can find patterns with greater flexibility. Credit: Neuroscience News

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.

The group’s results, published in the Nature Portfolio journal Scientific Reports are 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.”

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.

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Research shows that the average American spends just two hours per week on physical activity – half of the four hours recommended by the Centers for Disease Control and Prevention.

Lee, Choe and Kang used public data from the National Health and Nutrition Examination Survey, a government-sponsored survey, covering 2009-18.

“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.”

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.

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

 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.

“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.”

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.

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.

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Machine learning doesn’t have those limits, so it can find patterns with greater flexibility.

“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.”

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.

That could help trainers and fitness consultants produce workout regimens that people can actually stick with for the long haul.

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About this AI and exercise research news

Author: Clara Turnage
Source: University of Mississippi
Contact: Clara Turnage – University of Mississippi
Image: The image is credited to Neuroscience News

Original Research: Open access.
“Machine learning modeling for predicting adherence to physical activity guideline” by Seungbak Lee et al. Scientific Reports


Abstract

Machine learning modeling for predicting adherence to physical activity guideline

This study aims to create predictive models for PA guidelines by using ML and examine the critical determinants influencing adherence to the PA guidelines. 11,638 entries from the National Health and Nutrition Examination Survey were analyzed.

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Variables were categorized into demographic, anthropometric, and lifestyle categories. 18 prediction models were created by 6 ML algorithms and evaluated via accuracy, F1 score, and area under the curve (AUC).

Additionally, we employed permutation feature importance (PFI) to assess the variable significance in each model.

The decision tree using all variables emerged as the most effective method in the prediction for PA guidelines (accuracy = 0.705, F1 score = 0.819, and AUC = 0.542).

Based on the PFI, sedentary behavior, age, gender, and educational status were the most important variables.

These results highlight the possibilities of using data-driven methods with ML in PA research.

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Our analysis also identified crucial variables, providing valuable insights for targeted interventions aimed at enhancing individuals’ adherence to PA guidelines.

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Strategic Exercise Techniques to Maximize Mood Elevation – The Boca Raton Tribune

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Strategic Exercise Techniques to Maximize Mood Elevation – The Boca Raton Tribune
A Shift in Scientific Understanding Reveals That the ‘Runner’s High’ Stems from a Complex Cocktail of Chemicals, Including Endocannabinoids, Which Can Be Triggered by Adjusting Duration and Social Context. The widely reported phenomenon of exercise-induced euphoria—often known as the “runner’s high”—is rooted in specific alterations to neurochemistry that generate feelings of hope, calmness, and social […]
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Do you have sore hips? I asked a pain specialist why this happens and how to improve it

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Do you have sore hips? I asked a pain specialist why this happens and how to improve it

Hip soreness is a terribly common issue—it’s something that I certainly suffer with—so I’m always trying to get to the bottom of where this soreness originates from and what you can do about it.

According to Dr Shady Hassan, MD, an interventional pain and sports medicine physician and the founder of NefraHealth, immobility is the root cause of this discomfort.

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“No Pain No Gain” May Be Wrong: Science Says Slow Eccentric Exercise Builds Stronger Muscles

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“No Pain No Gain” May Be Wrong: Science Says Slow Eccentric Exercise Builds Stronger Muscles

Modern exercise culture has spent years glorifying exhaustion. The harder a workout feels, the more effective people assume it must be. Sore muscles became badges of honor, while gentle movements were often dismissed as ‘not real exercise.’ 

A man lifting a dumbbell. Image credits: Andres Ayrton/Pexels

However, according to a new study, some of the most efficient ways to build muscle strength may happen during the slow, controlled moments people usually ignore—walking downstairs, lowering weights, or carefully sitting into a chair. 

Study author Kazunori Nosaka, who is the director of exercise and sports science at Edith Cowan University, argues that eccentric exercise—a type of muscle action that occurs while muscles lengthen under tension, may offer a more practical alternative. Its opposite, concentric exercise, is the shortening (lifting) phase where muscles produce force to overcome resistance.

Instead of demanding maximum effort, these movements appear to train muscles while placing less stress on the body.  

“The idea that exercise must be exhausting or painful is holding people back. Instead, we should be focusing on eccentric exercises which can deliver stronger results with far less effort than traditional exercise – and you don’t even need a gym,” Nosaka said.

Muscles work differently on the way down

The study examines decades of earlier research on eccentric exercise rather than presenting a single laboratory experiment. It focuses on a simple but often overlooked detail of human movement, which is how muscles behave differently depending on whether they are shortening or lengthening.

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When someone lifts a dumbbell, climbs stairs, or rises from a chair, muscles shorten as they generate force. Scientists call this a concentric contraction. Eccentric contractions happen during the opposite phase—when the muscle stays active while stretching. 

Examples include lowering the dumbbell back down, descending stairs, or slowly lowering the body into a seated position. According to the review, muscles can tolerate and produce greater force during eccentric actions while using comparatively less energy and oxygen. 

“Eccentric contractions are distinguished by their ability to generate greater force than concentric or isometric contractions, while requiring less metabolic cost,” Nosaka notes.

Researchers believe this happens because muscles act more like controlled braking systems during lengthening movements, resisting gravity rather than directly overpowering it. As a result, people may gain strength without putting the same level of demand on the cardiovascular system. 

This difference could make eccentric exercise especially useful for individuals who find traditional workouts physically overwhelming.

“Eccentric exercise training provides numerous benefits for physical fitness and overall health, making it suitable for a wide range of individuals from children to older adults, clinical populations to athletes, and sedentary to highly active people,” Nosaka added.

Gravity may be doing more training than we realized

To support this argument, the study brings together findings from several earlier research works. For instance, one study from 2017 tracked elderly women with obesity who repeatedly walked either upstairs or downstairs over a 12-week period. 

While climbing stairs is normally considered the tougher workout, the women assigned to walk downstairs showed stronger improvements in measures including blood pressure, heart rate, and physical fitness. The results suggested that resisting gravity during downward movement may provide a surprisingly powerful training effect.

YouTube videoYouTube video

The review also discusses eccentric cycling, where participants resist pedals driven backward by a motor instead of pushing them forward in the usual way. 

Although the movement feels unusual and requires concentration, earlier studies found it improved muscle power, balance, and cardiovascular health while feeling less exhausting than standard cycling workouts.

Another important part of the review addresses muscle soreness, one of the main reasons eccentric exercise never became widely popular outside rehabilitation settings. People often experience delayed onset muscle soreness, or DOMS, after unfamiliar eccentric workouts. 

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“Unaccustomed eccentric exercise is often associated with muscle damage characterized by delayed onset muscle soreness (DOMS) and a reduction in muscle force-generating capacity lasting more than a day. However, this effect diminishes or at least is attenuated when the same eccentric exercise is repeated (known as the repeated bout effect),” Nosaka explained

Many eccentric exercises require little or no equipment. Slow squats into a chair, heel-lowering movements, controlled wall push-ups, or even maintaining posture against gravity can activate eccentric muscle work. 

Moreover, some studies referenced in Nosaka’s review suggest that just a few minutes of these exercises each day can still produce measurable improvements in health and strength.

The future of fitness may feel less punishing

The findings challenge the mindset surrounding fitness itself. Many people abandon exercise routines because they associate physical activity with pain, fatigue, or lack of time. Eccentric exercise suggests that effective movement does not always need to feel extreme. 

If future research continues to support these findings, eccentric exercise could influence far more than gym routines. It may reshape physical rehabilitation, elderly care, injury recovery programs, and public-health recommendations aimed at increasing physical activity among sedentary populations. 

These exercises also place lower demands on the heart and lungs while still strengthening muscles. They could help people who are unable or unwilling to follow intense training programs.

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Nosaka suggests that “we should establish eccentric exercise as standard practice, and make it common, accessible, and widely accepted as the ‘new normal’ of exercise to improve life performance and high (athletic) performance.”

However, this does not mean eccentric exercise is a universal replacement for all forms of physical activity. The current paper is a review of previous studies, and its findings still need to be validated through experiments and large-scale clinical trials.

Nosaka also notes that “Future studies should investigate mechanisms underpinning the effects of eccentric exercises in comparison to other types of exercises (e.g., isometric exercises, concentric exercises, aerobic exercises),”  

This could help scientists design safer and more personalized exercise programs for different age groups and health conditions.

The study is published in the Journal of Sport and Health Science.

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