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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.

Fitness

The Case for Ditching Your Fitness Trackers

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The Case for Ditching Your Fitness Trackers

Credit: René Ramos/Lifehacker/ZaZa studio/Adobe Stock/Andriy Onufriyenko/Moment/Vadym Kalitnyk/iStock/Getty Images


I have a love-hate relationship with the smartwatch on my wrist. This relationship is no doubt shaped by the fact that I write about fitness tech for a living, but I know I’m not alone in succumbing to an obsession with numbers from my wearables. Did I hit 10,000 steps? What’s my resting heart rate today? Is my sleep score better than yesterday’s? When did progressive overload turn into screen time overload, too?

The fitness tech boom is showing no signs of slowing down any time soon—and with it, we consume a constant stream of promises that this data will make us healthier, stronger, and faster. With the sheer amount of health insights potentially available to us at any time, it’s easy to get overwhelmed. I’ve watched my least health-anxious friends become consumed by metrics they’d never heard of two years ago. They’re tracking bone density trends, obsessing over cortisol levels, panicking about stress scores that fluctuate for reasons no algorithm can fully explain. I can feel my fitness trackers pull me away from genuine wellness and into a mental health disaster. The good news: When I look up from my screens and start talking to real people, I see I’m not alone in wanting to unplug and push back against the overly quantified self.

A growing anti-tech fitness movement

When I put out a call on Instagram asking people about their relationship with posting workout data and fitness content, I received hundreds of responses from people exhausted by the performance of fitness. Even if your only audience is your own reflection, simply owning a wearable can create a real barrier between feeling good about your body and your fitness journey. Did I work out enough today? Will my friends see that I skipped a workout? Should I push through injury to maintain my streak?

For these reasons, celebrity trainer Lauren Kleban says she doesn’t like to rely on wearables at all. “Counting steps or calories can quickly spiral into a bit of an obsession,” says Kleban, and that “takes the joy out of movement and away from learning what’s truly best for us.” She says her clients want to focus on their mind and body connection, now more than ever. There’s a real, growing desire to rebuild a sense of intuition that doesn’t depend on feedback from a watch.

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Similarly, Marshall Weber, a certified personal trainer and owner of Jack City Fitness, says that he’s “definitely been surprised by the growing push towards unplugged fitness,” but that he “totally gets it.” Weber says he’s had clients express feeling “overwhelmed with their Fitbit or Apple Watch micromanaging their training.” When every workout becomes about numbers and keeping up with an average, it’s all too easy to lose touch with your body. “The anti-tech movement is about taking back that personal connection,” Weber says. After all, when was the last time you finished a workout and didn’t immediately look at your stats, but instead just noticed how you felt?

This is the paradox at the heart of fitness technology. Tools designed to help us understand our bodies have created a new kind of illiteracy. Maybe you can tell me why you’re aiming for Zone 2 workouts, but can’t actually recognize what that effort feels like without a screen telling you. In a sense, you might be outsourcing your own intuition to algorithms.

If nothing else, the data risks are real. (Because if you think you own all your health data, think again.) Every heart rate spike, every missed workout, every late-night stress indicator gets recorded, stored, and potentially shared. Still, for me, the more insidious risk is psychological: the erosion of our ability to know ourselves without consulting a device first.


What do you think so far?

How to unplug and exercise intuitively

So what does unplugged fitness actually look like in practice? It’s not about rejecting all technology or pretending GPS watches and heart rate monitors don’t have value—I promise. Look, I crave data and answers as much as—and maybe more than—the average gym-goer. I’m simply not woo-woo enough to ditch my Garmin altogether.

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Instead, I argue for re-establishing a hierarchy in which technology serves your training, not the other way around. “Sometimes, the best performance boost is just learning to listen to what your body is saying and feeling,” says Weber. But what does “listening to your body” actually look like?

If you’re like me, and need to rebuild a connection with your body from the ground-up, try these approaches:

  • Start with tech-free workouts. Designate certain runs, yoga sessions, or strength workouts as completely unplugged. No watch, no phone, no tracking. Notice what changes when there’s no device to check.

  • Relearn your body’s signals. Can you gauge your effort level without looking at a heart rate monitor? Do you actually know what “recovery pace” feels like for you, or are you just matching a number? Practice assessing fatigue, energy, soreness, and readiness without checking your watch.

  • Replace metrics with sensory awareness. Instead of tracking pace, notice your breathing pattern. Instead of counting calories burned, pay attention to how your muscles feel. Instead of obsessing over sleep scores, ask yourself a simple question in the morning: how do I actually feel?

  • Set goals that can’t be gamified. Rather than chasing step counts or streak days, aim for qualitative improvements. Can you hold a plank with better form? Does that hill feel easier than last month? Are you enjoying your workouts more? These are the markers of real progress.

  • Create tech boundaries. Maybe you use your GPS watch for long runs but leave it home for everything else. Perhaps you track workouts but delete the social features. Find the minimum effective dose of technology that serves your goals without dominating your headspace.

  • Reconnect with in-person community. The loss of shared gym culture—people actually talking to each other instead of staying plugged into individual screens—represents more than just nostalgia. There’s real value in working out alongside others, in having conversations about training instead of just comparing data, in building knowledge through shared experience rather than algorithm-driven insights.

The bottom line

Unplugging is easier said than done, but you don’t need to go cold turkey. Maybe in the new year, you can set “body literacy” as a worthwhile resolution. At the end of the day, exercise should add to your life, not become another source of performance anxiety. It should be energizing, not exhausting—and I don’t just mean physically. The never-ending irony of modern fitness culture is that in our pursuit of optimal health, we keep inventing new forms of stress and anxiety. When all forms of wellness come with trackable metrics and social pressure, I think we’ve fundamentally missed the point.

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How to avoid exercise burnout and still build muscle, according to an expert

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How to avoid exercise burnout and still build muscle, according to an expert

Many of us have experienced the overwhelming feeling of mental and physical exhaustion that comes with exercise burnout. When you push yourself too hard without sufficient rest and recovery, it ultimately becomes counterproductive to your fitness goals, and your energy will tank along with your motivation. Not only that, your performance will suffer when you overtrain and under-recover, and you’re left sinking further into the couch, wondering how you’ll lift that next weight, swim that next lap, or run that next mile.

With a combo of the right nutrition, rest, recovery, and lowering your training intensity, you can get back on track. To learn more about avoiding burnout and torching fat while sculpting muscle for men, I asked certified personal trainer and Vice President of Education for Body Fit Training, Steve Stonehouse, to share some of his vast knowledge on the subject. With decades of experience in fitness education, fitness programming, and personal training, Steve Stonehouse developed an in-depth knowledge of weight loss, improving body fat composition, building muscle, and the best exercise plans that generate serious results. 

Expert advice on burning fat

The Manual: As the Vice President of Education for Body Fit Training, what are your top tips for burning fat and improving body composition for men? 

Steve Stonehouse: As the programmer and head of education, this is a little cliché, but I go for balance. Not every workout can be this CrossFit type, give it all you’ve got, smoke yourself, and work out — that’s not sustainable. The other end of the spectrum is just walking at a moderate pace for 20 minutes on a treadmill three times a week, because that’s not going to do it either. There’s value in both of those scenarios. 

It’s best to have a session or two each week where the intensity is very high, and you’re testing yourself and pushing yourself closer to your limits. That’s anaerobic exercise, which is 90% intensity or above. It’s fine, safe, and healthy to get there occasionally, but every workout can’t be one of those. Your body isn’t built to train that way; you’re gonna burn out, and you could get injured, or both.

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There’s a place for some moderate intensity as well, so if I were focusing on heart rate, I would say in the 80s, so it’s hard but not max effort, and it’s more sustainable. When you’re in that 70 to high 80s range, we categorize that as building aerobic capacity. Overall, I suggest an approach with recovery, moderate intensity, and then high intensity every now and again to test yourself. 

The best cardio for fat loss

TM: How does cardio help with fat loss, and what types of cardio do you recommend?

Steve Stonehouse: I’m a big fan of high-intensity cardio. Sometimes, people think if some is good, more is probably better, but more isn’t always better. If I were putting a program together for six days a week, I’d have three days as some type of cardio-driven day, and three of those days I would have some version of resistance training. Maybe some days are heavier, and other days are a little lighter with higher rep targets and less rest.

Of those three cardio days, I’d recommend that one of them be a high-intensity max effort type HIIT session. Another could be hard with a heart rate in the 80s, but not max effort. That third cardio day could be more metabolic conditioning, like kettlebell swings, sled pushes, rower, or SkiErg, and things like that.

Ramping up muscle growth

TM: What types of exercise are the most effective for ramping up muscle growth?

Steve Stonehouse: We’re moving into a great space right now in fitness, and it seems like every 10 or 15 years, there’s this new movement. CrossFit first popped up and led the charge for metabolic conditioning and no days off. It’s the idea that if you still feel good at the end of a workout, you didn’t train hard enough. I think we’re phasing out of that and into wanting to lift heavy again. People who wouldn’t have touched a barbell ten years ago are lifting heavy now.

Keep in mind that heavy is a relative term. You can get stronger with some lighter dumbbells, but there are limits to that. A blend is nice, but you do need to include those times when you’re lifting heavy and challenging yourself at a low rep target.

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Say, I’m going to do barbell deadlifts for five reps. If I can do eight, then that weight is too light. It’s intended to be a weight that you can’t get 15 reps of. There are advantages to lifting heavy with low-rep targets and longer rest times. For example, we’re going to do four sets of five reps of barbell deadlifts with two minutes of rest in between sets. If you can do more than five or six reps, that weight is too light. There’s a lot of value in lifting heavy.

TM: We know it’s probably difficult to choose, but what are your top three favorite fat-burning, muscle-building exercises right now?

Steve Stonehouse:

  • Barbell Zercher squat
  • Barbell deadlift
  • Flat barbell bench press

TM: How often should you work out to build muscle?

Steve Stonehouse: For the heavy session with five or six reps and longer rest periods, you could have a day each week that’s primarily focused on upper-body heavy strength training. Then, you could split it up and have another day that’s primarily focused on the lower body. You could do that, so you’re not in the gym for two hours; it’s more like a reasonable 45 or 50 minutes. If you were feeling ambitious, you could get a third one in toward the end of the week and have a bit of a mixed session where there’s not as much volume, but you have upper-body and lower-body focus. 

With that type of heavy volume, you’re going to need a decent amount of time to rest. So, if I were doing a heavy bench press today, I probably wouldn’t do that again until next week — same thing with squats, deadlifts, or any larger main lifts. 

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Incorporating sufficient rest days and progressive overload

TM: Are rest days important for the best results?

Steve Stonehouse: Yes. Rest and recovery are two different things. A recovery session would include a bit of activity, but at a lower intensity. Recovery is restoring to a natural, healthy state, and rest is inactivity. 

TM: With resistance training, do you recommend incorporating progressive overload, where you gradually increase the weights over time to develop muscle strength and mass?Steve Stonehouse: 100%. We do strength training regularly at BFT. We have a portion of our performance app, and you can enter your five-rep max. On different days, the performance app tells you how much weight you should be lifting on that day to appropriately follow that progressive overload model.

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