Latest news with #Ju-PilChoe
Phone Arena
5 days ago
- Health
- Phone Arena
Peer-reviewed study puts Apple Watch fitness tracking to the test: Here's the takeaway
A peer-reviewed meta-analysis published recently provides potential answers for Apple Watch users who have questioned the accuracy of their Activity Ring data. The researchers at the University of Mississippi examined 56 studies which measured Apple Watch performance against medical-grade devices and obtained both positive and warning signals. Apple Watches tend to have some of the best heart rate tracking among wearables. | Image by PhoneArena Let's start with the good news first. According to the findings, Apple Watches are highly accurate when it comes to tracking heart rate and counting steps. This is in line with our own experience with Apple Watches, including during our Apple Watch Series 10 review (Apple's latest smartwatch). The mean absolute percent error (MAPE) for these metrics in the meta-analysis was just 4.43% for heart rate and 8.17% for step count — both well below the 10% threshold typically used to define excellent performance in consumer wearables. The researchers also noted that newer Apple Watch models performed better across the board. While the study doesn't specify model-by-model results, doctoral student Ju-Pil Choe (one of the study authors) says: Smartwatches are still not that great at tracking calories, but that doesn't mean you can't use them for motivation. | Image by PhoneArena But not all metrics scored so well. The study found that estimates of energy expenditure — aka how many calories you burn — were far less reliable. The average error rate here jumped to 27.96%, with inaccuracies present across various activities, including walking, running, cycling, and high-intensity workouts. That puts Apple Watch roughly on par with other wearables, like the Fitbit Surge, which also struggles with calorie estimates. This shouldn't come as a surprise, though, as it is an extremely difficult task to measure calories burned. There are all kinds of things to factor in like body weight, movement type, metabolic efficiency, and even skin tone. Smartwatches often rely on general algorithms that combine heart rate data, sensor input, and user profile info to estimate calorie burn. But that process leaves plenty of room for error. Other studies have found that calorie estimates on wearables can be off by as much as 40% to 80%, depending on the device and activity. Use the Apple Watch — or any smartwatch, for that matter — as a guide, not gospel. The University of Mississippi study provides a helpful reality check: Apple Watches are excellent at tracking heart rate and step count but should not be relied on for precise calorie measurements. Personally, I find a smartwatch most valuable as a way to log workouts and monitor consistency. There's something genuinely motivating about seeing your progress visualized, whether it's a graph of completed runs or a streak of active said, I've also left my watch behind on occasion, and there's a certain freedom in it. Without the pressure of stats or metrics, you start to tune into how your body feels in the moment — and sometimes, that's exactly the kind of workout you need.
Arab Times
19-04-2025
- Health
- Arab Times
AI uncovers what keeps people committed to exercise: Study
NEW YORK, April 19: A team of researchers at the University of Mississippi has harnessed the power of machine learning to identify the strongest predictors of long-term exercise habits. Analyzing data from nearly 12,000 individuals, the study reveals that sedentary time, gender, and education level are the top indicators of whether someone consistently meets recommended physical activity guidelines. The findings, published in the journal Scientific Reports, could reshape how public health officials and fitness professionals tailor strategies to promote regular physical activity. Led by doctoral students Seungbak Lee and Ju-Pil Choe, and professor Minsoo Kang from the Department of Health, Exercise Science and Recreation Management, the research team trained machine learning models on data drawn from the National Health and Nutrition Examination Survey (NHANES), spanning 2009 to 2018. The survey includes detailed lifestyle, demographic, and health-related responses. 'We wanted to use advanced data analytic techniques, like machine learning, to predict this behavior,' Kang explained. 'Physical activity adherence to the guidelines is a public health concern because of its relationship to disease prevention and overall health patterns.' The U.S. Department of Health and Human Services recommends adults engage in at least 150 minutes of moderate exercise or 75 minutes of vigorous activity weekly. Yet, the average American spends only about two hours per week on physical activity—far short of the four-hour weekly benchmark set by the CDC. The researchers explored a wide range of factors including age, gender, race, income, education, marital status, BMI, waist circumference, sleep, alcohol use, and smoking habits. Participants with certain diseases or missing data on physical activity were excluded, resulting in a final sample size of 11,683. While several variables were considered, the machine learning models consistently identified three as most predictive: time spent sitting, gender, and education level. These factors appeared across all high-performing predictive models, suggesting a strong link to exercise commitment. 'I expected that factors like gender, BMI, race or age would be important,' said Choe, the study's lead author. 'But I was surprised by how significant educational status was. While gender or BMI are innate, education is an external factor that might influence behavior over time.' The study's reliance on self-reported activity levels is one of its limitations, as participants often overestimate their physical activity. The researchers note that future studies could be strengthened by incorporating wearable fitness data for more objective results. Nevertheless, the team believes that machine learning offers a powerful approach to understanding health behavior patterns. Unlike traditional statistical methods that assume linear relationships, machine learning can identify more nuanced connections between variables. Looking ahead, the researchers plan to use similar methods to explore other health behaviors, such as diet and supplement use, with the goal of helping fitness experts and health policymakers develop personalized, sustainable workout strategies. 'Ultimately, we want to support programs that help people stay active long-term,' said Kang. 'Understanding what keeps them going is the first step.'
