A new Florida International University study published in PLOS One reveals that popular fitness trackers overestimate exercise calories by 15% to 25%, with error rates spiking dramatically for wearers with higher body fat percentages.
Smartwatches have cemented their place on the wrists of millions worldwide as wellness companions. Yet new research demonstrates that one of the most frequently consulted metrics on these devices—the estimated number of calories burned during physical activity—carries systemic flaws that could undermine weight management efforts.
How the Study Put Four Popular Smartwatches to the Test
Researchers at Florida International University’s Medical Photonics Laboratory recruited 58 Hispanic adults in Miami to evaluate how well consumer wearables measure physiological responses. Each participant completed a standardized cycling session on a recumbent bicycle, alternating between moderate and vigorous intensity across 10-minute intervals.
During the workouts, participants wore four popular consumer devices simultaneously: the Apple Watch Series 8, Fitbit Sense 2, Samsung Galaxy Watch 5, and Garmin Forerunner 955. To establish a baseline for true energy expenditure, researchers outfitted participants with a COSMED K5 metabolic analyzer, a clinical-grade system that captures inhaled and exhaled gases via a face mask.
Substantial Overestimates and the Body Fat Discrepancy
The findings showed that typical errors in smartwatch calorie estimates ranged from 15% to 25%, with worst-performing devices exceeding that threshold significantly. Apple proved to be the most accurate brand tested, while Garmin and Samsung systematically produced the largest overestimates.
Fitbit presented a different technical hurdle.
Most notably, the study uncovered a strong correlation between wearer body composition and measurement error.
“You can’t treat the calories burned number it gives you as an accurate number, because it’s not. You could be off hundreds of calories each week and easily end up in a calorie surplus when you think you’re in a calorie deficit.”
Jason Kostrna, lead author and associate professor of kinesiology and exercise science at Florida International University
Why Algorithms May Fall Short
While smartwatches utilize hardware sensors like accelerometers for movement and light-based sensors for heart rate, caloric expenditure cannot be measured directly. Instead, devices rely on algorithms to translate accessible physiological data into estimated energy output.
Because researchers do not know where the test data for these algorithms comes from, they remain uncertain about the demographic and physiological characteristics of the populations used to train them. This may contribute to why devices struggle disproportionately with specific body compositions.
“When the companies are developing those algorithms, we don’t know where the test data is coming from. That’s why I think it’s beneficial for the companies to see this type of data, so they can refine their products over time.”
Jason Kostrna, associate professor of kinesiology and exercise science at Florida International University
Practical Guidance for Smartwatch Users
Despite these limitations, researchers emphasize that wearables still have an important role to play
in health tracking. Experts suggest treating a workout’s calorie readout as an upper ceiling rather than an exact figure, advising users to round numbers downward when managing dietary intake.
Rather than fixing on single-day calorie counts, users can rely more reliably on metrics that wearables handle with greater consistency, such as heart rate and time spent within specific heart-rate zones.
“Track your own trends over time. And if tracking helps, enjoy it. If it becomes too much, don’t fixate on it. Focus on how you’re feeling and use data to get a little better over time.”
Jason Kostrna, associate professor of kinesiology and exercise science at Florida International University
The research team is already expanding its scope. Kostrna is currently studying how accurately popular fitness trackers measure energy expenditure during traditional strength-training workouts, where short bursts of intensity present distinct tracking challenges.