03-07-2025
New Wearable Algorithm Improves Fitness Tracking in Obesity
A new algorithm enabled smartwatch fitness trackers to more accurately estimate energy expenditure by individuals with obesity.
In two studies conducted in a total of 52 people for 1838 minutes in a lab and 14,045 minutes in a 'free-living' situation, the algorithm performed mostly as well or better than 11 'gold-standard' algorithms designed by other researchers using research-grade devices, achieving over 95% accuracy in people with obesity in real-world situations.
The algorithm bridges an important gap in fitness technology — that most wearable devices are using algorithms validated mainly in people without obesity, said Nabil Alshurafa, PhD, Feinberg School of Medicine, Northwestern University, in Chicago.
Alshurafa was motivated to create the algorithm after attending an exercise class with his mother-in-law, who had obesity. His mother-in-law worked hard, but her effort barely showed on the leaderboard. He realized that most current fitness trackers use activity-monitoring algorithms developed for people without obesity.
'Commercial devices calibrate their accelerometer-to-calorie models using data mostly from people with normal BMI, using algorithms that rely on 'average' gait and metabolism,' he told Medscape Medical News. 'But people with obesity are known to exhibit differences in walking gait, speed, resting energy expenditure, and physical function. When you feed 'average' motion to kcal [kilocalories] mappings for people with different gait patterns, the math does not always line up.'
This may be particularly true for people with obesity who wear fitness trackers throughout the day on their hip, rather than their wrist, because of differences in gait patterns and other body movements.
The study was published online in Scientific Reports . The anonymized dataset and code and documentation are publicly available for use by other researchers.
'More Inclusive and Reliable'
Researchers in Alshurafa's lab developed and tested the open-source, dominant-wrist algorithm specifically tuned for people with obesity. The algorithm estimated metabolic equivalent of task (MET) values per minute from commercial smartwatch sensor data and compared them to actigraphy-based energy estimates in people with obesity.
In an in-lab study, 27 participants performed activities of varying intensities while wearing a smartwatch and a metabolic cart — a mask that measures the volume of oxygen the wearer inhales and the volume of carbon dioxide the wearer exhales to calculate their energy burn in kcals and their resting metabolic rate.
The activities included, among others, typing on a computer, lying still on the floor doing nothing, walking slowly on a treadmill, doing pushups against a door, and following along with an aerobics video. Each activity was performed for 5 minutes, followed by 5 minutes of rest. The researchers compared the fitness tracker results against the metabolic cart results.
Another 25 participants wore a smartwatch and a body camera for 2 days in a free-living study. The body camera enabled the researchers to visually confirm when the algorithm over- or under-estimated kcals.
The in-lab analysis included 2189 minutes of data and the free-living analysis included 14,045 minutes of data.
Compared to the metabolic cart, the new algorithm achieved lower root mean square error across various sliding windows (analyses of continuous and overlapping data streams).
In the free-living study, the algorithm's estimates fell within ±1.96 SDs of the best actigraphy-based estimates for 95.03% of minutes.
'Our proposed method accurately estimated METs compared to 11 algorithms primarily validated in nonobese populations, suggesting that commercial wrist-worn devices can provide more inclusive and reliable [energy expenditure] measures using our algorithm,' the authors wrote.
Challenges Ahead
More work needs to be done before apps for iOS and android driven by the new algorithm are available for use later this year, Alshurafa said. Because the model is tuned for users with obesity, 'we need a reliable way to obtain BMI or body composition, and possibly ways of turning on and off the algorithm over time or perhaps modifying the algorithm as people's fitness level changes.'
'Because we've optimized for the dominant hand, we'll need clear user guidance, and possibly user-interface prompts, to drive this cultural shift in watch placement,' he said.
To ensure accuracy across diverse users, activities, and wear styles, the team will conduct field testing and pool anonymized data.
'Power, size, and regulatory requirements may force trade-offs, so we'll work closely with device manufacturers on adaptive calibration routines and streamlined firmware,' Alshurafa said. 'But the real priority is training and tailoring our systems on truly diverse data and being transparent about who's represented in that data. Too many commercial devices skip this, leading users to assume they work universally when their models actually have limitations.'
For now, clinicians should be aware that the app has only been validated so far in individuals with obesity wearing their tracker on the dominant wrist and use outside that population or on the nondominant wrist may yield less accurate calorie estimates, he added. 'Beyond those parameters, though, the algorithm is ready for deployment and offers a powerful new tool for personalized activity monitoring.'
Mir Ali, MD, a bariatric surgeon and medical director of MemorialCare Surgical Weight Loss Center at Orange Coast Medical Center in Fountain Valley, California, agreed that an algorithm that more accurately reflects exercise and energy expenditure of patients with obesity would be helpful, and that 'any improvements' would likely be beneficial for patients and clinicians.
That said, 'a larger study comparing the new algorithm vs currently available devices would provide more validation,' Ali, who was not involved in the study, told Medscape Medical News .
In addition, 'research elucidating exercise goals and calorie expenditure for obese patients could be helpful to better counsel patients on what is the optimal goal for weight loss,' he said.
Ali noted that 'trackers for heart disease and pulmonary problems may be useful to help patients attain cardio-pulmonary improvement' — and indeed, Alshurafa's team will be looking at ways to tailor fitness trackers for diabetes and hypertension going forward.
This study is based on work supported by the National Institute of Diabetes and Digestive and Kidney Diseases, the National Science Foundation, the National Institute of Biomedical Imaging and the National Institutes of Health's National Center for Advancing Translational Sciences.
Alshurafa and Ali declared no competing interests.
