New Algorithm Enhances Accuracy of Fitness Trackers for Obese Users

In a groundbreaking development, researchers at Northwestern University have introduced a new algorithm aimed at improving the accuracy of fitness trackers for individuals with obesity. This advancement, which will be detailed in a study published on June 19, 2025, in *Nature Scientific Reports*, addresses long-standing issues in wearable technology that often underreports physical activity for this demographic. Current algorithms in fitness trackers have largely been designed for individuals without obesity, resulting in significant discrepancies in calorie expenditure measurements for those with higher body weights.

The research, spearheaded by Nabil Alshurafa, Associate Professor of Behavioral Medicine at Northwestern University's Feinberg School of Medicine, focuses on the unique physiological characteristics of people with obesity that affect their gait, energy consumption, and overall physical activity. According to Alshurafa, existing wrist-worn fitness trackers frequently misread energy burn due to differences in how obese individuals walk and how these devices are positioned on the wrist, leading to inaccurate data collection.

"Without a validated algorithm for wrist devices, we’re still in the dark about exactly how much activity and energy people with obesity really get each day," stated Alshurafa. The new algorithm, referred to as the 'dominant-wrist algorithm', has been rigorously tested and shows over 95% accuracy in real-world scenarios, making it comparable to gold-standard methods employed in clinical settings.

The development of this algorithm was motivated by Alshurafa's personal experiences, particularly an exercise class he attended with his mother-in-law, who has obesity. Despite her significant effort, her performance was inadequately represented on the leaderboard, prompting a critical evaluation of how fitness metrics are captured and interpreted.

The research team conducted a comprehensive study involving two groups of participants. The first group, consisting of 27 individuals, utilized both a fitness tracker and a metabolic cart, which measures oxygen consumption and carbon dioxide production to accurately assess energy expenditure. The second group, comprising 25 participants, wore a fitness tracker alongside a body camera during their daily routines to allow researchers to visually confirm the accuracy of the algorithm. This approach not only enabled the team to identify instances where the algorithm miscalculated but also emphasized the need for a more inclusive understanding of physical activity metrics.

The implications of this research are profound. With higher accuracy in tracking physical activity, individuals with obesity can receive more reliable feedback on their exercise efforts, potentially leading to better health outcomes. Alshurafa emphasized the need to rethink traditional standards of fitness success, stating, "We celebrate 'standard' workouts as the ultimate test, but those standards leave out so many people."

The algorithm is currently being prepared for deployment in an activity-monitoring app, which will be available on both iOS and Android platforms later this year. This innovation is expected to empower users with obesity by providing them with actionable insights into their daily activities and energy expenditures. The study's findings underscore a significant step towards making fitness technology more inclusive and effective for all individuals, regardless of body type.

In summary, the new algorithm developed at Northwestern University represents a crucial advancement in wearable technology, addressing the specific needs of users with obesity. By enhancing the accuracy of fitness trackers, this research not only has the potential to improve individual health outcomes but also to reshape the broader conversation around fitness and inclusivity in health technology.