New Study Reveals Smartwatches Overestimate Calorie Burn by Up to 25%

Key points

• A Florida International University study tested four major smartwatch brands against gold-standard clinical equipment.
• All devices overestimated calorie burn, with typical errors ranging from 15% to 25%.
• Apple Watch was the most accurate, while Garmin and Samsung showed the highest overestimation.
• Fitbit produced inconsistent data, including implausibly low readings, leading to partial data exclusion.
• Higher body fat percentages were associated with greater measurement errors.

Background

Wearable technology has become central to fitness tracking, but questions about the accuracy of calorie burn estimates have persisted. Previous discussions often highlighted the limitations of consumer-grade sensors compared to clinical laboratory equipment. This new research provides a controlled, comparative analysis of current market leaders.

How outlets are covering it

The Washington Post highlights the general tendency of smartwatches to exaggerate calorie burn, noting the involvement of major brands like Apple, Garmin, Samsung, and Fitbit. The Tampa Bay Times and Digital Information World provide more granular details, specifying that Apple was the most accurate while Garmin and Samsung overestimated the most. Digital Information World additionally notes that higher body fat percentages correlate with greater errors, a nuance not emphasized in the Washington Post report. Yahoo Style UK provided no relevant content, only a privacy notice.

Why it matters

Inaccurate calorie tracking can lead to significant miscalculations in energy balance, potentially causing users to remain in a calorie deficit or surplus without realizing it. This affects weight management strategies and overall fitness progress, making it crucial for consumers to understand the limitations of their devices.

What to watch

Researchers are planning further studies to evaluate smartwatch accuracy during strength training, which involves short, intense bursts of effort that may challenge current algorithms. Manufacturers may use these findings to refine their estimation models.