Background: Smartwatches have gained popularity for their potential to provide accurate measurements of various physiological parameters. However, the validity of energy expenditure (EE) across different smartwatch models remains a topic of ongoing investigation. Discrepancies between results obtained from different models and gold standard methods are particularly critical across varying exercise intensities and types, as validation studies have demonstrated overestimation when wearable activity monitors are compared with indirect calorimetry. Objective: This study investigated the accuracy of 2 versions of the Samsung smartwatch (Galaxy Watch [GW] 6 and 7) in measuring EE during intermittent moderate-intensity running exercises, using indirect calorimetry as the gold standard method. Methods: This study included 148 healthy adults, comprising 80 men and 68 women. Participants performed intermittent treadmill running, consisting of walking at 5 km·h⁻¹ for 1 minute and running between 8 and 16 km·h⁻¹ for 2 minutes, based on participant preference, for a total duration of 27 minutes. The GW6 and GW7 models were used and EE was measured by indirect calorimetry using a wearable portable metabolic gas analysis system (K5; Cosmed), which is considered a gold standard method. Results: No statistically significant differences were found between the GW models and the K5. The K5 showed a mean EE of 213.60 (SD 43.04) kilocalories, compared with 219.53 (SD 35.70) kilocalories for the GW6 and 202.67 (SD 47.42) kilocalories for the GW7 (all >.05). Good Spearman correlations (0.63‐0.70) and moderate intraclass correlation coefficients (0.65‐0.74) were found. Mean absolute percentage error values ranged from 10.10% to 12.55%. Bland-Altman analysis revealed limits of agreement for all comparisons (K5 vs GW6 and GW7, −61.93 to 65.80 kcal). Conclusions: The GW6 and GW7 devices showed moderate validity for estimating EE during intermittent running exercises, demonstrating the suitability of the GW as a low-cost and practical wearable option for daily physical activities.

Online today in Nature Metabolism: 🚨 New Global Standards for Metabolic Research: Unifying Indirect Calorimetry Experiments! 🔬🧬 Understanding how our bodies use energy is vital for tackling obesity, ...

This video shows how the accuracy of energy expenditure measurements is dependent on the flow rate of air through mouse metabolic cages.

The significant energy consumption of current artificial intelligence systems is a serious problem that could limit the spread of AI. In this article, we will show that in so-called wet-neural networks, i.e., non-solid-state networks, energy consumption is negligible. Introduction The thorny issue of the high energy expenditure of artificial intelligence systems will be addressed at the World Conference on Statistical Physics, which opened in Florence, Italy, on July 13, 2025.