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<p>With the rapid growth of wearable technology, smartwatches have become widely used for tracking health metrics, including energy expenditure (EE). This study involved 20 athletes performing aerobic dance under three protocols—High-Intensity Interval Training (HIIT), Moderate-Intensity Continuous Training (MICT), and TABATA—while wearing four popular smartwatches alongside ActiGraph accelerometers. Indirect calorimetry and Scott’s method served as reference standards. Anaerobic energy accounted for 8%–11% of total EE, with ActiGraph showing the highest accuracy [average mean absolute percentageerror (MAPE): 14.51%]. Smartwatch errors ranged from 16.56% to 42.84%, with Samsung Galaxy Watch 6 performing best (MAPE: 21.12%) and Huawei Watch 4 performing worst (MAPE: 42.14%). Accuracy declined further when anaerobic energy was included, highlighting current limitations of consumer wearables in estimating EE during aerobic dance. Future improvements may come from hybrid models that integrate multiple sensor data—including motion, heart rate, and lactate measurements—to enhance the accuracy of EE estimation.</p>

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Validity of Smartwatches for Estimating Energy Expenditure During Aerobic Dance

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • Xueyuan Zhao,
  • Xingyi Ding,
  • Junchao Yang,
  • Shuting Yan,
  • Hong Wang,
  • Kuan Tao,
  • Junqiang Qiu

摘要

With the rapid growth of wearable technology, smartwatches have become widely used for tracking health metrics, including energy expenditure (EE). This study involved 20 athletes performing aerobic dance under three protocols—High-Intensity Interval Training (HIIT), Moderate-Intensity Continuous Training (MICT), and TABATA—while wearing four popular smartwatches alongside ActiGraph accelerometers. Indirect calorimetry and Scott’s method served as reference standards. Anaerobic energy accounted for 8%–11% of total EE, with ActiGraph showing the highest accuracy [average mean absolute percentageerror (MAPE): 14.51%]. Smartwatch errors ranged from 16.56% to 42.84%, with Samsung Galaxy Watch 6 performing best (MAPE: 21.12%) and Huawei Watch 4 performing worst (MAPE: 42.14%). Accuracy declined further when anaerobic energy was included, highlighting current limitations of consumer wearables in estimating EE during aerobic dance. Future improvements may come from hybrid models that integrate multiple sensor data—including motion, heart rate, and lactate measurements—to enhance the accuracy of EE estimation.