Validation of the Fitbit Wireless Activity Tracker for Prediction of Energy Expenditure
Validation of the Fitbit Wireless Activity Tracker for Prediction of Energy Expenditure
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DOI：
10.1123/jpah.2012-0495
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发表时间：
2015-02-01
影响因子：
3.1
通讯作者：
Freedson, Patty S.
中科院分区：
文献类型：
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作者：
Sasaki, Jeffer Eidi;Hickey, Amanda;Freedson, Patty S.
目的：这项研究的目的是检查Fitbit无线活动跟踪器在评估不同活动的能量消耗（EE）方面的准确性。研究方法：20名参与者（10名男性，10名女性）在臀部佩戴Fitbit Classic无线活动跟踪器和Oxycon移动的便携式代谢系统（标准）。参与者在跑步机上进行步行和跑步试验，并模拟自由生活活动。使用配对t检验来检验每种活动的估计（Fitbit）和标准（Oxycon）kcal之间的差异。结果如下：所有活动的估计能量消耗的平均偏倚为-4.5 +/- 1.0 kcals/6 min（95%一致性限值：-25.2至15.8 kcals/6 min）。Fitbit显著低估了骑自行车，洗衣，耙，跑步机（TM）3英里每小时在5%的等级，上升/下降楼梯，TM 4英里每小时在5%的等级，并显着高估EE携带杂货。对于9项活动，Fitbit估计的能量消耗与Oxycon移动的计算的BE无显著差异。结论：佩戴在臀部的Fitbit显著低估了活动的BE。由于准确的EE估计对于跟踪/监测能量不足是重要的，因此不同活动的EE低估的可变性对于减肥管理应用可能是有问题的。
Objective: The purpose of this study was to examine the accuracy of the Fitbit wireless activity tracker in assessing energy expenditure (EE) for different activities. Methods: Twenty participants (10 males, 10 females) wore the Fitbit Classic wireless activity tracker on the hip and the Oxycon Mobile portable metabolic system (criterion). Participants performed walking and running trials on a treadmill and a simulated free-living activity routine. Paired t tests were used to test for differences between estimated (Fitbit) and criterion (Oxycon) kcals for each of the activities. Results: Mean bias for estimated energy expenditure for all activities was -4.5 +/- 1.0 kcals/6 min (95% limits of agreement: -25.2 to 15.8 kcals/6 min). The Fitbit significantly underestimated EE for cycling, laundry, raking, treadmill (TM) 3 mph at 5% grade, ascent/descent stairs, and TM 4 mph at 5% grade, and significantly overestimated EE for carrying groceries. Energy expenditure estimated by the Fitbit was not significantly different than BE calculated from the Oxycon Mobile for 9 activities. Conclusion: The Fitbit worn on the hip significantly underestimates BE of activities. The variability in underestimation of EE for the different activities may be problematic for weight loss management applications since accurate EE estimates are important for tracking/monitoring energy deficit.