Comparative accuracy of fitness tracking modalities in quantifying energy expenditure

Abstract

The purpose of this study is to compare the accuracy of wrist-worn, consumer-grade pedometers and earbud sensor technology in quantifying energy expenditure. Nineteen participants (24.6 ± 3.1 years) performed 5 min exercise stages on a treadmill (80.5 m/min, 107.3 m/min, 134.1 m/min and 160.9 m/min) while measuring energy expenditure via the Fitbit Charge pedometer and Jabra Sport Pulse Wireless Earbuds, comparing with indirect calorimetry. Paired samples t-tests were used to calculate differences and Pearson correlations to examine associations between kcals measured from the Fitbit and Jabra earbuds from indirect calorimetry. Results indicate significant underestimations of kcals for both devices at all speeds except one (Jabra earbuds at 107.3 m/min). Moderate correlations were observed for the Fitbit at 107.3 m/min and 107.3 m/min, and 134.1 m/min for the Jabra earbuds. Both devices considerably underestimated kcals, suggesting that caution be used when incorporating such data into fitness/health goals.

How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies

“…In this context, TEE may be considered the most important activity subgroup in this meta-analysis; however, the most variable and unpredictable component of TEE is EE during activity 6. In agreement with previous studies,13 45 52 we have shown that the accuracy of devices differs by activity and this may be related to the inability of devices to differentiate between activity types. For a device to accurately estimate TEE between individuals, it must accurately estimate the energy cost of a wide range of activities; however, some activities may require greater focus.…”

“…Four studies could not be synthesised by meta-analysis as mean difference between activity monitors and criterion measurements were not provided12 31–33; thus, 60 studies were included in the meta-analysis (figure 1). 10 13 34–88 A total of 1946 participants were included, with a mean age of 35 years (range 20–86 years). The mean body mass index (BMI) was 24.9 kg/m 2 (range 21.8–31.6 kg/m 2 ).…”

07 / How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis (2)

“…In this context, TEE may be considered the most important activity subgroup in this metaanalysis, however, the most variable and unpredictable component of TEE is EE during activity [6]. In agreement with previous studies [13,45,52], we have shown that the accuracy of devices differs by activity and this may be related to the inability of devices to differentiate between activity types. For a device to accurately estimate TEE between individuals, it must accurately estimate the energy cost of a wide range of activities however, some activities may require greater focus.…”

“…; thus, 60 studies were included in the meta-analysis (figure 1)[10,13,[41][42][43][44][45][46][47][48][49][50]20,[51][52][53][54][55][56][57][58][59][60]34,[61][62][63][64][65][66][67][68][69][70]35,[71][72][73][74][75][76][77][78][79][80]36,[81][82][83][84][85]…”

Validating Fitbit for Evaluation of Physical Activity in Patients with Knee Osteoarthritis: Do Thresholds Matter?

“…Although ActiGraphs are the gold standard for objective PA assessment, commercially available accelerometers like the Fitbit have distinct advantages for use in PA‐focused investigations. Such devices have a significantly lower cost and greater ease of wear, factors that make them appealing for scalability in large trials conducted over extended periods of time, leading to their increased use in research . Although studies have begun to investigate agreement between medical‐ and commercial‐grade accelerometers , standardized thresholds across devices have not been delineated, particularly those specific to at‐risk populations with unique gait patterns, such as older individuals with knee OA .…”