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Validity and reliability of Nike?+?Fuelband for estimating physical activity energy expenditure.

ABSTRACT: The Nike?+?Fuelband is a commercially available, wrist-worn accelerometer used to track physical activity energy expenditure (PAEE) during exercise. However, validation studies assessing the accuracy of this device for estimating PAEE are lacking. Therefore, this study examined the validity and reliability of the Nike?+?Fuelband for estimating PAEE during physical activity in young adults. Secondarily, we compared PAEE estimation of the Nike?+?Fuelband with the previously validated SenseWear Armband (SWA).Twenty-four participants (n?=?24) completed two, 60-min semi-structured routines consisting of sedentary/light-intensity, moderate-intensity, and vigorous-intensity physical activity. Participants wore a Nike?+?Fuelband and SWA, while oxygen uptake was measured continuously with an Oxycon Mobile (OM) metabolic measurement system (criterion).The Nike?+?Fuelband (ICC?=?0.77) and SWA (ICC?=?0.61) both demonstrated moderate to good validity. PAEE estimates provided by the Nike?+?Fuelband (246?±?67 kcal) and SWA (238?±?57 kcal) were not statistically different than OM (243?±?67 kcal). Both devices also displayed similar mean absolute percent errors for PAEE estimates (Nike?+?Fuelband?=?16?±?13 %; SWA?=?18?±?18 %). Test-retest reliability for PAEE indicated good stability for Nike?+?Fuelband (ICC?=?0.96) and SWA (ICC?=?0.90).The Nike?+?Fuelband provided valid and reliable estimates of PAEE, that are similar to the previously validated SWA, during a routine that included approximately equal amounts of sedentary/light-, moderate- and vigorous-intensity physical activity.

SUBMITTER: Tucker WJ

PROVIDER: S-EPMC4705763 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Validity and reliability of Nike + Fuelband for estimating physical activity energy expenditure.

Tucker Wesley J WJ Bhammar Dharini M DM Sawyer Brandon J BJ Buman Matthew P MP Gaesser Glenn A GA

BMC sports science, medicine & rehabilitation 20150630

<h4>Background</h4>The Nike + Fuelband is a commercially available, wrist-worn accelerometer used to track physical activity energy expenditure (PAEE) during exercise. However, validation studies assessing the accuracy of this device for estimating PAEE are lacking. Therefore, this study examined the validity and reliability of the Nike + Fuelband for estimating PAEE during physical activity in young adults. Secondarily, we compared PAEE estimation of the Nike + Fuelband with the previously vali ...[more]

PMID: 26751385

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Project description:ObjectivesTo examine the validity and reliability of the Fitbit Flex against direct observation for measuring steps in the laboratory and against the Actigraph for step counts in free-living conditions and for moderate-to-vigorous physical activity (MVPA) and activity energy expenditure (AEE) overall.MethodsTwenty-five adults (12 females, 13 males) wore a Fitbit Flex and an Actigraph GT3X+ during a laboratory based protocol (including walking, incline walking, running and stepping) and free-living conditions during a single day period to examine measurement of steps, AEE and MVPA. Twenty-four of the participants attended a second session using the same protocol.ResultsIntraclass correlations (ICC) for test-retest reliability of the Fitbit Flex were strong for walking (ICC = 0.57), moderate for stair stepping (ICC = 0.34), and weak for incline walking (ICC = 0.22) and jogging (ICC = 0.26). The Fitbit significantly undercounted walking steps in the laboratory (absolute proportional difference: 21.2%, 95%CI 13.0-29.4%), but it was more accurate, despite slightly over counting, for both jogging (6.4%, 95%CI 3.7-9.0%) and stair stepping (15.5%, 95%CI 10.1-20.9%). The Fitbit had higher coefficients of variation (Cv) for step counts compared to direct observation and the Actigraph. In free-living conditions, the average MVPA minutes were lower in the Fitbit (35.4 minutes) compared to the Actigraph (54.6 minutes), but AEE was greater from the Fitbit (808.1 calories) versus the Actigraph (538.9 calories). The coefficients of variation were similar for AEE for the Actigraph (Cv = 36.0) and Fitbit (Cv = 35.0), but lower in the Actigraph (Cv = 25.5) for MVPA against the Fitbit (Cv = 32.7).ConclusionThe Fitbit Flex has moderate validity for measuring physical activity relative to direct observation and the Actigraph. Test-rest reliability of the Fitbit was dependant on activity type and had greater variation between sessions compared to the Actigraph. Physical activity surveillance studies using the Fitbit Flex should consider the potential effect of measurement reactivity and undercounting of steps.

Project description:Background:Objective assessments of sedentary behavior and physical activity (PA) by using accelerometer-based wearable devices are ever expanding, given their importance in the global context of health maintenance. This study aimed to determine the reliability and validity of a new accelerometer-based analyzer (Fibion) for detecting different PAs and estimating energy expenditure (EE) during a simulated free-living day. Methods:The study consisted of two parts: a reliability (n = 18) and a validity (n = 19) test. Reliability was assessed by a 45 min protocol of repeated sitting, standing, and walking (i.e., 3 × 15 min, repeated twice), using both Fibion and ActiGraph. Validity was assessed by a 12 h continuous sequence tasks of different types (sitting, standing, walking, and cycling) and intensities (light [LPA], moderate [MPA], and vigorous [VPA]) of PA. Two Fibion devices were worn on the thigh (FT) and in the pocket (FP), respectively and were compared with criteria measures, such as direct observation (criterion 1) and oxygen consumption by a portable gas analyzer, K4b2 (criterion 2). Results:FT (intra-class correlation coefficients (ICCs): 0.687-0.806) provided similar reliability as the Actigraph (ICCs: 0.661-0.806) for EE estimation. However, the measurement error (ME) of FT compared to the actual time records indicated an underestimation of duration by 5.1 ± 1.2%, 3.8 ± 0.3% and 14.9 ± 2.6% during sitting, walking, and standing, respectively. During the validity test, FT but not FP showed a moderate agreement but lager variance with the criteria (1 and 2) in assessing duration of sitting, long sitting, LPA, MPA, and VPA (p > 0.05, ICCs: 0.071-0.537), as well as for EE estimation of standing, LPA, MPA, and VPA (p > 0.05, ICCs: 0.673-0.894). Conclusions:FT provided similar reliability to that of the Actigraph. However, low correlations between subsequent measurements of both devices indicated large random MEs, which were somewhat diminished during the simulated 12 h real-life test. Furthermore, FT may accurately determine the types, intensities of PA and EE during prolonged periods with substantial changes in postures, indicating that the location of the accelerometer is essential. Further study with a large cohort is needed to confirm the usability of Fibion, especially for detecting the low-intensity PAs.

Project description:Active video games that require physical exertion during game play have been shown to confer health benefits. Typically, energy expended during game play is measured using devices attached to players, such as accelerometers, or portable gas analyzers. Since 2010, active video gaming technology incorporates marker-less motion capture devices to simulate human movement into game play. Using the Kinect Sensor and Microsoft SDK this research aimed to estimate the mechanical work performed by the human body and estimate subsequent metabolic energy using predictive algorithmic models. Nineteen University students participated in a repeated measures experiment performing four fundamental movements (arm swings, standing jumps, body-weight squats, and jumping jacks). Metabolic energy was captured using a Cortex Metamax 3B automated gas analysis system with mechanical movement captured by the combined motion data from two Kinect cameras. Estimations of the body segment properties, such as segment mass, length, centre of mass position, and radius of gyration, were calculated from the Zatsiorsky-Seluyanov's equations of de Leva, with adjustment made for posture cost. GPML toolbox implementation of the Gaussian Process Regression, a locally weighted k-Nearest Neighbour Regression, and a linear regression technique were evaluated for their performance on predicting the metabolic cost from new feature vectors. The experimental results show that Gaussian Process Regression outperformed the other two techniques by a small margin. This study demonstrated that physical activity energy expenditure during exercise, using the Kinect camera as a motion capture system, can be estimated from segmental mechanical work. Estimates for high-energy activities, such as standing jumps and jumping jacks, can be made accurately, but for low-energy activities, such as squatting, the posture of static poses should be considered as a contributing factor. When translated into the active video gaming environment, the results could be incorporated into game play to more accurately control the energy expenditure requirements.

Project description:Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) for the prediction of EE using room calorimetry and doubly labeled water (DLW) and established accelerometry cut points for PA levels.Fifty preschoolers, mean ± SD age of 4.5 ± 0.8 yr, participated in room calorimetry for minute-by-minute measurements of EE, accelerometer counts (AC) (Actiheart and ActiGraph GT3X+), and HR (Actiheart). Free-living 105 children, ages 4.6 ± 0.9 yr, completed the 7-d DLW procedure while wearing the devices. AC cut points for PA levels were established using smoothing splines and receiver operating characteristic curves.On the basis of calorimetry, mean percent errors for EE were -2.9% ± 10.8% and -1.1% ± 7.4% for CSTS models and -1.9% ± 9.6% and 1.3% ± 8.1% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. On the basis of DLW, mean percent errors were -0.5% ± 9.7% and 4.1% ± 8.5% for CSTS models and 3.2% ± 10.1% and 7.5% ± 10.0% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. Applying activity EE thresholds, final accelerometer cut points were determined: 41, 449, and 1297 cpm for Actiheart x-axis; 820, 3908, and 6112 cpm for ActiGraph vector magnitude; and 240, 2120, and 4450 cpm for ActiGraph x-axis for sedentary/light, light/moderate, and moderate/vigorous PA (MVPA), respectively. On the basis of confusion matrices, correctly classified rates were 81%-83% for sedentary PA, 58%-64% for light PA, and 62%-73% for MVPA.The lack of bias and acceptable limits of agreement affirms the validity of the CSTS and MARS models for the prediction of EE in preschool-aged children. Accelerometer cut points are satisfactory for the classification of sedentary, light, and moderate/vigorous levels of PA in preschoolers.

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Validity and reliability of Nike?+?Fuelband for estimating physical activity energy expenditure.

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Validity and reliability of Nike + Fuelband for estimating physical activity energy expenditure.

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