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1.
The purpose of this investigation was to examine the validity of energy expenditure (EE), steps, and heart rate measured with the Apple Watch 1 and Fitbit Charge HR. Thirty-nine healthy adults wore the two monitors while completing a semi-structured activity protocol consisting of 20 minutes of sedentary activity, 25 minutes of aerobic exercise, and 25 minutes of light intensity physical activity. Criterion measures were obtained from an Oxycon Mobile for EE, a pedometer for steps, and a Polar heart rate strap worn on the chest for heart rate. For estimating whole-trial EE, the mean absolute percent error (MAPE) from Fitbit Charge HR (32.9%) was more than twice that of Apple Watch 1 (15.2%). This trend was consistent for the individual conditions. Both monitors accurately assessed steps during aerobic activity (MAPEApple: 6.2%; MAPEFitbit: 9.4%) but overestimated steps in light physical activity. For heart rate, Fitbit Charge HR produced its smallest MAPE in sedentary behaviors (7.2%), followed by aerobic exercise (8.4%), and light activity (10.1%). The Apple Watch 1 had stronger validity than the Fitbit Charge HR for assessing overall EE and steps during aerobic exercise. The Fitbit Charge HR provided heart rate estimates that were statistically equivalent to Polar monitor.  相似文献   

2.
ABSTRACT

A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of activities using acceleration, physiological signals (e.g., heart rate, body temperature, galvanic skin response), and participant characteristics (e.g., sex, age, height, weight, body composition) collected from wearable devices (Fitbit charge 2, Polar H7, SenseWear Armband Mini and Actigraph GT3-x) as potential inputs. By utilising a leave-one-out cross-validation approach in 59 subjects, we investigated the predictive accuracy in sedentary, ambulatory, household, and cycling activities compared to indirect calorimetry (Vyntus CPX). Over all activities, correlations of at least r = 0.85 were achieved by the models. Root mean squared error ranged from 1 to 1.37 METs and all overall models were statistically equivalent to the criterion measure. Significantly lower error was observed for Actigraph and Sensewear models, when compared to the manufacturer provided estimates of the Sensewear Armband (p < 0.05). A high degree of accuracy in EE estimation was achieved by applying non-linear models to wearable devices which may offer a means to capture the energy cost of free-living activities.  相似文献   

3.
ABSTRACT

Purpose: To compare children’s energy expenditure (EE) levels during object projection skill performance (OPSP; e.g., kicking, throwing, striking) as assessed by hip- and wrist-worn accelerometers. Method: Forty-two children (female n = 20, Mage = 8.1 ± 0.8 years) performed three, nine-minute sessions of kicking, over-arm throwing, and striking at performance intervals of 6, 12, and 30 seconds. EE was estimated using indirect calorimetry (COSMED k4b2) and accelerometers (ActiGraph GT3X+) worn on three different locations (hip, dominant-wrist, and non-dominant-wrist) using four commonly used cut-points. Bland-Altman plots were used to analyze the agreement in EE estimations between accelerometry and indirect calorimetry (METS). Chi-square goodness of fit tests were used to examine the agreement between accelerometry and indirect calorimetry. Results: Hip- and wrist-worn accelerometers underestimated EE, compared to indirect calorimetry, during all performance conditions. Skill practice at a rate of two trials per minute resulted in the equivalent of moderate PA and five trials per minute resulted in vigorous PA (as measured by indirect calorimetry), yet was only categorized as light and/or moderate activity by all measured forms of accelerometry. Conclusion: This is one of the first studies to evaluate the ability of hip- and wrist-worn accelerometers to predict PA intensity levels during OPSP in children. These data may significantly impact PA intervention measurement strategies by revealing the lack of validity in accelerometers to accurately predict PA levels during OPSP in children.  相似文献   

4.
This study examines the validity of the SenseWear Armband in different temperatures using the old (SenseWear v2.2) and newest version of the algorithm (SenseWear v5.2) against indirect calorimetry (IC). Thirty-nine male and female students (21.1 ± 1.41 years) completed an exercise trial in 19°C, 26°C and 33°C consisting of 5 min standing followed by alternating walking/running at 35% and 65% of their maximal oxygen uptake. The accuracy of the algorithms was evaluated by comparing estimated energy expenditure (EE) to IC using a mixed-model design. No difference was reported in EE between the different temperatures for IC. Both algorithms estimated EE significantly higher when exercising at high intensity in 33°C compared to 19°C. Compared to IC, SenseWear v2.2 accurately estimated EE during standing and light intensity exercise but underestimated EE when exercising in a hot environment and at high intensity. SenseWear v5.2 showed a difference when exercising at high intensity in thermoneutral and warm conditions. The new algorithm improved EE estimation in hot environments and at high intensity compared to the old version. However, given the inherent inaccuracy of the EE estimates of SenseWear, greater weight should be given to direct monitor outputs rather than the ability of a monitor to estimate EE precisely.  相似文献   

5.
ABSTRACT

Purpose: The purpose of this study was to evaluate the agreement of five commercially available accelerometers in estimating energy expenditure while performing an acute bout of high-intensity functional training (HIFT). Methods: Participants (n = 47; average age: 28.5 ± 11.6 years) consisted of recreationally active, healthy adults. Each participant completed a session of HIFT: a 15-minute workout consisting of 12 repetitions each of air-squats, sit-ups, push-ups, lunges, pull-ups, steps-ups, and high-knees; performed circuit-style by completing as many rounds as possible. During this session, each participant wore the Cosmed K4b2 portable metabolic analyzer (PMA) and five different accelerometers (ActiGraph GT3X, Nike Fuelband, Fitbit One, Fitbit Charge HR, and Jawbone UP Move). Results: Four of the five activity trackers reported lower (p < .05) total EE values compared to the PMA during the acute bout of HIFT. The waist-mounted device (ActiGraph, 182.55 ± 37.93 kcal) was not significantly different from, and most closely estimated caloric expenditure compared to the PMA (144.99 ± 37.13 kcal) (p = .056). A repeated-measures ANOVA showed that all activity trackers were significantly different from the reference measure (PMA) (p < .05). Systematic relative agreement between the activity trackers was calculated, exhibiting a significant ICC = 0.426 (F [46,230] = 5.446 [p < .05]). Conclusion: The wrist- and hip-mounted activity trackers did not accurately assess energy expenditure during HIFT exercise. With the exception of the ActiGraph GT3X, the remaining four activity trackers showed inaccurate estimates of the amount of kilocalories expended during the HIFT exercise bout compared to the PMA.  相似文献   

6.
Wearable activity trackers have become popular for tracking individual’s daily physical activity, but little information is available to substantiate the validity of these devices in step counts. Thirty-five healthy individuals completed three conditions of activity tracker measurement: walking/jogging on a treadmill, walking over-ground on an indoor track, and a 24-hour free-living condition. Participants wore 10 activity trackers at the same time for both treadmill and over-ground protocol. Of these 10 activity trackers three were randomly given for 24-hour free-living condition. Correlations of steps measured to steps observed were r?=?0.84 and r?=?0.67 on a treadmill and over-ground protocol, respectively. The mean MAPE (mean absolute percentage error) score for all devices and speeds on a treadmill was 8.2% against manually counted steps. The MAPE value was higher for over-ground walking (9.9%) and even higher for the 24-hour free-living period (18.48%) on step counts. Equivalence testing for step count measurement resulted in a significant level within ±5% for the Fitbit Zip, Withings Pulse, and Jawbone UP24 and within ±10% for the Basis B1 band, Garmin VivoFit, and SenseWear Armband Mini. The results show that the Fitbit Zip and Withings Pulse provided the most accurate measures of step count under all three different conditions (i.e. treadmill, over-ground, and 24-hour condition), and considerable variability in accuracy across monitors and also by speeds and conditions.  相似文献   

7.
This study compared heart rate (HR) measurements for the Fitbit Charge HR 2 (Fitbit) and the Apple Watch devices with HR measurements for electrocardiogram (ECG). Thirty young adults (15/15 females/males, age 23.5 ± 3.0 years) completed the Bruce Protocol. HR measurements were recorded from the ECG and both devices every minute. Average HR for each participant was calculated for very light, light, moderate, vigorous and very vigorous intensities based on ECG-measured HR. A concordance correlation coefficient (CCC) was calculated to examine the strength of the relationship between ECG measured HR and HR measured by each device. Relative error rates (RER) were also calculated to indicate the difference between each device and ECG. An equivalence test was conducted to examine the equivalence of HRs measured by devices and ECG. The Apple Watch showed lower RER (2.4–5.1%) compared with the Fitbit (3.9–13.5%) for all exercise intensities. For both devices, the strongest relationship with ECG-measured HR was found for very light PA with very high CCC (>.90) and equivalence. The strength of the relationship declined as exercise intensity increased for both devices. These findings indicate that the accuracy of real-time HR monitoring by the Apple Watch and Fitbit Charge HR2 is reduced as exercise intensity increases.  相似文献   

8.
This study compared the energy expenditure (EE) levels during object projection skill performance (OPSP) as assessed by indirect calorimetry and accelerometry. Thirty-four adults (female n = 18) aged 18–30 (23.5 ± 2.5 years) performed three, 9-min sessions of kicking, over-arm throwing, and striking performed at 6-, 12-, and 30-sec intervals. EE was estimated (METS) using indirect calorimetry (COSMED K4b2) and hip-worn accelerometry (ActiGraph GT3X+). EE using indirect calorimetry demonstrated moderate-intensity physical activity (3.4 ± 0.7 METS––30-sec interval, 5.8 ± 1.2 METS––12-sec interval) to vigorous intensity physical activity (8.3 ± 1.7 METS––6-sec interval). However, accelerometry predicted EE suggested only light-intensity physical activity (1.7 ± 0.2 METS––30-sec interval, 2.2 ± 0.4 METS––12-sec interval, 2.7 ± 0.6 METS––6-sec interval). Hip-worn, ActiGraph GT3X+ accelerometers do not adequately capture physical activity intensity levels during OPSP, regardless of differences in skill performance intervals.  相似文献   

9.
Activity monitors are frequently used to assess activity in many settings. But as technology advances, so do the mechanisms used to estimate activity causing a continuous need to validate newly developed monitors. The purpose of this study was to examine the step count validity of the Yamax Digiwalker SW-701 pedometer (YX), Omron HJ-720 T pedometer (OP), Polar Active accelerometer (PAC) and Actigraph gt3x+ accelerometer (AG) under controlled and free-living conditions. Participants completed five stages of treadmill walking (n = 43) and a subset of these completed a 3-day free-living wear period (n = 37). Manually counted (MC) steps provided a criterion measure for treadmill walking, whereas the comparative measure during free-living was the YX. During treadmill walking, the OP was the most accurate monitor across all speeds (±1.1% of MC steps), while the PAC underestimated steps by 6.7–16.0% per stage. During free-living, the OP and AG counted 97.5% and 98.5% of YX steps, respectively. The PAC overestimated steps by 44.0%, or 5,265 steps per day. The Omron pedometer seems to provide the most reliable and valid estimate of steps taken, as it was the best performer under lab-based conditions and provided comparable results to the YX in free-living. Future studies should consider these monitors in additional populations and settings.  相似文献   

10.
This study examined the validity of the Actical accelerometer step count and energy expenditure (EE) functions in healthy young adults. Forty-three participants participated in study 1. Actical step counts were compared to actual steps taken during a 200 m walk around an indoor track at self-selected pace and during treadmill walking at different speeds (0.894, 1.56 and 2.01 m · s–1) for 5 min. The Actical was also compared to three pedometers. For study 2, 15 participants from study 1 walked on a treadmill at their predetermined self-selected pace for 15 min. Actical EE was compared to EE measured by indirect calorimetry. One-way analysis of variance and t-tests were used to examine differences. There were no statistical difference between Actical steps and actual steps in self-selected pace walking and during treadmill walking at moderate and fast speeds. During treadmill walking at slow speed, the Actical step counts significantly under predicted actual steps taken. For study 2, there was no statistical difference between measured EE and Actical-recorded EE. The Actical provides valid estimates of step counts at self-selected pace and walking at constant speeds of 1.56 and 2.01 m · s–1. The Actical underestimates EE of walking at constants speeds ≥1.38 m · s–1.  相似文献   

11.
The purpose of the current study was to determine the accuracy of the Fitbit Charge HR and Hexoskin smart shirt. Participants (n = 32, age: 23.5 ± 1.3 years) wore a Fitbit and Hexoskin while performing 14 activities in a laboratory and on a track (lying, sitting, standing, walking various speeds and inclines, jogging, and cycling). Steps, kcals, heart rate, breathing rate, depth, and volume were measured by the Fitbit and Hexoskin and compared to criterion measures. The Fitbit and Hexoskin had low mean absolute percent error for steps (9.7%, 9.4%). The mean absolute percent error was low for heart rate (6.6% and 2.4%), with the Fitbit underestimating heart rate at higher intensities. Both devices had high mean absolute percent error for kcals (43.7% and 27.9%, respectively), and the Hexoskin had high mean absolute percent error for breathing rate, depth, and volume (19.4%, 35.6%, and 33.6%, respectively). The Fitbit and Hexoskin have utility for measurement of some, but not all, physical activity and physiologic variables which they measure.  相似文献   

12.
ABSTRACT

Accelerometer cut points are an important consideration for distinguishing the intensity of activity into categories such as moderate and vigorous. It is well-established in the literature that these cut points depend on a variety of factors, including age group, device, and wear location. The Actigraph GT9X is a newer model accelerometer that is used for physical activity research, but existing cut points for this device are limited since it is a newer device. Furthermore, there is not existing data on cut points for the GT9X at the ankle or foot locations, which offers some potential benefit for activities that do not involve arm and/or core motion. A total of N = 44 adults completed a four-stage treadmill protocol while wearing Actigraph GT9X sensors at four different locations: foot, ankle, wrist, and hip. Metabolic Equivalent of Task (MET) levels assessed by indirect calorimetry along with Receiver Operating Characteristic (ROC) curves were used to establish cut points for moderate and vigorous intensity for each wear location of the GT9X. Area under the ROC curves indicated high discrimination accuracy for each case.  相似文献   

13.
Accurate assessment of resting metabolic rate (RMR) is necessary for calorie-based recommendations in diet and exercise training interventions. BodyMetrix? is an ultrasound-based device that provides an estimate of RMR based on body composition, but has not been proven valid or reliable. Therefore, we evaluated the agreement between Katch–McArdle prediction equation used by BodyMetrix?, with indirect calorimetry, Harris–Benedict, WHO, and Sabounchi prediction equations of RMR. In total, 32 men and 22 women were measured for body composition via BodyMetrix? and RMR via indirect calorimetry. All prediction equations demonstrated significantly lower RMR values (p < .001) relative to indirect calorimetry. Katch–McArdle equation strongly correlated with other prediction equations (p < .001), and had a moderate (r = .658, p < .001) correlation with indirect calorimetry. There was a tendency toward underestimation for obese individuals. Therefore, we suggest that estimates from BodyMetrix? may be used as a relative, rather than an absolute measure of RMR.  相似文献   

14.
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15.
The purpose of this study was to examine the accuracy of the ePulse Personal Fitness Assistant, a forearm-worn device that provides measures of heart rate and estimates energy expenditure. Forty-six participants engaged in 4-minute periods of standing, 2.0 mph walking, 3.5 mph walking, 4.5 mph jogging, and 6.0 mph running. Heart rate and energy expenditure were simultaneously recorded at 60-second intervals using the ePulse, an electrocardiogram (EKG), and indirect calorimetry. The heart rates obtained from the ePulse were highly correlated (intraclass correlation coefficients [ICCs] ≥0.85) with those from the EKG during all conditions. The typical errors progressively increased with increasing exercise intensity but were <5 bpm only during rest and 2.0 mph. Energy expenditure from the ePulse was poorly correlated with indirect calorimetry (ICCs: 0.01-0.36) and the typical errors for energy expenditure ranged from 0.69-2.97 kcal · min(-1), progressively increasing with exercise intensity. These data suggest that the ePulse Personal Fitness Assistant is a valid device for monitoring heart rate at rest and low-intensity exercise, but becomes less accurate as exercise intensity increases. However, it does not appear to be a valid device to estimate energy expenditure during exercise.  相似文献   

16.
Abstract

A comparative evaluation of the ability of activity monitors to predict energy expenditure (EE) is necessary to aid in the investigation of the effect of EE on health. The purpose of this study was to validate and compare the RT3, the SWA and the IDEEA at measuring EE in adults and children. Twenty-six adults and 22 children completed a resting metabolic rate (RMR) test and performed four treadmill activities at 3 km.h?1, 6 km.h?1, 6 km.h?1 at a 10% incline, 9 km.h?1. EE was assessed throughout the protocol by the RT3, the SWA and the IDEEA. Indirect calorimetry (IC) was used as a criterion measure of EE against which each monitor was compared. Mean bias was assessed by subtracting EE from IC from EE from each monitor for each activity. Limit of agreement plots were used to assess the agreement between each monitor and IC. Limits of agreement for resting EE were narrowest for the RT3 for adults and children. Although the IDEEA displayed the smallest mean bias between measures at 3 km.h?1, 6 km.h?1 and 9 km.h?1 in adults and children, the SWA agreed closest with IC at 6 km.h?1, 6 km.h?1 at a 10% incline and 9 km.h?1. Limits of agreement were closest for the SWA at 9 km.h?1 in adults representing 42% of the overall mean EE. Although the RT3 provided the best estimate of resting EE in adults and children, the SWA provided the most accurate estimate of EE across a range of physical activity intensities.  相似文献   

17.
Abstract

The purpose of this study was to examine the accuracy of the ePulse Personal Fitness Assistant, a forearm-worn device that provides measures of heart rate and estimates energy expenditure. Forty-six participants engaged in 4-minute periods of standing, 2.0 mph walking, 3.5 mph walking, 4.5 mph jogging, and 6.0 mph running. Heart rate and energy expenditure were simultaneously recorded at 60-second intervals using the ePulse, an electrocardiogram (EKG), and indirect calorimetry. The heart rates obtained from the ePulse were highly correlated (intraclass correlation coefficients [ICCs] ≥0.85) with those from the EKG during all conditions. The typical errors progressively increased with increasing exercise intensity but were <5 bpm only during rest and 2.0 mph. Energy expenditure from the ePulse was poorly correlated with indirect calorimetry (ICCs: 0.01–0.36) and the typical errors for energy expenditure ranged from 0.69–2.97 kcal · min?1, progressively increasing with exercise intensity. These data suggest that the ePulse Personal Fitness Assistant is a valid device for monitoring heart rate at rest and low-intensity exercise, but becomes less accurate as exercise intensity increases. However, it does not appear to be a valid device to estimate energy expenditure during exercise.  相似文献   

18.
Abstract

To determine the time course of performance responses after an acute bout of plyometric exercise combined with high and low intensity weight training, a 3-group (including a control group), repeated-measures design was employed. Changes in performance were monitored through jumping ability by measuring countermovement and squat jumping, and strength performance assessment through isometric and isokinetic testing of knee extensors (at two different velocities). Participants in both experimental groups performed a plyometric protocol consisting of 50 jumps over 50 cm hurdles and 50 drop jumps from a 50 cm plyometric box. Additionally, each group performed two basic weight exercises consisting of leg presses and leg extensions at 90–95% of maximum muscle strength for the high intensity group and 60% of maximum muscle strength for the low intensity group. The results of the study suggest that an acute bout of intense plyometric exercise combined with weight exercise induces time-dependent changes in performance, which are also dependent on the nature of exercise protocol and testing procedures. In conclusion, acute plyometric exercise with weight exercise may induce a substantial decline in jumping performance for as long as 72 hours but not in other forms of muscle strength.  相似文献   

19.
目的:探讨儿童体力活动水平与健康体适能之间的相关性。方法:对大连市3所小学7~9岁儿童,运用ActiGraph GT9X Link型三轴加速度计进行体力活动水平测量,选取平均每天中等及以上运动强度的时间和平均每天消耗的卡路里2项指标,并完成相应的健康体适能指标测试,包括身体成分、柔韧性、心肺耐力、肌力和肌耐力。结果:平均每天中等及以上运动强度的时间越长,身体成分中体重指数越低,肌耐力测试中卷腹的次数越多,心肺耐力测试中20m折返跑往返的次数越多。结论:增加儿童平均每天中等及以上运动强度的时间,有助于提高儿童健康体适能水平,但是儿童平均每天消耗的卡路里与健康体适能水平之间没有显著的相关性。  相似文献   

20.
Wrist-based accelerometers are increasingly used to assess physical activity (PA) in population-based studies; however, cut-points to translate wrist-based accelerometer counts into PA intensity categories are still needed. The purpose of this study was to determine wrist-based cut-points for moderate- and vigorous-intensity ambulatory PA in adults for the Actical accelerometer. Healthy adults (n = 24) completed a four-phase treadmill exercise protocol (1.9, 3.0, 4.0 and 5.2 mph) while wearing an Actical accelerometer on their wrist. Metabolic equivalent of task (MET) levels were assessed by indirect calorimetry. Receiver operating characteristics (ROC) curves were generated to determine accelerometer counts that maximised sensitivity and specificity for classification of moderate (≥3 METs) and vigorous (>6 METs) ambulatory activity. The area under the ROC curves to discriminate moderate- and vigorous-intensity ambulatory activity were 0.93 (95% confidence interval [CI]: 0.90–0.97; P < 0.001) and 0.96 (95% CI: 0.94–0.99; P < 0.001), respectively. The identified cut-point for moderate-intensity ambulatory activity was 1031 counts per minute, which had a corresponding sensitivity and specificity of 85.6% and 87.5%, respectively. The identified cut-point for vigorous intensity ambulatory activity was 3589 counts per minute, which had a corresponding sensitivity and specificity of 88.0% and 98.7%, respectively. This study established intensity-specific cut-points for wrist-based wear of the Actical accelerometer which are recommended for quantification of moderate- and vigorous-intensity ambulatory activity.  相似文献   

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