scholarly journals Accuracy of 12 Wearable Devices for Estimating Physical Activity Energy Expenditure Using a Metabolic Chamber and the Doubly Labeled Water Method: Validation Study

10.2196/13938 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e13938 ◽  
Author(s):  
Haruka Murakami ◽  
Ryoko Kawakami ◽  
Satoshi Nakae ◽  
Yosuke Yamada ◽  
Yoshio Nakata ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Correlation Coefficients ◽  
Wearable Devices ◽  
Doubly Labeled Water ◽  
Free Living ◽  
Daily Lives ◽  
Self Monitoring ◽  
Metabolic Chamber ◽  
Positive Correlations

Background Self-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established. Objective We examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method. Methods A total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices. Results On the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P>.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P>.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P>.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to .37) and Spearman (ρ=−.25 to .46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P>.05) and Omron CaloriScan (707.5±172.7 kcal/day; P>.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P=.045) and Panasonic Actimarker (r=.48; P=.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P=.03), Omron CaloriScan (ρ=.48; P=.04), and Omron Active style Pro (ρ=.48; P=.04), could be ranked in PAEEdlw. Conclusions Most wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE.

scholarly journals Accuracy of 12 Wearable Devices for Estimating Physical Activity Energy Expenditure Using a Metabolic Chamber and the Doubly Labeled Water Method: Validation Study (Preprint)

2019 ◽  
Author(s):  
Haruka Murakami ◽  
Ryoko Kawakami ◽  
Satoshi Nakae ◽  
Yosuke Yamada ◽  
Yoshio Nakata ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Correlation Coefficients ◽  
Wearable Devices ◽  
Doubly Labeled Water ◽  
Free Living ◽  
Daily Lives ◽  
Self Monitoring ◽  
Metabolic Chamber ◽  
Positive Correlations

BACKGROUND Self-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established. OBJECTIVE We examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method. METHODS A total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices. RESULTS On the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P>.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P>.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P>.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to .37) and Spearman (ρ=−.25 to .46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P>.05) and Omron CaloriScan (707.5±172.7 kcal/day; P>.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P=.045) and Panasonic Actimarker (r=.48; P=.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P=.03), Omron CaloriScan (ρ=.48; P=.04), and Omron Active style Pro (ρ=.48; P=.04), could be ranked in PAEEdlw. CONCLUSIONS Most wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE.

scholarly journals Validity of Wrist-Wearable Activity Devices for Estimating Physical Activity in Adolescents: Comparative Study (Preprint)

2020 ◽  
Author(s):  
Yingying Hao ◽  
Xiao-Kai Ma ◽  
Zheng Zhu ◽  
Zhen-Bo Cao
Keyword(s):  
Physical Activity ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Activity Cycle ◽  
Living Conditions ◽  
Free Living ◽  
Self Monitoring ◽  
Metabolic Chamber ◽  
Absolute Percent Error ◽  
Free Living Conditions

BACKGROUND The rapid advancements in science and technology of wrist-wearable activity devices offer considerable potential for clinical applications. Self-monitoring of physical activity (PA) with activity devices is helpful to improve the PA levels of adolescents. However, knowing the accuracy of activity devices in adolescents is necessary to identify current levels of PA and assess the effectiveness of intervention programs designed to increase PA. OBJECTIVE The study aimed to determine the validity of the 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure (TEE) in healthy adolescents under simulated free-living conditions. METHODS Nineteen (10 male and 9 female) participants aged 14 to 18 years performed a 24-hour activity cycle in a metabolic chamber. Each participant simultaneously wore 11 commercial wrist-wearable activity devices (Mi Band 2 [XiaoMi], B2 [Huawei], Bong 2s [Meizu], Amazfit [Huamei], Flex [Fitbit], UP3 [Jawbone], Shine 2 [Misfit], GOLiFE Care-X [GoYourLife], Pulse O2 [Withings], Vivofit [Garmin], and Loop [Polar Electro]) and one research-based triaxial accelerometer (GT3X+ [ActiGraph]). Criterion measures were total EE from the metabolic chamber (mcTEE) and total steps from the GT3X+ (AGsteps). RESULTS Pearson correlation coefficients r for 24-hour TEE ranged from .78 (Shine 2, Amazfit) to .96 (Loop) and for steps ranged from 0.20 (GOLiFE) to 0.57 (Vivofit). Mean absolute percent error (MAPE) for TEE ranged from 5.7% (Mi Band 2) to 26.4% (Amazfit) and for steps ranged from 14.2% (Bong 2s) to 27.6% (Loop). TEE estimates from the Mi Band 2, UP3, Vivofit, and Bong 2s were equivalent to mcTEE. Total steps from the Bong 2s were equivalent to AGsteps. CONCLUSIONS Overall, the Bong 2s had the best accuracy for estimating TEE and total steps under simulated free-living conditions. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions.

scholarly journals Validity of Wrist-Wearable Activity Devices for Estimating Physical Activity in Adolescents: Comparative Study

10.2196/18320 ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. e18320
Author(s):  
Yingying Hao ◽  
Xiao-Kai Ma ◽  
Zheng Zhu ◽  
Zhen-Bo Cao
Keyword(s):  
Physical Activity ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Activity Cycle ◽  
Living Conditions ◽  
Free Living ◽  
Self Monitoring ◽  
Metabolic Chamber ◽  
Absolute Percent Error ◽  
Free Living Conditions

Background The rapid advancements in science and technology of wrist-wearable activity devices offer considerable potential for clinical applications. Self-monitoring of physical activity (PA) with activity devices is helpful to improve the PA levels of adolescents. However, knowing the accuracy of activity devices in adolescents is necessary to identify current levels of PA and assess the effectiveness of intervention programs designed to increase PA. Objective The study aimed to determine the validity of the 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure (TEE) in healthy adolescents under simulated free-living conditions. Methods Nineteen (10 male and 9 female) participants aged 14 to 18 years performed a 24-hour activity cycle in a metabolic chamber. Each participant simultaneously wore 11 commercial wrist-wearable activity devices (Mi Band 2 [XiaoMi], B2 [Huawei], Bong 2s [Meizu], Amazfit [Huamei], Flex [Fitbit], UP3 [Jawbone], Shine 2 [Misfit], GOLiFE Care-X [GoYourLife], Pulse O2 [Withings], Vivofit [Garmin], and Loop [Polar Electro]) and one research-based triaxial accelerometer (GT3X+ [ActiGraph]). Criterion measures were total EE from the metabolic chamber (mcTEE) and total steps from the GT3X+ (AGsteps). Results Pearson correlation coefficients r for 24-hour TEE ranged from .78 (Shine 2, Amazfit) to .96 (Loop) and for steps ranged from 0.20 (GOLiFE) to 0.57 (Vivofit). Mean absolute percent error (MAPE) for TEE ranged from 5.7% (Mi Band 2) to 26.4% (Amazfit) and for steps ranged from 14.2% (Bong 2s) to 27.6% (Loop). TEE estimates from the Mi Band 2, UP3, Vivofit, and Bong 2s were equivalent to mcTEE. Total steps from the Bong 2s were equivalent to AGsteps. Conclusions Overall, the Bong 2s had the best accuracy for estimating TEE and total steps under simulated free-living conditions. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions.

Energy requirements and physical activity in free-living older women and men: a doubly labeled water study

1998 ◽  
Vol 85 (3) ◽  
pp. 1063-1069 ◽  
Author(s):  
Raymond D. Starling ◽  
Michael J. Toth ◽  
William H. Carpenter ◽  
Dwight E. Matthews ◽  
Eric T. Poehlman
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Energy Expenditure ◽  
Older Women ◽  
Fat Free Mass ◽  
Energy Requirements ◽  
Physical Activity Energy Expenditure ◽  
Doubly Labeled Water ◽  
Free Living ◽  
Activity Energy

Determinants of daily energy needs and physical activity are unknown in free-living elderly. This study examined determinants of daily total energy expenditure (TEE) and free-living physical activity in older women ( n = 51; age = 67 ± 6 yr) and men ( n = 48; age = 70 ± 7 yr) by using doubly labeled water and indirect calorimetry. Using multiple-regression analyses, we predicted TEE by using anthropometric, physiological, and physical activity indexes. Data were collected on resting metabolic rate (RMR), body composition, peak oxygen consumption (V˙o 2 peak), leisure time activity, and plasma thyroid hormone. Data adjusted for body composition were not different between older women and men, respectively (in kcal/day): TEE, 2,306 ± 647 vs. 2,456 ± 666; RMR, 1,463 ± 244 vs. 1,378 ± 249; and physical activity energy expenditure, 612 ± 570 vs. 832 ± 581. In a subgroup of 70 women and men, RMR andV˙o 2 peakexplained approximately two-thirds of the variance in TEE ( R 2 = 0.62; standard error of the estimate = ±348 kcal/day). Crossvalidation of this equation in the remaining 29 women and men was successful, with no difference between predicted and measured TEE (2,364 ± 398 and 2,406 ± 571 kcal/day, respectively). The strongest predictors of physical activity energy expenditure ( P < 0.05) for women and men were V˙o 2 peak( r = 0.43), fat-free mass ( r = 0.39), and body mass ( r = 0.34). In summary, RMR andV˙o 2 peak are important independent predictors of energy requirements in the elderly. Furthermore, cardiovascular fitness and fat-free mass are moderate predictors of physical activity in free-living elderly.

scholarly journals Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

2019 ◽  
Vol 8 (4) ◽  
pp. 45-54
Author(s):  
Matteo Vandoni ◽  
Vittoria Carnevale Pellino ◽  
Stefano Dell'Anna ◽  
Elena Ricagno ◽  
Giulia Liberali ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Wearable Devices ◽  
Activity Monitoring ◽  
Percentage Error ◽  
Free Living ◽  
Sensewear Armband ◽  
Absolute Percentage Error ◽  
Physical Activity Monitoring ◽  
Monitoring Devices

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

scholarly journals Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

2019 ◽  
Vol 8 (4) ◽  
pp. 45-54
Author(s):  
Matteo Vandoni ◽  
Vittoria Carnevale Pellino ◽  
Stefano Dell'Anna ◽  
Elena Ricagno ◽  
Giulia Liberali ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Wearable Devices ◽  
Activity Monitoring ◽  
Percentage Error ◽  
Free Living ◽  
Sensewear Armband ◽  
Absolute Percentage Error ◽  
Physical Activity Monitoring ◽  
Monitoring Devices

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

scholarly journals Influence of body composition on physical activity validation studies using doubly labeled water

2004 ◽  
Vol 96 (4) ◽  
pp. 1357-1364 ◽  
Author(s):  
Louise C. Mâsse ◽  
Janet E. Fulton ◽  
Kathleen L. Watson ◽  
Matthew T. Mahar ◽  
Michael C. Meyers ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Energy Expenditure ◽  
Body Mass ◽  
Resting Metabolic Rate ◽  
Fat Free Mass ◽  
Doubly Labeled Water ◽  
Comparison Analysis ◽  
Methods Comparison ◽  
Statistical Procedures

This study investigated the influence of two approaches (mathematical transformation and statistical procedures), used to account for body composition [body mass or fat-free mass (FFM)], on associations between two measures of physical activity and energy expenditure determined by doubly labeled water (DLW). Complete data for these analyses were available for 136 African American (44.1%) and Hispanic (55.9%) women (mean age 50 ± 7.3 yr). Total energy expenditure (TEE) by DLW was measured over 14 days. Physical activity energy expenditure (PAEE) was computed as 0.90 × TEE - resting metabolic rate. During week 2, participants wore an accelerometer for 7 consecutive days and completed a 7-day diary. Pearson's product-moment correlations and three statistical procedures (multiple regressions, partial correlations, and allometric scaling) were used to assess the effect of body composition on associations. The methods-comparison analysis was used to study the effect of body composition on agreement. The statistical procedures demonstrated that associations improved when body composition was included in the model. The accelerometer explained a small but meaningful portion of the variance in TEE and PAEE after body mass was accounted for. The methods-comparison analysis confirmed that agreement with DLW was affected by the transformation. Agreement between the diary (transformed with body mass) and TEE reflected the association that exists between body mass and TEE. These results suggest that the accelerometer and diary accounted for a small portion of TEE and PAEE. Most of the variance in DLW-measured energy expenditure was explained by body mass or FFM.

scholarly journals Reliability and validity of a new accelerometer-based device for detecting physical activities and energy expenditure

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5775 ◽  
Author(s):  
Yanxiang Yang ◽  
Moritz Schumann ◽  
Shenglong Le ◽  
Shulin Cheng
Keyword(s):  
Energy Expenditure ◽  
Real Life ◽  
Reliability And Validity ◽  
Correlation Coefficients ◽  
Test Reliability ◽  
Free Living ◽  
Health Maintenance ◽  
Intra Class Correlation ◽  
Global Context ◽  
Different Types

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.

scholarly journals Simultaneous Validation of Seven Physical Activity Questionnaires Used in Japanese Cohorts for Estimating Energy Expenditure: A Doubly Labeled Water Study

2018 ◽  
Vol 28 (10) ◽  
pp. 437-442 ◽  
Author(s):  
Hiroyuki Sasai ◽  
Yoshio Nakata ◽  
Haruka Murakami ◽  
Ryoko Kawakami ◽  
Satoshi Nakae ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Doubly Labeled Water ◽  
Activity Questionnaires
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