scholarly journals Prediction of activity-related energy expenditure using accelerometer-derived physical activity under free-living conditions: a systematic review

2016 ◽  
Vol 40 (8) ◽  
pp. 1187-1197 ◽  
Author(s):  
S Jeran ◽  
A Steinbrecher ◽  
T Pischon
2013 ◽  
Vol 2 ◽  
Author(s):  
Marie Löf ◽  
Hanna Henriksson ◽  
Elisabet Forsum

AbstractActivity energy expenditure (AEE) during free-living conditions can be assessed using devices based on different principles. To make proper comparisons of different devices' capacities to assess AEE, they should be evaluated in the same population. Thus, in the present study we evaluated, in the same group of subjects, the ability of three devices to assess AEE in groups and individuals during free-living conditions. In twenty women, AEE was assessed using RT3 (three-axial accelerometry) (AEERT3), Actiheart (a combination of heart rate and accelerometry) (AEEActi) and IDEEA (a multi-accelerometer system) (AEEIDEEA). Reference AEE (AEEref) was assessed using the doubly labelled water method and indirect calorimetry. Average AEEActi was 5760 kJ per 24 h and not significantly different from AEEref (5020 kJ per 24 h). On average, AEERT3 and AEEIDEEA were 2010 and 1750 kJ per 24 h lower than AEEref, respectively (P < 0·001). The limits of agreement (± 2 sd) were 2940 (Actiheart), 1820 (RT3) and 2650 (IDEEA) kJ per 24 h. The variance for AEERT3 was lower than for AEEActi (P = 0·006). The RT3 classified 60 % of the women in the correct activity category while the corresponding value for IDEEA and Actiheart was 30 %. In conclusion, the Actiheart may be useful for groups and the RT3 for individuals while the IDEEA requires further development. The results are likely to be relevant for a large proportion of Western women of reproductive age and demonstrate that the procedure selected to assess physical activity can greatly influence the possibilities to uncover important aspects regarding interactions between physical activity, diet and health.


2008 ◽  
Vol 20 (2) ◽  
pp. 181-197 ◽  
Author(s):  
David Xiaoqian Sun ◽  
Gordon Schmidt ◽  
Sock Miang Teo-Koh

This is a validation study of the RT3 accelerometer for measuring physical activities of children in simulated free-living conditions. Twenty-five children age 12–14 years completed indoor testing, and 18 of them completed outdoor testing. Activity counts from the RT3 accelerometer estimated activity energy expenditure (AEE) and the Cosmed K4b2 analyzer measured oxygen uptake. Correlations were found between activity counts and metabolic cost (r = .95, p < .001), metabolic cost and RT3 estimated AEE (r = .96, p < .001) in the indoor test, activity counts and RT3 estimated AEE (r = .97, p < .001) in the outdoor test, and activity counts and metabolic cost when all activities were combined (r = .77, p < .001). Results indicate that the RT3 accelerometer might be used to provide acceptable estimates of free-living physical activity in children.


2017 ◽  
Vol 69 ◽  
pp. 128-134 ◽  
Author(s):  
Romain Guidoux ◽  
Martine Duclos ◽  
Gérard Fleury ◽  
Philippe Lacomme ◽  
Nicolas Lamaudière ◽  
...  

2013 ◽  
Vol 38 (1) ◽  
pp. 49-56 ◽  
Author(s):  
Pedro B. Júdice ◽  
João P. Magalhães ◽  
Diana A. Santos ◽  
Catarina N. Matias ◽  
Ana Isabel Carita ◽  
...  

Research on the effect of caffeine on energy expenditure (EE), physical activity (PA), and total sleep time (TST) during free-living conditions using objective measures is scarce. We aimed to determine the impact of a moderate dose of caffeine on TST, resting EE (REE), physical activity EE (PAEE), total EE (TEE), and daily time spent in sedentary, light, moderate, and vigorous intensity activities in a 4-day period and the acute effects on heart rate (HR) and EE in physically active males. Using a double-blind crossover trial (ClinicalTrials.gov ID: NCT01477294) with two conditions (4 days each with 3-day washout) randomly ordered as caffeine (5 mg/kg of body mass/day) and placebo (maltodextrin) administered twice per day (2.5 mg/kg), 30 nonsmoker males, low-caffeine users (<100 mg/day), aged 20–39, were followed. Body composition was assessed by dual-energy X-ray absorptiometry. PA was assessed by accelerometry, while a combined HR and movement sensor estimated EE and HR on the second hour after the first administration dose. REE was assessed by indirect calorimetry, and PAEE was calculated as [TEE − (REE + 0.1TEE)]. TST and daily food records were obtained. Repeated measures ANOVA and ANCOVA were used. After a 4-day period, adjusting for fat-free mass, PAEE, and REE, TST was reduced (p = 0.022) under caffeine intake, while no differences were found between conditions for REE, PAEE, TEE, and PA patterns. Also, no acute effects on HR and EE were found between conditions. Though a large individual variability was observed, our findings revealed no acute or long-term effects of caffeine on EE and PA but decreased TST during free-living conditions in healthy males.


2013 ◽  
Vol 10 (5) ◽  
pp. 617-625 ◽  
Author(s):  
Dac Minh Tuan Nguyen ◽  
Virgile Lecoultre ◽  
Andrew P. Hills ◽  
Yves Schutz

Background:Increases in physical activity (PA) are promoted by walking in an outdoor environment. Along with walking speed, slope is a major determinant of exercise intensity, and energy expenditure. The hypothesis was that in free-living conditions, a hilly environment diminishes PA to a greater extent in obese (OB) when compared with control (CO) individuals.Methods:To assess PA types and patterns, 28 CO (22 ± 2 kg/m2) and 14 OB (33 ± 4 kg/m2) individuals wore during an entire day 2 accelerometers and 1 GPS device, around respectively their waist, ankle and shoulder. They performed their usual PA and were asked to walk an additional 60 min per day.Results:The duration of inactivity and activity with OB individuals tended to be, respectively, higher and lower than that of CO individuals (P = .06). Both groups spent less time walking uphill/downhill than on the level (20%, 19%, vs. 61% of total walking duration, respectively, P < .001). However OB individuals spent less time walking uphill/downhill per day than CO (25 ± 15 and 38 ± 15 min/d, respectively, P < 0.05) and covered a shorter distance per day (3.8 km vs 5.2 km, P < 0.01).Conclusions:BMI and outdoor topography should also be considered when prescribing extra walking in free-living conditions.


2021 ◽  
Vol 8 ◽  
Author(s):  
Amine Guediri ◽  
Louise Robin ◽  
Justine Lacroix ◽  
Timothee Aubourg ◽  
Nicolas Vuillerme ◽  
...  

The World Health Organization has presented their recommendations for energy expenditure to improve public health. Activity trackers do represent a modern solution for measuring physical activity, particularly in terms of steps/day and energy expended in physical activity (active energy expenditure). According to the manufacturer's instructions, these activity trackers can be placed on different body locations, mostly at the wrist and the hip, in an undifferentiated manner. The objective of this study was to compare the absolute error rate of active energy expenditure measured by a wrist-worn and hip-worn ActiGraph GT3X+ over a 24-h period in free-living conditions in young and older adults. Over the period of a 24-h period, 22 young adults and 22 older adults were asked to wear two ActiGraph GT3X+ at two different body locations recommended by the manufacturer, namely one around the wrist and one above the hip. Freedson algorithm was applied for data analysis. For both groups, the absolute error rate tended to decrease from 1,252 to 43% for older adults and from 408 to 46% for young participants with higher energy expenditure. Interestingly, for both young and older adults, the wrist-worn ActiGraph provided a significantly higher values of active energy expenditure (943 ± 264 cal/min) than the hip-worn (288 ± 181 cal/min). Taken together, these results suggest that caution is needed when using active energy expenditure as an activity tracker-based metric to quantify physical activity.


2018 ◽  
Vol 124 (3) ◽  
pp. 780-790 ◽  
Author(s):  
M. Garnotel ◽  
T. Bastian ◽  
H. M. Romero-Ugalde ◽  
A. Maire ◽  
J. Dugas ◽  
...  

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions.NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated. Here we developed and validated an activity-specific model which, coupled with an automatic activity-recognition algorithm, improved the variance explained by the predictions from accelerometry counts by 43% of daily PAEE compared with models relying on a simple relationship between accelerometry counts and EE.


2021 ◽  
Author(s):  
Kaja Kastelic ◽  
Marina Dobnik ◽  
Stefan Loefler ◽  
Christian Hofer ◽  
Nejc Šarabon

BACKGROUND Wrist worn consumer-grade activity trackers are popular devices, developed mainly for personal use, but with the potential to be used also for clinical and research purposes. OBJECTIVE The objective of this study was to explore the validity, reliability and sensitivity to change of movement behaviours metrics from three popular activity trackers (POLAR Vantage M, Garmin Vivosport and Garmin Vivoactive 4s) in controlled and free-living conditions when worn by older adults. METHODS Participants (n = 28; 74 ± 5 years) underwent a videotaped laboratory protocol while wearing all three activity trackers. On a separate occasion, participants wore one (randomly assigned) activity tracker and a research grade physical activity monitor ActiGraph wGT3X-BT simultaneously for six consecutive days for comparisons. RESULTS Both Garmin activity trackers showed excellent performance for step counts, with mean absolute percentage error (MAPE) below 20 % and intraclass correlation coefficient (ICC2,1) above 0.90 (P < .05), while Polar Vantage M substantially over counted steps (MAPE = 84 % and ICC2,1 = 0.37 for free-living conditions). MAPE for sleep time was within 10 % for all the trackers tested, while far beyond 20 % for all the physical activity and calories burned outputs. Both Garmin trackers showed fair agreement (ICC2,1 = 0.58–0.55) for measuring calories burned when compared with ActiGraph. CONCLUSIONS Garmin Vivoactive 4s showed overall best performance, especially for measuring steps and sleep time in healthy older adults. Minimal detectible change was consistently lower for an average day measures than for a single day measure, but still relatively high. The results provided in this study could be used to guide choice on activity trackers aiming for different purposes – individual use/care, longitudinal monitoring or in clinical trial setting.


2000 ◽  
Vol 84 (4) ◽  
pp. 531-539 ◽  
Author(s):  
Jérôme Ribeyre ◽  
Nicole Fellmann ◽  
Jean Vernet ◽  
Michel Delaître ◽  
Alain Chamoux ◽  
...  

The objectives of the study were to determine: (1) daily energy expenditure (EE) of athletic and non-athletic adolescents of both sexes in free-living conditions; (2) day-to-day variations in daily EE during 1 week; (3) energy costs of the main activities; and (4) the effect of usual activity on EE during sleep, seated and miscellaneous activities. Fifty adolescents (four groups of eleven to fifteen boys or girls aged 16–19 years) participated in the study. Body composition was measured by the skinfold-thickness method, and VO2max and external mechanical power (EMP) by a direct method (respiratory gas exchanges) on a cycloergometer. Daily EE and partial EE in free-living conditions were computed from heart-rate (HR) recordings during seven consecutive days using individual prediction equations established from the data obtained during a 24 h period spent in whole-body calorimeters with similar activities. Fat-free mass (FFM), VO2max, EMP, daily EE and EE during sleep were significantly higher in athletic than in non-athletic subjects. After adjustment for FFM, VO2max, EMP, daily EE and EE during exercise were still higher in athletic than in non-athletic adolescents (P<0·001). However, adjusted sleeping EE was not significantly different between athletic and non-athletic adolescents. Increases in exercise EE were partly compensated for by significant reductions in EE during schoolwork and miscellaneous activities. Thus, the differences in daily EE between athletic and non-athletic subjects resulted mainly from increases in FFM and EE during exercise (duration and energy cost).


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