Measurement of energy expenditure by activity monitors

Ryan, Jennifer M; Gormley, John

doi:10.1179/1743288x13y.0000000063

Cited by 6 publications

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“…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 ).…”

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confidence: 99%

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

et al. 2018

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Section: Resultsmentioning

confidence: 99%

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

et al. 2018

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“…More recently some manufacturers have included inbuilt proprietary algorithms to convert counts into energy expenditure (EE) [typically in kilocalories (kcal)]. Although by providing a direct output of EE accelerometers may be more accessible and easy to use, particularly if marketed towards the general population, the ability of an accelerometer to provide a point estimate of EE from limited information is questionable [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A comparison of three accelerometry-based devices for estimating energy expenditure in adults and children with cerebral palsy

Ryan

Walsh

Gormley

2014

J NeuroEngineering Rehabil

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BackgroundAdvanced accelerometry-based devices have the potential to improve the measurement of everyday energy expenditure (EE) in people with cerebral palsy (CP). The aim of this study was to investigate the ability of two such devices (the Sensewear ProArmband and the Intelligent Device for Energy Expenditure and Activity) and the ability of a traditional accelerometer (the RT3) to estimate EE in adults and children with CP.MethodsAdults (n = 18; age 31.9 ± 9.5 yr) and children (n = 18; age 11.4 ± 3.2 yr) with CP (GMFCS levels I-III) participated in this study. Oxygen uptake, measured by the Oxycon Mobile portable indirect calorimeter, was converted into EE using Weir’s equation and used as the criterion measure. Participants’ EE was measured simultaneously with the indirect calorimeter and three accelerometers while they rested for 10 minutes in a supine position, walked overground at a maximal effort for 6 minutes, and completed four treadmill activities for 5 minutes each at speeds of 1.0 km.h−1, 1.0 km.h−1 at 5% incline, 2.0 km.h−1, and 4.0 km.h−1.ResultsIn adults the mean absolute percentage error was smallest for the IDEEA, ranging from 8.4% to 24.5% for individual activities (mean 16.3%). In children the mean absolute percentage error was smallest for the SWA, ranging from 0.9% to 23.0% for individual activities (mean 12.4%). Limits of agreement revealed that the RT3 provided the best agreement with the indirect calorimeter for adults and children. The upper and lower limits of agreement for adults were 3.18 kcal.min−1 (95% CI = 2.66 to 3.70 kcal.min−1) and -2.47 kcal.min−1 (95% CI = -1.95 to -3.00 kcal.min−1), respectively. For children, the upper and lower limits of agreement were 1.91 kcal.min−1 (1.64 to 2.19 kcal.min−1) and -0.92 kcal.min−1 (95% CI = -1.20 to -0.64 kcal.min−1) respectively. These limits of agreement represent -67.2% to 86.3% of mean EE for adults and -36.5% to 76.3% of mean EE for children, respectively.ConclusionsAlthough the RT3 provided the best agreement with the indirect calorimeter the RT3 could significantly overestimate or underestimate individual estimates of EE. The development of CP-specific algorithms may improve the ability of these devices to estimate EE in this population.

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“…2 Conducting a meta-analysis may have indeed strengthened the review. The decision not to conduct a metaanalysis was, however, made for a number of reasons.…”

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Response to commentary on: ‘Measurement of energy expenditure by activity monitors: Is it feasible to measure energy expenditure using tiny portable monitors?’

Ryan

Gormley

2013

Physical Therapy Reviews

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Dr Berntsen 1 makes some interesting points about our review on the ability of accelerometry-based devices to estimate energy expenditure in adults and children. 2 Conducting a meta-analysis may have indeed strengthened the review. The decision not to conduct a metaanalysis was, however, made for a number of reasons. As discussed in the review, a major finding during compilation of the review was the need for a standardized protocol or the development of best practices to guide research with accelerometers. As it stands, there is large variation in the characteristics of sample populations, protocols employed, outcome measures, data extraction methods, and analytical methods. In an attempt to minimize some of this variation, and include samples that are reasonably representative of the general population, only studies of apparently healthy adults and children were included in the review. Instead of selecting discrete groups such as children (younger than 10 years), adolescents (10 to 20 years), adults (20 to 60 years), and older adults (60 years and older) however, many studies included participants within a narrow age range of one to two years e.g. 12 to 14 years, 3 within a wide age range e.g. 15 to 61 years, 4 or participants exclusively of one sex. 5 Pooling this data would provide little insight into how variable results would be if the sample population was more representative of the general population.A further problem with combining results was the lack of appropriate analytical methods used by studies. The majority of studies compared group estimates from the monitor of interest and the criterion method, which would allow for pooling of data but may be misleading. This analysis may reveal non-significant differences between groups despite there being significant errors in individual estimates. Although many studies reported correlation coefficients as an indication of the association between the estimated energy expenditure and the criterion measure, correlations are not sufficient for establishing monitor validity. 6,7 It is possible for two measures to be correlated but provide different estimates. The aim when validating a monitor is ultimately to determine how much the monitor differs from an established measure, and if the difference is minimal enough not to cause problems when making clinical decisions or developing guidelines. Bland-Altman 95% limits of agreement provide the range within which most differences between measurements by two methods will lie by calculating the mean difference between the methods ¡1.96 standard deviations of the differences. 8 If differences within the observed limits of agreement are not clinically important the two measurement methods can be used interchangeably. Few studies calculated limits of agreement, making it difficult to determine if the difference between the monitor and the criterion measure is acceptable. It is also not appropriate to pool Bland-Altman limits of agreement as it would be impossible to interpret the clinical relevance of the pooled limits of...

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Measurement of energy expenditure by activity monitors

Cited by 6 publications

References 83 publications

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

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

A comparison of three accelerometry-based devices for estimating energy expenditure in adults and children with cerebral palsy

Response to commentary on: ‘Measurement of energy expenditure by activity monitors: Is it feasible to measure energy expenditure using tiny portable monitors?’

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