A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor.

Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation.

Energy Expenditure Validation of an Exergame Platform: Ring Fit Adventure Use in Adults with Overweight and Obesity.

Objective: This study aims at comparing the energy expenditure (EE) and heart rate (HR) data from Ring Fit Adventure (RFA) with those from indirect calorimetry (COSMED) and a heart rate monitor (Polar FT7). A secondary goal is to evaluate self-reported enjoyment and perceived effort levels. Materials and Methods: Thirty participants (age = 21.8 ± 2.2; body mass index = 31.8 ± 4.4) were recruited for two laboratory visits. The first visit involved baseline measurements. In the second visit, participants performed a 55-minute ad libitum exercise session with the RFA in adventure mode with moderate difficulty. During this session, EE, HR, perceived effort, and enjoyment of physical activity were recorded. Results: Although no statistically significant overestimation of EE was found between the RFA and the metabolic cart, two-way analysis of variance results show a main effect of condition (RFA vs. Polar FT7) on HR (122.8 ± 20.1 bpm and 129.0 ± 18.6 bpm; P = 0.007; ᶯp2 = 0.235). Based on comprehensive statistical evaluations, including the mean absolute percent error, intraclass correlations, typical error of measurement, and limits of agreement, the data suggest that the RFA provides reliable estimates for EE and HR. Overall, participants enjoyed the game considerably (71.3 ± 5.9/80 arbitrary units), and their reported perceived exertion was low. Conclusion: This study underscores that the RFA values are relatively accurate and precise, and thus it can be safely suggested for individuals with overweight and obesity to adopt an active lifestyle.

The effect of cisatracurium infusion on the energy expenditure of critically ill patients: an observational cohort study.

BACKGROUND: Both overfeeding and underfeeding of intensive care unit (ICU) patients are associated with worse outcomes. A reliable estimation of the energy expenditure (EE) of ICU patients may help to avoid these phenomena. Several factors that influence EE have been studied previously. However, the effect of neuromuscular blocking agents on EE, which conceptually would lower EE, has not been extensively investigated. METHODS: We studied a cohort of adult critically ill patients requiring invasive mechanical ventilation and treatment with continuous infusion of cisatracurium for at least 12 h. The study aimed to quantify the effect of cisatracurium infusion on EE (primary endpoint). EE was estimated based on ventilator-derived VCO2 (EE in kcal/day = VCO2 × 8.19). A subgroup analysis of septic and non-septic patients was performed. Furthermore, the effects of body temperature and sepsis on EE were evaluated. A secondary endpoint was hypercaloric feeding (> 110% of EE) after cisatracurium infusion. RESULTS: In total, 122 patients were included. Mean EE before cisatracurium infusion was 1974 kcal/day and 1888 kcal/day after cisatracurium infusion. Multivariable analysis showed a significantly lower EE after cisatracurium infusion (MD - 132.0 kcal (95% CI - 212.0 to - 52.0; p = 0.001) in all patients. This difference was statistically significant in both sepsis and non-sepsis patients (p = 0.036 and p = 0.011). Non-sepsis patients had lower EE than sepsis patients (MD - 120.6 kcal; 95% CI - 200.5 to - 40.8, p = 0.003). Body temperature and EE were positively correlated (Spearman's rho = 0.486, p < 0.001). Hypercaloric feeding was observed in 7 patients. CONCLUSIONS: Our data suggest that continuous infusion of cisatracurium in mechanically ventilated ICU patients is associated with a significant reduction in EE, although the magnitude of the effect is small. Sepsis and higher body temperature are associated with increased EE. Cisatracurium infusion is associated with overfeeding in only a minority of patients and therefore, in most patients, no reductions in caloric prescription are necessary.

Use of consumer monitors for estimating energy expenditure in youth.

The purpose of this study was to compare energy expenditure (EE) estimates from 5 consumer physical activity monitors (PAMs) to indirect calorimetry in a sample of youth. Eighty-nine youth (mean (SD); age, 12.3 (3.4) years; 50% female) performed 16 semi-structured activities. Activities were performed in duplicate across 2 visits. Participants wore a Cosmed K4b2 (criterion for EE), an Apple Watch 2 (left wrist), Mymo Tracker (right hip), and Misfit Shine 2 devices (right hip; right shoe). Participants were randomized to wear a Samsung Gear Fit 2 or a Fitbit Charge 2 on the right wrist. Oxygen consumption was converted to EE by subtracting estimated basal EE (Schofield's equation) from the measured gross EE. EE from each visit was summed across the 2 visit days for comparison with the total EE recorded from the PAMs. All consumer PAMs estimated gross EE, except for the Apple Watch 2 (net Active EE). Paired t tests were used to assess differences between estimated (PAM) and measured (K4b2) EE. Mean absolute percent error (MAPE) was used to assess individual-level error. The Mymo Tracker was not significantly different from measured EE and was within 15.9 kcal of measured kilocalories (p = 0.764). Mean percent errors ranged from 3.5% (Mymo Tracker) to 48.2% (Apple Watch 2). MAPE ranged from 16.8% (Misfit Shine 2 - right hip) to 49.9% (Mymo Tracker). Novelty Only the Mymo Tracker was not significantly different from measured EE but had the greatest individual error. The Misfit Shine 2 - right hip had the lowest individual error. Caution is warranted when using consumer PAMs in youth for tracking EE.

Handheld Indirect Calorimetry as a Clinical Tool for Measuring Resting Energy Expenditure in Children with and without Obesity.

Background: Resting energy expenditure (REE) is a valuable measure in clinical management of obesity and other chronic illnesses. Gold standard methods for measuring REE (e.g., Douglas bags and metabolic cart) are too expensive and cumbersome for an outpatient clinical setting. The purpose of this study was to determine the accuracy of a handheld indirect calorimeter (HHIC) and prediction equations (PEs) for measurement of REE in youth with and without obesity. Methods: Fifty-three children and adolescents (12.8 ± 4.3 years, 50.9% female) had REE measured first with a MedGem™ HHIC for 10 minutes, followed by a reference indirect calorimeter system (ParvoMedics TrueOne 2400™) with hood canopy and dilution pump for 30 minutes. REE was also estimated using nine PEs as follows: Henry-1, Henry-2, Schofield, World Health Organization, Molnar, Muller, Herrmann, Schmelzle, and Harris-Benedict. Concordance correlation coefficients and Bland-Altman analyses were used for comparisons among PEs, MedGem HHIC, and metabolic cart. Results: The observed correlation between the HHIC and the reference system was rc = 0.89 with a mean bias of 2.27 ± 3.41 kcal/(kg·d) (9.1% ± 14.7%). Regarding PE, Molnar had the highest agreement with the reference system [rc = 0.93, bias of 2.17 ± 2.04 kcal/(kg·d); 9.8% ± 8.1%], followed by Harris-Benedict (rc = 0.89; 13.8% ± 8.9%), Henry-2 (rc = 0.89; 15% ± 7.6%), and Henry-1 (rc = 0.86; 16.7% ± 7.3%). All PEs were less accurate for children with overweight/obesity. Conclusions: Compared to PE, the HHIC provided more accurate REE estimates for children across the age and BMI spectrum, although positive bias was present throughout. Difference in positive bias between the HHIC and the Molnar equation may be clinically significant for youth with overweight/obesity.

Accuracy of a Portable Indirect Calorimeter for Measuring Resting Energy Expenditure in Individuals With Cancer.

BACKGROUND: Determining optimal caloric intake for an individual with cancer is complicated by metabolic changes that occur, namely, alterations in resting energy expenditure (REE). There is currently no validated clinically available equation or tool to measure energy expenditure in these patients. METHODS: Patients with newly diagnosed solid tumors underwent REE assessments using the FitMate GS portable indirect calorimeter and reference VMax metabolic cart; both used canopy hoods. REE was also estimated from the Harris-Benedict, Mifflin St. Jeor, and Henry equations for comparison. Data were analyzed using paired samples t-test and the Bland-Altman approach to assess group-level and individual-level agreement compared with the metabolic cart. RESULTS: A total 26 patients (19 males; body mass index: 27.8 ± 5.5 kg/m2 ; age: 62 ± 10 years) participated in the study. Biases for the FitMate GS and both equations were low (ranging from -44 to -92 kcal or -2.3% to -5.1%), indicating good group-level accuracy. The FitMate GS had low bias, but the widest limits of agreement (-28.0% to 21.2%) compared with the 3 equations (Harris-Benedict: -15.8% to 11.2%; Mifflin St. Jeor: -17.1% to 6.9%; Henry: -15.4% to 11.5%). These differences were not due to volume of oxygen, BMI category, or sex. CONCLUSION: FitMate GS performed well on a group level, but its accuracy was poor on an individual level. Further research should develop better equations and validate tools to measure energy expenditure for accurate dietary recommendations for patients at nutrition risk.

Innovations in energy expenditure assessment.

PURPOSE OF REVIEW: Optimal nutritional therapy has been associated with better clinical outcomes and requires providing energy as closed as possible to measured energy expenditure. We reviewed the current innovations in energy expenditure assessment in humans, focusing on indirect calorimetry and other new alternative methods. RECENT FINDINGS: Although considered the reference method to measure energy expenditure, the use of indirect calorimetry is currently limited by the lack of an adequate device. However, recent technical developments may allow a broader use of indirect calorimetry for in-patients and out-patients. An ongoing international academic initiative to develop a new indirect calorimeter aimed to provide innovative and affordable technical solutions for many of the current limitations of indirect calorimetry. New alternative methods to indirect calorimetry, including CO2 measurements in mechanically ventilated patients, isotopic approaches and accelerometry-based fitness equipments, show promises but have been either poorly studied and/or are not accurate compared to indirect calorimetry. Therefore, to date, energy expenditure measured by indirect calorimetry remains the gold standard to guide nutritional therapy. SUMMARY: Some new innovative methods are demonstrating promises in energy expenditure assessment, but still need to be validated. There is an ongoing need for easy-to-use, accurate and affordable indirect calorimeter for daily use in in-patients and out-patients.

Comparison of Resting Energy Expenditure Assessment in Pediatric Oncology Patients.

BACKGROUND: Evaluation of energy requirements is an important part of the nutrition assessment of pediatric oncology patients. Adequate provision of energy in this population is of extreme importance because of the prevalence of malnutrition and its effect on growth, development, quality of life, morbidity, and mortality. Numerous methods are used in clinical practice for estimating the resting energy expenditures (REE), specifically indirect calorimetry and predictive equations. A relatively new instrument used to assess REE is the hand-held indirect calorimeter. The purpose of this quality improvement project was to compare the accuracy of REE measurements taken by a hand-held indirect calorimeter and predictive equations to that of a standard indirect calorimeter metabolic cart. METHODS: Patients receiving therapy for pediatric cancer, aged 7-18 years, and having a weight ≥15 kg and scheduled for a REE nutrition assessment were eligible. Sequentially, the patient's REE was assessed with the cart and the hand-held indirect calorimeter along with the predictive equation calculation. RESULTS: Post hoc pairwise comparisons revealed that all 3 methods were significantly different from one another (P < .0001). When compared with the cart, the portable hand-held calorimeter was found to underestimate REE by 11.9%, whereas predictive equations overestimated REE by 12.4%. CONCLUSION: Our quality improvement project suggests that the hand-held indirect calorimeter underestimated REE, and predictive equations overestimated REE in pediatric oncology nutrition assessment. Therefore, we recommend that these limitations in assessment be considered when assessing REE using a hand-held indirect calorimeter or predictive equations.

Indirect calorimetry using a ventilated hood may be easier than using a facemask to achieve steady state when measuring resting energy expenditure.

Previous studies have demonstrated differences in subjective comfort between different gas collecting methods during resting energy expenditure (REE) measurement. We hypothesized that gas collecting methods may have an influence on the time to achieve steady state and the optimum abbreviated period to estimate REE. Gas exchange variables were obtained using IIM-IC-100 (ventilated hood) and VO2000 (facemask) alternately among 32 healthy adults. A 10-minute test period was divided into 5-minute sliding time windows to obtain a subtle profile process of the coefficient of variation (CV) and REE. Friedman's test was used to compare the CVs during the test process. To compare the REE between abbreviated measurements and the reference, paired t test, linear regression, and Bland-Altman test were used. There were no significant differences between the CVs in each adjacent group of 5-minute intervals for IIM-IC-100 (P = .896). CV1 and CV2 were significantly higher than CV3-CV6 for VO2000 (P = .001). The proportion of subjects achieving steady state was lower with VO2000 (59.4-81.3%) than with IIM-IC-100 (84.4-91.8%). REE of the 2nd and 3rd 5-minute intervals did not differ from the reference, and they explained 94.7% (P < .001) and 94.9% (P < .001) of the last 5-minute REE variance, with the 95% limits of agreement ranging from -100.0 to 139.2 kcal/d and -139.5 to 182.5 kcal/d for IIM-IC-100 and VO2000, respectively. In conclusion, using a ventilated hood may be easier than using a facemask to achieve steady state and reduce the time required to estimate REE.

Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors.

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

Assessing resting energy expenditure in overweight and obese adolescents in a clinical setting: validity of a handheld indirect calorimeter.

BACKGROUND: Accurately determining energy requirements is key for nutritional management of pediatric obesity. Recently, a portable handheld indirect calorimeter, MedGem (MG) has become available to measure resting energy expenditure (REE). Our work aims to determine the clinical validity and usefulness of MG to measure REE in overweight and obese adolescents. METHODS: Thirty-nine overweight and obese adolescents (16 male (M): 23 female (F), 15.2 ± 1.9 y, BMI percentile: 98.6 ± 2.2%) and 15 normal weight adolescents (7M: 8F, age 15.2 ± 2.0 y, BMI percentile: 39.2 ± 20.9%) participated. REE was measured with both MG and standard indirect calorimeter (VMax) in random order. RESULTS: MG REE (1,600 ± 372 kcal/d) was lower than VMax REE (1,727 ± 327 kcal/) in the overweight and obese adolescents. Bland Altman analysis (MG -VMax) showed a mean bias of -127 kcal/d (95% CI = -72 to -182 kcal/d, P < 0.001), and a proportional bias existed such that lower measured REE by VMax was underestimated by MG, and higher measured REE by VMax were overestimated by MG. CONCLUSION: MG systematically underestimates REE in the overweight and adolescent population, thus the MG portable indirect calorimeter is not recommended for routine use. Considering that it is a systematic underestimation of REE, MG may be clinically acceptable, only if used with caution.

New generation indirect calorimeters for measuring energy expenditure in the critically ill: a rampant or reticent revolution?

To lower the risk of incorrectly feeding critically ill patients, indirect calorimetry (IC) is proposed as the most ideal method to evaluate energy expenditure and to establish caloric goals. New IC devices are progressively introduced but validation of this new generation remains challenging and arduous.

Measuring energy expenditure in the intensive care unit: a comparison of indirect calorimetry by E-sCOVX and Quark RMR with Deltatrac II in mechanically ventilated critically ill patients.

BACKGROUND: Indirect calorimetry allows the determination of energy expenditure in critically ill patients by measuring oxygen consumption (VO2) and carbon dioxide production (VCO2). Recent studies have demonstrated variable performance of "breath-by-breath" instruments compared to mixing chamber technology. The aim of this study was to validate two modern devices (E-sCOVX and Quark RMR) against a reference method (Deltatrac II). METHOD: Measurements of VO2/VCO2 with the test and reference devices were performed simultaneously over a 20-min period in mechanically ventilated adult intensive care unit patients. Accuracy and precision of instruments were analyzed using Bland-Altman plots. RESULTS: Forty-eight measurements in 22 patients were included for analysis. Both E-sCOVX and Quark RMR overestimated VO2 and VCO2 compared to Deltatrac II, corresponding to a 10% higher mean resting energy expenditure. Limits of agreement of resting energy expenditure within ± 2 standard deviations were ± 461 kcal/24 h (± 21% expressed as percentage error) for ΔE-sCOVX-Deltatrac II and ± 465 kcal/24 h (± 22%) for ΔQuark RMR-Deltatrac II. CONCLUSION: Both test devices overestimate VO2 and VCO2 compared to Deltatrac II. The observed limits of agreement are comparable to those commonly accepted in evaluations of circulatory monitoring, and significantly less than results from predictive equations. We hypothesize that the discrepancy between methods is due to patient/ventilator-related factors that affect the synchronization of gas and spirometry waveforms. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, Trial ID ACTRN12615000205538. Date registered 3 March 2015.

Evaluation of a Portable Armband Device to Assess Resting Energy Expenditure in Patients With Anorexia Nervosa.

BACKGROUND: In women with anorexia nervosa (AN), resting energy expenditure (REE) is decreased due to reduced energy intake and severe underweight. The assessment of REE allows estimating individual metabolic downregulation and better understanding body weight regulatory mechanisms in severely underweight patients with AN. However, REE predictive equations are known to have considerable shortcomings in patients with AN. Our aim was to evaluate a portable armband device (SenseWear armband [SWA]; BodyMedia, Inc, Pittsburgh, PA) for the assessment of REE against the measurement with indirect calorimetry (IC) as the reference method. METHODS: We assessed REE simultaneously by IC and SWA in 50 women with AN at the start of inpatient therapy and calculated REE using 2 predictive equations. RESULTS: Reliable data for IC measurement were obtained for 34 patients (age: 27.0 ± 8.0 years; body mass index: 14.4 ± 2.0 kg/m²). REE assessed with SWA was overestimated by 23% ± 27% compared with REE measured by IC (1166 ± 174 vs 979 ± 198 kcal/d, P < .001). REE estimation with SWA gave an accurate prediction within 10% deviation of REE measured with IC in 35% of the patients. In contrast, REE calculated with 2 predictive equations underestimated REE measured with IC by -26% ± 17% and -5% ± 20%, respectively. CONCLUSIONS: A mean difference of 187 kcal/d between both techniques for the assessment of REE may be of methodological relevance. Therefore, SWA and IC are not interchangeable methods for the assessment of REE in underweight females with AN.

Exploration of Energy Metabolism in the Mouse Using Indirect Calorimetry: Measurement of Daily Energy Expenditure (DEE) and Basal Metabolic Rate (BMR).

Current comprehensive mouse metabolic phenotyping involves studying energy balance in cohorts of mice via indirect calorimetry, which determines heat release from changes in respiratory air composition. Here, we describe the measurement of daily energy expenditure (DEE) and basal metabolic rate (BMR) in mice. These well-defined metabolic descriptors serve as meaningful first-line read-outs for metabolic phenotyping and should be reported when exploring energy expenditure in mice. For further guidance, the issue of appropriate sample sizes and the frequency of sampling of metabolic measurements is also discussed.

Validity of the SenseWear Armband to assess energy expenditure in graded walking.

BACKGROUND: Accurate assessments of physical activity and energy expenditure (EE) are needed to advance research on positive and negative graded walking. PURPOSE: To evaluate the validity of 2 SenseWear Armband monitors (Pro3 and the recently released Mini) during graded walking. METHODS: Twenty healthy adults wore both monitors during randomized walking activities on a motorized treadmill at 7 grades (0%, ±5%, ±15%, and ±25%). Estimates of total EE from the monitors were computed using different algorithms and compared with values derived from indirect calorimetry methodology using a 2-way mixed model ANOVA (Device × Condition), correlation analyses and Bland-Altman plots. RESULTS: There was no significant difference in EE between the 2 armbands in any of the conditions examined. Significant main effects for device and condition, as well as a consistent bias, were observed during positive and negative graded walking with a greater over- and under-estimation at higher slope. CONCLUSIONS: Both the armbands produced similar EE values and seem to be not accurate in estimation of EE during activities involving uphill and downhill walking. Additional work is needed to understand factors contributing to this discrepancy and to improve the ability of these monitors to accurately measure EE during graded walking.

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

BACKGROUND: Advanced 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. METHODS: Adults (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. RESULTS: In 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. CONCLUSIONS: Although 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.

Validation and feasibility of a caloric expenditure measuring device in women with early-stage breast cancer.

PURPOSE: The purpose of this study is to validate the Bodybugg (BB), a caloric expenditure measuring device, in breast cancer patients undergoing adjuvant and neoadjuvant chemotherapy for early-stage breast cancer. METHODS: Twenty-five women with stages I-III breast cancer who were to receive adjuvant dose-dense doxorubicin/cyclophosphamide were recruited. Participants were asked to wear the BB and record activity logs for seven pretreatment days (prior to commencing chemotherapy) and seven posttreatment days (upon completing cycle 4 of chemotherapy). The BB's caloric expenditure measurements were used to calculate metabolic equivalent (MET) values of patients' recorded activities. BB-calculated METs were compared with matching METs from the 2011 Compendium of Physical Activities Tracking Guide to assess accuracy of the device. RESULTS: The overall patient sample wore the device for an average of 5.32 (SD 1.75) pre- and 4.88 (SD 2.01) posttreatment days. The mean pairwise difference between BB and Compendium METs was 0.043 (SD 0.77) for 308 pretreatment activities recorded by 12 patients and 0.065 (SD 0.61) for 108 posttreatment activities recorded by 6 patients, indicating close to zero bias between the BB's and Compendium's measurements. Hierarchical linear modeling showed that Compendium METs strongly predict for BB METs (P < 0.00001). CONCLUSIONS: The BB is feasible to use in study designs involving defined time periods of measurement and provides accurate and objective measurements of caloric expenditure in breast cancer patients.

Predicting energy expenditure through hand rim propulsion power output in individuals who use wheelchairs.

AIM: To examine the relationship between hand rim propulsion power and energy expenditure (EE) during wheelchair wheeling and to investigate whether adding other variables to the model could improve on the prediction of EE. METHODS: Individuals who use manual wheelchairs (n=14) performed five different wheeling activities in a wheelchair with a PowerTap power meter hub built into the right rear wheel. Activities included wheeling on a smooth, level surface at three different speeds (4.5, 5.5 and 6.5 km/h), wheeling on a rubberised track at one speed (5.5 km/h) and wheeling on a sidewalk course that included uphill and downhill segments at a self-selected speed. EE was measured using a portable indirect calorimetry system. Stepwise linear regression was performed to predict EE from power output variables. A repeated-measures analysis of variance was used to compare the measured EE to the estimates from the power models. Bland-Altman plots were used to assess the agreement between the criterion values and the predicted values. RESULTS: EE and power were significantly correlated (r=0.694, p<0.001). Regression analysis yielded three significant prediction models utilising measured power; measured power and speed; and measured power, speed and heart rate. No significant differences were found between measured EE and any of the prediction models. CONCLUSION: EE can be accurately and precisely estimated based on hand rim propulsion power. These results indicate that power could be used as a method to assess EE in individuals who use wheelchairs.