Measurement Of Energy Expenditure Research Articles

Accuracy of the Apple Watch Series 9 for Measures of Energy Expenditure and Heart Rate at Rest and During Exercise: Impact of Skin Pigmentation

Background: The Apple Watch provides promising health data that could aid in increasing exercise adherence; regular exercise can help individuals manage and prevent diseases such as obesity and cardiovascular disease. However, the impact of skin pigmentation on the accuracy of the Apple Watch Series 9 for measures of energy expenditure (EE) and heart rate (HR) is unknown. Purpose: The purpose of this study was to determine the accuracy of the Apple Watch Series 9 on various skin pigmentations for measures of EE and HR. Methods: Thirty young, healthy individuals were assigned to one of three groups based on their scores on the Fitzpatrick skin survey. Participants completed a 10 min treadmill protocol with varying speeds and inclines while wearing an Apple Watch Series 9, a two-way non-rebreathing mouthpiece connected to a Parvo Medics metabolic cart, and a Polar H7 chest strap to measure EE and HR. Results: Overall, EE was found to be inconsistent for all skin pigmentation groups. However, for HR, the Apple Watch Series 9 was more variable (i.e., less accurate) for darker skin pigmentations compared to lighter skin pigmentations. Conclusions: The Apple Watch Series 9 was found to vary in both EE and HR measures from criterion across intensity and skin pigmentation, with greater discrepancies for individuals in Group 3 for measures of HR. Further investigation might aim to study the impact of skin pigmentations and wrist subcutaneous fat on the accuracy of the latest Apple Watch Series 9 for measures of EE and HR.

Cadence-based energy expenditure in able-bodied male people comparing cadence-based energy expenditure and oxygen cost indices in able-bodied male people.

Walking is a fundamental aspect of daily life and exercise, with clinical benefits for cardiovascular health and muscle strength. However, accurately measuring energy efficiency during walking poses challenges due to equipment and spatial constraints. In this study, we proposed the cadence-based energy expenditure index (cEEI) and analyzed its correlation with the previously proposed index for measuring energy expenditure under various gait conditions. We enrolled 15 healthy participants and conducted an experimental protocol on a treadmill to measure the following energy expenditure-related indices: oxygen cost index (OCI), energy expenditure index (EEI), and cEEI. The participants underwent stages of walking at different speeds and inclinations that comply with the modified Bruce protocol, whereas their heart rate, oxygen uptake, and cadence were recorded. Participants showed significant increases in heart rate, oxygen uptake, and cadence with higher walking speeds and inclinations. Correlation analysis revealed strong associations between cEEI and OCI, especially during walking conditions. Bland-Altman plots and interclass correlation coefficient analysis demonstrated a favorable agreement between cEEI and OCI, outperforming EEI. In conclusion, this study proposes cEEI as a reliable metric for estimating energy expenditure during walking by proving a strong correlation and agreement with OCI across various gait conditions. This suggests the potential for cEEI to provide real-time, individualized feedback on energy expenditure during walking, facilitating more personalized exercise prescriptions.NEW & NOTEWORTHY We proposed cEEI as a reliable metric for estimating energy expenditure under various walking conditions. Its demonstrated accuracy and potential for real-time individualized feedback could revolutionize personalized exercise prescriptions for diverse sexes and age groups, as well as individuals with disabilities requiring real-time physical monitoring and home care.

Energy expenditure of golf range practice: A beta investigation

How does Energy Expenditure (EE) in golf practice compare to golf play? Golf has demonstrated a moderate level of EE during play, especially when walking, which can contribute to health. Golf practice on the driving range has never been assessed. Can driving range practice qualify as meaningful exercise? Ten subjects regularly participating in golf performed two short-range/practice sessions while connected to a portable metabolic system to determine potential benefits. Data points were recorded and averaged for O2/L/min and converted to kcal per hour, oxygen consumption in VO2 ml/kg/min, and heart rate (HR). Subjects performed their regular physical warm-up routine and hit range balls using their standard progression of clubs and pace. Sessions ranged from 10-19 minutes, followed by a short break, with another session of the same time and progression. Using oxygen consumed to measure energy expenditure, the group averaged 3.5 kcal per minute for the first session/3.71 kcal per minute for the two sessions. MET values were 2.31/2.59, respectively, below the required 3.0 met level for moderate activity. Statistical analysis revealed a significant relationship between subject weight and EE of p=0.0001. From this beta study, golf practice can contribute to overall health as part of a weekly exercise and recreation schedule. Further rigorous study is needed to explore this form of activity's quantity further.

Measuring energy expenditure in narcolepsy using doubly-labelled water and respiration chamber calorimetry.

Hypocretin deficiency causes type 1 narcolepsy, a condition characterised by excessive daytime sleepiness, cataplexy, and fragmented nocturnal sleep. Two-thirds of people with narcolepsy are also overweight, of which half are obese. The pathophysiology behind weight gain in people with narcolepsy remains unknown. We assessed a possible decrease in energy expenditure as a cause for overweight in narcolepsy using respiration chamber calorimetry and doubly labelled water. Ten males with type I narcolepsy and nine matched (for age, sex, and BMI) healthy controls were enrolled. Subjects stayed in a respiration chamber for 24 hours. They subsequently received doubly labelled water and wore an accelerometer for two weeks to assess energy expenditure and physical activity under daily living conditions. Total daily energy expenditure, resting energy expenditure, overnight metabolic rate, physical activity level and activity-induced energy expenditure were measured. No significant differences were found in resting energy expenditure, mean 24-hour respiration chamber energy expenditure, overnight metabolic rate and activity-induced energy expenditure when comparing people with narcolepsy type 1 to controls. Physical activity was also comparable between groups. Energy expenditure in narcolepsy type 1 is similar to matched controls, suggesting comparable metabolism and physical activity rates. It remains possible that metabolic changes are most pronounced around disease onset. In addition, patients had to discontinue their medication which may have influenced the results. Still, our findings suggest that other factors may also play a role in weight gain in narcolepsy, such as differences in dietary behaviour.

Metabolic stress in patients with acute severe ulcerative colitis - a single-center cohort study.

Acute severe ulcerative colitis (ASUC) is characterized by systemic inflammation, which may initiate an acute-phase response leading to hypercatabolism. Patients with ASUC are usually treated with high-dose steroids that may further accelerate the metabolic response and lead to hyperglycemia and insulin resistance. Nevertheless, the degree of synergy between inflammation and steroid treatment and their influence on the insulin resistance remains unknown. We aimed to measure the degree of metabolic stress including insulin resistance in patients with ASUC during admission and three weeks after discharge. This single-center cohort study was conducted in adult patients with ASUC, defined and assessed by Truelove and Witt's criteria. Indirect calorimetry, bioelectrical impedance analysis, and the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) were applied at baseline and at follow-up three weeks after discharge. Among the 22 patients admitted during the project period, 15 provided consent for participation in the study. Median C-reactive protein at inclusion was 37.6 [4; 154.7]. Both median HOMA-IR and fasting plasma glucose were markedly increased at inclusion (median 8.6 [3.8; 14.1] and 7.1 [6; 8.7], respectively), and both had decreased significantly three weeks after discharge (p=0.0036 and p=0.0039, respectively). No significant differences were observed in resting energy expenditure or anthropometric measurements from baseline to follow-up. Patients with ASUC presented with marked insulin resistance, indicating that the days following admission and high-dose steroid treatment are particularly vulnerable. Despite improvement at three-week follow-up, patients still exhibited insulin resistance compared with relevant control groups. ClinicalTrials.gov, identifier NCT0527183.

COURSE OF MEASURED ENERGY EXPENDITURE OVER THE FIRST 10 DAYS OF CRITICAL ILLNESS: A NESTED PROSPECTIVE STUDY IN AN ADULT SURGICAL ICU

Background & aimsVarious factors may cause significant daily variations in energy expenditure in and between critically patients. This nested, prospective study (ClinicalTrials.gov Identifier: NCT04099108) in a predominantly trauma surgical ICU, aimed to determine the course of measured energy expenditure over the first 10 days of critical illness, and to identify factors contributing to energy expenditure. MethodsIndirect calorimetry was performed on alternate days from ICU Day 3±1 until Day 10±1. The mean daily measured energy expenditure, respiratory quotient and total energy delivery as a percentage of measured energy expenditure were modelled using linear mixed regression with two fractional polynomial terms to accommodate non-linear responses over time. ResultsFifty ICU patients (mean age 36.9+-11.8 years, Acute Physiology and Chronic Health Evaluation (APACHE II) 13.5±6.6, Sequential Organ Failure Assessment (SOFA) 4.5±3.2) were included. Mean body mass index (BMI) was 24.8±4.00 kg/m2 and mean ventilation duration 7.7±2.7 days. Mean daily measured energy expenditure showed a significant non-linear response (p=0.006) increasing over the first 4 days peaking on day 5 and then plateauing. Mean daily respiratory quotient increased over the first 7 days, thereafter plateauing with a slight downward trend from day 8 despite a progressive increase in total energy delivery as a percentage of measured energy expenditure. Mean daily measured energy expenditure was significantly lower in the early than in the late acute phases (p=0.024), whereas the late- and post-acute phases were similar. Age, sex and BMI significantly influenced measured energy expenditure. ConclusionMeasured energy expenditure showed a significant non-linear response over the first 10 days in ICU, increasing over the first 4 days peaking on day 5 then plateauing. The observed variability highlights the complexity of managing critically ill patients and the importance of personalised nutrition therapy. Additionally, the observed trend with a peak in measured energy expenditure around day five could inform timing and strategies for nutritional intervention in this patient cohort.

A Novel Method to Predict Carbohydrate and Energy Expenditure During Endurance Exercise Using Measures of Training Load.

Sports nutrition guidelines recommend carbohydrate (CHO) intake be individualized to the athlete and modulated according to changes in training load. However, there are limited methods to assess CHO utilization during training sessions. We aimed to (1) quantify bivariate relationships between both CHO and overall energy expenditure (EE) during exercise and commonly used, non-invasive measures of training load across sessions of varying duration and intensity and (2) build and evaluate prediction models to estimate CHO utilization and EE with the same training load measures and easily quantified individual factors. This study was undertaken in two parts: a primary study, where participants performed four different laboratory-based cycle training sessions, and a validation study where different participants performed a single laboratory-based training session using one of three exercise modalities (cycling, running, or kayaking). The primary study included 15 cyclists (five female; maximal oxygen consumption [ O2max], 51.9 ± 7.2mL/kg/min), the validation study included 21 cyclists (seven female; O2max 53.5 ± 11.0mL/kg/min), 20 runners (six female; O2max 57.5 ± 7.2mL/kg/min), and 18 kayakers (five female; O2max 45.6 ± 4.8mL/kg/min). Training sessions were quantified using six training load metrics: two using heart rate, three using power, and one using perceived exertion. Carbohydrate use and EE were determined separately for aerobic (gas exchange) and anaerobic (net lactate accumulation, body mass, and O2 lactate equivalent method) energy systems and summed. Repeated-measures correlations were used to examine relationships between training load and both CHO utilization and EE. General estimating equations were used to model CHO utilization and EE, using training load alongside measures of fitness and sex. Models were built in the primary study and tested in the validation study. Model performance is reported as the coefficient of determination (R2) and mean absolute error, with measures of calibration used for model evaluation and for sport-specific model re-calibration. Very-large to near-perfect within-subject correlations (r = 0.76-0.96) were evident between all training load metrics and both CHO utilization and EE. In the primary study, all models explained a large amount of variance (R2 = 0.77-0.96) and displayed good accuracy (mean absolute error; CHO = 16-21g [10-14%], EE = 53-82kcal [7-11%]). In the validation study, the mean absolute error ranged from 16-50g [15-45%] for CHO models to 53-182kcal [9-31%] for EE models. The calibrated mean absolute error ranged from 9-20g [8-18%] for CHO models to 36-72kcal [6-12%] for EE models. At the individual level, there are strong linear relationships between all measures of training load and both CHO utilization and EE during cycling. When combined with other measures of fitness, EE and CHO utilization during cycling can be estimated accurately. These models can be applied in running and kayaking when used with a calibration adjustment.

Improving quality control procedures for the measurement of total daily energy expenditure using the two-point doubly labeled water method.

The precision of the doubly labeled water (DLW) method is determined by the precision and accuracy of the isotopic measurements. Quality control (QC) procedures to mitigate sample variability require additional measurements if sample duplicates differ more than a factor of instrument precision. We explored the effect of widening QC ranges on total daily energy expenditure (TDEE) determined using the two-point sampling method. We screened DLW data from 121 individuals for instances where samples were analyzed more than twice using our existing QC criteria (±2.0 per mil [δ] for 2H and ±0.5 δ for 18O). We then applied wider QC ranges for accepting duplicate measures and recalculated TDEE. Widening the 2H QC range to ±10.0 δ in samples collected on the first day (most enriched) and to ±5.0 δ in samples collected on the final day (less enriched) produced almost identical mean TDEE compared to the originally calculated TDEE (2684 ± 508 vs. 2687 ± 512 kcal/day, p= 0.40). There was a strong correlation with the originally calculated TDEE (r2=0.97, p< 0.001). Expanding the 2H QC range to ±10.0 δ for samples collected on the first day and ±5.0 δ for samples collected on the final day provides similar mean TDEE results. These findings may help DLW labs optimize QC criteria and reduce analytical costs.

Mapping the Evidence for Measuring Energy Expenditure and Indicating Hypermetabolism in Motor Neuron Disease: A Scoping Review.

To map the international methods used to measure energy expenditure of adults living with motor neuron disease (MND) and to highlight discrepancies when indicating hypermetabolism in the MND literature. A decline in the nutritional status of patients is associated with exacerbated weight loss and shortened survival. Assessments of energy expenditure, using a variety of methods, are important to ensure an adequate energy intake to prevent malnutrition-associated weight loss. Assessments of energy expenditure are also commonly used to indicate hypermetabolism in MND, although these approaches may not be optimal. A protocol based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews Guidelines was developed. Three electronic databases (Medline [Ovid], CINAHL [EBSCO], and Web of Science) were exhaustively searched. Identified publications were systematically screened according to predefined PICOS eligibility criteria. The primary outcome was the identification of methods used to measure energy expenditure in MND. The secondary outcome was the identification of applications of energy expenditure assessments to indicate hypermetabolism in MND. Thirty-two observational primary research publications were identified. Thirteen (40.6%) were longitudinal in design, with data on repeated measurements of energy expenditure presented in 3 (9.4%). Thirteen (40.6%) were case-control studies, of which 11 use a matched control group. Pulmonary function was used to assess eligibility in 10 publications. Energy expenditure was measured using indirect calorimetry (IC) in 31 studies. Discrepancies in the durations of fasted, measurement, and washout periods were observed. Of all included publications, 50% used assessments of resting energy expenditure to identify hypermetabolism. Bioelectrical impedance analysis was used to assess body composition alongside energy expenditure in 93.8% of publications. Resting energy expenditure is most frequently measured using an open-circuit IC system. However, there is a lack of a standardized, validated protocol for the conduct and reporting of IC and metabolic status in patients with MND.

Association of a composite trait for anthropometrics, adiposity and energy expenditure with cardiometabolic diseases: An age-stratified cohort and genetic risk score analysis.

Various anthropometric measures capture distinct as well as overlapping characteristics of an individual's body composition. To characterize independent body composition measures, we aimed to reduce easily-obtainable individual measures reflecting adiposity, anthropometrics and energy expenditure into fewer independent constructs, and to assess their potential sex- and age-specific relation with cardiometabolic diseases. Analyses were performed within European ancestry participants from UK Biobank (N = 418,963, mean age 58.0 years, 56% women). Principal components (PC) analyses were used for the dimension reduction of 11 measures of adiposity, anthropometrics and energy expenditure. PCs were studied in relation to incident type 2 diabetes mellitus (T2D) and coronary artery disease (CAD). Multivariable-adjusted Cox regression analyses, adjusted for confounding factors, were performed in all and stratified by age. Genome-wide association studies were performed in half of the cohort (N = 156,295) to identify genetic variants as instrumental variables. Genetic risk score analyses were performed in the other half of the cohort stratified by age of disease onset (N = 156,295). We identified two PCs, of which PC1 reflected lower overall adiposity (negatively correlated with all adiposity aspects) and PC2 reflected more central adiposity (mainly correlated with higher waist-hip ratio, but with lower total body fat) and increased height, collectively capturing 87.8% of the total variance. Similar to that observed in the multivariable-adjusted regression analyses, we found associations between the PC1 genetic risk score and lower risks of CAD and T2D [CAD cases <50 years, odds ratio: 0.91 (95% confidence interval 0.87, 0.94) per SD; T2D cases <50 years, odds ratio: 0.76 (0.72, 0.81)], which attenuated with higher age (p-values 8.13E-4 and 2.41E-6, respectively). No associations were found for PC2. The consistently observed weaker associations of the composite traits with cardiometabolic disease suggests the need for age-specific cardiometabolic disease prevention strategies.

Food Quotients From Assessments Of Dietary Intake Reduce Bias In Measurement Of Total Energy Expenditure

Food Quotients From Assessments Of Dietary Intake Reduce Bias In Measurement Of Total Energy Expenditure

Comparison of energy expenditure measurement in critically ill patient between indirect calorimetry and modified Weir’s formula using time-based capnography and arterial blood gas

Comparison of energy expenditure measurement in critically ill patient between indirect calorimetry and modified Weir’s formula using time-based capnography and arterial blood gas

Time-energy budgets outperform dynamic body acceleration in predicting daily energy expenditure in kittiwakes, and estimate a very low cost of gliding flight relative to flapping flight.

Energy is a common currency for any living organism, yet estimating energy expenditure in wild animals is challenging. Accelerometers are commonly used to estimate energy expenditure, via a dynamic body acceleration (DBA) or time-energy budget approach. The DBA approach estimates energy expenditure directly from acceleration but may lead to erroneous estimates during inactivity when acceleration is zero but energy expenditure is not. The time-energy budget approach uses accelerometers and other data streams to assign a behaviour to each time step, and then calculates energy expenditure based on activity-specific metabolic rates assigned to each behaviour. Here, we used GPS-accelerometry in breeding black-legged kittiwakes (Rissa tridactyla, n=80) to calculate DBA and time-energy budgets derived from simple biologging metrics (speed, wingbeat frequency, GPS position). We then compared these two approaches with estimates of energy expenditure from doubly labelled water (DLW). Energy expenditure estimated from DLW correlated with DBA, but the best model to estimate energy expenditure was based on time-energy budgets. Energetic costs of flapping flight were higher than all other kittiwake behaviours (5.54×basal metabolic rate, BMR). Energetic costs of gliding flight (0.80×BMR) were the lowest of all behaviours, and equivalent to the cost of resting at the colony. DEE for our birds estimated from our calibration coefficients was similar to DEE for our birds estimated with the model coefficient published using different methods. We conclude that once calibrated with DLW, GPS-accelerometry provides a simple method for measuring energy expenditure in wild kittiwakes based on time-energy budgets.

Dynamic soaring decouples dynamic body acceleration and energetics in albatrosses.

Estimates of movement costs are essential for understanding energetic and life-history trade-offs. Although overall dynamic body acceleration (ODBA) derived from accelerometer data is widely used as a proxy for energy expenditure (EE) in free-ranging animals, its utility has not been tested in species that predominately use body rotations or exploit environmental energy for movement. We tested a suite of sensor-derived movement metrics as proxies for EE in two species of albatrosses, which routinely use dynamic soaring to extract energy from the wind to reduce movement costs. Birds were fitted with a combined heart-rate, accelerometer, magnetometer and GPS logger, and relationships between movement metrics and heart rate-derived V̇O2, an indirect measure of EE, were analyzed during different flight and activity modes. When birds were exclusively soaring, a metric derived from angular velocity on the yaw axis provided a useful proxy of EE. Thus, body rotations involved in dynamic soaring have clear energetic costs, albeit considerably lower than those of the muscle contractions required for flapping flight. We found that ODBA was not a useful proxy for EE in albatrosses when birds were exclusively soaring. As albatrosses spend much of their foraging trips soaring, ODBA alone was a poor predictor of EE in albatrosses. Despite the lower percentage of time flapping, the number of flaps was a useful metric when comparing EE across foraging trips. Our findings highlight that alternative metrics, beyond ODBA, may be required to estimate energy expenditure from inertial sensors in animals whose movements involve extensive body rotations.

Energy Deficit and Factors Associated with Energy Balance during a Combat Deployment in U.S. Army Special Operation Forces Soldiers.

Forty-six SOF Soldiers (age: 30.1 ± 3.5 yrs, body mass index: 27.7 ± 4.1 kg/m2) completed surveys on demographic data, mission activity characteristics, gastrointestinal issues, ration consumption, resilience, mood state, and dietary intake using a 127-question food frequency questionnaire at the end of a six-month deployment. EE was estimated using a SOF-specific prediction equation with a physical activity factor of 2.1. A paired t-test compared reported energy intake (EI) with estimated energy expenditure (EE). Pearson correlations identified significant variables associated with energy balance, which were then incorporated into a multiple linear regression model. The regression analysis included Profile of Mood States (POMS) anger and POMS depression as predictor variables to determine their influence on energy balance. Reported mean EI was 2512 ± 1059 kcal·d-1, while estimated mean EE was 5272 ± 525 kcal·d-1. The mean energy imbalance was -2854 kcal/d (95% CI: -2655 to -3055, p < 0.001), with all participants in negative energy balance (range: -492 to -3813 kcal/d). POMS depression (r = 0.517, p < 0.01) and POMS anger (r = 0.363, p = 0.020) were associated with energy balance. The regression model was significant (R2 = 0.23, F (2, 38) = 7.02, p < 0.01), with POMS depression significantly predicting energy balance (β = 50.76, p = 0.011). Deployed SOF Soldiers reported high EE and limited EI, which may negatively impact performance. Higher POMS depression scores were associated with lower energy deficits. Future studies should investigate the relationship between mood and energy balance, using direct measures of EI and EE.

Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry

BackgroundAccurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling.MethodsWe first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure.ResultsThe activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively.ConclusionsThe integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

Predicting Steady-State Metabolic Power in Cerebral Palsy, Stroke, and the Elderly During Walking With and Without Assistive Devices.

Individuals with walking impairment, such as those with cerebral palsy, often face challenges in leading physically active lives due to the high energy cost of movement. Assistive devices like powered exoskeletons aim to alleviate this burden and improve mobility. Traditionally, optimizing the effectiveness of such devices has relied on time-consuming laboratory-based measurements of energy expenditure, which may not be feasible for some patient populations. To address this, our study aimed to enhance the state-of-the-art predictive model for estimating steady-state metabolic rate from 2-min walking trials to include individuals with and without walking disabilities and for a variety of terrains and wearable device conditions. Using over 200 walking trials collected from eight prior exoskeleton-related studies, we trained a simple linear machine learning model to predict metabolic power at steady state based on condition-specific factors, such as whether the trial was conducted on a treadmill (level or incline) or outdoors, as well as demographic information, such as the participant's weight or presence of walking impairment, and 2 minutes of metabolic data. We demonstrated the ability to predict steady-state metabolic rate to within an accuracy of 4.71 ± 2.7% on averageacross allwalking conditionsand patient populations, including with assistive devices andondifferent terrains. This work seeks to unlock the use of in-the-loop optimization of wearable assistive devices in individuals with limited walking capacity. A freely available MATLAB application allows other researchers to easily apply our model.

Bioelectrical Impedance Analysis to Assess Energy Expenditure in Critically Ill Patients: A Cross-Sectional Study.

Evaluating energy expenditure is important for establishing optimal goals for nutrition treatment. However, indirect calorimetry, the reference standard for measuring energy expenditure, is difficult to apply widely in clinical practice. To test the consistency of bioelectrical impedance analysis (BIA) relative to indirect calorimetry for evaluating energy expenditure in critically ill patients. A cross-sectional study of 140 critically ill adult patients was conducted. Within 24 hours of a patient being transferred to the intensive care unit, trained researchers assessed the patient's energy expenditure by use of BIA and indirect calorimetry simultaneously. Consistency of the 2 measurements was detected by intraclass correlation coefficients with a 2-way random-effects model. Factors affecting consistency were analyzed. Median energy expenditure measured by indirect calorimetry was 1430.0 kcal/d (IQR, 1190.5-1650.8 kcal/d). Median energy expenditure measured by BIA was 1407.0 kcal/d (IQR, 1248.5-1563.5 kcal/d). The correlation coefficient between indirect calorimetry and BIA was 0.813 (95% CI, 0.748-0.862; P < .001). The consistency of the 2 measurements was lower in patients with comorbidities than in those without (P = .004). Results of BIA were highly consistent with indirect calorimetry assessments of energy expenditure in critically ill patients. Few factors except comorbidity affect the accuracy of BIA when assessing energy expenditure. Therefore, as a low-cost, easy-to-use, and noninvasive method, BIA is a valuable clinical tool for assessing energy expenditure in critically ill patients.

Validity of Wearable Trackers for Measuring Steps, Energy Expenditure and Heart Rate in Korean Elderly

PURPOSE: This study aimed to assess the validity of a physical activity tracking device by comparing physical activity data from older adults, including the number of steps, energy expenditure, and heart rate (HR), with data obtained from research accelerometers, wearable watches, and mobile apps.METHODS: A total of 24 healthy older adults (aged 65-79 years) wore a wearable tracking device and used a respiratory gas analyzer while walking and running on a treadmill at speeds of 3.8 km/h, 4.8 km/h, 5.6 km/h, 6.4 km/h, and 8.0 km/h, in a randomized order. This study evaluated seven wearable devices: one research accelerometer (ActiGraph GT3X+), five smartwatches/fitness trackers (Samsung Galaxy Watch Active 2, Fitbit Versa 2, Xiaomi Mi Band 4, Garmin Forerunner 945, and Apple Watch Series 2), and one mobile application. Step counts were measured directly, HR was compared using a Polar H7 HR Sensor, and energy expenditure was assessed using a Quarkb2 respiratory gas analyzer.RESULTS: Commercial wearable watches and mobile applications were more accurate than research-grade accelerometers for monitoring steps during walking and running in older adults. HR measurements from wearable watches were more accurate than those from reference devices. Although energy expenditure measurements from mobile applications and wearable watches were less accurate than measurements of steps and HRs their accuracy was comparable to that of research-grade accelerometers.CONCLUSIONS: This study provides insights into the strengths of using wearable devices in healthcare and fitness fields, which seek to monitor and actively manage the physical activity of older adults using wearable trackers.

How to interpret and apply the results of indirect calorimetry studies: A case-based tutorial

Evidence is growing that the individual adjustment of energy targets guided by indirect calorimetry (IC) can improve outcome. With the development of a new generation of devices that are easier to use and rapid, it appears important to share knowledge and expertise that may be used to individualize nutrition care. Despite the focus of this tutorial being on one contemporary device, the principles of IC apply across existing devices and can assist tailoring the nutrition prescription and in assessing response to nutrition therapy. The present tutorial addresses its clinical application in intubated mechanically ventilated and spontaneously breathing adult patients (canopy), i.e. it covers the range from critical illness to outpatients.The cases that are presented show how the measured energy expenditure (mEE), and the respiratory quotient (RQ), i.e. the ratio of expired CO2 to consumed O2, should be applied in different cases, to adapt and individualize nutrition prescription, as it is a good marker of over- or underfeeding at the different stages of disease. The RQ also informs about the patient's body’s capacity to use different substrates: the variations of RQ indicating the metabolic changes revealing insufficient or excessive feeding. The different cases reflect the use of a new generation device as a metabolic monitor that should be combined with other clinical observations and laboratory biomarkers. The tutorial also points to some shortcomings of the method, proposing alternatives.