Perioperative estimations of oxygen consumption from LiDCO™plus-derived cardiac output and Ca-cvO2 difference: Relationship with measurements by indirect calorimetry in elderly patients undergoing major abdominal surgery.

BACKGROUND: Feasible estimations of perioperative changes in oxygen consumption (VO2) could enable larger studies of its role in postoperative outcomes. Current methods, either by reverse Fick calculations using pulmonary artery catheterisation or metabolic by breathing gas analysis, are often deemed too invasive or technically requiring. In addition, reverse Fick calculations report generally lower values of oxygen consumption. METHODS: We investigated the relationship between perioperative estimations of VO2 (EVO2), from LiDCO™plus-derived (LiDCO Ltd, Cambridge, UK) cardiac output and arterial-central venous oxygen content difference (Ca-cvO2), with indirect calorimetry (GVO2) by QuarkRMR (COSMED srl. Italy), using data collected 2017-2018 during a prospective observational study on perioperative oxygen transport in 20 patients >65 years during epidural and general anaesthesia for open pancreatic or liver resection surgery. Eighty-five simultaneous intra- and postoperative measurements at different perioperative stages were analysed for prediction, parallelity and by traditional agreement assessment. RESULTS: Unadjusted bias between GVO2 and EVO2 indexed for body surface area was 26 (95% CI 20 to 32) with limits of agreement (1.96SD) of -32 to 85 ml min-1m-2. Correlation adjusted for the bias was moderate, intraclass coefficient(A,1) 0.51(95% CI 0.34 to 0.65) [F (84,84) = 3.07, P<0.001]. There was an overall association between GVO2 and EVO2, in a random coefficient model [GVO2 = 73(95% CI 62 to 83) + 0.45(95% CI 0.29 to 0.61) EVO2 ml min-1m-2, P<0.0001]. GVO2 and EVO2 changed in parallel intra- and postoperatively when normalised to their respective overall means. CONCLUSION: Based on this data, estimations from LiDCO™plus-derived cardiac output and Ca-cvO2 are not reliable as a surrogate for perioperative VO2. Results were in line with previous studies comparing Fick-based and metabolic measurements but limited by variability of data and possible underpowering. The parallelity at different perioperative stages and the prediction model can provide useful guidance and methodological tools for future studies on similar methods in larger samples.

Nutritional Issues among Children with Duchenne Muscular Dystrophy-Incidence of Deficiency and Excess Body Mass.

The progression of Duchenne muscular dystrophy (DMD)requires the assessment of nutritional disturbances at each stage of the disease. The purpose of this study was to assess the nutritional status in various ages of boys with DMD using screening and in-depth evaluation methods. Body composition by Dual X-ray Absorptiometry (DXA), basal metabolic rate (BMR) by indirect calorimetry, a questionnaire of nutritional status-Pediatric Nutrition Screening Tool (PNST)-and laboratory parameters were performed. In the cohort of 93 boys aged 8.54 (5.9-12.6 years), inappropriate nutritional status occurred in 41.8% of boys (underweight 11.8%, overweight 16.0%, and obesity 14.0%). In the 10-13 age group, the occurrence of overweight and underweight was the highest. Based on PNST, 15.1% of patients were at nutritional risk (≥2 points)-the most in the 14-17 age group (29%). A negative correlation was identified between PNST and z-scores of body weight, BMI, and FFMI (r Spearman = -0.49, -0.46, and -0.48, respectively; p < 0.05). There were no differences between BMR results from indirect calorimetry and calculations from the Schofield formula for any age group. In obese boys, the caloric requirement in indirect calorimetry was significantly lower than that indicated by the calculations according to the Schofield formula (p < 0.028). Inappropriate nutritional status occurred in almost half of the children with DMD. The age group in which nutritional disorders were most frequently identified was 10-13 years old. PNST could be considered a tool for screening malnutrition after testing a larger group of DMD patients.

Methods of Determining Energy Expenditure in Individuals with Prader-Willi Syndrome: A Systematic Literature Review.

Prader-Willi syndrome (PWS) is a rare disorder characterised by varying nutritional phases that occur throughout the lifespan, ranging from failure to thrive to hyperphagia. If uncontrolled, the imbalance between energy intake and expenditure results in obesity development and increased morbidity and mortality risk. Although measures of energy requirements for accurate nutrition assessment are vital, the evidence appears sparse and heterogeneous; hence, the aim of this review was to examine the available literature on energy expenditure predicted or measured using various methods in individuals with PWS. Studies were sought that presented methods and results on resting energy expenditure or basal metabolic rate. A narrative synthesis was completed to present the study characteristics and results. Methods of determining energy requirements included predictive equations and indirect calorimetry. Differences amongst ages, growth hormone therapy, fasting status, and measures in which results were presented were limitations to appropriately summarising and identifying trends in energy expenditure. Indirect calorimetry was identified as the most accurate method; however, it is not widely available in all settings. Further research is encouraged to support the development of valid and reliable predictive equations that will better inform and improve the efficiency of clinical practice in supporting people with PWS.

The Prevalence of Low Energy Availability in Cross-Country Skiers during the Annual Cycle.

BACKGROUND AND OBJECTIVES: A sustained mismatch between energy intake (EI) and exercise energy expenditure (EEE) can lead to Low Energy Availability (LEA), as well as health and performance impairments characteristic of Relative Energy Deficiency in Sport (RED-S). Research in females has identified specific LEA cut-points for the risks of developing physiological and performance disturbances. Cut-points in males have yet to be evaluated; therefore, this study examined the prevalence of LEA in highly trained male cross-country skiers. The key purpose of this study was to analyze EI, resting energy expenditure (REE), EEE, and energy availability (EA) in highly trained cross-country skiers during the preparation and competition periods. The secondary objective of our study was to evaluate the relative contribution of fats and carbohydrates to EI, REE, and EEE. MATERIALS AND METHODS: EI was determined by an estimated 24 h diet recall method, REE was assessed by indirect calorimetry, and EEE was estimated from heart rate in 27 cross-country skiers. RESULTS: EI amounted to 4050 ± 797 kcal/day on a typical training day (TD) and 5986 ± 924 kcal/day (p < 0.001) on a typical competition day (CD). REE on TDs (2111 ± 294 kcal/day or 30 ± 6 kcal/day/kg) was higher (p < 0.05) than on CDs (1891 ± 504 kcal/day or 27 ± 7 kcal/day/kg). The EA in the athletes was <15 kcal∙kg FFM-1·d-1 on TDs and <65 kcal∙kg FFM-1·d-1 on CDs. EI was not optimal, as indicated by low EA throughout TDs (June). This could be associated with insufficient EI along with a high amount of EEE (3690.7 ± 485.2 kcal/day). During the transition from TD to CD, an increase in the contribution of fats to EI and EEE was observed in cross-country skiers. CONCLUSION: The conception of LEA and REDs and their potential implication for performance is underestimated among coaches and athletes. The importance of appropriate dietary strategies is essential to ensure that enough calories are consumed to support efficient training.

Comparison between Measured and Predicted Resting Metabolic Rate Equations in Cross-Training Practitioners.

This study aimed to investigate the resting metabolic rate (RMR) in cross-training practitioners (advanced and novice) using indirect calorimetry (IC) and compare it with predictive equations proposed in the scientific literature. METHODS: A cross-sectional and comparative study analyzed 65 volunteers, both sexes, practicing cross-training (CT). Anthropometry and body composition were assessed, and RMR was measured by IC (FitMate PRO®), bioimpedance (BIA-InBody 570®), and six predictive equations. Data normality was tested by the Kolgomorov-Smirnov test and expressed as mean ± standard deviation with 95% confidence intervals (CI), chi-square test was performed to verify ergogenic resources, and a Bland-Altman plot (B&A) was made to quantify the agreement between two quantitative measurements. One-way ANOVA was applied to body composition parameters, two-way ANOVA with Bonferroni post hoc was used to compare the RMR between groups, and two-way ANCOVA was used to analyze the adjusted RMR for body and skeletal muscle mass. The effect size was determined using Cohen's d considering the values adjusted by ANCOVA. If a statistical difference was found, post hoc Bonferroni was applied. The significance level was p < 0.05 for all tests. RESULTS: The main results indicated that men showed a higher RMR than women, and the most discrepant equations were Cunningham, Tinsley (b), and Johnstone compared to IC. Tinsley's (a) equation indicated greater precision in measuring the RMR in CM overestimated it by only 1.9%, and BIA and the Harris-Benedict in CW overestimated RMR by only 0.1% and 3.4%, respectively. CONCLUSIONS: The BIA and Harris-Benedict equation could be used reliably to measure the RMR of females, while Tinsley (a) is the most reliable method to measure the RMR of males when measuring with IC is unavailable. By knowing which RMR equations are closest to the gold standard, these professionals can prescribe a more assertive diet, training, or ergogenic resources. An assertive prescription increases performance and can reduce possible deleterious effects, maximizing physical sports performance.

Validity of dietary intake methods in cancer cachexia.

PURPOSE OF REVIEW: Accurate assessment of dietary intake, especially energy and protein intake, is crucial for optimizing nutritional care and outcomes in patients with cancer. Validation of dietary assessment methods is necessary to ensure accuracy, but the validity of these methods in patients with cancer, and especially in those with cancer cachexia, is uncertain. Validating nutritional intake is complex because of the variety of dietary methods, lack of a gold standard method, and diverse validation measures. Here, we review the literature on validations of dietary intake methods in patients with cancer, including those with cachexia, and highlight the gap between current validation efforts and the need for accurate dietary assessment methods in this population. RECENT FINDINGS: We analyzed eight studies involving 1479 patients with cancer to evaluate the accuracy and reliability of 24-hour recalls, food records, and food frequency questionnaires in estimating energy and protein intake. We discuss validation methods, including comparison with biomarkers, indirect calorimetry, and relative validation of dietary intake methods. SUMMARY: Few have validated dietary intake methods against objective markers in patients with cancer. While food records and 24-hour recalls show potential accuracy for energy and protein intake, this may be compromised in hypermetabolic patients. Additionally, under- and overreporting of intake may be less frequent, and the reliability of urinary nitrogen as a protein intake marker in patients with cachexia needs further investigation. Accurate dietary assessment is important for enhancing nutritional care outcomes in cachexia trials, requiring validation at multiple time points throughout the cancer trajectory.

Physiological response during exercise is preserved in children exposed to a suboptimal gestational environment.

OBJECTIVE: This study investigated whether exposure to suboptimal gestational factors (SGFs) alters mechanical efficiency (ME) and substrate oxidation during rest and exercise in children as a mechanism contributing to obesity. METHODS: Data from the Quebec Adiposity and Lifestyle Investigation in Youth cohort were used. Children aged 8 to 10 years performed an incremental maximal cycling test with indirect calorimetry. Their ME was measured during submaximal and maximal effort. The substrate oxidation during rest and submaximal effort was also computed. ME and substrate oxidation results between children exposed to each SGF during pregnancy (gestational diabetes mellitus: n = 68; hypertensive disorders: n = 49; maternal smoking: n = 77) and nonexposed children (n = 370) were compared. RESULTS: No difference was observed for ME during submaximal (F[3,540] = 0.46, p = 0.713) and maximal effort (F[3,545] = 0.86, p = 0.463) between exposed and nonexposed children. The percentage contributions of lipids and carbohydrates did not differ during rest (F[3,545] =1.68, p = 0.169) or submaximal exercise (F[3,544] = 0.31, p = 0.534) between exposed and nonexposed children. CONCLUSIONS: Children exposed to investigated SGFs display a similar physiological response regarding ME and substrate oxidation during rest and exercise compared to nonexposed children. Future studies should confirm these novel results and continue investigating other research avenues to explain the higher risk of obesity in this population.

Daily energy expenditure and water turnover in female netball players from the Netball Super League: A doubly labeled water observation study.

To establish the criterion-assessed energy and fluid requirements of female netball players, 13 adult players from a senior Netball Super League squad were assessed over 14 days in a cross-sectional design, representing a two- and one-match microcycle, respectively. Total energy expenditure (TEE) and water turnover (WT) were measured by doubly labeled water. Resting and activity energy expenditure were measured by indirect calorimetry and Actiheart, respectively. Mean 14-day TEE was 13.46 ± 1.20 MJ day-1 (95% CI, 12.63-14.39 MJ day-1). Resting energy expenditure was 6.53 ± 0.60 MJ day-1 (95% CI, 6.17-6.89 MJ day-1). Physical activity level was 2.07 ± 0.19 arbitrary units (AU) (95% CI, 1.95-2.18 AU). Mean WT was 4.1 ± 0.9 L day-1 (95% CI, 3.6-4.7 L day-1). Match days led to significantly greater TEE than training (+2.85 ± 0.70 MJ day-1; 95% CI, +1.00- +4.70 MJ day-1; p = 0.002) and rest (+4.85 ± 0.70 MJ day-1; 95% CI, +3.13-+6.56 MJ day-1; p < 0.001) days. Matches led to significantly greater energy expenditure (+1.85 ± 1.27 MJ; 95% CI, +0.95-+2.76 MJ day-1; p = 0.001) than court-based training sessions. There was no significant difference in TEE (+0.03 ± 0.35 MJ day-1; 95% CI, -0.74-+0.80 MJ day-1; p = 0.936) across weeks. Calibrated Actiheart 5 monitors underestimated TEE (-1.92 ± 1.21 MJ day-1). Energy and fluid turnover were greatest on match days, followed by training and rest days, with no difference across weeks. This study provides criterion-assessed energy and fluid requirements to inform dietary guidance for female netball players.

Evaluating Airflow Sensor Methods: Precision in Indirect Calorimetry.

This study assesses the impact of three volumetric gas flow measurement methods-turbine (fT); pneumotachograph (fP), and Venturi (fV)-on predictive accuracy and precision of expired gas analysis indirect calorimetry (EGAIC) across varying exercise intensities. Six males (Age: 38 ± 8 year; Height: 178.8 ± 4.2 cm; V ̇ O 2 peak $$ \dot{V}{\mathrm{O}}_2\mathrm{peak} $$ : 42 ± 2.8 mL O2 kg-1 min-1) and 14 females (Age = 44.6 ± 9.6 year; Height = 164.6 ± 6.9 cm; V ̇ O 2 peak $$ \dot{V}{\mathrm{O}}_2\mathrm{peak} $$ = 45 ± 8.6 mL O2 kg-1 min-1) were recruited. Participants completed physical exertion on a stationary cycle ergometer for simultaneous pulmonary minute ventilation ( V ̇ $$ \dot{V} $$ ) measurements and EGAIC computations. Exercise protocols and subsequent conditions involved a 5-min cycling warm-up at 25 W min-1, incremental exercise to exhaustion ( V ̇ O 2 $$ \dot{V}{\mathrm{O}}_2 $$ ramp test), then a steady-state exercise bout induced by a constant Watt load equivalent to 80% ventilatory threshold (80% VT). A linear mixed model revealed that exercise intensity significantly affected V ̇ O 2 $$ \dot{V}{\mathrm{O}}_2 $$ measurements (p < 0.0001), whereas airflow sensor method (p = 0.97) and its interaction with exercise intensity (p = 0.91) did not. Group analysis of precision yielded a V ̇ O 2 $$ \dot{V}{\mathrm{O}}_2 $$ CV % = 21%; SEM = 5 mL O2 kg-1 min-1. Intra- and interindividual analysis of precision via Bland-Altman revealed a 95% confidence interval (CI) precision benchmark of 3-5 mL kg-1 min-1. Agreement among methods decreased at power outputs eliciting V ̇ $$ \dot{V} $$ up to 150 L min-1, indicating a decrease in precision and highlighting potential challenges in interpreting biological variability, training response heterogeneity, and test-retest comparisons. These findings suggest careful consideration of airflow sensor method variance across metabolic cart configurations.

Validity and reliability of a new whole room indirect calorimeter to assess metabolic response to small calorie loads.

We overview of our whole room indirect calorimeter (WRIC), demonstrate validity and reliability of our WRIC, and explore a novel application of Bayesian hierarchical modeling to assess responses to small carbohydrate loads. To assess WRIC validity seven gas infusion studies were performed using a gas blender and profiles designed to mimic resting and postprandial metabolic events. Sixteen participants underwent fasting and postprandial measurements, during which they consumed a 75-kcal drink containing sucrose, dextrose, or fructose in a crossover design. Linear mixed effects models were used to compare resting and postprandial metabolic rate (MR) and carbohydrate oxidation. Postprandial carbohydrate oxidation trajectories for each participant and condition were modeled using Bayesian Hierarchical Modeling. Mean total error in infusions were 1.27 ± 0.67% and 0.42 ± 0.70% for VO2 and VCO2 respectively, indicating a high level of validity. Mean resting MR was similar across conditions ([Formula: see text] = 1.05 ± 0.03 kcal/min, p = 0.82, ICC: 0.91). While MR increased similarly among all conditions (~13%, p = 0.29), postprandial carbohydrate oxidation parameters were significantly lower for dextrose compared with sucrose or fructose. We provide evidence validating our WRIC and a novel application of statistical methods useful for research using WRIC.

Resting energy expenditure in women with polycystic ovary syndrome.

STUDY QUESTION: Is resting energy expenditure (REE) altered in women with polycystic ovary syndrome (PCOS)? SUMMARY ANSWER: Women with PCOS have a reduction in REE, when corrected for fat-free mass, independent of PCOS clinical phenotypes and BMI categories. WHAT IS KNOWN ALREADY: Obesity is an important issue in women with PCOS, in terms of frequency and pathophysiological implications. It has been hypothesized that obesity may be favoured by alterations in REE, but the studies have been limited and conflicting. STUDY DESIGN, SIZE, DURATION: This case-control study was a comparison of 266 women with PCOS and 51 healthy controls, recruited in the Verona 3P study from 2010 to 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS: Women with PCOS diagnosed by the Rotterdam criteria, with normal thyroid function and no interfering medications, were referred to the outpatient clinic of a tertiary care centre of endocrinology and metabolism for a measurement of REE. Healthy controls were recruited in the same period and submitted to the same procedure. In all subjects, REE was measured by indirect calorimetry and serum androgens were measured by LC-MS/MS. In women with PCOS, insulin sensitivity was assessed using the hyperinsulinemic-euglycemic clamp. MAIN RESULTS AND THE ROLE OF CHANCE: REE was similar in women with PCOS and controls. However, REE corrected for fat-free mass (REE/FFM) was significantly lower in women with PCOS than in controls (31.8 ± 4.0 vs 35.4 ± 3.9 kcal/kgFFM·day, P < 0.001). REE/FFM did not differ between normal-weight, overweight, or obese women with PCOS, and each of these subgroups showed lower REE/FFM values than controls. Reduced REE/FFM values were found in each phenotype of the syndrome. In multiple regression analysis, REE/FFM was independently associated with age and PCOS status, but not with fat mass. In PCOS women, REE/FFM was independently and directly associated with ovarian follicle number. LIMITATIONS, REASONS FOR CAUTION: Limitations of the study are the cross-sectional design, which limits the causal inference of the results, and the unavailability of precise information about lifestyle factors, which may be potential confounders. Further prospective studies are needed to establish the importance of this phenomenon in contributing to the weight excess of PCOS. WIDER IMPLICATIONS OF THE FINDINGS: A reduction of REE could potentially favour weight gain in women with PCOS and possibly contribute to the altered metabolic profile typical of this condition, even counteracting the therapeutic strategies aimed to reduce excess body fat in these women. Nevertheless, the presence of this abnormality in both obese/overweight and normal-weight patients suggests that other factors must play a role in this phenomenon. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by academic grants to PM from the University of Verona (FUR 2010-2022). All authors declare no conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

Effects of Energy Delivery Guided by Indirect Calorimetry in Critically Ill Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: The utility of using indirect calorimetry (IC) to estimate energy needs and methods for its application to this purpose remain unclear. This systematic review investigated whether using IC to estimate energy expenditure in critically ill patients is more meaningful for improving survival than other estimation methods. METHODS: Comprehensive searches were conducted in MEDLINE using PubMed, Cochrane Central Register of Controlled Trials, and Igaku-Chuo-Zasshi up to March 2023. RESULTS: Nine RCTs involving 1178 patients were included in the meta-analysis. The evidence obtained suggested that energy delivery by IC improved short-term mortality (risk ratio, 0.86; 95% confidence interval [CI], 0.70 to 1.06). However, the use of IC did not appear to affect the length of ICU stay (mean difference [MD], 0.86; 95% CI, -0.98 to 2.70) or the duration of mechanical ventilation (MD, 0.66; 95% CI, -0.39 to 1.72). Post hoc analyses using short-term mortality as the outcome found no significant difference by target calories in resting energy expenditure, whereas more frequent IC estimates were associated with lower short-term mortality and were more effective in mechanically ventilated patients. CONCLUSIONS: This updated meta-analysis revealed that the use of IC may improve short-term mortality in patients with critical illness and did not increase adverse events.

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.

The use of accelerometers to improve estimation of the thermic effect of food in whole room calorimetry studies.

We tested whether spontaneous physical activity (SPA) from accelerometers could be used in a whole room calorimeter to estimate thermic effect of food (TEF). Eleven healthy participants (n = 7 females; age: 27 ± 4 yr; body mass index: 22.8 ± 2.6 kg/m2) completed two 23-h visits in randomized order: one "fed" with meals provided and one "fasted" with no food. SPA was measured by ActivPAL and Actigraph accelerometers. Criterion TEF was calculated as the difference in total daily energy expenditure (TDEE) between fed and fasted visits and compared with three methods of estimating TEF: 1) SPA-adjusted TEF (adjTEF)-difference in TDEE without SPA between visits, 2) Wakeful TEF-difference in energy expenditure obtained from linear regression and basal metabolic rate during waking hours, 3) 24-h TEF-increase in TDEE above SPA and sleeping metabolic rate. Criterion TEF was 9.4 ± 4.5% of TDEE. AdjTEF (difference in estimated vs. criterion TEF: activPAL: -0.3 ± 3.3%; Actigraph: -1.8 ± 8.0%) and wakeful TEF (activPAL: -0.9 ± 6.1%; Actigraph: -2.8 ± 7.6%) derived from both accelerometers did not differ from criterion TEF (all P > 0.05). ActivPAL-derived 24-h TEF overestimated TEF (6.8 ± 5.4%, P = 0.002), whereas Actigraph-derived 24-h TEF was not significantly different (4.3 ± 9.4%, P = 0.156). TEF estimations using activPAL tended to show better individual-level agreement (i.e., smaller coefficients of variation). Both accelerometers can be used to estimate TEF in a whole room calorimeter; wakeful TEF using activPAL is the most viable option given strong group-level accuracy and reasonable individual agreement.NEW & NOTEWORTHY Two research-grade accelerometers can effectively estimate spontaneous physical activity and improve the estimation of thermic effect of food (TEF) in whole room calorimeters. The activPAL demonstrates strong group-level accuracy and reasonable individual-level agreement in estimating wakeful TEF, whereas a hip-worn Actigraph is an acceptable approach for estimating 24-h TEF. These results highlight the promising potential of accelerometers in advancing energy balance research by improving the assessment of TEF within whole room calorimeters.

Resting energy expenditure differs among individuals with different levels of perceived thermal sensitivity: A cross-sectional study.

Metabolic rate has been used in thermophysiological models for predicting the thermal response of humans. However, only a few studies have investigated the association between an individual's trait-like thermal sensitivity and resting energy expenditure (REE), which resulted in inconsistent results. This study aimed to explore the association between REE and perceived thermal sensitivity. The REE of healthy adults was measured using an indirect calorimeter, and perceived thermal intolerance and sensation in the body were evaluated using a self-administered questionnaire. In total, 1567 individuals were included in the analysis (women = 68.9%, age = 41.1 ±â€…13.2 years, body mass index = 23.3 ±â€…3.3 kg/m2, REE = 1532.1 ±â€…362.4 kcal/d). More women had high cold intolerance (31.8%) than men (12.7%), and more men had high heat intolerance (23.6%) than women (16.1%). In contrast, more women experienced both cold (53.8%) and heat (40.6%) sensations in the body than men (cold, 29.1%; heat, 27.9%). After adjusting for age, fat-free mass, and fat mass, lower cold intolerance, higher heat intolerance, and heat sensation were associated with increased REE only in men (cold intolerance, P for trend = .001; heat intolerance, P for trend = .037; heat sensation, P = .046), whereas cold sensation was associated with decreased REE only in women (P = .023). These findings suggest a link between the perceived thermal sensitivity and REE levels in healthy individuals.

Indirect calorimetry directed feeding and cycling in the older ICU population: a pilot randomised controlled trial.

BACKGROUND: Older critically ill patients experience rapid muscle loss during stay in an intensive care unit (ICU) due to physiological stress and increased catabolism. This may lead to increased ICU length of stay, delayed weaning from ventilation and persistent functional limitations. We hypothesized that with optimal nutrition and early physical therapy acting in synergism, we can reduce muscle mass loss and improve functional outcomes. METHODS: This was a prospective, single blinded randomized, controlled single-center pilot study to compare the lean muscle mass (measured at bilateral quadriceps femoris using ultrasound) of older ICU patients at 4 time points over 14 days between the control and intervention groups. The control group received standard weight-based empiric feeding and standard ICU physiotherapy. The intervention group received indirect calorimetry directed feeding adjusted daily and 60 min per day of cycle ergometry. 21 patients were recruited and randomized with 11 patients in the control arm and 10 patients in the intervention arm. Secondary outcome measures included ICU and hospital mortality, length of stay, functional assessments of mobility and assessment of strength. RESULTS: Median age was 64 in the control group and 66 in the intervention group. Median calories achieved was 24.5 kcal/kg per day in the control group and 23.3 kcal/kg per day in the intervention group. Cycle ergometry was applied to patients in the intervention group for a median of 60 min a day and a patient had a median of 8.5 sessions in 14 days. Muscle mass decreased by a median of 4.7cm2 in the right quadriceps femoris in the control group and 1.8cm2 in the intervention group (p = 0.19), while the left quadriceps femoris decreased by 1.9cm2 in the control group and 0.1cm2 in the intervention group (p = 0.51). CONCLUSION: In this pilot study, we found a trend towards decrease muscle loss in bilateral quadriceps femoris with our combined interventions. However, it did not reach statistical significance likely due to small number of patients recruited in the study. However, we conclude that the intervention is feasible and potentially beneficial and may warrant a larger scale study to achieve statistical significance. TRIAL REGISTRATION: This study was registered on Clinicaltrials.gov on 30th May 2018 with identifier NCT03540732.

Prediction of resting energy expenditure for adolescents with severe obesity: A multi-centre analysis.

BACKGROUND AND OBJECTIVES: Resting energy expenditure (REE) assessments can help inform clinical treatment decisions in adolescents with elevated body mass index (BMI), but current equations are suboptimal for severe obesity. We developed a predictive REE equation for youth with severe obesity and obesity-related comorbidities and compared results to previously published predictive equations. METHODS: Data from indirect calorimetry, clinical measures, and body composition per Dual x-ray absorptiometry (DXA) were collected from five sites. Data were randomly divided into development (N = 438) and validation (N = 118) cohorts. A predictive equation was developed using Elastic Net regression, using sex, race, ethnicity, weight, height, BMI percent of the 95th%ile (BMIp95), waist circumference, hip circumference, waist/hip ratio, age, Tanner stage, fat and fat-free mass. This equation was verified in the validation cohort and compared with 11 prior equations. RESULTS: Data from the total cohort (n = 556, age 15 ± 1.7 years, 77% female, BMIp95 3.3 ± 0.94) were utilized. The best fit equation was REE = -2048 + 18.17 × (Height in cm) - 2.57 × (Weight in kg) + 7.88 × (BMIp95) + 189 × (1 = male, 0 = female), R2 = 0.466, and mean bias of 23 kcal/day. CONCLUSION: This new equation provides an updated REE prediction that accounts for severe obesity and metabolic complications frequently observed in contemporary youth.

Development and validation of a smartwatch algorithm for differentiating physical activity intensity in health monitoring.

To develop and validate a machine learning based algorithm to estimate physical activity (PA) intensity using the smartwatch with the capacity to record PA and determine outdoor state. Two groups of participants, including 24 adults (13 males) and 18 children (9 boys), completed a sequential activity trial. During each trial, participants wore a smartwatch, and energy expenditure was measured using indirect calorimetry as gold standard. The support vector machine algorithm and the least squares regression model were applied for the metabolic equivalent (MET) estimation using raw data derived from the smartwatch. Exercise intensity was categorized based on MET values into sedentary activity (SED), light activity (LPA), moderate activity (MPA), and vigorous activity (VPA). The classification accuracy was evaluated using area under the ROC curve (AUC). The METs estimation accuracy were assessed via the mean absolute error (MAE), the correlation coefficient, Bland-Altman plots, and intraclass correlation (ICC). A total of 24 adults aged 21-34 years and 18 children aged 9-13 years participated in the study, yielding 1790 and 1246 data points for adults and children respectively for model building and validation. For adults, the AUC for classifying SED, MVPA, and VPA were 0.96, 0.88, and 0.86, respectively. The MAE between true METs and estimated METs was 0.75 METs. The correlation coefficient and ICC were 0.87 (p < 0.001) and 0.89, respectively. For children, comparable levels of accuracy were demonstrated, with the AUC for SED, MVPA, and VPA being 0.98, 0.89, and 0.85, respectively. The MAE between true METs and estimated METs was 0.80 METs. The correlation coefficient and ICC were 0.79 (p < 0.001) and 0.84, respectively. The developed model successfully estimated PA intensity with high accuracy in both adults and children. The application of this model enables independent investigation of PA intensity, facilitating research in health monitoring and potentially in areas such as myopia prevention and control.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults.

BACKGROUND: Predicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults. OBJECTIVES: To generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y. METHODS: TEE derived from "gold standard" methods from n = 1657 (n = 1019 females, age range 65-90 y), was used to generate PAL values. PAL ranged 1.28-2.05 for males and 1.26-2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations. RESULTS: Equations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = -5680.17 - 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = -5290.72 - 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%. CONCLUSIONS: These new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.