Measurement Of Expenditure Research Articles

Evaluating the effects of continuous veno-venous hemodiafiltration on O2 and CO2 removal and energy expenditure measurement using indirect calorimetry

BackgroundContinuous veno-venous hemodiafiltration (CVVHDF) is used in critically ill patients, but its impact on O₂ and CO₂ removal, as well as the accuracy of resting energy expenditure (REE) measurement using indirect calorimetry (IC) remains unclear. This study aims to evaluate the effects of CVVHDF on O₂ and CO₂ removal and the accuracy of REE measurement using IC in patients undergoing continuous renal replacement therapy. DesignProspective, observational, single-center study.MethodologyPatients with sepsis undergoing CVVHDF had CO₂ flow (QCO₂) and O₂ flow (QO₂) measured at multiple sampling points before and after the filter. REE was calculated using the Weir equation based on V̇CO₂ and V̇O₂ measured by IC, using true V̇CO₂ accounting for the CRRT balance, and estimated using the Harris-Benedict equation. The respiratory quotient (RQ), the ratio of V̇CO₂ to V̇O₂, was evaluated by comparing measured and true values.ResultsThe mean QCO₂ levels measured upstream of the filter were 76.26 ± 17.33 ml/min and significantly decreased to 62.12 ± 13.64 ml/min downstream of the filter (p < 0.0001). The mean QO₂ levels remained relatively unchanged. The mean true REE was 1774.28 ± 438.20 kcal/day, significantly different from both the measured REE of 1758.59 ± 434.06 kcal/day (p = 0.0029) and the estimated REE of 1619.36 ± 295.46 kcal/day (p = 0.0475). The mean measured RQ value was 0.693 ± 0.118, while the mean true RQ value was 0.731 ± 0.121, with a significant difference (p < 0.0001).ConclusionsCVVHDF may significantly alter QCO₂ levels without affecting QO₂, influencing the REE and RQ results measured by IC. However, the impact on REE is not clinically significant, and the REE value obtained via IC is closer to the true REE than that estimated using the Harris-Benedict equation. Further studies are recommended to confirm these findings.Graphical

Accuracy of the Huawei GT2 Smartwatch for Measuring Physical Activity and Sleep Among Adults During Daily Life: Instrument Validation Study.

Smartwatches are increasingly popular for physical activity and health promotion. However, ongoing validation studies on commercial smartwatches are still needed to ensure their accuracy in assessing daily activity levels, which is important for both promoting activity-related health behaviors and serving research purposes. This study aimed to evaluate the accuracy of a popular smartwatch, the Huawei Watch GT2, in measuring step count (SC), total daily activity energy expenditure (TDAEE), and total sleep time (TST) during daily activities among Chinese adults, and test whether there are population differences. A total of 102 individuals were recruited and divided into 2 age groups: young adults (YAs) and middle-aged and older (MAAO) adults. Participants' daily activity data were collected for 1 week by wearing the Huawei Watch GT2 on their nondominant wrist and the Actigraph GT3X+ (ActiGraph) on their right hip as the reference measure. The accuracy of the GT2 was examined using the intraclass correlation coefficient (ICC), Pearson product-moment correlation coefficient (PPMCC), Bland-Altman analysis, mean percentage error, and mean absolute percentage error (MAPE). The GT2 demonstrated reasonable agreement with the Actigraph, as evidenced by a consistency test ICC of 0.88 (P<.001) and an MAPE of 25.77% for step measurement, an ICC of 0.75 (P<.001) and an MAPE of 33.79% for activity energy expenditure estimation, and an ICC of 0.25 (P<.001) and an MAPE of 23.29% for sleep time assessment. Bland-Altman analysis revealed that the GT2 overestimated SC and underestimated TDAEE and TST. The GT2 was better at measuring SC and TDAEE among YAs than among MAAO adults, and there was no significant difference between these 2 groups in measuring TST (P=.12). The Huawei Watch GT2 demonstrates good accuracy in step counting. However, its accuracy in assessing activity energy expenditure and sleep time measurement needs further examination. The GT2 demonstrated higher accuracy in measuring SC and TDAEE in the YA group than in the MAAO group. However, the measurement errors for TST did not differ significantly between the 2 age groups. Therefore, the watch may be suitable for monitoring several key parameters (eg, SC) of daily activity, yet caution is advised for its use in research studies that require high accuracy.

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

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. 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. 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. 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. 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.

Federally Qualified Health Centers and Performance of Medicare Accountable Care Organizations

Federally qualified health centers (FQHCs) have increasingly participated in the Medicare Shared Savings Program (MSSP) accountable care organizations (ACOs), one of the most widespread value-based programs. Although FQHCs may strengthen ACOs' ability to provide affordable care to diverse Medicare beneficiaries, evidence on ACOs' performance by FQHC participation is limited. To compare beneficiary characteristics, utilization, expenditure, and quality between ACOs with and without FQHC participation and assess changes in ACO performance after including first FQHCs. Using MSSP public use files, this cross-sectional study compared performance of ACOs that always had FQHC participation with ACOs that never had FQHC participation from January 1, 2016, to December 31, 2022, supplemented with staggered difference-in-differences analyses of ACOs' first-time inclusion of FQHCs on performance measures. Data analysis was performed from December 1, 2023, to February 29, 2024. Participation of FQHCs in the MSSP. Measures of ACO-assigned beneficiaries, utilization, expenditure, and quality per ACO-year. Among 752 ACOs in the descriptive analysis, 140 ACOs always had at least 1 FQHC participant, whereas 612 ACOs never had FQHC participants. Compared with ACOs that never had FQHC participation, those that always had FQHC participation provided care to more socioeconomically disadvantaged beneficiaries (mean [SD] with dual eligibility, 2035.8 [2110.6] vs 1040.9 [1084.2] person-years; with disability, 3341.1 [3474.9] vs 1705.1 [1664.9] person-years; in racial and ethnic minoritized groups, 3690.6 [4118.4] vs 2515.1 [2762.9] person-years), with fewer primary care visits (mean [SD], 9956.6 [1926.3] vs 10 858.8 [2383.4] per 1000 person-years), more emergency department visits (mean [SD], 771.6 [190.9] vs 657.2 [160.0] per 1000 person-years), and lower levels of several quality measures. In the difference-in-differences analysis, 43 ACOs included FQHCs for the first time. Including first FQHCs was associated with increases of 872.9 dual-eligible (95% CI, 345.9-1399.8), 1137.6 disability (95% CI, 390.1-1885.1), and 1350.8 racial and ethnic minority (95% CI, 447.4-2254.1) person-years, with increases in rates of influenza immunization (5.9 percentage points [pp]; 95% CI, 1.4-10.4 pp), tobacco screening and cessation intervention (11.8 pp; 95% CI, 3.7-20.0 pp), and depression screening and follow-up (8.9 pp; 95% CI, 0.5-17.4 pp). No associations were observed between FQHC inclusion and utilization or expenditure. In this repeated cross-sectional study, MSSP ACOs with FQHC participation served more socioeconomically disadvantaged Medicare beneficiaries than those without FQHC participation. The inclusion of first FQHCs was associated with increased rates of several preventive services without increasing costs. Participation of safety net practices appeared to improve access to ACOs among beneficiaries from underserved communities.

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.

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.

Impact of fiscal spending, financial inclusion on financial stability

The existing literature requires further comprehensive study on the relationship among fiscal expenditure, financial inclusion, and financial stability. This empirical study analyzes the impact of fiscal expenditure and financial inclusion on financial stability using panel data from 31 Chinese provinces (autonomous regions and municipalities directly under the central government) from 2011 to 2022. The study findings suggest that fiscal expenditures, financial inclusion, and technological development all improve financial stability. This study serves as a reference for designing fiscal expenditure measures and improving financial stability in the region.

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.

Adaptation of the Foster-Greer-Thorbecke poverty measures for the measurement of catastrophic health expenditures.

In this paper we provide an adaptation of the Foster-Greer-Thorbecke (FGT) family of poverty measures for the measurement and analysis of catastrophic health expenditure (CHE). The adaptation entails introducing the FGT-type family of CHE measures with a single CHE aversion parameter whose value can be increased to put greater emphasis on the health expenditure proportions that overshoot the prescribed threshold proportions for CHE characterization by the greatest margins. The subgroup decomposition property of the FGT-type family of CHE measures (i.e., the ability to isolate the contributions of the various mutually exclusive population subgroups to the overall FGT-type CHE measure) is discussed along with other normative properties. We also show how the estimation and subgroup decomposition of the FGT-type family of CHE measures can be conveniently accomplished using ordinary least squares regression. An illustrative example is also provided to show how the FGT approach can provide valuable insights into the distribution of CHE among the healthcare spending units that incur CHE.

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

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.

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.

The impact of financial inclusion on rural–urban households welfare inequality in Ghana: A decomposition analysis

AbstractThis article examined the impact of financial inclusion on rural–urban households' welfare inequality in Ghana using nationally representative survey data sourced from the seventh round of the Ghana Living Standard Survey. The article used a multidimensional financial exclusion index and the real per adult equivalent measures of total annual household consumption expenditure as measures of financial inclusion and welfare, respectively. The article employed the instrumental variable estimation technique to account for the issues of endogeneity and sample selectivity bias associated with measuring financial inclusion. The estimated results from recentered influenced function‐Ordinary Least Squares of the unconditional quantile regression and the Oaxaca‐Blinder techniques show significant rural–urban welfare gaps in favor of urban households at the mean and across the selected quantiles. The study also found that differences in financial inclusion between rural and urban households contribute significantly to the welfare gaps, while the economic returns to financial inclusion reduce the welfare gaps both at the mean and the selected quantiles. The study recommends that policies aimed at improving financial inclusion as well as strengthening financial institutions in rural areas be put in place to help reduce the rural–urban welfare inequality in Ghana.

Abnormal hedonic process in patients with stable schizophrenia: Relationships to negative symptoms and social functioning

BackgroundAnhedonia is a deficit of dynamic reward process, and a large proportion of schizophrenia patients continue to experience anhedonia even during the stable phase. However, few studies have examined the multiple aspects of performance in reward processing in patients with stable schizophrenia and evidence suggests that physical and cognitive effort may involve different neural mechanisms. MethodsParallel measures of effort-based expenditure for reward tasks (EEfRT) and self-report questionnaires of pleasure were applied in 61 patients with stable schizophrenia (SSZ) and 46 healthy controls (HCs), and percentages of hard task choices (HTC%) were used to assess motivation in reward processing. Negative symptoms, neurocognitive and social function were evaluated in SSZ patients, and associations with performance in reward tasks were explored. ResultsSSZ patients reported more severe consummatory and anticipatory anhedonia and social anhedonia. HTC% in reward tasks of SSZ patients were significantly lower than that of HCs, especially in cognitive-effort tasks. HTC% in cognitive tasks were correlated with motivation and pleasure dimension of negative symptoms, whereas HTC% in physical tasks were associated with expression dimension. Anticipatory anhedonia and negative symptoms were correlated with Personal and Social Performance Scale (PSP) scores. ConclusionPatients with stable schizophrenia have social anhedonia, physically consummatory and anticipatory anhedonia and reduced reward motivation. They are less willing to make cognitive effort than physical effort for reward. The different associations of physical and cognitive effort with negative symptoms indicate physical and cognitive effort may represent disparate neuropsychological processes. Anticipatory anhedonia is closely related to social functioning.

Measurement of Energy Expenditure Using a Triaxial Accelerometer and Estimation of Energy Intake from Dietary Records During Decathlon Competition Days

Background: Energy requirement (ER) for a competition day depends on the amount of energy consumed. Planning energy intake strategies is particularly important for decathlon athletes, who compete in track and field events over two days. However, few studies have reported how decathletes manage their energy intake. The aim of this study was to estimate the total energy intake (TEI) and total energy expenditure (TEE) of decathletes during competition days, considering with specific factors related to energy balance (EB). Methods: Eight athletes were analyzed who completed the decathlon in official track and field events. The TEI was calculated using photographs of all the food and beverages consumed by the athlete. TEE was estimated using a triaxial accelerometer-based method. The EB was calculated by the difference between TEI and TEE. Results: Over the two competition days (48 hours), the TEE for decathlon athletes was 7,984±202 kcal, with a negative EB observed on Day 2. The physical activity level (PAL) exceeded 2.3 on each day of the competition. EB was more strongly associated with TEI/kg than with TEE/kg, and was negative on Day 2. Conclusion: Competition days for decathlon athletes involve high intensity exercise. On Day 2 of the competition and the following competition day, there might be an energy deficiency. It is necessary to focus on strategies for energy intake after the competition ends in the future.

Body Composition and Dietary Intake Profiles of Elite Iranian Swimmers and Water Polo Athletes.

This study aimed to conduct a detailed and comparative analysis of body composition and dietary habits in elite swimming and water polo athletes. Through the examination of these key parameters, this study seeks to compare the dietary intake of these two distinct aquatic sports disciplines. A total of 10 top-level swimmers and 13 water polo athletes participated in anthropometric and body composition assessments, as well as a detailed analysis of nutritional intake. To compare the two groups, an independent samples t-test was used, and variance homogeneity was checked using Levene's test. The effect size of the group differences was evaluated using Hedges' g. Water polo athletes showed significantly greater height (189.4 ± 2.9 vs. 186.5 ± 2.0 cm, p = 0.013), body mass index (24.3 ± 1.4 vs. 22.1 ± 0.5 kg/m2, p < 0.001), fat-free mass (62.9 ± 1.4 vs. 61.1 ± 1.38 kg, p < 0.001), skeletal muscle mass (47.1 ± 1.3 vs. 43.9 ± 1.6 kg, p < 0.001), and overall weight (86.9 ± 6.9 vs. 76.7 ± 2.2 kg, p < 0.001) in comparison to swimmers. Swimmers consumed greater amounts of mean daily energy (60.0 ± 1.0 vs. 39.0 ± 1.0 kcal/kg, p < 0.001), carbohydrate (7.8 ± 0.3 vs. 4.4 ± 0.5 g/kg, p < 0.001), protein (1.7 ± 0.5 vs. 1.4 ± 0.5 g/kg, p < 0.001), and fat (2.4 ± 0.5 vs. 1.7 ± 0.5 g/kg, p < 0.001) compared to water polo athletes. Our findings highlight the need for differentiated targeted nutritional interventions to enhance athletic performance in different types of water sports. Compared to water polo athletes, swimmers consumed significantly higher amounts of calories, matching their increased calorie demand from their specific training regime. However, this is an observational study and the differential needs of energy and macronutrients in water sports should be confirmed by studies with energy expenditure measurements.

Dietary macronutrient composition and its effect on 24-h substrate oxidation: A study of diurnal variations in carbohydrate and fat intake

IntroductionIn recent times, the complexity of food styles and meal content has increased, leading to significant variations in macronutrient composition between meals. This has coincided with a rise in obesity rates. ObjectiveTo determine if a larger variation in macronutrient composition between meals results in lower fat oxidation. MethodsA cross-over study was conducted on 13 healthy young male individuals using whole-body indirect calorimetry to test energy metabolic responses under three conditions: regular meals (R), high-carbohydrate breakfast (CB), or high-fat breakfast (FB), each with different macronutrient contents. R included three meals daily with the same macronutrient composition. CB included a high-carbohydrate meal at breakfast, a high-fat meal at lunch, and a high-carbohydrate meal at dinner. FB included a high-fat meal at breakfast, a high-carbohydrate meal at lunch, and a high-carbohydrate meal at dinner. The daily macronutrient compositions were similar across the three conditions, except CB and FB had larger variations in carbohydrate and fat balance between meals than R. The participants were tested in random order. ResultsThere were no significant differences in the measures of energy expenditure among the three conditions. However, after adjusting for the sleeping respiratory quotient (RQ) on the preceding day, the 24-hour RQ was lower under FB than under R (β=−0.0087, P=0.0077) or CB conditions (β=−0.0078, P=0.016). There was no difference in the magnitude of the 5-hour RQ change from lunch to dinner under the CB condition and in the magnitude of change from breakfast to lunch under the FB condition. ConclusionsA large variation in carbohydrate and fat balance between meals does not decrease daily fat oxidation. A FB may increase daily fat oxidation compared to a CB when the daily food quotient is equal, but this impact may not involve radical acceleration of fat oxidation.

Body Composition and Energy Expenditure in Youth With Spina Bifida: Protocol for a Multisite, Cross-Sectional Study.

Obesity prevalence in youth with spina bifida is higher than in their typically developing peers. Obesity is associated with lifelong medical, psychological, and economic burdens. Successful prevention or treatment of obesity in individuals with spina bifida is compromised by (1) the lack of valid and reliable methods to identify body fat in a clinical setting and (2) limited data on energy expenditure that are necessary to provide daily caloric recommendations. The objectives of this study will be to develop 2 algorithms for use in youth with spina bifida in a clinical setting, one to model body fat and one to predict total daily energy expenditure. In addition, physical activity and dietary intake will be described for the sample. This multisite, prospective, national clinical study will enroll 232 youth with myelomeningocele aged 5 to 18 years (stratified by age and mobility). Participants will be enrolled for 1 week. Data obtained include 4 measures of body composition, up to 5 height measures, a ramped activity protocol, and a nutrition and physical activity screener. Participants will wear an accelerometer for the week. On the final study day, 2 samples of urine or saliva, which complete the doubly labeled water protocol, will be obtained. The analysis will include descriptive statistics, Bland-Altman plots, concordance correlation, and regression analysis. The study received extramural federal funding in July 2019. Data collection was initiated in March 2020. As of April 2024, a total of 143 (female participants: n=76, 53.1%; male participants: n=67, 46.9%) out of 232 participants have been enrolled. Data collection is expected to continue throughout 2024. A no-cost extension until November 2025 will be requested for data analysis and dissemination of findings. This study furthers previous pilot work that confirmed the acceptability and feasibility of obtaining alternate height, body composition, and energy expenditure measures. The findings from this study will enhance screening, prevention, and treatment of abnormal weight status by facilitating the accurate identification of youths' weight status category and recommendations of daily caloric needs for this population that is at higher risk of obesity. Furthermore, the findings have the potential to impact outcomes for youth diagnosed with disabilities other than spina bifida who experience similar challenges related to alterations in body composition or fat distribution or measurement challenges secondary to mobility issues or musculoskeletal problems. DERR1-10.2196/52779.

Modeling approaches for assessing device-based measures of energy expenditure in school-based studies of body weight status

BackgroundObesity has become an important threat to children’s health, with physical and psychological impacts that extend into adulthood. Limited physical activity and sedentary behavior are associated with increased obesity risk. Because children spend approximately 6 h each day in school, researchers increasingly study how obesity is influenced by school-day physical activity and energy expenditure (EE) patterns among school-aged children by using wearable devices that collect data at frequent intervals and generate complex, high-dimensional data. Although clinicians typically define obesity in children as having an age-and sex-adjusted body mass index (BMI) value in the high percentiles, the relationships between school-based physical activity interventions and BMI are analyzed using traditional linear regression models, which are designed to assess the effects of interventions among children with average BMI, limiting insight regarding the effects of interventions among children categorized as overweight or obese.MethodsWe investigate the association between wearable device–based EE measures and age-and sex-adjusted BMI values in data from a cluster-randomized, school-based study. We express and analyze EE levels as both a scalar-valued variable and as a continuous, high-dimensional, functional predictor variable. We investigate the relationship between school-day EE (SDEE) and BMI using four models: a linear mixed-effects model (LMEM), a quantile mixed-effects model (QMEM), a functional mixed-effects model (FMEM), and a functional quantile mixed-effects model (FQMEM). The LMEM and QMEM include SDEE as a summary measure, whereas the FMEM and FQMEM allow for the modeling of SDEE as a high-dimensional covariate. The FMEM and FQMEM allow the influence of the time of day at which physical activity is performed to be assessed, which is not possible using the LMEM or the QMEM. The FMEM assesses how frequently collected SDEE data influences mean BMI, whereas the FQMEM assesses the effects on quantile levels of BMI.ResultsThe LMEM and QMEM detected a statistically significant effect of overall mean SDEE on log (BMI) (the natural logarithm of BMI) after adjusting for intervention, age, race, and sex. The FMEM and FQMEM provided evidence for statistically significant associations between SDEE and log (BMI) for only a short time interval. Being a boy or being assigned a stand-biased desk is associated with a lower log (BMI) than being a girl or being assigned a traditional desk. Across our models, age was not a statistically significant covariate, and white students had significantly lower log (BMI) than non-white students in quantile models, but this significant effect was observed for only the 10th and 50th quantile levels of BMI. The functional regression models allow for additional interpretations of the influence of EE patterns on age-and sex-adjusted BMI, whereas the quantile regression models enable the influence of EE patterns to be assessed across the entire BMI distribution.ConclusionThe FQMEM is recommended when interest lies in assessing how device-monitored SDEE patterns affect children of all body types, as this model is robust and able to assess intervention effects across the full BMI distribution. However, the sample size must be sufficiently large to adequately power determinations of covariate effects across the entire BMI distribution, including the tails.