Prediction of activity-related energy expenditure under free-living conditions using accelerometer-derived physical activity.

The purpose of the study was to develop prediction models to estimate physical activity (PA)-related energy expenditure (AEE) based on accelerometry and additional variables in free-living adults. In 50 volunteers (20-69 years) PA was determined over 2 weeks using the hip-worn Actigraph GT3X + as vector magnitude (VM) counts/minute. AEE was calculated based on total daily EE (measured by doubly-labeled water), resting EE (indirect calorimetry), and diet-induced thermogenesis. Anthropometry, body composition, blood pressure, heart rate, fitness, sociodemographic and lifestyle factors, PA habits and food intake were assessed. Prediction models were developed by context-grouping of 75 variables, and within-group stepwise selection (stage I). All significant variables were jointly offered for second stepwise regression (stage II). Explained AEE variance was estimated based on variables remaining significant. Alternative scenarios with different availability of groups from stage I were simulated. When all 11 significant variables (selected in stage I) were jointly offered for stage II stepwise selection, the final model explained 70.7% of AEE variance and included VM-counts (33.8%), fat-free mass (26.7%), time in moderate PA + walking (6.4%) and carbohydrate intake (3.9%). Alternative scenarios explained 53.8-72.4% of AEE. In conclusion, accelerometer counts and fat-free mass explained most of variance in AEE. Prediction was further improved by PA information from questionnaires. These results may be used for AEE prediction in studies using accelerometry.

Dietary Macronutrient Composition in Relation to Circulating HDL and Non-HDL Cholesterol: A Federated Individual-Level Analysis of Cross-Sectional Data from Adolescents and Adults in 8 European Studies.

BACKGROUND: Associations between increased dietary fat and decreased carbohydrate intake with circulating HDL and non-HDL cholesterol have not been conclusively determined. OBJECTIVE: We assessed these relations in 8 European observational human studies participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) using harmonized data. METHODS: Dietary macronutrient intake was recorded using study-specific dietary assessment tools. Main outcome measures were lipoprotein cholesterol concentrations: HDL cholesterol (mg/dL) and non-HDL cholesterol (mg/dL). A cross-sectional analysis on 5919 participants (54% female) aged 13-80 y was undertaken using the statistical platform DataSHIELD that allows remote/federated nondisclosive analysis of individual-level data. Generalized linear models (GLM) were fitted to assess associations between replacing 5% of energy from carbohydrates with equivalent energy from total fats, SFAs, MUFAs, or PUFAs with circulating HDL cholesterol and non-HDL cholesterol. GLM were adjusted for study source, age, sex, smoking status, alcohol intake and BMI. RESULTS: The replacement of 5% of energy from carbohydrates with total fats or MUFAs was statistically significantly associated with 0.67 mg/dL (95% CI: 0.40, 0.94) or 0.99 mg/dL (95% CI: 0.37, 1.60) higher HDL cholesterol, respectively, but not with non-HDL cholesterol concentrations. The replacement of 5% of energy from carbohydrates with SFAs or PUFAs was not associated with HDL cholesterol, but SFAs were statistically significantly associated with 1.94 mg/dL (95% CI: 0.08, 3.79) higher non-HDL cholesterol, and PUFAs with -3.91 mg/dL (95% CI: -6.98, -0.84) lower non-HDL cholesterol concentrations. A statistically significant interaction by sex for the association of replacing carbohydrates with MUFAs and non-HDL cholesterol was observed, showing a statistically significant inverse association in males and no statistically significant association in females. We observed no statistically significant interaction by age. CONCLUSIONS: The replacement of dietary carbohydrates with fats had favorable effects on lipoprotein cholesterol concentrations in European adolescents and adults when fats were consumed as MUFAs or PUFAs but not as SFAs.

Variability and reliability study of overall physical activity and activity intensity levels using 24 h-accelerometry-assessed data.

BACKGROUND: 24 h-accelerometry is now used to objectively assess physical activity (PA) in many observational studies like the German National Cohort; however, PA variability, observational time needed to estimate habitual PA, and reliability are unclear. METHODS: We assessed 24 h-PA of 50 participants using triaxial accelerometers (ActiGraph GT3X+) over 2 weeks. Variability of overall PA and different PA intensities (time in inactivity and in low intensity, moderate, vigorous, and very vigorous PA) between days of assessment or days of the week was quantified using linear mixed-effects and random effects models. We calculated the required number of days to estimate PA, and calculated PA reliability using intraclass correlation coefficients. RESULTS: Between- and within-person variance accounted for 34.4-45.5% and 54.5-65.6%, respectively, of total variance in overall PA and PA intensities over the 2 weeks. Overall PA and times in low intensity, moderate, and vigorous PA decreased slightly over the first 3 days of assessment. Overall PA (p = 0.03), time in inactivity (p = 0.003), in low intensity PA (p = 0.001), in moderate PA (p = 0.02), and in vigorous PA (p = 0.04) slightly differed between days of the week, being highest on Wednesday and Friday and lowest on Sunday and Monday, with apparent differences between Saturday and Sunday. In nested random models, the day of the week accounted for < 19% of total variance in the PA parameters. On average, the required number of days to estimate habitual PA was around 1 week, being 7 for overall PA and ranging from 6 to 9 for the PA intensities. Week-to-week reliability was good (intraclass correlation coefficients, range, 0.68-0.82). CONCLUSIONS: Individual PA, as assessed using 24 h-accelerometry, is highly variable between days, but the day of assessment or the day of the week explain only small parts of this variance. Our data indicate that 1 week of assessment is necessary for reliable estimation of habitual PA.

Joint Data Analysis in Nutritional Epidemiology: Identification of Observational Studies and Minimal Requirements.

Background: Joint data analysis from multiple nutrition studies may improve the ability to answer complex questions regarding the role of nutritional status and diet in health and disease. Objective: The objective was to identify nutritional observational studies from partners participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) Consortium, as well as minimal requirements for joint data analysis. Methods: A predefined template containing information on study design, exposure measurements (dietary intake, alcohol and tobacco consumption, physical activity, sedentary behavior, anthropometric measures, and sociodemographic and health status), main health-related outcomes, and laboratory measurements (traditional and omics biomarkers) was developed and circulated to those European research groups participating in the ENPADASI under the strategic research area of "diet-related chronic diseases." Information about raw data disposition and metadata sharing was requested. A set of minimal requirements was abstracted from the gathered information. Results: Studies (12 cohort, 12 cross-sectional, and 2 case-control) were identified. Two studies recruited children only and the rest recruited adults. All studies included dietary intake data. Twenty studies collected blood samples. Data on traditional biomarkers were available for 20 studies, of which 17 measured lipoproteins, glucose, and insulin and 13 measured inflammatory biomarkers. Metabolomics, proteomics, and genomics or transcriptomics data were available in 5, 3, and 12 studies, respectively. Although the study authors were willing to share metadata, most refused, were hesitant, or had legal or ethical issues related to sharing raw data. Forty-one descriptors of minimal requirements for the study data were identified to facilitate data integration. Conclusions: Combining study data sets will enable sufficiently powered, refined investigations to increase the knowledge and understanding of the relation between food, nutrition, and human health. Furthermore, the minimal requirements for study data may encourage more efficient secondary usage of existing data and provide sufficient information for researchers to draft future multicenter research proposals in nutrition.

Validity and reliability of total body volume and relative body fat mass from a 3-dimensional photonic body surface scanner.

OBJECTIVE: Three-dimensional photonic body surface scanners (3DPS) feature a tool to estimate total body volume (BV) from 3D images of the human body, from which the relative body fat mass (%BF) can be calculated. However, information on validity and reliability of these measurements for application in epidemiological studies is limited. METHODS: Validity was assessed among 32 participants (men, 50%) aged 20-58 years. BV and %BF were assessed using a 3DPS (VitusSmart XXL) and air displacement plethysmography (ADP) with a BOD POD® device using equations by Siri and Brozek. Three scans were obtained per participant (standard, relaxed, exhaled scan). Validity was evaluated based on the agreement of 3DPS with ADP using Bland Altman plots, correlation analysis and Wilcoxon signed ranks test for paired samples. Reliability was investigated in a separate sample of 18 participants (men, 67%) aged 25-66 years using intraclass correlation coefficients (ICC) based on two repeated 3DPS measurements four weeks apart. RESULTS: Mean BV and %BF were higher using 3DPS compared to ADP, (3DPS-ADP BV difference 1.1 ± 0.9 L, p<0.01; %BF difference 7.0 ± 5.6, p<0.01), yet the disagreement was not associated with gender, age or body mass index (BMI). Reliability was excellent for 3DPS BV (ICC, 0.998) and good for 3DPS %BF (ICC, 0.982). Results were similar for the standard scan and the relaxed scan but somewhat weaker for the exhaled scan. CONCLUSIONS: Although BV and %BF are higher than ADP measurements, our data indicate good validity and reliability for an application of 3DPS in epidemiological studies.

24 h-accelerometry in epidemiological studies: automated detection of non-wear time in comparison to diary information.

Estimation of physical activity using 24 h-accelerometry requires detection of accelerometer non-wear time (NWT). It is common practice to define NWT as periods >60 minutes of consecutive zero-accelerations, but this algorithm was originally developed for waking hours only and its applicability to 24 h-accelerometry is unclear. We investigated sensitivity and specificity of different algorithms to detect NWT in 24 h-accelerometry compared to diary in 47 ActivE and 559 KORA participants. NWT was determined with algorithms >60, >90, >120, >150, or >180 minutes of consecutive zero-counts. Overall, 9.1% (ActivE) and 15.4% (KORA) of reported NWT was >60 minutes. Sensitivity and specificity were lowest for the 60-min algorithm in ActivE (0.72 and 0.00) and KORA (0.64 and 0.08), and highest for the 180-min algorithm in ActivE (0.88 and 0.92) and for the 120-min algorithm in KORA (0.76 and 0.74). Nevertheless, when applying these last two algorithms, the overlap of accelerometry with any diary based NWT minutes was around 20% only. In conclusion, only a small proportion of NWT is >60 minutes. The 60-min algorithm is less suitable for NWT detection in 24 h-accelerometry because of low sensitivity, specificity, and small overlap with reported NWT minutes. Longer algorithms perform better but detect lower proportions of reported NWT.

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

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

Long-lasting improvements in liver fat and metabolism despite body weight regain after dietary weight loss.

OBJECTIVE: Weight loss reduces abdominal and intrahepatic fat, thereby improving metabolic and cardiovascular risk. Yet, many patients regain weight after successful diet-induced weight loss. Long-term changes in abdominal and liver fat, along with liver test results and insulin resistance, are not known. RESEARCH DESIGN AND METHODS: We analyzed 50 overweight to obese subjects (46 ± 9 years of age; BMI, 32.5 ± 3.3 kg/m2; women, 77%) who had participated in a 6-month hypocaloric diet and were randomized to either reduced carbohydrates or reduced fat content. Before, directly after diet, and at an average of 24 (range, 17-36) months follow-up, we assessed body fat distribution by magnetic resonance imaging and markers of liver function and insulin resistance. RESULTS: Body weight decreased with diet but had increased again at follow-up. Subjects also partially regained abdominal subcutaneous and visceral adipose tissue. In contrast, intrahepatic fat decreased with diet and remained reduced at follow-up (7.8 ± 9.8% [baseline], 4.5 ± 5.9% [6 months], and 4.7 ± 5.9% [follow-up]). Similar patterns were observed for markers of liver function, whole-body insulin sensitivity, and hepatic insulin resistance. Changes in intrahepatic fat und intrahepatic function were independent of macronutrient composition during intervention and were most effective in subjects with nonalcoholic fatty liver disease at baseline. CONCLUSIONS: A 6-month hypocaloric diet induced improvements in hepatic fat, liver test results, and insulin resistance despite regaining of weight up to 2 years after the active intervention. Body weight and adiposity measurements may underestimate beneficial long-term effects of dietary interventions.