For the same BMI, South Asians have a higher body fat percentage, a higher liver fat content and a more adverse metabolic profile than whites. South Asians may have a lower fat oxidation than whites, ...which could result in an unfavorable metabolic profile when exposed to increased high-fat foods consumption and decreased physical activity as in current modern lifestyle.
To determine substrate partitioning, liver fat accumulation and metabolic profile in South Asian and white men in response to overfeeding with high-fat diet under sedentary conditions in a respiration chamber.
Ten South Asian men (BMI, 18-29 kg/m
) and 10 white men (BMI, 22-33 kg/m
), matched for body fat percentage, aged 20-40 year were included. A weight maintenance diet (30% fat, 55% carbohydrate, and 15% protein) was given for 3 days. Thereafter, a baseline measurement of liver fat content (1H-MRS) and blood parameters was performed. Subsequently, subjects were overfed (150% energy requirement) with a high-fat diet (60% fat, 25% carbohydrate, and 15% protein) over 3 consecutive days while staying in a respiration chamber mimicking a sedentary lifestyle. Energy expenditure and substrate use were measured for 3 × 24-h. Liver fat and blood parameters were measured again after the subjects left the chamber.
The 24-h fat oxidation as a percentage of total energy expenditure did not differ between ethnicities (P = 0.30). Overfeeding increased liver fat content (P = 0.02), but the increase did not differ between ethnicities (P = 0.64). In South Asians, overfeeding tended to increase LDL-cholesterol (P = 0.08), tended to decrease glucose clearance (P = 0.06) and tended to elevate insulin response (P = 0.07) slightly more than whites.
Despite a similar substrate partitioning and similar accretion of liver fat, overfeeding with high-fat under sedentary conditions tended to have more adverse effects on the lipid profile and insulin sensitivity in South Asians.
Low cardiorespiratory fitness (CRF) increases risk of all-cause mortality and cardiovascular events. Periodic CRF assessment can have an important preventive function. The objective of this study was ...to develop a protocol-free method to estimate CRF in daily life based on heart rate (HR) and body acceleration measurements. Acceleration and HR data were collected from 37 subjects (men = 49%) while they performed a standardized laboratory activity protocol (sitting, walking, running, cycling) and during a 5-day free-living monitoring period. CRF was determined by oxygen uptake (V̇o
) during maximal exercise testing. A doubly labeled water-validated equation was used to predict total energy expenditure (TEE) from acceleration data. A fitness index was defined as the ratio between TEE and HR (TEE-pulse). Activity recognition techniques were used to process acceleration features and classify sedentary, ambulatory, and other activity types. Regression equations based on TEE-pulse data from each activity type were developed to predict V̇o
. TEE-pulse measured within each activity type of the laboratory protocol was highly correlated with V̇o
(
from 0.74-0.91). Averaging the outcome of each activity-type specific equation based on TEE-pulse from the laboratory data led to accurate estimates of V̇o
root mean square error (RMSE): 300 mL O
/min, or 10%. The difference between laboratory and free-living determined TEE-pulse was 3.7 ± 11% (
= 0.85). The prediction method preserved the prediction accuracy when applied to free-living data (RMSE: 367 mL O
/min, or 12%). Measurements of body acceleration and HR can be used to predict V̇o
in daily life. Activity-specific prediction equations are needed to achieve highly accurate estimates of CRF.
This is among the very few studies validating, in free-living conditions, a method to estimate cardiorespiratory fitness using heart rate and body acceleration data. A novel parameter called TEE-pulse, which was defined as the ratio between accelerometer-determined energy expenditure and heart rate, was highly correlated with maximal oxygen uptake (V̇o
). Activity classification and the use of activity-selective prediction equations outperformed previously published methods for estimating V̇o
from heart rate and acceleration data.
The Apple Watch (AW) Series 1 provides energy expenditure (EE) for wheelchair users but was found to be inaccurate with an error of approximately 30%, and the corresponding error for heart rate (HR) ...provided by the Fitbit Charge 2 was approximately 10% to 20%. Improved accuracy of estimated EE and HR is expected with newer editions of these smart watches (SWs).
This study aims to assess the accuracy of the AW Series 4 (wheelchair-specific setting) and the Fitbit Versa (treadmill running mode) for estimating EE and HR during wheelchair propulsion at different intensities.
Data from 20 manual wheelchair users (male: n=11, female: n=9; body mass: mean 75, SD 19 kg) and 20 people without a disability (male: n=11, female: n=9; body mass: mean 75, SD 11 kg) were included. Three 4-minute wheelchair propulsion stages at increasing speed were performed on 3 separate test days (0.5%, 2.5%, or 5% incline), while EE and HR were collected by criterion devices and the AW or Fitbit. The mean absolute percentage error (MAPE) was used to indicate the absolute agreement between the criterion device and SWs for EE and HR. Additionally, linear mixed model analyses assessed the effect of exercise intensity, sex, and group on the SW error. Interclass correlation coefficients were used to assess relative agreement between criterion devices and SWs.
The AW underestimated EE with MAPEs of 29.2% (SD 22%) in wheelchair users and 30% (SD 12%) in people without a disability. The Fitbit overestimated EE with MAPEs of 73.9% (SD 7%) in wheelchair users and 44.7% (SD 38%) in people without a disability. Both SWs underestimated HR. The device error for EE and HR increased with intensity for both SWs (all comparisons: P<.001), and the only significant difference between groups was found for HR in the AW (-5.27 beats/min for wheelchair users; P=.02). There was a significant effect of sex on the estimation error in EE, with worse accuracy for the AW (-0.69 kcal/min; P<.001) and better accuracy for the Fitbit (-2.08 kcal/min; P<.001) in female participants. For HR, sex differences were found only for the AW, with a smaller error in female participants (5.23 beats/min; P=.02). Interclass correlation coefficients showed poor to moderate relative agreement for both SWs apart from 2 stage-incline combinations (AW: 0.12-0.57 for EE and 0.11-0.86 for HR; Fitbit: 0.06-0.85 for EE and 0.03-0.29 for HR).
Neither the AW nor Fitbit were sufficiently accurate for estimating EE or HR during wheelchair propulsion. The AW underestimated EE and the Fitbit overestimated EE, and both SWs underestimated HR. Caution is hence required when using SWs as a tool for training intensity regulation and energy balance or imbalance in wheelchair users.
Objective: Physical activity (PA) begins to decline in adolescence with a concomitant increase in weight. We hypothesized that a vicious circle may arise between decreasing PA and weight gain from ...adolescence to early adulthood.
Methods and Procedures: PA and self‐perceived physical fitness assessed in adolescents (16–18 years of age) were used to predict the development of obesity (BMI ≥30 kg/m2) and abdominal obesity (waist ≥88 cm in females and ≥102 cm in males) at age 25 in 4,240 twin individuals (90% of twins born in Finland, 1975–1979). Ten 25‐year‐old monozygotic (MZ) twin pairs who were discordant for obesity (with a 16 kg weight difference) were then carefully evaluated for current PA (using a triaxial accelerometer), total energy expenditure (TEE, assessed by means of the doubly labeled water (DLW) method), and basal metabolic rate (BMR, assessed by indirect calorimetry).
Results: Physical inactivity in adolescence strongly predicted the risk for obesity (odds ratio (OR) 3.9, 95% confidence interval (CI) 1.4–10.9) and abdominal obesity (4.8, 1.9–12.0) at age 25, even after adjusting for baseline and current BMI. Poor physical fitness in adolescence also increased the risk for overall obesity (5.1, 2.0–12.7) and abdominal obesity (3.2, 1.5–6.7) in adulthood. Physical inactivity was both causative and secondary to the development of obesity discordance in the MZ pairs. TEE did not differ between the MZ co‐twins. PA was lower whereas BMR was higher in the obese co‐twins.
Discussion: Physical inactivity in adolescence strongly and independently predicts total (and especially) abdominal obesity in young adulthood, favoring the development of a self‐perpetuating vicious circle of obesity and physical inactivity. Physical activity should therefore be seriously recommended for obesity prevention in the young.
In this work, we propose to use pattern recognition methods to determine submaximal heart rate (HR) during specific contexts, such as walking at a certain speed, using wearable sensors in free ...living, and using context-specific HR to estimate cardiorespiratory fitness (CRF). CRF of 51 participants was assessed by a maximal exertion test (V̇o2 max). Participants wore a combined accelerometer and HR monitor during a laboratory-based simulation of activities of daily living and for 2 wk in free living. Anthropometrics, HR while lying down, and walking at predefined speeds in laboratory settings were used to estimate CRF. Explained variance (R(2)) was 0.64 for anthropometrics, and increased up to 0.74 for context-specific HR (0.73-0.78 when including fat-free mass). Next, we developed activity recognition and walking speed estimation algorithms to determine the same contexts (i.e., lying down and walking) in free living. Context-specific HR in free living was highly correlated with laboratory measurements (Pearson's r = 0.71-0.75). R(2) for CRF estimation was 0.65 when anthropometrics were used as predictors, and increased up to 0.77 when including free-living context-specific HR (i.e., HR while walking at 5.5 km/h). R(2) varied between 0.73 and 0.80 when including fat-free mass among the predictors. Root mean-square error was reduced from 354.7 to 281.0 ml/min by the inclusion of context-specific HR parameters (21% error reduction). We conclude that pattern recognition techniques can be used to contextualize HR in free living and estimated CRF with accuracy comparable to what can be obtained with laboratory measurements of HR response to walking.
The present study was designed to objectively assess the effects of 3-months submarine deployment on behavioural and metabolic determinants of metabolic health. In 13 healthy, non-obese volunteers, ...we using stable isotope dilution, and plasma and urinary biochemistry to characterize metabolic health before and after a 3-month submarine deployment. Volunteers worked in 6-h shifts. After deployment, we observed reduced fat-free mass (mean ± SD, -4.1 ± 3.3 kg,
= 0.003) and increased adiposity (21.9 ± 3.2% fat mass to 24.4 ± 4.7%,
0.01). Changes in fat-free mass were positively associated with physical activity (+0.8 kg per 0.1 increase in PAL,
0.03). The average physical activity level was 1.64 ± 0.26 and total energy expenditure during deployment was 2937 ± 498 kcal/d, while energy intake was 3158 ± 786 kcal/d. Fasting glucose (
0.03), and triglycerides (
0.01) declined, whereas fasting free fatty acids increased (
0.04). Plasma vitamin D and B12 concentrations decreased (-14%,
0.04, and -44%,
0.001, respectively), and plasma calcium, and magnesium increased (+51%,
0.01, and +5%,
0.02). Haemoglobin was unchanged, but haematocrit decreased (-2.2 ± 2.1%,
0.005). In conclusion, submarine deployment impairs fat-free mass maintenance and promotes adiposity. High physical activity may prevent the decline in fat-free mass. Our study confirms the need to counteract Vitamin D and B12 deficiencies, and suggests impairments in erythrocyte metabolism.
The aim of this study was to investigate the ability of a novel activity monitor designed to be minimally obtrusive in predicting free‐living energy expenditure. Subjects were 18 men and 12 women ...(age: 41 ± 11 years, BMI: 24.4 ± 3 kg/m2). The habitual physical activity was monitored for 14 days using a DirectLife triaxial accelerometer for movement registration (TracmorD) (Philips New Wellness Solutions, Lifestyle Incubator, the Netherlands). TracmorD output was expressed as activity counts per day (Cnts/d). Simultaneously, total energy expenditure (TEE) was measured in free living conditions using doubly labeled water (DLW). Activity energy expenditure (AEE) and the physical activity level (PAL) were determined from TEE and sleeping metabolic rate (SMR). A multiple‐linear regression model predicted 76% of the variance in TEE, using as independent variables SMR (partial‐r2 = 0.55, P < 0.001), and Cnts/d (partial r2 = 0.21, P < 0.001). The s.e. of TEE estimates was 0.9 MJ/day or 7.4% of the average TEE. A model based on body mass (partial‐r2 = 0.31, P < 0.001) and Cnts/d (partial‐r2 = 0.23, P < 0.001) predicted 54% of the variance in TEE. Cnts/d were significantly and positively associated with AEE (r = 0.54, P < 0.01), PAL (r = 0.68, P < 0.001), and AEE corrected by body mass (r = 0.71, P < 0.001). This study showed that the TracmorD is a highly accurate instrument for predicting free‐living energy expenditure. The miniaturized design did not harm the ability of the instrument in measuring physical activity and in determining outcome parameters of physical activity such as TEE, AEE, and PAL.
The prevalence of overweight and obesity is on the rise around the world, not only in the West, but also in Asian countries. South Asian countries in particular are experiencing a rapid increase in ...overweight and obesity, that coexists with the rapid increase in non-communicable diseases linked to obesity such as diabetes and cardiovascular disease than any other country in Asia. The phenomena observed in South Asian countries are due to the size of the population, the ageing of the population, the high degree of urbanization and the lifestyle changes in favor of increased energy consumption and reduced physical activity. The imbalance between energy consumption and energy expenditure results in the development of a positive energy balance that, over time, accumulates in higher body fat. South Asians were reported to have a more unfavorable body composition with a higher percentage of body fat than Caucasians with an equivalent BMI. Body composition is a major determinant of resting energy expenditure. It has been reported that South Asians have a lower resting energy expenditure than Caucasians with the same BMI. Resting energy expenditure accounts for the majority of total daily energy expenditure and, therefore, plays a crucial role in achieving the balance between energy intake and expenditure.
This dataset includes continuously measured walking biomechanics collected using three-dimensional motion capture and gas exchanges of 18 healthy participants (9 male/9 female, mean ± standard ...deviation age 24.8 ± 3.3 years, height 1.71 ± 0.81 meter, weight 65.9 ± 8.1 kilogram). Walking biomechanics were recorded during four different self-paced speeds (comfortable, very slow, slow, fast) in randomized order and four fixed-paced speeds on an instrumented treadmill. The average walking speed during the last two minutes of a 6 minute self-paced walking familiarization period was determined as the comfortable walking speed and used to set the target speed ranges for the other self-paced conditions: slow (20% slower than comfortable), very slow (40% slower) and fast (20% faster) Oxygen consumption (O2) and carbon dioxide (CO2) production were measured continuously and computed at 5-second intervals throughout the resting metabolic rate (RMR) measurement and walking trials. RMR (J·24 hours) was computed from the average O2 and CO2 measured during the last 5 minutes of the 35 minutes RMR measurement using Weir's non-protein equation. The energy consumption of walking (J·min−1) at each speed was computed from the average O2 and CO2 measured during the last 2 minutes of each condition using similar procedures. RMR (J·min−1) was subtracted from the energy consumption of walking to determine net walking energy consumption. The net cost of walking was then expressed as J·kg·0.67−1·m−1. All participants avoided strenuous activity 24 hours, and eating and drinking (with the exception of water) up to 3hours before the session. Height was measured using a stadiometer (SECA, model 213, Hamburg, Germany). Body mass was measured by force platforms.
Both the exported data files from the CAREN software (D-flow) (.mox files), and processed data files with a custom-made Matlab script are included. Marker and force plate data in the .mox and processed files were low-pass filtered using a 2nd order Butterworth with a cut-off frequency of 6 Hz. C3D files with raw marker and ground reaction force data are available upon request.
Continuously measured spatiotemporal parameters, energetics, 3D lower lumb plus trunk kinematics, 3D kinetics and surface muscle activation during walking at both self-paced and imposed(fixed) speeds on a treadmill. Resting metabolism.
1) Assessing self-paced and fixed speed treadmill walking biomechanical and energetics, 2) assessing biomechanics and energy expenditure at multiple or particular speeds, 3) investigating the relationship between walking biomechanics and energetics, 4) reference database of walking biomechanics and energetics of healthy adults.
When the doubly labeled water (DLW) method is used to measure total daily energy expenditure (TDEE), isotope measurements are typically performed using isotope ratio mass spectrometry (IRMS). New ...technologies, such as off-axis integrated cavity output spectroscopy (OA-ICOS) provide comparable isotopic measurements of standard waters and human urine samples, but the accuracy of carbon dioxide production (V̇co
) determined with OA-ICOS has not been demonstrated. We compared simultaneous measurement V̇co
obtained using whole-room indirect calorimetry (IC) with DLW-based measurements from IRMS and OA-ICOS. Seventeen subjects (10 female; 22 to 63 yr) were studied for 7 consecutive days in the IC. Subjects consumed a dose of 0.25 g H
O (98% APE) and 0.14 g
H
O (99.8% APE) per kilogram of total body water, and urine samples were obtained on days 1 and 8 to measure average daily V̇co
using OA-ICOS and IRMS. V̇co
was calculated using both the plateau and intercept methods. There were no differences in V̇co
measured by OA-ICOS or IRMS compared with IC when the plateau method was used. When the intercept method was used, V̇co
using OA-ICOS did not differ from IC, but V̇co
measured using IRMS was significantly lower than IC. Accuracy (~1-5%), precision (~8%), intraclass correlation coefficients ( R = 0.87-90), and root mean squared error (30-40 liters/day) of V̇co
measured by OA-ICOS and IRMS were similar. Both OA-ICOS and IRMS produced measurements of V̇co
with comparable accuracy and precision compared with IC.
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