Physical activity, fat intake and body fat

The body fatness of a subject is a long-term reflection of the energy balance, the more intake exceeds expenditure the more energy is stored as fat. There is not yet a clear answer on the question whether the current obesity epidemic is a consequence of gluttony or sloth. Review studies do not show a reduction of physical activity over the years, and food intake is difficult to measure in daily life conditions. Food intake can only be derived from self-report, where under-reporting of food intake and selective underreporting of fat intake are major issues. Fat intake might be an important factor in the increase of body weight. Many studies suggest the capacity of the body to oxidize dietary fat is a major risk factor for a positive energy balance. Additionally, there is evidence that most of the fat consumed is stored before oxidation. Obesity prone subjects might be characterized by a higher storage of dietary fat. The only way to increase the oxidation of dietary fat, other than consuming more dietary fat, is to increase energy expenditure by an increase of physical activity. Indeed, there are indications that physical activity is an important determinant of fat oxidation. Based on the evidence presented, it is concluded that the obesity epidemic is mainly due to a high dietary intake, especially as fat, and that physical activity can be a tool to modulate the effect of fat intake on body fat. AD - Care and Health Applications, Philips Research, Eindhoven, The Netherlands

Aspects of activity behavior as a determinant of the physical activity level.

This study investigated which aspects of the individuals' activity behavior determine the physical activity level (PAL). Habitual physical activity of 20 Dutch adults (age: 26-60 years, body mass index: 24.5+/-2.7 kg/m(2)) was measured using a tri-axial accelerometer. Accelerometer output was used to identify the engagement in different types of daily activities with a classification tree algorithm. Activity behavior was described by the daily duration of sleeping, sedentary behavior (lying, sitting, and standing), walking, running, bicycling, and generic standing activities. Simultaneously, the total energy expenditure (TEE) was measured using doubly labeled water. PAL was calculated as TEE divided by sleeping metabolic rate. PAL was significantly associated (P<0.05) with sedentary time (R=-0.72), and the duration of walking (R=0.49), bicycling (R=0.77), and active standing (R=0.62). A negative association was observed between sedentary time and the duration of active standing (R=-0.87; P<0.001). A multiple-linear regression analysis showed that 75% of the variance in PAL could be predicted by the duration of bicycling (Partial R(2)=59%; P<0.01), walking (Partial R(2)=9%; P<0.05) and being sedentary (Partial R(2)=7%; P<0.05). In conclusion, there is objective evidence that sedentary time and activities related to transportation and commuting, such as walking and bicycling, contribute significantly to the average PAL

Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer.

BACKGROUND: Accelerometers are often used to quantify the acceleration of the body in arbitrary units (counts) to measure physical activity (PA) and to estimate energy expenditure. OBJECTIVE: The present study investigated whether the identification of types of PA using one accelerometer could improve the estimation of energy expenditure as compared to activity counts. METHOD: Total energy expenditure (TEE) of 15 subjects was measured using doubly-labeled water. The physical activity level (PAL) was derived dividing TEE by sleeping metabolic rate. Simultaneously, PA was measured using one accelerometer. Accelerometer output was processed to calculate activity counts per day (ACD) and to determine the daily duration of 6 types of common activities identified using a classification tree model. A daily metabolic value (METD) was calculated as mean of the MET compendium value of each activity type weighed by the daily duration. RESULTS: TEE was predicted by ACD and body weight and by ACD and fat free mass with a standard error of estimate (SEE) of 1.47 MJ(.)d(-1), and 1.2 MJ(.)d(-1), respectively. The replacement in these models of ACD with METD increased the explained variation in TEE by 9%, decreasing SEE by 0.14 MJ*d(-1), and 0.18 MJ*d(-1), respectively. The correlation between PAL and METD (R(2)=51%) was higher than PAL and ACD (R(2)=46%). CONCLUSION: Identification of activity types combined with MET intensity values improves the assessment of energy expenditure as compared to activity counts. Future studies could develop models to objectively assess activity type and intensity to further increase accuracy of the energy expenditure estimation. Key words: doubly-labeled water, motion sensor, activity recognition, classification tree

Detection of type, duration, and intensity of physical activity using an accelerometer

OBJECTIVE: The aim of this study was to develop models for the detection of type, duration, and intensity of human physical activity using one triaxial accelerometer. METHODS: Twenty subjects (age = 29 +/- 6 yr, BMI = 23.6 +/- 3.2 kg.m) performed 20 selected activities, including walking, running, and cycling, wearing one triaxial accelerometer mounted on the lower back. Identification of activity type was based on a decision tree. The decision tree evaluated attributes (features) of the acceleration signal. The features were measured in intervals of defined duration (segments). Segment size determined the time resolution of the decision tree to assess activity duration. Decision trees with a time resolution of 0.4, 0.8, 1.6, 3.2, 6.4, and 12.8 s were developed, and the respective classification performances were evaluated. Multiple linear regression was used to estimate speed of walking, running, and cycling based on acceleration features. RESULTS: Maximal accuracy for the classification of activity type (93%) was reached when the segment size of analysis was 6.4 or 12.8 s. The smaller the segment size, the lower the classification accuracy achieved. Segments of 6.4 s gave the highest time resolution for measuring activity duration without decreasing the classification accuracy. The developed models estimated walking, running, and cycling speeds with a standard error of 0.20, 1.26, and 1.36 km.h, respectively. CONCLUSIONS: This study demonstrated the ability of a triaxial accelerometer in detecting type, duration, and intensity of physical activity using models based on acceleration features. Future studies are needed to validate the presented models in free-living conditions

Daily energy expenditure through the human life course

Total daily energy expenditure ("total expenditure") reflects daily energy needs and is a critical variable in human health and physiology, but its trajectory over the life course is poorly studied. We analyzed a large, diverse database of total expenditure measured by the doubly labeled water method for males and females aged 8 days to 95 years. Total expenditure increased with fat-free mass in a power-law manner, with four distinct life stages. Fat-free mass-adjusted expenditure accelerates rapidly in neonates to similar to 50% above adult values at similar to 1 year; declines slowly to adult levels by similar to 20 years; remains stable in adulthood (20 to 60 years), even during pregnancy; then declines in older adults. These changes shed light on human development and aging and should help shape nutrition and health strategies across the life span

Total daily energy expenditure has declined over the past three decades due to declining basal expenditure, not reduced activity expenditure.

Obesity is caused by a prolonged positive energy balance <sup>1,2</sup> . Whether reduced energy expenditure stemming from reduced activity levels contributes is debated <sup>3,4</sup> . Here we show that in both sexes, total energy expenditure (TEE) adjusted for body composition and age declined since the late 1980s, while adjusted activity energy expenditure increased over time. We use the International Atomic Energy Agency Doubly Labelled Water database on energy expenditure of adults in the United States and Europe (n = 4,799) to explore patterns in total (TEE: n = 4,799), basal (BEE: n = 1,432) and physical activity energy expenditure (n = 1,432) over time. In males, adjusted BEE decreased significantly, but in females this did not reach significance. A larger dataset of basal metabolic rate (equivalent to BEE) measurements of 9,912 adults across 163 studies spanning 100 years replicates the decline in BEE in both sexes. We conclude that increasing obesity in the United States/Europe has probably not been fuelled by reduced physical activity leading to lowered TEE. We identify here a decline in adjusted BEE as a previously unrecognized factor

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P=1 × 10) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P=8.4 × 10). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies

A standard calculation methodology for human doubly labeled water studies

The doubly labeled water (DLW) method measures total energy expenditure (TEE) in free-living subjects. Several equations are used to convert isotopic data into TEE. Using the International Atomic Energy Agency (IAEA) DLW database (5,756 measurements of adults and children), we show considerable variability is introduced by different equations. The estimated rCO(2) is sensitive to the dilution space ratio (DSR) of the two isotopes. Based on performance in validation studies, we propose a new equation based on a new estimate of the mean DSR. The DSR is lower at low body masses (<10 kg). Using data for 1,021 babies and infants, we show that the DSR varies non-linearly with body mass between 0 and 10 kg. Using this relationship to predict DSR from weight provides an equation for rCO(2) over this size range that agrees well with indirect calorimetry (average difference 0.64%; SD = 12.2%). We propose adoption of these equations in future studies

Under-Reporting of energy intake in elderly Australian women is associated with a higher body mass index

Design: Dietary intake was assessed using a 3-day weighed food record. Protein intake was validated by 24-hour urinary nitrogen. To examine under-reporting, participants were grouped according to their energy intake and compared to the Goldberg cut-off equation. Logistic regression was performed to assess the influence of body mass index (BMI) and social-demographic factors on under-reporting. Setting: Community dwelling elderly women from Perth, Western Australia. Participants: 217 elderly women aged 70–80 years. Results: Under-reporters had a higher physical activity level (p<0.001) compared with acceptable-reporters. The under-reporters also had a higher body weight (p=0.006), body mass index (BMI) (p=0.001), waist (p=0.011), hip circumference (p<0.001), whole body fat mass (p<0.001) and percentage body fat (p<0.001) than acceptable-reporters. Under-reporters had a significantly lower intakes of protein, fat, carbohydrate and alcohol (p<0.001) and fewer reported food items, compared with acceptable reporters. However, 24-hour urinary nitrogen was only marginally different between the two groups (p=0.053). Participants with a higher BMI were more likely to under-report their energy intake (BMI=25–29.9: odds ratio=2.98[95% CI=1.46–6.09]; BMI≥30: 5.84[2.41–14.14]). Conclusion: Under-reporting energy intake in elderly women was associated with a higher BMI, body fat and higher self-reported physical activity levels. A higher BMI (≥25) appears to be most significant factor in determining if elderly women will underreport their food intake and may be related to body image. These results have implications for undertaking surveys of food intake in elderly women