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.

Exercise-induced oxidative stress in older adults as a function of habitual activity level.

OBJECTIVES: It has been suggested that regular physical activity might maintain and promote the antioxidant defense capacity against oxidative stress. Therefore, we assessed exercise-induced oxidative stress in relation to habitual physical activity level (PAL) in older adults. DESIGN: The study included a 2-week observation period for the measurement of average daily metabolic rate (ADMR) and PAL. Exercise-induced oxidative stress was measured during a 45-minute cycling test at submaximal intensity. SETTING: A university medical research center. PARTICIPANTS: Twenty-six subjects volunteered for the study (n = 26; mean age +/- standard deviation 60 +/- 1; body mass index 27 +/- 1 kg/m2). MEASUREMENTS: PAL was determined as ADMR combined with a measurement of basal metabolic rate (BMR): PAL = ADMR/BMR. ADMR was measured over 2 weeks with the doubly labeled water method, preceded by a BMR measurement with a ventilated hood. Antipyrine oxidation was used as marker for oxidative stress in vivo. Reaction of antipyrine with hydroxyl radicals results in the formation of para-hydroxyantipyrine (p-APOH) and ortho-hydroxyantipyrine (o-APOH), where o-APOH is not formed through alternative oxygenetic pathways. RESULTS: PAL was inversely related to the exercise-induced increase in the ratio of o-APOH to native antipyrine (r = 0.49, P = .010). The relationship between PAL and exercise-induced increase in the ratio of p-APOH (r = 0.30, P = .140) or thiobarbituric acid reactive species (r = 0.31, P = .130) did not reach the level of significance. CONCLUSION: Physically active older adults have a reduced exercise-induced oxidative stress than older adults with a lower level of physical activity. It seems that regular physical activity improves the antioxidant defense capacity.

Weight-loss induced changes in physical activity and activity energy expenditure in overweight and obese subjects before and after energy restriction.

Activity energy expenditure (AEE) is the component of daily energy expenditure that is mainly influenced by the amount of physical activity (PA) and by the weight of the body displaced. This study aimed at analyzing the effect of weight loss on PA and AEE. The body weight and PA of 66 overweight and obese subjects were measured at baseline and after 12 weeks of 67% energy restriction. PA was measured using a tri-axial accelerometer for movement registration (Tracmor) and quantified in activity counts. Tracmor recordings were also processed using a classification algorithm to recognize 6 common activity types engaged in during the day. A doubly-labeled water validated equation based on Tracmor output was used to estimate AEE. After weight loss, body weight decreased by 13±4%, daily activity counts augmented by 9% (95% CI: +2%, +15%), and this increase was weakly associated with the decrease in body weight (R(2) = 7%; P<0.05). After weight loss subjects were significantly (P<0.05) less sedentary (-26 min/d), and increased the time spent walking (+11 min/d) and bicycling (+4 min/d). However, AEE decreased by 0.6±0.4 MJ/d after weight loss. On average, a 2-hour/day reduction of sedentary time by increasing ambulatory and generic activities was required to restore baseline levels of AEE. In conclusion, after weight loss PA increased but the related metabolic demand did not offset the reduction in AEE due to the lower body weight. Promoting physical activity according to the extent of weight loss might increase successfulness of weight maintenance.

Validation of the TracmorD triaxial accelerometer to assess physical activity in preschool children.

OBJECTIVES: To assess validity evidence of TracmorD to determine energy used for physical activity in 3-4-year-old children. DESIGN AND METHODS: Participants were randomly selected from GECKO Drenthe cohort (n = 30, age 3.4 ± 0.3 years). Total energy expenditure (TEE) was measured using the doubly labeled water method. Sleeping metabolic rate (SMR) was measured by indirect calorimetry (Deltatrac). TEE and SMR were used to calculate physical activity level (PAL) and activity energy expenditure (AEE). Physical activity was monitored using a DirectLife triaxial accelerometer, TracmorD with activity counts per minute (ACM) and activity counts per day (ACD) as outcome measures. RESULTS: The best predictor for PAL was ACM with gender and weight, the best predictor for AEE was ACM alone (backward regression, R(2) = 0.50, P = 0.010 and R2 = 0.31, P = 0.011, respectively). With ACD, the prediction model for PAL included ACD, height, gender, and sleep duration (R2 = 0.48, P = 0.033), the prediction model for AEE included ACD, gender and sleep duration (R2 = 0.39, P = 0.042). The accelerometer was worn for 5 days, but 3 days did not give a different estimated PAL. CONCLUSION: TracmorD provides moderate-to-strong validity evidence that supports its use to evaluate energy used for physical activity in 3-4-year-old children.

Estimation of free-living energy expenditure using a novel activity monitor designed to minimize obtrusiveness.

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/m(2)). The habitual physical activity was monitored for 14 days using a DirectLife triaxial accelerometer for movement registration (Tracmor(D)) (Philips New Wellness Solutions, Lifestyle Incubator, the Netherlands). Tracmor(D) 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-r(2) = 0.55, P < 0.001), and Cnts/d (partial r(2) = 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-r(2) = 0.31, P < 0.001) and Cnts/d (partial-r(2) = 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 Tracmor(D) 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.

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.

Water loss as a function of energy intake, physical activity and season.

Although water is an important nutrient, there are no recommended intake values. Here, water intake, energy intake, physical activity and water loss was measured over 1 week in summer and in winter. Subjects were healthy volunteers, forty-two women and ten men, mean age of 29 (SD 7) years and mean BMI 21.8 (SD 2.2) kg/m2. Water intake was measured with a 7 d food and water record. Physical activity level (PAL) was observed as the ratio of total energy expenditure, as measured with doubly labelled water, to resting energy expenditure as measured in a respiration chamber. Water loss was measured with the deuterium elimination method. Water loss was highly reproducible and ranged from 0.20 to 0.35 l/MJ, independent of season and activity level, with higher values in women. Water loss was related to water and energy intake in summer (r 0.96, P<0.0001 and r 0.68, P<0.001, respectively) as well as in winter (r 0.98, P<0.0001 and r 0.63, P<0.01, respectively). Water loss was, for men, higher in subjects with a higher physical activity in summer (r 0.94, P<0.0001) and in winter (r 0.70, P<0.05). Normalizing water loss for differences in energy expenditure by expressing water loss in litres per MJ resulted in the same value for men in summer and winter. For women, physical activity-adjusted values of water loss were higher, especially in summer. In men, water turnover was determined by energy intake and physical activity, while seasonal effects appeared through energy expenditure. Women showed a higher water turnover that was unrelated to physical activity.

Measuring free-living energy expenditure and physical activity with triaxial accelerometry.

OBJECTIVE: To investigate the ability of a newly developed triaxial accelerometer to predict total energy expenditure (EE) (TEE) and activity-related EE (AEE) in free-living conditions. RESEARCH METHODS AND PROCEDURES: Subjects were 29 healthy subjects between the ages of 18 and 40. The Triaxial Accelerometer for Movement Registration (Tracmor) was worn for 15 consecutive days. Tracmor output was defined as activity counts per day (ACD) for the sum of all three axes or each axis separately (ACD-X, ACD-Y, ACD-Z). TEE was measured with the doubly labeled water technique. Sleeping metabolic rate (SMR) was measured during an overnight stay in a respiration chamber. The physical activity level was calculated as TEE x SMR(-1), and AEE was calculated as [(0.9 x TEE) - SMR]. Body composition was calculated from body weight, body volume, and total body water using Siri's three-compartment model. RESULTS: Age, height, body mass, and ACD explained 83% of the variation in TEE [standard error of estimate (SEE) = 1.00 MJ/d] and 81% of the variation in AEE (SEE = 0.70 MJ/d). The partial correlations for ACD were 0.73 (p < 0.001) and 0.79 (p < 0.001) with TEE and AEE, respectively. When data on SMR or body composition were used with ACD, the explained variation in TEE was 90% (SEE = 0.74 and 0.77 MJ/d, respectively). The increase in the explained variation using three axes instead of one axis (vertical) was 5% (p < 0.05). DISCUSSION: The correlations between Tracmor output and EE measures are the highest reported so far. To measure daily life activities, the use of triaxial accelerometry seems beneficial to uniaxial.

Validity of the assessment of dietary intake: problems of misreporting.

PURPOSE OF REVIEW: This is a review of recent studies on the analysis of misreporting of food intake and on the consequences of misreporting for the interpretation of dietary surveys. Bias in the assessment of dietary intake was analysed from studies comparing reported intake with doubly labelled water assessed energy expenditure. RECENT FINDINGS: There is not yet a method for the accurate determination of dietary intake. Physical and psychological characteristics of study participants play an important role in the observed reporting bias. The degree of misreporting might increase with repeated dietary assessment in the same subjects, confounding the results of intervention studies. SUMMARY: Campaigns aimed at changing food intake might not be as successful as concluded from the results of national food consumption measurements. Subjects might be reporting according to expected instead of real intake. In a clinical setting, the increased awareness of the nursing staff has been observed to result in overreporting of intake.

Habitual meal frequency in relation to resting and activity-induced energy expenditure in human subjects: the role of fat-free mass.

Habitual meal frequency was assessed as a possible function of components of energy expenditure (EE) in human subjects. Fifty-six subjects participated (four categories differing in body composition): ten older women (fat-free mass (FFM) 42.0 (sd 6.3) kg, aged 59 (sd 2) years, BMI 27.5 (sd 6.9) kg/m(2)), fifteen younger women (FFM 45.5 (sd 5.2) kg, aged 34 (sd 10) years, BMI 21.9 (sd 2.3) kg/m(2)), twelve older men (FFM 56.8 (sd 5.9) kg, aged 62 (sd 4) years, BMI 25.7 (sd 3.3) kg/m(2)) and nineteen younger men (FFM 63.9 (sd 7.5) kg, aged 23.1 (sd 3.9) years, BMI 22.9 (sd 1.8) kg/m(2)). Measurements consisted of habitual meal frequency by validated food-intake diaries, physical activity by tri-axial accelerometers and resting EE by a ventilated hood system. Habitual meal frequency was expressed as a function of resting EE (including resting EE as a function of FFM), and of activity-induced EE, using regression analysis. FFM differed according to gender and age categories (P<0.01). Physical activity level was higher in the younger men than in the other categories (P<0.05). No relationship of meal frequency with the variables assessed was observed in subjects with a low FFM (the women). In the subjects with a medium FFM (the older men), meal frequency was positively related to resting EE (r(2) 0.4, P<0.05), but not to the residuals of resting EE as a function of FFM, and inversely related to activity-induced EE (r(2) 0.3, P<0.05). Resting EE explained 40 % of the variation in meal frequency; adding activity-induced EE increased this to 60 %. In the subjects with a high FFM (the younger men), meal frequency was inversely related to resting EE (r(2) 0.8, P<0.0001) and to the residuals of resting EE as a function of FFM (P=0.03), and positively related to activity-induced EE (r(2) 0.6, P<0.0001). Resting EE explained 85 % of the variation in meal frequency; adding activity-induced EE increased this to 89 %. Habitual meal frequency was a function of components of EE, namely resting EE and activity-induced EE, only in subjects with a medium to high FFM (men). FFM-related differences in these relationships suggest a role of physical activity.

Alcohol energy intake and habitual physical activity in older adults.

Alcohol forms a significant component of many diets and it supplements rather than displaces daily energy intake. Surprisingly, alcohol intake does not systematically increase body weight. The present study assessed whether a higher level of habitual physical activity in the daily environment is associated with a higher alcohol intake. Alcohol intake as part of total food intake was measured with a 7 d dietary record while at the same time physical activity was monitored with a tri-axial accelerometer for movement registration. Subjects were twenty women and twenty-four men, aged 61+/-5 years, of BMI 27.1+/-4.6 kg/m(2). Between subjects, there was a positive association between the level of habitual physical activity and alcohol intake (r 0.41; P<0.01). The subjects with higher alcohol intake had a higher activity level. On days with and days without alcohol consumption there was no difference in physical activity within subjects. In conclusion, it was shown that subjects with higher alcohol consumption are habitually more active. This may explain the lack of increasing body weight through additional energy intake from alcohol.

Energy balance in depleted ambulatory patients with chronic obstructive pulmonary disease: the effect of physical activity and oral nutritional supplementation.

Patients with chronic obstructive pulmonary disease (COPD) often suffer from weight loss. The aim of the present study was to gain insight into the energy balance of depleted ambulatory COPD patients, in relation to their habitual level of physical activity and consumption of oral nutritional supplements. Clinically stable and weight-stable patients (n 20; BMI 19.8+/- SD 2.0 kg/m2) were studied 1 and 3 months after rehabilitation or recovery in the clinic and were at random assigned to a control or intervention group with regard to nutritional supplementation. Energy intake was measured with a 7 d food record. Energy expenditure was estimated from a simultaneous 7 d assessment of physical activity with a tri-axial accelerometer for movement registration in combination with measured BMR. Body mass was measured at several time points. The body mass remained stable in both groups after 1 or 3 months and mean energy balances were comparable for both groups. The mean body-mass change between month 1 and 3 was negatively related to the mean physical activity level (r -0.49; P=0.03). Weight change over the 3 months was negatively associated with the physical activity level. These results suggest that knowledge about the individual physical activity level is necessary for the estimation of the energy need of the COPD patient.