Physical Activity Energy Expenditure Research Articles

Energy Expenditure in Free-Living Physical Activities and Sedentary Behaviors in Mexican Schoolchildren

ABSTRACT Purpose: Metabolic equivalents for youth (METy) are derived using the estimated basal metabolic rate (BMR). However, is unknown whether this METy can be different when measured resting energy expenditure (REE) is used. The purposes of this article are to: (a) To determine whether there is equivalence between METy values calculated using measured REE (METy-mea) and METy using predicted BMR (METy-est). (b) To determine whether METy values of different activities are dependent on age, sex, and body composition. Methods: A cross-sectional study with a sample of 122 Mexican children (5–11 years old) was conducted. With indirect calorimetry, energy expenditure was measured at rest and during 16 sedentary- to vigorous-intensity physical activities. METy were obtained in two forms: METy-mea and METy-est. Equivalence testing was used to compare METy-mea and METy-est. To examine the individual-level agreement, Bland-Altman plots were graphed, and intra-class correlation coefficients (ICC) were calculated. Linear regression models were estimated having METy as the outcome. Results: For 15 activities, there was equivalence between METy-mea and METy-est (t > 2.05, p < 0.050). However, at the individual-level, for 7 activities the agreement between METy-eREE and METy-mREE was inadequate (ICC<0.75). In the Bland-Altman plots was evident that in 8 activities METy-est was biased compared to METy-mea, METy-est had more correlations with body mass index and body fat. Conclusions: In conclusion, at group-level, for most activities METy-mea and METy-est were equivalent. However, at the individual level, METy-est of many activities is not a valid estimate of METy-mea. METy-est of many physical activities were dependent on adiposity, which could be an artifact introduced when BMR is predicted.

Physical activity estimated by osteogenic potential and energy expenditure has differing associations with bone mass in young adults: the raine study

SummaryGround impacts during physical activity may be important for peak bone mass. We found differences in how energy expenditure and impact scores estimated from a physical activity questionnaire related to bone health in young adults. Using both estimate types can improve our understanding of the skeletal benefits of physical activity.PurposeIt is unclear whether mechanical loading during physical activity, estimated from physical activity questionnaires which assess metabolic equivalents of task (METs), is associated with skeletal health. This longitudinal study investigated how physical activity loading scores, assessed at ages 17 and 20 years, (a) compares with physical activity measured in METs, and (b) is associated with bone mass at age 20 years.MethodsA total of 826 participants from the Raine Study Gen2 were assessed for physical activity energy expenditure via the International Physical Activity Questionnaire (IPAQ) at age 17 and 20 years. Loading scores (the product of peak force and application rate) per week were subsequently estimated from the IPAQ. Whole-body and appendicular bone mineral density (BMD) at age 20 years were assessed by dual-energy X-ray absorptiometry.ResultsBland–Altman minimal detectable difference for physical activity Z-scores at age 17 and 20 years were 1.59 standard deviations (SDs) and 1.33 SDs, respectively, greater than the a priori minimal clinically important change of 0.5 SDs. Loading score, but not IPAQ score, had significant positive associations with whole-body and leg BMD after adjustment for covariates (β = 0.008 and 0.012 g/cm2, respectively, for age 17 and 20 years loading scores). IPAQ score at age 20 years, but not loading score, had a significant positive association with arm BMD (β = 0.007 g/cm2).ConclusionThis study revealed disagreement in associations of self-reported METs and loading score estimates with bone health in young adults. Coupling traditional energy expenditure questionnaire outcomes with bone-loading estimates may improve understanding of the location-specific skeletal benefits of physical activity in young adults.

Association between physical activity energy expenditure and cardiometabolic risk factor clustering among Chinese adults in 2015

To understand the association between cardiometabolic risk factor (CMRF) clustering and physical activity (PA) levels, we included 86520 Chinese adults aged 18–64 years having at least one CMRF (hypertension, diabetes, dyslipidemia, or obesity) from the China Chronic Disease and Nutrition Surveillance survey in 2015, a nationally and provincially representative investigation with a multistage clustering sampling design. Self-reported PA information was collected with the Global Physical Activity Questionnaire through face-to-face interviews. In view of the obesity epidemic in CMRF patients, PA energy expenditure (PAEE) per kilogram body weight was used, and was defined into four categories: (i) inactivity: 0 ​kJ/kg/day; (ii) low activity: 0–5 ​kJ/kg/day; (iii) moderate activity: 6–11 ​kJ/kg/day; and (iv) vigorous activity: ≥ 12 ​kJ/kg/day. The estimated weighted prevalence (95% confidence interval [CI]) of having 1, 2, 3, and 4 CMRFs was 60.57% (59.48%–61.67%), 28.10% (27.40%–28.79%), 9.82% (9.22%–15.42%) and 1.50% (1.37%–1.63%), respectively. The rate (95%CI) of inactivity, low activity, moderate activity, and vigorous activity was 34.52% (32.69%–36.35%), 22.22% (21.37%–23.37%), 15.98% (15.38%–16.58%) and 27.28% (26.02%–28.53%), respectively. For those having 2, 3 and 4 CMRFs (compared to those having 1 CMRF), the adjusted odds ratio (95%CI) for moderate activity and vigorous activity were 0.91 (0.85–0.98) and 0.92 (0.85–0.99), 0.87 (0.80–0.95) and 0.84 (0.77–0.92), and 0.77 (0.67–0.89) and 0.85 (0.72–1.00), respectively. In conclusion, CMRF clustering was a pandemic among Chinese adults in 2015 and was inversely associated with PA level. PAEE (in kJ/kg/day) may be introduced into PA management practice, especially for populations with high body weight.

Socioecological Factors Associated With Physical Activity and Sedentary Behavior Among Workers: Using the PRECEDE-PROCEED Model.

Physical inactivity is a major global health problem. Industrial automation has led to an increased number of workers who are sedentary at work. We examined whether three socioecological factors (i.e., predisposing, reinforcing, and enabling factors) derived from the PRECEDE-PROCEED model would be significantly associated with the physical activity and sedentary behavior among workers. A cross-sectional study was conducted among 539 employees at an electronic manufacturing plant in Gumi, South Korea. Physical activity variables of energy expenditure (MET-min/week) and sitting time at work (minutes/day) were measured by the International Physical Activity Questionnaire and the Workforce Sitting Questionnaire, respectively. Of the socioecological factors, a predisposing factor defined as self-determined motivation was measured by the Exercise Self-Regulation Questionnaire; a reinforcing factor defined as autonomy support was measured by the Work Climate Questionnaire; and an enabling factor defined as supportive workplace environment was measured by the Perceived Workplace Environment Scale. Self-determined motivation (i.e., autonomous and controlled forms of motivation), autonomy support, and a supportive workplace environment were all significantly associated with increased physical activity energy expenditure during leisure-time. However, they were not significantly associated with sitting time on working and non-working days. Three socioecological factors of the PRECEDE-PROCEED model were significantly associated with leisure-time physical activity among workers. Our findings may help occupational health nurses use a socioecological approach for designing effective workplace strategies to increase leisure-time physical activity among workers.

Levels and correlates of physical activity and capacity among HIV-infected compared to HIV-uninfected individuals.

IntroductionIn the HIV-infected individuals, physical activity improves physical strength, quality of life and reduces the risk of developing non-communicable diseases. In Sub-Saharan Africa, HIV-infected patients report being less active compared to HIV-uninfected individuals. We assessed the levels and correlates of objectively measured physical activity and capacity among HIV-infected antiretroviral therapy (ART)-naive individuals compared to HIV-uninfected individuals in Mwanza, Tanzania.MethodWe conducted a cross-sectional study among newly diagnosed HIV-infected ART-naive individuals and HIV-uninfected individuals frequency-matched for age and sex. Socio-demographic data, anthropometrics, CD4 counts, haemoglobin level, and C-reactive protein (CRP) were collected. Physical activity energy expenditure (PAEE) was assessed as measure of physical activity whereas sleeping heart rate (SHR) and grip strength were assessed as measures of physical capacity. Multivariable linear regression was used to assess the correlates associated with physical activity and capacity.ResultsA total of 272 HIV-infected and 119 HIV-uninfected individuals, mean age 39 years and 60% women participated in the study. Compared to HIV-uninfected individuals, HIV-infected had poorer physical activity and capacity: lower PAEE (-7.3 kj/kg/day, 95% CI: -11.2, -3.3), elevated SHR (7.7 beats/min, 95%CI: 10.1, 5.3) and reduced grip strength (-4.7 kg, 95%CI: -6.8, -2.8). In HIV-infected individuals, low body mass index, moderate-severe anaemia, low CD4 counts and high CRP were associated with lower physical activity and capacity. In HIV-uninfected individuals, abdominal obesity and moderate anaemia were associated with lower physical activity and capacity.ConclusionHIV-infected participants had lower levels of physical activity and capacity than HIV-uninfected participants. Correlates of physical activity and capacity differed by HIV status. Management of HIV and related conditions needs to be provided effectively in health care facilities. Interventions promoting physical activity in these populations will be of importance to improve their health and reduce the risk of non-communicable diseases.

The Influence of Small-Sided Football Games with Numerical Variability in External Training Load

Small-sided games (SSGs) are a motivational strategy and effective training method to develop skills and physical fitness at optimal intensity. This study investigates the influence of variability in the number of players on the physiological response using SSGs. The sample was composed of 10 field players with an average age of 15.9 ± 0.50 years old. Three sessions were held on 3 different days to collect information. The 10 min SSG exercise with temporal numerical variability was repeated twice on each day. The numerical ratio of players involved changed every 2 min without disrupting the practice. In a game space with 35 × 25 m, the exercise always started in a situation of 3 vs. 3. External training load metrics (i.e., physical activity counts, activity intensity, and energy expenditure) were assessed using Actigraph WGT3X accelerometers. The 3 vs. 3 format game had higher physical activity counts (i.e., higher vector magnitude) and requested more time spent in very vigorous physical activity intensity. Consequently, the 3 vs. 3 format in young football players had higher energy expenditure (i.e., a higher METs) than that in the other studied game formats. This study confirms the influence of SSGs with numerical variability in external training load.

Association of PC Use and TV Viewing Hours with Women’s Recreational Physical Activity and Its Impact on Their Obesity Indices

Background: Television (TV) viewing and computer (PC) use have been associated with poor health outcomes.&#x0D; Aim: To investigate the association of TV viewing and PC use with recreational physical activity energy expenditure (RPAEE), obesity indices, physical activity levels (PAL) and body fat percentage (BF%) of adult women.&#x0D; Methods: Bodyweight (BW), height, waist (WC) and hip (HC) circumferences of 150 adult women were measured. Body mass index (BMI) and waist-to-hip ratio (WHR) were calculated. The BF% was measured by bioelectrical impedance analysis. The Athens Physical Activity Questionnaire was used to assess PAL, RPAE, TV viewing and PC hours.&#x0D; Results: The majority of women (53%) were overweight and obese, watched TV for &gt;14 hr/wk (59%), and almost half (47%) of them used computers for &gt;1 hr/day. Slightly more than half of them (54%) had a sedentary lifestyle and reported exercise participation for &lt;2 hr/wk. No walking for leisure was reported by one-third of the subjects, while an alarming low percentage (0.7%) walked only 2.5 hr/wk. No participation in informal physical activity was reported by 69%. RPAEE was estimated at 982 ± 973 kcal/wk and negatively correlated to TV-watching hours (r = –0.31, p &lt; 0.05), computer use (r = –0.3, p &lt; 0.05), BMI (r = –0.44, p &lt; 0.01), BW (r = –0.44, p &lt; 0.01), WC (r = –0.41, p &lt; 0.01), WHR (r = –0.31, p = 0.01). Moreover, RPAEE and BF% were negatively correlated (r = –0.44, p &lt; 0.01). BF% was associated with long hours of PC use and TV watching (R2 = 0.11, F1.148 = 17.94, p = 0.00; R2 = 0.14, F1.148 = 5.4, p = 0.002).&#x0D; Conclusion: Screen use affecting obesity indices seemed to overrun recreational time for physical activity participation and dominate women’s lifestyle. Further research targeting behavioral change practices is recommended.&#x0D; Keywords: obesity, women, physical activity, recreational physical activity expenditure

A recurrent neural network architecture to model physical activity energy expenditure in older people

Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being.

The Acute Effects of Diet-Induced Energy Restriction on Physical Activity Energy Expenditure and Basal Metabolic Rate in Overweight and Obese Men and Women

The Acute Effects of Diet-Induced Energy Restriction on Physical Activity Energy Expenditure and Basal Metabolic Rate in Overweight and Obese Men and Women

Physical Activity Compliance to American Cancer Society Recommendations Amongst Hematopoietic Stem Cell Transplant Survivors.

The aims of this study were to determine the extent to which hematopoietic cell transplantation (HCT) survivors adhere to the American Cancer Society recommendations for weekly physical activity and identify potential demographic and transplant characteristics associated with the lack of compliance. This cross-sectional study included adults who had undergone HCT and were at least 1 year post transplantation. Physical activity was assessed using the screening tool of the Block 2014. The type of activity, frequency, and intensity were converted into the metabolic equivalent of task (MET) scores (0-499.0 MET min/week, inadequate activity; 500-1000 MET min/week, adequate activity; >1000 MET min/week, highly vigorous activity). Participants (n = 81) reported a median MET score of 153 min/week, and 83% failed to reach the physical activity guideline of >500 MET min/week. Only 17.3% met the ACS recommendations, with three reporting above 1000 MET min/week. Median daily moderate and vigorous physical activity minute totals were 18.0 and 5.9 min/d, with 85.2% and 60.5% of participants involved, respectively. The median total physical activity energy expenditure was 744 kcal/d. Only race was associated with MET score, with Whites reporting higher MET scores. Most HCT survivors assessed in this study did not meet the ACS physical activity recommendations. These findings reinforce the need to incorporate screening for physical activity into HCT survivorship care, offer counseling to those who do not meet the recommended levels, and encourage a physically active lifestyle among HCT survivors.

Mechanisms of obesity in children and adults with phenylketonuria on contemporary treatment.

Obesity prevalence in people with phenylketonuria (PKU) is comparable to that of the general population but the underlying aetiology remains unknown. To assess body composition, dietary intake, moderate physical activity duration (MPAD) and energy expenditure (MPAEE), resting metabolic rate (RMR), diet-induced thermogenesis (DIT), fasting and postprandial fat (FOx) and carbohydrate oxidation (CHOOx), in PKU people and healthy Controls. Participants were PKU people (n=16) and healthy controls (n=15). Body composition was measured with stable isotopes using deuterium as tracer, dietary intake from 4-day food diaries, MPAD and MPAEE from 7-day activity counts measured by triaxial accelerometers, calibrated against individual rates of oxygen consumption and carbon dioxide production, RMR, DIT, FOx and CHOOx by indirect calorimetry. Body composition, DIT, FOx, CHOOx and RMR did not differ between the PKU and the Control groups. MPAD (PKU, 73±26min/week; Control, 152±43min/week) and MPAEE (PKU, 404±127kcal/week; Control, 741±153kcal/week) were lower (P<0.05) in the PKU than the Control group. Raised phenylalanine levels were inversely related with MPAD and MPAEE. Energy intake and energy provided by protein did not differ between the groups, while energy proportion obtained from carbohydrate was higher (PKU, 60±2%; Control, 51±2%; P<0.05) and from fat lower (PKU, 24±2%; Control, 35±3%; P<0.05) in the PKU than in the Control group. People with PKU spent less time and expend less energy in moderate physical activity and have a higher intake of energy from CHO which may be involved in the underlying mechanisms of obesity in PKU.

Breakfast Consumption Suppresses Appetite but Does Not Increase Daily Energy Intake or Physical Activity Energy Expenditure When Compared with Breakfast Omission in Adolescent Girls Who Habitually Skip Breakfast: A 7-Day Randomised Crossover Trial

With concerns that adolescent girls often skip breakfast, this study compared the effects of breakfast consumption versus breakfast omission on free-living physical activity (PA) energy expenditure (PAEE) and dietary intakes among adolescent girls classified as habitual breakfast skippers. The participants went through two 7-day conditions in a trial with a crossover design: daily standardised breakfast consumption (energy content: 25% of resting metabolic rate) before 09:00 (BC) and daily breakfast omission (no energy-providing nutrients consumed) until 10:30 (BO). Free-living PAEE, dietary intakes, and perceived appetite, tiredness, and energy levels were assessed. Analyses were linear mixed models. Breakfast manipulation did not affect PAEE or PA duration. Daily fibre intake was higher (p = 0.005; d = 1.31), daily protein intake tended to be higher (p = 0.092; d = 0.54), post-10:30 carbohydrate intake tended to be lower (p = 0.096; d = 0.41), and pre-10:30 hunger and fullness were lower and higher, respectively (p ≤ 0.065; d = 0.33–1.01), in BC versus BO. No other between-condition differences were found. Breakfast-skipping adolescent girls do not compensate for an imbalance in energy intake caused by breakfast consumption versus omission through subsequent changes in PAEE but may increase their carbohydrate intakes later in the day to partially compensate for breakfast omission. Furthermore, breakfast can make substantial contributions to daily fibre intake among adolescent girls.

Does Google Fit provide valid energy expenditure measurements of functional tasks compared to those of Fibion accelerometer in healthy individuals? A cross-sectional study

Background and aimsSmartphone applications (e.g., Google Fit) may be a good alternative tool for accelerometers in estimating energy expenditure of physical activities because they are affordable, easy to use, and freely downloadable on smartphones. We aimed to determine the concurrent validity of the Fibion and Google Fit for measuring energy expenditure of functional tasks in healthy individuals. MethodsIn this cross-sectional study, 28 healthy individuals (21.25 ± 1.84 years) performed certain tasks (lying, standing, 6-min walk test, treadmill walking, stair climbing and cycling) for ∼90 min, while wearing a Fibion accelerometer on their thigh and having the Google Fit application in a smartphone placed in their trouser pocket. Concurrent validity between the energy expenditure data of the Google Fit and Fibion was assessed using the Spearman rho correlation coefficient (data were not normally distributed), Bland-Altman plots and linear regression. ResultsNeither energy expenditure for the whole duration nor for the tasks, except sitting + treadmill walking (r = 0.419, p = 0.027), showed significant correlations between the Google Fit and Fibion measurements. A proportional bias was evident for almost all comparisons. ConclusionsThe Google Fit did not provide valid energy expenditure measurements compared to the Fibion for most of the investigated tasks in healthy individuals.

Validation of the Apple Watch for Estimating Moderate-to-Vigorous Physical Activity and Activity Energy Expenditure in School-Aged Children.

The Apple Watch is one of the most popular wearable devices designed to monitor physical activity (PA). However, it is currently unknown whether the Apple Watch accurately estimates children’s free-living PA. Therefore, this study assessed the concurrent validity of the Apple Watch 3 in estimating moderate-to-vigorous physical activity (MVPA) time and active energy expenditure (AEE) for school-aged children under a simulated and a free-living condition. Twenty elementary school students (Girls: 45%, age: 9.7 ± 2.0 years) wore an Apple Watch 3 device on their wrist and performed prescribed free-living activities in a lab setting. A subgroup of participants (N = 5) wore the Apple Watch for seven consecutive days in order to assess the validity in free-living condition. The K5 indirect calorimetry (K5) and GT3X+ were used as the criterion measure under simulated free-living and free-living conditions, respectively. Mean absolute percent errors (MAPE) and Bland-Altman (BA) plots were conducted to assess the validity of the Apple Watch 3 compared to those from the criterion measures. Equivalence testing determined the statistical equivalence between the Apple Watch and K5 for MVPA time and AEE. The Apple Watch provided comparable estimates for MVPA time (mean bias: 0.3 min, p = 0.91, MAPE: 1%) and for AEE (mean bias: 3.8 kcal min, p = 0.75, MAPE: 4%) during the simulated free-living condition. The BA plots indicated no systematic bias for the agreement in MVPA and AEE estimates between the K5 and Apple Watch 3. However, the Apple Watch had a relatively large variability in estimating AEE in children. The Apple Watch was statistically equivalent to the K5 within ±17.7% and ±20.8% for MVPA time and AEE estimates, respectively. Our findings suggest that the Apple Watch 3 has the potential to be used as a PA assessment tool to estimate MVPA in school-aged children.

Effects of COVID-19 Confinement on Physical Activity and Health-Related Quality of Life in Rehabilitation Patients

› Aim: This article investigates the influence of the confinement from March 14th, 2020 until mid-May 2020 induced by the Co-vid-19 outbreak on the exercise behavior of rehabilitation patients and their health-related quality of life (HRQoL). › Methods: A total of 118 rehabilitation patients completed a fully standardized online survey. The Short Form 12 Health Survey (SF-12) and the International Physical Activity Questionnaire in its short form (IPAQ) was used to assess HRQoL and weekly physical activity (PA) energy expenditure before and during the confinement. › Results: Vigorous PA decreased by 64.7% (p=.001, r=.31) during confinement. There was also a decrease in moderate PA of 29.6% (p=.017, r=.20) and a decrease in total PA of 21.9% (p=.001, r=.31). Both physical and psychological HRQoL domains changed sig-nificantly during the confinement: the value of the physical component scale (PCS) decreased by 14.4% (p=<.001, r=.73), the value of the mental component scale (MCS) by 0.05% (p=.018, r=.22). Correlation analysis showed significant associations between vigorous PA and PCS value (p<.001), and between total PA and PCS value (p<.05) during the confinement. There were neither significant relationships between moderate PA and PCS nor between walking and PCS. Furthermore, no correlations between any PA intensities and MCS values were observed. › Conclusion: The confinement induced a significant decrease of weekly physical activity (PA) energy expenditure, especially vigorous PA, in rehabilitation patients. Both total PA (p<.05) and vigorous PA (p<.001) show significant positive correlations with the PCS values of the SF-12 questionnaire. © 2021, Dynamic Media Sales Verlag. All rights reserved.

Directly measured aerobic fitness in male Maasai of Tanzania.

The agro-pastoralist Maasai of East Africa are highly physically active, but their aerobic fitness has so far only been estimated using heart rate (HR) response to submaximal exercise and not directly measured. Thus, we aimed to measure aerobic fitness directly using respiratory gas analysis in a group of Maasai, and habitual physical activity energy expenditure (PAEE) as explanatory variable. In total, 21 (10 rural, 11 semi-urban) of 30 volunteering Tanzanian Maasai men were eligible to participate. Respiratory gas exchange was measured during a graded exercise test until exhaustion on a stationary bicycle to determine aerobic fitness. Maximal effort criteria were at least two of the following (1) leveling off, (2) respiratory exchange ratio (RER) >1.10, and (3) maximum HR within 10bpm of age-estimated maximum HR. Habitual PAEE was estimated using combined accelerometry and HR monitoring. Anthropometry, biochemistry, blood pressure, resting HR, and dietary intake information were collected for background information. Mean age was 43.2 (range 26-60) years, and hemoglobin was higher in the rural versus semi-urban Maasai (16.9 vs. 15.4 g/dl, p=.02). Mean aerobic fitness (34.4 vs. 33.3 mlO2 /min/kg, p=.79), and mean PAEE (58.5 vs. 52.9 kJ/day/kg, p=.64) were similar in rural and semi-urban Maasai, respectively. Aerobic fitness was low to moderate in male rural and semi-urban Maasai. This may be explained by relatively low PAEE in comparison to previous objectively measured activity levels in Maasai, which indicates recent lifestyle changes.

Exploring the Association of a Total Physical Activity Energy Expenditure and Diabetes Mellitus in Adults: A Cross-Sectional Analysis of the Canadian Health Measures Survey.

Background: Physical activity (PA) guidelines for adults recommend participation in aerobic activities of moderate-to-vigorous intensity and a minimum of two sessions of resistance training (RT) weekly. These guidelines account for a small amount of the total PA energy expenditure and include no recommendation for low intensity activities (sleeping, sedentary behavior, and light intensity PA). Consequently, there is a need to investigate the benefits of total PA energy expenditure and diabetes mellitus (DM); to investigate the association between total PA energy expenditure and DM in adults aged 45 years or above. Methods: Data from the Canadian Health Measures Survey (CHMS; n = 5591) were used for the cross-sectional analysis. DM was measured using hemoglobin glycated (A1c) and questionnaires in adults aged 45 and above. PA and sedentary behavior were estimated using accelerometry. Sleep and RT were self-reported. Total PA energy expenditure was computed using the sum of metabolic equivalent of task-min/week. Results: The mean age of the sample was 58.0 ± 0.2 years old. No associations were observed between total PA energy expenditure and self-reported T2DM in all models (P > 0.05). For objectively measured DM, this association was significant when adjusted for age, sex, ethnicity, and smoking [OR: 0.45; 95% CI (0.25-0.80)]; however, the association was no longer significant once adjusted for waist circumference and further adjusted for meeting the International PA Guidelines [OR: 0.64; 95% CI (0.33-1.27)] (P > 0.05). Conclusion: Total PA energy expenditure performed weekly is not associated with DM when considering other known risk factors, including waist circumference and meeting the PA guidelines.

The association between motor capacity and motor performance in school-aged children with cerebral palsy: An observational study

BackgroundThis study aimed to investigate the association between motor capacity and motor performance in children with cerebral palsy (CP) aged 6–12 years with Gross Motor Function Classification System (GMFCS) levels I to III. MethodsForty-six children with CP (24 boys and 22 girls) classified as GMFCS levels Ⅰ, Ⅱ, or Ⅲ were included. Motor capacity was measured by the Gross motor function measure (GMFM), Pediatric balance scale (PBS), Timed up and go (TUG), and 6-min walk test (6MWT). Motor performance was measured by triaxial accelerometers. Estimations of physical activity energy expenditure (PAEE) (kcal/kg/day), percentage of time spent on physical activity (% sedentary physical activity; %SPA; % light physical activity, %LPA; % moderate physical activity, %MPA; % vigorous physical activity %VPA; and moderate-to-vigorous physical activity, %MVPA), and activity counts (counts/minute) were obtained. ResultsChildren with GMFCS level I showed a significantly higher motor capacity (GMFM-66, GMFM-88, D-dimension and E-dimension, PBS and 6MWT) than those with level II or III. Children with GMFCS level II and/or III had significantly lower physical activity (PAEE, % MPA, % VPA, %MVPA, and activity counts) than children with GMFCS level I. Multiple linear regression analysis (dependent variable, GMFM-66) showed that %MVPA was positively associated with GMFM-66 in the GMFCS level II & III children but not in GMFCS level I children. ConclusionsThese findings highlight the importance of increasing %MVPA in children with CP, especially GMFCS levels II and III.

Validation of ambulatory monitoring devices to measure energy expenditure and heart rate in a military setting

Objectives.To investigate the validity of different devices and algorithms used in military organizations worldwide to assess physical activity energy expenditure (PAEE) and heart rate (HR) among soldiers. Design. Device validation study. Methods. Twenty-three male participants serving their mandatory military service accomplished, firstly, nine different military specific activities indoors, and secondly, a normal military routine outdoors. Participants wore simultaneously an ActiHeart, Everion, MetaMax 3B, Garmin Fenix 3, Hidalgo EQ02, and PADIS 2.0 system. The PAEE and HR data of each system were compared to the criterion measures MetaMax 3B and Hidalgo EQ02, respectively. Results. Overall, the recorded systematic errors in PAEE estimation ranged from 0.1 (±1.8) kcal.min−1 to −1.7 (±1.8) kcal.min−1 for the systems PADIS 2.0 and Hidalgo EQ02 running the Royal Dutch Army algorithm, respectively, and in the HR assessment ranged from −0.1 (±2.1) b.min−1 to 0.8 (±3.0) b.min−1 for the PADIS 2.0 and ActiHeart systems, respectively. The mean absolute percentage error (MAPE) in PAEE estimation ranged from 29.9% to 75.1%, with only the Everion system showing an overall MAPE <30%, but all investigated devices reported overall MAPE <1.4% in the HR assessment. Conclusions. The present study demonstrated poor to moderate validity in terms of PAEE estimation, but excellent validity in all investigated devices in terms of HR assessment. Overall, the Everion performed among the best in both parameters and with a device placement on the upper arm, the Everion system is particularly useful during military service, as it does not interfere with other relevant equipment.

Physical Activity and Total Daily Energy Expenditure in Older US Adults: Constrained versus Additive Models.

This study aimed to examine the shape of the relationship between physical activity (PA) and total energy expenditure (TEE) and to explore the role of energy balance status (negative, stable, positive) in influencing this association. Cross-sectional. Participants were 584 older adults (50-74 yr) participating in the Interactive Diet and Activity Tracking in AARP study. TEE was assessed by doubly labeled water and PA by accelerometer. The relationship between PA and TEE was assessed visually and using nonlinear methods (restricted cubic splines). Percent weight change (>3%) over a 6-month period was used as a proxy measurement of energy balance status. TEE generally increased with increasing deciles of PA averaging 2354 (SD, 351) kcal·d-1 in the bottom decile to 2693 (SD, 480) kcal·d-1 in the top decile. Cubic spline models showed an approximate linear association between PA and TEE (linear relation, P < 0.0001; curvature, P = 0.920). Results were similar in subgroup analyses for individuals classified as stable or positive energy balance. For those in negative energy balance, TEE was generally flat with increasing deciles of PA averaging 2428 (SD, 285) kcal·d-1 in the bottom decile to 2372 (SD, 560) kcal·d-1 in the top decile. Energy balance status seems to play an important role in the relationship between PA and TEE. When in a positive energy balance, the relationship between TEE and PA was consistent with an additive model; however, when energy balance was negative, TEE seems to be consistent with a constrained model. These findings support PA for weight gain prevention by increasing TEE; however, the effect of PA on TEE during periods of weight loss may be limited. An adequately powered, prospective study is warranted to confirm these exploratory findings.