BackgroundIn the pre-biologics era, employment of patients with ankylosing spondylitis (AS) was decreased when compared to the general population. However, information on standardized employment ...since the introduction of biologicals is lacking. Also, while mastery (control over disease) has been identified as strong predictor of work outcome within patients with AS, it is not known whether such personality trait plays a similar role in patients compared to population subjects.ObjectivesTo update the knowledge on employment and contributing factors, in particular personal factors, among Dutch patients with AS compared to general population subjects.MethodsData from patients and population controls participating in the Dutch cross-sectional multicenter survey-based Social Participation in AS Study (SPASS) and ≤65 years were used. Standardized employment ratios (SERs) were calculated using indirect standardization after adjusting for age, gender and education and were stratified by disease duration tertiles. Adjusted absolute employment rate (%) was calculated as “SERAS*employment rate controls”. Modified Poisson regressions were performed to understand the role of mastery as a personal factor (Pearlin's Mastery scale) in patients opposed to controls, independent of socio-demographics (age, gender, education) and health-related factors (comorbidities, Physical Component Summary (PCS) of the SF-36).Results214 patients and 470 controls (127 59.3% and 323 68.7% males; mean age of 48.3 SD 10.4 and 39.3 SD 12.7 years, respectively) completed the online questionnaire in 2011. SERs (95% CI) of patients with AS with controls set as reference (1.00) were 0.83 (0.69–0.98) for the total group, 0.84 0.67–1.04 for males and 0.83 0.59–1.07 for females. There was no significant difference in SER between those with short or long disease duration (Figure 1). Adjusted absolute employment rate (%) of patients with AS was 14% lower compared to controls (69% vs. 84%). In both patients and controls, higher PCS (SF-36) was associated with being employed. While AS patients with higher (better) mastery were more likely to be employed, such association was not seen in controls (p<0.01 for interaction group*mastery) (Table).Table 1.Multivariate Poisson regression exploring determinants of work participation, stratified by group (AS vs. controls)VariableAS (n=213)Controls (n=465) IRR95% CIpIRR95% CIp Age, years0.990.98–1.00<0.011.000.99–1.000.07Gender, male1.130.91–1.390.261.080.99–1.190.09Education, high–ns†1.091.01–1.180.02Comorbidity (SCQ, 0–39)0.970.94–1.010.120.970.94–1.010.14SF-36 PCS (0–100)1.021.00–1.03<0.011.011.00–1.020.02Mastery (7–28)1.031.00–1.050.03–ns††Value not significant and no confounder.ConclusionsIn the biologics era, employment among Dutch patients with AS remains reduced when compared to the general population, also in those with less than 15 years disease duration. As mastery in patients is independently associated with work outcome, it is worthwhile exploring whether improving personal self-management skills supports future worker participation.Disclosure of InterestNone declared
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 × SMR−1, and AEE was calculated as (0.9 × 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.
There is considerably greater variation in metabolic rates between men than between women, in terms of basal, activity and total (daily) energy expenditure (EE). One possible explanation is that EE ...is associated with male sexual characteristics (which are known to vary more than other traits) such as musculature and athletic capacity. Such traits might be predicted to be most prominent during periods of adolescence and young adulthood, when sexual behaviour develops and peaks. We tested this hypothesis on a large dataset by comparing the amount of male variation and female variation in total EE, activity EE and basal EE, at different life stages, along with several morphological traits: height, fat free mass and fat mass. Total EE, and to some degree also activity EE, exhibit considerable greater male variation (GMV) in young adults, and then a decreasing GMV in progressively older individuals. Arguably, basal EE, and also morphometrics, do not exhibit this pattern. These findings suggest that single male sexual characteristics may not exhibit peak GMV in young adulthood, however total and perhaps also activity EE, associated with many morphological and physiological traits combined, do exhibit GMV most prominently during the reproductive life stages.
We recently reported on a new method to assess physical fitness, based on the combined use of accelerometry and heart rate (HR) registration. This study tested the validity of the prediction formula ...in a group of healthy young adults.
Twenty-six healthy subjects performed a maximal incremental test on a bicycle ergometer to determine VO2max. A triaxial accelerometer and a HR monitor were worn for 7 d under free-living conditions. The prediction formula developed in a previous experimental group (EXP) was applied on the cross-validation group (CV).
No difference was found in subjects' characteristics between the EXP and CV groups except for accelerometer output (activity counts). Although measured VO2max could be predicted for 80% (P < 0.0001), a paired t-test showed a significant difference between measured and predicted VO2max (178 mL.min(-1); P = 0.015). Because of the difference in activity between the EXP and the CV groups, all data were combined and sorted according to activity counts, then two new groups were formed. As a result, EXP and CV groups were created that did not significantly differ in activity or any other parameters. The formula developed in the new experimental group (R2 = 0.74; P < 0.0001) explained 72% (P < 0.0001) of the variation in VO2max in the cross-validation group, a paired t-test showed no difference between measured and predicted VO2max, and Bland-Altman plotting showed no systematic bias.
Although a good correlation was seen between measured and predicted VO2max in the cross-validation group, care should be taken in applying the prediction formula on groups that differ in physical activity from the current study population.
1 Departments of Human Biology and
2 Methodology and Statistics, Maastricht University,
6200 MD Maastricht, The Netherlands
Submitted 7 November 2002
; accepted in final form 17 March 2003
We ...investigated seasonal variation in sleeping metabolic rate (SMR) and the
possible relation to body composition, thyroid activity, and leptin.
Twenty-five healthy volunteers were examined four times during the year: in
spring (April, May), summer (July, August), autumn (October, November), and
winter (January, February). Body composition was determined using a
three-compartment model based on underwater weighing and the deuterium
dilution method. SMR was measured during an overnight stay in a respiration
chamber. A blood sample was taken for the analysis of free and total
thyroxine, TSH, and leptin. SMR showed a significant seasonal variation
( P < 0.01) with a maximum in winter (4.54 kJ/min) and a minimum in
summer (4.34 kJ/min). The amplitude was 0.10 ± 0.02 kJ/min, and the
phase was November 5th. Season explained 17% of the intraindividual variation
in SMR. The circannual rhythm in SMR could not be explained by changes in body
composition, thyroid activity, or leptin. Interindividual variation in SMR was
explained by fat-free mass ( P < 0.001) and leptin ( P <
0.001).
body composition; thyroid-stimulating hormones; thyroxine; ambient temperature
Address for reprint requests and other correspondence: G. Plasqui, Dept. of
Human Biology, Maastricht University, PO Box 616, 6200 MD Maastricht, The
Netherlands (E-mail:
G.Plasqui{at}HB.Unimaas.NL ).
This study focused on developing a new method to assess VO2max outside laboratory conditions and without the need for maximal exertion. We hypothesized that the combined use of accelerometry and HR ...monitoring, under daily life conditions, could provide a good estimate of physical fitness.
Twenty-six healthy subjects (15 women, 11 men), aged 28 +/- 7 yr, performed a maximal incremental test on a bicycle ergometer to determine VO2max. Body composition was measured with underwater weighing and deuterium dilution using a three-compartment model. A triaxial accelerometer (Tracmor) and an HR monitor were worn for seven consecutive days under free-living conditions. The ratio of HR to activity counts per minute (ACM) was used as a fitness index (HR.ACM(-1)).
As hypothesized, HR.ACM(-1) was significantly correlated with VO2max. Using fat-free mass (FFM) (P < 0.0001), age (P = 0.025), and HR.ACM(-1) (P = 0.021) as the independent variables, the explained variation in VO2max was 76% (P < 0.0001, SEE = 363 mL x min(-1)). In order to generate a prediction formula that is applicable in the field when no data on body composition are available, the same analysis was done with body mass and gender in the model instead of FFM. HR.ACM(-1) was significantly (P = 0.023) correlated with VO2max. The total explained variation of the model was 71%, with a SEE of 409 mL x min(-1), or 13.7% of the average VO2max.
After correction for body composition, VO2max was significantly related to HR.ACM(-1). It is, to our knowledge, the first tool that yields a measure of VO2max by monitoring people in their daily life activities without the need for a specific protocol or for maximal exertion, and therefore is applicable to a large variety of subjects.
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.
The study aimed to compare differences in physical activity, the relationship between physical activity and body composition, and seasonal variation in physical activity in outpatients with anorexia ...nervosa (AN) and healthy controls. Physical activity (CM-AMT) and time spent in different intensities of 10 female individuals with AN and 15 female controls was assessed across three seasons along with the percentage body fat. The two groups did not differ in their physical activity and both demonstrated seasonal variation. The percentage body fat of individuals with AN, but not that of the controls, was negatively related to CM-AMT and time spent in low-moderate intensity activity (LMI). Seasonal variation in physical activity emerged with increases in engagement in LMI during the summer period for both groups. Possible interpretations of the finding that decreased physical activity was related to a normalization of percentage body fat in the individuals with AN are discussed and implications for treatment are highlighted.
Background: The purpose of this pilot study was to assess the feasibility of a structured activity protocol in a room calorimeter among young children. Methods: Five healthy children (age 5.2 plus or ...minus 0.4 y) performed an activity protocol in a room calorimeter, ranging from sedentary--to vigorous-intensity activities. Energy expenditure (EE) was calculated from continuous measurements of O sub(2)-consumption and CO sub(2)-production using Weir's formula. Resting EE was defined as EE during the first 30 min of the study where participants were seated while watching television. The children wore an ActiGraph accelerometer on the right and left hip. Results: The protocol was well tolerated by all children, and lasted 150 to 175 min. Further, differences were seen in both EE and accelerometer counts across 3 of the 4 activity intensities. Conclusions: It is feasible for young children to perform a structured activity protocol in a room calorimeter enhancing the possibility of conducting future studies to cross-validate existing accelerometer prediction equations.
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