Energy costs of pregnancy approximate 320 MJ in well-nourished women, but whether or not these costs may be partly covered by modifications in activity behavior is incompletely known. In healthy Swedish women: (1) to evaluate the potential of the Intelligent Device for Energy Expenditure and Physical Activity (IDEEA) to assess energy expenditure during free-living conditions, (2) to assess activity pattern, walking pace and energy metabolism in pregnant women and non-pregnant controls, and (3) to assess the effect on energy expenditure caused by changes in physical activity induced by pregnancy.
Activity pattern was assessed using the IDEEA in 18 women in gestational week 32 and in 21 non-pregnant women. Activity energy expenditure (AEE) was assessed using IDEEA, as well as using the doubly labelled water method and indirect calorimetry.
AEE using the IDEEA was correlated with reference estimates in both groups (r=0.4-0.5; P<0.05). Reference AEE was 0.9 MJ/24 h lower in pregnant than in non-pregnant women. Pregnant women spent 92 min/24 h more on sitting, lying, reclining and sleeping (P=0.020), 73 min/24 h less on standing (P=0.037) and 21 min/24 h less on walking and using stairs (P=0.049), and walked at a slower pace (1.1 ± 0.1 m/s versus 1.2±0.1 m/s; P=0.014) than did non-pregnant controls. The selection of less demanding activities and slower walking pace decreased energy costs by 720 kJ/24 h and 80 kJ/24 h, respectively.
Healthy moderately active Swedish women compensated for the increased energy costs of pregnancy by 0.9 MJ/24 h. The compensation was mainly achieved by selecting less demanding activities.
"This compares favorably with a recent English study conducted in obese and overweight nondiabetic pregnant women, which found that only 2 h out of a total 12.4 daytime hours (16%) was spent in light intensity activities, with 9.7 h (81%) spent sedentary, although an equivalent of 33 min were spent in moderate intensity exercise (23). Studies of healthy pregnancy from Switzerland (24) and Sweden (25) also reported lesser amounts of light intensity exercise (5.3 and 4.1 h, respectively), although the women in those studies spent rather more time in moderate to vigorous intensity exercise (57 and 63 min, respectively). "
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVE
To describe activity patterns in pregnant women with type 1 diabetes and evaluate the impact of increased structured physical activity on glucose control.RESEARCH DESIGN AND METHODS
Physical activity energy expenditure (PAEE) and glucose levels (continuous glucose monitoring) were measured in 10 pregnant women with type 1 diabetes (age 33.2 years, gestation 20 weeks, BMI 27.9 kg/m(2), diabetes duration 16.6 years, HbA(1c) 6.5% [48 mmol/mol], insulin pump duration 2.4 years) during a day at home (free-living) and during a 24-h visit incorporating controlled diet and structured physical activity with light intensity activity (three 20-min self-paced walks) and moderate intensity activity (two 50-min sessions of brisk treadmill walking). PAEE was evaluated through individually calibrated combined heart rate and movement sensing.RESULTSFree-living PAEE was comparable to that under controlled study conditions (3.8 and 5.1 kcal/kg/day, P = 0.241), with women achieving near to the recommended 30 min of moderate physical activity (median 27 min [interquartile range 14-68]). During the free-living period, more time was spent in light activity (10.3 vs. 7.2 h, P = 0.005), with less sedentary time (13.0 vs. 14.9 h, P = 0.047) and less moderate activity (27 vs. 121 min, P = 0.022). The free-living 24-h mean glucose levels by continuous glucose monitoring were significantly higher (7.7 vs. 6.0 mmol/L, P = 0.028). The effect of controlled diet and exercise persisted overnight, with significantly less time spent hyperglycemic (19% vs. 0%, P = 0.028) and less glucose variability (glucose SD 1.3 vs. 0.7 mmol/L, P = 0.022).CONCLUSIONSA controlled diet and structured physical activity program may assist women with type 1 diabetes in achieving optimal glucose control during pregnancy.
Diabetes care 02/2013; 36(5). DOI:10.2337/dc12-1567 · 8.42 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Few valid, objective methods exist to quantify physical activity and predict energy expenditure (EE) during pregnancy.
The purpose of this study was to evaluate the validity of the SenseWear Mini armband monitor (SWA) (BodyMedia, Pittsburgh, PA) to estimate EE in pregnant women.
Thirty healthy pregnant women (22-24 wk of gestation) completed a series of activities of daily living (typing, laundry, sweeping, and treadmill walking: 2.0, 2.5, 3.0, and 3.0 mph, 3% incline) while EE was estimated by the SWA and measured by indirect calorimetry (IC). The SWA data were processed using both the v2.2 algorithm and the newer v5.2 algorithm. The estimated EE values were compared with the measured EE values using a three-way (method × algorithm × activity) mixed model ANOVA. Least square means ± SE were estimated in the model. Significance was set at P < 0.05.
The analyses revealed a significant method (IC vs. SWA) × algorithm (v5.2 vs. v2.2) interaction with significantly smaller error (IC-SWA) for the newer v5.2 algorithm (-0.57 ± 0.06 kcal·min) than the older v2.2 algorithm (-0.78 ± 0.06 kcal·min). The SWA significantly overestimated EE for all activities, except inclined walking. The average mean absolute percentage error was considerably lower for the new algorithm (22%) than that for the older algorithm (35%). The average individual correlation coefficients revealed good overall agreement between the SWA and the IC (v5.2, mean r = 0.93; v2.2, mean r = 0.87).
Overall, the SWA correlated well with IC; however, EE was significantly overestimated during most activities. Future studies should develop pregnancy-specific algorithms and assess validity of the SWA at all stages of pregnancy to further improve prediction of EE in this population.
Medicine and science in sports and exercise 05/2012; 44(10):2001-8. DOI:10.1249/MSS.0b013e31825ce76f · 3.98 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The assessment of physical activity in healthy populations and in those with chronic diseases is challenging. The aim of this systematic review was to identify whether available activity monitors (AM) have been appropriately validated for use in assessing physical activity in these groups. Following a systematic literature search we found 134 papers meeting the inclusion criteria; 40 conducted in a field setting (validation against doubly labelled water), 86 in a laboratory setting (validation against a metabolic cart, metabolic chamber) and 8 in a field and laboratory setting. Correlation coefficients between AM outcomes and energy expenditure (EE) by the criterion method (doubly labelled water and metabolic cart/chamber) and percentage mean differences between EE estimation from the monitor and EE measurement by the criterion method were extracted. Random-effects meta-analyses were performed to pool the results across studies where possible. Types of devices were compared using meta-regression analyses. Most validation studies had been performed in healthy adults (n = 118), with few carried out in patients with chronic diseases (n = 16). For total EE, correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices. For active EE, correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices. Uniaxial devices tended to underestimate TEE (-12.07 (95%CI; -18.28 to -5.85) %) compared to triaxial (-6.85 (95%CI; -18.20 to 4.49) %, p = 0.37) and were statistically significantly less accurate than multisensor devices (-3.64 (95%CI; -8.97 to 1.70) %, p<0.001). TEE was underestimated during slow walking speeds in 69% of the lab validation studies compared to 37%, 30% and 37% of the studies during intermediate, fast walking speed and running, respectively. The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome. Triaxial and multisensor devices tend to be more valid monitors. Since activity monitors are less accurate at slow walking speeds and information about validated activity monitors in chronic disease populations is lacking, proper validation studies in these populations are needed prior to their inclusion in clinical trials.
International Journal of Behavioral Nutrition and Physical Activity 07/2012; 9(1):84. DOI:10.1186/1479-5868-9-84 · 4.11 Impact Factor
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