Original Article

European Journal of Clinical Nutrition (2011) 65, 1295–1301; doi:10.1038/ejcn.2011.129; published online 27 July 2011

Physical activity pattern and activity energy expenditure in healthy pregnant and non-pregnant Swedish women

M Löf1

1Division of nutrition, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden

Correspondence: Dr M Löf, Division of nutrition, Department of Clinical and Experimental Medicine, Linköping University, SE 581 85 Linköping, Sweden. E-mail: marie.lof@liu.se

Received 22 November 2010; Revised 10 June 2011; Accepted 14 June 2011; Published online 27 July 2011.

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Abstract

Background/Objectives:

 

Energy costs of pregnancy approximate 320MJ 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.

Subjects/Methods:

 

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.

Results:

 

AEE using the IDEEA was correlated with reference estimates in both groups (r=0.4–0.5; P<0.05). Reference AEE was 0.9MJ/24h lower in pregnant than in non-pregnant women. Pregnant women spent 92min/24h more on sitting, lying, reclining and sleeping (P=0.020), 73min/24h less on standing (P=0.037) and 21min/24h less on walking and using stairs (P=0.049), and walked at a slower pace (1.1±0.1m/s versus 1.2±0.1m/s; P=0.014) than did non-pregnant controls. The selection of less demanding activities and slower walking pace decreased energy costs by 720kJ/24h and 80kJ/24h, respectively.

Conclusion:

 

Healthy moderately active Swedish women compensated for the increased energy costs of pregnancy by 0.9MJ/24h. The compensation was mainly achieved by selecting less demanding activities.

Keywords:

activity energy expenditure; activity pattern; IDEEA; pregnancy; walking pace

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Introduction

During pregnancy an optimal intake of dietary energy is important. Excessive gestational weight gain increases the risk for pregnancy complications (Institute of Medicine and National Research Council, 2009), and fetal over- and undernutrition are both associated with impaired health in adult life (Robinson, 2001; Baird et al., 2005; Michels and Xue, 2006). Energy requirements increase during pregnancy because of tissue growth, an elevated basal metabolic rate (BMR) and the increased energy costs of moving a heavier body. The increased costs average 320MJ (World Health Organization, 2004). As early as 1971, Hytten and Leitch suggested that this increase may be offset at least in part by decreased physical activity, and in 1985, the World Health Organization suggested a lower allowance in women who reduce their activity during pregnancy. Prentice et al. concluded in 1996 that about half of the energy costs could theoretically be spared by reductions in physical activity. However, in 2005 Butte and King pointed out that many women may not have the possibility of reducing their activity after conception. Current World Health Organization (2004) recommendations do not take such reductions into account, but recent British guidelines suggest an additional energy intake during the last trimester of only 0.8MJ/day, motivated by ‘adaptive changes in energy expenditure’ (Scientific Advisory Committee on Nutrition, 2009).

During pregnancy, the interactions between physical activity and energy metabolism are complex. Pregnant women may reduce the energy expended in response to physical activity by selecting less demanding activities or by reducing the pace of activities. However, during pregnancy the energy cost of physical activity is increased because of a heavier body. To obtain relevant information in relation to dietary energy needs during pregnancy, studies should be carried out during free-living conditions using appropriate methodology. When studying intensity and duration of activities during pregnancy most authors have used questionnaires or diaries (Prentice et al., 1996; Poudevigne and O’Connor, 2006), methods with an inherent risk for bias, while few studies have used objective methods (Lof and Forsum, 2006; Poudevigne and O’Connor, 2006; Rousham et al., 2006; Melzer et al., 2009). Only two of these studies included simultaneous assessments of energy metabolism (Lof and Forsum, 2006; Melzer et al., 2009). Van Raaij et al. (1990) reported that when women were told to walk at their own pace on a treadmill the self-selected pace declined in late gestation, but so far no study has investigated whether pregnant women modify the pace of their activities using objective methodology during free-living conditions.

The Intelligent Device for Energy Expenditure and Physical Activity (IDEEA), a multiple accelerometer-based system, can identify time spent lying down, sitting, standing, walking or running, and can estimate the pace at which activities are performed (Zhang et al., 2003, 2004). This device has been used to describe activity patterns during shorter time periods (<24h; for example, Saremi et al., 2006; Marsh et al., 2007; Welk et al., 2007). However, it also has potential for use in studies with longer time periods, but so far no study has taken advantage of this possibility.

The aims of this study in healthy Swedish women were: (1) to evaluate the potential of the IDEEA to assess energy metabolism for several days 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 the changes in physical activity induced by pregnancy. The hypothesis was that in pregnant women the amount of energy expended in response to physical activity is reduced when compared with non-pregnant controls, because of modifications in activity pattern and walking pace.

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Subjects and methods

Subjects and study outline

A total of 39 healthy, non-smoking women (21 non-pregnant/non-lactating and 18 pregnant) were recruited by means of advertisements in the local press during 2007 and 2008. Data on energy metabolism for the non-pregnant women have been reported (Bexelius et al., 2010). The pregnant women, all with uncomplicated pregnancies, were investigated in gestational week 32. Each woman collected two to three urine samples at home and brought them to the measurement session that took place on day 0 of the study and started with a measurement of BMR. The woman was then given a dose of doubly labelled water, and was asked to collect six urine samples during the subsequent 14 days (days 1–15) to measure her total energy expenditure (TEE) during days 1–5 (TEEref) as well as during days 1–15. The urine samples were to be taken in the morning on days 1, 4, 6, 8, 11 and 15 and the woman was asked to carefully note the time of sampling. Reference activity energy expenditure (AEEref) was TEEref minus BMR. The IDEEA was attached to the woman's body on day 0 to assess her activity pattern during days 1–5. This pattern was used to calculate TEE (TEEIDEEA) and AEE (AEEIDEEA). Background variables (for example, age, occupation and exercise habits) were assessed by means of a questionnaire. This study was approved by the Central Ethics Board, Stockholm, Sweden. Verbal informed consent, witnessed and formally recorded, was obtained from all women.

Energy expenditure using reference methods

CO2-production and O2-consumption were measured during 20min after an overnight fast and 45min of rest (Deltratrac Metabolic Monitor, Datex Instrumentarium Corp, Helsinki, Finland), and converted to BMR (Weir, 1949). Each woman was given an accurately weighed dose of isotopes (0.09g 2H20 and 0.23g H2180 per kg body weight). Isotopic enrichments of dose and urine samples were analyzed as previously described (Lof et al., 2003). Analytical precision for results expressed in p.p.m. were 0.22 for 2H and 0.03 for 18O. CO2-production was calculated assuming 30% of water losses to be fractionated (Coward, 1988). TEE was calculated from CO2-production by means of the equation by Weir (1949) assuming a food quotient of 0.85. According to Black et al. (1986), this value is typical for a Western diet, and all women in this study reported to be omnivores and eating a Western diet. The ratio between 2H and 18O-dilution spaces was 1.033±0.006 and 1.031±0.004 for non-pregnant and pregnant women, respectively. When the same dose and urine samples for one subject were analyzed nine times, the following coefficients of variation were obtained: TEE (1.2%), total body water (0.3%) and fractional turnover rate constants (0.3% or less), all well within the recommended criteria (International Atomic Energy Agency, 2009).

The IDEEA

Data collection
 

The IDEEA device (Minisun, Fresno, CA, USA) consists of a microprocessor/storage unit, which is attached to the waist, and five sensors connected with wires that are attached to the front of the thighs, the soles of the feet and the sternum. The sensors send information about angles of body segments and accelerations in two orthogonal directions to the microprocessor unit for identification of eight activities (lying down, reclining, transition, sitting, standing, walking, using stairs and running), and for calculation of their duration and the speed at which they are performed. Initiation, calibration, recordings and data analysis were conducted according to the manufacturer (http://www.minisun.com and Ming Sun, MiniSun LLC, Fresno, CA, USA, personal communication). The woman was instructed to wear the IDEEA all the time, except when in water or sleeping, and to record in a notebook when the device was taken off, as well as the activities that were performed then (for example, showering or sleeping). She was also instructed to change batteries twice during the study period, as the battery capacity of the IDEEA is only 48h. The results are based on recordings during 3, 4 and 5 days for 2, 2 and 14 pregnant women, respectively. The corresponding figures for non-pregnant women were 2, 3, 4 and 5 days for 2, 1, 4 and 14 women, respectively. The readings obtained during these days covered 98±2% (non-pregnant women) and 97±5% (pregnant women) of all time in the waking state.

Activity pattern and walking pace
 

To obtain the activity pattern of a woman, that is, the average number of minutes spent in each activity category per 24h, the total number of minutes spent in each of the eight activity categories identified using the IDEEA (that is, lying down, reclining, transition, sitting, standing, walking, using stairs and running) as well as sleeping (obtained from the notebook) was divided by the number of recorded 24h-periods. Furthermore, the number of minutes spent sleeping, lying down, reclining and sitting per 24h was summarized as time spent on sedentary activities (metabolic equivalent or MET <1.5), whereas walking and using stairs was summarized as moderate activities (MET: 3–6) (American College of Sports Medicine, 2006). For each woman, walking pace, representing the average value for the time recorded as walking during the complete study period, was obtained from the IDEEA output.

Calculation of TEEIDEEA and AEEIDEEA
 

For each woman, energy expenditure in each of the nine activity categories was the number of minutes spent in that particular category according to her activity pattern, obtained as described above, multiplied by an appropriate MET-value and by her BMR in kJ/min. To obtain TEEIDEEA, energy expended in each of the nine categories was summarized. For non-pregnant women, the MET-values were: sleeping (0.9), lying down (1.0), reclining (1.8), transition (1.8), sitting (1.5), standing (2.8), walking (3.4), using stairs (5.5) and running (10; Ainsworth et al., 2000). Except for lying down and sleeping, these MET-values were multiplied by 0.88 for pregnant women. This figure is based on data by Prentice et al. (1996) for cycling and walking (average 0.88 for these two activities) and on our own data where MET-values for sitting, cycling, walking and running were assessed using indirect calorimetry in pregnant and non-pregnant women (average 0.88 for all four activities; Löf M et al., unpublished). AEEIDEEA was calculated as TEEIDEEA minus BMR.

Calculating how a pregnant versus a non-pregnant activity pattern influences TEE of a pregnant woman

To assess the effect of the pregnancy-induced changes in physical activity on energy expenditure, TEE of a pregnant woman was calculated using activity patterns typical for the pregnant as well as for the non-pregnant state. Subsequently, the difference in TEE thus obtained for the two activity patterns was calculated. Furthermore, energy expenditure (in kJ/min) during walking, obtained using MET factors (Ainsworth et al., 2000) appropriate for the recorded walking speed, was calculated for a ‘pregnant’ and ‘a non-pregnant’ walking pace. During these calculations, a BMR appropriate for a pregnant woman and MET-values multiplied by 0.88 to adjust for pregnancy as described above were used.

Statistics

Values are means±s.d. Significant differences between groups were identified using independent t-tests if the data were normally distributed, and otherwise the Mann–Whitney method was used. Significance was accepted when P<0.05. Statistical analyses were conducted using the Statistica software, version 8.0 (Statsoft, Scandinavia AB, Uppsala, Sweden).

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Results

Characteristics of the women

Characteristics of the women are given in Table 1. Means and ranges for age, height and non-pregnant body mass index were similar among the two groups. Before conception, pregnant women were engaged in similar occupations and had similar exercise habits as non-pregnant controls. The pregnant women gained 12±3kg of body weight during the complete pregnancy. They delivered healthy full-term infants (birth weight and length: 3430±460 grams and 51±2cm).


Energy expenditure assessed by means of reference methods

In Table 2, TEEref, AEEref and BMR are given. BMR was significantly higher in pregnant than in non-pregnant women. On average, AEEref was 930kJ/24h (or 18%) lower in pregnant than in non-pregnant women (P<0.05). When expressed per kg body weight, AEEref was on average 21.4kJ per 24h and kg body weight (or 28%) lower in the pregnant state (P<0.001). Furthermore, as shown in Table 2, TEEref/BMR or physical activity levels (PALs) were significantly lower in pregnant women than in non-pregnant women.


Activity pattern and walking pace

All women accepted to wear the IDEEA for 5 days, although several of them reported some discomfort from wearing the sensors with wires. All women were also able to apply the device and successfully complete the two battery changes. Table 3 shows the average number of minutes spent per 24h in the different activity categories by pregnant and non-pregnant women. There were no significant differences between the two groups in times spent sleeping, lying down, sitting, reclining, in transition, walking, using stairs or running. However, pregnant women spent 73min less on standing activities (P=0.037). Instead, they spent 92min more on sedentary activities (sitting, sleeping, reclining and lying down; P=0.020), and 21min less (P=0.049) on moderate activities (walking and using stairs) than did non-pregnant women. On average, pregnant women walked at a slower pace than non-pregnant women (1.1±0.1m/s compared with 1.2±0.1m/s; P=0.014).


Energy expenditure assessed by means of the IDEEA

Energy expenditure assessed by means of the IDEEA for each of the investigated activities as well as TEEIDEEA are shown in Table 4 for the women in the study. TEEIDEEA was not significantly different between pregnant and non-pregnant women. TEEIDEEA minus TEEref was −1.0±1.1 and −1.0±1.1MJ/24h in non-pregnant and pregnant women, respectively. TEEIDEEA was correlated with TEEref both in non-pregnant (r=0.66; P=0.001) and pregnant (r=0.81; P<0.001) women. Correspondingly, AEEIDEEA was correlated with AEEref in non-pregnant (r=0.44; P<0.05) as well as in pregnant (r=0.49; P<0.05) women.


TEE for a pregnant woman having a pregnant versus a non-pregnant activity pattern

Table 5 shows the calculation of energy expenditure for a pregnant woman with the non-pregnant versus the pregnant activity patterns shown in Table 3. The difference in TEE between the two patterns represents ~720kJ/24h. Furthermore, walking for 1h per day at the ‘non-pregnant walking speed’, 1.2m/s, rather than at the ‘pregnant walking speed’, 1.1m/s, reduced TEE further by ~80kJ/24h. Thus, the total energy savings obtained by modifications in physical activity were estimated to be 800kJ/24h.


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Discussion

The prevalence of overweight and obesity among the women in this study was in agreement with such figures for Swedish women of corresponding age (Swedish National Institute of Public Health, 2011), and the birth weight of their babies was comparable to that of healthy Swedish newborns (National Board of Health and Welfare, 2011). The non-pregnant women had an average PAL value of 1.86, which is comparable to earlier studies using the doubly labelled water method in Western women (Forsum et al., 1992; Lof et al., 2003; Food and Nutrition Board and Institute of Medicine, 2005), and hence they are classified as moderately active (World Health Organization, 2004). The controls had similar body mass index levels, occupations and exercise habits as pregnant women before conception. Thus, the participants in both groups represent healthy, moderately active Swedish women.

TEE minus BMR was considered to represent energy expenditure in response to physical activity. This estimate includes dietary-induced thermogenesis, a component representing 5–10% of TEE and not considered to be affected by pregnancy (Prentice et al., 1996). In competent hands, the doubly labelled water method is able to produce estimates of TEE in non-pregnant individuals with an accuracy of 1–3% (Speakman, 1998; International Atomic Energy Agency, 2009). Prentice (1988) discussed potential errors associated with the pregnant state, and concluded that TEE estimates obtained using the doubly labelled water method are as valid in pregnant as in non-pregnant women. TEE and AEE were assessed during 5 instead of 10–14 days, which is considered as the standard procedure (International Atomic Energy Agency, 2009). However, using TEE based on samples collected during days 1–15 when evaluating this study did not affect the results and conclusions in any important way.

When assessing activity pattern it is crucial that all time is covered. The amount of time not wearing the IDEEA during recorded days was small in both groups. In each group, 14 women provided data for 5 complete days. The results and conclusions of this study were the same when calculations were based on data for these women only.

The women in this study accepted to wear the IDEEA for several days during normal free-living conditions, although a few reported some discomfort from the wires. All of the women were able to change batteries and apply the sensors without interrupting the measurements. Activity pattern assessed using the IDEEA was converted to energy expenditure by assigning MET-values to the different activity categories. For several reasons, these calculations are relatively imprecise. Thus, in the present study activities were classified into only nine categories, whereas the paper by Ainsworth et al. (2000) gives MET-values for more than 600 activities. Furthermore, the same factors were used for all subjects, although individual variation in MET-values is certainly present. Nevertheless, in the control group TEEIDEEA was correlated with TEEref (r=0.66). Furthermore, in this group average TEEIDEEA agreed well with average TEEref. These results support the statement that the IDEEA is able to provide valid assessments of activity pattern in humans during free-living conditions. Furthermore, in pregnant women TEEIDEEA was also correlated with TEEref (r=0.81), and on average, these two estimates were also in good agreement in this group. These results indicate that when activity pattern was assessed by means of the IDEEA, the accuracy was as high in the pregnant group as in the controls.

Compared with non-pregnant controls, pregnant women spent less time (1.5h/24h) on standing and moderate activities and more time (1.5h/24h) on sedentary activities, and their AEE was 18% lower. These findings agree with an American study in which AEE in late pregnancy was reduced by 13%, a decrease confirmed by activity records (Butte et al., 2004). Also, a recent Swiss study on pregnant women reported modifications in activity behavior obtained using a combined accelerometer and heart rate recorder (Melzer et al., 2009). However, in a previous study on healthy Swedish women, we found no major effect of pregnancy on activity pattern or on AEE (Lof and Forsum, 2006).

In this study, average PAL was 1.59 in pregnant women and this value was significantly lower than the corresponding value for their non-pregnant controls (1.86). However, as pointed out by Prentice et al. (1996) the use of PAL in pregnant women is not comparable to non-pregnant individuals since even if AEE (TEE–BMR) is unchanged, PAL will decrease as BMR increases during pregnancy. Thus, investigations of the effect of pregnancy on energy expenditure in response to physical activity should not rely on PAL values only.

A unique and interesting observation in the present study is that, on average, pregnant women walked more slowly than did non-pregnant women, supporting the suggestion by Prentice et al. 1996 that pregnant women may compensate for the increased energy costs of movements by performing activities at a slower pace. Such behavior has previously been shown in laboratory settings (Banerjee et al., 1971; Blackburn and Calloway, 1974), but not during free-living conditions. For the women in the present study, who walked for about 1h/day, the decreased walking pace reduced TEE by only 80kJ/24h, whereas about 720kJ/24h was saved by selecting less demanding activities. Further savings by reducing the pace of other activities are theoretically possible, but likely to be small considering the moderate activity level of the women in the present study.

This study suggests that moderately active Swedish women in late pregnancy reduce their energy expenditure by about 0.9MJ/24h by modifications in their behavior. The lower AEE in pregnant women (−0.9MJ/24h) was almost of the same magnitude as the difference in BMR when compared with their non-pregnant controls (+1.1MJ/24h). Furthermore, the proposed reduction is also quite large considering that the total additional energy needed during the third trimester has been calculated to be 2MJ/24h according to the World Health Organization (2004). However, all pregnant women are certainly not able to reduce their physical activity. Until more knowledge is available it is important to emphasize that individual women may be very different in this respect, and more studies in this area are warranted.

In conclusion, healthy moderately active Swedish women were found to compensate for the increased energy costs of pregnancy by about 0.9MJ/24h. The compensation was mainly achieved by selecting less demanding activities, and it corresponded to nearly half of the estimated additional energy needs in the third trimester. The findings are important in relation to energy requirements and dietary guidelines during pregnancy.

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Conflict of interest

The author declares no conflict of interest.

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Acknowledgements

This study was supported by FORMAS, the Magnus Bergvall Foundation, the Thuring Foundation and the Swedish Society of Medicine.

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