Participation in physical activity during pregnancy may reduce the risk of gestational diabetes mellitus (12,13) and preeclampsia (23,25) and help prevent excess maternal weight gain (10). These potential benefits are reflected in the recent guidelines of the American College of Obstetricians and Gynecologists (ACOG) (1), which recommend that, in the absence of either medical or obstetric complications, pregnant women adopt the same guidelines for exercise as those issued by the Surgeon General (28) for nonpregnant women. Specifically, healthy pregnant women are advised to accumulate 30 min or more of moderate-intensity activity per day on most, if not all, days of the week.
According to national surveys, women tend to report less physical activity than men (8). However, most of these surveys were developed and validated in men and emphasize participation in moderate- and vigorous-intensity sports. In addition, most failed to include household or child care activity, which comprise a substantial portion of physical activity among women (4). Patterns of activity for women also change during pregnancy. Specifically, physical activity tends to be of lower duration, frequency, and intensity as compared with nonpregnant women (16,21,30). In addition, household, child care, and occupational activities contribute more to both moderate-intensity and overall energy expenditure during pregnancy than recreational and sport activities (24).
Therefore, questionnaires designed for nonpregnant women and men that fail to measure total physical activity (household, occupational, and recreational) may be less sensitive to differences in activity levels among pregnant women. These questionnaires may include activities that fail to discriminate among pregnant women or may omit activities that do discriminate (e.g., low-intensity activities), thus misclassifying pregnant women as sedentary when the opposite is true (2). This misclassification can bias studies designed to assess the relationship between pregnancy physical activity and maternal and fetal health, limiting the ability to detect important associations with disease (2,19). Therefore, the challenge for a pregnancy physical activity questionnaire is to discriminate within a potentially narrower range of physical activity, in other words, to rank pregnant women in categories from sedentary to most active.
To our knowledge, the Pregnancy Physical Activity Questionnaire (PPAQ) is the only validated instrument designed specifically to assess physical activity during pregnancy (9). However, it is also critical to evaluate the ability of existing questionnaires to assess pregnancy physical activity. The use of questionnaires validated in nonpregnant populations of women would facilitate comparisons between pre- and postpartum physical activity and pregnancy physical activity. This is important for national surveys that use existing instruments to collect long-term trends on physical activity among women. In addition, use of a questionnaire validated for both prepregnancy and pregnancy would also facilitate analyses that combine pre- and early pregnancy units of activity into a composite risk factor for maternal/fetal outcomes.
The Kaiser Physical Activity Survey (KPAS), adapted from the Baecke physical activity survey, was designed specifically to assess physical activity in women (6). Unique features of the KPAS include the assessment of multiple domains of physical activity (household/caregiving, occupational, active living, and sports/exercise) (26). The KPAS is substantively different than the PPAQ. For example, activities were selected for the PPAQ based on their ability to discriminate between subjects regarding physical activity energy expenditure. This approach prioritizes the ability of the questionnaire to correctly classify subjects into activity rankings (e.g., quartiles of activity) and avoids unnecessarily lengthy questionnaires. Alternatively, the KPAS was designed to measure the full range of activity in women. It provides a comprehensive assessment of each domain of activity, which may be more useful for studies in which physical activity is the primary exposure measure. The KPAS has been validated in nonpregnant women (5); however, the validity and reliability of the KPAS in estimating activity levels among pregnant women have not been evaluated.
Therefore, the purpose of the current study was to evaluate the reliability and validity of the KPAS in estimating physical activity in pregnant women. Specific goals were 1) to estimate the 1-wk test–retest reliability of the KPAS measures of total activity as well as indices of household/caregiving, occupational, active living, and sports/exercise; 2) to estimate the validity of the KPAS measures using an objective comparison measure of physical activity (the Actigraph accelerometer); and 3) to estimate the validity of the KPAS measures using a subjective comparison measure of physical activity (the PPAQ).
Study design and population.
Participants for this analysis were recruited from June 2002 through January 2003 as part of a larger study primarily designed to develop and validate a pregnancy physical activity questionnaire. The design of the study has been previously described (9). Specifically, we recruited 63 participants from Western Massachusetts via flyers posted at local health clinics, recruitment at a hospital prenatal care clinic, and advertisements in local papers. Women at any stage of pregnancy were eligible for the study unless they had any one of the following characteristics: type 2 diabetes, hypertension, heart disease, chronic renal disease, nonsingleton pregnancy, or younger than 16 yr of age. Participants read and signed a written informed consent approved by the institutional review board of the University of Massachusetts, Amherst. Participants first completed the self-administered PPAQ followed by the interviewer-administered KPAS. Participants were then fitted with an ActiGraph accelerometer by ActiGraph LLC (Fort Walton Beach, FL) (formerly the MTI Actigraph/accelerometer) and instructed to wear it for the following 7 d. At the end of the 7-d period, participants again completed the PPAQ and the KPAS in the same order as described above.
We used a modified version of the KPAS to obtain detailed information on physical activity patterns during pregnancy. Questions in the KPAS are grouped into four sections titled: 1) “Household and Family Care Activities” (11 items), which includes child and elder care activities, meal preparation, major cleaning, shopping, gardening, and yard work; 2) “Occupational Activities” (11 items), which includes sitting, standing, walking, lifting heavy loads, and sweating from exertion; 3) “Active Living Habits” (four items), which includes TV watching, walking and bicycling to and from work, school, or errands; and 4) “Participation in Sports and Exercise” (up to 15 items), which includes the frequency and duration of up to three frequently performed sports or exercises.
For the purposes of the current study, several modifications to the KPAS were made. The KPAS was interviewer administered as opposed to self-administered to reduce potential language or literacy barriers. A visual handout of the categorical responses was used during the interview to maximize subject understanding. Finally, all questions were assigned the time frame of the current trimester of pregnancy. To aid recall, patients were provided with the start date of the current trimester calculated from their self-reported last menstrual period.
Activity indices were created for each domain of activity (household/caregiving, occupational, active living, sports/exercise) by summing the domain-specific categorical responses and dividing by the number of items, giving an average value that ranged from 1 to 5. Specifically, the household/caregiving index was calculated as the average categorical response to questions about the hours per day or week spent in household/caregiving activities with each categorical response assigned a value of 1 to 5 representing increasing levels of participation. The occupational index was calculated as the average of five-level categorical responses to questions about occupational activity with category options ranging from never to always. For the purposes of the current study, we used seven items instead of eight to calculate the index of occupational activity. Specifically, we eliminated the “occupational intensity code” (which uses job title and characteristics to assign occupational intensity) due to the lack of occupational intensity data available for pregnant women and the substantial potential for misclassification. For women reporting no occupational physical activity, the occupational index was assigned a value of 1 (“none”). The active living index was calculated as the average of five-level categorical responses to questions about the hours or sessions per day or week spent in active living habits. The sports/exercise index was calculated as the average of five-level categorical responses to global questions about participation in sports/exercise (three items) plus a five-level ordinal mapping of a continuous activity score derived from frequency and duration of participation in specific sports and exercise activities. Frequency was multiplied by duration and by MET value and summed over all activities. One MET is equivalent to the oxygen consumption required at rest or approximately 1 kcal·kg−1·h−1. The MET values of 3.5, 5, and 7 were used to represent the average MET values of the activities in each intensity category.
A total activity index was calculated as the simple sum of all four indices for those participants with none of the four indices missing, specifically:
A second measure of total activity was calculated as the weighted sum of each activity index according to the relative contribution of each type of activity to total activity energy expenditure during pregnancy (24), specifically:
These weights reflect the greater proportion of energy expenditure attributable to household/caregiving activities as opposed to sports/exercise during pregnancy (24).
The Actigraph accelerometer.
The KPAS measure of current trimester activity was compared with the ActiGraph accelerometer as an objective measure of current trimester physical activity. The Actigraph accelerometer is a uniaxial Actigraph that detects vertical accelerations ranging in magnitude from 0.05 to 2.00 g with frequency response from 0.25 to 2.50 Hz. The above parameters detect normal human movement while filtering out high-frequency movements such as vibrations. The filtered acceleration signal is digitized and the magnitude is summed over a user-specified time interval (epoch). A 1-min epoch was used in the current study. At the end of each epoch, the activity count is stored in memory and the accumulator is reset to zero (11). The Actigraph accelerometer has been strongly correlated with energy expenditure during treadmill walking and running measured via open-circuit spirometry (r = 0.88) (20) as well as directly measured oxygen consumption during moderate-intensity activities in the field (r = 0.59 for all activities combined and r = 0.77 for outdoor walking) (15).
The accelerometer was affixed with an expandable elastic belt on the right hip under clothing and worn during the waking hours of the 7 d between questionnaire administrations, thus providing an objective measure of physical activity during the current trimester. Women were instructed to wear the belt snugly and to check the monitor placement regularly. While wearing the accelerometer, women were given a daily wear log on which to note whether they removed the accelerometer for longer than 1 h to swim, shower, or nap. None of the participants self-reported removing the accelerometer.
The accelerometer data for each subject were downloaded into a computer using the reader interface unit. Through examining minute-by-minute accelerometer output, we identified time during which the accelerometer was not worn. Specifically, we assumed that the accelerometer was not worn during periods when the output was equal to zero for ≥15 continuous minutes. We used this objective estimate to exclude days when the accelerometer was not worn for at least 8 h. A custom software program was used to determine the number of minutes per day spent in activity of moderate intensity and above using three published count cut points: ≥191 (Hendelman et al. (15)), ≥574 (Swartz et al. (27)), and ≥1952 (Freedson et al. (14)). Average counts per minute was defined as the mean accelerometer output per 1-min epoch, reflecting raw accelerometer output without any categorization according to activity intensity.
The KPAS was also compared with the PPAQ, a questionnaire designed to assess the duration, frequency, and intensity of total physical activity during the current trimester in pregnant women (9). The PPAQ is a self-administered semiquantitative questionnaire with 35 questions including household/caregiving, occupational, sports/exercise, transportation, and sedentary activities. Activities were selected for inclusion on the PPAQ based on their ability to discriminate between women, with the goal of ranking women into groups of least to most active. For each activity, respondents were asked to select the category best estimating the amount of time spent in that activity per day or week during the current trimester. At the end of the PPAQ, an open-ended section allowed the respondent to add activities not already listed. Recently, our group reported statistically significant correlations between the PPAQ and accelerometer measures ranging from r = 0.08 to 0.43 for total activity supporting the validity of the PPAQ for use during pregnancy (9).
From the PPAQ, the number of hours spent in each activity was multiplied by activity intensity to arrive at a measure of average daily energy expenditure (MET·h·d−1) attributable to each activity. Intensity values were derived from the compendium of physical activities (3), with the exception of walking and light housework activities, for which field-based direct measures of energy expenditure for pregnant women were used (9). Activities of light intensity and above were summed to derive average MET-hours per day for total activity and the average number of MET-hours per day spent in each activity type was calculated. Three domain-specific measures of energy expenditure were calculated (household/caregiving, occupational, and sports/exercise) to correspond to KPAS domain-specific activity indices. Activity estimates obtained from the two PPAQ administrations were averaged to obtain summary values.
Date of birth, prepregnancy height and weight, race/ethnicity, education, income, date of last menstrual period, and delivery due date were collected via interviewer-administered questionnaire. Date of last menstrual period, or if missing, delivery due date, was used to determine each participant’s trimester of pregnancy.
All analyses were conducted using the Statistical Analysis System (SAS Institute, Cary, NC) version 9.1. Median (25th and 75th quartiles) values of activity from both administrations of the KPAS were calculated. In addition, both mean (SD) and median (25th and 75th quartiles) values from the PPAQ (average of both administrations) and accelerometer (averaged over 7 d of measurement) were calculated. Intraclass correlation coefficients were used to describe the reproducibility of the KPAS and were calculated as the proportion of total variance explained by between-subject variance. Between- and within-subject variance components were estimated using log-transformed data assuming a compound symmetric covariance structure using SAS PROC MIXED.
Because the activity scores were not normally distributed, Spearman correlation coefficients were used to measure the correlation between the KPAS, PPAQ, and the accelerometer values to evaluate the validity of the KPAS. In addition, KPAS index scores for each activity type were correlated to like estimates from the PPAQ (i.e., KPAS occupational index vs PPAQ occupational energy expenditure). Pearson correlation coefficients did not differ substantively and are therefore not presented.
Finally, because the goal of questionnaires in epidemiologic research is to measure relative rather than absolute activity levels, we grouped the participants into tertiles of energy expenditure according to the KPAS data, and, for each tertile, calculated the mean accelerometer as well as PPAQ values. In this manner, we evaluated whether the grouping of participants into tertiles based on the KPAS yielded groups with different “true” activity.
A total of 61 of the 63 women completed both administrations of the KPAS and the PPAQ. Accelerometer data were available for 54 of these women, as two participants experienced accelerometer failure and five women failed to complete the validation study protocol. To prevent significant underestimation of daily activity, we excluded 1–3 d of measurement for 14 participants (a total of 23 d overall) because the accelerometer was not worn for at least 8 h during those days. Therefore, the final validation sample included 54 participants with a mean length of accelerometer wear of 6.6 d.
Participants ranged in age from 18 to 47 yr (mean age = 29.5 yr) and represented all three trimesters of pregnancy; 30% were in the first trimester, 31% were in the second trimester, and 39% were in the third trimester. Based on prepregnancy body mass index (BMI) values, 36.1% of women were classified as overweight or obese (BMI ≥ 25 kg·m−2). In terms of race/ethnicity, 65.6% of women were white, 31.2% were Hispanic, 1.6% were African American, and 1.6% were of other ethnicity. Almost half (47.5%) of the women were college educated, whereas 14.8% had less than a high school education. Approximately 52.5% of women reported annual household incomes of at least $30,000, although a fifth (21.3%) of participants had household incomes of less than $15,000.
The median index score for total activity from the first KPAS administration was slightly higher than, but comparable with, that obtained from the second administration (Table 1). A similar trend was observed for the median index values of household/caregiving and sports/exercise, whereas the median index values of occupational and active living remained constant between administrations. Within each administration, the median values for the four domain-specific activity index scores were fairly comparable with each other. For example, in the first administration slightly lower median index values observed for household/caregiving (2.44) and occupational activity (2.29) and higher median index values were observed for active living (2.75) and sports/exercise (3.00). The median weighted total activity score (10.00) was slightly lower than the unweighted total activity score (10.70) due to the smaller weight (0.05 vs 0.25) assigned to sports/exercise activity, which had the highest index value.
The mean ± SD minutes per day spent in activities of moderate intensity and above varied depending on the cut point used to classify accelerometer output (Table 2). Specifically, the Hendelman cut point resulted in the highest estimate of moderate/vigorous activity (283.9 ± 74.1 min·d−1), whereas the Swartz (136.7 ± 48.9 min·d−1) and Freedson cut points (22.3 ± 20.7 min·d−1) produced substantially lower estimates. The mean average counts per minute, representing the raw output from the accelerometer, was 316.3 ± 130.1. Mean ± SD MET-hours per day spent in total activity (activities of light intensity and above) as measured by the PPAQ was 28.5 ± 15.7 MET·h·d−1 with household/caregiving (13.8 ± 9.3 MET·h·d−1) and occupational activity (11.3 ± 11.1 MET·h·d−1) making the largest contributions to total energy expenditure.
The reproducibility of the KPAS was generally strong, with intraclass correlation coefficients of r = 0.84 and r = 0.76 for unweighted and weighted total activity indices, respectively (Table 3). Intraclass correlation coefficient values were similar for the domain-specific activity index scores, ranging from r = 0.76 for the active living index to r = 0.86 for the occupational index. Intraclass correlation coefficients were homogeneous across trimesters of pregnancy.
To assess the validity of the KPAS, total and domain-specific activity index scores from the KPAS were compared with both objective (accelerometer) and subjective (PPAQ) criterion measures of pregnancy physical activity (Table 4). Overall, Spearman correlations between the KPAS total activity index and the accelerometer measures were fair to good. Correlations were somewhat higher for the weighted total activity index, ranging from r = 0.57 (Freedson cut point) to r = 0.66 (Swartz cut point), as compared with the unweighted total activity index, ranging from r = 0.49 (Hendelman cut point) to r = 0.59 (Freedson cut point). Spearman correlations were homogeneous across trimesters of pregnancy, categories of race/ethnicity (white vs nonwhite), and income level (<$45,000 vs >$45,000).
The Spearman correlations between the domain-specific activity indices and accelerometer measures of moderate activity and above were more modest. For example, correlations between the household/caregiving index and the accelerometer measures ranged from r = 0.12 (Freedson cut point) to r = 0.26 (Hendelman cut point), whereas correlations for the occupational index (r = 0.25–0.33) and the active living index (r = 0.31–0.36) were somewhat higher. Sports/exercise was the component index most strongly correlated with the accelerometer, with correlations ranging from r = 0.34 to 0.51.
To compare the KPAS with a subjective criterion measure of pregnancy activity, KPAS indices were correlated with like measures of energy expenditure from the PPAQ (Table 4). Similar to the accelerometer results, the KPAS weighted index of total activity correlated more strongly (r = 0.51) with the PPAQ measure of total activity (≥light intensity) than the unweighted index (r = 0.37). Correlations between the KPAS and PPAQ domain-specific activity indices were strong and ranged from r = 0.71 for household/caregiving to r = 0.84 for sports/exercise.
To evaluate whether grouping of women into tertiles based on the KPAS summary index of total activity would result in groups with different “true” activity levels, we calculated mean accelerometer and PPAQ values within each tertile of total activity (unweighted) as calculated by the KPAS (Table 5). There was a significant linear trend of increasing accelerometer activity of moderate intensity and above across tertiles of activity based on the KPAS. Mean average counts per minute from the accelerometer and total MET-hours per day from the PPAQ also increased significantly across KPAS tertiles. Results were nearly identical when tertiles were created using the KPAS weighted total activity index.
This study evaluated the reliability and validity of the KPAS when used to assess physical activity during pregnancy. Results from these analyses indicate good to excellent reproducibility of KPAS measures of total activity as well as domain-specific activity indices (household/caregiving, occupational, active living, and sports/exercise). Validation coefficients were near or above r = 0.50 for both weighted and unweighted KPAS summary measures of total activity when compared with objective estimates of activity from the accelerometer. Correlations between KPAS domain-specific activity indices and accelerometer measures of moderate-intensity activity and above were more modest.
When compared with the PPAQ, a subjective measure of pregnancy physical activity, the weighted measure of total physical activity from the KPAS was also above r = 0.50, although the validation coefficient for unweighted total activity was not as strong. In addition, correlations for total activity between the KPAS and PPAQ were lower than those between KPAS domain-specific activity indices and like indices from the PPAQ. This may be due to differences in active living questions between the two questionnaires. In addition to the three PPAQ domains described in the analysis, the PPAQ also includes a section on “going places” (three items) as well as four questions on sedentary activities. Whereas these seven questions could be grouped into a PPAQ “active living” domain, the comparability between this domain and the KPAS “active living” domain would be limited due to several factors. First, the KPAS “active living” domain includes a smaller proportion of sedentary activities (one of four) than would the PPAQ “active living” domain (four of seven). More importantly, the sedentary activities included in the KPAS are reverse weighted, meaning that increased sedentary activity will result in a lower KPAS index score whereas increased sedentary activity would result in a higher PPAQ “active living” score (because the PPAQ calculates actual energy expenditure). Due to these differences, we decided not to compare the KPAS “active living” living domain with an artificially created PPAQ “active living” domain. However, because we were ultimately interested in the ability of the KPAS to estimate total pregnancy activity, we believe that the use of the PPAQ estimate of total energy expenditure (which includes these seven questions), rather than the sum of the three PPAQ domains, was the most appropriate criterion measure to use.
Our estimates for the reproducibility and validity of the KPAS in this sample of pregnant women are comparable with or slightly higher than those observed for the KPAS by Ainsworth et al. (5) in a sample of 50 nonpregnant women. Women were aged 20–60 yr and were predominantly white (94%), college educated (78%), and employed full time (70%). Reproducibility coefficients ranged from r = 0.79 for housework to r = 0.85 for occupation, differing by 0.06, or less than the reproducibility coefficients observed in the current study. To validate the KPAS, the Caltrac accelerometer was used as an objective measure and physical activity records as a subjective measure of physical activity. Although our study differed in that we used the Actigraph accelerometer as an objective measure and the PPAQ as a subjective measure, we observed similar validation coefficients for KPAS total activity. For example, Ainsworth et al. observed a Spearman correlation between total activity and Caltrac MET-minutes per day of r = 0.49 (compared with r = 0.49–0.66 vs the accelerometer in the current study) and r = 0.35 against the physical activity record measure (compared with r = 0.37–0.51 vs the PPAQ in the current study). For the activity-specific indices, Ainsworth et al. observed validation coefficients ranging from r = 0.35 to 0.73 using the physical activity records, lower overall than those observed in the current study using the PPAQ (ranging from r = 0.71 to 0.84). Our findings of higher validity were likely due to the higher level of correlated error (i.e., due to self-report) between the KPAS and the PPAQ as opposed to an activity record. Validation coefficients for the domain-specific activity indices using the Caltrac measures in Ainsworth et al. ranged from r = −0.03 to 0.57, comparable with those observed for the domain-specific activity indices in the current study using the accelerometer (ranging from r = 0.12 to 0.51).
A prior study evaluating the validity of the PPAQ for assessing pregnancy physical activity reported statistically significant correlations between the PPAQ and accelerometer measures ranging from r = 0.08 to 0.43 for total activity, r = −0.04 to 0.14 for household/caregiving activity, r = −0.10 to 0.42 for occupational activity, and r = 0.30–0.48 for sports/exercise activity (9). In the current study, we observed stronger correlations between the KPAS and accelerometer measures for total and household/caregiving activity and comparable correlations for occupational and sports/exercise activities. These findings may be due to the more comprehensive assessment of total activity in the KPAS as compared with the PPAQ. Alternatively, findings of higher validity for the KPAS could be due, in part, to the order of questionnaire administration. At both administrations, the PPAQ was administered before the KPAS. Completing the PPAQ first may have served as a memory cue, enhancing the recall of physical activity in response to the KPAS and thereby somewhat inflating validity estimates.
Error in the estimates of KPAS reliability may arise from several sources. First, due to the relatively short time interval between KPAS administrations, some women may have been influenced by recall of their responses to the first KPAS administration in completing the second KPAS. This would have resulted in inflated estimates of reliability. Alternatively, reliability estimates may have been underestimated if wear of the accelerometer in the interval between KPAS administrations led to a heightened awareness of activity patterns while completing the second KPAS. Any true changes in activity that occurred between administrations would have also led to underestimated reliability coefficients.
Estimates of the validity of the KPAS are limited by the lack of a true gold-standard criterion measure of physical activity. In other words, validity estimates are influenced by errors in both the accelerometer and PPAQ as well as in the KPAS measures. For example, when the accelerometer is worn on the hip, error results from the inability of the accelerometer to accurately measure activities involving upper-body movement, pushing or carrying a load, stationary exercise (e.g., cycling), weight lifting, and water activities (7). In addition, the positioning of the accelerometer varies slightly from woman to woman, regardless of pregnancy, due to variability in stomach size. While we do not expect variability in positioning to have a substantial effect on our findings, any decrease in the accuracy of accelerometry in pregnant women would likely be nondifferential (i.e., would affect all pregnant women at the same stage of pregnancy similarly) and therefore lead to an underestimate of the correlation with the KPAS.
Because the accelerometer does not discriminate between different types of activity and accelerometer cut points for light-intensity activity are not available, correlations with specific types of activity may be lower than correlations with total activity, as was observed in this study. For example, the lower correlations observed between KPAS household/caregiving activity and the accelerometer measures of moderate activity and above may be attributed, in part, to the lower intensity of many household/caregiving tasks (e.g., cooking, dishwashing). However, because the sources of error in the accelerometer are unlikely to be correlated with errors in the KPAS, the validity coefficients between the accelerometer and KPAS are likely conservative (29).
A number of studies have been conducted to determine how many measurement days are needed to reliably estimate habitual physical activity using an accelerometer. In these studies, the number of days has varied between 4 and 12 depending on the precision that is required, the accuracy of the reference method, and the intraindividual variation in activity (22). In light of these factors and given that the majority of these questionnaires are designed to estimate normal activity over ≥1 yr as opposed to a 3-month trimester, we believed that 7 d of Actigraph use was appropriately conservative (22).
Both the KPAS and the PPAQ share errors due to subject inaccuracy in self-reporting physical activity (17,18), which may result in inflated estimates of validity when the PPAQ is used as the criterion measure. However, other uncorrelated sources of error specific to each instrument work to counterbalance any potential inflation. For example, errors in the KPAS arise from difficulty in combining domain-specific activity indices into a measure of total activity. For example, not all respondents participate in all four activity domains. In this sample, 30% of participants were not employed and were assigned the lowest possible value for the occupational activity index. This assignment may skew the distribution and artificially decrease the unemployed group’s total activity level relative to those who are employed. In addition, the KPAS sums the four domain-specific estimates of physical activity such that the relative contribution of an activity domain to KPAS total activity is the same for each woman. The meaning of such a combined measure may be obscure.
Unique to the current study was an alternative measure of total activity calculated as the weighted average of the KPAS domain-specific activity indices. Weights were assigned based on the relative contribution of each type of activity to total physical activity energy expenditure observed in a prior study conducted among a similar sample of pregnant women and reflect the greater proportion of energy expenditure attributable to household/caregiving activities as opposed to sports/exercise during pregnancy (24). As in this prior study, the current study population was also from western Massachusetts and did not differ significantly in terms of race/ethnicity or prepregnancy BMI. However, those in the current study tended to be slightly older (mean age = 29.5 vs 26.2 yr, P = 0.002), better educated (47.5 vs 21.4% with a college degree, P < 0.0001), and have a higher income level (52.5 vs 43.6% with household income >$30,000, P = 0.01). To the extent that these characteristics are associated with proportion of time spent in domain-specific activities, the weights used in the calculation of “total weighted activity” may have introduced some error into the observed results. However, a number of prior studies conducted among nonpregnant women have also observed a greater proportion of energy expenditure attributable to household/caregiving activities as opposed to sports/exercise (4,8). Finally, compared with the simple sum of the domain-specific activity indices, the weighted measure of total activity was more strongly associated with both PPAQ and accelerometer measures of activity, with the exception of accelerometer activity calculated using the Freedson cut point. These results suggest that weighting KPAS component indices according to the relative contribution of each index to total activity in the study population may result in a more valid measure of total activity.
Although our participants were likely typical of pregnant women, our sample should not be considered fully representative of pregnant women. As the majority of validation studies demand substantial time and cooperation from participants, subjects who participate may be more likely to accurately report levels of physical activity or be more active than the general population. This may result in inflation of validity and reproducibility estimates. However, unlike the majority of prior validation studies that used accelerometers as the criterion measure, our study population was sociodemographically and ethnically diverse, facilitating the generalization of our results to wider populations. Larger studies would be required, however, to detect trimester-specific differences in reproducibility and validity.
In summary, our data indicate that the KPAS is a reliable and reasonably accurate instrument for estimating physical activity among pregnant women. These findings suggest that the KPAS can be used for investigations of total physical activity (household/caregiving, occupational, active living, and sports/exercise) during pregnancy and its impact on maternal and fetal outcomes.
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