Regular physical activity (PA) is widely believed to confer important health benefits, including a decrease in the risk of cardiovascular disease and colon cancer. Although vigorous activity has traditionally been associated with improvements in health, moderate-intensity activities (40–60% O2max) have been shown to confer such benefits as well (17). In addition, longitudinal training studies have documented that moderate intensity activity reduces blood pressure, increases glucose tolerance, and improves the blood lipid profile (6,14,24).
On the basis of such observations, public health officials are now advocating moderate intensity PA for the U.S. public. In 1995, the Centers for Disease Control and Prevention (CDC) and American College of Sports Medicine (ACSM) issued a recommendation that every American accumulate at least 30 min·d−1 of moderate intensity physical activity on most, and preferably all, days of the week (17). They specified however, that this activity need not be performed at one time, but could be carried out in three 10-min bouts during the day. A year after the CDC/ACSM recommendation, the U.S. Surgeon General’s report on Physical Activity and Health recommended that every American obtain 30 min·d−1 of moderate- or vigorous-intensity activity (22).
To assess how many Americans are meeting these new recommendations, researchers can use surveys or tools such as motion sensors to obtain activity data for individual persons (9). From the Behavioral Risk Factor Surveillance System (BRFSS) survey, for example, researchers found that only 19.6% of the adult U.S. population met the new recommendations (at least five times per week of ≥ 30 min·d−1 at moderate intensity or above) (unpublished data from the Centers for Disease Control and Prevention, 1998).
At present, several methodologic issues in the assessment of PA merit special attention from researchers (16). One is the design of questionnaires (8). In 1997–98 three of us (BEA, CAM, and DAJ) developed a new PA survey designed to reflect occupational PA patterns, capture time spent in nonoccupational moderate-intensity and vigorous activities, and help monitor progress toward national health objectives. More information is needed on how accurately the survey can reflect free-living PA patterns among adults.
Another critical area for research is the use of motion sensors. Previous studies have found motion sensors to provide valid and reliable data (16,23), but few researchers have tried to use motion sensors to measure the duration and frequency of activity bouts in intensity categories (9,12). The CDC-ACSM position statement (17) identified three different intensity categories for PA (light, < 3 METs; moderate, 3–6 METs; vigorous, > 6 METs); 1 MET is equal to the metabolic rate for a specific activity divided by the resting metabolic rate for an average person. Most accelerometers have the potential to provide an objective measure of time spent in various intensity categories, by estimating energy expenditure and storing data for later recall. The most work in setting “activity cut points” has been done with the Computer Science and Applications, Inc. (CSA) (Shalimar, FL). Currently, a need exists to determine how well information derived from motion sensors compares with other measures of PA, especially activity of moderate intensity.
The primary purpose of the present study was to compare various methods of assessing PA in a field setting during a 3-wk period. Specifically, we compared three methods for quantifying the amount of time spent in moderate, and hard PA: 1) selected items from the new PA questionnaire 2), PA logs, and 3) the CSA monitor. A secondary purpose of this study was to determine the ability of the CSA monitor to reflect three 10-min bouts per day of moderate intensity activity in a field setting.
The study was conducted at the University of South Carolina. Volunteers participating in the study resided in the Columbia, SC, metropolitan area were recruited by word of mouth and from announcements and advertisements posted in the community. Most of the final study group members were well educated, employed in professional settings, and healthy. A total of 101 people (51 men, 50 women) enrolled, but three dropped out due to a lack of time (N = 2) or inability to complete the study as designed (N = 1). All participants signed a written informed consent statement approved by the University of South Carolina Institutional Review Board. Among the 98 participants who completed the study, 15 were dropped from data analysis because of incomplete data for the CSA monitor (N = 10) or PA logs (N = 5), leaving 38 men and 45 women.
Participants completed four study sessions. In the first session, they read and signed the informed consent form; completed demographic, health history, and PA questionnaires; were measured for height and weight; and received instructions in using a daily PA log and wearing the CSA monitor. Height was measured without shoes by using a wall-mounted tape measure, and weight was measured with a digital laboratory scale (Seca, Model 770, Olney, MD). Body mass index (BMI) was computed as weight in kilograms divided by height in square meters. Participants were instructed to wear the CSA monitor daily attached to the waistband of their clothing during waking hours and to complete the PA log each night before going to bed. All study participants were instructed to maintain their usual physical activity patterns, except a random subsample of 16 who were asked to complete at least three 10-min bouts of moderate activity each day. The purpose of the subsample was to determine whether the CSA monitor would reflect intermittent 10-min bouts of moderate activity in a free-living field setting. All the subsample participants were given examples of activities listed as 3–6 METs in the 1993 Compendium of Physical Activities (2).
Once a week, an investigator telephoned participants at their home or worksite to complete the PA questionnaire. The phone call was also used to answer participants’ questions about the study and to determine compliance with the study protocol. At the end of the 3 wk, participants returned to the study center with their PA logs and CSA monitors. Data from the CSA monitors were downloaded into a personal computer for storage, and the PA logs were scored by using the established MET values from the Compendium of Physical Activities (2). Participants were given feedback about their PA habits but were not provided monetary compensation.
Physical activity logs.
Participants kept three 1-wk PA logs of their physical activities (see Fig. 1).
The seven-page PA log (a page for each day) contained 48 items (7 resting/light [< 3 METs], 25 moderate [3–6 METs], and 16 hard/very hard [> 6 METs]) intensity organized as home, transportation, occupation, conditioning, sports, and leisure activities (intensity was not identified in the form). Additional spaces were provided for participants to write down any activities not listed. Every evening, participants circled the activities they performed that day and wrote down for each its duration (hours and minutes of actual movement) and the approximate time they began the activity that day. Participants were instructed to make entries only for activities with a duration of ≥ 10 min. The logs, which take less than 5 min to complete and require minimal literacy, were scored by assigning each activity a five-digit code from the 1993 Compendium of Physical Activities which shows a MET for each code. The activity codes, duration, and the times at which participants performed each activity were keyed into the PC-SAS FSEDIT data entry program (Cary, NC). The total minutes for each five-digit code (e.g., 17030 for walking for exercise) were summed across the 21 d and aggregated by intensity level and then divided by 21 to represent the average minutes spent in resting/light, moderate, and hard/very hard activities.
Physical activity questionnaire.
The new PA questionnaire assesses occupational PA habits, time spent in nonoccupational walking (considered a moderate intensity activity), moderate-intensity recreational activities, vigorous-intensity recreational activities, and strength or toning activities. Comparisons of the nonoccupational walking and strength or toning activities with a 1-wk PA log are reported elsewhere (3,13).
Three questions on the PA questionnaire reflecting the time spent in nonoccupational walking, vigorous (herein described as hard/very hard), and moderate, activities were used in the current study to compare associations between the activity levels indicated by responses to the questions and CSA and PA log data. The questions administered in the following order:
I am going to ask you questions about three different levels of PA that you do when you are not at work. Last week, did you walk continuously for at least 10 min for recreation, exercise, or to get to and from places? If yes, how many days last week did you spend walking each day? On average, how many total min did you spend walking each day?
I am going to ask you about activities other than walking. Last week, did you do hard/very hard activities continuously for at least 10-min that caused large increases in breathing or heart rate, such as running, swimming, aerobics, fast bicycling, competitive sports, or heavy yard work? If yes, how many days last week did you do hard/very hard activities? On days when you did hard/very hard activities for at least 10 min at time, how much total time did you spend doing these activities?
Last week, did you do moderate activities continuously for at least 10 min that caused some increase in breathing or heart rate, such as bicycling, tennis, dancing, vacuuming, gardening, or other yard work? If yes, how many days last week did you do moderate activities? On average, how many total min did you spend doing moderate activities each day?
The activity monitor worn by participants, a small uniaxial accelerometer (CSA, Model 7164), measures vertical acceleration and deceleration. The acceleration signal is filtered and digitized by an 8-bit analog-digital (A-D) converter at 10 samples per second. The A-D converter measures the magnitudes of the accelerations, which are then summed over a given period of time (epoch). The monitors were initialized 60 min before the first study and programmed to record data in 60-s epochs. The monitors were placed in a carrying pouch, and participants were shown how to place the pouch on a belt at waist level in the right anterior axillary line. Proper consistent placement was emphasized, and participants were told to wear the monitor from morning until retirement at night (except when bathing or swimming) for the next 20 d. Participants also were instructed to call the study coordinator if they had questions. At the end of the 21-d recording period, a study investigator used a reader interface unit to download the CSA data into a desktop computer, which was stored in an Excel file until further analysis.
A computer program was written in SAS (Cary, NC) to sum the CSA counts·min−1 over 21 d and to compute the average min·d−1 spent in resting/light (<3 METs), moderate (3–6 METs), hard (7–8 METs), and very hard (≥9 METs) activities. Specific cut points relating CSA counts·min−1 to resting/light, moderate, and hard/very hard activities were used to compute the min per intensity levels. The cut points were determined from published regression equations relating CSA counts·min−1 to gross energy expenditure in METs and are presented in Table 1.
The regression equation of Freedson et al. (9) was developed from a study of treadmill walking and jogging, and equations of Hendelman et al. (10) and Swartz et al. (21) were derived from field studies of lifestyle activities, including walking. The CSA data in the present study are presented as minutes per day spent in resting/light, moderate, and hard/very hard categories, using each cut point method and average total counts per day. In addition, the number of 10-min bouts of activity per day were calculated using each of the cut point values. A 10-min bout was defined as 10 or more consecutive minutes where the intensity fell continuously within a given range for CSA counts that represented 3–6 METs for moderate intensity and > 6 METs for hard/very hard intensity for each cut point method.
Means and standard deviations were computed for data from the CSA monitors and the PA logs, medians and 25th to 75th percentiles were computed for the PA questionnaire data, and percentages were computed to describe the study population by race or ethnicity and educational attainment. Spearman rank-order correlations were used to calculate associations between CSA minutes per day values obtained by the three methods and to identify associations between the CSA total counts per day and the PA log min per day for resting/light, moderate, and hard/very hard activities. Spearman rank-order correlations were also used to identify associations between the PA questionnaire data in min per day and data from the PA log and CSA monitor. We considered a P value of < 0.05 to be significant throughout.
Average ages of participants (Table 2) were 47.2 ± 15.3 yr for men and 45.4 ± 15 yr for women. BMIs were 26.6 ± 4.9 for men and 24.4 ± 4.2 for women. About three-fourths of participants were white. Activity data (CSA, PA log, and PA questionnaire) were similar by sex. Overall, according to the PA logs, participants spent about 2 h·d−1 in moderate activities and less than 10 min·d−1 in hard/very hard activities (mostly running or other conditioning activities). In contrast, for the PA questionnaire, participants averaged 35 min·d−1 in walking or moderate intensity activities but an average of 16-min·d−1 in hard/very hard intensity activities.
Total CSA minutes per day and average 10-min bouts·d−1 of resting/light, moderate, and hard/very hard activity from the CSA counts are shown in Figure 2 for the cut point methods of Freedson et al. (9), Hendelman et al. (10), and Swartz et al. (21). Activity duration from the PA logs is also shown in the figure. For the CSA, the duration of moderate-intensity PA was highest for the Hendelman et al. (10) method and lowest for the Freedson et al. (9) method. Minutes of moderate activity computed with the Swartz et al. (21) method came closest to matching minutes of such activity recorded in the PA log. For hard/very hard activity, all CSA cut-point methods produced a shorter duration than that recorded in the PA log, with the Swartz et al. (21) method providing the highest value and the Hendelman et al. (10) method the lowest.
The Spearman rank-order correlation coefficients between the CSA minutes per day scores for the three cut-point methods were all significant (P < 0.001) for resting/light (r = 0.43–0.88), moderate (r = 0.45–0.89), and hard/very hard intensities (r = 0.76–0.94) (data not shown). Coefficients were highest between the methods of Hendelman et al. (10) and Swartz et al. (21) for resting/light and moderate min·d−1 (both r = 0.89) and between the methods of Freedson et al. (9) and Swartz et al. (21) for hard/very hard minutes per day (r = 0.94).
The number of 10-min bouts of moderate and hard/very hard activity all varied by method. For moderate activity, the Hendelman et al. (10) method yielded the most 10-min bouts (means ± SD, Hendelman et al. = 6.5 ± 6.2, Swartz et al. = 2.0 ± 2.8, Freedson et al. = 0.60 ± 0.88). Among the 20 participants performing hard/very hard activity, there was little difference between the methods with all three yielding ≤ 0.2 10-min bouts·d−1 for all subjects. We also compared the subsample of participants told to complete three 10-min bouts·d−1 of moderate-intensity activity with the remaining study participants (who were told to maintain their usual activity habits). There were no differences in minutes per day of activity by intensity level or in the number of 10-min bouts·d−1 between participants in the subsample and those told to maintain their usual activity habits (P > 0.05).
The Spearman rank-order correlation coefficients for the associations between the PA logs and CSA scores are shown in Table 3. Coefficients ranged from r = 0.24 to r = 0.35 for the association between the PA log and CSA moderate intensity scores (P < 0.05) and they ranged from r = 0.31 to r = 0.36 for the correlation between the PA log and CSA hard/very hard scores. Correlation coefficients were similar to the intensity-specific CSA scores when the total CSA count·d−1 was compared with the PA log moderate and hard/very hard scores.
The Spearman rank-order correlation coefficients between the PA questionnaire items and the CSA and PA log minutes per day are shown in Table 4. Nonoccupational walking correlated highest with the moderate-intensity score from the Freedson et al. (9) CSA method (r = 0.41), and to a lesser amount with total CSA counts·d−1 (r = 0.26) and PA log moderate intensity scores (r = 0.38). Correlation coefficients between the questionnaire’s hard/very hard activity item and the total CSA counts per day and the CSA hard/very hard min per day for all methods were on the order of r = 0.30–0.33 (P < 0.01). The correlation between the questionnaire hard/very hard item and the PA log was low and not significant (r = 0.09) (P > 0.05).
In the present study, we compared free-living daily activity using CSA monitors, PA logs, and PA surveys during a 3-wk period. Our sample size of 83 adults provided adequate statistical power to compare results from all three measures. Correlation coefficients for the two direct measures of PA, CSA monitors and PA logs, were modest, but they were statistically significant for the moderate and hard/very hard–intensity scores. For moderate intensity scores, correlations with PA logs were higher for CSA scores developed from a variety of moderate intensity activities (Hendelman et al. (10) and Swartz et al. (21) methods than they were for scores using cut points developed from treadmill walking and jogging (Freedson et al. (9) method). Correlation coefficients relating the PA questionnaire, an indirect measure of activity, to the comparable CSA scores and the PA logs were on the order of r = 0.01–0.41 for walking and moderate activities and r = 0.09–0.33 for hard/very hard activities, which suggests considerable variability between the several methods used to measure moderate and hard/very hard activity.
From the PA logs, we found that participants spent about 2 h·d−1 in moderate-intensity activity, of which occupational activities accounted for about one-fourth. The most common types of moderate activity recorded included walking at work and for exercise, household and yard or garden chores, and conditioning activities. Relatively few participants reported organized sports activities.
According to the PA questionnaire, participants spent a median amount of 15 min·d−1 in nonoccupational walking and another 15 min·d−1 in moderate activities, for a combined median total of about 35 min·d−1. This contrasts sharply with the nearly 2 h·d−1 reported in the PA logs for moderate intensity activities, with much of the walking reported on the PA logs scored as occupational moderate activity. However, the estimate of about 7 min·d−1 for hard/very hard from the PA log is about half of the corresponding estimate of 16 min·d−1 from the PA questionnaire.
The correlation coefficients relating minutes per day from the PA questionnaire with minutes per day from the PA log were r = 0.38 (P = 0.001) for walking and r = 0.26 (P = 0.02) for moderate activity. These coefficients were similar to correlation coefficients reported by other PA validation studies that used PA logs and records as a direct measure to validate PA studies (4,18–20). Although these correlations are modest in size, they show it is possible for a telephone-administered questionnaire to reflect participation in moderate-intensity walking and recreational activities. In contrast, the correlation coefficient between the PA questionnaire and the PA log for hard/very hard intensity minutes per day scores were quite low (r = 0.09, P = 0.43). This last finding is unusual, as most validation studies of hard/very hard intensity PA survey questions have shown a strong correlation with direct measures of physical activity (11,18–20).
One explanation for the lack of agreement on hard/very activity is that participants may have reported to the telephone interviewer’s question about hard/very hard activities some activities that were classified on the PA log as moderate. For example, on the PA questionnaire, participants were instructed to recall activities that caused large increases in breathing or heart rate; they were also given specific examples of hard/very hard activities (running, swimming, aerobics, fast bicycling, competitive sports, and heavy yard work). Conceivably, participants recalled other activities that caused them to have large increases in breathing or heart rate and reported them as hard/very hard intensity. When participants recorded the same activity in the PA log, however, it would have been objectively classified by the 1993 Compendium of Physical Activities (2) and may well have been included as moderate activity. Participants whose aerobic fitness level was low or who were older or overweight may have been more prone to such misreporting, but our data do not address this issue. Studies designed to better understand the cognitive processes people experience when completing PA questionnaires may be helpful in this regard (7). Another possibility for the lower correlations is that few participants reported doing hard/very hard activities on the PA questionnaire or log resulting in little variability between the scores reported.
Comparisons of min of activity per day between the CSA and the PA logs showed modest agreement for moderate-intensity scores regardless of the cut points used. Correlation coefficients were higher using the cut points of Hendelman et al. (10) and Swartz et al. (21) than with those of Freedson et al. (9). For hard/very hard activity, the correlation coefficients between the PA logs and CSA scores using the Hendelman et al. (10) and Freedson et al. (9) methods were similar in magnitude and higher than that observed using the Swartz et al. (21) method. Interestingly, the correlation coefficients between the total CSA counts per day and the PA log moderate and hard/very hard scores (r = 0.22–0.34) were similar in magnitude to those for the intensity-specific CSA and PA log values. Thus, the total CSA counts per day may be a simple but useful marker of PA that exceeds resting and low metabolic requirements.
Using the CSA cut points proposed by Freedson et al. (9), Hendleman et al. (10), and Swartz et al. (21), we estimated the time spent in moderate activity to be approximately 25 min, 4.3 h, and 2 h, respectively. The moderate-intensity cut point of Freedson et al. (9) (1952 counts·min−1) was far higher than the other two, and it may well be set too high to capture a broad range of moderate activity. The equation accurately predicts the metabolic cost of walking and jogging, but it has been shown to underpredict the cost of activities involving upper body movement, pushing or carrying a load, and walking on varying terrain. Thus, it is very likely that Freedson et al.’s (9) high cut point leads to the misclassification of some moderate activities. In contrast, Hendelman et al.’s (10). method had the smallest cut point (190.7) for the bottom of the 3–6 MET range and the highest (7525.7) for the top, which was consistent with its largest total min of moderate activity. Of interest, we note that the estimate of moderate intensity activity using the cut point of Swartz et al. (21) (approximately 129 min·d−1) is roughly consistent with the PA logs.
One of the limitations of this study is that we were unable to determine from the PA logs whether participants completed their moderate and hard/very hard activities in multiple 10-min bouts or performed the activities during one continuous period. All activities entered in the PA log were performed, minimally, for 10-min bouts; if we had had participants report activities in multiples of 10-min bouts (e.g., two bouts, three bouts), we could have compared the frequency of 10-min bouts of moderate and hard/very hard activity detected by the CSA with PA log data. Instead, we were able to compare accumulated minutes of activity during the day.
This study has strengths that contribute to the understanding of measuring PA using direct and indirect methods. The methods used permit simultaneous evaluation of the CSA, PA logs, and a PA survey. Few studies have used concurrent measures of direct and indirect PA to evaluate PA surveys (5,11,15,18), and even fewer have monitored participants continuously for 21 d or longer (1). To our knowledge, this is the first study to attempt a detailed analysis of the amount of time spent in different PA intensity categories over an extended period.
In summary, our results found the CSA monitor, PA logs, and a PA questionnaire to reflect moderate intensity and hard/very hard–intensity PA patterns. Modest but statistically significant correlation coefficients were observed between nonoccupational walking, moderate-, and hard/very hard– intensity activity scores from a PA questionnaire and scores on the CSA and PA logs. Scores computed using CSA cut points developed from studies of lifestyle activities yielded the higher correlation coefficients with PA logs than scores for moderate activities developed from treadmill walking and jogging. All of the methods examined in the present study reflect PA, but these methods do not always provide similar estimates of the amount of time spent in moderate or hard/very hard PA. This finding has important implications for studies designed to assess the prevalence of U.S. adults who meet the national PA recommendations. Additional studies are needed to compare the three different CSA cut points before a single method can be suggested to identify the time spent in moderate and hard/very hard life-style activities.
This study was funded by a cooperative agreement from the Centers for Disease Control and Prevention and was supported by the International Life Sciences Institute Center for Health Promotion (ILSI CHP). The use of trade names and commercial sources in this document is for identification only and does not imply endorsements. The views expressed herein are those of the individual authors and/or their organizations, and do not necessarily reflect those of ILSI CHP. We thank Angela Morgan, Katrina Drowatzky, Charity Moore, Rodney Velliquette, Dawn Tittsworth, Dr. Ming Fang Zhao, Dr. Melicia Whitt, Dr. Jennifer Hootman, and Dr. Melinda Irwin from the University of South Carolina and Dr. Michael Pratt from the CDC for their assistance with the project.
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