The epidemiology of physical activity in children, college students, middleaged men, menopausal females and monkeys

J ChronDis Vol. 35, pp. 787 to 795, 1982 Printed

in Great

0021.9681’82ilOO787-09~03.00:0 Pergamotl Press Ltd

Britain

THE EPIDEMIOLOGY OF PHYSICAL ACTIVITY IN CHILDREN, COLLEGE STUDENTS, MIDDLEAGED MEN, MENOPAUSAL FEMALES AND MONKEYS RONALD E. LAPORTE’, WAYNE

JANE A. CAULEY’,

COLLIN M. KINSEY’,

CORBETT~, ROBERT ROBERTSON~, RIVKA BLACK-SANDLER~, LEWIS H. KULLER’

and

JEFF FALKEL’

‘Department of Epidemiology. Graduate School of Public Health. Universityof Pittsburgh, Pittsburgh, ‘Human Energy Laboratories,Trees Hall. University of Pittsburgh, Pittsburgh, ‘Bowman-Gray School of Medicine, Winston-Salem, NC 27103 and %chool of Health Related Professions Pennsylvania Hall, University of Pittsburgh. Pittsburgh, PA 15261. U.S.A. (Rewired in recised form

16 Junuary 1982)

Abstract-Although the inverse relationship between physical activity and coronary heart disease (CHD) has been well documented, little is known concerning the epidemiology of physical activity. A primary reason for the lack of knowledge has been a problem of quantification of physical activity. We have employed the Large-Scale-Integrated (LSI)Activity Monitor in five diverse populations to measure individual physical activity levels. The results indicated that the instrument can accurately index individual physical activity levels. as well as to provide important information concerning the epidemiology of physical activity.

INTRODUCTION

THE IMPORTANCE of increased physical activity to the prevention of Coronary Heart Disease (CHD) has long been recognized. Numerous epidemiologic papers have determined that increased physical activity is related to a reduced risk of heart attack [l-3]. An important feature, often neglected when discussing the beneficial aspects of physical activity, is the stronger epidemiologic evidence linking physical activity to heart attack than linking physical fitness to heart attack [4-81. Additionally, measures of physical activity, rather than fitness, appear to be more highly related to high density lipoprotein cholesterol (HDLc), a primary determinant for heart attack risk [9, lo]. The problem with physical activity as contrasted with physical fitness is quantification. Fitness ca_n be reliably measured through maximal oxygen intake tests (v02). Moreover, physical activity is much more difficult to assess than the other major risk factors for heart disease. Blood pressure can be quantified by trained technicians; serum cholesterol measures are standardized through certified laboratories; cigarette consumption can be reliably reported by participants as well as validated by chemical tests (i.e. thiocyanate or carboxihemoglobin). Physical activity, however, cannot be directly quantified by laboratory tests. Therefore, there is marked variability between and within studies concerning the measurement of physical activity. The focus of the current research is the quantification of physical activity in nonexercising populations. People not engaged in structured exercise programs vary considerably in their free living physical activity. It is relatively simple and accurate to 787

788

RONALD E. LAPORTE rt al.

quantify physical activity in exercising populations--e.g. self report of miles run, holes of golf played or amount of time playing basketball. However, only a small percentage of the American public exercise regularly. The quantification of physical activity in populations not engaged in structured exercise is a major concern for epidemiologists to determine the role of physical activity for the protection against disease. The current research was designed to further evaluate the Large-Scale-Integrated Activity Monitor (LSI) as an objective measure of physical activity for epidemiologic research. The activity monitor is an extremely accurate motion sensor. Within the instrument is a cylinder with a ball of mercury. A 3% inclination or declination from the horizontal causes the ball of mercury to roll down the cylinder into a mercury contact switch. The closing of the switch is registered as a movement. Sixteen movements are registered as a count in an internal counter. The contents of the counter become available by placing a magnet to the side of the instrument which activates an LED display presenting the accumulation of motion counts. The sensor does not difierentiate between different intensities of movements. We feel that in non-exercising populations the intensity of movement across individuals is relatively constant on a daily basis and individuals can therefore be best indexed according to their rates of movement. Placement on the hip measures trunkal movement which we have found to be a very good indicator of physical activity. Validation experiments indicate that the instrument (1) does not interfere with behavior; (2) is simple to use; (3) is directly related to measures of energy expenditure as determined by specific logging of activity; and (4) can discriminate individuals and populations having different activity levels [ 111. A simple objective measure of physical activity in a free living population would be of great importance. It would be then possible to directly relate physical activity to heart attack and heart disease risk factors without resorting to questionnaires. In some epidemiologic research the only feasible assessment is through surveys. However, objective monitoring can be employed on a sampling basis to validate the surveys. Therefore, an objective monitor need not serve as the primary measurement tool but rather as an instrument to validate other possible more easily administered physical activity assessments. In epidemiology there have been two primary measures of physical activity: job titles and logging procedures. There are problems associated with job titles, including the variable levels of physical activity with a job title [12] as well as the difficulty of defining “strenuous” work. The second type of measurement, logging, is currently the most common physical activity measure. Three different logging procedures have been used: immediate, delayed (e.g. 24 hr later), and a general questionnaire. The immediate procedure is a very accurate measure of physical activity as validated by Passmore et al. [13] and Eldholm [14]. However, immediate logging may become a major focus of the individual for the day and may interfere with normal activity [14] and one needs very motivated subjects. The delayed and generalized procedures do not interfere with normal activity but the accuracy of recall is reduced. Durnin, for example, has indicated that generalized logging produces extremely poor measures of physical activity [15]. Use of generalized questionnaires as have been presented by Taylor [16] and Paffenbarger [7], can probably discriminate individuals who are extremely active from those who are inactive. It is not clear as to whether they can validly discriminate individual physical activity levels in the moderate ranges. Furthermore, logging procedures could not be employed effectively in certain populations: children, people who are below a certain intelligence level, or are non-motivated, or with animals. Also, cross cultural physical activity surveys using logging procedures are probably not valid. Clearly, there are problems concerning the currently available measures of physical activity for epidemiologic research. More objective measures are needed, as direct measures or as validation measures. The current research is designed to further evaluate the applicability of the LSI activity monitor for examining physical activity in diverse populations and under various conditions in order to determine the relationship of the

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monitor readings to other measures of physical activity and to determine the characteristics of physical activity. EXPERIMENT

Analysis of physical

1

activity in eighth grade males

In experiment 1 the physical activity of eighth grade boys, aged 12-14 from two area schools, was examined. The research employed various measures of physical activity, nutrition and health fitness in order to determine how measures of daily activity relate to each other and to measures of cardiovascular fitness. At the initial interview, the Taylor Leisure Time Activity Survey was administered by an interviewer, using the procedures outlined by Taylor [16]. Following this, the participants recorded their food intake for three days, and wore the LSI for two days. A record of school activity was obtained by the LSI by having the individuals record the counts immediately before and after school. Leisure time activity was determined by the after school and the before retiring readings. Due to the limited availability of the LSI, only 22 students were tested with the units. Thus, the direct measures of daily activity were the activity readings and the survey activity. The caloric intake from the food records served as an indirect physical activity measure. The second stage consisted of exercise testing performed at their schools including the maximum number of sit-ups in one minute, a one mile run for time and a sit and reach test for flexibility. In addition, a Maximal Oxygen Uptake Test on a motorized treadmill was administered according to Taylor’s protocol. The maximal oxygen uptake was determined by expired air. Results

Table 1 presents the interrelationship of the daily activity measures. There are a few important features to note. First, the school activity was highly related to the after school activity. Thus, children active in school also appeared to be active after school. The second interest was the relationship between the activity as measured by the activity monitors and the activity derived from the activity log and caloric expenditure from the food logs. There did not appear to be any significant relationships between the activity measurements of the monitors and the surveys. The monitored activity was significantly related to the caloric intake where the more active students had the highest caloric intake. Total activity was also positively, but not significantly, related to the food intake. The relationships between the activity monitor readings, and the daily activity readings to the health fitness measures were examined (Table 2). It was of interest that the activity monitor readings and the Taylor survey were not related to any of the fitness measures. Furthermore, activity monitor measurements were not highly related to body composition measures. Discussion

The results indicated that the after school rate of activity was significantly related to the indirect measure of physical activity, caloric intake. The activity monitor measurements were not significantly related to the Taylor survey. TABLE I. CORRELATIONANALYSISOFDAILYACTIVITY

LSI school LSI after school LSI total Leisure time activity questionnaire Caloric intake *Significant

0.43* 0.83*

-0.17 - 0.02

at the 0.05 level.

LSI after school

MEASURES:EIGHTHGKADEBOYS

LSI total

Leisure time activity

0.02 0.30

0.16

0.86*

0. I 7 0.48*

RONALDE. LAPORTEet al.

790

TABLE2. RELATIONSHIP BETWEEN DAILYPHYSICAL ACTIVITY ANDHEALTHFITNESS MEASURES Sit and reach LSI school LSI after school LSI total Leisure time activity questionnaire Caloric intake

vo,

Situps

Mile time

Treadmill time

max

0.02 -0.11 -0.05

-0.10 0.22 0.08

0.19 0.00 0.10

0.16 0.29 0.28

- 0.05 -0.20 -0.16

-0.21 - 0.09

0.08 0.06

0.02 -0.19

0.04 0.24

-0.10

-0.12

-

Sum of skin folds

Weight/ height’

0.25 0.07 0.17

0.06 0.05 0.06

- 0.02 -0.14

0.11 0.02

It was somewhat disturbing to not find a relationship between activity and fitness. A review of the epidemiologic research indicates that the relationship is somewhat elusive to find. Two population studies have examined the relationship between physical activity and fitness [17, 181 on a population basis and did not find a strong relationship. A recent report, however, found a high relationship between the Taylor survey and work capacity [19]. It is clear that other factors such as genetics have a strong influence on vo2 [20]. Moreover, these children were in different stages in their growth which may have accounted for the lack of relationship. We were concerned, however, that we were not able to demonstrate a relationship between the activity monitor readings and the surveys. This question was examined in the following research. The sampling frame was increased from two days to three. Instead of only work or school activity, off-day activity was also examined. It was felt that perhaps a longer sampling frame was needed to define individual activity levels. EXPERIMENT

Analysis of physical

2

activity in middle-aged

men

The second experiment examined the relationship among work, after work and off-day activity, as measured by the LSI and the Taylor Leisure Time Activity Survey in 42 participants of the Multiple Risk Factor Intervention Trial (MRFIT). The MRFIT project was a large multi-center, clinical trial attempting to assess the effect of heart disease risk factor intervention upon subsequent morbidity and mortality. The men recruited into the project represented men in the top l&15% risk for myocardial infarction. The men wore an activity monitor on their hip for 3 days; 2 work days and one off-day. During the work days, a reading was taken prior to going to work, immediately after work, and before retiring for the evening. The Taylor survey was administered following the procedures indicated by Taylor [16]. Results

Table 3 presents the frequency distribution of activity as measured by movement counts per hr for the MRFIT population and the eighth grade students from the first experiment. In addition, physical education majors and non-physical education majors from a previous report [l l] and post-menopausal women from experiment 3 are presented. As can be seen, the activity levels of these primarily blue collar workers were extremely low. The activity levels for the MRFIT group of men were similar to the activity levels of the post-menopausal women. Thus, although these men would be classified by job titles as laborers primarily in steel mills, the levels of physical activity appeared to be very sedentary. Work activity was considerably higher than after work or off-day activity where work activity represented an average activity rate per hr of 42.2, after work activity, 24.0 and the off-day rate was 23.5. In addition, work represented a very high proportion of the total daily activity (69%).

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TABLE 3. MEASUREMENT OF ACTIVITYCOUNTS(MEANCOUNTSPERHR) FOR VARIOUSPOPULATIONS

Mean counts per hr G-19 20-39 4t359 60-79 NH9 100-119 120-139 14&159 16&179 18&199 200-219 22@239 24fb259 26&219 Z 36G369 X activity =

College

students* freq.

Eighth graders freq.

Post-menopausal women freq.

MRFIT freq.

2

14 16 8

20 33 11 6 2

1

3 9 5 3

2 1

2

I 126.7

102.1

*There were 10 physical activity majors (x = 182.4) and (x = 86.6). See Ref. 9 for indepth description of the study.

32.3

33.2 10 non-physical

activity

majors

Table 4 presents the correlation coefficients among the various activity measures. Three things were of primary interest in the interrelationship among the variables. First, there was a significant correlation between work activity and after work activity, indicating that those individuals who were active at work, also tended to be active after work. This was consistent with the previously reported student study where activity in school correlated highly with after school activity. Second, the after work activity was not related to the off-day activity. This is important because it suggests that leisure time may have to be conceptualized in two forms: an after work activity and an off-day activity. The third finding concerned the correlation between the Taylor generalized activity survey and the activity monitor readings where the Taylor survey was significantly related to off-day activity but not to after work or work activity. This suggests that the Taylor survey may not necessarily reflect work day activity (either during work, after work or total activity). Instead, the Taylor survey appeared to be assessing off day activity. Discussion

The results indicated that work activity accounted for the major proportion of the activity of these middle aged men. The LSI monitors again showed an association between work and off-work activity. The relationship of the off-day activity with the Taylor activity survey, suggests that the Taylor survey primarily assesses non-work day activity.

TABLE 4. CORRELATIONS BETWEEEN ACTIVITYMEASURES:MRFIT

After work activity Total activity Off-day activity Taylor leisure time activity survey

Work

After work

Total

0.36* 0.92** 0.03

0.64** 0.05

0.06

0.05

0.16

0.1

* = p < 0.05; ** = p < 0.01.

(‘.D.

35.IO--C

1

MEN

Off-day

0.45*

RONALD E. LAPORTE et al.

792

EXPERIMENT

Analysis of physical activity

3

post-menopausal women

in

Experiment 3 was designed to test the applicability of employing the activity monitors to measure physical activity in older women. Little is known about physical activity in older women. In addition, the interest was to relate the measures of physical activity to high density lipoprotein cholesterol levels (HDLc). The HDLc results have recently been published elsewhere [21]. Seventy-two white post-menopausal women (age range 45-74, x = 61.2) were recruited to participate. At the initial visit, the triceps skinfold, grip strength, height and weight of the women were measured and a blood sample obtained for lipoprotein analysis. At the meeting, the women completed the Paffenbarger survey for physical activity [7]. This was converted to kilocalories of energy expenditure per week. The participants recorded their food intake and activity for three days. The activity readings were taken upon arising in the morning, before dinner and before retiring for the evening, thus providing for a day, evening and total daily activity reading. Results Table 3 presented the mean total activity of the women compared with the various populations of men. As evident, the women had a much lower level of physical activity than the college students or the eighth grade students. The low activity of the women, however, was not much different than the middle aged MRFIT men. In analyzing the activity of the women, the day activity (40.0 counts/hr) was almost twice as high as the evening activity (21.0) demonstrating, not too surprisingly, that the day activity is greater than evening activity. Table 5 presents the correlations among the various measures of physical activity. There was a significant relationship between the monitor readings and the Paffenbarger survey. The results indicated that the activity monitor readings for the women on a daily basis were related to the Paffenbarger Leisure Time Activity Survey. Furthermore, the day activity was more related to the Paffenbarger activity than the evening activity. Table 6 presents the relationship between the various physical activity measures and the HDLc levels of the women. The kilocalories per week were significantly correlated with the HDLc levels but the activity counts were not. Discussion The activity monitors proved to be very effective for analyzing the activity of a relatively sedentary population: post-menopausal women. The results indicated again that individuals who are most active during the day were also the most active during the evening. Additionally, in this population, the daily activity was significantly related to the Paffenbarger survey. The majority of these women were not employed outside the

TABLE 5. RELATIONSHIP AMONG PHYSICAL ACTIVITY MEASUREMENTS:POSTMENOPAUSALWOMEN

Day

Evening

Total

Paffenbarger

Day activity (LSI) Evening activity (LSI) Total activity (LSI) Paffenbarger kilocalories Grip strength *p < 0.05.

0.43* o.s7* 0.33; -0.06

0.70* 0.08 0.03

0.23* 0.01

0.09

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TABLE 6. UNIVARIATE CORRELATION COEFFICIENTS:HDLc LEVEL TO PHYSICAL ACTIVITY AND STRENGTH MEASURES

Variable LSI morning activity’ LSI evening activity’ LSI total activity’ Kilocalories/week’ Vigorous hours’ Moderate hours’ Light hours’ Grip strength ‘LSI Activity Monitors; Survey. *p < 0.05.

0.09 0.03 0.09 0.25* 0.27* -0.15 0.10 -0.01 ‘Paffenbarger

home, suggesting that this association observed between the LSI and the Paffenbarger survey is consistent with the observed correlation between the Taylor survey and off day activity in the MRFIT men. EXPERIMENT

Analysis of physical

4

activity in monkeys

The activity monitors have been used in a population where other epidemiologic measures of physical activity are impossible. A jacket has been developed where the activity monitor can be placed on monkeys’ backs. The monkeys are anesthesized in order to put on the harness and then followed for a period of days to measure activity. In order to determine the face validity of the monitor for measuring physical activity, three groups were defined through inspection of female monkeys of the Macaca Fascicularis species who were approximately the same age. Four monkeys were classified into each of the three groups. The first group was labelled as being very active, the second moderately active and the third as exhibiting little physical activity. Following the classification of the monkeys, the activity was measured for a three day time period. The second day activity was employed for analysis because during the first day the monkeys were overcoming the effect of the anesthetic. Additionally, there was a very high correlation between the second and third day activity levels. One reading was taken per day, therefore, the physical activity measurement represented a rate for 24 hr in comparison to the rate/hr for the human population. The interest was to determine if the activity monitor readings could discriminate the three physical activity groups. Results

The measurement of the activity counts proved to perfectly discriminate the three groups. The high activity group had a mean activity count of 10,948 + 3300 (456/hr). The middle group had a 24 hr activity count of 2611 + 1973 (109/hr). The low group had an activity count of 484 + 316 (20/hr). The groups were significantly different (F(2,3) = 10.68, p < 0.004). Discussion

The results of the pilot animal research work demonstrated that the monitors could easily discriminate animal groups having different activity levels. The monitors could, therefore, effectively evaluate physical activity in a group where other measures would be impractical. Future research measuring the activity and changes of free living activity in relationship to cardiovascular risk factors in animals now appears to be feasible.

794

RONALD E. LAPORTE c>ral. SUMMARY

We have employed the activity monitors in a series of diverse population studies. In addition to the previously described college students, the monitors have proven to be effective in discriminating individual activity levels in eighth grade males, MRFIT participants, older women and monkeys. Furthermore, we have begun to develop an understanding of the epidemiology of free living physical activity differences between and within populations and what they might mean in terms of physiologic differences. Clearly, the comparison of these diverse groups could not have been accomplished with the available epidemiologic physical activity measurement tools. Thus, the activity monitoring devices and measuring techniques appear to have an excellent potential as an objective measure of physical activity in large population studies to either validate surveys or as techniques unto themselves. With the activity monitors, it may be possible to develop an understanding of the role of moderate levels of physical activity to health on a population basis since moderate levels of physical activity can be more accurately classified. Furthermore, change in activity over time probably could be assessed more precisely, especially in relationship to change in other cardiovascular risk factors, such as HDLc, blood pressure or weight. The instrument could also be used to evaluate changes in activity such as part of a clinical trial designed to increase physical activity levels following a heart attack. Lastly, this objective measure could also be employed as a feedback mechanism to inform individuals how active they really are. Thus, the LSI or instruments of its type may have an important potential for future research on the epidemiology of physical activity. A critical, yet neglected, issue has been the measurement of free living physical activity for the majority of people in our society who do not engage in structured exercise programs. Conclusions about the relationships of physical activity to fitness, heart attack, other diseases and risk factors are dependent upon the accuracy of measurement of physical activity. If measurement is poor, so will be the conclusions of the research. Acknowledyements~Research requests to Dr LaPorte.

supported

by the NIH

Post-Doctoral

Training

Grant

NO. S-34369.

Reprint

REFERENCES 1. 2. 3. 4. 5. 6. I. 8. 9. 10.

11. 12. 13. 14. 15.

Leon AS, Blackburn H: The relationship of physical activity to coronary heart disease and life expectancy. Ann NY Acad Sci 301: 561-578, 1977 Morris JN, Kagan A, Pattison DC, et rti.: Incidence and prediction of ischemic heart disease in London busmen. Lancet 2: 553-559, 1966 Paffenbarger RS, Gima AS, Laughlin ME, et al.: Characteristics of longshoremen related to fatal coronary heart disease and stroke. Am J Pub Hlth 61: 1362-1370, 1971 Kannel WB, Sorlie P: Some health benefits of physical activity, The Framingham Study. Arch Int Med 139:8577861, 1979 Morris JN, Headv JA, Raffle PAB, et al.: Coronary heart disease and physical activity at work. Lancet 2:1053-1057,111i~1120,1953 Paffenbarger RS, Hale WE: Work activity and coronary heart mortality. N Engl J Med 292:545-550, 1975 Paffenbarger RS, Wing AL, Hyde RT: Physical activity as an index of heart attack risk in college alumni. Am J Epid 108: 161-175, 1978 Wilhelmsen L, Tibblin G, Aurell M, et al.: Physical activity, physical fitness and risk of myocardial infarction. Adv Cardiol, 18:217-230, 1976 Haskel WL, Taylor HL, Wood PD, et al.: Strenuous physical activity, treadmill exercise test performance and plasma high density lipoportein cholesterol. Circulation 62(Suppl. IV):IV53361, 1980 Hulley SB: The high density lipoproteins: An epidemiologic review. (From the Multiple Risk Factor Intervention Trial). Presented at the 6th International Symposium on Drugs Affecting Metabolism. September, 1977, Philadelphia, Pennsylvania. LaPorte RE, Kuller LH, Kupfer DJ, et al.: An objective measure of physical activity for epidemiologic research. Am J Epid 109: 158-168, 1979 Maffield ME: The direct measurement of energy expenditure in industrial situations. Am J Clin Nutr 24:11261138,1971 Passmore R and Durnin JVGA: Human energy expenditure. Physiol Rev 35:801-840, 1955 Eldholm OG, Fletcher JG, Widdowson EM, et al: The energy expenditure and food intake of individual men. Br J Nutr 9:286-300, 1955 Durnin JVGA: Activity patterns in the community. Can Med Assoc J 96:882X887, 1967

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