An older adult exercise status inventory: reliability and validity.
Part of the problem in assessing physical activity is in defining it. Exercise has been defined as a subset of physical activity that is planned, structured, repetitive, and has as its objective the improvement or maintenance of physical fitness (Caspersen, Powell, & Christensen, 1985). In this study, exercise is used interchangeably with physical activity although researchers recognize that "it has characteristics that separate it from many other physical activities" (Powell & Paffenbarger, 1985, p. 118).
Since work requirements have become increasingly sedentary in recent decades, contemporary survey assessments have focussed on "leisure-time" physical activity (Godin & Shephard, 1985; LaPorte, Montoye, & Caspersen, 1985; Taylor et al., 1978; Yasin et al., 1967). Furthermore, work-time activity and leisure-time exercise are rarely examined together, even though both are likely to play an important role in maintaining functional status in the elderly.
Unfortunately, no single standardized measure of physical activity behavior is universally supported; at this point in time there is still a vigorous search for a powerful but simple survey instrument. As many as 30 different methods have been used to assess physical activity from calorimetry, job classification, survey assessment, physiological markers, behavioral observation, mechanical monitors, and dietary records. Yet no single instrument fulfils the criteria of being valid, reliable, and practical while not affecting behavior. Laporte and colleagues (1985) sum up this problem best:
The instruments that are very precise tend to be impractical on a population basis. Surveys' are the most practical approach in large-scale studies, although little is known about their reliability and validity, (p. 132).
The lack of detailed information on habitual exercise is, ironically, at least partially a result of the success of simple one-statement questions, and other coarse assessments to obtain epidemiological estimates which are statistically predictive of physical fitness, health outcomes, morbidity status, and mortality. Such simple instruments, while providing statistical explanation, offer little assistance to health counselling professionals who need more technical information. Recent findings suggest that more detailed instruments have better accuracy in assessing moderate to vigorous forms of activity among older adults (Cartmel & Moon, 1992). For the field professional, an instrument is needed which is comprehensive enough to capture the type, duration, intensity, and frequency of the person's activity. Furthermore, an instrument should be age and gender sensitive so that elderly adults, a majority of whom are women, find the instrument easy to use and relevant to their life-styles.
The objective of this study was to develop an instrument which would have utility for geriatricians, public health professionals, fitness programmers, and exercise leaders. The requirement was to develop a brief field survey instrument which would capture a great deal of information about physical activity patterns without markedly increasing respondent burden. The data collected on the instrument would need to be a valid and reliable reflection of a person's actual activity level so that the professional using it would be able to accurately assess the adequacy of exercise and prescribe for the activity deficiencies of the respondent.
The purpose of this paper is to report on the utility of an older adult exercise inventory using seven-day recall. In addition to providing scientific support for the validity and reliability of the Older Adult Exercise Status Inventory, this paper discusses the merits and limitations of the instrument in three studies with elderly adults.
The Validity of Self-Reported Physical Activity
Self-report has become the measure of choice because a researcher can acquire a vast amount of information about physical activity patterns with near clinical accuracy, relatively little inconvenience to subjects, and with little time and expense (Baranowski, 1988; Grey & Kennedy, 1993; Godin, Jobin, & Bouillon, 1986). Recent studies have succeeded in demonstrating significant relationships between self-reported physical activity and health indicators with simple activity questions about strenuous activities. For example, the single question "Do you regularly engage in strenuous exercise or hard physical labor?," was a better predictor of HDL cholesterol in the blood than was direct fitness measurement on the treadmill (Haskell, Taylor, Wood, Schrott, & Heiss, 1980). Other researchers have provided evidence of construct validity; significant relationships have been found between self-reported activity and maximal oxygen uptake (Blair et al., 1985; Godin & Shephard, 1985; Haskell, 1984; Horowitz et al., 1987; Siconolfi et al., 1985), and with other assessments of exercise such as blocks walked, stairs climbed, and sweat episodes per week (LaPorte et al., 1983).
Despite the crudeness and simplicity in some of these instruments, research has been unhindered in finding significant relationships of self-reported physical activity with caloric intake (Alderson & Yasin, 1966), with smaller body fat skinfolds (Epstein, Wing, & Thompson, 1978); with daily activity diaries (Taylor et al., 1984); with self-motivation to persevere at exercise (Dishman & Ickes, 1981); with heart attack risk (Paffenbarger & Hale, 1975; Paffenbarger, Wing, & Hyde, 1978); with improved levels of HDL blood cholesterol; with physical health status (Belloc & Breslow, 1972); with longevity (Paffenbarger et al., 1987); and with socioeconomic status (Ford, Merritt, Heath, Powell, Washburn, Kriska, & Haile et al., 1991). However, for all of these studies, the activity patterns and their relationships among elderly adults, and especially older women, are not represented.
Reliability of Self-Reported Exercise Among the Elderly
In general, reliability coefficients for self-reported exercise status are over .7 in adults under age 65 especially if the exercise is intense (Baecke, Burema, & Frijters, 1982; LaPorte et al., 1983, 1985) or sweat-inducing (Godin & Shephard, 1985; Kohl et al., 1988). Few reproducibility studies have been specifically conducted on older adults. Sallis and colleagues (1985) reported test-retest data on 2,126 men and women, aged 20 to 74. The test-retest coefficient between the two weekly reports of the number of vigorous activities was .83 (p [less than] 0.0001). For moderate activities, r was somewhat lower at .75 (p [less than] 0.0001). This compares favorably with the two-week test-retest correlations for the Yale Physical Activity Survey on adults aged 60 to 86 which were only .42 to .65 (Dipietro, Casperson, Ostfeld, & Nadel, 1993). In another study, outpatients 18 to 80 years of age reported days of weekly physical exercise as 2.5 days at Time 1 and 2.6 days at Time 2 (r = .82) (Skinner, Palmer, Sanchez-Craig, & Mcintosh, 1987).
Support for the Seven-day Recall
The seven-day self-report for leisure-time activity assessment is gathering research support in contemporary studies. The seven-day recall format was used for this study because it does not appear to surpass the recall memory of elderly individuals and does not create too great a respondent burden (Blair, 1984) compared to the yearly assessment of the Canada Fitness Survey (1983). Initially, consideration was given to Taylor's one year "recall" of the Leisure Time Physical Activities Questionnaire for middle-aged men which was conducted by interview and validated against physical work capacity (Taylor et al., 1978). But a one-year recall was considered too onerous for older adult research. The seven-day recall has the advantage of brevity. Seven-day recall has been used in a number of important studies and appears to have adequate scientific merit in terms of reliability and validity.
An age-appropriate and activity-prompted style of survey information is recommended for reducing report error in research with the elderly (Cauley et al., 1987; Washburn, Jette, & Janney, 1990). Baranowski (1988) suggested that self-report accuracy of physical activity might be increased using memory-enhancing procedures such as checklists. Certainly, the recall accuracy of older people is important to this study. Ridley, Bachrach, and Dawson (1979) examined recall ability of females aged 66 to 76 in the birth cohort of 1901 to 1910. Recall of fertility history over the life course revealed a "high and invariant level of recall ability" (p. 103) with over 90% accuracy to 15 items asked. Reliability was highest for subject matter which was factual more than attitudinal. Item retests conducted three weeks later ranged from 44% to over 90% accuracy.
Description of the OA-ESI
The Older Adult Exercise Status Inventory (OA-ESI) is a seven-day self-assessment inventory which provides information on the type, frequency, duration, and approximate intensity of the weekly physical activity pursuits of older adults. Both work-time activity and leisure-time physical activity are documented. The OA-ESI is a two-page inventory which prompts subjects with activity categories organized in columns by the seven days of the week and organized in rows by lists of work-time and leisure-time physical activities. Five categories of indoor and outdoor work activity, along with a list of 38 categories of leisure-time exercise and sport activity were provided. The inventory aims to provide more detailed assessment of physical activity than currently is available and account for the unique activities of older adults as recommended by Washburn and colleagues (1990).
The 38 leisure-time activities are considered to be age-appropriate for older adults because they were based on field observation of the activities available to older people in urban recreation and community centers as well as activities attractive to rural people in Canada in Winter and Summer. Two open categories called "Other" accommodated any other activities that were not already included on the main list.
The exercise categories acted as memory prompts and were listed alphabetically from "aerobic fitness class" to "walking (no sweating)." Subcategories were used to reduce error in estimating the intensity of a particular activity and thereby improve the estimate of energy expenditure for weekly exercise. For example, aquacize activity was subdivided into "vigorous" and "gentle," while cycling, gardening, jogging, and walking were subdivided into "sweat-inducing" and "no sweating." To aid precision, subjects were asked to report the "time spent in minutes" (rather than hours) for each activity on each day.
Using the OA-ESI
"Amount of exercise in the past week" was calculated using published metabolic charts giving MET (metabolic equivalent) units for physical activities (Cantu, 1980; Passmore & Durnin, 1955; Taylor, 1994; Taylor et al., 1978; Wilson et al., 1986). The MET is the ratio of working metabolic rate to resting metabolic rate and is a convenient method of expressing energy expenditure (Sallis et al., 1985). It can be thought of as the ability of an individual to tolerate multiples of their resting energy level (P.O. Astrand, personal communication). Where the reported MET estimates differed in the literature, the more conservative estimate was used.
One MET = 1 kcal/kg/hour, represents an equivalent of one kilocalorie of energy expended by a 60 kg. person sitting for one minute. MET units account for the intensity of the activity, the duration of the activity, as well as the body weight of the individual (if they are not too different from 60 kg.). For sake of convenience, many studies assume an average body weight of 60 kg., meaning that the average individual, sitting at rest, spends about 60 kilocalories per hour or 1.0 kilocalorie/kg./hour, or 1.0 MET. If the researcher plans to adjust for people of extreme weight, the individual's actual body weight in kilograms is exchanged in the calculation for the assumed weight of 60 kg.
In this study, a shortcut method of calculation was used whereby the MET unit was multiplied by the individual's reported participation time in total minutes over the seven days for each activity. For example, to estimate the total weekly energy spent on walking (slow strolling), first the daily minutes reported for slow walking across the seven days are summed and then multiplied by the MET unit for that specific activity.
Slow Walking Calculation
* Walking Duration (total weekly minutes) x Intensity (MET)
* [135 minutes x 3.0 kcal x 60 kg./60 min. (kcal/kg/hr)]
* 135 x 3.0
* 405 kilocalories spent on this activity in one week
Next, the energy estimates are summed for all the activities below an intensity of 4 METS (MILDKCAL), for all the activities of a moderate level of 4.0 to 5.9 METS (MODKCAL), and all the "vigorous" activities 6 MET units or more (VlGKCAL). The three intensity categories provide a breakdown of how much energy is spent on the different intensity levels, and each can be used as independent or dependent variables for regression analysis. MILDKCAL, MODKCAL, and VIGKCAL can then be summed to form a Weekly Exercise score in the form of estimated kilocalories spent on the total amount of leisure-time physical activity in the past week.
Weekly Exercise Status (TOTKCAL)
SUM of MILDCAL ([less than] 4 METS) + MODKCAL (4 to 5.9 METS) + VIGKCAL (6+ METS)
From the information provided on the OA-ESI, a number of useful measures are obtained in addition to TOTKCAL, MILDKCAL, MODKCAL, and VIGKCAL such as the total number of active days (ACTDAYS), total hours of activity (ACTHOURS), average time spent per activity session (TIMESESS), and total number of weekly exercise sessions (TOTSESS) over the seven days. Separate days of the week can be examined for differences in activity patterns as well.
SYSTAT 5.01 (1991) was used to analyze the data of the OA-ESI and associated variables. Means and standard deviations of the variables were calculated. In preparation for regression analysis all variables were standardized and exercise status was logged to correct for positive skewness. Pearson correlation coefficients were calculated between variables with interval and ratio data
Study 1 and 2: Tests of Reliability
In the first study of reproducibility, the OA-ESI was administered twice in a four-week period to an athletic sample of 17 Edmonton women from Edmonton aged 58 to 80 (mean age of 67). In Study 2, a coed test-retest study, was conducted with 29 adults aged 65 to 90 years of age with an average age of 71 years. The 22 women and 7 men reported on two different weeks of activity spaced by one week apart.
Study 3: Tests of Validity
The purpose of Study 3 was to assess the physical activity involvement and to examine predictive and concurrent validity of the OA-ESI with community-based elderly women who were 70 years or older. A geographically strategic sample with randomization was employed within the city of Vancouver. The sampling procedure first identified all community facilities where seniors could be found in formal and informal settings. All available seniors programs which were publicized in the Community Resource Directory for Seniors, the Recreation Program Guide, The Vancouver Courier Program Guide, and the B.C. Tel Yellow Pages, were listed. From these public resources, a list of 120 older adult facilities (program sites) and seniors' residences (but not extended care centers) was identified for the greater metropolitan area. Using the geographic boundaries of the city limits, the list was reduced to 69 sites. As with other North American cities, Vancouver has West to East bands of high, middle, and low economic status. The 69 sites were marked on a map and 18 clusters of 3 to 4 proximate facilities were drawn. Each cluster had a one to two mile radius. One research site was randomly drawn from each cluster using a table of random numbers (Havilcek & Crain, 1988).
Facility managers were contacted for permission to distribute the survey by explaining the study to formal classes (e.g., watercolor painting), scheduled group meetings e.g. bridge groups), and individual elderly women visiting the premises (e.g. lunch program). The research study was explained to all available groups and individuals who were female and over the age of 70. The survey questionnaire was distributed to as many volunteers at the chosen sites that were willing to participate. All participating women were unpaid volunteers and signed informed consent forms. Of 520 surveys distributed, 327 were returned in a usable form, a return rate of 63%. Participants represented over 30 different types of community activities for seniors ranging from sedentary pursuits such as bridge and bingo, to tap dancing and aquafit exercise.
Of the three activity types, MILDKCAL activities demonstrated the poorest reproducibility (r = .114; n.s.) and undermined the reproducibility of the total amount of exercise reported (r = .340; p = .198, n.s.). Moderate exercise was reported more consistently (r = .756; p [less than] .001) while vigorous exercise was modestly reliable (r = .505; p [less than] .05)
Ironically these inconsistent findings for mild exercise patterns provide support for construct validity. The initial survey and retest were conducted in the last two weeks of August and the first two weeks of September during which the test-retest sample entered various fall sport and fitness programs. Unforeseen were these substantial changes in the nature and quantity of physical activity over the four-week study. All of the women in the reliability study were re-initiating participation in structured, supervised, and vigorous forms of exercise (seniors gymnastics club) and forfeiting some of their less structured and milder Summer activities, such as walking, at that time of year. In this regard the piloted questionnaire was sensitive to these seasonal changes in activity patterns of the women. In doing so, self-reported activity as measured by the OA-ESI demonstrated sweat reproducibility, but, at the same time, validated the known changes in participation. This finding provided a degree of confidence that the seven-day recall had adequate sensitivity to be administered to a larger sample. Indeed, closer examination of the test-retest data for total weekly exercise level indicated that variance more than doubled from test to retest ([S.sup.2] = 392.1 to 968.2) suggesting that by September, some members of the group were taking up substantially larger activity programs compared to others in the group.
The two weekly exercise scores, unadjusted for body weight, were correlated [r.sub.totkcal] = .771. p [less than] .000.
The women participating in Study 3 ranged in age from 70 to 98 with an average age of 77. Compared to Vancouver and Canadian census data, women in this study were less overweight, better educated, and in better health. The sample was culturally representative of the Vancouver ethnic scene and was virtually identical to Canadian women over age 65 in terms of economic status and reported medical symptoms (60% with one or more serious health problems). In response to a question about lifelong activity patterns, about one-third of the sample reported that over their entire life course, they had "always been physically active." While over 60% said they were no longer active or had "never been much involved with physical activity."
The low intercorrelations of mild, moderate, and vigorous exercise in Table 1 suggest that older women tended to participate at similar intensities of activity throughout their week rather than within the full range of mild to vigorous forms of exercise. Table 1 also shows that people who were active more days per week or in more activity sessions per week were spending less time at each session.
Predictive and Concurrent Validity of the OA-ESI
An assessment tool should have predictive validity, concurrent validity, and construct validity if it is to serve a variety of measurement purposes (Chen, Calderone, & Pellarin, 1987). The Older Adult Exercise Status Inventory (OA-ESI) integrated the best assessment strategies of other validated survey instruments for leisure-time physical activity (7-day recall; checklist; time in minutes) and thus had a degree of assumed construct validity. But in addition, other forms of validity were examined as part of a larger study and are presented next. Because objective assessments for validating work activity in and around the home were not available at the time, the validation study included only the leisure-time aspect of the OA-ESI.
Predictive validity was demonstrated in the strength of the statistical relationships between weekly leisure-time exercise and a number of psychological constructs assessed at the same time. Weekly exercise, as assessed by the OA-ESI, was significantly and positively associated with beliefs about self-efficacy to participate in fitness activities (r = .324, p [less than] .0001); with perceptions about social support to exercise from family, friends, and physician (r = .333, p [less than] .0001); and negatively with beliefs about personal risks related to exercise (r = -.185, p [less than] .01). Previous research acknowledges that exercise activity is lower in older individuals and those in poor health. In this study, TOTKCAL supported these relationships with an r = -0.257 (p [less than] .0001) with increasing age and r = .222 (p [less than] .0001) with a positive health self-rating. Weekly exercise was able to explain 10.5% of the variance of self-efficacy to exercise (F = 34.283, p [less than] .0001) and [TABULAR DATA FOR TABLE 1 OMITTED] 11% of perceived social support to exercise (F = 39.358, p [less than] .000). These significant relationships with cognitive beliefs provide further support for the statistical capabilities of the OA-ESI.
Concurrent validity was demonstrated by examining the correlations of weekly exercise with other previously-validated physical activity indicators on the same survey. For example, TOTKCAL (weekly exercise) had a significant coefficient of r = .450 (p [less than] .01) with a subjective question about lifelong activity involvement in physical activity similar to that used by Godin and colleagues (1987). This question was worded, "How would you describe your physical fitness activity over your entire life course?" The five response choices ranged from "never been much involved" to "have always been involved." Concurrent validity for TOTKCAL was also found with the separate assessment of number of active days reported per week (r = .491; p [less than] .001).
A question from Gaston Godint's (1982) unpublished survey instrument (PAST4MON) was included in the survey. Subjects were asked "How often did you participate in vigorous physical activities long enough to get sweaty within the past four months?" Response categories were: "not at all; less than once a month; about once a month; about 2 to 3 times a month; about once a week; two or more times a week" (Godin & Shephard, 1982). With this single question, the TOTKCAL score was correlated r = .411 (p [less than] .0001). In summary, the OA-ESI is significantly related to other more simple assessments already used in the field. The substantial detail of the OA-ESI makes it unlikely that correlations above .45 to 50 will be found.
Self-report using the Older Adult Exercise Status Inventory appears to be an effective way to acquire detailed information about activity patterns among the elderly. Using a small sample of 17 elderly women, the estimate of test-retest reliability compares favorably with other activity instruments: r = .77 compared to Paffenbarger's Physical Activity Index (.73) and Godin, Jobin, & Bouillones question about sweating (.64). However, many older adults do not read well, have poor vision, or have language barriers. Therefore oral surveys using the OA-ESI as an interview guide are recommended if time is not a limiting factor. In the interview setting, the researcher can clarify certain responses and probe in more detail about the specific nature of the activity, thereby potentially reducing error in estimating activity intensity. While the 38 leisure-time activities act as memory prompts, there may be other activities that are not listed, or recalled at the time of assessment. The reported activity duration and frequency in the past week may also contain significant reporting error through exaggeration or reporting of the "ideal week" or the "intended" activity level. Social desirability bias was not assessed and thus also could be acting to inflate self-reports.
Other error is introduced when a sample is biased through self-selection. One difficulty in surveying older adults is that a substantial minority are simply not well enough to participate. By seeking volunteers from community settings, this study intentionally excluded the very ill and institutionalized older women - women who represent up to 10% of this age cohort. Thus the inventory may only be workable with independent, community dwelling and mobile older women. Also unintentionally excluded were women who may have had adequate health, but for other reasons were not venturing into their community at the time of the study. Thus, the bias inherent in studying the well elderly, the available, and the self-selected volunteer further strengthens the statement that "those who have survived to old age often represent a special case" (Branch & Jette, 1984, p. 1128).
The OA-ESI brings together the strengths of a number of instruments which have been used in prominent epidemiological research projects (Blair, 1984; Paffenbarger et al., 1986; Canada Fitness Survey, 1988; Taylors et al., 1978). In general, the Older Adult Exercise Status Inventory (OA-ESI) used in this study compromises some instrument brevity for increased detail and rigor than in other studies which have used the seven-day self-report. The OA-ESI is among the first physical activity assessment tools to both describe and quantify the weekly physical activity patterns of women over age 70.
In terms of reported leisure-time exercise, the participants in Study 1, 2, and 3 averaged 2150, 3400, and 1400 kcal per week respectively. This is substantially more than the 777 kcal per week reported by the women over age 65 in the Stanford Five City Project Community Health Survey (Blair et al., 1985) and more than the 1042 kcal per week reported by LaPorte and colleagues (1983) for 76 women averaging age 61. These differences may reflect errors in activity measures, errors caused by the self-selecting process of obtaining volunteers, real differences in the life-styles of seniors in recent years, or possibly national or regional differences.
As with the Canada Fitness Survey, the OA-ESI examined exercise as a form of leisure behavior, and therefore did not initially include domestic work, nor employed work activity as part of the weekly energy estimate. The work energy of women on domestic tasks has, unfortunately, received little interest by researchers. At least two studies claim that domestic activity accounts for much of women's everyday physical activity and must be documented in the future (Cauley et al., 1987; Mattiasson-Nilo et al., 1990). However, recent and very preliminary research suggests that women are more alike than different in their domestic work patterns; if this is true, leisure-time activity measures may offer more variability than domestic work activity measures (O'Brien-Cousins & Keating, in press). Researchers are therefore warned that reported work activity around the home, which is generally light, brief, and intermittent, may not add much explanation to the differential health outcomes of the elderly. Moreover, combining the two types of measures may reduce the activity variability needed for statistical explanation. Thus researchers should analyze physical work and physical leisure patterns separately until the issue is clarified.
In this study, concurrent validity was tested against other kinds of self-reported physical activity rather than "objective" measures of movement since other researchers have encountered problems with mechanical instruments. Few concurrent validity studies using objective standards have been conducted because even with mechanical measures of exercise activity, true validity is suspect. LaPorte and colleagues (1983) compared a movement-activated mercury sensor (LSI Activity Monitor) and the seven-day recall survey of Paffenbarger, Wing, and Hyde (1978) in assessing the physical activity of older women. After finding concurrent validity of only r = .23 they reported that the LSI activity monitoring and Paffenbarger survey were measuring different aspects of physical activity. The LSI measured physical activity associated with any movement, whereas the surveys captured the more intense components of energy expenditure. They concluded that:
...it is important to evaluate the characteristics of the activity of interest in order to select a physical activity tool for assessing activity patterns in older women. (Laporte et al., 1983 p. 394).
In younger adult populations, a seven-day recall significantly agrees with daily diaries or log entries of physical activity and direct measures of physical activity (Taylor et al., 1984, p. 823).
It is concluded that a seven-day activity recall accurately reflects mean kcal/day expenditure, with conditioning activities being the best recalled. (Taylor et al., 1984, p. 818).
Similarly, Washburn, Jette, and Janney (1990) reported that their questionnaire underestimated light and moderate standing work for elderly men and women aged 65 to 91 years while strenuous activities were reported with more accuracy.
Debate still exists whether the weight of an individual should be used in the calculation of energy expenditure, since the MET unit is meant to be a metabolic ratio, independent of body weight. The work/rest ratio method assumes that a task performed by a heavy person raises metabolic rate to the same extent as the same task performed by a person weighing less, even though the caloric expenditure might be different (Reiff, Montove, Remington, Napier, Metzner, & Epstein, 1967). In the Five City Project, researchers suggested that the measurement of exercise in "kilocalories per kilogram per day was not an acceptable measure for overweight populations" (Sallis et al., 1985, p. 95). Being overweight was considered to add to the energy estimate of exercise and was considered to negatively affect the reliability of their self-report data.
To test this concern in the present study, the estimated energy expended on weekly exercise was calculated as both a body weight-adjusted and non-adjusted estimate. Later analysis revealed that exercise status (adjusted for body weight) and exercise status (unadjusted) were virtually identical estimates (r = .975) and both measures produced similar regression equations (O'Brien-Cousins, 1993).
The Older Adult Exercise Status Inventory appears to have merit in evaluating older people's weekly physical activity. The inventory is quick and easy for reporting and at the same time provides a great deal of information about an individual's weekly physical activity Preliminary research suggests that the OA-ESI is more reliable at the moderate and vigorous intensity levels. The inventory exhibits concurrent validity with lifelong status in physical activity (r = .450), with frequency of sweating in the past four months (r = .411), and with active days per week (r = .491). Leisure-time exercise, as estimated by the OA-ESI, has significant statistical associations with psychological constructs such as self-efficacy for exercise, social support for physical activity and perceptions about risk in activity settings. In addition, the OA-ESI acts as a useful criterion variable which can be predicted from demographic characteristics such as age and health status.
Even with the amount of detailed information generated on the inventory, older adults are capable of self-reporting their weekly activity with adequate accuracy. From a research point of view, the instrument is simple to work with in terms of calculating row sums (duration of each activity across the days) and then adding up the vertical column (down the activity list) to acquire a weekly estimate of energy spent on physical activity. The OA-ESI provides all the important information required for type, intensity, frequency, and duration of each activity using only two pages. The instrument is easily suited to collect oral data in an interview setting. Both work-time and leisure-time activity are assessed, but at the present time, reliability and validity data are only available for the leisure-time activities.
Future studies need to clarify the utility of the OA-ESI with other older populations. In particular, the nature and scope of physical activities for elderly men have yet to be adequately examined with this inventory. Previous research suggests that elderly men are likely to report less domestic physical activity and more sport and physical recreation activity than same-age women (Mattiasson-Nilo et al., 1990).
The inventory may be improved in the future by evaluating the reliability and validity of work-time activities and their relationship to leisure-time activity as well as the utility of combining work and leisure-time activity into a single activity score. At present, only studies examining leisure-time activity can be readily compared. Contemporary research, while very preliminary, has not been convincing that work-time physical activity in the elderly will offer much strength to the present leisure-time assessments. For women, at least, domestic activity is not likely to differentiate women in terms of life-style behavior as significantly as has leisure-time physical activity.
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Appreciation is extended to the Social Sciences and Humanities Research Council of Canada for support of this research. Parts of this paper were presented at the 1993 Annual Scientific Meeting of the Canadian Society for Exercise Physiology, London, Ontario, October 23, 1993.
For further information concerning the instrument, contact:
Sandra O'Brien-Cousins, Ed.D. Faculty of Physical Education & Recreation The University of Alberta Edmonton, AB Canada T6G 2H9
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|Publication:||Journal of Sport Behavior|
|Date:||Dec 1, 1996|
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