Journal of Aging and Physical Activiiy, 1996,4, 377-389 O 1996 Human Kinetics Publishers, Inc.

Relationship Between Self-Reported and Physiological Indicators of Exercise Behavior in Older Women

Joanne Kraenzle Schneider

The purpose of this study was to examine therelationship between self-reported exercise behavior and physiological indicators of exercise behavior (body composition and oxygen consumption measures) in older women. Three self- report exercise behavior instruments were administered in counterbalanced order. Body mass index and sums of skinfold thicknesses were used as measures of body composition. Oxygen consumption was measured using a metabolic cart during a treadmill test while women walked at approximately 70% of their heart rate reserve. Fifty-nine women participated (68.7 f 6.0 years). Results showed that self-reported exercise behavior was moderately related to body composition measures. However, predicted maximal oxygen consumption was only weakly related to self-reported exercise behavior.

Key Words: self-reported exercise behavior, body composition measures, predicted maximal oxygen consumption

As the older adult population expands and an increasing number of research- ers study health and well-being, the need for instruments that accurately measure exercise behavior in older individuals also increases. Because of the challenges associated with measuring physical performance in older adult populations, self- report instruments are frequently selected as alternative measures of exercise behavior. In large-scale epidemiological studies, self-report instruments are espe- cially important because of their efficiency and cost-effectiveness.

Many researchers have selected physiological measures to assess physical fitness and exercise behavior in older adults. Both laboratory stress tests (e.g., treadmill and bicycle ergometer tests) and field tests (e:g., AAHPERD Functional Fitness test) have been used to assess functional capacity. While there are many advantages to assessing physical performance, there are also a number of shortcom- ings. For example, there is evidence that many older individuals may not be able to reach the plateau required for maximal stress tests (Sidney & Shephard, 1977). Furthermore, many submaximal tests require an estimation of maximal heart rate from age, which may not be accurate. In addition, older individuals may not meet the relatively stringent medical criteria set by the American College of Sports

Joanne Kraenzle Schneider is with the Department of Internal Medicine, Division of Geriatrics and Gerontology, Washington University, Campus Box 81 13,4566 Scott Ave., St. Louis, MO 63 110.

378 Schneider

Medicine, restricting exercise behavior studies to a healthy subsample of the older adult population (Chodzko-Zajko & Ringel, 1987).

In general, field tests of physical and functional fitness (e.g., AAHPERD Functional Fitness test) have less stringent exclusionary criteria. However, these tests also have their limitations. For example, many field tests may be affected by practice or learning effects (Shaulis, Golding, & Tandy, 1994). Furthermore, because field tests often do not require subjects to respond near their functional capacity (Evans, Hopkins, & Toney, 1996), they may not be sensitive enough to discriminate among high-functioning individuals. Because of these limitations, self-report instruments are likely to remain important alternative approaches to assessing exercise behavior.

Several self-report exercise behavior instruments have been developed. However, these instruments have generally been tested only in young and middle- aged populations (Ainsworth, Jacobs, & Leon, 1993; Baecke, Burema, & Frijters, 1982; Cauley, LaPorte, Sandler, Schrarnm, & Kriska, 1987; Gionet & Godin, 1989; Godin, Jobin, & Bouillon, 1986; Godin & Shephard, 1985; Goldsmith & Hale, 1971; Kohl, Blair, Paffenbarger, Macera, & Kronenfeld, 1988; Schechtman, Barzilai, Rost, &Fisher, 1991; Siconolfi, Lasater, Snow, & Carleton, 1985; Slattery & Jacobs, 1987). In addition, existing instruments are seldom appropriate for use with sedentary older people who require a sensitive instrument that distinguishes between slight differences in exercise behavior (DiPietro, Caspersen, Ostfeld, & Nadel, 1993).

There are other limitations to existing self-report exercise behavior instru- ments. Many older people are retired, making questions about activity related to their occupation inappropriate. In addition, diary-type instruments may be hard to read by individuals with limited vision or difficult to complete by those with arthritic hands (Cauley et al., 1987), and these instruments contributeconsiderably to subject burden (Washburn, Jette, & Janney, 1990). Thus, age-specific self-report exercise behavior instruments need to be developed and tested in older populations (Cauley et al., 1987; Washburn et al., 1990).

The purpose of this study was to examine the relationships among three self- report exercise behavior instruments and selected physiological indicators of exercise behavior (predicted maximal oxygen consumption and body composition measures) in women 55 years of age and older.

Methods

PARTICIPANTS

Womenvolunteers were recruited from senior centers, malls, and exercise facilities. Based on personal report, women were excluded from this study if they (a) had a history of a heart attack, (b) were taking prescribed medications with a known primary effect on heart rate, or (c) had a resting diastolic blood pressure of 120 mrnHg or greater or a resting systolic blood pressure of 200 mmHg or greater (American College of Sports Medicine, 1991).

Women were excluded for use of medications having a known primary effect on heart rate. However, taking other medications did not cause women to be excluded, because the goal was to select a sample that closely represented the

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normal population of older women. Thirty women in the sample (50.8%) were on hormone replacement therapy.

Fifty-nine women, aged 68.7 f 6.0 years (range 56.6 to 85.0), were enrolled in the study. Fifty-five women were white (93.2%), 1 woman (1.7%) was Hispanic, 1 woman (1.7%) was black, and 2 women (3.4%) were Asian or Pacific Islander. Fifty-four women (91.5%) had completed high school, and 34 women (57.6%) had at least some college education.

Prior to fitness testing, participants completed three exercise behavior instru- ments: Exercise Behavior Analysis, Godin's Leisure Time Exercise Questionnaire, and the Leisure Index of the Minnesota Heart Health Program Physical Activity Questionnaire. Administration of the instruments was counterbalanced to control for order effects.

EXERCISE BEHAVIOR ANALYSIS

I developed and pilot tested the Exercise Behavior Analysis (EBA). For this instrument, exercise was defined as activity performed for the purpose of maintain- ing or improving physical fitness; this definition does not include gardening or housekeeping. The EBA is different from other instruments because response choices provide interval or ratio-level data (as opposed to categorical) and are concrete, such as minutes or times per week. Participants are asked to check the appropriate response, thus making it relatively easy to complete.

The EBA includes eight items, generated from previous qualitative work, that ask participants about their usual exercise frequency, duration, and intensity during both warm and cold weather (see the appendix). For the intensity component, participants are asked to check two different scales: a Borg scale indicating how hard they usually work, and a shortness of breath scale indicating how short of breath they usually get when they exercise. Scoring involves computing separate frequency, duration, and intensity scores by averaging across warm and cold weather, then multiplying frequency, duration, and intensity to obtain two different scores: EBArpe, a score using Borg's RPE scale as the measure of intensity, and EBAsob, a score using shortness of breath as the measure of intensity.

I established face validity for this instrument by having a group of 7 older women critique the items and suggest changes. Then, I examined the EBA for test- retest reliability by asking women (n = 51; 70.2 + 7.4 years) from a larger study (Schneider, 1995) to complete the EBA twice about 2 weeks apart. Forty-one women (80.4%) were white, 9 women (17.6%) were black, and 1 woman (2.0%) was Asian or Pacific Islander. Number of days between test and retest was 15.2 + 3.4, with a range of 8 to 28. Missing data were deleted listwise, resulting in 44 EBAsob and43 EBArpe scores. Intraclass correlations (ICC) were calculated using the formula for a random sample of judges and an individual unit of analysis (Armstrong, 198 1; Portney & Watkins, 1993; Shrout & Heiss, 1979). ICCs for the EBAsob and EBArpe were 0.81 and 0.82, respectively.

LEISURE TIME EXERCISE QUESTIONNAIRE

Godin and Shephard's (1985) Leisure Time Exercise Questionnaire (LTEQ) asks respondents to consider a 7-day period and report the number of times they

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exercised for more than 15 min at each of three intensities: strenuous, moderate, and mild. Scoring includes multiplying the number of times reported for each intensity by the respective metabolic equivalents (METs; 9 for strenuous, 5 for moderate, and 3 for mild; Gionet & Godin, 1989) and summing the three products to produce a total score. A final question asks participants how often they engage in any regular activity long enough to work up a sweat. For the sweat item, participants check one of three choices: often, sometimes, or neverlrarely.

Godin and Shephard's LTEQ has been tested in several studies of young and middle-aged people. Self-reported strenuous exercise correlated (r = .25 to .38) with maximal oxygen consumption (Gionet & Godin, 1989; Godin & Shephard, 1985); with the Caltrac, a portable accelerometer (r = .37); with the Caltrac corrected by self-report activity during the time it was off (r = .45; Miller, Freedson, & Kline, 1994); and with muscle endurance measured by the maximum number of sit-ups performed in 1 min (r = .36; Gionet & Godin, 1989). However, self-reported moderate exercise and mild exercise were only mildly correlated with maximal oxygen consumption, body mass index, and muscular endurance (r = -.06 to .11). The total score was correlated (r= .O1 to .24) with physiological measures (oxygen consumption and oxygen consumption percentile, body mass index and body fat percentile, and muscular endurance measured by number of sit-ups; Gionet & Godin, 1989; Godin & Shephard, 1985). Finally, in a sample of 28 men and 50 women with a mean age of 37 years, with adjustment for age and gender, the total score correlated with maximal oxygen consumption (r = .56), the minutes required to reach a heart rate of 160 on the treadmill (r = .57), and percent body fat measured by hydrostatic weighing (r=-.43; Jacobs, Ainsworth, Hartman, &Leon, 1993). The LTEQ was chosen because it performed moderately well with younger people and was easy to complete for older people.

LEISURE INDEX OF THE MINNESOTA HEART HEALTH PROGRAM PHYSICAL ACTIVITY QUESTIONNAIRE

The Leisure Index of the Minnesota Heart Health Program Physical Activity Questionnaire (MPAQ) also was administered (Jacobs, Ainsworth, Hartman, & Leon, 1993). The MPAQ consists of two items that assess the duration and intensity of exercise. These questions are answered by checking categorical responses. Another question asks participants to enter the frequency of exercise sessions per week. Scoring involves weighting each categorical response (duration and intensity) and multiplying all three (duration, intensity, and frequency) to obtain a single score.

For 28 men and 50 women with a mean age of 37 years, the Leisure Index of the MPAQ was correlated with maximal oxygen consumption (r = .56), with the minutes required to reach a heart rate of 160 on the treadmill (r = .56), and with percent body fat measured by hydrostatic weighing (r = -.37; Jacobs et al., 1993). The MPAQ was chosen because it performed well with this younger population and because it was easy for older people to complete.

PROCEDURE

Participants were instructed not to smoke, drink alcohol or caffeine, or eat for 3 hr prior to testing. To be tested, participants were required to sign an informed consent

Indicators of Exercise Behavior 381

that was approved by the human subjects committee. Next, a health history form was administered by interview.

Height (nearest 0.5 in.) and weight (nearest 0.25 lb) were measured on a standard scale manufactured for use by physicians. Skinfold thicknesses were measured as described in the Anthropometric Standardization Reference Manual (Lohman, Roche, & Martorell, 1988) using a Lange skinfold caliper, which was periodically checked for accuracy. Five sites were measured including the triceps, biceps, subscapula, suprailium, and thigh. Three measures were averaged for each site. I performed all physiological measures to maintain consistency.

Participants practiced walking on the treadmill for 1 to 2 min to determine their preferred speed. Participants then were asked to sit and complete the three exercise behavior instruments in counterbalanced order to control for order effects. This took 10 to 15 min and allowed time to determine participants' lowest heart rate. Heart rate at 70% of heart rate reserve was calculated using the resting heart rate and the age-predicted maximal heart rate (220 minus age; American College of Sports Medicine, 1991). When the exercise behavior instruments were completed, a resting 12-lead electrocardiogram (ECG) was obtained and blood pressure was measured by auscultation.

Minute ventilation, oxygen consumption, and carbon dioxide production were measured every 15 s during the treadmill test using a metabolic cart (SensorMedics MMC 4400tc). The metabolic cart was calibrated with standard gases prior to each testing session, and the treadmill speed was calibrated periodi- cally.

Fitness testing involved measurement of submaximal oxygen consumption during a treadmill test. The treadmill test consisted of progressive 3-min levels. If heart rate did not reach steady-state during the second and third minutes of each level, the participant remained at that level for a fourth minute. Steady-state was defined as consecutive heart rates, 1 min apart, differing by five beats per minute or less (Siconolfi, Cullinane, Carleton, & Thompson, 1982). All but 8 participants reached steady-state heart rate at each exercise level they completed.

Treadmill speed was adjusted to participants' abilities as determined during the practice session. The treadmill was started at a 0% elevation for the first level and was raised 3.5% in elevation for each progressive level. The test was terminated when the participant signaled the desire to stop or when heart rate reached approximately 70% of heart rate reserve. Throughout treadmill testing, ECG was monitored continuously and heart rate was recorded every minute. Blood pressure was taken between minutes 2 and 3 of each incremental exercise level and following exercise until it returned to resting values. Participants were strongly encouragednot to hold the treadmill bar; however, about one-half of the participants preferred to hold on.

DATA ANALYSES

Descriptive statistics were calculated for each variable. Pearson Product Moment Correlation Coefficients were used to examine the relationships among self- reported exercise behavior and the physiological indicators (estimated maximal oxygen consumption and body composition measures). In addition, partial correla- tions were computed to control for age differences between subjects.

382 Schneider

Results

Of the 59 women who participated in this study, the majority of women (38 women, or 64.4%) reached or exceeded their 70% heart rate reserve. Twenty-one women (36%) did not reach their 70% heart rate reserve before the treadmill test was stopped. Only 7 women (11.9%) did not reach their 60% heart rate reserve. Treadmill tests were terminated for the following reasons: at the request of participants, because of increased dysrhythmias, or because, in my opinion, one additional level would have greatly exceeded 70% heart rate reserve.

The three exercise behavior instruments were scored as previously described, yielding four total scores (EBArpe, EBAsob, MPAQ, and LTEQ). Table 1 presents the descriptive statistics for these scores. Because previous researchers examined relation- ships between physiological indicators and the individual components making up total scores, descriptive statistics for the components are also included in the table. Order effects for each exercise behavior score were tested using one-way ANOVAs. There were no differences in scores when compared by order of administration.

Physiological measures included estimated maximal oxygen consumption and body composition measures. Submaximal oxygen consumption was measured via metabolic cart while women walked at increasing levels of intensity until reaching approximately 70% of heart rate reserve. Then, a regression line was calculated (slope and intercept) for each participant using submaximal oxygen consumption as the dependent variable and heart rate as the independent variable. Maximal oxygen consumption was estimated by substituting age-predicted maximal heart rate as the independent variable. Table 2 presents descriptive statistics for estimated maximal oxygen consumption.

Table 1 Descriptive Statistics for the Exercise Behavior Instruments (N = 59)

Variable Mean SD Min Max

EB Arpe Frequency (daystweek) Duration (midday) Intensity

EBAsob Intensity

MPAQ" LTEQ

Strenuous score Moderate score Mild score Sweat

Note. EBArpe = Exercise Behavior Analysis using Rating of Perceived Exertion as the measure of intensity; EBAsob = Exercise Behavior Analysis using shortness of breath as the measure of intensity; MPAQ = Minnesota Heart Health Program Physical Activity Questionnaire; LTEQ = Leisure Time Exercise Questionnaire. "Available for only 58 participants.

Indicators of Exercise Behavior 383

Table 2 Descriptive Statistics for Physiological Indicators

Variable Mean SD Min Max

Weight (kg) Height (cm) BMI Skinfolds(4) (mm) Skinfolds(3) (mm) V0,max (mlkglmin)

Note. Skinfolds(3) = suprailium, triceps, and thigh. Skinfolds(4) = biceps, hiceps, subscapula, and suprailium.

Height, weight, and two skinfold measures were collected. Body mass index (BMI) was calculated by dividing body weight in kilograms by height in meters squared (ACSM, 1991). Skinfolds were summed to create two measures. One measure, Skinfolds(3), included the sum of suprailium, triceps, and thigh thick- nesses (Pollock, Schmidt, & Jackson, 1980). The other measure, Skinfolds(4), included the sum of biceps, triceps, subscapula, and suprailium sites (Durnin & Womersley, 1974). Table 2 displays descriptive statistics for these body composi- tion measures.

Statistical assumptions were evaluated by checking these data for normality, constant variances, andlinearity. The four exercise behavior scores, two component scores (frequency and strenuous), and two skinfold measures were transformed using square root transformations. One component score, intensity (RPE), was transformed using a power transformation. Age was transformed using a logarithm transformation.

RELATIONSHIP BETWEEN SELF-REPORTED AND PHYSIOLOGICAL INDICATORS OF EXERCISE BEHAVIOR

Zero-order correlations between self-reported exercise behavior and the physi- ological indicators were examined (Table 3). The strongest relationships were between the self-reported exercise behavior scores and the body composition measures, especially Skinfolds(4). Among self-report total scores, the highest correlations were between the LTEQ total score and the body composition measures (r = -.23 to -.28) and between Skinfolds(4) and the EBArpe (r = -.29). Among the component scores, the moderate score of the LTEQ had higher correlations with body composition measures than all other self-report scores (r = -.30 to -.38). The EBArpe and EBAsob were negatively correlated (r = -.29 and -.26, respectively) with Skinfolds(4). Frequency was the EBA component score that had the largest negative correlation (r = -.30) with Skinfolds(4). Estimated maximal oxygen consumption was poorly correlated with all self-reported exercise behavior scores (r =-.lo to .18) including the sweat item from the LTEQ (r = -.09). None of these correlations attained statistical significance after application of the Bonferroni adjustment for multiple comparisons. These data suggest that the association

384 Schneider

between self-reported and physiological indicators of exercise behavior is weak. In contrast, the exercise behavior total scores were moderately to highly correlated to each other (r = .47 to .88; Table 4) and were statistically significant.

The research question guiding this study assessed the significance of the relationship between self-reported exercise behavior and physiological indicators

Table 3 Correlations Between Exercise Behavior Scores and Physiological Indicators and Between Age and Physiological Indicators (N = 59)

Weight BMI Skinfolds(4) Skinfolds(3) VO,maxa

EBArpe Frequency Duration Intensity

EBAsob Intensity

MPAQ LTEQ

Strenuous Moderate Mild Sweat

Age

Note. EBArpe = Exercise Behavior Analysis using Rating of Perceived Exertion as the measure of intensity; EBAsob = Exercise Behavior Analysis using shortness of breath as the measure of intensity; MPAQ = Minnesota Heart Health Program Physical Activity Questionnaire; LTEQ = Leisure Time Exercise Questionnaire. Skinfolds(3) = suprailium, triceps, and thigh. Skinfolds(4) = biceps, triceps, subscapula, and suprailium. Correlations are 2-tailed. "Predicted from submaximal oxygen consumption values and estimated maximal heart rate. *Significant after Bonferroni adjustment (p I .001).

Table 4 Correlations Among Exercise Behavior Scores

Moderate LTEQ MPAQ EBAsob

MPAQ EBAsob EBArpe

Note. EBArpe = Exercise Behavior Analysis using Rating of Perceived Exertion as the measure of intensity; EBAsob = Exercise Behavior Analysis using shortness of breath as the measure of intensity; MPAQ = Minnesota Heart Health Program Physical Activity Questionnaire; LTEQ = Leisure Time Exercise Questionnaire. Correlations are 2-tailed. *Significant after Bonferroni adjustment 0,s .006).

Indicators of Exercise Behavior 385

of exercise behavior (estimated maximal oxygen consumption and body composi- tion). Because age was moderately correlated with the physiological indicators (Table 3), it was controlled for by calculating partial correlations. Thus, first-order partial correlations were computed between the exercise behavior total scores and each physiological indicator while controlling for age. First-order partial correla- tions between the moderate score of the LTEQ (moderate) and the physiological indicators also were examined (Table 5).

When age was partialed out, the moderate component score of the LTEQ was negatively correlated with the body composition measures BMI, Skinfolds(4), and Skinfolds(3). EBArpe and LTEQ were the exercise behavior total scores that exhibited the strongest negative association with the body composition measures. Estimated maximal oxygen consumption and exercise behavior correlated poorly even when age was partialed out; the highest correlation was with LTEQ (r = .15). Once again, however, when Bonferroni adjustments were made, none of the partial correlations attained statistical significance.

Discussion

Overall, after age was controlled for, the strongest relationships were found between self-reported exercise behavior and body composition measures. These data suggest that women who have less body fat tend to report more exercise behavior than women with more body fat. Dipietro and colleagues (1993) found percent body fat to correlate negatively with vigorous activity (r=-.30) but not with indices reflecting leisure walking, moving, standing, and sitting (r = -.00 to -. 19). In their study, BMI correlated most strongly with the moving, standing, and sitting indices (r = -.37, .20, and .30, respectively) and correlated weakly with vigorous activity (r = .11; Dipietro et al., 1993). It is likely that the low correlations between body composition measures and the strenuous component score in the present study

Table 5 First-Order Partial Correlations Between Physiological Indicators and Self-Reported Exercise Behavior Controlling for Age

V0,max Skinfolds(3) Skinfolds(4) BMI

EBArpe EBAsob MPAQ LTEQ Moderate

Note. EBArpe = Exercise Behavior Analysis using Rating of Perceived Exertion as the measure of intensity; EBAsob = Exercise Behavior Analysis using shortness of breath as the measure of intensity; MPAQ = Minnesota Heart Health Program Physical Activity Questionnaire; LTEQ = Leisure Time Exercise Questionnaire. Skinfolds(3) = suprailium, triceps, and thigh. Skinfolds(4) = biceps, triceps, subscapula, and suprailium. Correlations are 2-tailed. No significant correlations after Bonferroni adjustment (p < .003).

386 Schneider

(r = -.02 to .05) were the result of such few women (n = 6) reporting strenuous exercise.

Correlations between self-reported exercise behavior and estimated maximal oxygen consumption were lower than expected (r = -.04 to .15). Singleton, Fitzgerald, and Neale (1994) found similar results after their subjects walked on a treadmill at 70% maximal heart rate. Time to 70% maximal heart rate was correlated, r= .15 to .22, with an exercise index score computed from interview data. However, other researchers have reported stronger associations than were found in the present study. Gionet and Godin (1989) estimated maximal oxygen consump- tion from a three-stage 12-min submaximal cycle ergometer test and found it to be modestly correlated with self-reported strenuous exercise (r= .25 to .28). Godin and Shephard (1985), using the Canadian Home Fitness step test to predict maximal oxygen consumption, dichotomized participants above and below the 50th percen- tile. The authors found percentile and strenuous exercise to be related (r = .38). They also found predicted maximal oxygen consumption percentile to be related (r = .26) with a sweat item asking how often participants sweated in a 7-day period (often, sometimes, or neverIrarely). Estimated oxygen consumption and the same sweat score were unrelated in this present study (r = -.09). using an interview format, Dipietro and colleagues (1993) found vigorous and total physical activity to be correlated (r = .60 and .58, respectively) with estimated maximum oxygen con- sumption from 80% predicted maximal heart rate. Leisure walking, moving, standing, total activity time, and sitting were weakly related to estimated maximal oxygen consumption (r = .12 to -.20).

Other researchers have found moderate to high correlations between the direct measurement of maximal oxygen consumption and self-reported exercise behavior (r= .47 to .54; Ainsworth et al., 1993). However, Siconolfi and colleagues (1985) found weak correlations between maximal oxygen consumption using a cycle ergometer and a physical activity index (r = .08 to .29). Jacobs and colleagues (1993) found a range of correlations between maximal oxygen consumption and a variety of self-reported exercise behavior instruments (r = -.01 to -.63). Most notable across the questionnaires they used were the higher correlations for heavy or vigorous exercise as compared to light or moderate exercise. The findings of ~ n a p i k and colleagues (1993) supported these differences across intensities, as heavy activity correlated better (r = .28) with peak oxygen consumption than did moderate and light activity (r = -.01 and -.06). In summary, these studies support the notion that maximal oxygen consumption mostly reflects heavy intensity activity (Jacobs et al., 1993). This is likely due to the fact that vigorous activity brings about physiological adaptations that improve maximal oxygen consumption. Oxygen consumption may provide a measure of strenuous activity, while self- reported exercise behavior may provide a measure of all exercise behavior, heavy as well as light and moderate. Thus, self-reported exercise behavior and maximal oxygen consumption are not strongly associated because they measure different aspects of exercise behavior.

Several limitations mav contribute to the low correlations between estimated maximal oxygen consumption and self-reported exercise behavior in the present study. First, many of the women held on to the treadmill bar despite being discouraged from doing so. It was impossible to determine from this study how holding on affected heart rate and oxygen consumption responses. Second, the

Indicators of Exercise Behavior 387

women came to the laboratory for treadmill testing without having had an accom- modation treadmill test. Women who lack familiarity with the treadmill test may have higher heart rates and oxygen consumption values, whereas accommodation may produce more accurate heart rate and oxygen consumption values. Third, because the maximal allowable exercise intensity was approximately 70% of heart rate reserve, many women reached this level during the second exercise stage, and only two points were available from which to calculate the slope and intercept to predict their maximal oxygen consumption. Thus, this estimation of maximal oxygen consumption from measures of submaximal oxygen consumption and the estimated maximal heart rate may be far less accurate than a direct measure of maximal oxygen consumption. However, the submaximal measure was chosen in this study to maintain the safety of the older participants. Finally, several women were excluded from this data set for a variety of reasons, such as excessively high blood pressure, dysrhythmias, reaching their 70% heart rate reserve on their first level, or requesting the test be terminated. Thus, these findings are not generalizable to all older women, further supporting the need for self-reported measures of exercise behavior.

There are limitations to the self-report measures as well. For example, it is not possible to determine the accuracy of participants' self-reported exercise behavior. Women who came to the laboratory interacted one-to-one with me and thus may have tended to report more favorable responses. In fact, other researchers (Klesges et al., 1990; Taylor et al., 1984) found subjects to under- or overestimate their activity level.

Weak and largely nonsignificant correlations were observed between three self-report measures and two physiological indicators of exercise behavior. These data suggest that extreme caution is warranted when interpreting the findings of both questionnaire and submaximal stress test data in older adult populations.

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Appendix: Questions From the Exercise Behavior Analysis

For these questions, exercise is defined as activity done for the purpose of maintaining or improving physical fitness.

1. How often do you usually exercise during warm weather? 2. How often do you usually exercise during cold weather? 3. About how long do your exercise sessions usually last in warm weather? 4. About how long do your exercise sessions usually last in cold weather? 5. How short of breath do you usually get when you exercise in warm weather? 6. How short of breath do you usually get when you exercise in cold weather? 7. How hard do you usually work or how much do you usually exert yourself when you

exercise in warm weather? 8. When you exercise, how hard do you usually work or how much do you usually exert

yourself in cold weather?

Acknowledgments

This work was supported by an Individual National Research Service Award (NINR F31 NR06864), which the investigator received during doctoral study at the University of Kansas School of Nursing, and Sigma Theta Tau International, Honor Society of Nursing, Epsilon Gamma Chapter-At-Large. Many thanks to Drs. Wendy Kohrt, Mary Visser, and Robert Spina for their critical review of earlier versions of this manuscript and to Drs. Edna Harnera and Ken Pitetti and Ms. Jarny Flikkema for their devoted support and assistance. Most importantly, thanks to the women whogave their time to participate in this project.