Self-Favoring Bias for Physical Activity in Middle-Aged and Older Adults

Self-Favoring Bias for Physical Activity in Middle-Aged and Older Adults’

SARA wILCOX2 AND ABBY c. KING Stanford Center,Jbr Research in Disease Prevention

Stanford University School of Medicine

A self-favoring bias for physical activity (PA) was examined in a community-based sample of middle-aged and older adults (N= 3,211). Participants’ actual level of PA relative to peers was compared with their perceived PA relative to peers. It was estimated that 38% were realistic, 46% self-favoring, and 16% other-favoring in their perceptions. Among participants whose actual PA level was similar to peers, increasing age was associated with a self-favoring bias. Among less and more physically active participants, however, age was not associated with this bias. Better self-rated health and being male were also associated with a self-favoring bias. These results suggest that a self-favoring bias for PA exists in a significant proportion of middle-aged and older adults, and it is more pro- nounced with increasing age, among those with better self-rated health, and among men.

Research examining a wide range of health hazards has shown that people tend to believe that they are at less risk than are their peers (e.g., Hoorens & Buunk, 1993; Weinstein, 1982, 1984, 1987). That is, people tend to have unrealistic optimism regarding their risk of diseases, injuries, and illnesses (Weinstein, 1989). In fact, across multiple domains, people tend to view themselves in an overly positive manner, tend to believe that they have more control over events than they actually do, and tend to view their future in an unrealistically positive way (Taylor & Brown, 1988, 1994). These tendencies to underestimate health hazards ( e g , risk of heart disease, cancer, automobile accidents) and to hold positive illusions are seen as ways of improving one’s mood and enhancing one’s self-esteem.

Although unrealistic optimism and related theories have been studied in some detail in relation to health and disease, fewer studies have applied these theories to health behaviors such as diet, physical activity, and smoking. Raats and Sparks (1995) reviewed several studies suggesting that unrealistic optimism in the area

‘This research was supported by Public Health Service grants (AG-09991 and AG-I 2358) and by a National Institutes of Health training grant (2T32 HL07034).

*Correspondence concerning this article should be addressed to Sara Wilcox, who is now at the Department of Exercise Science, School of Public Health, University of South Carolina, Columbia, SC 29208.

1773

Journal ofApplied Social Psychology, 2000, 30, 9, pp. 1773-1 789. Copyright 0 2000 by V. H. Winston & Son, Inc. All rights reserved.

1774 WlLCOX AND KING

of dietary perceptions may undermine positive behavioral change. Strecher, Kreuter, and Kobrin ( I 995) found that smokers underestimated their risk of heart attack, cancer, and stroke. They discuss this bias as a likely barrier to smoking cessation. Similarly, Weinstein (1982) found that unrealistic optimism was related to less interest in risk reduction because it decreased one’s worry about health hazards. In contrast, however, Taylor et al. (1992) found that optimism was unrelated to risk-related sexual behavior in men who were seropositive for HIV.

The current study extends previous research by examining the self-favoring bias in an older age range and by examining this bias in relation to a health behavior that has received relatively little study in this area: physical activity. Rates of participation in leisure-time physical activity are substantially lower in older age, despite the well-documented benefits of regular physical activity for older individuals (U.S. Department of Health and Human Services [USDHHS], 1996). Research on the self-favoring bias in this domain could help to explain the low rates of exercise participation and could provide directions for intervention strate- gies. The term selfTfavoring bias (Hoorens, 1995) is used throughout this paper, rather than optimistic bias, because we were interested in how participants rated a current domain rather than a future event. Many terms exist to describe the phe- nomenon of viewing oneself and one’s future events in an unrealistically positive manner; our use of the term self-favoring is more general (Hoorens, 1995).

Consistent with past research in other health-related domains, i t is hypothesized that a self-favoring bias will be seen in perceptions of physical activity relative to peers. In the absence of such a bias, the majority of participants should rate their level of physical activity as similar to peers, and the percentage who rate their physical activity as more and less active than peers should be equal (i.e., perceptions approximate a normal distribution). If a self-favoring bias exists, however, the percentage of participants who rate themselves as more active than peers should be greater than the percentage who rate themselves as less active than peers. Similarly, when participants’ actual physical activity relative to peers is compared to their perceived activity relative to peers, in the absence of bias, most participants should be realistic (i.e., actual and perceived activity are in agreement). Although some participants are expected to show dis- agreement in their actual and perceived physical activity, in the absence of a self- favoring bias, the percentage who are self-favoring (unrealistically positive) and other-favoring (unrealistically negative) should be about equal. The classification of participants as self-favoring, realistic, and other-favoring is based on Strecher et al.’s (1995) work on the optimistic bias and is shown in Table 1. We predict that the percentage of self-favoring participants will be greater than the percentage of other-favoring participants.

In addition to examining the relationship between actual and perceived physical activity relative to peers, this study examines other factors hypothesized to drive perceptions of physical activity. I f participants are realistic in their

SELF-FAVORING BIAS FOR PHYSICAL ACTIVITY 1775

Table 1

Classifcation of Participants According to Actual and Perceived Physical Activity Relative to Peers

Actual physical activity relative to peers Perceived physical activity relative to peers Less active About as active More active

Less active realistic other-favoring other-favoring About as active self- favoring realistic other-favoring More active self-favoring self-favoring realistic

perceptions of physical activity relative to peers, actual physical activity relative to peers should be the primary factor associated with perceptions. If other factors are uniquely associated with perceptions, these factors can be thought of as correlates of bias. We hypothesize that age and self-rated health will be two correlates of the self-favoring bias. Specifically, we predict that the self-favoring bias will increase with increasing age. We also hypothesize that individuals who report better health will be more prone to a self-favoring bias, as compared with those who report poorer health. Weinstein ( 1 987) found that participants believed that because they had not yet experienced a disease, they were unlikely to experience it in the future. Thus, individuals who saw themselves as healthy might assume that they were more active than were peers because they had not yet experienced disability. We also examine whether the self-favoring bias differs according to gender, but no specific hypotheses are made for this variable.

Method

Participants

Participants were residents of Sunnyvale, California, who participated in a community-based telephone survey. Two surveys were conducted: one of middle-aged adults (50 to 64 years), and one of older-aged adults (65 years and older). The middle-aged sample included 1,877 residents between the ages of 50 and 65 years, and the older-aged sample included 1,526 residents aged 65 years and older. Complete data on the variables of interest were available for 1,787 of the middle-aged and 1,424 of the older participants. These samples were com- bined to form a sample of 3,2 1 1 participants.

Procedure

Residents of Sunnyvale, California were contacted through a telephone ran- dom-digit-dialing procedure (King & Brassington, 1997; King, Harris, &


Haskell, 1994). Men and women who fell within the age range for each study were invited to complete a 20-min telephone survey about health habits. The interviews of the middle-aged adults were conducted between October 1986 and April 1987. The interviews of the older adults were conducted between February 1993 and February 1994. The overall response rate for both surveys was 70% (King & Brassington, 1997; King et al., 1994).

Measures

The telephone survey was adapted from the National Health Interview Survey (NHIS; US. Bureau of the Census, 1985), the NHIS Health Promotion and Dis- ease Prevention Supplement (NHIS-HPDP; Thornberry, Wilson, & Golden, 1986), and the NHIS Supplement on Aging (USDHHS, 1984). Participants were asked about a variety of health behaviors and health conditions. In addition to asking participants their age in years and gender, study measures included:

Physical activity measures. Self-report measures of physical activity, such as the ones used in this study, are used extensively in epidemiologic research in this field and are predictive of mortality and morbidity (Folsom et al., 1985; Leon, Connett, Jacobs, & Rauramaa, 1987; Paffenbarger, Hyde, Wing, & Hsieh, 1986). Middle-aged participants in this study were asked whether or not they had participated in any of the following activities over the past 2 weeks: walking for exercise, jogging or running, gardening or yardwork, aerobicsljazzerciseiaerobic dancing, other dancing, calisthenics, golf, tennis, biking, swimming or water exercises, yoga, weight lifting or training, hiking, bowling, basketball, baseball or softball, football, volleyball, handball/racquetbalI/squash, iceholler skating, or waterisnow skiing. Participants could list an additional two physical activities not included in this list. Older-aged participants were only asked about the first 12 activities in this list (walking through weight lifting or training), but could also list an additional two physical activities not included in the list. After an affirma- tive response on any activity, participants were asked the frequency and average duration of these activities.

For each exercise, the reported frequency of participation was multiplied by the average duration (in minutes) of participation. These values were summed across physical activities to create a composite score of total minutes of physical activity during the past 2 weeks. Because this distribution was positively skewed, a log transformation was used.

After providing information regarding each of these exercises, perceived physical activity relative to peers was measured by asking participants, “Would you say that you are physically more active, less active, or about as active as other persons of your age and sex?” The responses were recorded on a 3-point scale ranging from 1 (less active) to 3 (more active).

Health. Participants were asked, “All in all, would you say your health is excellent, good, fair, or poor?” Responses were coded on a 4-point scale, with


higher scores indicating better health ratings. In addition to being used in national samples such as the NHIS, this measure has been shown to correlate with physician ratings of health (LaRue, Bank, Jarvik, & Hetland, 1979) and is predictive of mortality in older adults (Idler & Kasl, 1991; Mossey & Shapiro, 1982).

Participants were also asked whether or not they had a number of health conditions. For the middle-aged sample, these conditions were hypertension, high cholesterol, diabetes, previous stroke, previous hospitalization for a heart problem, and up to one additional medical condition (open-ended response). For the older-aged sample, the number of conditions was greater so as to be age appropriate. These medical conditions were hypertension, high cholesterol, diabetes, previous stroke, previous hospitalization for a heart problem, arthritis, osteoporosis, chronic obstructive pulmonary disease, arteriosclerosis (hardening of the arteries), valvular heart disease, and cancer treatment within the past year.

The total number of medical conditions endorsed was summed, with possible scores ranging from 0 to 6 for middle-aged adults and 0 to 1 1 for older adults. The correlation between self-rated health and reported number of medical conditions was -.36 in the middle-aged sample and -.39 in the older-aged sample (ps < . O O l ) . Because a different number of conditions was measured in the two samples, self-rated health was used as the measure of health in the primary analyses.

Additionally, participants were asked whether or not they had a physical limitation that made exercise difficult (1 = Yes, 2 = No). This variable was used only in post-hoc analyses.

Results

Sample Characteristics

The demographic characteristics of participants, according to sample and gender, are shown in Table 2. Similar to the population from which they were drawn, the majority of participants were White and tended to be fairly well educated (King, Taylor, & Haskell, 1990). Participants were similar to the general population in terms of basic health characteristics, such as the prevalence of chronic illnesses and diseases, including hypertension, high cholesterol, arthritis, and osteoporosis (US. Bureau of the Census, 1996).

Based on the physical activity classification system used by Caspersen, Christenson, and Pollard (1986) and by Young, King, and Oka (1995), 16% of participants were regularly active; 73% were underactive; and 1 1 % were completely sedentary. Thus, similar to nationally reported data, the majority of participants were not engaging in leisure-time physical activity at a level consistent with national recommendations (USDHHS, 1996).


Table 2

Demographic Characteristics of the Community Samples of Middle-Aged und Older Adults by Gender

Middle-aged sample Older sample

Variable Men Women Men Women ~

N 718 Age (in years)

M 56.83 SD 4.39

Education (in years) M 15.19 SD 2.98

Marital status (YO) Married 78.38 Widowed 3.07 Divorced 10.04 Single 8.5 1

Caucasian 88.89 Asian American 4.92 Hispanic American 4.50 African American 0.98 Native American 0.70 Other race 0.00

Race (Yo)

Self-reported health problems Hypertension (%) 33.57 High cholesterol (%) 25.1 1 Diabetes (%) 7.8 1 Hospitalized for heart problem (%) 10.24 Ever had a stroke (YO) 2.80 Arthritis (YO) -

Osteoporosis (%) -

1,069

56.90 4.48

13.75 2.69

59.74 12.83 17.88 9.55

89.68 4.13 4.78 0.84 0.56 0.00

33.68 20.02

4.78 5.82 1.87 -

-

516

71.89 5.62

14.38 3.22

77.48 12.04 6.80 3.69

88.95 4.84 1.74 0.58 0.78 3.10

41.67 30.74 12.02 28.32

6.0 1 39.53

1.55

908

73.01 6.04

13.15 2.79

40.90 44.54 10.47 4.08

87.67 3.52 4.19 0.55 0.55 3.52

46.97 37.64

6.50 13.84 5.18

60.64 18.72

(table continues)


Table 2 (Continued) ~ ~

Middle-aged sample Older sample

Variable Men Women Men Women

11.63 8.37 Valvular heart disease (YO) - - 10.85 8.83 Chronic obstructive pulmonary

disease (YO) - - 6.01 5.64 3.29 2.54

Arteriosclerosis (%) - -

Cancer treatment in past year (%) - -

Other medical condition (%) 14.89 20.15 - -

Total medical conditions M 0.93 0.85 2.14 1.91 SD 1.00 0.92 1.46 1.53

Note. Dashed line indicates that the sample was not asked whether or not they had the medical condition.

SelfFavoring Bias for Physical Activity

It was hypothesized that the percentage of participants who reported that they were more active than their peers would be greater than the percentage who reported that they were less active than their peers. Consistent with a self-favoring bias, significantly more participants described their physical activity level as more active (45%) than less active (20%) and about as active (35%) relative to peers, x’( I , N = 2,080) = 3 10.78, and x2( 1, N = 2,573) = 37.59, respectively, ps < .001.

We also hypothesized that the percentage of participants who overestimated their physical activity relative to their peers would be greater than the percentage of participants who underestimated their physical activity relative to their peers. In order to test this hypothesis and to compare participants’ actual level of physical activity in a manner similar to the perceived comparison that they were asked to make, participants’ actual reported activity was classified into categories of less active than peers, about as active as peers, and more active than peers. This categorization was done by computing the mean minutes of physical activity for each age decade according to gender in the target sample. Participants whose log- transformed minutes of physical activity fell at or one standard deviation below the mean of their age/gender group’s mean in the target sample were classified as less active than peers. Participants whose log-transformed minutes of physical activity fell at or one standard deviation above the mean of their agelgender group’s mean were classified as more active than peers. The remaining participants were classified as about as active as peers.


Table 3

Agreement Between Perceived and Actual Physical Activity Relative to Peers

Actual physical activity relative to peers Perceived physical activity Less active About as active More active

Less active 5.64 (181) 13.73 (441) 0.50 (16) About as active 5.14 (166) 28.71 (922) 1.37 (44) More active 2.99 (96) 38.3 1 (1,230) 3.61 (116)

Note. Percentages o f the total sample are presented, with ns in parentheses

The 3 x 3 table comparing actual with perceived physical activity relative to peers is shown in Table 3. As expected, significantly more participants had a self- favoring (unrealistically positive) than an other-favoring (unrealistically negative) bias, x2( I , N = 1,992) = 4 9 2 . 0 2 , ~ < ,001. We found that 46% of participants were self-favoring in their perceived physical activity (their perceived physical activity was greater than their actual physical activity). Only 38% of participants were realistic in their perceived physical activity (their perceived physical activity was in agreement with their actual physical activity). Finally, 16% of participants were other-favoring in their perceived physical activity (their perceived physical activity was less than their actual physical activity).

Correlates of the Self-Favoring Bias

Correlates of the self-favoring bias were examined with a simultaneous ordered logistic multiple regression analysis. The trichotomous dependent variable in this analysis was perceived physical activity relative to peers (less active, about as active, or more active than peers). The entry of actual physical activity (less active, about as active, or more active than peers) as an independent variable served as a statistical test of the association between actual and perceived physical activity relative to peers. Two dummy-coded variables were created to repre- sent actual physical activity. Less active than peers was coded 1 in the first dummy variable, and more active than peers was coded 1 in the second dummy variable. The independent variables of primary interest were self-rated health, age (in years), and gender (Men = I , Women = 2). All interaction terms were also tested in the model.

Two interaction terms were statistically significant in the model; therefore, the model was run again with all of the main effects and these two interaction terms. The results of this analysis are shown in Table 4. The overall model was statistically significant, ~ ~ ( 7 , N = 3,2 1 I ) = 6 3 4 . 9 5 , ~ < .001. Actual and perceived


Table 4

Summav of Simultaneous Logistic Regression Analysis for Variables Associated With Perceived Physical Activity Relative to Peers

~ ~ ~~

Independent variable B S E B P X 2 P Actual act i vitya Less active 1.33 0.66 0.25 4.08 .043 More active 3.60 1.15 0.45 9.84 .002 Self-rated health 0.90 0.05 0.39 355.04 .OOO Age (in years) 0.04 0.00 0.23 106.82 ,000 Genderb -0.22 0.07 -0.06 9.28 .002 Actual Activity x Age Less Active x Age -0.04 0.01 -0.45 12.70 .OOO More Active x Age -0.04 0.02 -0.36 6.40 .011 Intercept 1 -5.4374 Intercept 2 -3.5649 Note. N=3,211. aDummy coded; about as active is the reference group. bMen = I, Women = 2.

physical activity relative to peers were significantly associated. Participants who were less physically active than their peers were more likely to see themselves as less physically active than their peers; whereas participants who were more physically active than their peers were more likely to see themselves as more physically active than their peers. Self-rated health, however, was a stronger independent correlate of perceived physical activity. As predicted, healthier participants showed a greater self-favoring bias than did less healthy participants. That is, independent of actual physical activity, participants who rated themselves as healthy were more likely to see themselves as more active than their peers, as compared to participants who rated themselves as less healthy. As predicted, age was also positively associated with a self-favoring bias. Independent of actual physical activity relative to peers, participants were more likely to see themselves as more active than peers with increasing age. Additionally, gender was an independent correlate of the self-favoring bias. Independent of actual physical activity, men were more likely than were women to see themselves as more active than their peers. Finally, the Actual Physical Activity x Age inter- actions were significant.

In order to examine the nature of the Actual Physical Activity x Age interaction, slopes for age in relation to perceived physical activity were computed and


tested in each of the three groups (as recommended in Jaccard, Turrisi, & Wan, 1990): those who were more active than peers, those who were less active than peers, and those who were about as active as peers. The slopes for those who were more and less active than peers were not significantly different from zero, ~(3,203) = 0.05 and t(3,203) = 0 . 9 7 , ~ s > .05. Among those who were about as active as their peers, the slope indicated that increasing age was associated with the self-favoring bias, t(3,203) = 1 0 . 3 5 , ~ < .001.

Several methods exist for estimating explained variance (R2) in logistic regression analyses. We chose to use a measure discussed in Menard (1995) called pseudo-R2. This measure is most appropriate when the dependent variable represents a latent interval scale, but i t tends to underestimate the true R2 (Menard, 1995). Pseudo-R2 is equal to GM/(GM + N), where N is the sample size and GM is the difference between the -2 log-likelihood statistic with no independent variables and the -2 log-likelihood statistic for the fall model. Calculated in this manner, our pseudo-R* was equal to .17, representing a medium effect size (Cohen & Cohen, 1983). We also estimated R2 by conducting a simultaneous linear regression analysis using the identical variables as in our logistic analysis. This analysis yielded a similar R2 o f . 18. In reality, the observed effect size is likely attenuated because our dependent variable of perceived physical activity and our independent variable of actual physical activity level were trichotomized.

Pos t-Hoe A nalvs es

Follow-up descriptive analyses were conducted to examine whether the percentage of participants who reported that they had a physical limitation that made exercise difficult and the total number of reported medical conditions differed according to actual physical activity level and age. Significantly more participants who were less active than their peers reported that they had a physical limitation that made exercise difficult (54%), as compared with participants who were about as active as their peers (34%) and participants who were more active than their peers (33%), x2(2, N = 3,208) = 6 4 . 4 0 , ~ < .001. In addition, increasing age was positively associated with the self-reported presence of a physical limitation that made exercise difficult, point-biserial r(3,208) = .21,p < .0001. A different pattern of association, however, occurred within the three physical activity levels. Among participants who were less active than their peers and among participants who were about as active as their peers, age was positively associated with the self-reported presence of a physical limitation that made exercise difficult, r(441) = .36,p < .0001, and r(2,597) = .18,p < .0001, respectively. These correlations, tested with a Fisher’s z’ transformation, were significantly different, z = 3 . 7 8 , ~ < .0001. Thus, the correlation between age and the presence of a self- reported physical limitation was significantly greater among those who were less


Table 5

Participants (%) With a Self-Reported Physical Limitation Making Exercise Dijjficult, by Actual Physical Activity Level Relative to Peers and Age Group

Actual physical activity level relative to peers

Age (in years) Less active About as active More active

50-54 25 23 33 55-59 36 28 35 60-64 55 33 27 65-69 59 36 32 70-74 67 39 23 75+ 81 52 43

active than their peers as compared with those who were about as active as their peers. Among participants who were more active than their peers, age and the self-reported presence of a physical limitation were not associated, r( 170) = .04, p > .60. Table 5 shows the relationship between actual physical activity level, age, and the self-reported presence of a physical limitation.

The relationship between age, actual physical activity level, and total number of reported medical conditions was examined in a multiple linear regression analysis. The model predicting total medical conditions was statistically significant, F(5. 3205) = 132.69, p < .0001, R2 = .17. Older age was associated with a greater number of self-reported medical conditions ( B = 0.056, SE = .002, t = 2 1.44, p < .OOO 1). Additionally, the Age x Actual Physical Activity interaction was significant ( B = 0.015, SE = .006, p .05). Slopes for age in relation to total number of medical conditions were computed and tested for statistical significance in each of the three groups: those who were more active than their peers, those who were less active than their peers, and those who were about as active as their peers. The slopes for all three groups were significantly different from zero, fs(3,203) =

5.3 1, 12.18, and 2 I .44, respectively, ps < .O 1. Furthermore, the slope was significantly greater among less active individuals relative to about as active individuals, t(3,203) = 2.37, p < .05. No other differences between groups were significant.

Discussion

This study examined the self-favoring bias for perceptions of physical activity and correlates of this bias in a large, community-based sample of middle-aged and older adults. When participants’ actual physical activity relative to peers was


compared to their perceived physical activity relative to peers, only 38% of participants were realistic in their perceptions, and close to half were self-favoring in their perceptions. These results are consistent with the positive-illusions, unrealistic-optimism, and self-favoring literatures (e.g., Taylor & Brown, 1988, 1994; Weinstein, 1980, 1982, 1984, 1987). thus extending these results to a health behavior (i.e., physical activity) in a middle- and older-aged sample.

The relationship between age and the self-favoring bias for physical activity relative to peers was of particular interest in this study. Despite the benefits of regular physical activity in old age, older adults are more likely than their younger counterparts to be sedentary (USDHHS, 1996). A better understanding of age differences in social psychological processes related to health behaviors could provide useful information for developing age-appropriate theories and interventions. We predicted that increasing age would be associated with a greater self-favoring bias. Although age was found to be related to the self-favoring bias, the relationship was not consistent across levels of physical activity. Among those whose actual physical activity level was similar to peers (the larg- est subgroup in the sample), increasing age was associated with the perception of being more active than peers. These participants, therefore, were less realistic and more self-favoring with increasing age. In contrast, among those whose physical activity level was less active than peers or more active than peers, increasing age was not associated with perceived activity.

Post-hoc analyses indicated that older participants who were less active than their peers were also more physically restricted as a result of health and mobility problems, likely making one’s physical activity status more obvious and easy to evaluate. Older participants who were about as active as their peers, in contrast, may have invoked images and stereotypes of frail, older adults, and thereby concluded that they were actually more active than their peers. Thus, participants might have been unrealistically negative regarding their peers’ behavior, rather than unrealistically positive regarding their own behavior.

This speculation is consistent with the social-comparison literature. Engaging in downward social comparisons (i.e., comparing oneself with less fortunate others) appears to be a way that older adults maintain their well-being despite age- related declines in health (Heidrich & Ryff, 1993a, 1993b; Robinson-Whelen & Kiecolt-Glaser, 1997; Suls, Marco, & Tobin, 199 1 ; VanderZee, Buunk, & Sanderman, 1995), and the use of social comparative processes in general has been associated with better psychological adjustment among adults with chronic diseases (Ameck, Tennen, Pfeiffer, & Fifield, 1988; Blalock, Afifi, DeVellis, Holt, & DeVellis. 1990; Blalock, DeVellis, & DeVellis, 1989; Heidrich, 1996; Helgeson & Taylor, 1993; Taylor, 1983). Downward social comparisons are thought to be more common when a domain is threatened. Anticipated or actual age-related declines in physical functioning could be a perceived threat to physical functioning, thus prompting downward social comparisons and self-favoring biases.


The self-favoring bias for physical activity was also more prevalent in participants who rated themselves as healthier, regardless of one’s actual physical activity relative to peers. In fact, self-rated health was a stronger independent correlate than was actual physical activity. Although our data cannot directly test the mechanisms underlying this finding, Weinstein ( 1987) has reported that the optimistic bias is greater when individuals are evaluating a disease or illness that they have not yet experienced. Older individuals who are in good health and who are physically mobile may believe that they are therefore more physically active than their peers because they may not have begun to experience activity restrictions. It is also possible that older adults equate physical mobility with good health.

Finally, the self-favoring bias for physical activity was more prevalent in men than in women. Men, independent of their actual physical activity level relative to peers, were more likely than were women to see themselves as more active than their peers. Physical fitness could be a domain that is more central to a man’s identity than a woman’s identity (Hennessy, 1989). Men have traditionally had more experience with sport and exercise than have women (O’Brien & Vertinsky, 199 1 ), and men may perceive their fitness to be a domain that is more threatened with increasing age. If this hypothesis is true, and consistent with social-comparison theories (Wills, 198 I ) , men may therefore be more likely to engage in downward social comparisons and thereby believe that they are more active than are their peers.

Weinstein (1982) has shown that participants who underestimate their risk of diseases have less interest in risk reduction because they are less worried about health hazards. Realistic appraisal of disease risks is an important prerequisite to behavior change, according to the health belief model (Strecher & Rosenstock, 1997). It would be important, as a next step, to examine whether a self-favoring or optimistic bias impedes actual behavior change and, if so, whether educational interventions could modify self-perceptions in the elderly and thereby lead to behavior change.

There are several limitations to the present study. First, the cross-sectional design does not allow for an examination of changes in health behavior over time. Furthermore, statements regarding causation cannot be made in cross- sectional designs, and statements regarding age changes cannot be concluded. Second, this sample represents a largely White and well-educated community and should not be considered representative of the entire United States population. It is possible that the sample was more active, on average, than are at least subsegments of the population that were not represented. However, the fact that the majority of the participants were underactive according to national recommendations (USDHHS, 1996) and looked similar to the general population in terms of the presence of health conditions (U.S. Bureau of the Census, 1996) sug- gests that the sample was similar to the general population on important and potentially confounding variables. Third, the surveys of middle-aged and older


adults were conducted at two different times. Although the confound of time period cannot be completely dismissed, there is evidence from national studies that physical-activity participation rates did not substantially change over this time period (Caspersen & Merritt, 1995; USDHHS, 1996). The benefits of com- bining the samples and thus being able to examine a broader age range, in our opinion, outweighed the risk of this potential confound. A fourth and major limitation is that we relied exclusively on self-report data to classify participants’ actual level of physical activity relative to peers. By using a relatively brief self- report measure of physical activity that could by administered by telephone, however, we were able to reach a large number of participants in a manner that increased the likelihood of reaching a more representative sample. In addition, as noted earlier, these types of self-report physical-activity measures are used extensively and have been shown to be valid.

The substantial increase in older adults in the United States population com- bined with the increase in chronic disease and disability with age underscores the importance of examining determinants and correlates of health behaviors. Our study contributes to the literatures on unrealistic optimism, self-favoring bias. and social comparisons by examining the self-favoring bias for physical activity and correlates of this bias in a large, community-based sample of middle-aged and older adults. We found that more participants were self-favoring than were realistic in their perceptions of physical activity relative to peers. In addition, among participants who were about as active as their peers, increasing age was associated with a self-favoring bias for physical activity. We also found that those with better self-rated health and men were more likely than were those with poorer self-rated health and women to hold a self-favoring bias for physical activity. Our results suggest that interventions that challenge misperceptions regarding physical activity may be especially important for older adults.

References

Affleck, G., Tennen, H., Pfeiffer, C., & Fifield, J . (1988). Social comparisons in rheumatoid arthritis: Accuracy and adaptational significance. Jourrial qf Social and Clinical Psychology, 6, 2 19-234.

Blalock, S. J., Afifi, R. A., DeVellis, B. M., Holt, K., & DeVellis, R. F. (1990). Adjustment to rheumatoid arthritis: The role of social comparison processes. Health Education Research, 5, 36 1-370.

Blalock, S. J., DeVellis. B. M., & DeVellis, R. F. (1989). Social comparison among individuals with rheumatoid arthritis. Journal of Applied Social Psy- chologv, 19,665-680.

Caspersen, C. J., Christenson, G . M., & Pollard, R. A. (1986). Status of the 1990 physical fitness and exercise objectives: Evidence from NHIS 1985. Public Health Reports, 101, 587-592.


Caspersen, C. J., & Merritt, R. K. (1995). Physical activity trends among 26 states, 1986- 1990. Medicine and Science in Sports and Exercise, 27, 7 13-720.

Cohen, J., & Cohen, P. ( 1983). Applied multiple regression/correlation analysis for /he behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Folsom, A. R., Caspersen, C. J., Taylor, H. L., Jacobs, D. R., Luepker, R. V., Gomez-Marin, O., Gillum, R. F., & Blackburn, H. (1985). Leisure time physical activity and its relationship to coronary risk factors in a population-based sample. American Journal of Epidemiology. 121, 570-579.

Heidrich, S. M. ( 1 996). Mechanisms related to psychological well-being in older women with chronic illnesses: Age and disease comparisons. Research in Nursing and Health, 19, 225-235.

Heidrich, S. M., & Ryff, C. D. (1993a). Physical and mental health in later life: The self-system as mediator. Psychology and Aging, 8, 327-338.

Heidrich, S. M., & Ryff, C. D. (l993b). The role of social comparisons processes in the psychological adaptation of elderly adults. Journal of Gerontology, 48,

Helgeson, V. S., & Taylor, S. E. (1993). Social comparisons and adjustment among cardiac patients. Journal of Applied Social Psychologv, 23, I 1 7 1 - 1 195.

Hennessy, C. H. (1 989). Culture in the use, care, and control of the aging body. Joirrnal ofAging Studies, 3, 39-54.

Hoorens, V. ( 1995). Self-favoring biases, self-presentation, and the self-other asymmetry in social comparisons. Journal of Personality, 63, 793-8 17.

Hoorens, V., & Buunk, B. P. (1993). Social comparison of health risks: Locus of control, the person-positivity bias, and unrealistic optimism. Journal of Applied Social Psychology, 23,29 1-302.

Idler, E. L., & Kasl, S. (1991). Health perceptions and survival: Do global evaluations of health status really predict mortality? Journal of Gerontolog): 46,

Jaccard, J., Turrisi, R., & Wan, C. K. (1990). Interaction efecls in multiple regression. Newbury Park, CA: Sage.

King, A. C., & Brassington, G . (1997). Enhancing physical and psychological functioning in older family caregivers: The role of regular physical activity. Annals of Behavioral Medicine, 19,9 1 - 100.

King, A. C., Harris, R. B., & Haskell, W. L. ( 1 994). Effect of recruitment strategy on types of subjects entered into a primary prevention clinical trial. Annals sf’ Epidemiology, 4 , 3 12-320.

King, A. C., Taylor, C. B., & Haskell, W. L. (1990). Smoking in older women. Is being female a “risk factor” for continued cigarette use? Archives qf ln/ernal Medicine, 150, 1 84 1 - 1 846.

LaRue, A., Bank, L., Jarvik, L., & Hetland, M. ( 1979). Health in old age: How do physicians’ ratings and self-ratings compare? Journal of Gerontolog,v, 34,

PI 27-P136.

S55-S65.

687-691.


Leon, A. S., Connett, J . , Jacobs, D. R., & Rauramaa, R. (1987). Leisure-time physical activity levels and risk of coronary heart disease and death. Journal of the American Medical Association, 258, 2388-2395.

Menard, S. (1995). Applied logistic regression analysis. Thousand Oaks, CA: Sage.

Mossey, J. M., & Shapiro, E. (1982). Self-rated health: A predictor of mortality among the elderly. American Journal of Public Health, 72, 800-808.

O’Brien, S. J., & Vertinsky, P. A. (1991). Unfit survivors: Exercise as a resource for aging women. The Gerontologist, 31, 347-357.

Paffenbarger, R. S., Hyde, R. T., Wing, A. L., & Hsieh, C. (1986). Physical activity, all-cause mortality, and longevity of college alumni. The New England Journal of Medicine, 314.605-61 3.

Raats, M. M., & Sparks, P. (1995). Unrealistic optimism about diet-related risks: Implication for interventions. Proceedings of the Nutrition Society, 54, 737- 745.

Robinson-Whelen, S., & Kiecolt-Glaser, J. (1997). The importance of social versus temporal comparison appraisals among older adults. Journal of Applied Social Psychology, 2 I, 95 9 - 966,

Strecher, V. J., Kreuter, M. W., & Kobrin, S. C. (1995). Do cigarette smokers have unrealistic perceptions of their heart attack, cancer, and stroke risk? Journal ofBehaviora1 Medicine, 18,45-54.

Strecher, V. J., & Rosenstock, I . M. (1997). The health belief model. In K. Glanz, F. M. Lewis, & B. K. Rimer (Eds.), Health behavior and health education. Theory, research, andpractice (pp. 4 1-59). San Francisco, CA: Jossey-Bass.

Suls, J., Marco, C. A., & Tobin, S. (1991). The role of temporal comparison, social comparison, and direct appraisal in the elderly’s self-evaluations of health. Journal of Applied Social Psychology, 21, 1 125- 1 144.

Taylor, S. E. (1983). Adjustment to threatening events. A theory of cognitive adaptation. American Psychologist, 38, 1 16 1 - 1 173.

Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: A social psychological perspective on mental health. Psychological Bulletin, 103, 193-2 10.

Taylor, S. E., & Brown, J. D. (1994). Positive illusions and well-being revisited: Separating fact from fiction. Psychological Bulletin, 116, 21 -27.

Taylor, S. E., Kemeny, M. E., Aspinwall, L. G., Schneider, S. G., Rodriguez, R., & Herbert, M. (1992). Optimism, coping, psychological distress, and high- risk sexual behavior among men at risk for acquired immunodeficiency syn- drome (AIDS). Journal of Personality and Social Psychology, 63,460-473.

Thornberry, 0. T., Wilson, R. W., & Golden, P. M. (1986). The 1985 health promotion and disease prevention survey. Public Health Reports, 101, 566-570.

U.S. Bureau of the Census. ( 1 985). Narional Health Interview Survey. Washing- ton, DC: U.S. Department of Commerce, Bureau of the Census, U.S. Public Health Service.


U S . Bureau of the Census. (1996). Statistical abstract of the United States. Washington, DC: U S . Government Printing Office.

U.S. Department of Health and Human Services. ( I 984). National Health Inter- view Survey, 1984 supplement on aging (DHHS Publication No. PHS 87- 1323). Washington, DC: U.S. Government Printing Office.

U.S. Department of Health and Human Services. ( 1 996). Physical activity and health: A report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

VanderZee, K. I . , Buunk, B. P., & Sanderman, R. (1995). Social comparison as a mediator between health problems and subjective health evaluations. British Journal of Social Psychology, 34, 53-65.

Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal qf Personality and Social Psychology, 39, 806-820.

Weinstein, N. D. (1982). Unrealistic optimism about susceptibility to health problems. Journal of Behavioral Medicine, 5,44 1-460.

Weinstein, N. D. (1984). Why it won’t happen to me: Perceptions of risk factors and susceptibility. Health Psychology, 3,43 1-457.

Weinstein, N. D. (1987). Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample. Jozrrnal of Behav- ioral Medicine, 10,481-500.

Weinstein, N. D. (1989). Optimistic biases about personal risks. Science, 246,

Wills, T. A. (1981). Downward comparison principles in social psychology. Psy- chological Bulletin, 90, 245-271.

Young, D. R., King, A. C., & Oka, R. K. (1995). Determinants of exercise level in the sedentary versus underactive older adult: Implications for physical activity program development. Journal of Aging and Physical ActiviQ, 3, 4- 25.

1232-1 233.

Documents

Self-Favoring Bias for Physical Activity in Middle-Aged and Older Adults