Medicine & Science in Sports & Exercise:
APPLIED SCIENCES: Psychobiology and Behavioral Strategies
Lapses and Psychosocial Factors Related to Physical Activity in Early Postmenopause
CONROY, MOLLY B.1,2; SIMKIN-SILVERMAN, LAUREY R.1; PETTEE, KELLEY K.3; HESS, RACHEL1,2; KULLER, LEWIS H.1; KRISKA, ANDREA M.1
1Department of Epidemiology and 2Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA; and Department of Exercise and Wellness, Arizona State University, Mesa, AZ
Address for correspondence: Molly B. Conroy, M.D., MPH, Center for Research on Health Care, 230 McKee Place, Suite 600, Pittsburgh, PA 15213; E-mail: firstname.lastname@example.org.
Submitted for publication August 2006.
Accepted for publication May 2007.
Purpose: After menopause, leisure physical activity (PA) levels seem to decline for reasons that are not completely understood. This study examines the associations between PA, lapses in PA, and psychosocial factors in early postmenopausal women.
Methods: This cross-sectional analysis included 497 women from the Women on the Move through Activity and Nutrition study. PA was assessed with a past-year, interviewer-administered Modifiable Activity Questionnaire. Measures of activity lapses of ≥ 2 wk in the past 6 months, exercise decision making, processes of change, and self-efficacy were collected along with Beck Depression Inventory, State-Trait Anxiety Inventory, Cohen Perceived Stress Scale, and Short Form-36.
Results: Mean age of participants was 56.9 yr. Compared with less active women, women with significantly higher activity levels reported greater exercise self-efficacy (r = 0.31), more frequent use of behavioral exercise processes of change (r = 0.31), greater perceived benefits for PA (r = 0.22), and better physical quality of life (r = 0.16) (all P < 0.001). Women reporting no activity lapses had higher reported activity levels than regularly active women with lapses or occasionally active women with lapses (P < 0.0001 for trend). Of the women who reported lapses, 24% reported low self-confidence, 43% reported difficulty controlling their weight, and 55% reported difficulty maintaining their diet when they lapsed from PA. Thirty-nine percent of women reporting lapses did not resume PA (i.e., relapsed to inactivity). Higher anxiety and depressive symptoms, and less frequent use of behavioral exercise processes of change, were associated with relapse to inactivity.
Conclusions: Future interventions for early postmenopausal women should consider psychosocial factors when attempting to encourage and maintain higher levels of PA. Addressing and preventing PA lapses may help to achieve PA goals in this population.
Countless reports support the role of regular physical activity (PA) in achieving and maintaining optimal health. However, recent population studies show that only a minority of American adults accrue enough PA to achieve appreciable health benefits (10,11). Women are at particularly high risk for physical inactivity (11). Although opportunities exist across the lifespan of women to better understand and, ultimately, increase PA levels, few times are as crucial as menopause and early postmenopause. After menopause, women experience a dramatic increase in their risk of cardiovascular disease (CVD) (1,42), which may be partially attributable to lifestyle factors such as decreasing levels of PA (11). PA has been shown to reduce risk of CVD in postmenopausal women (31,34). However, there is a gap between these observations and the widespread adoption of PA in postmenopausal women.
An incomplete understanding of factors influencing PA adoption and maintenance in early postmenopausal women may be one reason for the gap between the evidence for the benefits of PA and actual PA levels in this population. PA is a complex behavior that is influenced by sociodemographic, personal, environmental, and psychosocial factors (5,50,57). Women in early postmenopause may also experience vasomotor symptoms or fatigue attributable to altered sleep patterns (2,28) that may make them vulnerable to disruption of PA patterns. Psychosocial factors are particularly important to understand because they predict health behaviors, and also because they may mediate the relationships between health behaviors and health outcomes (5,23,55). PA researchers have studied psychosocial factors related to PA in the context of theoretical models; our understanding of early postmenopausal PA may benefit from being evaluated in this manner.
Theoretical models such as social cognitive theory (3) and the transtheoretical model (47,48,60) have been used to guide PA interventions. Social cognitive theory includes the widely studied construct of self-efficacy, or confidence in one's abilities to successfully manage challenging or high-risk situations. Adapted from Bandura's self-efficacy theory, self-efficacy for PA has been shown to be positively associated with PA behavior in both population (37,38) and more recent intervention studies (19,23,33,46). For example, Gallagher and colleagues (19) report that weight loss and greater levels of PA were associated with higher PA self-efficacy in a comprehensive weight-loss intervention in overweight women. However, in a population of postmenopausal women, Irwin and colleagues (23) found that exercise self-efficacy was not significantly associated with yearlong exercise adherence in a population of overweight, postmenopausal women. More data about the relevance of self-efficacy to PA behavior in early postmenopausal women are, therefore, needed.
The transtheoretical model (TTM) has also been employed to underscore and explain the dynamic nature of PA behavior (36,39). In the TTM, persons move through a series of stages (precontemplation, contemplation, preparation, and action) before achieving maintenance of PA levels. Movement through these stages towards more consistent PA behavior is accomplished by using behavioral and cognitive processes of change and correlates with a predictable shift in decisional balance (i.e., the weighing of the perceived benefits of PA relative to the negative aspects or cons). Self-efficacy also plays a key role in stage progression and prevention of relapse. Although maintenance is usually defined as a period of at least 6 months of consistent behavior, Rothman (49) argues that this time-dependent definition of maintenance does not account for other psychological factors such as expectations and satisfaction with outcomes that may strongly influence whether a behavior is maintained. Rothman's approach argues for a more detailed analysis of the TTM, particularly the factors that may derail PA maintenance.
A better understanding of PA maintenance in the early postmenopausal population could help to clarify why PA levels decrease after menopause, and how this may be prevented. One concept to explore is lapses, that is, brief periods of ≥ 2 wk without PA. Lapses are important because they are related to the concepts of PA maintenance and adherence. Whereas maintenance refers to a stage of behavioral change at which PA has become a regular behavior for at least 6 months (47,48), adherence refers to the level of PA participation after deciding to participate in a PA regimen or intervention (26). Lapses may also be related to relapse, that is, a complete return to sedentary behavior (47,48,50). Adhering to or maintaining a PA regimen is critical to realizing the health benefits of PA (59); however, about 25-50% of individuals who initiate a PA program will drop out in the first 6 months (i.e., before reaching maintenance) (14,27).
A third theoretical model that has been applied to PA behavior is the relapse-prevention model (RP), which was originally described by Marlatt and Gordon (41) to better explain relapse in addictions, and to guide interventions (30). Briefly, this model states that lapse and, eventually, relapse result from responding to a high-risk situation with inadequate cognitive and behavioral coping skills. Moreover, inadequate coping leads to decreased self-efficacy, outcome expectations, and the abstinence-violation effect before eventual relapse. When testing the RP in relation to PA, Simkin and Gross (53) found that PA lapses were quite common (seen in 66% of their sample of healthy, college-aged women), and relapse was more likely in those reporting fewer behavioral and cognitive coping strategies. In a recent report describing the application of RP to PA behavior, Stetson and colleagues (56) discuss the high-risk situations and coping strategies in a population of long-term exercisers, and they point out the need for expanded research in this area with regard to PA history and gender. An opportunity exists to better describe PA lapse and relapse among early postmenopausal women.
Reports published to date on PA adherence and maintenance have shown that interruptions in PA are common (40), but adherence to PA can be difficult to define or measure (14,63). Wilbur and colleagues (64) found that in a sample of midlife women, self-efficacy for overcoming barriers to PA and adherence during an initial intervention phase were predictors of longer-term adherence. To date, researchers have not thoroughly described how the construct of brief lapses in PA may relate to overall PA levels and psychosocial concepts relevant to PA in early postmenopausal women.
The current report examines the relationships between detailed measures of PA, PA lapses, and psychosocial factors in a cohort of early postmenopausal women who were overweight and participating in a randomized clinical trial. The following research questions were examined:
* What psychosocial factors are most strongly associated with PA in early postmenopausal women?
* What factors are associated with lapses in PA behavior, and how do these lapses influence overall PA levels?
The Women on the Move through Activity and Nutrition (WOMAN) study is a randomized clinical trial of a nonpharmacological lifestyle intervention of diet and PA to modify subclinical markers of CVD by achieving 10% weight loss. Primary intervention goals of the trial are to achieve 150 min·wk−1 of moderate PA, and to follow a low-fat, reduced-calorie eating pattern. The WOMAN study cohort includes 508 postmenopausal women ages 52-62, recruited by direct mailing from selected zip codes in Allegheny County, Pennsylvania, from April 2002 through October 2003. The current analyses include women with complete PA, PA lapses, and psychosocial data at baseline (N = 497, Table 1).
Eligibility criteria included a waist circumference ≥ 80 cm, LDLc between 100 and 160 mg·dL−1, BMI between 25 and 39.9 kg·m−2, weight stable, blood pressure < 160/95 mm Hg at initial screening but < 140/90 mm Hg at randomization (on or off drug therapy), no current use of cholesterol-lowering or weight-loss medication, no diagnosis of diabetes, no history of cancer in the past 2 yr, and the ability to complete a 400-m corridor walk. Participants who reported engaging in more than 4 h of regular PA per week were excluded. Potential participants were also excluded if they had a Beck Depression Inventory (BDI (6)) score > 20, history of psychiatric hospitalization in the past 5 yr, had been treated for depression in the past year, or had uncontrolled depression in the past 3 months. Women who self-reported diagnoses of schizophrenia, bipolar disorder, panic disorder, or eating disorder, or who were in treatment for substance use, were also excluded.
All participants provided written informed consent, and all protocols were approved by the institutional review board at the University of Pittsburgh. PA, PA lapses, psychosocial factors, demographic factors, dietary intake, and lipids were assessed at the baseline clinic visit.
PA was measured using a past-year version of the Modifiable Activity Questionnaire (MAQ), an interviewer-administered questionnaire (29). This questionnaire assesses current leisure and occupational activities, as well as extreme levels of inactivity attributable to disability. The current report focuses on the leisure activity estimates because there was little reported occupational activity in our study population. Study participants were asked to report whether they had participated in specific activities, such as walking for exercise, at least 10 times during the past year (12 months). Those who had participated in a specific activity were asked which months they had participated in that specific activity during the past year; then, they estimated the number of times each month and the length of time that they spent doing the specific activity. PA levels were calculated as the product of the duration and frequency of each activity (in hours per week), weighted by an estimate of the metabolic equivalent (MET) of that activity and summed for all activities performed. One MET represents the energy expenditure for an individual at rest, whereas a 10-MET activity requires 10 times the resting energy expenditure. PA data were expressed as metabolic equivalent hours per week (MET·h·wk−1). The MAQ has been shown to be both reliable and valid, and it has been widely used in a number of populations (29).
At baseline, PA data were also obtained objectively with the Accusplit Eagle AE120 (Accusplit Inc., Pleasanton, CA) pedometer in a subgroup of WOMAN study participants (N = 170; 33.5%). The participants were instructed to wear the pedometer clipped to their waistband over the dominant hip for 1 wk and, at the end of each day, to record the number of steps taken in a diary. At the end of the week, the participant returned the activity diary to the investigator. The number of steps recorded in the diary from the pedometer was averaged for the week to obtain a 7-d average of the number of steps taken per day. The Accusplit pedometer used in WOMAN has the same internal mechanism as a Digiwalker pedometer, which has been widely demonstrated to be a reliable, valid measure of PA (4,13,52,58).
The measure of lapses was based on previous research applying the RP model to PA behavior (53). Participants reported whether they had engaged in regular exercise (three or more times per week) or occasional exercise (fewer than three times per week) with or without lapses of longer than 2 wk during the past 6 months at baseline. Those reporting any lapse in the past 6 months were asked whether they returned to PA after the lapse; those who indicated they had not were considered to have relapsed. Furthermore, participants who endorsed an exercise lapse during the past 6 months were then asked to select among a list of perceived barriers the one most important reason why they stopped exercising. Perceived barriers were adapted from previous studies (24,25). Participants were also asked to report their feelings at the time of the PA lapse (adapted from Grilo et al. (21)) and their motivations to resume PA after a lapse. Feelings included statements such as "I felt guilty when I stopped exercising" with responses on a five-point Likert scale (1 = disagree; 3 = undecided; 5 = agree). Motivations included statements such as "I feel physically better when I exercise" with responses on a five-point Likert scale (1 = not at all important; 3 = moderately important; 5 = extremely important).
The BDI (6) is a validated, reliable, 21-item survey intended to assess the burden of depressive symptoms (Cronbach's alpha = 0.74). Scores for the BDI range from 0 to 63. Scores in the range of 0-9 (i.e., the majority of our population) represent minimal depressive symptoms. The Cohen Perceived Stress Scale (CPSS) (12) is a four-item instrument that measures the degree to which situations in one's life are appraised as stressful (Cronbach's alpha = 0.78). Items were designed to tap how unpredictable, uncontrollable, and overloaded respondents find their lives. This scale assesses the amount of stress in one's life during the past month rather than in response to a specific stressor. The Spielberger State-Trait Anxiety Inventory (STAI) (54) is a widely used, validated measure of anxiety. It consists of two 20-item, self-reported scales that measure both the temporary condition of state anxiety (Cronbach's alpha = 0.90) and the more general, long-standing quality of trait anxiety (Cronbach's alpha = 0.88). The STAI ranges from 20 to 80; mean values (norms) in a population of working adult females ages 50-69 are 32.30 (state) and 31.79 (trait).
Women also reported their health-related quality of life (HRQoL) with the Short Form-36 (SF-36) (62), which is designed to report composite physical and mental health scores (Cronbach's alpha = 0.89 and 0.87, respectively, for physical and mental composites) as well as eight specific subscales. The median normative composite scores for the SF-36 in females for mental and physical health are approximately 52, and a normal range is between 42 and 52 for mental health and between 47 and 52 for physical health. The current analyses focus on the four subscales hypothesized by the authors to be most related to PA: physical functioning (Cronbach's alpha = 0.85), physical role limitation (Cronbach's alpha = 0.80), general health (Cronbach's alpha = 0.72), and energy/fatigue (Cronbach's alpha = 0.86).
Women also responded to a number of psychosocial measures specific to PA, including exercise self-efficacy (40), decisional balance for PA (35,36), and exercise processes of change (37). Exercise self-efficacy was assessed using a previously validated and reliable instrument designed to assess confidence in exercising in five challenging situations, including being tired, in a bad mood, or on vacation (40). Respondents were asked to rate their confidence using a five-point Likert-type scale, with higher numbers indicating greater confidence about exercising (Cronbach's alpha = 0.76). The range for exercise self-efficacy is 1-5. Decisional balance (36,37) reflects how an individual personally weighs the advantages (pros) and disadvantages (cons) of being physically active, asking them to endorse the importance of statements such as "I would feel more confident if I exercised regularly" on a five-point Likert-type scale. The total score was calculated as the difference of the t-scores for pros and cons (Cronbach's alpha = 0.82). The exercise processes-of-change questionnaire (38) is a measure of cognitive and behavioral strategies a person employs or plans to employ to affect his or her exercise habits (e.g., "I put things around my home to remind me of exercise."). Participants used a five-point Likert-type scale to indicate how frequently they used each strategy in the past month. Each of the 20 items was categorized as one of five behavioral or five cognitive processes; the 10 items representing behavioral processes (Cronbach's alpha = 0.84) and 10 items representing cognitive processes (Cronbach's alpha = 0.86) were each added together to calculate a mean score.
Univariate analyses were conducted on PA and psychosocial factors. Because PA levels were not normally distributed in this population, we report PA data as medians (25th, 75th percentiles). Correlations between PA and psychosocial factors were assessed with Spearman correlation coefficients. The sample size of 497 gives power of 83.1% to yield a statistically significant result. We created separate multivariable linear regression models to examine both 1) the relationship between PA and psychosocial factors that controlled for other variables that might confound the relationship between PA and psychosocial factors: age, BMI, and HT use; and 2) the total variance in PA explained by the psychosocial factors. For the first set of models, a linear regression model was created for each psychosocial factor with a significant (P < 0.05) univariate relationship with PA, adjusted for age, BMI, and HT use, which were entered as a block of variables. For the second, stepwise multivariable linear regression was used to create a model with continuous leisure PA (measured by MAQ in MET-hours per week) as the dependent variable to assess the association of the various psychosocial factors. A P value of 0.15 was required for entry into the model, and 0.05 to stay in the model. Psychosocial factors were added to the model in a sequential fashion, starting with that factor with the lowest P value. After addition of the psychosocial factors, the model was further adjusted for age, BMI, and HT use, which were entered as a block of variables.
Descriptive statistics were used to report the characteristics of PA lapses and relapses in this population. Demographic, PA, and psychosocial variables were compared across the categories of PA lapses, using chi-square tests for categorical variables and ANOVA for continuous variables. Tukey post hoc tests were used to further examine significant main effects. Among the participants who reported PA lapses, we further classified women who did or did not return to activity after the lapse (no relapse vs relapse). Demographic and psychosocial variables were compared across relapse categories using chi-square tests for categorical variables and t-tests for continuous variables. SAS version 9.0 (Cary, NC) was used for all analyses.
Characteristics of study population.
Table 1 shows the baseline demographic characteristics of this cohort and the mean values of the psychosocial factors and median values of PA (MAQ). At baseline, participants had a mean age of 56.9 yr and a mean BMI of 30.8 kg·m−2, and they were predominantly white and well educated, with about 60% on HT. Participants with higher BMI reported less PA (P = 0.003), and HT users reported higher levels of leisure PA than did nonusers (P = 0.02). In general, the mean values of the BDI, CPSS, Spielberger STAI, and physical health composite (PHC) of the SF-36 were in a normal or nonimpaired range.
Correlations between PA and psychosocial factors.
Table 2 shows the correlations between leisure PA as measured by the MAQ and the various continuous psychosocial factors. In this cohort of postmenopausal women, PA was moderately, positively correlated with exercise self-efficacy, cognitive and behavioral processes associated with exercise, and decisional balance for PA (i.e., tending to see more advantages than disadvantages associated with PA). Higher levels of PA were also associated with several domains of the SF-36. Better PHC scores, physical functioning, general health, and higher energy levels were all associated with higher levels of PA. There was a trend towards an inverse relationship between PA levels and anxiety, as well as between PA and better mental health composite scores on the SF-36.
A set of multivariable linear regression models examining the relationship between PA and psychosocial factors (Table 2) with a P value < 0.05 were then created to adjust for age, BMI, and HT use. All of the psychosocial factors were still significantly related to PA (P < 0.05) after adjustment in multivariable models, with the exception of physical role limitation, taken from the SF-36. The results of the separate stepwise linear regression model are included in Table 3. Exercise self-efficacy and behavioral processes of change remain the only independent psychosocial predictors of PA. BMI also had a significant impact on PA in this model, which had an R2 of 0.13.
Frequency of and associations with PA lapses.
PA lapses were very common in this group of overweight postmenopausal women, with 61% (N = 301) of participants reporting PA lapses in the past 6 months. Of women who lapsed, 39% (N = 116) did not resume PA after the lapse. The most frequently cited reasons for lapsing were lack of motivation (29%), lack of time because of work (18%), and weather change (13%). Moreover, of the women who reported lapses, 39% endorsed feeling guilty at the time of the lapse, 24% reported less self-confidence, 43% reported difficulty controlling their weight, and 55% reported difficulty maintaining their diet when they lapsed from PA. The following reasons were deemed extremely important for resuming PA: weight loss or maintenance (55%), feeling better when active (52%), health reasons (42%), and physical appearance (31%).
Demographic/psychosocial characteristics by PA lapse status.
Table 4 presents the demographic and psychosocial characteristics across categories of PA lapses (i.e., no activity, occasional with lapses, regular with lapses, regular without lapses). There were no meaningful differences in age, race, or HT use; women who reported no activity had lower levels of educational attainment than women in other groups. Women who reported regular PA without lapses had significantly lower mean BMI than did those who reported no activity. Regardless of lapses, women who were regularly active were more likely to have higher exercise self-efficacy, employ behavioral strategies for increasing activity more frequently, see more pros than cons for being physically active, and have better energy levels than women reporting occasional activity with lapses or no activity. Women who were regularly active also employed more frequent use of cognitive strategies for increasing activity than did women who reported no activity.
Predictors of failure to return to PA after a lapse.
Among the 301 women who experienced PA lapses, we examined factors associated with failure to return to PA after lapsing (i.e., relapse to inactivity). Age, BMI, and hormone therapy were not significantly associated with relapses to inactivity. Not surprisingly, women who were only occasionally active and lapsed were more frequently relapsed to inactivity compared with women who were regularly active and lapsed (45 vs 29%; P = 0.004). Higher levels of stress and depressive symptoms were modestly associated with relapses to inactivity (5.3 vs 4.5; P = 0.02 and 4.3 vs 3.4; P = 0.04, respectively). Behavioral processes related to exercise were highly protective against relapses to physical inactivity after lapses, as women who relapsed had less frequent use compared with those who did not relapse (2.5 vs 2.9; P < 0.0001). Exercise self-efficacy and the remaining psychosocial factors were not significantly associated with failure to return to PA after lapsing (data not shown).
PA levels by PA lapse status.
Figure 1 shows the levels of PA measured by both MAQ and pedometer across PA lapse categories. Overweight postmenopausal women who reported performing regular PA with no lapses in the past 6 months had a significantly higher level of reported PA than did women who reported regular activity with lapses, occasional activity with lapses, or no regular activity, regardless of the method of measuring PA (P for both trends < 0.0001).
FIGURE 1- Median phy...Image Tools
This paper examined the relationships among PA, PA lapses, and psychosocial factors among 497 overweight and obese early postmenopausal women participating in a lifestyle intervention trial. Lapses in PA were quite commonly reported in this group and were associated with significantly lower PA levels. The present study adds to the current research on psychosocial factors influencing PA by focusing on early postmenopausal women, a group in which little previous research has been reported (23). Although our participants volunteered for a clinical trial, our findings are consistent with reports in other populations showing that self-efficacy, cognitive and behavioral processes of change, and decisional balance are all positively associated with PA (5,17,32,51,57). These findings also reinforce the utility of social cognitive theory and the transtheoretical model in this population. Although we did not formally evaluate stages of change for PA in our study, key components of the transtheoretical model were important constructs in our findings. Self-efficacy was the strongest predictor of PA in our multivariable analyses, which is consistent with previous research (37,38,43,44). Moreover, behavioral processes of change were associated with both high PA levels and a lower risk of relapse to physical inactivity.
Psychosocial factors unrelated to either of these theoretical models played a somewhat more modest role in our findings. HRQoL has been shown to correlate positively with an active lifestyle in many research studies (7,8,18) in the general population (20,61) and in menopausal women (16). Although of borderline statistical significance, the inverse relationships between PA and depressive symptoms and anxiety are concordant with previous reports of the association between PA and mood (9,15,22). It is possible that more significant associations were not found because the participants in the WOMAN trial were recruited from the general population and not a clinical sample, and they had very low depressive symptoms.
To our knowledge, ours is the first study that has looked specifically at the concept of PA lapses in early postmenopausal women who were overweight. Lapses in the present study were associated with negative feelings (guilt, low self-confidence) and perceptions of difficulty maintaining weight or healthy diet. On the other hand, in regularly active women, lapses did not seem to be significantly associated with decreased exercise self-efficacy and HRQoL, which may suggest that women can lapse and still accrue PA benefits. However, lapses were also associated with lower levels of PA, and many women experienced a relapse to inactivity after a lapse, particularly those with higher anxiety and depressive symptoms and less frequent use of behavioral exercise processes of change. How can one reconcile these seemingly contradictory observations with what is known about psychosocial factors associated with PA? It may reinforce the concept that although lapses are strongly associated with overall PA levels, they are not a proxy, and they have their own unique characteristics. Our findings can also be considered in the context of the RP model. Negative feelings (such as guilt and low self-confidence) associated with lapses were reported by some participants; such feelings are described in the RP model as emotional reactions that may influence whether a lapse leads to relapse (i.e., the abstinence-violation effect). In our study, a lapse was less likely to lead to a relapse in women reporting more frequent use of behavioral processes of change related to PA, which may be related to the positive influence of effective coping strategies described in the RP model and the findings of Simkin and Gross (53) and Stetson et al. (56). These relationships should be explored in more detail in future studies, because we did not set out to formally test the RP model or to directly measure coping strategies used regarding the high-risk situations (i.e., barriers) reported in the current study.
Although lapses per se are not commonly addressed in the PA literature, there has been published work on related concepts such as maintenance and adherence. In recent studies, Wilbur and colleagues (63,64) have examined adherence to walking during the maintenance phase of a PA intervention. Women were defined as having consistent adherence if they completed 80% of walks and had no lapses, with a lapse defined as 1 wk with no walks. They found six dynamic and mutually exclusive patterns of walking behavior, including both consistently adherent and occasional lapse patterns. Higher adherence during maintenance was associated with exercise self-efficacy, but other psychosocial factors were not investigated, nor were lapses and predictors of lapses described in detail. In a recent descriptive study, women in a walking program said that making time, problem solving, internal motivation, and support of family and friends were all important to achieving and maintaining PA (45). The findings of the current study are consistent with these reports in highlighting the complexity of PA behavior and the many influences on it. Our examination of the psychosocial factors associated with lapses and relapse might help to inform how to address and prevent lapses. For example, because employing frequent use of behavioral processes related to exercise was highly protective against a relapse to physical inactivity after a lapse, interventions could reinforce behavioral processes such as having things at home and work to remind the participant of exercise, or having someone to provide feedback about exercise.
Our study suggests that such interventions for early postmenopausal women should address both common barriers or high-risk situations for lapses (i.e., motivation, time constraints, and inclement weather) and the benefits that women in this age group connect with consistent PA (i.e., weight loss or maintenance, feeling mentally and physically well, and improved health). Future interventions might also address the perceptions of a lapse, which might more constructively be viewed as a learning experience rather than a negative experience associated with guilt and low self-confidence (30). Although the measure of PA lapses used in this study has not been previously validated, it has good face validity, and it was shown to be strongly related to three separate measures of PA in our analyses, including an objective measure (pedometer steps), thus providing support for the concurrent validity of this measure.
Our findings must be interpreted with several limitations in mind. No inferences can be made about the directionality or causality of the various relationships, given the cross-sectional nature of the data. It is quite possible, given the complex nature of PA behavior, that the relationships are truly bidirectional; for example, high levels of PA may improve physical functioning, which, in turn, predicts future PA. In general, precise estimates of PA cannot feasibly be obtained from subjective measures such as the self-reported MAQ. However, the estimates obtained by the activity questionnaire are valuable in relative terms, and they can be used to rank individuals or groups of subjects within a population from the least to the most active. In addition, PA questionnaires are limited in their ability to assess activities of low intensity. Our primary PA measure is, therefore, not as sensitive to lower-intensity activities that may be preferred by some women in this age group, and the results we present cannot account for how psychosocial factors or lapses may relate to lower-intensity activity.
Our findings are based on responses from a sample of overweight and obese women who volunteered for a clinical trial; therefore, their motivations for PA may be different from those of other women of comparable age. The women in our sample were likely more active than average overweight or obese postmenopausal women and more ready to adopt PA behavior, given that they had volunteered for a lifestyle study. The participants in the WOMAN trial were recruited from the general population rather than a clinical sample; as such, they were relatively free of significant mental or physical impairments. Although this presents an opportunity to examine the data from a "primary prevention" perspective, it also limits the generalizability of our findings to a more diverse group of women. Lastly, there may be psychosocial and other factors important to PA in postmenopausal women that were not included in this study. This is reflected in the small amount of total variance explained by our final multivariable linear regression model (Table 3). Future studies should both address the robustness of these factors in more diverse populations, and explore psychosocial and other constructs that could also be important to PA in this age group.
Our findings have several possible implications for understanding and promoting PA in early postmenopausal women. First, providers or community programs designed to promote PA in this population should appreciate the complex array of factors that are related to this behavior. Special attention should be paid to psychosocial and motivational factors such as decisional balance and exercise self-efficacy. Moreover, programs should include strategies to avoid or minimize activity lapses, and to make lapses that do occur into learning experiences. Future research should address these various topics in a longitudinal manner and in more diverse groups of postmenopausal women.
The authors would like to acknowledge the contributions of the staff as well as the 508 dedicated participants of the WOMAN study. We also appreciate the programming and statistical assistance of Mr. Al-haji Buhari and Dr. Doris Rubio. This research was funded by National Heart, Lung, and Blood Institute contract R01-HL-66468. Dr. Conroy is supported by a Building Interdisciplinary Research in Women's Health (BIRCWH) career development award (K12 HD043441-05).
1. American Heart Association. Heart disease and stroke statistics-2007 update. Circulation
2. Avis, N. E., R. Stellato, S. Crawford, et al. Is there a menopausal syndrome? Menopausal status and symptoms across racial/ethnic groups. Soc. Sci. Med.
3. Bandura, A. Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev.
4. Bassett, D. R., and B. E. Ainsworth. Accuracy of five electronic pedometers for measuring distance walked. Med. Sci. Sports Exerc.
5. Baumann, A. E., J. F. Sallis, D. A. Dzewaltowski, and N. Owen. Toward a better understanding of the influences on physical activity: the role of determinants, correlates, causal variables, mediators, moderators and confounders. Am. J. Prev. Med.
6. Beck, A. T., C. H. Ward, M. Mendeson, J. Mock, and J. Erbaugh. An inventory for measuring depression. Arch. Gen. Psychiatry
7. Brown, D. W., L. S. Balluz, G. W. Heath, et al. Associations between recommended levels of physical activity and health-related quality of life. Findings from the 2001 Behavioral Risk Factor Surveillance System (BRFSS) survey. Prev. Med.
8. Brown, D. W., D. R. Brown, G. W. Heath, et al. Associations between physical activity dose and health-related quality of life. Med. Sci. Sports Exerc.
9. Brown, W. J., J. H. Ford, N. W. Burton, A. L. Marshall, and A. J. Dobson. Prospective study of physical activity and depressive symptoms in middle-aged women. Am. J. Prev. Med.
10. Centers for Disease Control (CDC). Prevalence of physical activity, including lifestyle activities among adults-United States, 2000-2001. MMWR Morb. Mortal. Wkly. Rep.
11. Centers for Disease Control (CDC). Trends in leisure-time physical inactivity by age, sex, and race/ethnicity-United States, 1994-2004. MMWR Morb. Mortal. Wkly. Rep.
12. Cohen, S., T. Kamarck, and R. Mermelstein. A global measure of perceived stress. J. Health Soc. Behav.
13. Crouter, S. E., P. L. Schneider, M. Karabulut, and D. R. Bassett. Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Med. Sci. Sports Exerc.
14. Dishman, R. Determinants of participation in physical activity. In: Exercise, Fitness, and Health
, C. Bouchard, R. J. Shephard, T. Stephens, J. R. Sutton, and B. D. McPherson (Eds.). Champaign, IL: Human Kinetics, 1990, pp. 75-102.
15. Dunn, A. L., M. H. Trivedi, and H. A. O'Neal. Physical activity dose-response effects on outcomes of depression and anxiety. Med. Sci. Sports Exerc.
33(6 Suppl.):S587-S597, 2001.
16. Elavsky, S., and E. McAuley. Physical activity, symptoms, esteem, and life satisfaction during menopause. Maturitas
17. Eyler, A. Correlates of physical activity: who's active and who's not? Arthritis Care Res.
18. Fabre, C., J. Massé-Biron, K. Chamari, et al. Evaluation of quality of life in elderly healthy subjects after aerobic and/or mental training. Arch. Gerontol. Geriatr.
19. Gallagher, K. I., J. M. Jakicic, M. A. Napolitano, and B. H. Marcus. Psychosocial factors related to physical activity and weight loss in overweight women. Med. Sci. Sports Exerc.
20. Galper, D. I., M. H. Trivedi, C. E. Barlow, A. L. Dunn, and J. B. Kampert. Inverse association between physical inactivity and mental health in men and women. Med. Sci. Sports Exerc.
21. Grilo, C. M., S. Shiffman, and R. R. Wing. Relapse crises and coping among dieters. J. Consult. Clin. Psychol.
22. Hassmen, P., N. Koivula, and A. Uutela. Physical exercise and psychological well-being: a population study in Finland. Prev. Med.
23. Irwin, M. L., S. S. Tworoger, Y. Yasui, et al. Influence of demographic, physiologic, and psychosocial variables on adherence to a yearlong moderate-intensity exercise trial in postmenopausal women. Prev. Med.
24. Johnson, C. A., S. A. Corrigan, P. M. Dubbert, and S. E. Gramling. Perceived barriers to exercise and weight control in community women. Women Health
25. King, A. C., C. Castro, S. Wilcox, A. A. Eyler, J. F. Sallis, and R. C. Brownson. Personal and environmental factors associated with physical inactivity among different racial-ethnic groups of U.S. middle-aged and older-aged women. Health Psychol.
26. King, A., R. Harris, and W. Haskell. Effect of recruitment strategy on types of subjects entered into a primary prevention clinical trial. Ann. Epidemiol.
27. King, A. C., W. L. Haskell, D. R. Young, R. K. Oka, and M. L. Stefanik. Long-term effects of varying intensities of varying intensities and formats of physical activity on participation rates, fitness and lipoproteins in men and women aged 50 to 60 years. JAMA
28. Kravitz, H. M., P. A. Ganz, J. Bromberger, L. H. Powell, K. Sutton-Tyrrell, and P. M. Meyer. Sleep difficulty in women at midlife: a community survey of sleep and the menopausal transition. Menopause
29. Kriska, A. M., and C. J. Caspersen. Introduction to a collection of physical activity questionnaires. Med. Sci. Sports Exerc.
30. Larimer, M. E., R. S. Palmer, and G. A. Marlatt. Relapse prevention. An overview of Marlatt's cognitive-behavioral model. Alcohol Res. Health
31. Lee, I., K. Rexrode, N. Cook, J. Manson, and J. Buring. Physical activity and coronary heart disease in women: is "no pain, no gain" passe? JAMA
32. Lewis, B. A., B. H. Marcus, R. R. Pate, and A. L. Dunn. Psychosocial mediators of physical activity behavior in adults and children. Am. J. Prev. Med.
23(2 Suppl.):26-35, 2002.
33. Linde, J., A. J. Rothman, A. S. Baldwin, and R. W. Jeffery. The impact of self-efficacy on behavior change and weight change among overweight participants in a weight loss trial. Health Psychol.
34. Manson, J. E., P. Greenland, A. Z. LaCroix, et al. Walking compared with vigorous exercise for the prevention of cardiovascular events in women. N. Engl. J. Med.
35. Marcus, B. H., W. Rakowski, and J. S. Rossi. Assessing motivational readiness and decision making for exercise. Health Psychol.
36. Marcus, B. H., J. S. Rossi, V. C. Selby, R. S. Niaura, and D. B. Abrams. The stages and processes of exercise adoption and maintenance in a worksite sample. Health Psychol.
37. Marcus, B. H., V. C. Selby, R. S. Niaura, and J. S. Rossi. Self-efficacy and the stages of exercise behavior change. Res. Q. Exerc. Sport
38. Marcus, B. H., B. M. Pinto, L. R. Simkin, J. E. Audrain, and E. R. Taylor. Application of theoretical models to exercise behavior among employed women. Am. J. Health Promot.
39. Marcus, B. H., and L. R. Simkin. The transtheoretical model: applications to exercise behavior. Med. Sci. Sports Exerc.
40. Marcus, B. H., P. M. Dubbert, L. H. Forsyth, et al. Physical activity and behavior change: issues in adoption and maintenance. Health Psychol.
19(1 Suppl.):S32-S41, 2000.
41. Marlatt, G. A., and J. R. Gordon. Relapse Prevention: Maintenance Strategies in Effective Behavior Change
. New York, NY: Guilford Press, 1985.
42. Matthews, K. A., E. Meilahn, L. H. Kuller, S. F. Kelsey, A. W. Caggiula, and R. R. Wing. Menopause and risk factors for coronary heart disease. N. Engl. J. Med.
43. McAuley, E. Self-efficacy and the maintenance of exercise participation in older adults. J. Behav. Med.
44. McAuley, E., G. J. Jerome, S. Elavsky, D. X. Marquez, and S. N. Ramsey. Predicting long-term maintenance of physical activity in older adults. Prev. Med.
45. Nies, M. A., and C. L. Motyka. Factors contributing to women's ability to maintain a walking program. J. Holist. Nurs.
46. Pinto, B. M., H. Lynn, B. H. Marcus, J. Depue, and M. G. Goldstein. Physician-based activity counseling: intervention effects on mediators of motivational readiness for physical activity. Ann. Behav. Med.
47. Prochaska, J. O., and C. C. DiClemente. Stages and processes of self-change of smoking: toward an integrative model of change. J. Consult. Clin. Psychol.
48. Prochaska, J. O., C. C. DiClemente, and J. C. Norcross. In search of how people change: applications to addictive behaviors. Am. Psychol.
49. Rothman, A. J. Toward a theory-based analysis of behavioral maintenance. Health Psychol.
50. Sallis, J. F., M. F. Hovell, C. R. Hofstetter, et al. Lifetime history of relapse from exercise. Addict. Behav.
51. Sallis, J. F., and N. Owen. Physical Activity & Behavioral Medicine
. Thousand Oaks, CA: Sage Publications, 1999.
52. Schneider, P. L., and S. E. Crouter, and D. R. Bassett. Pedometer measures of free-living physical activity: comparison of 13 models. Med. Sci. Sports Exerc.
53. Simkin, L. R., and A. M. Gross. Assessment of coping with high-risk situations for exercise relapse among healthy women. Health Psychol.
54. Spielberger, C. D., R. L. Gorsuch, and R. E. Lushene. Manual for the State-Trait Anxiety Inventory
. Palo Alto, CA: Consulting Psychologists Press, 1976.
55. Steptoe, A., E. Rink, and S. Kerry. Psychosocial predictors of changes in physical activity in overweight sedentary adults following counseling in primary care. Prev. Med.
56. Stetson, B. A., A. O. Beacham, S. J. Frommelt, et al. Exercise slips in high-risk situations and activity patterns in long-term exercisers: an application of the relapse prevention model. health. Ann. Behav. Med.
57. Trost, S. G., N. Owen, A. E. Bauman, J. F. Sallis, and W. Brown. Correlates of adults' participation in physical activity: review and update. Med. Sci. Sports Exerc.
58. Tudor-Locke, C., D. R. Bassett, A. M. Swartz, et al. A preliminary study of one year of pedometer self-monitoring. Ann. Behav. Med.
59. U.S. Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General
. Washington, DC: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 1996.
60. Velicer, W. F., J. O. Prochaska, J. L. Fava, G. J. Norman, and C. A. Redding. Smoking cessation and stress management: applications of the transtheoretical model of behavior change. Homeostasis
61. Vuillemin, A., S. Boini, S. Bertrais, et al. Leisure time physical activity and health-related quality of life. Prev. Med.
62. Ware, J. E., and C. D. Sherbourne. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med. Care
63. Wilbur, J., P. Chandler, and A. M. Miller. Measuring adherence to a women's walking program. West J. Nurs. Res.
64. Wilbur, J., A. Vassalo, P. Chandler, J. McDevitt, and A. M. Miller. Midlife women's adherence to home-based walking during maintenance. Nurs. Res.
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