Growth trajectories of exercise self-efficacy in older adults: influence of measures and initial status.

Self-efficacy expectations reflect one’s beliefs in his or her ability to successfully carry out a course of action (Bandura, 1997). Such perceptions influence the activities in which individuals choose to engage, the amount of effort they will invest in those activities, and the extent to which they will persist when they encounter barriers and/or failures. As the central active agent in Bandura’s social cognitive theory, self-efficacy has been consistently identified as a determinant of an array of health behaviors including physical activity (Bandura, 1997; McAuley & Blissmer, 2000). There is evidence that the salience of self-efficacy perceptions may differ depending upon which stage of the exercise process the individual is currently in. Bandura (1997) posits that cognitive variables such as self-efficacy have the greatest impact on behavior when the task is physiologically and/or psychologically demanding. The physical activity literature provides evidence to support this position whereby the influence of self-efficacy is considered to be strongest during the initial stages of an exercise program, when the behavior is novel, and barriers such as fatigue and time constraints are likely to augment the perceived difficulty of maintaining an exercise routine (McAuley, Courneya, Rudolph, & Lox, 1994; Oman & King, 1998). Once the behavior becomes more habitual, the role of efficacy cognitions diminishes. However, in exercise trials, it is likely that self-efficacy shifts again as the organized intervention terminates and the individual is faced with the challenge of continuing to exercise regularly without the structured routine to which he or she has become accustomed (McAuley, 1993). There are multiple sources from which one may derive efficacy, including mastery experiences, social persuasion, social modeling, and the interpretation of physiological and affective responses (Bandura, 1997). In the context of an exercise trial, one might expect self-efficacy to increase as a function of engagement in and exposure to activity, interactions with their exercise leader and peers, and through their affective states. From a social cognitive perspective, self-efficacy might be expected to increase with repeated exposures to physical activity. However, several studies detailing findings from randomized controlled physical activity trials report either no change in efficacy across varying lengths of intervention time or reductions in efficacy from baseline to the end of the interventions and beyond. For example, Moore et al. (2006) employed an eight-week lifestyle modification intervention to improve exercise maintenance in individuals enrolled in a cardiac rehabilitation program. They reported a small decrease in barriers self-efficacy (d = −.09) and a moderate decrease in exercise self-efficacy (d = −.67) at intervention end. At 12-month follow-up, barriers efficacy remained stable whereas exercise efficacy declined further. McAuley, Jerome, Marquez, Elavsky, and Blissmer (2003) examined the effects of a six-month exercise program on barriers and exercise efficacy in older adults and found a significant decline in both measures across the trial. However, there was a greater reduction in exercise efficacy (d = −.92) than in barriers efficacy (d = −.18). Finally, Hughes and her colleagues (2004) conducted an eight-week, multi-component, center-based physical activity intervention followed by home-based activity in older adults with lower extremity osteoarthritis. Once again, there were declines in barriers efficacy from baseline at two (d = −.56) and six months (d = −.59) and smaller declines in exercise efficacy at two (d = −.16) and six months (d = −.36). Why would self-efficacy decline with continued participation in an exercise intervention? We believe that there may be three issues to consider here. First, as McAuley and Mihalko (1998) have suggested, in the context of relatively inactive older adults, participants may simply not have the appropriate previous experiences upon which to form accurate efficacy expectations and, therefore, over-estimate their capabilities at baseline. In essence, as they become exposed to the intervention they recalibrate their personal efficacy. Second, in the event that recalibration takes place and the true baseline self-efficacy is lower than measured, one might expect to see increases throughout the program (i.e., at mid-point), and then a reduction at program end as individuals consider the challenges associated with exercising independently. A third possible explanation is that not all exercise self-efficacy measures might be expected to have similar trajectories. For example, barriers efficacy measures and measures which assess efficacy for adherence to exercise prescriptions over time may not fare as well as those measures which assess gradations of task (e.g., walking further or longer). This may be particularly true when participants have performance-based tests on a frequent basis. This supposition was evidenced in a study by Rejeski et al. (2008) whereby efficacy for a 400 meter walk (i.e., a task-related measure) increased at six months but reverted to baseline at 12 months. Further, it is often assumed that individuals in health-related interventions are drawn from a single population and have similar trajectories across these interventions. This assumption is the basis of linear growth curve modeling (Bollen & Curran, 2006). However, a more realistic assumption may be that different sub-groups (e.g., combinations of demographic factors, health status, or adherence to an intervention) exist within intervention studies. It is wholly possible that such groups display different trajectories of growth across time. The notion of the existence of “latent classes” or subgroups who exhibit heterogeneity in their behavior is an assumption of growth mixture modeling (McLachlan & Peel, 2000) and such an approach has been gaining popularity, particularly in the study of health behavior (e.g., Barnett, Guavin, Craig, Katzmarzyk, 2008; Jackson & Sher, 2005). Identifying sub-groups within clinical trials that evidence different trajectories of growth across time could have significant implications for treatment outcomes, identification of determinants of these trajectories, and for the implementation of different intervention strategies for different sub-groups. Here, our focus was on sub-groups dually-defined by baseline efficacy scores and efficacy trajectories. We report data examining the differential effects of a 12-month randomized controlled exercise trial on three measures of self-efficacy in a sample of older men and women. In doing so, we attempt to answer several questions. First, do individuals recalibrate their efficacy expectations in a downward trajectory after being exposed to the exercise intervention? We hypothesized that barriers and exercise self-efficacy would be overestimated at baseline, be reduced with exposure to the intervention (i.e., a true baseline), increase at six months and then decline at program termination. Statistically speaking, for each of these measures we compared a linear growth curve (i.e., single growth process) with a piecewise growth model (i.e., three growth processes accounting for hypothesized transition points). Second, we were interested in whether task-related efficacy measures behave differently than barriers/adherence type measures in terms of growth. We hypothesized that the task-related measure (i.e. self-efficacy for walking) would increase across the trial, as a function of personal assessments of progress and physical testing (i.e., treadmill testing and 1-mile walk test). Finally, an exploratory question focused upon whether there were different sub-groups within our sample (i.e., classes) relative to self-efficacy and whether the trajectory of growth for these classes was different.