Part Two
Background Papers



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When I’m 64 Part Two Background Papers

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When I’m 64 Initiatives to Motivate Change: A Review of Theory and Practice and Their Implications for Older Adults Alexander J. Rothman University of Minnesota INTRODUCTION Unhealthy behaviors and the disorders they cause pervade modern life. As even a cursory review of Healthy People 2010 (U.S. Department of Health and Human Services, 2001) makes perfectly clear, significant improvements in physical and mental health critically depend on changes in human behavior. The reductions in disease morbidity and premature mortality that would come from increased rates of physical activity; improved compliance with medical recommendations; reduced rates of obesity; and reduced rates of utilization of tobacco, alcohol, and other controlled substances are tantalizing. Moreover, these changes would result in meaningful improvements in quality of life and also increase the potential for dramatic reductions in health care costs. Yet even though the benefits of these behavioral changes are clear, eliciting consistent changes in people’s behavior has proven to be a formidable problem (Baumeister, Heatherton, and Tice, 1994; Rothman, 2000). For example, even though people recognize the health costs posed by smoking, the impact of those costs on behavioral decision making has proven to be quite complex—people downplay the importance of health concerns (Gerrard, Gibbons, Benthin, and Hessling, 1996), underestimate their own personal risk (Weinstein, 1998), or believe their fate is sealed and that cessation will make little difference. Moreover, behavioral decisions unfold in environments that too often hinder people’s ability or opportunity to act in a healthy manner. Although across the life span there are significant shifts in the types of

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When I’m 64 behavioral decisions that people must grapple with, there remains a consistent need for people to recognize and adopt healthy patterns of behavior (Leventhal, Rabin, Leventhal, and Burns, 2001). Given the difficulty people have initiating and maintaining changes in their behavior, there is continued demand for intervention strategies that effectively motivate healthy behavior. To date, efforts to specify and test these strategies have tended not to focus on the behavioral decisions facing older adults. In a similar manner, the theoretical models that have guided these intervention efforts do not address, directly or indirectly, whether the underlying processes generalize to older adults. In order to stimulate both theoretical and practical initiatives directed at the behavioral practices of older adults, this paper provides an overview of current models of how people decide to initiate and maintain healthy patterns of behavior and the implications of these models for intervention design and implementation. The review is divided into four sections. First, I consider whether people are ready to make changes in their behavior. Second, I examine what can be done to motivate people to initiate changes in their behavior, with a particular focus on communication strategies that have been shown to be effective. Third, I consider whether the factors that guide efforts to initiate a new pattern of behavior are distinct from those that determine whether those actions are sustained over time. Finally, because the development of initiatives to promote healthy behavioral decisions depends on communication between investigators who are working to advance theory and those working to advance practice (Rothman, 2004), I consider what can be done to facilitate this type of collaborative activity. In each section, consideration is given to the implications of the current state of basic and applied science for motivating change in older adults. IS EVERYONE READY TO CHANGE THEIR BEHAVIOR? Modern society is replete with opportunities for people to make changes in their behavior. Yet the proportion of adults who act on these opportunities has been modest at best (e.g., Schmid, Jeffery, and Hellerstedt, 1989). In response to this observation, investigators have asserted that people differ in their readiness to change and that it is overly simplistic (and optimistic) to expect that most or all people are prepared to avail themselves of the opportunity to modify their behavior (Prochaska, DiClemente, and Norcross, 1992). Thus, initiatives that provide people with effective methods to modify their behavior must be complemented with initiatives that persuade people that it is both important and possible for them to change their behavior. A rich array of factors has been invoked to explain why people differ in their readiness to make changes in their behavior (Salovey, Rothman, and

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When I’m 64 Rodin, 1998). In some cases, especially those involving new hazards, it may be that people are unaware of the dangers posed by their actions. Alternatively, people’s understanding of the health problem may lead them to believe (inaccurately) that their current actions are appropriate (Leventhal, Nerenz, and Steele, 1984). Even if people are reasonably well informed, they may choose not to modify their behavior because they perceive the value of the new pattern of behavior to be insufficient to justify a change in their behavior, or because they may not have confidence in their ability to perform the new pattern of behavior (Bandura, 1997). Structural factors may also constrain people’s behavioral options, acting either directly on behavior (e.g., the absence of low-fat snack foods at one’s workplace may hamper one from taking action) or indirectly by influencing people’s beliefs about the behavior (e.g., the absence of low-fat snacks may lead people to perceive them as unpopular or undesirable). Given the myriad of factors that determine people’s behaviors, investigators have worked to formulate systematic accounts of the factors that regulate people’s behavioral decisions with respect to health issues (e.g., health belief model [Rosenstock, Strecher, and Becker, 1988]; precaution adoption process model [Weinstein, 1988]; social cognitive theory [Bandura, 1997]; theory of planned behavior [Ajzen, 1991]; theory of reasoned action [Ajzen and Fishbein, 1980]; transtheoretical model of behavior change [Prochaska et al., 1992]).1 Although these models are remarkably similar in many respects, two general approaches can be identified (see Salovey et al., 1998, for a description of the specific features of each model). Some investigators have proposed that the process by which people decide to make changes in their behavior is best conceptualized as a series of stages and that unique factors determine whether people are able to transition from one stage to the next (e.g., transtheoretical model; precaution adoption process model). The critical implication of the stage-based approach is that intervention strategies are effective only if they target the specific needs of individuals at each stage and therefore interventions need to be stage matched. This approach can be contrasted with the conceptual framework that underlies continuum-based models such as the theory of reasoned action (Ajzen and Fishbein, 1980) or social cognitive theory (Bandura, 1997). In this framework, a single set of factors is identified that affects people’s behavioral decisions and, regardless of their readiness to change, people will benefit from information highlighting any or all of these factors 1   These models strive to specify the psychological factors that direct behavior, but provide limited information about the processes by which these factors (i.e., attitudes and beliefs) are formed or altered. Contemporary models of attitude formation and change (Petty and Wegener, 1998) delineate these processes and are considered in the next section of the paper.

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When I’m 64 (for a detailed discussion of the relative features of stage- and continuum-based models, see Weinstein, Rothman, and Sutton, 1998). There is little doubt that a continuum-based framework is more parsimonious than a stage-based framework. Stage models require not only a detailed articulation of the determinants of each stage transition, but also empirical evidence of the predicted dissociations among stage predictors. To date, nearly all of the empirical work that has been conducted cannot distinguish between predictions derived from the two frameworks and thus it is difficult to formulate strong arguments for one perspective over the other (Rothman, Baldwin, and Hertel, 2004; Weinstein et al., 1998).2 However, it is critical that investigators recognize explicitly which approach is guiding their efforts to motivate people to change their behavior (e.g., is the intervention expected to work consistently across people or will its impact be contingent on where people are in the behavior change process?). The observation that most people are not ready to implement changes in their behavior does not appear to be less true for older people (Leventhal et al., 2001). In fact, if anything, it has been suggested that older adults are somewhat less interested in opportunities for new experiences (e.g., Fredrickson and Carstensen, 1990), which would suggest an even lower level of readiness to change. Of the two approaches identified above, a stage-based approach may provide some interesting insights into the behavioral practices of older adults as it more readily affords investigators the opportunity to respond to the practical and psychological demands posed by the situations in which people find themselves. The psychological needs and prior experiences that older adults bring to a decision are likely to have a significant effect on where they are most likely to be in the behavior change process. When younger adults express limited interest in modifying their behavior, it is typically thought to reflect a failure to attend to or think through the behavioral decision. However, among older adults, limited interest in modifying, for example, one’s diet may indicate that a decision has been made to not make a change, as opposed to simply failing to consider the behavioral alternative (see Weinstein, 1988). This may be particularly true when examining behavioral decisions that involve issues that have been addressed repeatedly throughout the life span. If true, this has implications for initiatives to encourage behavior change. In this case, the failure to act would represent an active choice and thus initiatives would be needed to persuade people to reconsider their decision. Because a 2   Even if subsequent empirical work reveals that a stage-based approach provides a more complete account of behavioral decision making, its impact on intervention efforts may be constrained by the logistical and financial costs associated with its implementation.

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When I’m 64 decision to not take action may lead people to reaffirm the set of beliefs that guided that decision, persuading people to alter the decision is likely to be more difficult than persuading them to consider a new behavioral alternative. Asking for Change: Communication Strategies that Motivate Behavior Change A primary strategy for motivating people to change their behavior has been to provide them with information that will persuade them to alter their behavior (Eagly and Chaiken, 1993).3 Although in some cases this may involve providing new information about an issue, messages are typically designed to help people recognize or confront issues that are familiar but not seen as important enough to motivate a change in behavior. Thus, the primary challenge in efforts to communicate information is getting people to not only attend to the message but also process it in a manner that maximizes its impact on how they think and feel about the issue (Petty and Wegener, 1998). A critical step in this process is to create a context in which people recognize an issue to be personally relevant. Heightened personal relevance motivates people to not only seek out new information but also process the information in a systematic manner (Petty and Cacioppo, 1990). There is considerable empirical evidence that people strive to interpret information about themselves in a favorable light (Kunda, 1990). In the health domain, people readily accept information that indicates they are healthy, but are critical of and adopt higher standards of evidence for information that indicates a health problem (e.g., Ditto and Lopez, 1992; Liberman and Chaiken, 1992; see Rothman and Kiviniemi, 1999, for a review). For example, in an elegant series of experiments, Ditto and Croyle (1995) have shown that when people are provided with diagnostic information indicating a health problem, they respond to this information by minimizing its seriousness (“Oh, it’s not such a big deal”) and increasing perceptions of its prevalence (“Oh, everyone gets it”). Although these processes have not been studied systematically in populations of older adults, there is evidence that older adults misattribute symptoms to aging and that this tendency both reduces emotional distress about the symptoms and delays 3   Interventions targeted at structural or environmental factors (e.g., access to care, availability of facilities) can also afford significant changes in people’s behavior but, aside from brief consideration in the final section of this paper, they fall outside the purview of this paper, which focuses on the psychology of change.

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When I’m 64 interest in seeking care (Prochaska, Keller, Leventhal, and Leventhal, 1987). It is plausible that this tendency reflects a similar underlying interest in construing health information in a self-protective manner. Despite evidence that people may resist unfavorable information about their health, it must be recognized that people respond to health information in a myriad of ways. As suggested by Leventhal and colleagues (1984), people may adjust their beliefs to alleviate the sense of threat and anxiety posed by a health concern but still choose to act to reduce the danger posed by the health concern. If investigators assess only people’s beliefs about the seriousness of the health problem or how worried they are about the problem, they may develop an incomplete description of how people respond to health information. In fact, Croyle, Sun, and Louie (1993) found that even though people who were informed they had a health problem (high cholesterol) minimized the seriousness of the problem, they nonetheless expressed a heightened interest in information about how to treat the problem. It may be that people’s efforts to reduce the perceived seriousness of the problem allows them to manage their affective reaction to the threat to a sufficient degree that they have the psychological resources they need to focus on taking action to reduce their risks (e.g., Aspinwall and Brunhart, 1996). However, if people are too quick or too good at minimizing the problem and the appropriate behavioral response to the problem requires sustained action, over time people may find themselves with insufficient motivation to continue in the necessary action. The observation by Carstensen and her colleagues (Carstensen, Isaacowitz, and Charles, 1999) that older adults prioritize actions that address their emotional needs could indicate that older adults will be particularly interested in regulating their affective reactions to the health information. Such an approach might attenuate any behavioral response to the problem. On the other hand, if older adults are able to maintain a positive emotional state, they may find that they have the psychological resources to deal with challenges to their health in an optimal manner (Salovey, Rothman, Detweiler, and Steward, 2000; Taylor, Kemeny, Reed, Bower, and Gruenewald, 2000). Any strong conclusion about the processes that guide how adults (young and old) respond to health information must wait until investigators assess a sufficiently broad set of indicators to provide a complete description of people’s psychological and behavioral response. Given the challenges associated with providing people with information about their health, are there communication strategies that have been shown to be effective ways to motivate behavior change? Message tailoring and message framing represent two conceptual approaches that not only have been shown to alter people’s behavior systematically but also have received sufficient empirical scrutiny that investigators have begun to formulate an understanding of the factors that underlie their effectiveness. The

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When I’m 64 state of the science for each of these approaches and their implications for efforts to motivate behavior change in older adults are examined in turn. Message Tailoring Message tailoring is guided by the premise that people will pay more attention to and be more persuaded by information that speaks directly to their own personal concerns.4 For example, a smoker who is concerned about the social stigma of smoking would be sent a message focusing on that topic, whereas a smoker who is concerned about how smoking is harming the health of his wife would be sent a message focusing on that topic. There is a growing body of empirical evidence indicating that tailored health messages are more effective than generic messages that provide all individuals with the same information (Brug, Glanz, van Assema, Kok, and van Breukelen, 1998; Dijkstra, De Vries, and Roijackers, 1998a, 1998b; Kreuter and Strecher, 1996; Kreuter, Oswald, Bull, and Clark, 2000; see Skinner, Campbell, Rimer, Curry, and Prochaska, 1999, for review). Why might tailoring messages be more effective? Contemporary models of attitude change and persuasion may provide a rich conceptual framework for understanding how and when tailored messages are maximally effective (Petty, Barden, and Wheeler, 2002). In particular, to the extent that tailored messages are perceived to be personally relevant, recipients of the message will process the information more extensively, which, in turn, should increase its influence on people’s thoughts and feelings about the health issue. All else being equal, greater elaboration of a strong health message is desirable as well-reasoned attitudes are more stable over time and better predictors of behavior (Petty and Wegener, 1998). Tailoring information has been shown to increase the attention people pay to a message. Tailored messages are more likely to be read and remembered than are nontailored messages (e.g., Skinner, Strecher, and Hospers, 1994), are more likely to be discussed with others (e.g., Brug, Steenhuis, van Assema, and De Vries, 1996), and are perceived as more interesting and engaging (e.g., Brug et al., 1996; Kreuter, Bull, Clark, and Oswald, 1999). To the extent that the information addresses an individual’s concerns (i.e., the perception that “it speaks to me”), it may be less likely to elicit feelings of reactance and it may be more difficult for people to assert that the 4   It should be noted that tailored messages are distinct from targeted messages. Message tailoring focuses on matching information to individual-level characteristics (e.g., why Alex resists exercising), whereas message targeting focuses on matching information to group-level characteristics (e.g., why men resist exercising).

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When I’m 64 message doesn’t apply to them (see Gump and Kulik, 1995). In many ways, tailored messages invoke some of the strengths of a self-persuasion strategy, which puts people in situations in which they themselves bring to mind the relevant arguments or reasons for modifying their behavior (Aronson, 1999; McGuire and McGuire, 1991). As with self-persuasion, people who receive tailored messages may be more likely to construe their motivation to modify their behavior as reflecting intrinsic concerns, which have been shown to be predictive of behavior change (e.g., Williams, Ryan, Rodin, Grolnick, and Deci, 1998). Advances in technology have made the prospect of tailoring messages to a person’s unique set of personal concerns feasible, but it is not yet clear how investigators are to determine or prioritize which information should be tailored. For instance, is it more important that people’s understanding of the determinants of a health problem are well matched so that they readily infer that they either suffer from or are at risk for a health problem, or should the recommended response to the problem be well matched so that people more easily infer that they can perform the new pattern of behavior? Although stage-based models of behavior change could provide a theoretical framework for selecting the dimensions on which to tailor a message, to date investigators have only compared tailored messages to generic messages. Any conclusions regarding the value of tailoring messages along specific dimensions (e.g., self-efficacy vs. perceived risk) or to a person’s stage of change must wait until investigators systematically compare the impact of differentially tailored messages (see Weinstein et al., 1998, for a general discussion of these issues). Although message tailoring has not been tested systematically with older adults, it provides a framework that can capitalize on the observation that as people age their primary concerns and goals shift. According to socioemotional selectivity theory, older adults prioritize goals that address emotional as opposed to informational needs and thus are more likely to focus on people and opportunities that can address those needs (Carstensen et al., 1999). This would suggest that tailored messages might prove to be particularly persuasive with older adults if they address how modifying one’s behavior can satisfy one’s emotional needs. However, research on the interplay between emotion and persuasion has found that people’s emotional goals (e.g., desire to maintain a positive mood) affect the manner in which they process a message (Wegener and Petty, 1994; Wegener, Petty, and Smith, 1996). To the extent that older adults prioritize their ability to regulate and optimize their current emotional needs and, in particular, strive to construct a world that both heightens the opportunity for positive emotional experiences and constrains the opportunity for negative emotional experiences (i.e., antecedent emotional regulation; Charles and

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When I’m 64 Carstensen, 1999; Gross, 1998), they may be particularly skilled at minimizing contact with messages about health problems. A critical assumption implicit in tailoring messages to an individual’s set of unique concerns is that the different ways in which people think about a health problem do not differentially predict behavior. For example, when asked to consider cessation, some smokers may focus on the good things they would like to get if they were not smokers (e.g., the extra time and money that they would have available for other interests), whereas other smokers may focus on the unpleasant things about smoking they want to avoid (e.g., the hassle and expenses associated with buying cigarettes and finding a place to smoke). Is it correct to assume that both of these construals are equally likely to motivate behavior? Andrew Elliot and his colleagues have argued that all goals are not equally motivating and that, in particular, approach goals (i.e., goals that are characterized by a desire to reach a favorable goal state) are more strongly associated with favorable outcomes than are avoidance goals (i.e., goals that are characterized by a desire to stay away from an unfavorable goal state; Elliot and Church, 1997; Elliot and McGregor, 1999). This conclusion rests primarily on data from academic settings that have consistently revealed that the more avoidance goals students report (e.g., desire to not do poorly in the class), the poorer their subsequent academic performance. In a similar vein, contemporary statements of social comparison theory have emphasized that upward social comparisons (i.e., comparing one’s performance or condition to that of someone who is doing better) are more likely to elicit improvements in performance than are downward social comparisons (i.e., comparing one’s performance or condition to that of someone who is doing worse; Taylor and Lobel, 1989). Translated into the health domain, this perspective suggests that people who focus on approach goals or upward comparison standards for the behavior (e.g., an interest in dieting predicated on a desire to be thin) will be more successful than those who focus on avoidance goals or downward comparison standards for the behavior (e.g., an interest in dieting predicated on a desire not to be fat), and that tailoring messages to approach goals will be more effective than tailoring them to avoidance goals. Although this prediction follows clearly from empirical findings obtained in academic settings, the extent to which it will be confirmed in the health area is uncertain. In prior studies, avoidance goals have almost always involved the desire to prevent an unpleasant future outcome (e.g., not failing a class). Although similar health goals exist (e.g., not wanting to develop cancer), avoidance goals in the health domain can also involve eliminating the risk of or curing an existing unpleasant state (e.g., shortness of breath). In an initial study of smokers enrolled in a cessation program, my colleagues and I found that smokers whose cessation efforts were motivated by avoidance

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When I’m 64 Moving from Initiation to Maintenance: Why Do People Sustain Behavior Change? Even when efforts to motivate people to modify their behavior prove successful, the changes that are elicited more often than not prove transient (e.g., diet and exercise to produce weight loss [Jeffery et al., 2000], smoking cessation [Ockene et al., 2000], recovery from substance abuse [Marlatt and Gordon, 1985]). Although some health behaviors need only be done once, the overwhelming majority of behaviors that people are urged to adopt (such as those identified in Healthy People 2010) require sustained action and their benefits are contingent on maintenance. Why is it that people who are able to successfully initiate changes in their behavior are more often than not unable to sustain those changes over time? One possibility from dual process models of persuasion is that some behavioral changes are ephemeral because the underlying attitudes that support the changes are weak (e.g., based on little cognitive elaboration) and thus unlikely to persist (Petty and Wegener, 1998). In addition, there may be meaningful differences in the processes that underlie the decision to initiate and the decision to maintain a pattern of behavior. Current models of health behavior, cited above, have focused on elucidating how people determine whether to adopt a given behavior and have assumed, either implicitly or explicitly, that the factors underlying the decision to maintain a pattern of behavior are no different from those that govern its initiation. For example, the health belief model (Rosenstock et al., 1988), theory of reasoned action (Ajzen and Fishbein, 1980), and theory of planned behavior (Ajzen, 1991) make no direct reference to issues regarding behavioral maintenance other than to define it as a course of action sustained over a specified period of time.6 By comparison, stage models have identified maintenance as a distinct stage in the behavior change process. However, the primary focus of these theoretical approaches has been to delineate the processes through which people become ready to initiate a change in their behavior (Prochaska et al., 1992; Weinstein, 1988). The distinction between people in the action and maintenance stages is predicated solely on the length of time the behavior has been adopted, and thus the set of cognitive and behavioral strategies predicted to facilitate initial 6   To the extent that investigators have actively focused on time, it has been to determine how to increase the likelihood that people act on their intentions (Gollwitzer, 1999). Although instructing people to articulate how they will implement their behavioral intention has been shown to increase the likelihood that people will take action, these approaches do not address whether a change in behavior, once enacted, will be maintained.

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When I’m 64 action are similarly predicted to help sustain that action over time (Prochaska and Velicer, 1997). Social cognitive theory (Bandura, 1997) does explicitly consider how people regulate their behavior over time and identifies self-efficacy beliefs as a critical determinant of both the initiation and the maintenance of a change in behavior. Confidence in one’s ability to take action serves to sustain effort and perseverance in the face of obstacles. The successful implementation of changes in behavior bolsters people’s confidence, which, in turn, facilitates further action, whereas failure experiences serve to undermine personal feelings of efficacy. Although the reciprocal relation between perceived self-efficacy and behavior is well documented, this relation needs to be reconciled with the observation that successfully enacted changes in behavior are not always maintained. I have recently proposed that there are important differences in the decision criteria that guide the initiation and maintenance of behavior change and that these differences may serve to explain why people who are able to make changes in their behavior may subsequently choose not to sustain those changes (Rothman, 2000; Rothman et al., 2004). Behavioral decisions by definition involve a choice between behavioral alternatives. What differentiates decisions concerning initiation from those concerning maintenance are the criteria on which the decision is based. Decisions regarding behavioral initiation involve a consideration of whether the potential benefits afforded by a new pattern of behavior compare favorably to one’s current situation, and thus the decision to initiate a new behavior depends both on people holding favorable expectations regarding future outcomes and on their ability to obtain those outcomes. This premise is well grounded by a broad tradition of research (for reviews, see Bandura, 1997; Salovey et al., 1998). Whereas decisions regarding behavioral initiation are based on expected outcomes, decisions regarding behavioral maintenance involve a consideration of the experiences people have had engaging in the new pattern of behavior and a determination of whether those experiences are sufficiently desirable to warrant continued action. Consistent with Leventhal’s Self-Regulatory Model of Illness Behavior (Leventhal and Cameron, 1987), the decision to continue a pattern of behavior reflects an ongoing assessment of the behavioral, psychological, and physiological experiences that accompany the behavior change process. According to the model, people’s assessment of these experiences is ultimately indexed by their satisfaction with the experiences resulting from the new pattern of behavior and people will maintain a change in behavior only if they are satisfied with what they have accomplished. The feeling of satisfaction indicates that the initial decision to change the behavior was correct and furthermore provides justification for the continued effort people must put

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When I’m 64 forth to monitor their behavior and minimize vulnerability to relapse (for a more complete description of the model, see Rothman, 2000; Rothman et al., 2004). Although there is evidence consistent with the premise that satisfaction is associated with behavioral maintenance in the health domain (e.g., Klem, Wing, McGuire, Seagle, and Hill, 1997; Urban, White, Anderson, Curry, and Kristal, 1992) as well as in the area of consumer behavior (e.g., Oliver, 1993), investigators have only begun to test systematically for dissociations between predictors of behavioral initiation and behavioral maintenance (see King, Rothman, and Jeffery, 2002). In order to test predictions regarding the differential determinants of initial and long-term behavior change, investigators have to capture the unique effect that a particular psychological state (e.g., self-efficacy) has on each phase of the behavior change process. To date, claims regarding the determinants of behavioral maintenance have relied on tests of whether a psychological state (e.g., self-efficacy at baseline) can predict a distal behavioral outcome (e.g., smoking status 18 months later). However, this analytic approach is inconclusive regarding the factors that underlie behavioral maintenance as it cannot determine whether, for example, people’s initial feelings of self-efficacy influence their ability to maintain a behavior over and above its effect on their initial behavioral efforts. Across a series of intervention studies that have been designed to test predictions from the model in the contexts of smoking cessation and of weight loss, my colleagues and I have begun to accumulate evidence consistent with the model. For example, cessation self-efficacy has been shown to predict people’s initial ability to quit smoking but not whether the quit is sustained. People’s satisfaction with the outcomes afforded by cessation predicted whether people were able to sustain their quit over an extended period of time (Baldwin, Rothman, Hertel, Linde, Jeffry, Finch, and Lando, in press). As efforts to understand the distinctions between the processes that guide behavioral initiation and behavioral maintenance move forward, it is critical that investigators examine how these processes operate in older adults. To date, investigators have found in samples of younger adults that people tend to have more difficulty maintaining than initiating a new pattern of behavior. Whether this pattern will hold for older adults is uncertain. Given the premise that older adults are somewhat less interested in new opportunities and find it more difficult to imagine the benefits afforded by a new behavior, it may be that older adults find it particularly hard to initiate new behaviors and thus greater attention must be paid to initiatives that can motivate them to take action. However, the self-regulatory strategies that are typically employed by older adults may make them better able to maintain a new pattern of behavior (although declines in cognitive functioning may, over time, miti-

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When I’m 64 gate the impact of these strategies). To the extent that there may be greater continuity in the environments in which older adults live, it may be easier for them to develop habits. However, little is known about the process by which repeated behaviors become habits. My colleagues and I have suggested that what differentiates habits from behaviors that are maintained over time is that habits are behaviors for which people no longer feel any need to question their value (Rothman et al., 2004). For example, seat belt use may readily become a habit because people feel little need to continually reassess its worth. On the other hand, it may be difficult for a set of dietary behaviors to become a habit as people will continually reassess whether they are satisfied with the outcomes of the behavior. To the extent that habits are regulated by stored representations of the behavior, innovations in how people form and change implicit or automatically activated attitudes (Wilson, Lindsey, and Schooler, 2002) may provide critical insights into the design of initiatives that can create and sustain healthy habits. If satisfaction proves to be a critical determinant of whether a new pattern of behavior is maintained, investigators need to formulate a better understanding of the factors that cause people to feel more or less satisfied with their actions. This is likely to be a daunting task as one would expect assessments of satisfaction to reflect not only the set of concerns highlighted by the behavioral domain but also the set of priorities specified by the individual. For example, people who want to lose weight in order to improve their social life will focus their assessment of their actions on a different set of criteria than will those who want to lose weight in order to be more physically active. In order to apply this model to the behavioral practices of older adults, investigators need to develop a rich understanding of what older adults are likely to attend to when assessing the value of their behavior. According to socioemotional selectivity theory (Carstensen et al., 1999), older adults would be expected to base their evaluation on a behavior’s ability to address or meet their emotional needs. To the extent that older adults focus their attention to a greater degree on their present psychological needs, they may find it particularly difficult to forestall an assessment of a behavior’s value. This would lead to the prediction that older adults would have a particularly difficult time sustaining behaviors for which the initial costs are high and the focal benefits are delayed (e.g., smoking cessation). From the perspective of an intervention, older adults would therefore be likely to benefit from activities that both highlight any immediate benefits and heighten the critical long-term benefits afforded by the new behavior. Older adults’ ability to evaluate the consequences of their actions may also be constrained by cognitive impairments that can cause uncertainty as to whether the behavior has been performed (Purdie and McCrindle, 2002). If people fail to recognize that they have forgotten to perform a behavior

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When I’m 64 (e.g., take medication), when they subsequently find that the benefits of the behavior are diminished, they will mistakenly conclude that the behavior is not effective. People’s commonsense model of the health issue can also affect the conclusions they draw about their behavior as their model provides a framework for interpreting both the psychological and the physiological changes attributed to the behavior (Leventhal et al., 2001). For example, investigators have observed that patients misinterpret the feelings elicited by physical exertion as signs of cardiac distress and that this (mis)interpretation is associated with a reduction in activity (e.g., Petrie, Cameron, Ellis, Buick, and Weinman, 2002). Initiatives that shape what people expect to experience when engaging in a particular pattern of behavior may have the ability to increase the likelihood that people are satisfied with the actions they have taken and thus enhance the probability of sustained behavior change. Taking the Next Steps: Enhancing Collaboration Between Theory and Practice Given the critical impact that people’s behavioral choices have on their health and well-being, the need for intervention strategies that can elicit changes in behavior continues unabated. Successfully meeting this challenge depends on advances in both theory and practice. Thus, we need to increase simultaneously the impact of our interventions and the relevance and precision of our theoretical models. Although there is widespread acknowledgment that theory and practice must progress in tandem, linking these efforts has proven to be a formidable challenge (Rothman, 2004; Suls and Rothman, 2004). One essential strategy is to improve the ways in which basic and applied behavioral scientists communicate with each other. In particular, the two broad groups of investigators must recognize and value the outcomes that can arise from close collaboration between theory and practice. On the intervention side, it is critical not only that intervention strategies be predicated on theoretical principles but also that their impact be assessed in a manner that can, in turn, inform theory. Investigators need to be able to articulate not only why an intervention will be effective but also how it will affect people’s behavior. At the same time, theorists must specify the predicted relations in their models to a sufficient degree that the models can inform decisions regarding intervention design. Because interventions focus primarily on altering the status of a particular construct (e.g., perceptions of personal risk), it would be helpful if theorists elaborated more on the processes by which people formulate their beliefs. In this regard, social cognitive theory (Bandura, 1997) serves as an exemplary model as it clearly articulates the determinants of self-efficacy. By actively promoting the reciprocal relation between theory and practice, the

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When I’m 64 information gleaned from intervention activities should lead to improvements in the quality and precision of our theoretical models—advances that, in turn, should enhance the development of new, innovative intervention strategies. In the present context, efforts to apply current conceptualizations of behavioral decision making to older adults highlight the limits of our knowledge in this area. In particular, the less that is known about the processes underlying a given phenomenon, the more difficult it is to draw firm predictions about how or whether the effect might generalize new populations or situations. For instance, the applicability of message framing as a tool to motivate behavior change among older adults is constrained by limitations in our understanding of the processes that underlie its impact. The ability to specify the processes that underlie a particular intervention strategy also provides investigators with a framework within which to compare different types of intervention protocols. For example, an evaluation of a tailoring intervention might reveal not only that it was effective but also that its effectiveness could be attributed to its ability to heighten people’s confidence that they could change their behavior. Although tailoring might prove to be an effective way to raise people’s sense of personal efficacy, it is also quite possible that other intervention methods such as those that involve procedural or structural modification might prove to be a more effective way to target people’s feelings of efficacy and, in turn, motivate behavior change. In the absence of any clear information about the underlying processes, it can prove to be quite difficult to compare and evaluate different intervention protocols. If we are to successfully develop methods that can be used to motivate behavioral changes in older adults, investigators must embrace the value of a research program that addresses two distinct goals. The program must include activities that specify and detect both the psychological processes that regulate people’s behavioral decisions and those that involve the implementation and evaluation of intervention strategies. It is hoped that the next generation of research in this area will be designed to meet these challenges. REFERENCES Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. Ajzen, I., and Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall. Apanovitch, A.M., McCarthy, D., and Salovey, P. (2003). Using message framing to motivate HIV-testing among low-income, ethnic minority women. Health Psychology, 22, 60-67. Aronson, E. (1999). The power of self-persuasion. American Psychologist, 54, 875-884.

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