Human behavior plays a central role in the maintenance of health, and the prevention of disease. With an eye to lowering the substantial morbidity and mortality associated with health-related behavior, health professionals have turned to models of behavior change to guide the development of strategies that foster self-protective action, reduce behaviors that increase health risk, and facilitate effective adaptation to and coping with illness. Several decades of concerted effort to promote health and decrease risk through individual behavior change have produced successes, failures, and lessons learned.
This chapter addresses the models of behavior change and interventions designed to influence individual behaviors. It continues to explore the influence of family relationships on the management and outcomes of chronic disease.
MODELS OF BEHAVIOR CHANGE
Human behavior plays a central role in the maintenance of health and the prevention of disease. Growing evidence suggests that effective programs to change individual health behavior require a multifaceted approach to helping people adopt, change, and maintain behavior. For example, strategies for establishing healthy eating habits in children and adolescents might be quite ineffective for changing maladaptive eating behaviors—that is, when they are used to substitute one pattern for another—in the same population (e.g., Jeffery et al., 2000). Similarly, maintaining a particular behavior over time might require different strategies than will establishing that behavior in the first place (e.g., Ockene et al., 2000). Models of behavior change have been developed to guide strategies to promote healthy behaviors and facilitate effective adaptation to and coping with illness. Several models for individual behavior change are reviewed here.
Learning and Conditioning
Among the oldest, most widely researched, and yet most often misunderstood models of individual behavior applied to behavior change are those that deal with fundamental associative or classical conditioning and the related models of operant conditioning. Classical conditioning, pioneered by Pavlov, modifies behavior by repeatedly pairing a neutral stimulus with an unconditioned stimulus that elicits the desired response. Operant-conditioning builds on classical conditioning and focuses on the hypothesis that the frequency of a behavior is determined by its consequences (or reinforcements; Skinner, 1938). Although learning theory has been criticized for treating behavior in simplistic and mechanistic stimulus response terms, modern learning theory addresses complex components, including environmental cues and contexts, memory, expectancies, and underlying neurological processes related to learning (Rescorla, 1988). As Kehoe and Macrae (1998) note, today classical conditioning integrates cognition, brain science, associative learning, and adaptive behavior.
Classical conditioning introduced concepts that have been particularly important in the design of health-related interventions, such as reinforcement, stimulus—response relationships, modeling, cues to action, and expectancies. However, given the particular difficulty in maintaining behavior changes, the relapse of behaviors that have been eliminated (or “extinguished”) by an intervention is of particular interest. Relapse of extinguished behaviors is a major problem in health-related behavior change interventions, especially those that target alcohol use, smoking, and diet (Dimeff and Marlatt, 1998; Marlatt and George, 1998; Perri et al., 1992; Wadden et al., 1998). Extinction initially was conceptualized as a process in which original learning, and therefore behavior, was unlearned or destroyed. That is, it was assumed that extinguished behavior would no longer be elicited by the environmental cues that originally evoked it. However, extensive research shows that extinction does not involve unlearning, but rather new learning that does not overwrite the original learning. Furthermore, the physical environment and social context in which extinction takes place, as well as such internal states as emotions, drug-related states, and time, will influence the process of extinction (Bouton, 1998, 2000).
Those findings have important implications for health-related behavior change. Specifically, the effectiveness of an intervention to reduce or eliminate a health risk, such as cigarette-smoking, will be limited to the extent that it is bound to the context in which it is delivered. As noted by Bouton (2000, p. 58), “the reformed smoker who once habitually smoked in a particular setting at work, or under the influence of a particular drug or alcohol, or in the presence of negative affect will be ready to lapse when cigarettes are made available in one of those contexts again. We now think of extinction as inherently context-specific, with the term ‘context' being broadly defined.”
One important implication of those findings is the importance of eliciting extinction in different contexts, including various physical environments, times, and emotional states. For example, extinction trials that are more widely spaced and in separate locations are more likely to be effective than core sessions that occur within short periods or in similar physical circumstances. Behavior change efforts should recognize the possible influence of contextual cues, identify the cues that might be involved, and help people avoid (or cope with) the contexts connected with the original health-compromising behavior, whether physical environments, interpersonal relationships, or negative emotional states. The learning of the new behavior (or extinction of the old) should take place in the contexts in which the person will need it the most.
There is another important difference between original learning and extinction, namely, that original learning of a behavior readily generalizes across contexts, whereas extinction does not (Bouton, 2000, p. 61):
[F]irst-learned things seem much more likely to generalize over place and time. One implication of this is that if we really want to reduce cardiovascular risk, we should arrange a world in which healthy behaviors are the first things, not the second things, learned. One way of thinking about research on behavior change is that the organism seems to treat the second thing learned about a stimulus as a kind of exception to a rule. It is as if the learning and memory system is organized with a default assumption that the first-learned thing is correct, and everything else is conditional on the current context, place, or time.
That perspective provides support for the importance of preventive interventions that promote health-enhancing behaviors, as opposed to interventions designed to treat or change health-compromising behaviors.
The evidence that extinction depends on context is but one of several important results from basic research on learning and conditioning with important implications for explaining health-related behavior change. Closer ties between intervention research and basic learning theory and research could contribute to what O'Donohue (1998) called “third-generation behavior therapy,” behavioral interventions that are informed by recent developments in learning theory and other fields of basic behavioral science.
Cognitive Social Learning
Cognitive social-learning theory (e.g., Bandura, 1977, 1986, 1997) proposes that reinforcements are not the sole determinants of behavior, but that behavior changes with observations of others. According to cognitive social-learning theory, the most important prerequisite for behavior change is a person's sense of self-efficacy or the conviction that one is able successfully to execute the behavior required to produce the desired outcome. People can feel susceptible to an illness, expect to benefit if they change their behavior, and perceive their social environment as encouraging the change, but if they lack a belief that they can indeed change, their efforts are not likely to succeed. Substantial empirical evidence suggests that self-efficacy beliefs (and the related concept of optimism) are reliable predictors of behavior, and that they mediate the effects of intervention on behavior change, including a number of health-related behaviors (e.g., Bandura et al., 1987; Ewart, 1995; Kaplan et al., 1994; Scheier et al., 1989; Wiedenfeld et al., 1990). A growing body of literature supports the importance of self-efficacy in initiation and maintenance of behavioral change (Bandura, 1977, 1986; Marlatt and Gordon, 1985; Strecher et al., 1986).
Self-regulation is a concept that derives from cognitive social learning theory (see Bandura, 1986; Baumeister et al., 1998; Carver and Scheier, 1998; Compas et al., 1999; Eisenberg et al., 1997), and it includes what many people call “will power.” Self-regulation includes cognitive and behavioral processes that involve the initiation, termination, delay, modulation, modification, or redirection of a person's emotions, thoughts, behaviors, physiological responses, or environment (Compas et al., 1999). Self-regulation can be critical in such health-protective and health-maintaining behaviors as eating a healthy diet, engaging in regular exercise, and managing stress. Conversely, the failure or breakdown of self-regulatory efforts can be crucial in some risky behaviors, such as smoking, poor dietary management, and a sedentary lifestyle.
Although much research supports the utility of Social Learning Theory, limitations have been noted. It is difficult to evaluate the efficacy of theory-based interventions because the studies have involved only small numbers of subjects and the intervention designs have been very complex. In addition it is difficult to quantify and measure the conceptual elements of Social Learning Theory: self-efficacy, influence of observational learning, and emotional arousal.
Health Belief Model
One of the earliest theoretical models developed for understanding health behaviors was the health belief model (HBM; Hochbaum, 1958). The model was developed in the 1950s to explain why people did not engage in behaviors to prevent or detect disease early. It integrates elements of operant-conditioning and Cognitive Theory. Operant-conditioning theory focused on the hypothesis that the frequency of a behavior is determined by its consequences while Cognitive Theory gave more emphasis to expectations to explain behavior. For example, the desire to avoid becoming ill is a value, and belief that a specific health behavior can prevent an illness is an expectancy. Perceived susceptibility is the perception of personal risk of developing a particular condition, and it involves a subjective evaluation of risk rather than a rigorously derived level of risk. Perceived severity is the degree to which the person attributes negative medical, clinical, or social consequences to being diagnosed with an illness. Together, perceived susceptibility and perceived severity provide motivation for reducing or eliminating such threats. The type of action taken depends on perceived benefits (beliefs about the effectiveness of different actions) and perceived barriers (potential negative aspects of particular actions). People are thought to weigh an action's effectiveness in reducing a health threat against possible negative outcomes associated with that action.
The HBM has been applied, among other things, to influenza inoculation, screening for Tay-Sachs disease, exercise programs, nutrition programs, and smoking cessation (Strecher and Rosenstock, 1997). An important contribution of the model is the recognition that prevention requires people to take action in the absence of illness. This continues to be useful, for example, in explaining women's reluctance to perform breast self-examination or obtain mammograms (Rimer, 1990). The limitations of the HBM are reviewed by Janz and Becker (1984). Perhaps the most critical of these is the lack of predictive value for some of its central tenets. For example, the perceived severity of a risk does not reliably predict protective health behaviors (Rimer, 1990). Moreover, the HBM is more descriptive than explanatory and does not presuppose or imply a strategy for change (Rosenstock and Kirscht, 1974). The predictive utility of the HBM and its applicability to behavior change can be improved by adding variables, such as self-efficacy, or by integrating it with other models.
Theory of Reasoned Action
The Theory of Reasoned Action was first proposed by Ajzen and Fishbein (1980) to predict an individual's intention to engage in a behavior at a specific time and place. The theory was intended to explain virtually all behaviors over which people have the ability to exert self-control. Factors that influence behavioral choices are mediated through the variable of behavioral intent. In order to maximize the predictive ability of an intention to perform a specific behavior, it is critical that measures of the intent closely reflect the measures of the behavior, corresponding in terms of action, target, context, and time.
Behavioral intentions are influenced by the attitude about the likelihood that the behavior will have the expected outcome and the subjective evaluation of the risks and benefits of that outcome. The predictive power of the model depends significantly on the identification of most or all of the salient outcomes associated with a given behavior for any particular target population.
Stages-of-Change Model/Transtheoretical Model
Beginning with the first formulation of the HBM, Hochbaum (1958) assessed the “readiness” of adults to participate in screening. The inclusion of beliefs about susceptibility to illness and the personal benefits of screening was seen as an essential element in “readiness.” The concept was expanded into more elaborate models, such as the Transtheoretical Model (also known as the Stages-of-Change Model) first proposed by Prochaska and DiClemente (1983). This model characterizes the continuum of steps that people take toward change and includes the activities or processes to move people from one stage to another. The earliest stage of behavior change starts with moving from being uninterested, unaware, or unwilling to change (precontemplation) to considering a change (contemplation). This is followed by the decision to take action (preparation) and the first steps toward the behavioral change (action). With determined action, the requirement for maintenance and relapses are recognized as part of the process. In addition to these temporal stages, the Transtheoretical Model encompassed the concepts of decision criteria, self-efficacy, and change processes (consciousness-raising, relief from negative emotions associated with unhealthy behavior, self-reevaluation, environmental reevaluation, committing to change, seeking support, substituting healthier alternative behaviors, contingency management, stimulus control, and recognizing supportive social norms; Prochaska et al., 1997). The Transtheoretical Model has been influential in research on smoking and was recently extended to other health risk behaviors (Prochaska et al., 1994).
The theoretical validity of the Stages-of-Change Model for behavior change is a matter of controversy (Budd and Rollnick, 1997; Sutton, 1996). Although early cross-sectional studies provided support for the theory (DiClemente et al., 1991; Fava et al., 1995), recent longitudinal studies did not support the Transtheoretical Model (Herzog et al., 1999; Sutton, 1996). Furthermore, multivariate analyses of several behavioral predictors demonstrate that the stages are weak predictors of cessation (Farkas et al., 1996; Pierce et al., 1998). Variables from cognitive social learning—such as outcome expectancy, self-efficacy, and behavioral self-control—appear to be better predictors of change than are the stages and associated processes (Bandura, 1997; Herzog et al., 1999).
Despite questions about its theoretical validity, the model has contributed to the recognition that most potential recipients of health-related behavior change efforts are not motivated to change. Population surveys show 80% of the target group in the “precontemplation” or “contemplation” stages. That result draws attention to the potential of approaches that increase motivation for health promotion and illness prevention. The development of innovative motivational programs to encourage less interested people to consider healthier lifestyles represents a new direction in health and behavior change (e.g., Miller and Rollnick, 1995).
Social Action Theory
One important example of a model that attempts to integrate individual psychological processes with social contextual factors is Social-Action Theory (Ewart, 1991), which builds on Social Cognitive-Learning Theory, models of self-regulation, processes of social interdependence and social interaction, and underlying biological processes to predict health-protective behaviors and outcomes (Ewart, 1991). It views the person as influenced by environmental contexts or settings to which he or she brings a particular temperament and biological context. Thus, a person's capacity to practice healthy eating habits and to exercise is influenced by access to health-enhancing foods and safe places to exercise and by internal goal structures, self-efficacy beliefs, and problem-solving skills.
In Social-Action Theory, biology and social and environmental contexts determine the success of interventions to promote individual behavior change (Ewart, 1991). Most behavioral research, however, has focused on individual strategies to facilitate desired changes, and less is known about how social and other contextual factors can be mobilized to promote behavior change. Social-Action Theory specifies mediating mechanisms that link organizational structures to personal health and incorporates key concepts from the earlier theoretical models, including self-efficacy and outcome expectancies. Some applications of social-action theory focus on the mechanisms and maintenance of behavior change (Ewart, 1990), again placing the focus on the influence of context on individual behavior.
Social-Action Theory provides a framework for multilevel approaches to health promotion and illness prevention. It offers a theoretical rationale for intervening in health policy and for creating environments that are conducive to self-protective choices. It provides an approach for defining public health goals and modifiable social and personal influences that can be used to encourage individual health-related behavior change. Social-Action Theory fosters interdisciplinary collaborations by incorporating and coordinating the perspectives of the biological, epidemiologic, social, and behavioral sciences.
Summary of Models for Behavioral Change
Strong conceptual models are available to guide the development, implementation, and evaluation of health-related behavior change interventions. While the models are useful constructs for thinking about behavioral change, they each have their limitations and each addresses different behavioral attributes. Furthermore, only rarely have these models been appropriately applied to interventions (IOM, 2001). The IOM report (2001) suggests that contextual and individual factors contributing to behavior should be fully surveyed and assessed from the perspective of the various models to gain insights from each as to pathways and barriers. It is prudent for researchers to look beyond specific models and to draw on general concepts of behavior change.
Recent advances in research on classical conditioning and self-regulation have important implications for establishing, reducing, and maintaining health-related behaviors. Establishing a stronger link with basic behavioral science promises to provide important directions for the continued development of health-related behavior interventions. Social Action Theory provides a promising way to integrate elements of several broad models in an attempt to account for health-related behavior change.
INTERVENTIONS TARGETED AT INDIVIDUALS
In response to mounting evidence that behaviors, such as cigarette-smoking and consumption of high-fat diets, are risk factors for chronic diseases, several studies target interventions for medically at-risk individuals. Some landmark clinical trials, such as the Multiple Risk Factor Intervention Trial (MRFIT Research Group, 1982), have contributed to our understanding of risk factors in disease. Trials also focus on psychosocial interventions after disease onset to improve treatment adherence and medical outcomes. Other interventions arise from the concept of population-attributable risk, which measures the amount of disease in the population that can be attributed to a given exposure (Marmot, 1994). A large number of people exposed to a small risk might generate more cases than will a small number exposed to a high risk (Rose, 1992), so that when risk is widely distributed in the population, small changes in behavior across an entire population can yield larger improvements in population-attributable risk than would larger changes among a smaller number of highrisk individuals (Marmot, 1994; McKinlay, 1995; Rose, 1992). Both approaches are described below.
Clinical trials such as the Multiple Risk Factor Intervention Trial (MRFIT Research Group, 1982), the Lipid Research Clinics Coronary Primary Prevention Trials (Lipid Research Clinics Program, 1984a,b), and the Lifestyle Heart Trial (Ornish et al., 1990) have provided important contributions to the development of successful interventions and to the current understanding of risk factors for disease. Education and counseling can promote primary prevention measures reducing smoking and choosing a healthy diet. Interventions aimed at secondary prevention behaviors can influence early detection of illness. For instance, willingness to self-examine and participate in screening procedures is important for detection and treatment of cancer. Psychosocial interventions can improve people's coping skills and provide emotional support, thereby improving quality of life and medical outcomes among the chronically ill. The role of behavioral interventions for improving adherence to treatment is discussed below. Interventions addressing behavioral and psychosocial risk factors are also briefly reviewed.
Adherence, the match between a patient's behavior and health care advice (Haynes et al., 1979), mediates the effectiveness of treatment recommendations, the scientific evaluation of treatment protocols, and even public health. For example, when treating bacterial infections, some patients stop taking antibiotics when symptoms stop, but before all the targeted bacteria are eradicated, resulting in relapse for the patient and the development of resistant bacteriological strains. Failure to follow medical recommendations for treatment is a common problem that is not without controversy. The term “adherence” has been increasingly used to replace the previous label of “compliance” to convey the patient's active participation in following a treatment regimen, rather than the patient's submission to a provider's directive (Roter et al., 1998). Between 30% and 70% of patients do not adhere effectively to treatment recommendations. Nonadherence to difficult behavioral recommendations, such as smoking cessation or following a restrictive diet, occurs in more than 80% of patients (National Heart, Lung, and Blood Institute [NHLBI], 1998). The reasons are varied: Providers sometimes fail to describe the treatment regimen clearly, resulting in confusion on the part of the patient. Patients may also not fully appreciate the consequences of nonadherence. Some regimens interfere with daily activities, particularly those requiring multiple doses each day, or those with special instructions regarding meals (e.g., take on empty stomach). Side effects, such as hair loss, can be embarrassing; others, such as dry mouth or gastrointestinal problems, can be uncomfortable. Insurance limits on reimbursement for treatments also can affect adherence. Nonadherence is more than failure to take medications as prescribed or to follow other recommendations for health behavior changes. One survey of oncologists (Hoagland et al., 1983) showed that failure to return for recommended outpatient treatments was the most frequent source of nonadherence. Adherence often depends on the nature of treatment. Therapies that are simple or that produce prompt relief of pain or symptoms typically result in high levels of adherence (Dunbar-Jacob et al., 2000). Adherence is usually poor if therapies last a long time, if they are preventive rather than curative, or if they are complicated. Patients who experience psychological problems or substance abuse are less likely to adhere (NHLBI, 1998).
Renewed attention has been given to non-adherence in recent years, led by concerns about the development of multi-drug-resistant tuberculosis (Cohen, 1997) and HIV (Chesney et al., 1999). Multidisciplinary research efforts are developing new self-report assessments of adherence that show significant relationships with biological outcomes. Electronic medication monitors, which are being used increasingly in research, provide more accurate estimates of adherence to medication regimens and suggest that patients overestimate their own adherence (Cramer et al., 1989) and that provider estimates of adherence are not better than chance (Haubrich et al., 1999).
Effective interventions have been developed to improve cooperation in the acute-care setting. For example, adjunctive nonpharmacologic analgesia involving self-hypnosis has been shown in two randomized trials to reduce pain, anxiety, patient-controlled medication use and episodes of hemodynamic instability and to reduce procedure time by 22% (Lang et al., 1996, 2000).
There have been surprisingly few studies of interventions that might enhance adherence (Shumaker et al., 1998). A recent systematic review of randomized trials of interventions to help patients adhere to medications revealed that successful interventions were those that were multifaceted, including such features as more convenient care, information, counseling, reminders, self-monitoring, reinforcements, and other forms of supervision and attention (Haynes et al., 1996). Relatively few studies have evaluated the benefits of interventions that require permanent lifestyle changes. The difficulties in sustaining the cessation of smoking, weight loss, or initiation of exercise are well recognized (Marlatt and George, 1998). The relapse rates, however, are not uniform for these behaviors. The rate of relapse from treatment of serious obesity is more than 90%, leading to revision of goals in its treatment to more modest but sustainable weight loss (Wadden et al., 1999), but half of those who stop smoking will remain completely abstinent for 2 years (Spiegel et al., 1993).
Addressing Psychosocial Risk Factors
As described in Chapter 2, depression is a risk factor for mortality from multiple causes. Furthermore, “distressed high utilizers” of medical care are substantially more likely to suffer from psychiatric disorders, including major depression, generalized anxiety disorder, and substance abuse (Von Korff et al., 1992). Poor adjustment to illness can increase the cost of medical care by as much as 75% (Browne et al., 1990). These problems make the development of programmatic interventions to provide psychosocial support both humane and expedient. Thus, providing appropriate psychotherapeutic and psychopharmacologic treatment for them not only can improve coping and reduce patient discomfort but also can make the delivery of medical care more efficient. The contributions of clinical behavioral and psychosocial interventions to diabetes, cancer, and heart disease are explored briefly. A recent chapter (Baum, 2000) from an Institute of Medicine (IOM) report provides further discussion of the influence of stress in cancer and cardiovascular disease.
Diabetes Mellitus. To reduce the incidence and severity of complications of diabetes, including vascular, coronary, renal, and neurologic disease, blood sugar must be carefully regulated. Adherence to medication regimens, glucose testing, exercise, and diet influences medical outcomes. Research indicates that coping skills and family stresses influence the management of diabetes (see Glasgow et al., 1999, for a review). Furthermore, depression is a serious co-occurring problem in diabetes (Glasgow et al., 1999; Jacobson, 1996; Lustman et al., 1992) that can affect glycemic control (Lustman et al., 2000). Several reviews and meta-analyses have demonstrated the effectiveness of educational approaches aimed at increasing knowledge, control, and self-efficacy among diabetics (Brown 1990, 1992, 1999; Hampson et al., 2000; Padgett et al., 1988). On the other hand, education did not consistently improve metabolic control (Grey, 2000). Psychosocial interventions (for example, enhancing coping skills and peer support) seem to provide greater success in improving both metabolic outcomes and quality of life (Grey, 2000; Grey et al., 1999). Educational interventions could be more effective when used in combination with behavioral psychosocial interventions (e.g., Brown, 1999, Clement, 1995). However, concerns exist that the beneficial changes might not be sustained long beyond the intervention (Brown, 1992).
Cancer. There is evidence that psychosocial interventions can improve quality of life, psychological adjustment, health status, and survival of cancer patients (see reviews by Andersen, 1992; Blake-Mortimer et al., 1999; Compas et al., 1998; Fawzy et al., 1995; Helgeson and Cohen, 1996; Meyer and Mark, 1995, Montazeri et al., 1998). A meta-analysis of 116 studies on the effects of psychoeducational care provided to adult cancer patients concludes that interventions affect anxiety, depression, and mood (Devine and Westlake, 1995). Another analysis of 45 psychosocial interventions showed statistically significant emotional benefits in adults (Meyer and Mark, 1995). Various interventions have been tested, including teaching specific methods of coping with the stress of cancer (Edgar et al., 1992; Fawzy et al., 1990; Telch and Telch, 1986), providing education and information (Manne et al., 1994), and providing social support and facilitating expression of emotions (Spiegel and Classen, 1995; Spiegel et al., 1981, 1989). Their relative effectiveness has been difficult to assess (Devine and Westlake, 1995; Fawzy, 1999; Meyer and Mark, 1995).
Some evidence supports the effectiveness of psychosocial interventions to improve medical outcomes and prolong survival (for reviews, see Creagan, 1999; Greer, 1999). Spiegel and colleagues (1998) found that psychosocial group treatment in metastatic cancer patients doubled survival time to an average of 18 months, from the point of randomization. A study by Richardson et al. (1990) showed that lymphoma and leukemia patients assigned to 1 of 3 educational and home-visiting supportive interventions had significantly longer survival than did patients allocated to routine care (control). The effect was sustained even when differences in medication adherence were controlled. In a study of 125 patients with metastatic melanoma, quality of life was found to be associated with duration of survival (Butow et al., 1999). A randomized controlled trial of 6 weeks of intensive group therapy aimed at developing active coping among 80 malignant melanoma patients significantly reduced mortality at 6-year follow-up (Fawzy et al., 1993). The mechanisms through which psychosocial interventions exert their effect is unknown, but it has been suggested that depression exacerbates symptoms (Evans et al., 1999) and that psychotherapy augments the immune response (for reviews, see Kiecolt-Glaser and Glaser, 1999; Spiegel et al., 1998). These results should be explored further and confirmed.
Although the potential of psychosocial intervention to slow the progression of cancer is promising, the literature is limited and several reports refute the hypothesis (for example, Cunningham et al., 1998; Gellert et al., 1993; Ilnyckyj et al., 1994; Linn et al., 1982; Morgenstern et al., 1984). One meta-analysis (Meyer and Mark, 1995) showed a small effect of psychosocial interventions on medical measures that was not statistically significant. Carefully designed studies are needed to clarify this issue.
Coronary Disease. Primary prevention can reduce the incidence of coronary disease (Chapter 3), but psychosocial interventions also can affect morbidity and mortality in at-risk patients. As described in Chapter 2, several studies have recently demonstrated that social isolation, depression, and type A personality traits—especially hostility—can mediate medical outcomes for patients with coronary disease (also see Rozanski et al., 1999; Williams and Littman, 1996). Evidence is increasing that psychosocial interventions after the onset of disease are effective supplements to routine cardiac care. One recent meta-analysis of 37 studies (Dusseldorp et al., 1999) found that psychoeducational programs reduced mortality by 34% and decreased recurrence of myocardial infarction by 29%. Another meta-analysis (Linden et al., 1996) of 23 clinical trials on coronary artery disease reported a similar significant reduction in morbidity and mortality with psychosocial interventions, especially during the first 2 years.
The interventions included in the analysis by Linden et al. (1996) were diverse but consistently positive. Powell and Thoresen (1988) found that counseling designed to reduce hostility and impatience typical in type A people reduced mortality among acute myocardial infarction patients who had less serious cardiac disease. Ornish et al. (1990) demonstrated that an intensive program of group support, stress management, moderate exercise, smoking cessation, and strict vegetarian diet resulted in a measurable reversal of coronary artery disease. Blumenthal et al. (1997a) found that stress management in coronary artery disease patients significantly reduced the subsequent risk of a cardiac event.
Many studies support psychosocial interventions, but other evaluations show no significant effects. Black et al. (1998) provided psychiatric evaluation and behavioral therapy to 380 cardiac patients and reported decreases in depression but not in rehospitalization rates. A clinical trial by Jones and West (1996) revealed no benefit from relaxation training and stress management. In contrast to the results of an earlier study that indicated that simply monitoring for psychological distress in cardiac patients reduced mortality (Frasure-Smith and Prince 1985), a follow-up study (Frasure-Smith et al., 1997) could not replicate the results and recommended against implementing such programs into routine care. The discrepancies among studies probably result from methodologic limitations, including small study sizes, varied interventions (some of which may not be behaviorally effective), indefinite clinical endpoints, and lack of intention-to-treat analyses. To address these limitations, a national multicentered clinical trial has been initiated (Enhancing Recovery in Coronary Heart Disease [ENRICHD], 1999), to determine the effects of psychosocial interventions on 3000 patients. Interventions will target depression and social isolation in patients with a recently diagnosed myocardial infarction. Endpoints will include mortality, nonfatal infarctions, cardiovascular hospitalizations, and changes in risk factor profiles (Blumenthal, 1997b; ENRICHD, 2000).
Addressing Behavioral Risk Factors
The primary care physician is in an optimal position to provide advice on healthy behaviors. Many studies have indicated that counseling by a primary care physician can be effective in changing the behaviors of patients but the approaches are varied. Several fundamental characteristics contribute to the effectiveness of these interventions. Recognition of differing patient needs is one fundamental characteristic of practices dedicated to enhancing beneficial behavior change. Some patients need only visual cues as a reminder to ask for help with smoking cessation, to obtain timely mammograms, to exercise more regularly, or to follow up for management of depression (Pronk and O'Connor, 1997; Rogers, 1995). Others respond more favorably to printed materials, coaching via telephone-based counseling, or classes. Some patients cannot change health-related behavior without one-on-one structured education and counseling supplemented by frequent reinforcement from their physicians. Multiple modalities of support are used in the practices that are most heavily committed to encouraging beneficial behavior change and that target individual patients (Oxman et al., 1995). Similarly, multiple methods are necessary to communicate with physicians and other clinical staff to encourage behavior change on their part that reinforces patient behavior change (Green et al., 1988; Greer, 1988). Chart reminders, computerized medical records with automated protocols, and physician and other staff education have all shown promise (Buntinx et al., 1993; Davis et al., 1995).
A second beneficial approach to behavioral intervention is the organizational leadership to decide to focus on a problem and devote energy and resources to it (Greer, 1988; Hammer and Champy, 1993; Oxman et al., 1995; Patti and Resneck, 1972; Rossi, 1992). A clinical practice that has an enhanced capacity to change patients' health-related behavior has leadership able to relate to the physician staff members and to engender enough emotional, internal, political, and economic support to drive behavior-change efforts toward success (Davis and Taylor-Vaisey, 1997). That presents a major challenge because most clinical practices are organized to deliver acute care rather than to change patients' behavior to prevent illness (Walsh and McPhee, 1992). Engaging busy practices to reach into new health promotion endeavors for which there is little economic reward is challenging, no matter how dedicated the leadership and clinical staff (Fishman et al., 1997). Rising to such a challenge tests the leadership and organizational adaptability of any practice that also must comply with innumerable legal, business, and clinical regulations and requirements. Many variables peculiar to a given practice—such as physician attitudes, local competitive pressures, staff morale, and socioeconomic needs of the patient population—can enhance or inhibit change in the practice toward a greater focus on prevention or other innovation (Crabtree et al., 1998). For example, changing practice patterns to document brief but consistent efforts to encourage smoking cessation initially proved beyond the reach of many good practices (Kottke et al., 1988).
Health care systems and practices in the United States are moving toward use of methods to increase the predictable quality and efficiency of medical care (Berwick, 1989; Carlin et al., 1996; Grimshaw and Russell, 1993, 1994; McDonald, 1976; Miller et al., 1998; Mittman et al., 1992). Current quality improvement models propose a more active and continuous method of identifying problems and testing interventions. This is a change from traditional methods of identifying faulty practices and practitioners by investigating clinical cases that have unsatisfactory outcomes (Balas et al., 1996). Rather than a list of poorly performing health providers, the result of a continuous improvement model can be a testable hypothesis that outlines a series of steps for caring for patients with specific problems that can result in measurable improvement in outcomes or processes (Crabtree et al., 1998; McBride et al., 1993; Solberg et al., 1997).
A simplified continuous-improvement model has four steps: (1) design a guideline with active participation of clinicians; (2) implement the guideline; (3) measure the outcomes; (4) study the outcomes, compared with what was expected, and redesign as needed (Mosser and Sakowski, 1996). Working with two large managed-care organizations, Solberg et al. (1998) conducted an RCT to assess the effectiveness of a process to help primary care clinics develop systems for the delivery of preventive services. Previous research showed that even when external technical assistance succeeded in increasing preventive services, the services declined to baseline when the assistance ended (Magnan et al., 1998). To build practices' internal capacity to initiate and manage change, the IMPROVE (Improving Prevention through Organization, Vision, and Empowerment) trial (Solberg et al., 1998) used organizational development approaches, such as continuous quality improvement and process consultation. The intervention facilitated the formation of continuous improvement teams that instituted prevention processes (Solberg et al., 1995). However, the extent to which patients in the intervention practices are actually receiving more preventive services has not been determined.
Clinical practice guidelines are formal statements that provide guidance to health care practitioners regarding specific clinical circumstances. Ideally, guidelines are based on the best available scientific evidence and clinical judgment. They should lead to the best patient outcomes and should steer clinicians away from unnecessary or extravagant interventions. The appeal of practice guidelines has led to remarkable growth in their development. An editorial in Lancet (Fletcher and Fletcher, 1998) describes beleaguered clinicians faced with more than 2000 sets of guidelines. However, guidelines lack standards of quality and have been developed by fragmented groups that might have different goals, motivations, and capabilities. Furthermore, guidelines are often outdated by the time they are released, often ignore patient preferences (Eddy, 1990), and often emphasize peer consensus rather than outcome evidence.
Many focused interventions to encourage health-related behavior change would benefit from population databases that keep track of patients' medical histories, behaviors, and attitudes. One fundamental factor for practice-based interventions is the availability of a database that defines the population served, accepts searches of health parameters or disease targets, and allows tracking of measurable changes in the defined health behavior or health outcome. An ideal database can link names, addresses, telephone numbers, diagnoses, pharmacy use, and other use of health care visits and educational resources (Redding et al., 1993). An example of a practice-based intervention that requires such a database is improving the diet and exercise patterns of poorly controlled diabetes mellitus patients and tracking their metabolic-outcome measurements for improvement (Thomson O'Brien et al., 1999). However, there has been little systematic research on the benefits of such databases in the United States. Practice databases are available primarily in large, well-organized practices and in staff model health-maintenance organizations whose physicians or other providers are paid salaries. They are not often used in smaller group practices because of the cost and personnel required to maintain them. Their use also raises major legal and ethical issues of privacy and confidentiality that have been the topic of several reviews (Gostin, 1997; Sweeney, 1997; Woodward, 1997).
Need for Research on Practice
Much of the information in this section is based on evidence from uncontrolled trials and one-time interventions in large multispecialty group practices and well-organized staff model health maintenance organizations. Some of the information is based on the opinions of experts. Little of what is known about dissemination is based on well-controlled trials wherein a practice-level intervention is compared with reasonably controlled and parallel practice. Only occasional studies (e.g., Cohen et al., 1999; Cooper et al., 1997) have tried to assess interventions such as screening practices at the level of primary care physicians. Little research funding in the past has been applied to systematic evaluation of fundamental (systemic) changes in clinical practices that might support health-enhancing behavior change in defined populations. Future efforts should test various hypotheses that would encourage experimentation and practice-level interventions.
This section examines a sampling of studies that are representative of population-based intervention trials in a community, worksite, or school that are focused on changing individual behavior for primary prevention of disease. Given the importance of shifting the population distribution of disease risk, the effectiveness of interventions must be measured among the entire population for whom the intervention is intended, and not only among program participants. In addition, because of the importance of accounting for the influence of secular trends and for other factors not associated with the intervention that could affect behavior change, the studies discussed here included intervention and control conditions alike. Finally, to narrow the field of potential studies, a focus was given to those interventions conducted in the United States that targeted primary prevention of cancer or coronary heart disease, although the committee recognizes that considerable progress has been made using community interventions to address other public health problems.
Several early population-based community studies, including the Minnesota Heart Health Program (MHHP), the Stanford Five City Project (SFCP), and the Pawtucket Heart Health Program (PHHP), tracked changes in morbidity and mortality. For subsequent intervention studies, however, funding did not permit following participants long enough or in sufficient numbers to determine long-term costs and consequences of the interventions for survival, quality of life, or disease incidence. Instead, subsequent population-based intervention research rests on prior evidence linking behavioral outcomes to health benefits, such as reductions in morbidity and mortality (Chapter 3). Thus, for most population-based trials, behavior change is the primary outcome. The behaviors examined include dietary changes, tobacco use, and physical activity.
Large-scale studies. Two early studies targeting cardiovascular disease prevention set the stage for population-based community intervention trials: the North Karelia Project (Puska et al., 1983) and the 1977 Stanford Three Community Study (Farquhar et al., 1977; Fortmann et al., 1981). Although the North Karelia Study was not done in the United States, it is included here because of its importance as a groundbreaking study of community intervention trials. The North Karelia Project grew out of that community's concern about having the highest heart attack risk world-wide (Blackburn, 1983; Keys, 1970; Verschuren et al., 1995). Results of a community-wide intervention implemented in North Karelia were compared with a reference area in eastern Finland. After 10 years, the net effects among middle-aged males included significant reductions in smoking, mean serum cholesterol concentrations, mean systolic blood pressure, and mean diastolic blood pressure; significant declines in mean systolic and diastolic blood pressure were observed among women (Puska et al., 1983). The study set the stage for community-wide intervention studies in the United States, the first of which was the Stanford Three Community Study (SHDPP). Initiated in 1972, that study demonstrated the feasibility and potential effectiveness of mass-media-based educational campaigns combined with intensive instruction of individuals in group or home classes directed at entire communities (Farquhar et al., 1977; Fortmann et al., 1981; Maccoby and Solomon, 1981). Significant reductions in cholesterol and saturated fat were reported at the conclusion of the intervention and were sustained during a 1-year maintenance period (Fortmann et al., 1981).
In the late 1970s, three large community-wide intervention trials were funded by the National Heart, Lung, and Blood Institute: SFCP (Farquhar et al., 1990), MHHP (Luepker et al., 1994), and PHHP (Carleton et al., 1995). All targeted change in risk factors for coronary heart disease (CHD), including high blood pressure, elevated blood cholesterol, cigarette-smoking, and obesity. None was randomized; rather, communities were matched to optimize comparability of study conditions (Murray, 1995). The multiple-risk-factor intervention trials varied in length from 5 to 7 years, and they tracked changes in morbidity and mortality beyond the intervention period. The interventions were aimed at raising public awareness of CHD risk factors through media education. Other objectives were to change risk-related behaviors through public education in schools, worksites, and other community organizations; educate health professionals; and initiate environmental change programs, such as labeling of foods sold in grocery stores and restaurants. For SFCP, significant effects were observed in blood cholesterol, smoking, and systolic and diastolic blood pressure; and decreases in risk—shown in composite risk factor indices— were significantly larger in the intervention than in the comparison communities (Farquhar et al., 1990). At the 3-year follow-up, the possibility was suggested of sustaining at least some observed outcomes, although the magnitude of the long-term effects was small (Winkleby et al., 1996). Fewer significant results were observed in MHHP and PHHP. MHHP reported significant effects for smoking prevalence among women and for physical activity (Luepker et al., 1994). PHHP resulted in smaller increases in body mass index in the intervention communities than in the controls; no other significant results were reported (Carleton et al., 1995).
In 1989, the National Cancer Institute (NCI), building on methods used in cardiovascular disease studies, launched the Community Intervention Trial (COMMIT) for Smoking Cessation (COMMIT, 1991). The trial used 11 matched pairs of communities across North America, and it was designed to test the effectiveness of a multifaceted, 4-year community intervention to encourage smokers, particularly heavy smokers, to achieve and maintain cessation (COMMIT, 1991, 1995a). A significant effect was observed among light-to-moderate smokers, and it appeared to be greater among a less-educated subgroup of participants (COMMIT, 1995a). There was no effect among heavy smokers (COMMIT, 1995a).
Although not a randomized, controlled intervention trial, the American Stop Smoking Intervention Study (ASSIST) was a large-scale, 7-year demonstration project building on randomized community-wide intervention trials. The intervention was implemented in 17 states through a partnership among NCI, the American Cancer Society, state health departments, and other organizations. The primary goal was to reduce smoking prevalence and cigarette consumption. To assess the results, investigators compared data from ASSIST and non-ASSIST states. Comprehensive tobacco control programs emphasized policy interventions, including indoor air, pollution, youth access, advertising, and tobacco taxes, as well as mass-media interventions and program services such as cessation classes (Manley et al., 1997a,b). Per capita consumption of cigarettes was comparable in ASSIST and non-ASSIST states before the beginning of the 1993 intervention. By 1996, smokers in ASSIST states were smoking 7% fewer cigarettes per capita. The intervention also included guidelines for raising cigarette excise taxes as a means of reducing consumption. Inflation-adjusted cigarette prices were nearly identical in both groups of states before 1993. Although the tobacco industry reduced prices during this period, in 1994 the average price was more than $0.12/pack higher in intervention than in control states (Manley et al., 1997a,b).
Small-scale studies. Several recent community-wide studies have borrowed principles from the early large cardiovascular disease prevention trials, but they have been implemented on a smaller scale and with smaller budgets. It might be difficult for such studies to achieve the necessary intensity and reach to show significant intervention effects. The Bootheel Heart Health Project, for example, was conducted in a six-county area in southeastern Missouri (Brownson et al., 1996). This rural area has the largest African American population in Missouri, and it is characterized by high rates of poverty and low education levels. The intervention was tailored to the community through the participation of local coalitions, each establishing its own priorities for intervention. The researchers conducted population-based cross-sectional surveys before and after the intervention to compare results in communities where there were coalitions against results from communities that did not have coalitions. Physical inactivity decreased and the prevalence of self-reported cholesterol screening increased in communities with active coalitions. Differences observed in self-reported weight gain were in the right direction, although not statistically significant. No differences were found for fruit and vegetable consumption or for smoking prevalence. Similar results were observed in the Heart to Heart Project, which reported decreases in dietary fat consumption and increases in cholesterol screening (Croft et al., 1994; Heath et al., 1995).
A more targeted definition of “community” was used in the Physical Activity for Risk Reduction (PARR) Project, conducted with residents of rental communities administered by the housing authority in Birmingham, Alabama (Lewis et al., 1993). PARR targeted physical inactivity among African Americans of low socioeconomic status who were residents of public housing, and it was evaluated in eight communities randomly assigned to intervention through a staged design (n=6) and control (n=2). Baseline assessments confirmed the low levels of physical activity in the target population. Despite using community residents as interviewers, however, there were substantial problems in obtaining participation from randomly selected households, particularly in the initial survey. Pre- and post-intervention physical activity scores were not significantly different in the intervention and control communities.
In a move toward ensuring greater community input, the Kaiser Family Foundation's Community Health Promotion Grant Program (CHPGP) offered communities substantial flexibility in developing program targets that were responsive to local needs and priorities. This program was designed to foster community health promotion efforts targeting cardiovascular disease, cancer, substance abuse, adolescent pregnancy, and injuries (Tarlov et al., 1987; Wagner et al., 1991). Comparisons among 11 intervention and 11 control communities, however, indicated little evidence of positive changes in the outcomes selected by the intervention communities (Wagner et al., 1991) That project illustrates the challenges of interpreting results when the intervention is not standardized across communities; the lack of consistency in the results was due at least in part to differences in the interventions (Cheadle et al., 1995).
Conclusions. The ability to draw conclusions on the basis of these trials is limited by their designs and methods. Only a few included an adequate number of communities to provide sufficient statistical power. Most studies used random samples for project evaluation, but the response rates varied widely, and few studies had adequate response rates. Most studies used nonvalidated self-report of behaviors as outcome measures. Few studies reported the results of process tracking. The assignment of multiple communities is expensive, and ultimately might require multicenter collaborations, such as that used in the COMMIT (1991) study.
In the past 15 years, an increasing number of health promotion studies have been conducted in workplaces and worksites are now considered important channels for delivery of interventions to reduce chronic disease among adult populations (Abrams, 1991; Abrams et al., 1994; Fielding, 1984; Heimendinger et al., 1990; Tilley et al., 1999). The U.S. Department of Health and Human Services conducted two National Surveys of Worksite Health Promotion Activities, one in 1985 and another in 1992 (USDHHS, 1985, 1992). In 1985, 66% of private worksites with 50 or more employees offered health promotion activities. This increased to 81% by 1992 (McGinnis, 1993). Many worksite trials have targeted cancer and cardiovascular disease risk factors either as discrete trials (Byers et al., 1995; Emmons et al., 1999; Glasgow et al., 1995, 1997; Heirich et al., 1993; Jeffery et al., 1993, 1994; Salina et al., 1994; Sorensen et al., 1992, 1996, 1999; Tilley et al., 1999) or within the context of larger community-wide trials (Glasgow et al., 1996; Sorensen et al., 1993). Most of those studies used individual behaviors as the primary outcome. Intervention methods included strategies to incorporate employee input and a variety of activities based on tested behavior change theories. The reported interventions ranged from more intensive group behavioral counseling sessions of varying duration and number and supervised exercise prescriptions to less intense interventions with a wider reach, such as mailed self-help materials and newsletters. Several of the programs achieved statistically significant effects on smoking cessation (Jeffery et al., 1993; Salina et al., 1994; Sorensen et al., 1993, 1996). Jeffery and colleagues (1994) reported that where worksites changed from unrestrictive to restrictive tobacco control policies during the course of the intervention, there were significant reductions in smoking among employees. In the Working Well trial (Sorensen et al., 1996), no trialwide differences in smoking cessation were observed, but one of the four participating study centers reported significant effects for 6-month abstinence rates. That study center was unique in that it integrated an occupational health focus into the health promotion intervention, thereby targeting a key concern of workers in the participating worksites (Sorensen et al., 1995).
The Working Healthy Project (WHP), a multi-risk-factor study that was part of the Working Well trial, showed significant increases in self-reported exercise behavior in the intervention group as compared with controls (Emmons et al., 2000). Dishman and colleagues (1998) reviewed 26 studies of worksite interventions targeting physical activity, including those that did and did not use the worksite as the unit of analysis. The poor scientific quality of the studies precludes judgment about whether such interventions can increase physical activity, and the researchers concluded that there is a need for studies that use valid designs and methods.
Over the past two decades, extensive attention has been paid to health promotion and disease prevention among youth, particularly in schools. Schools provide an established setting in the community for reaching children and their families (Best, 1989; Perry et al., 1989; Stone and Perry, 1990; Stone et al., 1989). Several reviews summarize school-based smoking, physical activity, and nutrition education intervention trials from the 1980s and 1990s (Best, 1989; Contento et al., 1992; Flay et al., 1985; Stone et al., 1998). Some of those trials and analyses are reviewed here.
Reviews of youth smoking-control interventions generally conclude that social influence interventions can curb smoking onset (Best et al., 1988) although recent meta-analyses yielded a somewhat guarded picture of their efficacy. The first (Bruvold, 1993) found that effect sizes were largest for interventions that focus on social reinforcement, moderate for those with either a developmental orientation or a focus on increasing social norms, and small for interventions with a health information focus. A second meta-analysis (Rooney and Murray, 1996) reviewed 90 studies of school-based smoking prevention programs published in 1974–1991. They concluded that the influence of peer or social programs could be improved if they were delivered early in the transition from elementary to middle school (e.g., 6th grade), if same-age peer leaders were used, if they were part of a multicomponent health program, and if booster sessions were included in subsequent years. Although the average effects were small, with a reduction in smoking of as little as 5%, and only 20–30% under optimal conditions, school-based programs showed promise. The Life Skills Training (LST) program, a school-based intervention that teaches personal coping and social skills, has shown promising effects in both immediate and longer-term outcomes (Botvin et al., 1995). Dusenbury and Falco (1997) reported that the results of the 10 published studies of the LST program showed reductions up to 50% to 75% in tobacco, alcohol, and marijuana use at post-test, and a 6-year follow-up of over 4,000 participants indicated a 44% reduction in tobacco use.
Recognition of multilevel influences on smoking in youths has led to multifaceted interventions, including schoolwide media campaigns in combination with individual approaches. Such programs have been effective in reducing smoking prevalence throughout secondary school (Perry et al., 1992). A trial focusing on high-risk youths tested a combined program of mass media and standard school smoking prevention programs. This program was implemented in two schools; two other schools (the controls) had only the school program. At the 2-year followup, prevalence of smoking in the schools was compared; participants in the combined program showed a significantly lower prevalence of smoking than the controls (Flynn et al., 1997).
A recent school-based smoking prevention program (Peterson et al., 2000), The Hutchinson Smoking Prevention Project (HSPP), randomly assigned 40 school districts to experimental or control groups. Students were followed from grade 3 until 2 years after high school. An enhanced social-influence approach to the intervention was used, containing the 15 “essential elements” for school-based tobacco prevention developed by an NCI Advisory Panel (explained in Flay, 1985; Glynn, 1989). No significant differences between the control and experimental groups were evident at grade 12 or 2 years after high school suggesting that the intervention had little, if any, impact. The highly controlled, and well-designed nature of the study, including the high follow-up rates, high compliance with the intervention, the maintenance of the randomization by the school districts, well-matched control and treatment groups, and appropriate statistical analysis, strongly suggest that the failure to achieve change was a result of a failed intervention and not poor methodology. This conclusion implies that future interventions need to take a different approach, critically rethinking the interactions of biological, behavioral, and psychosocial risk factors at social and cultural contexts.
A review of the literature of school-based physical activity intervention research in 1980s and 1990s (Stone et al., 1998) found that the work was based on multiple theoretical approaches and incorporated simultaneous multicomponent interventions. In general, the studies found significant intervention effects for student knowledge and for psychosocial factors related to physical activity. Significant positive behavior changes were less common, but they were demonstrated in several studies (Dale et al., 1998; Homel et al., 1981; Killen et al., 1988; Leslie et al., 1998; Luepker et al., 1996; McKenzie et al., 1996; Sallis et al., 1999; Tell and Vellar, 1987). Two studies that conducted long-term follow-up found sustained significant differences up to 12 years after the intervention (Luepker et al., 1996; McKenzie et al., 1996; Tell and Vellar, 1987). The more extensive interventions typically had better results (Stone et al., 1998).
Most youth intervention programs to enhance physical activity have been conducted in school environments, typically through the physical education programs in elementary schools. The Child and Adolescent Trial of Cardiovascular Health (CATCH), a multicenter randomized trial for grades 3–5 involving 5,100 students in 96 schools, developed an intensive, teacher-based curriculum for enhancing health behaviors, including physical activity (Luepker et al., 1996). The program demonstrated significant differences in vigorous physical activity between experimental and control schools (Luepker et al., 1996); the differences were maintained three years after the intervention ended in the 5th grade (Stone et al., 1998).
Several school-based trials targeted dietary behaviors and found significant differences in knowledge, attitudes, and behavior change between intervention and control schools. Two exemplary programs are the Class of 1989 Study as part of the Minnesota Heart Health Program for 6th-12th graders (Kelder et al., 1994) and CATCH for 3rd–5th graders (Luepker et al., 1996; Perry et al., 1992). Both studies involved school-based interventions with large samples assessed for a long duration. Both interventions had beneficial effects on diet and eating habits (Nader et al., 1999); however, CATCH did not produce effects on physiological measures related to cardiovascular disease. In a review of interventions to promote healthy dietary behavior in children and adolescents, Perry et al. (1997) concluded that school-based nutrition education programs have been effective in improving aspects of children's eating behaviors, with positive effects also observed in physiological outcomes such as serum cholesterol.
Lessons from Behavioral Intervention Studies
There is clear evidence of efficacy of interventions to establish health-protective or health-enhancing behaviors, such as diet and physical activity; to reduce health-risk behaviors, such as smoking; and to facilitate adaptation to chronic illness, including cancer and heart disease. Yet the behavior changes frequently are difficult to maintain, which poses an important challenge to the field. The limited maintenance of behavioral change seen in initial intervention efforts may be due to the failure to take into account the contextual factors that allow relapse. Advances will require the practical application of new research on the role of contributing contextual factors that include intrapersonal, interpersonal, environmental, and temporal variables. A second challenge is the effective translation of trials to real-world settings. Generalization of effective interventions will require an expansion of the assessment of intervention outcomes delivered in diverse settings. Community-wide and organization-wide interventions have shown varied success. The findings are marred by poor designs and methods. In general, however, the interventions that were more broadly based and multifaceted were more likely to be effective. These challenges are not confined to advances in individual behavior change. As later chapters will reflect, similar challenges apply to all levels of interventions.
FAMILIES AND HEALTH
Another framework for examining health-related behavior change focuses on the family. The good influence of supportive family relationships is widely accepted in the scientific community (Broadhead et al., 1983; House et al., 1988; Uchino et al., 1996). Family relationships have greater emotional intensity than do most other social relationships, and research suggests that there is a substantive, positive association between the specific bonds within families and chronic-disease management and outcomes (Primomo et al., 1990).
The report defines a family as a group of intimates with strong emotional bonds (identification, attachment, loyalty, reciprocity, and solidarity) and a history and future as a group (Gilliss et al., 1989; Ransom and Vandervoort, 1973). In the United States, the incidence of a “traditional” family of a married couple with children has decreased from 40% of households in 1970 to only 25% in 1996 (US Bureau of the Census, 1998). Considering the family as the setting of disease management requires a definition of the family that is narrow enough to be useful in intervention but broad enough to include the multiple forms that families take in contemporary society. In this context, family members generally live together or close to one another. Our definition of family is not constrained by the number, configuration, sex, sexual orientation, age, or ethnicity of members (Doherty and Campbell, 1988; Holder et al., 1998). It assumes only three characteristics of family relationships: they persist over time, they are emotionally intense, and they involve high levels of intimacy in day-to-day life.
That definition sets family relationships apart from other social relationships that provide “social support.” It identifies the family as a unique setting with powerful continuing relationships that assume levels of complexity and organization that go beyond the individual people involved. Family members create a shared social reality that is linked to health (Kleinman et al., 1978; Reiss, 1981), and it is in this environment that most disease management takes place—whether by the patient alone or with other family members (Ell, 1996).
Family and Disease Management
Chronic disease is a long-term stressor for patient and family members alike. The nature and intensity of this chronic stress has three important determinants. The first is the magnitude of the change required of the patient and family members—in their daily activities and in the way they relate to one another (Rolland, 1984). The second determinant is the capacity of the patient, within the circumstances of the family and its approach to life, to make these changes. Parents, spouses, and other family members are assumed to be the primary source of support, and their ability to meet the needs of the patient is often confounded by the distress that illness generates in other family members (Baider et al., 1996; Boss et al., 1990). Distressed household members are less able to provide support and also might need help themselves (Helgeson, 1994). Third, the availability of medical assistance and community resources for support of people with chronic disease can mitigate or exacerbate the stress of illness.
Secure and supportive close personal relationships help patients and other family members regulate the emotional distress that disease can engender (Saarni and Crowley, 1990; Schmoldt, 1989; Wyke and Ford, 1992). Conflicted family relationships, however, can interfere with regulation of emotion (Fiscella et al., 1997; Levenson and Gottman, 1983; Levenson et al., 1994). The body's homeostatic and allostatic regulatory systems connect emotional experience to the physiologic stress response (McEwen, 1998; Sapolsky et al., 1986), and the resulting changes in hormonal, immunologic, and neurochemical systems can influence the outcomes of chronic disease (Kiecolt-Glaser et al., 1997).
Family and Behavioral Risk Factors
Behavior also defines the influence of family relationships on chronic disease. Stable, secure, and mutual family relationships enhance consistent disease management behavior by permitting a sharing of the burdens associated with disease. Such relationships enhance joint “ownership” of disease, which often includes a partitioning of disease management responsibilities among the patients and others and reduces patients' emotional and behavioral burdens. A family-focused approach is likely to maximize intervention effectiveness, whether or not family members other than the patient are directly involved. For example, at the simplest level, patient-focused interventions to alter diet might be only minimally effective if the patient's spouse shops for food and prepares meals (Cousins et al., 1992).
INTERVENTIONS TARGETED AT FAMILY INTERACTIONS
Given the importance of family relationships, it is surprising that they have not been addressed more systematically and extensively in intervention research on the management of chronic disease. Table 5–1 indicates the relative amount of family-focused intervention research for several chronic diseases. Most family-based clinical-intervention research has concerned chronic diseases of childhood and adolescence (e.g., insulin-dependent diabetes, asthma). Family-focused intervention studies of dementia in the elderly (especially Alzheimer's disease) are increasing, but relatively less attention has been directed to family-focused interventions for diseases of adulthood. For example, of the diseases with the highest cost to the United States health-care system—cardiovascular disease, chronic obstructive pulmonary disease, asthma, and non-insulin-dependent diabetes—the latter two have been the subject of very little family-focused intervention research (Campbell and Patterson, 1995).
Interventions for Children and Adolescents with Chronic Disease
An organizational framework of family-focused interventions is helpful for comparing outcomes across several diseases. Three categories are used here and defined in Table 5–2: psychoeducational interventions to provide information about the disease and methods for its management by multiple family members, modified psychoeducational interventions to strengthen and improve family relationship quality and functioning, and family therapy. These are described below with illustrative examples.
This is the most common type of family-focused intervention. It aims to increase family members' understanding of the disease and its management and to improve their capacity for management of the disease, including early recognition of the stress-induced changes occasioned by managing disease in the family setting and more effective adaptations that involve asking for help, reconfiguring expectations, reappropriating roles, and so on. Education and behavioral methods predominate. Parents and siblings of ill children are common targets for psychoeducational interventions, as are caregivers and their ailing adult relatives. A few interventions of this type targeted the families of adult patients. Secondary prevention—attempts to prevent recurrence, progression, or mortality among those with a disease—is the goal of most psychoeducational interventions.
For example, psychoeducational interventions for insulin-dependent diabetes have included groups for parents that run concurrently with groups for the children who are patients (McNabb et al., 1994; Mendez and Belendez, 1997; Thomas-Dobersen et al., 1993). Each of those randomized trials enrolled samples of 11–37 families with diabetic children. They included 6–14 weekly sessions aimed at adherence to treatment and coping with illness. No differences in metabolic control or self-care were found. However, improvement in disease knowledge and body image, decrease in barriers to adherence, and decrease in daily hassles were documented in the intervention groups.
There have been two well-controlled randomized trials of psychoeducational interventions for children with asthma. Tal et al. (1990) studied an intervention with 28 children and their families and found that patients in the intervention group took more responsibility for daily health care and demonstrated greater independence in the family than did patients in the control group. Medical outcomes were not assessed. Clark and colleagues (1984) randomly assigned 274 individuals to six 1-hour group sessions or to treatment as usual. One year later, the intervention group had fewer emergency room visits and better grades in school than did the control group.
One well-controlled study of a family-focused intervention for cystic fibrosis has been published (Bartholomew et al., 1997). It involved separate psychoeducational groups for parents, young children, school-age children, and adolescents, and it focused on increasing knowledge about cystic fibrosis, self-efficacy, self-management, and quality of life. Compared with controls, there were improvements in knowledge in all groups, improvements in self-efficacy in parents and children (but not adolescents), better management of the condition, and better health status of the children with cystic fibrosis in the first year after the intervention (Bartholomew et al., 1997).
There has been relatively little research on family interventions for childhood cancer. Two randomized trials of psychoeducational interventions focused on improving parental coping with the stress of the illness (Hoekstra-Weebers et al., 1998; Jay and Elliott, 1990). Each failed to show reduced patient distress, although the parents were less distressed in the study by Jay and Elliott (1990).
There have been two studies of family-focused interventions for children with congenital heart disease. Both were randomized trials of interventions that focused on decreasing distress related to cardiac procedures and their aftermath. Campbell and colleagues (1986) studied adjustment in children after they received cardiac catheterization. The study group, who received supportive counseling and stress management training, made a better adjustment than did a control group. The interventions were delivered separately for children and parents. Parental reports of stress were lower in the intervention group, but there were no differences in parents' or children's fear, affect, or cooperation during hospitalization.
A subsequent randomized intervention study of patients preparing for cardiac surgery compared individualized information and coping-skills training with routine presurgical information for primary caregivers and their children before and during hospitalization (Campbell et al., 1995). Children in the treatment group had a higher level of well-being with a gradual increase over time. There were no significant treatment effects on medical outcome measures or on children's or caregivers' anxiety. However, treatment group caregivers perceived themselves as substantially more competent to care for the children, and the intervention favorably affected the children's behavior in the hospital, at home, and at school.
Kaslow and colleagues (Collins et al., 1997; Kaslow and Brown, 1995; Kaslow et al., 1997) studied a family-focused intervention for sickle-cell disease. They developed a psychoeducational family intervention aimed at improving relationships in families of children and adolescents (aged 7–16 years) with sickle-cell disease. The intervention consisted of a culturally and developmentally sensitive manualized treatment in six sessions tailored to the needs and competencies of each child and family. It included education about the disease, provision of skills for enhancing stress management and coping, and methods to improve family and peer relationships. African American health-care counselors conducted all the interventions. Preliminary post-intervention results revealed that, compared with families randomly assigned to the usual control condition (n=20), youth and their caretakers assigned to the experimental condition (n=20) showed greater increases in knowledge about sickle cell disease. That change is important because treatment compliance has been found to be greater among patients who have a better understanding of the disease (Dunbar-Jacob et al., 2000). No reductions in psychological symptoms were noted for the children or caretakers at the end of the intervention. Six-month follow-up data were collected but have not been reported.
Interventions Affecting Family Relationship Quality and Functioning
This type of intervention focuses on family relationships and includes various methods to foster emotional expressiveness, reduce social isolation, prevent disease from dominating family life, help deal with loss, promote collaboration among family members, improve empathy, deal with stigma, reinforce developmental family roles, and resolve intrafamily conflict. Psychoeducation is often combined with family relationship interventions, which might be more effective than psychoeducation alone for secondary prevention. These interventions also appear to be helpful for tertiary prevention (reducing the duration and effects of established complications of disease and comorbid psychological disorders) when used selectively for reversing noncompliance with recommended treatments and for relieving subclinical psychological distress engendered by disease management burdens in patients and family members.
Children with newly diagnosed diabetes have been studied in individual families in intensive full-time programs (Sundelin et al., 1996), intensive home follow-up and crisis intervention (Galatzer et al., 1982), and weekly outpatient sessions with boosters at 6 and 12 months (Delamater et al., 1990). Sample sizes were 38, 223, and 36 families, respectively. Concentrations of hemoglobin A1C were used to monitor efficacy of the first and third of those interventions, with one confirmatory and one nonconfirmatory result, respectively. Improvement in family relationship quality was documented in the first two studies, and better treatment compliance was shown in the second. The interventions took place shortly after diagnosis, in contrast with the psychoeducational interventions described earlier, which were done at 1–5 years after diagnosis. The stronger effects of family relationship interventions on disease outcomes therefore might be attributable to timing rather than to type of intervention.
Interventions that affect family relationship quality and functioning in childhood asthma include those shown in a study by Hughes and colleagues (1991), in which 89 families had monthly sessions focused on supportive parent/child relationships and asthma management. As a group, children of the families randomly assigned to the intervention had better airway-function scores, fewer school absences, and fewer hospitalizations during the year after the intervention. The differences disappeared after that, so continuing reinforcement of family intervention might be needed to maintain the benefits.
In contrast with the need for chronic management of the medical problem in the diseases of childhood and adolescence described above, childhood cancer treatment is usually accompanied by a period of distress and disorganization followed by a general improvement in psychological health and family functioning as treatment demands lessen. Treatments can be traumatic, and symptoms of posttraumatic stress in patients and family members have been recognized as long-term sequelae. A study by Kazak and colleagues (1998) demonstrated effectiveness with a cognitive/ behavioral, family-oriented intervention for parents that focused on decreasing the child's distress related to painful medical procedures. For this disease-management situation, that study supports an intervention that goes beyond psychoeducation alone to a relationship-focused intervention targeting the parents' interaction with the child.
The use of multifamily groups is an efficient way to provide psychoeducation and family-relationship-functioning interventions, although some families might not work optimally in a multifamily setting (Gonzalez et al., 1989). A randomized study of 32 families compared six weekly multifamily group sessions with usual care for children with insulin-dependent diabetes (Satin et al., 1989). The intervention focused on metabolic control and psychosocial function of the child and on family function. The children in the intervention group had better hemoglobin A1C, attitudes, and self-care behavior than did those in the control group.
Wamboldt and Levin (1995) reported the results of a multifamily group intervention delivered to 72 children with asthma and their families. The intervention lasted 5 hours, and was delivered on 2 consecutive days for 17 groups of families. It included education, support, and group discussion. Preintervention and postintervention assessments revealed an increase in family members' reports of feeling understood by others, feeling open to help with the illness, and having a stronger belief that it is helpful for family members to share their feeling about the illness with each other. No medical outcomes or long-term follow-up were reported. However, increased sense of support and increased belief that communication is useful are associated with improved disease outcomes in descriptive studies, so this intervention potentially could improve asthma management and outcome.
Another multifamily group intervention pilot project included 19 families of survivors of childhood cancer (Kazak et al., 1999). A combination of cognitive/behavioral approaches and manualized family therapy was used during four sessions delivered on 1 day. Success in decreasing symptoms of posttraumatic stress and anxiety in parents, siblings, and survivors was documented with preintervention and postintervention assessment.
Family therapy has been used for secondary prevention, but it might be most appropriate for tertiary prevention. Screening programs can detect families in which serious psychological dysfunction predates the disease or complications of the chronic disease already constitute disorders to target for more intensive intervention.
A randomized trial of family therapy (n=25) in the setting of poorly controlled childhood diabetes (Ryden et al., 1993) documented better metabolic control, improved behavioral symptoms, and better patient/ family relations associated with intervention. A pilot study of family problem-solving therapy in 14 families documented efficacy in increasing adherence to treatment for diabetes and in decreasing family conflict (Auslander, 1993).
Family therapy was associated with greater clinical improvement than was medical management alone in a small (n=17) randomized study of children with severe asthma (Gustafsson et al., 1986). A larger study of children with severe asthma (n=32) by Lask and Matthew (1979) documented lower daily wheezing scores and lower thoracic gas volumes in the intervention group than in the control group.
None of those studies had well-validated measures of family functioning, so it is unclear whether the interventions exerted their effects by improving family functioning. Indeed, inasmuch as both interventions also taught asthma management strategies, it is possible that the interventions did not directly change family functioning. In contrast, improvement in family communication and parental discipline was documented with pre intervention and post intervention assessments of an intensive rehabilitation program that used family therapy and education for children with severe asthma (Weinstein et al., 1992).
Interventions for Adults and the Elderly with Chronic Disease
Chronic diseases of adulthood have received the least systematic attention with respect to family-focused interventions. There have been, however, many clinical reports and descriptive studies of informal interventions to assist families struggling with chronic disease. Most have been unsystematic and uncontrolled, but they indicate a growing recognition by the clinical community of the need to address family issues and of the utility of basing intervention in a family context. Although reported studies tend to use family-based intervention methods similar to those outlined for children and adolescents (psychoeducation and multifamily groups), there are so few studies on adults that categorizing them by method of intervention is not useful. The studies also appear somewhat scattered among several chronic diseases. Therefore, a few illustrative studies are reviewed below.
Pilot work on a family intervention during hospitalization of 56 stroke patients, which was followed up with telephone tracking for 3 months, showed decreased perceived criticism among family members and decreased use of health care, compared with standard medical follow-up. The study also showed a trend for improved performance of daily activities by stroke patients in the intervention group (Bishop et al., 1997; see also Glass et al., 2000).
Several descriptive studies indicate that spouse support is crucial for optimal risk reduction and management of existing heart disease. Family studies show that counseling about diet and exercise delivered to both marital partners reduced risk for both spouses over time (Family Heart Study Group, 1994; Knutsen and Knutsen, 1991). When interventions focused on changing the amount of support provided by the spouse, mixed results were found (Campbell and Patterson, 1995).
There have been five randomized, controlled trials of partner-supported smoking cessation. Although the data from descriptive studies show that such support is highly correlated with reduced smoking, most of the interventions were ineffective in increasing support or in reducing smoking (Campbell and Patterson, 1995). A similar pattern emerged in reducing the weight of obese patients (Black et al., 1990; Pearce et al., 1981). Involvement of spouses in cardiac-rehabilitation programs also has produced mixed results (Campbell and Patterson, 1995). It has been suggested that interventions to decrease patient smoking or promote weight loss might have failed for a couple of reasons: lack of integration of the social support intervention with other aspects of the treatment program (nicotine replacement, extrafamilial stressors, and so on) and failure to use a customized approach to address the complexities of married relationships.
A major study of a family intervention for hypertension (Morisky et al., 1983, 1985) is an exception to the trend of mixed findings. It demonstrated that a single home visit to develop a personalized plan for family medication and lifestyle changes resulted in improved patient compliance, reduced blood pressure, and reduced mortality. The responsiveness of hypertension to relationship-alter ing interventions was demonstrated by Ewart and colleagues (1984). Communication training of couples in which one member had hypertension decreased both blood pressure and hostility during discussion of a conflict compared to couples receiving treatment as usual.
Interventions for Caregivers
Caregivers face tremendous stress. It has been observed that family members caring for someone with progressive dementia are characterized by impaired wound-healing compared with controls matched for age and family income (Kiecolt-Glaser et al., 1995, 1998). Interventions to address this stress are challenging issues in health and behavior and deserves greater attention.
A series of reports by Mittleman et al. (1993, 1995, 1996) explored family-based interventions for the elderly with dementia. They demonstrated that an intervention with multiple members of the patient's family substantially improved caregiver well-being. The intervention also resulted in a significant delay in institutionalization of the demented elderly, compared with controls who received usual care. The intervention consisted of six psychoeducational sessions with individual families followed by long-term availability of the healthcare counselors to the family members. More studies on the effectiveness of interventions for caregivers are warranted.
Lessons from Family Intervention Studies
Family-focused interventions have multiple aims. They are intended to help family members agree on and collaborate in a program of disease management in ways that are consistent with their beliefs and operational style. Helping family members manage stress by preventing the disease from dominating family life and sacrificing normal developmental and personal goals is also important. Interventions help the family deal with the losses that chronic disease can create, mobilize the family's natural support system, provide education and support for family members, and reduce the social isolation and the resulting anxiety and depression accompanying disease management. Finally, interventions can provide new structure for the family, with adjustments of roles and expectations if needed, to ensure optimal patient self-care.
Studies of interventions that involve family members are more common in diseases of children than in diseases of adults. Most family-focused intervention studies of children's diseases have been conducted with groups of patients or groups of family members, not with whole families. Interventions have focused more on adherence to treatment and metabolic control than on family-behavior variables or family processes themselves. However, a few studies demonstrate improved family relationships associated with better health outcomes (for example, Delamater et al., 1990; Ryden et al., 1993; Satin et al., 1989). Results suggest that interventions focused on family relationships, rather than education of individual family members, might be a fruitful approach to improving the management of various chronic diseases in children and adolescents. Research on family interventions for management of chronic diseases in adults and the elderly is in its infancy and careful attention is needed to develop realistic and systematic trials. The available data suggest that recognizing and attending to the family relationship context adds considerably to improving the health and well-being of patients and family members struggling with the management of a chronic disease.
An increase in complexity occurs when a shift is made from addressing only the individual patient to addressing the broader social context in which the patient lives and in which the disease is managed. Accompanying the increased complexity, however, is the need to increase the flexibility of intervention approaches, the number of methods of intervention, and the number of risk and protective factors that are targeted for change so that the potential for effecting substantive improvement can be realized.
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