REVIEW

BEHAVIORAL RESEARCH ON DIABETES AT THE OREGON RESEARCH INSTITUTE

1,2

Russell E. Glasgow, Ph.D., D e b o r a h J. Toobert, Ph.D., Sarah E. H a m p s o n , Ph.D., and Willetta Wilson, Ph.D. Oregon Research Institute

ABSTRACT

INTRODUCTION

This article overviews the scope and progression of research on behavioral aspects of diabetes over the past decade at the Oregon Research Institute. Our research team has investigated several topics including: (a) conceptual models of self-management; (b) social learning factors associated with regimen adherence; (c) individual and group-based interventions to enhance diabetes self-management; (d) rates and determinants of participation in diabetes education; (e) determinants of glycemic control; and (f) patient models (beliefs) about diabetes and its treatment. We have employed a social learning theory approach to diabetes management, and over the past decade have come to adopt a broader public health perspective that addresses environmental influences on diabetes self-management at multiple levels (e.g. family, health care system, community). This approach has led us to conclude that increased attention should be devoted to the most prevalent types of diabetes, to the behavioral issues that create the most difficulty for the greatest number of patients, and to the social environment in which patients live and diabetes management education takes place. Our research focus has evolved over time and currently emphasizes: (a) assessment and tailoring of intervention based upon the patient's perspective; (b) patient-provider interactions; and (c) brief, low-cost, and system-wide interventions that can be implemented in medical office settings. Lessons learned from this research, the potential disseminability of our findings, and future directions are summarized.

Non-insulin-dependent diabetes mellitus ( N I D D M ) is one of the most c o m m o n chronic diseases in the United States (1,2). Based upon 1993 estimates, about 6.5 million people (about 5.2%) have diagnosed diabetes ( 1). O f these cases, approximately 90-95% have N ] D D M (2,3). It is estimated that almost as many people have undiagnosed diabetes (1,2). Furthermore, since N I D D M is strongly related to age, the prevalence o f diabetes will likely increase as our population ages. Medical care costs for persons with diabetes far exceed those of persons without diabetes (4). Diabetes is a m a j o r risk factor for heart disease, retinopathy, neuropathy, peripheral vascular circulation problems, stroke, and amputation (3). N I D D M is a significant public health problem (5) and is likely to be even more so in the future. The purpose of this article, as a contribution to the "Examples o f Programmatic Research in Behavioral Medicine" section, is to describe the development, influences on, and progression o f the diabetes self-management research program at Oregon Research Institute (ORD. Behavioral research on diabetes has been conducted at O R I for over ten years. The majority o f our research has focused on self-management behaviors among persons with N I D D M . We define diabetes self-management or self-care as behaviors performed to manage one's diabetes (6). Diabetes self-management behaviors include dietary and physical activity patterns, foot care, glucose testing, medication taking, and other behaviors that must be performed on a regular basis and in specific temporal relations to each other. These behaviors have a substantial influence on the outcome of diabetes (7-10). We view diabetes as a model for other chronic diseases, and feel that the behavioral context is such that findings on diabetes self-care should be generalizable to other chronic

(Ann Behav Med

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diseases. Research indicates that the incidence of long-term complications o f diabetes--such as kidney damage, cardiovascular problems, and blindness--is reduced by maintaining blood glucose levels that are close to normal. The recently completed Diabetes Control and Complications Trial conclusively demonstrated that the lower the average I-thAt,, the less likelihood o f developing complications such as retinopathy (7,9). In the intervention condition, there was a direct and "dose-response" relationship between H b A ~ level and probability o f developing retinopathy. The goal o f near-normal blood glucose is accomplished through adherence to an appropriately tailored and complex diabetes regimen. However, unless patients can cope with the multiple, ongoing, and changing challenges to diabetes adherence, intensive management treatment prescriptions will be o f little value. Given the central role o f diabetes self-manage-

Preparation of this manuscript was supported in part by grant #1 R29 HLS0181-01 from the National Heart, Lung, and Blood Institute; grant #5 R01 DK35524-09 from the National Institute of Diabetes and Digestive and Kidney Diseases; and grant #AG08837 from the National Institute on Aging. 2 Appreciation is expressed to our colleagues, Drs. Kevin McCaul, Lorrane Schafer, Matthew Riddle, David Calder, Jane Farmer, Dennis Ary, Anthony Biglan, Peter Lewinsohn, Edward Lichtenstein, Eddie McCauley, John Noel1, Janice Radcliff, and Ms. Jodie Donnelly Perry, R.D., who were instrumental in several of the directions and studies discussed in this manuscript. Any distortions or inappropriate conclusions are solely the responsibility of the authors.

Reprint Address: R. E. Glasgow, Ph.D., Oregon Research Institute, 1715 Franklin Boulevard, Eugene, OR 97403. 9 1995 by The Society of Behavioral Medicine.

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Diabetes Research at O R I ment in accomplishing treatment goals (11,12), we have focused our research on this behavioral aspect of diabetes. Achieving long-term adherence to diabetes regimens remains a challenge for patients, educators, and researchers (1315). Several factors combine to make diabetes self-management particularly difficult. First, as described above, diabetes regimens are complex. Second, component tasks of the regimen are difficuit in and of themselves. For example, following a dietary plan may include complying to high-fiber, low-fat, and reduced caloric levels, as well as eating meals at consistent times. Following exercise prescriptions may include timing physical activity to avoid periods o f low blood glucose levels; testing blood glucose before and after exercise and recording the results; establishing a habit of consistent daily physical activity; and remembering to carry sources o f glucose and diabetic identification for episodes of hypoglycemia. Third, the diabetes regimen is challenging because it is a lifetime regimen. It is much easier to adhere to short-term regimens designed for acute illness than it is to follow those which must be performed for the rest of one's life. Finally, many older persons with diabetes also suffer from other diseases or complications that introduce additional regimen complexities or functional limitations (subjects over 60 years of age in our studies have an average o f three or more chronic diseases such as arthritis, hypertension, or back pain, in addition to diabetes). The impact of this comorbidity on diabetes self-management, patient-provider interactions, and outcomes is only beginning to be studied. G O A L S A N D P U R P O S E S OF O R I R E S E A R C H The ultimate goal of our research is to contribute to the health and well-being of the public, especially older persons with diabetes or other chronic diseases. Toward this end, we seek to identify and to understand, from the perspective o f persons having diabetes, the social--environmental, psychological, economic, and biomedical factors related to diabetes self-management. We further strive to translate the results of our research into efficient, personalized interventions that will help patients to better manage their diabetes on a daily basis. To maximize impact, we use social learning principles and modern technology to develop assessment and treatment procedures that are: (a) feasible to use in medical offices and other settings frequented by patients; Co) applicable to and capable o f reaching a large percentage of persons with diabetes; (c) helpful to both patients and health care providers; and (d) potentially generalizable to self-management of other chronic diseases. M O D E L OF D I A B E T E S M A N A G E M E N T A N D EDUCATION Our work has been guided by a social learning theory based conceptual model o f diabetes management and education that continues to evolve (14,16-18). Although always relying upon social learning theory as its core conceptual basis, the model has become more complex over time and has included successively broader contextual factors [see (14) versus (18) for more details]. Figure 1 outlines our current model, which is intended to serve as a practical guide for focusing attention on important assessment and intervention issues. The model has several implications which will be discussed briefly prior to reviewing our research. The first component in this model, surrounding all other factors, consists of community, social environment, and contextual factors which are rarely con-

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1. C o m m u n i t y a n d Larger Social Context

r 2. Neighborhood and F a m i l y

Context a Resources

Available

4. C_!inic C o n t e x t a Behofs (Personal Models) b Self-Efficacy c Problem-Solving Skills

a. Health Care System Experiences b. Patient-Provider

Interactions:

b, Level of Social Support

-Support

-Listening to Patient -Goal Setting -Consistency

c. Barriers to

Adherence

-Follow-Up

Management

Behaviors

Physiologic Outcomes

a b c, d,

Quality of Life FunCtiomng Patient Satisfaction Health Care Utilizabo

FIGURE 1: A practical working model of diabetes management and education. sidered regarding diabetes education (19,20). These provide the setting or context within which diabetes education and management occurs. Community and social context (component # 1) refers to the extent to which policies, community resources (or lack thereof), media influences (e.g. TV, newspaper), and public opinion support diabetes self-management. For example, consider the context o f clean indoor air laws, worksite smoking restrictions, cigarette taxes, and public awareness o f the effects of cigarette smoking in the 1990s versus the 1950s to gain some perspective on the importance o f these societal or community level factors. Component #2 represents the next level of neighborhood and family influences, including harriers to adherence and degree of social support (or sabotage, or "misguided helping") (21) experienced from a variety o f sources including family, coworkers, neighbors, and friends (22). Component #3, patient characteristics, is the focus of much of the behavioral research on diabetes and will be discussed later. At this point, we would like to comment only on the omission o f one patient characteristic variable--knowledge-from the model. It is not that we consider knowledge unimportant. We feel knowledge, and especially abstract "knowledge transfer" approaches (e.g. how your pancreas works) have been overemphasized in diabetes education (17,23), and Figure 1 is not intended to include every factor, but only the most important ones, and we have found the social cognitive factors listed to be stronger predictors o f self-management than patient knowledge (24). Compared to patient characteristics, medical care factors (component #4) have been under-researched. These factors incorporate the experience that the patient has with the entire health care organization within which diabetes education is delivered. For example, the experience of the patient in the waiting room, the handling of insurance/billing aspects of the visit, as well as the actual patient-provider interaction, all impact medical care and patient satisfaction. Components #4, #5, and #6 together form what have been termed the "cycle of care" medical interactions by Lynda Anderson (25). They consist of the important and reciprocal interrelationships among patient and health care team interactions (component #4); patient self-management behaviors and the

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extent to which patients assume responsibility for these activities (component #5); and short-term physiologic outcomes (component #6). (Note the influence of the social learning theory principles of reciprocal determinism and the inter-relationships among cognitive, behavioral, and environmental factors.) Finally, component #7 in Figure 1 concerns long-term health outcomes and reflects the locus of the major societal costs and benefits of diabetes and its management. As Kaplan (26) has eloquently discussed, the important outcomes of any chronic disease do not end with short-term physiologic outcomes such as HbA~c, but are concerned with patient functioning and quality-of-life, as well as medical care utilization [see also (17)].

Implications of the Model As is obvious from the figure, diabetes self-management is complex. The first important implication of this model and its complexity is that poor adherence is not all the patient's fault-there are multiple determinants of self-management. At the same time, it is not all the provider's fault--as some publications in the patient-provider interaction field seem to imply. Rather, there are a variety of interacting--and sometimes conflicting-factors that influence self-management. There has been more than enough blaming the (frequent) failure of diabetes management on "victims," be they "non-compliant patients" or "insensitive physicians." A second implication is that attention to more facets of the model should produce better outcomes than will use of singlefactor interventions. A corollary of this implication is that longterm improvements are more likely to be achieved if both setting and patient-provider interaction factors are addressed. Stated differently, given what we know about correlates of long-term lifestyle behavior change, it is naive to expect that a single interaction in a physician's office--or even a series of diabetes education meetings--no matter how compelling or well organized, will produce long-term change. These interactions do not occur in a vacuum, and inattention to components #1, #2, and #4 in the model will result in interventions that are not effective in producing long-lasting changes. A third, less obvious, implication of this model is that at each of the background/environmental and cycle of care medical interaction levels there are both low-cost, system-wide actions as well as higher-cost, selective interventions, such as intensive management strategies, that can be implemented to improve self-management [see (18)]. Finally, consideration of the multiple determinants of patient self-management and health outcomes produces greater appreciation of the challenges involved and of potential reasons for the at times unpredictable effects of behavior change efforts. Patients or providers who feel that they have done everything possible, behaved perfectly, or done everything according to the guidelines and yet experience less than desired levels ofglycemic control or functional improvement can become frustrated and demoralized. Appreciation of the model and for the less-thanperfect links among the components in Figure 1 may help to mitigate this frustration and demoralization. REVIEW OF ORI DIABETES RESEARCH Our work in diabetes management and education has evolved to reflect the cumulative findings in behavioral research in diabetes and has had three major foci. One focus includes the identification and evaluation of psychosocial and behavioral

Glasgow et al. factors related to diabetes self-care. A second focus has been the conceptualization and measurement of diabetes selfmanagement. Our final focus has been the development and implementation of interventions to facilitate diabetes selfmanagement. Our work in diabetes commenced in 1982, with the funding of two separate grants by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). At the time these grants were funded, the principal investigators were not aware of each other's work, although both grants were remarkably similar and focused on identifying behavioral factors related to diabetes self-management. One grant was awarded to Anthony Biglan and Willetta Wilson at ORI. The other grant was awarded to Russell Glasgow and Kevin McCaul at the Department of Psychology at North Dakota State University. Upon moving to ORI, Glasgow began collaborating on the existing ORI diabetes grant, and both projects benefitted from the sharing and exchange of conceptual frameworks, measures, and analytic approaches.

Psychosocial Variables Associated with Self-Management The first diabetes research project at ORI was an assessment study that sought to identify, from the patient's perspective, the psychosocial factors related to diabetes self-care and glycemic control. This study was conducted with a community sample of 184 individuals having NIDDM (27). Health beliefs and social support were found to be the most consistent and the strongest predictors of self-care across regimen areas. Additional analyses were conducted with an aggregate sample (N = 208) of 24 individuals with insulin-dependent diabetes mellitus (IDDM) and 184 persons with NIDDM to determine whether type of diabetes was related to level of self-care or to reasons given for low levels of self-care. In general, levels of self-care and reasons given for low levels of self-care were similar for IDDM and NIDDM subsamples, Both groups reported: (a) high levels of self-care for medication-taking and glucose-testing behaviors; and Co) lower levels of self-care for dietary and exercise behaviors. Both samples reported being "too busy" as the most common reason for low levels of self-care across different components of the regimen (27). Ary and associates (28) conducted further analyses of the reasons given for low levels of self-care and of the situations that influenced this behavior. Data for these analyses were from the NIDDM subsample, but were coded from responses to openended questions. Diet and exercise were again found to be the most troublesome regimen areas. "Inappropriate offers of food" was the most frequently reported interpersonal situation that made it difficult to adhere to diet. "Negative physical reactions" (e.g. sickness, chest pain) was the reason most frequently given for low levels of exercise. During this same period of time, Glasgow and colleagues began research into behavioral aspects of diabetes [e.g. (16,29)]. The purpose of these early studies was to identify social learning factors (30,31 ) associated with self-management among 107 individuals with IDDM (24,32). This investigation found barriers to adherence (33), family support (34), and other social learning factors to prospectively predict diabetes self-management (24). Further, these factors were better predictors than were knowledge measures (24), and consistent with social learning theory, diabetes-specific measures of social support were found to be

Diabetes Research at O R I better predictors than were more general, state-of-the-art measures o f family functioning (34). Glasgow and McCaul obtained a competing renewal grant from N I D D K to replicate and extend these findings among persons with N I D D M (35). Space limitations preclude detailed discussion [see (35)], but these studies identified three sets o f factors that were consistently associated with self-care: (a) how often patients encountered environmental, social, and cognitive barriers to adherence (35,36); Co) self-efficacy (37); and (c) degree o f family support for diabetes adherence (38). Our research on barriers to diabetes self-care is summarized separately in a recent chapter by Glasgow (36). Assessment o f personalized barriers has become one o f the cornerstones o f our intervention work, as described later. Although we remain convinced o f the clinical importance o f self-efficacy, our assessments o f this construct (and the related social learning construct o f outcome expectations) have met with mixed success. The paper by Kingery and Glasgow (37) describes the strengths and limitations o f these assessments. Finally, our work in diabetes-specific family s u p p o r t - - i n cluding what Anderson (22) has termed the " d a r k side" o f social support (e.g. interference, nagging, setting poor examples)--is summarized in two publications, one on N I D D M (38) and one on I D D M (34). These studies have found brief diabetes-specific assessments o f family support for regimen adherence to be prospective predictors o f regimen adherence.

Conceptualization and Measurement of Adherence F r o m the outset, diabetes regimen adherence proved to be a complex and problematic construct (6). The classic definitions o f compliance are based upon the extent to which a person's behavior coincides with advice from a health care provider (39). In most settings, however, specific treatment prescriptions are not routinely recorded in medical records. Often, patients report that they have never been given any specific prescriptions, but only general recommendations (e.g. lose weight, get some exercise) or no advice at all. Thus, it is often not possible to compare self-management records or reports to a known standard. In the absence o f such a standard, we felt it most reasonable to report the behaviors that are observed (self-care behavior) and to distinguish such data from the compliance/adherence measures. For this reason, we have referred to our observations as diabetes self-care behaviors or self-management (6,14). A second problem with the construct o f regimen adherence was that the literature tended to report adherence as a unitary construct with the patient being referred to dichotomously as either a "good complier" or " p o o r compiler." Our assessment o f diabetes self-care behavior demonstrated that levels o f selfcare behavior for different aspects o f the regimen were essentially unrelated; that is, an individual might take their medications 95% o f the time, but follow their eating plan only 25% o f the time. These findings have been replicated numerous times by ourselves and others (14,40). Therefore, in reporting level o f self-care behavior, we do so in reference to a specific aspect o f the regimen and avoid using trait labels that imply that adherence is a unitary dimension. We have also found it important to distinguish between the constructs o f self-care and glycemic control. Unfortunately, many clinicians and investigators use these terms interehangeably. Our initial research was designed to identify predictors o f both diabetes self-care behaviors and glycemic control. We success-

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fully identified predictors o f diabetes self-care, but were unable to identify significant predictors o f glycemic control. We have found that self-care behaviors are often only weakly correlated with glycemic control (32). Similar results have been reported by other investigators (41). Spurred on by our collaboration with and the clinical observations o f Dr. Matthew Riddle, Chief o f Endocrinology at Oregon Health Sciences University, we are continuing to investigate this issue. We are hopeful that either stratifying patients into subgroups, or including moderating factors such as duration and severity o f diabetes, medication regimen, etc., in our models m a y help clarify this issue.

Assessing Diabetes Self-Management: The Summary of Diabetes Self-Care Activities Questionnaire: Valid and unbiased indices to assess diabetes self-care are needed to determine if estimates o f self-care vary as a function ofpsychosocial factors, other variables, or if they are an artifact o f one's measurement procedure. There are, however, several complexities in developing tools to measure adherence a n d / o r self-care behavior (14,15,40). These include: (a) comparison o f actual behavior to a known standard; Co) distinguishing between patient error due to skill deficits or misunderstanding versus intentional nonadherence; (c) deciding how to quantify adherence by patients who exceed their prescriptions; and (d) recognizing the relative independence o f different components o f the diabetes regimen. It is surprising, given the pivotal role o f diabetes self-management, that adherence measures are often developed anew for each study. To address these concerns, we recently summarized and reported on the psychometric properties of the S u m m a r y o f Diabetes Self-Care Activities (SDSCA) questionnaire (42) which we have used in several studies. We found support for the reliability and validity o f this brief self-report instrument (12-24 items, depending upon the scope o f the study) to assess both adherence and level o f self-care for each o f four diabetes regimen areas. Despite the difficulties in measuring self-reported adherence, the results o f the three studies which have specifically evaluated the properties o f the SDSCA indicate that its subscales: (a) were moderately stable over time (with the exception o f glucose testing); Co) had a high level o f internal consistency given the brevity of the scale; (c) should not be used to provide a total adherence/level o f self-care score but rather s u m m a r y scores for each regimen area; and (d) showed evidence o f concurrent validity as measured by other, more objective methods. The SDSCA appears to be a brief, practical measure o f change, and the overall pattern o f results provides support for its validity (42). When possible, we r e c o m m e n d use o f a multi-method assessment that combines the SDSCA with other assessment procedures.

More Recent Assessment Work

Problem-Solving:We feel that problem-solving skills are key determinants o f diabetes self-management, and include some form o f problem-solving training as a standard component o f all our interventions. Given the ever-changing set o f barriers to adherence faced by patients, flexible coping and problem-solving skills are considered to be essential to long-term maintenance o f self-care. We have completed a prospective study o f the relationship between problem-solving and self-care (43). We used a structured Diabetes Problem-Solving Interview to assess problem-solving skills used by N I D D M subjects in response to hypothetical situations which might interfere with self-care. These

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problem-solving measures were used to predict self-care at a six-month follow-up. Scores were obtained for both overall problem-solving skill ratings and frequency of use of different types of coping strategies (behavioral, cognitive, other). Behavioral strategies were reported most frequently for diet, exercise, medication taking, and general adherence situations; cognitive coping strategies were used most frequently for glucose testing situations. Hierarchical multiple regression analyses (separate analysis for each regimen area) were conducted to prospectively predict level of self-care from problem-solving variables and from patient characteristics. Patient characteristic variables were entered first, and generally were not strong predictors of self-care. The addition of problem-solving variables (mean skill rating, number of different strategies used) significantly improved the prospective prediction of both exercise and dietary self-care (43). Similar results were obtained for analyses of type of problemsolving strategies used.

Personal Models (Patient Beliefs): Recently, we have intensively researched patient beliefs about their diabetes and its treatment. Personal models of illness are patients' mental representations of their disease, including both beliefs and feelings (44). They reflect the experience of illness and its management from the patient's perspective. Personal models are typically elicited by interviews (45) or questionnaires (46) and similar patterns of beliefs have been found for a variety of illnesses (47). Personal models are believed to determine patients' reactions and responses to their illness, including their level of self-care. For our first investigation of personal models of diabetes, we developed an extensive structured interview (48). Participants (women only) were in general agreement about the nature of diabetes and reported few beliefs inconsistent with the prevailing medical science view of NIDDM. However, they also displayed individual differences on four aspects of their personal models. These dimensions were each assessed by several interview items and were: (a) cause (level of self-blame); (b) symptoms (number and variety of diabetes symptoms); (c) treatment value (perceived importance of self-management activities for controlling diabetes); and (d) seriousness (a combination of beliefs and feelings about the course and consequences of diabetes). Internal consistency and inter-coder reliability for these composite variables were quite good and are reported in Hampson et at. (48). After controlling for the effects of age and medication status, personal models constructs (particularly beliefs about treatment value and seriousness) significantly improved the prediction of dietary intake, and marginally improved the prediction of exercise. These findings have been replicated and extended using a larger, community sample of both men and women with NIDDM, in which personal models were related to levels of self-management assessed both concurrently and prospectively (49). The personal models interview was revised and shortened, and three composite variables were developed that were similar to those in the first study. Reliabilities (internal, inter-coder, and test-retest over one month) were again quite high. Personal models, especially treatment effectiveness, were predictive of dietary intake and physical activity, after controlling for demographic and medical history factors. Given these promising findings, we are extending the study of personal models of illness to other chronic conditions, such as osteoarthritis (50). We are also developing brief questionnaire versions of our measures

Glasgow et al. that are more suitable for clinical use, including touch-screen, computer-assisted assessment (51).

Summary of Assessment Studies: Identification of variables relevant to diabetes self-management has been facilitated by the continuity of our research, funded by the NIDDK, and through the use of social learning/social cognitive theory (14,31,52) as our conceptual framework. Its central feature--self-regulatory processes--is well-suited to conceptualizing interactions among biomedical, cognitive, behavioral, and environmental determinants of diabetes self-care. Much remains to be done, however, to understand the influence of community, media, and larger social units on individual behavior (19,20). Over the years, we have developed several methodological recommendations for diabetes research including: (a) the importance of multiple measures of adherence; (b) the advantages of treating different areas of the diabetes regimen separately, rather than as a unidimensional cluster of behaviors; (c) the need to recruit representative, population-based samples rather than self-selected, highly motivated volunteers (discussed below); (d) the importance of longer-term, prospective investigations (and relative lack of knowledge gained from one-shot, cross-sectional studies); and (e) that to be of practical utility, psychosocial variables should predict behavior better than demographic or medical history factors. Several of these points are summarized in a recent critique (53). In summary, our assessment studies have provided data relevant to the conceptualization of diabetes self-care, and on variables that predict self-management. An even greater challenge than predicting and understanding, however, is improving diabetes care. After identifying social learning predictors (social support, self-efficacy, problem-solving) of diabetes self-management, we have designed and tested interventions to determine if we can modify these variables to increase self-care. Intervention Stndies

Initial Intervention Research: In collaboration with Oregon State University colleagues, Wilson (54) conducted a study of the effects of diabetes education and peer support on weight loss and level of glycemic control among 79 elderly persons with NIDDM. Participants registered for the study through their local senior citizen centers and senior nutrition sites, which were spread over a largely rural, four-county area. It was not feasible to randomly assign subjects to conditions, so sites were randomized with three sites being assigned to each condition: education only; education and peer support; and wait-list control. The peer support intervention involved sharing and encouragement, behavioral prescriptions, modeling, positive reinforcement, and personalized goal setting. Peer support scores were highest in groups in which support was actively facilitated. The greatest weight loss and improvements in glycemic control occurred in the education and peer support condition, but did not reach significance between groups (54). Other psychosocial variables (adaptation to diabetes, problems with food, emotional stress, and social pressures) were found to be significantly correlated with reductions in weight and improvement in glycemic control (55). The first all ORI-staffed intervention study evaluated two diabetes self-management programs (56). Seventy-eight individuals with NIDDM were randomly assigned to three conditions: nutrition education; nutrition education with a social learning component (individualized goal setting, problem-solv-

D i a b e t e s Research at O R I ing, and relapse prevention); or a wait-list control condition. The purposes o f this study were to determine whether a timelimited nutrition education program would i m p r o v e dietary behaviors more than a control condition, and evaluate the impact o f adding social learning components to nutrition education. Subjects in the nutrition education plus social learning condition significantly reduced their total caloric intake and percent o f calories from fat; these reductions were maintained through the two-month follow-up. This condition also produced a statistically significant decrease in weight. Subjects in the nutrition education condition showed a significant within-group reduction from pre-test to follow-up only in calorie consumption. Subjects in the control condition did not show significant improvement on any measure. In general, the two interventions produced results superior to those o f the control condition. Although the pattern o f results was consistently in the predicted direction, the social learning condition was generally not significantly better than the nutrition education condition, possibly because o f the small sample size. This study demonstrated that our nutrition education plus social learning program was successful in facilitating short-term dietary change (56), and provided the background for the dietary portion o f our current medical office intervention.

Sixty Something:The Sixty Something . . . diabetes selfcare program was the most comprehensive intervention we have conducted. It addressed multiple regimen areas (blood glucose monitoring, exercise, and diet) and was o f longer duration than previous programs. Also, it was specifically tailored to meet the needs o f persons aged 60 and older. Subjects with N I D D M who obtained physician permission to participate and passed a subm a x i m a l graded exercise test were randomized to i m m e d i a t e intervention or to a delayed treatment control condition. We randomized 105 persons and collected posttest data from 102. The intervention was based upon findings from our assessment studies as well as from the previously described diabetes educational interventions. It featured problem-solving training focused on c o m m o n l y occurring barriers to dietary selfmanagement, as well as supervised walking sessions led by a trained exercise leader. We also included a mood/pleasant events module because reports [e.g. (57)] have consistently indicated that depression is prevalent among this population. Although this proved a difficult problem to address, we were fortunate to have the collaboration o f O R I colleagues Peter Lewinsohn and A n t h o n y Biglan in developing this aspect o f the intervention. The Sixty S o m e t h i n g . . . program contained a stronger group interaction component and was less didactic than our previous interventions. Classes were presented in a group format for ten sessions over twelve weeks. The weekly format was to: (a) review and discuss homework from the preceding week; (b) present and discuss a new topic; and (C) address personalized goal-setting and problem-solving as applied to the new topic (43,58). The Sixty Something . . . intervention was successful in improving self-care behaviors, both with respect to baseline and c o m p a r e d to the control condition. Significant i m p r o v e m e n t for treated subjects was seen on diet and glucose testing (self-report measures); mean calories per day and percent o f calories from fat (food record measures); and on weight and glycemic control. The intervention produced greater change than the control condition on all measures except for dietary fiber intake, and the changes, although modest, were generally maintained at the sixm o n t h follow-up.

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Summary and Participation Issues:Our intervention studies have demonstrated the positive change potential o f older adults and also clarified the limited reach o f traditional diabetes education programs. Assumptions are frequently m a d e about the unwillingness or inability o f older adults to make lifestyle changes. O u r experience contradicts this assumption. Indeed, our data indicate that this population is eager to gain informarion about dietary issues and reasonably successful in making lifestyle changes. Older persons with diabetes have traditionally been underserved and represent a group who have been, or are at high risk for, many other diseases and complications. A n important cautionary note is that, although we found that participants reported enjoying and derived benefit from the program, we had difficulty with recruitment. Despite being designed to overcome a number o f frequent barriers to participation among older adults (e.g. offered without charge during daylight hours in accessible, pleasant, and safe facilities), the Sixty S o m e t h i n g . . . program attracted only 53% o f all patients who were screened and met eligibility criteria, and only 36% o f a subsample that was more representative o f outpatients in general (59). The three most c o m m o n reasons for declining to participate were "too large a time c o m m i t m e n t " (28%); "already doing something on m y own about self-care" (15%); and " t o o ill to participate" (13%). P r o m p t e d by these recruitment difficulties, we reviewed all 1988 and 1989 issues o f The Diabetes Educator and Diabetes Care for studies on diabetes educational programs to determine what was known about participation. Our review revealed that few diabetes education studies even report on participation rates or representativeness o f participants. Those that d i d generally had low participation rates or paticipants who were not representative. These results are summarized in Glasgow et al. (59). Thus, generalizations regarding our (and we believe most other) research findings on diabetes should be restricted to volunteer populations rather than all patients. The public health implication o f our recruitment findings is that although effective diabetes education and self-care interventions have been developed, they do not appear to reach the majority o f patients who could benefit from such services. W e became convinced that to have an impact on the general population, diabetes education programs needed to reach out to patients. One way o f doing this is to conduct brief programs in settings in which patients are already present for other reasons. Because o f the key role o f the patient's health care team in facilitating diabetes adherence, the outpatient medical office setting seemed to be particularly advantageous. A parenthetical note is that this research direction was preceded by and undoubtedly influenced by similar shifts in research directions that Glasgow and O R I colleagues, led by Edward Lichtenstein, were making in smoking cessation [e.g. (60-62)]. Current Intervention Research Brief Medical Offtce-Based Intervention to Facilitate Diabetes Self-Management:The goal o f our office-based self-management intervention program (51) is to develop a practical intervention to help both patients and providers better address behavioral issues related to dietary self-management. The intervention is designed to be applicable to the majority o f adult diabetes outpatients during medical visits; uses touch-screen computer assessment to provide immediate feedback on key issues to patients and providers prior to their interaction; and provides gnal-setring and problem-solving assistance to patients

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following their interactions with the physician. Follow-up components include phone calls and videotape or interactive video instruction as needed. This project, also funded by N I D D K , originally proposed to address multiple regimen areas. Due to funding cuts, however, it was necessary to limit the scope of the intervention. Dietary intervention was chosen, as it is applicable to the vast majority of N I D D M patients, and is rated as the regimen area with which patients have the greatest difficulty and would most like assistance (27,36). Briefly, the procedures are as follows: patients arrive 20 minutes early for their appointment to complete baseline assessments (rather than sitting in the reception area). All patients complete the computerized assessment via a touch-screen color monitor and are then randomly assigned to either Special Intervention or Usual Care conditions. Special Intervention subjects then complete an additional touch-screen assessment to identify personal barriers to dietary self-care, and enter into a sequence o f activities that includes feedback to the patient and provider on: (a) fat intake patterns, Co) barriers to dietary selfcare, and (c) patient desire for involvement in self-management. Following the patient-provider interaction, intervention subjects meet briefly with staff for goal-setting and strategy planning. I f patient self-efficacy for attaining their personal goal is low, a return visit is scheduled to view an interactive video developed for this project. The video presents information compatible with the patient's baseline stage o f dietary change and type of barrier she is most likely to encounter. Patients with high self-efficacy are given a linear video to view at home. In either case, brief one- and three-week follow-up phone calls are conducted to review and reinforce progress. The above sequence is repeated at three- and twelve-month follow-up visits, supplemented by an intervention phone call at six months, and a mail-out o f tailored written materials at nine months. Preliminary results from this study indicate that based on baseline four-day food records, patients on average consumed approximately 38% of their calories from fat. From their scores on the Kristal Food Habits Questionnaire (63) the majority o f patients can benefit most from replacing and substituting for high-fat foods, from learning to avoid fats as seasoning, and from increasing their intake of fruit and vegetables. The class o f dietary adherence barriers reported most frequently involved eating at home (e.g. snacking while watching TV, seeing family members eat unhealthy snacks) rather than eating away from home. Subjects reported and displayed no difficulty using the touch-screen computer (even those with visual problems, arthritis, or no previous computer experience). Data from the oneweek and three-week follow-up phone calls have revealed that subjects are generally able to both remember and achieve the specific behavioral goals that they established. Although preliminary, the initial results from this brief office-based application are encouraging (51). CONCLUSIONS AND FUTURE DIRECTIONS As indicated previously, it is our goal to develop efficient procedures to improve diabetes self-care that: (a) can reach many persons--especially older N I D D M and high-risk patients; Co) are feasible to conduct in settings where patients are already present (e.g. physician offices, worksites); (c) are helpful to both patients and health care team members; and (d) are relatively brief and low-cost. We have made progress toward this goal. We have developed measures that identify variables predictive

Glasgow et al. o f diabetes self-management behaviors, as well as a data-based self-management intervention that is appropriate for older as well as younger individuals with N I D D M . We are currently conducting research directed toward our goals regarding brief procedures to help health care providers, and evaluating their cost-effectiveness and generalizability to more diverse populations.

Lessons Learned In addition to the conceptual model described earlier, our work has been guided by several informal lessons about conducting research on chronic disease in the U.S. in the 1990s. These perspectives are still evolving, but we attempt to explicate them below. Most of these lessons are neither novel nor specific to diabetes. They have, however, guided our research directions and both assessment and intervention approaches. These lessons are: (a) Having a conceptual framework is helpful--if this framework is flexible. Working models can help guide research, but should evolve over time. Co) Behavioral contributions to diabetes care should be practical. Part o f the reason that behavioral research findings have not been incorporated more rapidly into practice is that they are either not understood by other professionals or are considered to be impractical. (c) We need to truly listen to patients and better understand their perspective to really help them, a lesson learned from Robert Anderson (64,65). (d) A public health perspective [e.g. (61,62,66,67)] can add significantly to the relevance of behavioral contributions in this era of managed care and cost-effectiveness. Future Directions

Our future research directions are in both basic and applied areas. In more basic research, we are investigating health care provider-patient interactions and their relationship to both process factors (e.g. patient personal models, consumer satisfaction) and to outcomes such as self-management, quality-of-life, and medical care utilization. We are interested in identifying key aspects of the patient-provider interaction, through coding o f audiotapes, that are related to both subjective (e.g. patient satisfaction and perception of provider caring) and overt outcomes (e.g. implementation of recommended strategies). We are currently adapting the model presented in Figure 1 to focus specifically on what we view as key issues of patientprovider interactions related to diabetes self-management. We are guardedly optimistic that this basic conceptual approach (or one of its later modifications) will prove useful for health care providers and patients in the prevention and management o f not only diabetes, but also other chronic diseases. We hope that this research will result in straightforward recommendations for feasible ways to modify patient-provider interactions. Our applied goal is to determine which aspects of our program are generalizable to other populations and to other settings such as health maintenance organizations. Recently, we have consulted with organizations such as Group Health Cooperative o f Puget Sound (67), the Centers for Disease Control, and Johnson & Johnson Applied Behavioral Technologies to coUaboratively design assessment, intervention, and evaluation procedures to improve diabetes management. It is our hope that these approaches can be applied to diverse medical care systems in a cost-efficient manner that will improve the quality of diabetes management.

Diabetes Research at ORI REFERENCES (1) Centers for Disease Control: Diabetes Surveillance, 1993. Atlanta, GA: Centers for Disease Control, Divison of Diabetes Translation, 1993. (2) Harris MI: Prevalence of non-insulin-dependentdiabetes and impaired glucose tolerance. In Diabetes in America: Diabetes Data Compiled 1984, DHHS Publication No. (NIH) 85-1468. Washington, DC: U.S. Government Printing Office, 1985. (3) American Diabetes Association: Diabetes 1993 Vital Statistics. Alexandria, VA: American Diabetes Association, Inc., 1993. (4) American Diabetes Association: Direct and Indirect Costs of Diabetes in the United States in 1992. Alexandria, VA: American Diabetes Association, Inc., 1993. (5) Vinieor F: Is diabetes a public-health disorder? Diabetes Care. 1994, 17(Suppl. 1):22-27. (6) Glasgow RE, Wilson W, McCaul KD: Regimen adherence: A problematic construct in diabetes research. Diabetes Care. 1985, 8(3): 300-301. (7) American Diabetes Association: Implications of the diabetes control and complications trial. Diabetes Care. 1993, 16(11):15171520. (8) Abraira C, Emanuele N, Colwell J, et al: Glycemic control and complications in Type II diabetes. Diabetes Care. 1992, 15(11): 1560-1571. (9) Diabetes Control and Complications Trial: The effect of intensive treatment of diabetes on the development and progression of longterm complications in insulin-dependent diabetes mellitus. The New England Journal of Medicine. 1993, 329:977-986. (10) UK Prospective Diabetes Study Group: UK Prospective Diabetes Study (UKPDS). VIII. Study design, progress and performance. Diabetologia. 199 l, 34:877-890. (1 l) Drash AL: The child, the adolescent, and the diabetes control and complications trial. Diabetes Care. 1993, 16(11):1515-1516. (12) American Diabetes Association: Diabetes outpatient education: The evidence of cost savings. In Task Force on Financing Quality Health Carefor Persons with Diabetes. Alexandria, VA: American Diabetes Association, Inc., 1986. (13) Cox DJ, Gonder-Frederick L: Major developments in behavioral diabetes research. Journal of Consulting and Clinical Psychology. 1992, 60(4):628--638. (14) Glasgow RE: Compliance to diabetes regimens: Conceptualization, complexity, and determinants. In Cramer JA, Spilker B (eds), Patient Compliance in Medical Practice and Clinical Trials. New York: Raven Press, 1991, 209-221. (15) Kurtz SMS: Adherence to diabetes regimens: Empirical status and clinical applications. The Diabetes Educator. 1990, 16(1):50-59. (16) Glasgow RE, McCaul KD: Psychological issues in diabetes: A different approach. Diabetes Care. 1982, 5(6):645-646. (17) Glasgow RE, Osteen VL: Evaluating diabetes education: Are we measuring the most important outcomes? Diabetes Care. 1992, 15: 1423-1432. (18) Glasgow RE: A practical working model of diabetes management and education. Diabetes Care. 1995, 18:117-126. (19) Anderson B, Auslander WF, Epstein M, et al: The importance of social context in diabetes management. In Mazze RS (ed), Professional Education in Diabetes Management: Proceedings of the Diabetes Research and Training Centers Conference. Washington, DC: U.S. Department of Health and Haman Services, 1980, 3-42. (20) Biglan A, Glasgow RE: The need for a science of larger social units: A contextual approach. Behavior Therapy. 1990, 21:195-215. (21) Anderson BJ, Coyne JC: Miscarried helping in the families of children and adolescents with chronic diseases. In Johnson JH, Johnson SB (eds), Advances in ChiM Health Psychology. Gainesville, FL: University of Florida Press, 1993, 167-177. (22) Anderson B: Familly issues in intensive insulintherapy. Strategies for ImplementingTight Control in Patients with Type I and Type II Diabetes: American Diabetes Association. Boston: 1994.

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(23) Dunn SM: Rethinking the models and modes of diabetes education. Patient Education and Counseling. 1990, 16:281-286. (24) MeCaul KD, Glasgow RE, Schafer LC: Diabetes regimen behaviors: Predicting adherence. Medical Care. 1987, 25(9):868-881. (25) Anderson LA: Health-care communication and selected psychosocial correlates of adherence in diabetes management. Diabetes Care. 1990, 13(2):66-76. (26) Kaplan RM: Behavior as the central outcome in health care. American Psychologist. 1990, 45:1211-1220. (27) Wilson W, Ary DV, Biglan A, et al: Psychosocial predictors of selfcare behaviors (compliance) and glycemic control in non-insulindependent diabetes mellitus. Diabetes Care. 1986, 9(6):614-622. (28) ArT DV, Toobert DJ, Wilson W, Glasgow RE: Patient perspective on factors contributingto non-adherence to diabetes regimen. Diabetes Care. 1986, 9(2):168-172. (29) Schafer LC, Glasgow RE, McCaul KD, Dreher M: Adherence to IDDM regimens: Relationship to psychosocial variables and metabolic control. Diabetes Care. 1983, 6(5):493-498. (30) Bandura A: Self-efficacy:Toward a unifying theory of behavioral change. Psychology Review. 1977, 84:191-215. (31) Bandura A: Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall, 1986. (32) Glasgow RE, McCaul KD, Schafer LC: Self-care behaviors and glycemic control in Type I diabetes. Journal of Chronic Diseases. 1987, 40:399--412. (33) Glasgow RE, MeCaul KD, Schafer LC: Barriers to regimen adherence among persons with insulin-dependentdiabetes. Journal of Behavioral Medicine. 1986, 9(1):65-77. (34) Schafer LC, McCaul KD, Glasgow RE: Supportive and non-supportive family behaviors: Relationships to adherence and metabolic control in persons with Type I diabetes. Diabetes Care. 1986, 9(2): 179-188. (35) Glasgow RE, Toobert DJ, Riddle M, et al: Diabetes-specific social learning variablesand self-carebehaviors among persons with Type II diabetes. Health Psychology. 1989, 8(3):285-303. (36) Glasgow RE: Social-environmentalfactors in diabetes: Barriers to diabetes self-care. In Bradley C (ed), Handbook of Psychology and Diabetes Research and Practice. Berkshire, England: Hardwood Academic, 1994. (37) Kingery PM, Glasgow RE: Self-efficacyand outcome expectations in the self-regulationof non-insulin-dependent diabetes mellitus. Health Education. 1989, 20(7):13-19. (38) Glasgow RE, Toobert DJ: Social environment and regimen adherence among Type II diabetic patients.Diabetes Care. 1988, II: 377-386. (39) Haynes RB, Taylor WB, Sackett D L (eds): Compliance in Health Care. Baltimore, M D : Johns Hopkins University Press, 1979. (40) Johnson SB: Methodological issuesin diabetes research:Measuring adherence. Diabetes Care. 1992, 15(Suppl. 4):1658-1667. (41) Johnson SB: Health behavior and health status:Concepts, methods, and applications. Journal of Pediatric Psychology. 1994, 19: 129-141. (42) Toobert DJ, Glasgow RE: Assessing diabetes self-management: The summary of diabetes self-care activities questionnaire. In Bradley C (ed), Handbook of Psychology and Diabetes Research and Practice. Berkshire, England: Hardwood Academic, 1994. (43) Toobert DJ, Glasgow RE: Problem-solvingand diabetes self-care. Journal of Behavioral Medicine. 1991, 14(1):71-86. (44) Skelton JA, Croyle RT: Mental Representation in Health and Illness. New York: Springer-Verlag, 1991. (45) Leventhal H, Nerenz DR: The assessment of illness cognition. In Karoly P (ed), Measurement Strategies in Health Psychology. New York: Wiley, 1985, 517-553. (46) Turk DC, Rudy TE, Salovey P: Implicit models of illness. Journal of Behavioral Medicine. 1986, 9:453-474. (47) Leventhal H, Diefenbach M: The active side of illness cognition. In Skelton JA (ed), Mental Representation in Health and Illness. New York: Springer-Verlag, 1991, 246-272.

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(48) Hampson SE, Glasgow R, Toobert D J: Personal models of diabetes and their relations to self-care activities. Health Psychology. 1990, 9(5):632--646. (49) Hampson SE, Glasgow RE, Foster L: Personal models of diabetes among older adults: Relation to self-management and other variables. The Diabetes Educator (in press, 1995). (50) Hampson SE, Glasgow RE, Zeiss A: Personal models of osteoarthritis and their relation to self-management activities and qualityof-life. Journal of Behavioral Medicine. 1994, 17:143-158. (51) Glasgow RE, Toobert DJ, Hampson SE, Noell JW: A brief piecebased intervention to facilitate diabetes self-management. Health Education Research (in press, 1995). (52) Ewart CK: Social action theory for a public health psychology. American Psychologist. 1991, 46.

Behavioral research on diabetes at the Oregon Research Institute.

This article overviews the scope and progression of research on behavioral aspects of diabetes over the past decade at the Oregon Research Institute. ...
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