Psychosocial Barriers to Diabetes Self-Management and ...spectrum.diabetesjournals.org/content/diaspect/14/1/33.full.pdf · Psychosocial Barriers to Diabetes Self-Management and Quality

33Diabetes Spectrum Volume 14, Number 1, 2001

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Psychosocial Barriers to DiabetesSelf-Management and Quality of Life

Russell E. Glasgow, PhD, Deborah J.Toobert, PhD, and Cynthia D.Gillette, PhD, RD

Diabetes self-management can be difficult and frustrating for both patientsand practitioners. Information is needed about which barriers present thegreatest obstacles for which types of patients, and from this, practical, cost-effective interventions need to be developed. This article reviews existingresearch on psychosocial and interpersonal barriers to diabetes self-manage-ment and quality of life. It also presents new data on a self-efficacy-basedbarriers instrument and makes recommendations for future research.

This article discusses psychosocialbarriers to diabetes self-managementand quality of life. Although theboundaries between psychosocial andother types of barriers are permeableand at times diffuse, to organize thewealth of information that is availableand make sense of it, psychosocialbarriers will be discussed in this arti-cle, whereas external (systems), inter-nal symptom, and cultural barriers areaddressed in companion articles onpages 23, 28, and 13, respectively.

We begin by defining what wemean by psychosocial barriers.Webster’s dictionary defines a barrieras “something immaterial thatimpedes or separates.”1 In our case,we are concerned with psychologicaland interpersonal factors that impedediabetes management or diabetes-related quality of life.

Before summarizing the literatureon psychosocial barriers, it is neces-sary to review two decisions made indefining our scope of work. The firstwas inclusionary and involves consid-ering low levels of psychosocial sup-ports as barriers. For example,although high levels of self-efficacyand social support are generally facili-tative of self-management, low levelsof these factors can be considered asbarriers. In this way of thinking,much of the assistance provided bydiabetes educators is to help patientsmove along the continuum of key psy-chosocial resources and supports fromlower to higher levels.

Second, we have separated proxi-mal barriers arising from low levels ofsocial support from more distalsources of support. For example, we

will consider here support from familyand significant others, but not supportfrom health care team members,coworkers, or the community in gen-eral. This was challenging, since manyof the assessment instruments andstudies reviewed blend these differenttypes of barriers. Considered from theperspective of an increasingly broadseries of concentric rings of influence,the barriers discussed in this paper(see Figure 1) lie in the second level.They are more “external” than inter-nal barriers such as symptoms andbiological factors, but more proximalthan both systems barriers, such asthe organization of medical care, andcommunity/cultural influences.

Priority areas. To reduce the num-ber of studies available on every typeof psychological barrier and distur-bance imaginable, we have focused onthree psychological constructs andtwo social/interpersonal factors thatare both theoretically important andhave been applied successfully inother areas of behavioral medicine.The psychological factors are (lowlevels of) self-efficacy, personal illnessmodels and health beliefs, and depres-sion. The social factors are stress and(low levels of) support from closefriends and family.

The remainder of this article isorganized into three sections:1. A brief review of key diabetes stud-

ies on psychosocial barriers, includ-ing both assessment instrumentsand interventions designed to iden-tify and cope with psychosocialbarriers;

2. Data from a new scale that wehave developed that blends the

In Brief


concepts of self-efficacy and exter-nal barriers, to illustrate some ofthe issues in this area of research;and,

3. Discussion of the strengths andweaknesses of current research onpsychosocial barriers and recom-mendations for future research,focusing on steps that will expeditethe translation from research topractice.

REVIEW OF RECENT LITERATURETwo databases, National Library ofMedicine PubMed and OnlineComputer Library Center FirstSearch, were consulted combining thesearch terms diabetes and barrierswith the terms self-management, self-efficacy, motivation, empowerment,psychosocial, personal models, prob-lem solving, interventions, stress,depression, and social support. Weconsidered both assessment and inter-vention studies that addressed barrierspublished from 1992 to 2000.Additional references on the topic thatwere cited in journal articles fromthese searches were also included.

Classification Schema for StudiesThe classification schema recommend-ed by Greenwald and Cullen2 wasused to organize studies into fivestages of research. In this schema,Phase 1 studies identify and evaluatethe relative importance of barriers todiabetes self-management. In Phase 1,data are collected through interviews,focus groups, and cross-sectional andprospective evaluations. This informa-tion is prerequisite to developing

hypotheses and behavioral targets forinterventions. Phase 2 studies aredesigned to develop and test evalua-tion tools and methods. The validityand reliability of instruments areassessed. These types of studies areprerequisite to testing the efficacy ofinterventions. Phase 3 studies useideal scientific conditions to test theefficacy of interventions designed toreduce barriers and improve diabetesself-management. Building from theresults of all previous types ofresearch, Phase 4 studies use large,representative samples to test theeffectiveness of interventions whenapplied in clinical settings. Phase 5studies evaluate the dissemination ofbarriers-based intervention protocols.Phase 5 studies were not identifiedand therefore are not discussed fur-ther.

Phase 1 StudiesOur search identified 22 Phase 1 bar-riers studies, each exploring psychoso-cial predictors of diabetes self-man-agement. Only one study used aprospective, rather than cross-section-al design.3 Very few attempted to con-trol for moderators such as socialdesirability of instruments, body massindex, diabetic complications, and soforth. The vast majority of studiesused HbA1c as the primary indicatorof how well subjects adhered to dia-betes guidelines, although severalincluded both HbA1c and measures ofself-management behaviors. Finally, alarge percentage of these studies wereconducted with difficult-to-reach,low-income, and minority popula-

tions. While the attempt to reach low-income and minority populations rep-resents important progress, unfortu-nately, response rates were often low,ranging from 30 to 70%, whenreported at all.

The majority of Phase 1 studieswere designed around well-establishedpsychosocial constructs including theHealth Belief Model, Locus ofControl, Self-Efficacy, and SocialSupport. (See Table 1.) In these stud-ies, low positive correlations wereoften found between psychosocial pre-dictors and markers of self-manage-ment. The low correlation coefficientssuggest that these global constructsmay not be useful predictors of self-management behaviors and that inves-tigators should look for other mediat-ing targets when designing interven-tions. However, before coming to thisconclusion, it is important to considerthat the measurement of self-manage-ment behaviors and the measurementof psychosocial barriers are both chal-lenging. Low correlation coefficientsmay result from measurement errorrather than lack of construct signifi-cance.

Comparing two indicators of self-management behavior illustrates onedifficulty with measurement in barri-ers research. In much of the literaturereviewed, two dependent variableswere used, usually HbA1c and self-reported behaviors. The correlationsbetween these two variables were sur-prisingly low in each of the studiesthat looked at both. Moreover, in onestudy, the correlation between self-management and HbA1c disappearedcompletely when the analyses wererestricted to subjects with significantcomplications.4 This finding highlightsthe possibility that, for many individ-uals, physiological processes and med-ications may cause the relationshipbetween HbA1c and self-managementbehaviors to be tenuous or insignifi-cant. An important note made by sev-eral researchers examining HbA1c wasthat often patients were not aware ofthe marker’s significance. Severalauthors suggested that personalizedfeedback on HbA1c levels may be auseful tool that clinicians could use toeducate patients and help them avoidcomplications.

In a series of studies, Hampson andcolleagues3,5 have developed and test-ed Personal Models of Diabetes (e.g.,beliefs about the consequences of hav-ing diabetes and about the effective-ness of treatment) as predictors of

Figure 1. Visual representation of diabetes barriers

Internal(Physical)

Psychosocial

External - Systems

Cultural


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self-care behaviors. Building fromother models, Personal Models mayrepresent a significant advancementbecause they have been shown,through prospective designs, to pre-dict HbA1c levels and self-manage-ment after controlling for other fac-tors. Personal Models theory is an

extension of Self-Schema theory. Itidentifies those variables that patientsthemselves believe to be central totheir experience of illness and its man-agement. Findings suggest thatPersonal Models may mediate theassociation between social supportand behaviors. Hampson’s work and

other findings underscore the impor-tance of internal locus of control withrespect to cause of disease and theimportance of patients believing in theefficacy of their treatment regimen.

Cox and colleagues6-9 have high-lighted the psychosocial implicationsof insulin-induced hypoglycemia

Table 1. Number of Studies Reporting Different Types of Information

Type of research

Phase 1 (studiesdesigned to evaluatethe relative impor-tance of certain bar-riers)

Phase 2 (studiesdesigned to measurethe validity, reliabil-ity, and utility ofassessment instru-ments)

Phase 3 (studiesthat used idealexperimental condi-tions to test inter-ventions designedto reduce psycho-social barriers)

Phase 4 (studiesthat used typicalclinical conditionsto test interventionsdesigned to reducepsychosocial barri-ers)

Number of studies

identified

22

7

3, educationprogram orclasses

1, follow-upphone calls

1, computer-based educa-tion

1

Psychosocial Barriers(number looking ateach barrier)

Alternative therapies, 1Anxiety, 1Attitudes, 4Depression, 2Empowerment, 2Fear of hypoglycemia, 1Health beliefs, 5Interview responses, 6Knowledge, 2Locus of control, 7Motivation, 2Outcome expectancies, 2Personal Models, 3Problem solving, 1Self-efficacy, 1Self-esteem, 3Social support, 6Stress, 3

Anxiety, 1Attitudes, 1Depression, 1Coping, 1Emotional distress, 1Health beliefs, 1Locus of control, 1Personal Models, 1Self-efficacy, 4Social support, 2

Attitudes, 2Empowerment, 1Knowledge, 3Personal Models, 1Problem solving, 1Self-efficacy, 1Social support, 2Stress, 1

Self-efficacy

Self-care RegimenArea (numberaddressing eacharea)

Multiple (diet, exer-cise, glucose moni-toring, taking med-ications), 16

Glycemic control (HbA1c), 10

Quality of life, 1

Other, 6

Multiple, 4

Glycemic control(HbA1c), 4

Other, 3

Multiple, 3

Quality of life, 1

HbA1c, 4

Multiple, 1

Study Population(number usingeach type)

Convenient sample, 6

Low-income &minority, 12

Youth, 2

Type 1 diabetesonly, 2



Youth, 1



Youth, 1


Design Used(% each type)

Cross-sectional, 13

Descriptive, 5

Nonexperimentalprospective, longerthan 3 months, 2

Nonexperimentalprospective, shorterthan 3 months, 2

Interventionchange scores, 2

Correlation withother measures ofself-care, 4

Test-retest reliability, 2

Cluster analyses, 1

Prospective predic-tive validity, 2

Randomized con-trol trial, 3

Quasi-experimental, 2

Pre-post test, 1


among type 1 diabetes patients. Thesefindings are particularly relevantbecause intensive insulin therapy isbecoming more common. These stud-ies suggest that fear of severe hypo-glycemia may be a barrier to tightmetabolic control for some type 1patients. The significance of the barri-er is related to the number of pasthypoglycemic episodes and a patient’sability to detect and respond to hypo-glycemic symptoms. The series ofstudies by Cox and colleagues alsosuggests that hypoglycemic episodesmay adversely influence family rela-tionships. These authors recommendthat clinicians assess this barrier intheir type 1 diabetes patients and,when appropriate, provide hypo-glycemic awareness training.

While only one article was identi-fied that examined whether the use ofalternative therapies could be consid-ered a barrier to conventional self-management of diabetes, the findingswere important. In interviews of 43elderly, low-income, Hispanic dia-betes patients, Hunt et al.10 observedthat the use of herbs and prayer in thetreatment of diabetes was not a barri-er. Rather, those who used alternativetreatments also used conventionaltreatments, and those who did not useconventional treatments did not usealternative therapies either.

Six Phase 1 studies were identifiedthat examined the relationshipbetween social support barriers andself-management. Most studies report-ed moderate positive correlationsbetween levels of social support andmarkers of self-care (most oftenHbA1c). Most of the studies werecross-sectional in nature, and rarelywas the strength of the relationshiptested by determining whether signifi-cant relationships remained after con-trolling for other predictors.

Boehm et al.11 observed that it maybe possible to get too much socialsupport. In this study, subjects report-ing that they received more social sup-port than desired (using the DiabetesCare Profile)12 were less likely towork with a nurse to improve healthbehaviors using contingency contract-ing. The authors speculate thatpatients feared being nagged orharassed about their behaviors. Otherauthors noted that reliance on familyand friends can be risky because fami-ly and friends may have limited infor-mation about diabetes regimens andmay be unable to accurately evaluatethe extent of the patient’s adherence.

Moreover, if health care recommenda-tions are not consistent with valuesand beliefs, family or friends can sub-vert self-management.13 Finally, it wasnoted that family members can besupportive of a patient following aspecial diet for diabetes but oftentimes are not interested in followingsuch a diet themselves.14 Preparingtwo types of meals may not be feasi-ble for most families.

Several authors noted that assess-ment instruments may be useful foridentifying patients for whom low lev-els of social support is an importantbarrier. A well-validated, practicalinstrument would help practitionersand patients identify possible socialsupport problems, including too muchsocial support.

Phase 2 StudiesSeven studies were identified that pri-marily focused on development andvalidation of barriers-related assess-ment instruments. A majority of thesestudies focused on psychometric char-acteristics, especially internal consis-tency, and presented cross-sectionalvalidity data, often using HbA1c as thecriterion. Progress has been made inthis area, and, in particular, usefulinstruments have been developed tomeasure self-efficacy and empower-ment.15,16 In our opinion, however, themajority of the literature has focusedtoo much on internal consistency reli-ability and not enough on prospectivepredictive validity, sensitivity tochange as a result of intervention, oron the development of instrumentsthat are broadly applicable and feasi-ble to use in applied settings.

Important exceptions to these gen-eralizations are the work of Polonskyand colleagues on the Problem Areasin Diabetes (PAID) scale,17,18 as well asthat of Hampson and colleagues onPersonal Models of Diabetes and ofCox and colleagues on fear of hypo-glycemia, which were discussedabove. The PAID17,18 is a diabetes-spe-cific distress instrument that hasrespondents rate the degree to whicheach of 20 common situations is cur-rently problematic for them. Thisscale assesses diabetes-specific overallemotional distress, interpersonal dis-tress, regimen-related distress, andphysician-related distress. A recent 28-item revision of the PAID18 producessubscales on these four dimensions aswell as an overall score. The originalPAID has good construct and criteri-on validity and has been shown to be

responsive to psychosocial interven-tion.

Phase 3 and Phase 4 StudiesCompared to the number of Phase 1studies identified, only a handful ofPhase 3 barriers-based interventionefficacy studies were found. All fivePhase 3 intervention studies identifiedreported significant treatment-relatedimprovements. However, the effectsizes of interventions were not large,and most of the interventions wouldneed significant modification beforethey would appeal to a wide range ofeither health care practices or patientpopulations or would likely be main-tained after research funding was ter-minated.19

Interventions were often time-con-suming for both patients and practi-tioners. Excluding the two interven-tions specifically designed for follow-up or to be brief, the number of hoursinvested by subjects participating in adiabetes management interventionwas considerable. This time invest-ment, combined with transportationneeds, restricts participation to highlymotivated individuals who are able totravel repeatedly to intervention sitesduring usual work hours.

The intervention studies do addimportant information to our under-standing of how to reduce psychoso-cial barriers. Of interest are prospec-tive findings suggesting that care mustbe taken in developing interventionsto enhance social support.20 In thisstudy of adolescent type 1 patients,initial improvements in the likelihoodof socially skillful responses later dete-riorated, suggesting that assertivenesstraining may not have been sufficientand, in the long run, may have led tobehavioral inhibition and reducedsocial support.

Only one Phase 4 effectivenessintervention to reduce psychosocialbarriers was identified. In this study,self-efficacy for self-managementbehaviors was assessed in 115 insulin-requiring patients before and after 12home nursing visits.21 The interven-tion was shown to enhance self-effica-cy for self-management, particularlyamong those patients with low self-efficacy at the beginning of theintervention. Whether self-efficacyimprovement brought about by homecare nurses translates into improvedself-management behaviors, improvedpatient health, and reduced healthcare costs are important topics forfuture investigation.


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I L L U S T R A T I V E B A R R I E R SINSTRUMENT AND DATATo illustrate several of the conceptual,assessment, and analytic issuesinvolved, this section presents data ona new barriers instrument from ourresearch group. Earlier work by ourgroup on the Barriers to Self-Carequestionnaire is briefly reviewed toprovide a context for discussion of thenew instrument, called Challenges toIllness Management Scale (CIMS),and recent findings.

The original barriers scale wasdeveloped for use with type 1 diabetesand consisted of 15 items, including atleast 3 items from each of 4 regimenareas: diet, exercise, glucose testing,and insulin administration.22 It wasmodified in 1989 to be applicable topeople with type 2 diabetes and toolder adults.23,24

The second version of the Barriersto Self-Care scale was evaluated inone prospective assessment study andone intervention study of 127 and 78type 2 diabetes patients, respective-ly.23,24 There was consistency acrossstudies in the reported frequency ofbarriers to different regimen compo-nents, with barriers to dietary self-careoccurring most frequently, followedby exercise barriers. The psychometriccharacteristics of the instrumentincluded high Chronbach’s alphas forthe overall scale (0.84–0.86) and alsoindicated that the Barriers scores weremoderately stable over 6 months. TheBarriers scores were related to concur-rently collected self-report measures ofdietary and exercise self-management,with the regimen-specific Barriersscales being the best predictors ofthese respective behaviors. Barriers toself-care have tended to be unrelatedto HbA1c, given that some people whoencounter barriers will overcomethem, and others will not. Analyses topredict 6-month self-managementbehaviors from baseline Barriersscores indicated that, in general, theoverall Barriers score was the best pre-dictor of both self-reported and moreobjective measures of dietary andexercise performance.

To create the CIMS, the Barriers toSelf-Care instrument was expanded toinclude an individual’s disease man-agement challenges across several dif-ferent settings, ranging from moreproximal factors, such as personalchallenges and obstacles from familyand friends, to more distal factors,including health care team, worksite,organizational, and community barri-

ers. Barriers to engaging in the self-management tasks of lowering dietaryfat intake, engaging in physical activi-ty, and taking medication wereretained; barriers to engaging instress-management activities wereadded.

Two versions of the new instru-ment were constructed. One, theCIMS/Difficulty version, asked therespondent to indicate for each barri-er, using a scale from 1 to 5, how dif-ficult it was over the past 3 months toovercome that obstacle in trying toreach their self-management goals.The second, the CIMS/Confidenceversion, asked respondents to indi-cate, on a scale from 0 to 10, howconfident they were that, over thenext 3 months, they could follow theirself-management activities when facedwith each obstacle. This sectionreports on the use of the CIMS in tworecent studies, focusing only on thepsychosocial barriers aspects of thescale.25,26

One study was a randomized clini-cal intervention trial,25 and one26 wasa prospective observational study. Theobservational study included a conve-nience sample, whereas the interven-tion study drew a representative sam-ple of participants from lists of prima-ry care patients. The interventionstudy targeted dietary change, wasdelivered one-on-one by dietitians andresearch staff, included 40 physiciansfrom 12 medical practices, and wasconducted in a hospital wellness cen-ter setting.

Statistical AnalysesThe statistical software package SPSSfor Windows, version 9.0, was usedfor all analyses. The characteristicsand normative data for each studysample are expressed in means ± SDor percentages. Pearson correlationcoefficients were computed to evalu-ate the magnitude of associationbetween baseline and post-test (test-retest), of the CIMS/Difficulty version(at 6 months) and CIMS/Confidenceversion (at 12 months) for the inter-vention study. Pearson correlationcoefficients were also computed toassess relationships of regimen areasubscales (i.e., diet, exercise, stressmanagement, medication taking) andthe Overall Challenges scores to crite-rion variables (validity coefficients).Alpha reliability coefficients were usedto assess relationships among itemswithin a scale.

ResultsAll participants had type 2 diabetesfor at least 1 year (average durationsof 6.3 and 8.5 years) (Table 2). Themean ages of the two samples were 59and 62 years, and there were slightlymore women than men.

The mean levels and standard devi-ations for both the CIMS/Difficultyversion and the CMS/Confidence ver-sion are presented in Table 2 (highervalues indicate more obstacles to self-care and more confidence on allscales). These means show consistencyacross studies, with patients typicallyreporting higher levels of dietary,exercise, and stress-management bar-riers and lower levels of medication-taking barriers. The internal consis-tency of the scales, assessed by alphareliability coefficients, was veryacceptable (mean = 0.90).

Test-retest correlations over 4months were examined for the obser-vational study and over a 6-monthperiod for the control group only inthe intervention study. All were signif-icant, although the magnitude of thesecorrelations tended to be moderate(mean r = 0.6; range of r = 0.43 [formedication taking in the interventionstudy] to 0.80 [for the OverallChallenges scores in the observationalstudy]).

Correlations among the CIMSscales measuring different regimenbehaviors within each study (notshown in Table 2 but available fromthe authors) ranged from 0.20 to0.81, with an average correlation of0.50.

Correlations were calculated toevaluate relationships between theCIMS subscales and participant char-acteristics within each study (i.e., age,insulin status, gender, number of co-morbid conditions, diabetes duration,smoking status, ethnicity, income, liv-ing alone). Overall, there were fewsignificant correlations. Only onetrend emerged across both studies:older people had fewer barriers onmost scales, and this correlation wasmodest (mean r = 0.32). Social desir-ability was assessed in the observa-tional study26 and did not significantlycorrelate with any of the CIMS sub-scales.

Table 3 shows the correlationsbetween Overall Challenges score aswell as the dietary, exercise, andstress-management subscales, with cri-terion variables. For the interventionstudy, two versions of the CIMSinstrument were administered.


Therefore, both 6-month (using diffi-culty ratings) and 12-month (usingconfidence ratings) concurrent validitycoefficients are reported. Prospectivevalidity coefficients are also presentedusing 6-month CIMS scales to predict12-month behavioral outcomes. Fordietary comparisons, criterion mea-sures were derived from the KristalFood Habits Questionnaires (FHQ),27

the Summary of Diabetes Self-CareDiet subscale28 and the Block FatScreener.29 For exercise, comparisonswere with the Summary of DiabetesSelf-Care Exercise subscale28 and thePhysical Activity Scale for the Elderly(PASE).30 For stress management,comparisons were with the LifestyleAppraisal Questionnaire.31 In additionto these criterion variables, for theOverall Challenges score, compar-isons were also made with the Illness

Intrusiveness Scale,32,33 a measure ofquality of life. These correlations wereall significant.

For the observational study, thesesame criterion measures were used,except that the Perceived Stress Scale34

was used to validate the stress-man-agement scale; and to further validatethe Overall Challenges score, we alsoused the Center for EpidemiologicalStudies-Depression (CES-D) scale.

Concurrent validity coefficientswere of moderate magnitude, and allin the expected direction. As would beexpected given their strong interrela-tionship (even over the 6-month inter-val), concurrent validity correlationsfor CIMS/Difficulty version andCIMS/Confidence version ratings weregenerally similar. Stress- and exercise-dependent variables appeared to bepredicted better than dietary indices.

Prospective analyses were moremixed. Although significant relation-ships were reported in all self-manage-ment areas in the Glasgow andToobert (2000) intervention study,25

only stress management was predictedprospectively by CIMS/Difficulty rat-ings in the observational study or withan impressive magnitude. More defin-itive prospective data are not yetavailable for the confidence ratings.

In conclusion, these analyses sug-gest that the CIMS has reasonablepsychometric properties. It appearsthat the Overall Challenges scores hadbetter psychometric properties thansubscale scores, especially on theCIMS/Confidence version, while theregimen-specific subscales were gener-ally better predictors of their respec-tive criterion variables. A potentialadvantage of the CIMS is that it can

Table 2. Sample Characteristics, Reliability Estimates, Normative Data, and Test-RetestResults for Challenges Scales

Study

Glasgow &Toobert,

2000

Glasgow et al., 2000

Means andstandard

deviations forChallenges

Scales

2.1 ± 0.822.0 ± 0.872.3 ± 0.891.3 ± 0.561.9 ± 0.63

6.8 ± 2.05.9 ± 2.66.5 ± 1.88.0 ± 2.06.8 ± 1.8

2.3 ± 0.852.1 ± 1.002.5 ± 0.941.7 ± 0.971.6 ± 0.82

n

321

65

Sample characteristics

100% type 2 Mean age = 59, SD = 9 years57% female 15.5% on insulinMean years diagnosed = 6.3,

SD = 6.2 53.6% not working (retired)Mean occupation = Semi-skilledMean SES ** = 2.2 of 5

(5 is highest class)90.2% Caucasian

100% type 2 Mean age = 62, SD = 11 years51% femaleMean years diagnosed = 8.5,

SD = 8.2 yearsMean income: $10,000 to

$29,99991.3% Caucasian

Reliability estimates

Scale Items Coefficient(n) alpha (%)

CIMS/DifficultyVersion (1-5)

Diet 14 0.92Exercise 11 0.88Stress Mgt. 9 0.88Meds 5 0.74Overall Score 47 0.95

CIMS/ConfidenceVersion (0-10)


CIMS/DifficultyVersion


Test-retest

Interval ra

6 months(n = 262)

0.67†0.66†0.60†0.43†0.66†

4 months(n = 45)

0.64†0.64†0.73†0.75†0.80†

Average values are mean percentages ± SD unless otherwise specified. a “Test-retest” for the first study compares the Difficulty version versus the Confidence version of the scale, so the signs for thesecorrelations have been reversed. † P < 0.001** Socioeconomic status


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be used with patients having multiplechronic illnesses or to compare barri-ers across different disease groups.

DISCUSSIONWe have reviewed a number of differ-ent concepts, instruments, andapproaches related to psychosocialbarriers to diabetes self-managementand quality of life. The complexity ofthis area can be overwhelming, andwe still have much to learn, as dis-cussed below. Nevertheless, in thissection we attempt to summarize thekey conceptual issues, research find-ings, and lessons learned by the stud-ies conducted to date.

The first conceptual issue is to clar-ify what is a barrier and what is an

outcome. For example, depending onthe specific research question beingaddressed, depression can be either abarrier/independent variable or anoutcome/dependent variable. In thisarticle, we have defined (psychosocial)barriers as psychological and interper-sonal factors that impede diabetesself-management and quality of life. Inthis conceptualization, depressionwould be a barrier, due to its demon-strated inverse relationship to self-management and quality of life.

The underlying assumption in thisconceptualization is that psychosocialbarriers influence other longer-termoutcomes, such as glycemic control,cardiovascular status, and eventualdevelopment of diabetes complica-

tions indirectly via their influence onself-management and/or quality oflife. Brown and Hedges36 have foundempirical support for this assumption,but at least some psychosocial barri-ers, such as stress and possibly depres-sion and social support, may alsohave direct as well as indirect effectson metabolic outcomes.

As shown in Table 1, the vastmajority of studies in this area havebeen Phase 1 or hypothesis-generatingstudies. Some concepts have beentranslated into useful assessmentinstruments (Phase 2 research), butfew barriers concepts have yet beentranslated into effective or practicalinterventions. Although it may seemintuitively obvious that interventions

Concurrent Concurrent Prospectivevalidity validity validity

Barriers scale Criterion variables coefficients at t1 coefficients at t2 coefficientsGlasgow & Toobert, 2000 (n = 273) t1 Difficulty t2 Confidence t1 Difficulty

version with t1 version with t2 version withbehavior behavior t2 behavior

1) Regimen-Specific Barriers ScoresDiet:

Kristal Food Habits Questionnaire total score 0.28† 0.36† 0.36†Block Fat Screener 0.23† 0.24† 0.19‡Summary of Self-Care Diet Scale 0.41† 0.21† 0.41†

Exercise:Summary of Self-Care Exercise Scale 0.26† 0.49† 0.42†Physical Activity Scale for the Elderly (PASE) 0.44† 0.61† 0.54†

Stress Management:Lifestyle Appraisal Questionnaire 0.61† 0.58† 0.54†

2) Overall Barriers ScoreKristal Food Habits Questionnaire total score 0.23† 0.30† 0.30†Block Fat Screener 0.16‡ 0.20† 0.14§Summary of Self-Care Diet Scale 0.36† 0.43† 0.40†Summary of Self-Care Exercise Scale 0.14§ 0.31† 0.25†Physical Activity Scale for the Elderly (PASE) 0.32† 0.40† 0.38†Lifestyle Appraisal Questionnaire 0.63† 0.54† 0.51†Illness Intrusiveness Scale 0.57† 0.30† 0.35†

Glasgow et al., 2000 (n = 47) t1 Difficulty t2 Confidence t1 Difficultyversion with t1 version with t2 version with

behavior behavior t2 behavior

1) Regimen-Specific Barrier ScoresDiet:

Kristal Food Habits Questionnaire total score 0.20 ns – 0.08 nsExercise:

Physical Activity Scale for the Elderly (PASE) 0.13 ns – 0.02 nsStress Management:

Cohen Perceived Stress Scale 0.49† – 0.49†2) Overall Barriers Score

Kristal Food Habits Questionnaire total score 0.12 ns – 0.05 nsPhysical Activity Scale for the Elderly (PASE) 0.12 ns – -0.02 nsCohen Perceived Stress Scale 0.55† – 0.55†CES-D Depression Scale 0.41‡ – –Illness Intrusiveness Scale 0.32§ – 0.25 ns

Table 3. Concurrent and Prospective Validity Coefficients (r) for the Diet, Exercise, andStress Management, and Overall Scales of the Challenges to Illness Management Scalea

P values: †P < 0.001; ‡P < 0.01; §P < 0.05aAll correlations, with the exception of the one value with a negative sign, were in the predicted direction (e.g., more barriers or lessconfidence associated with lower levels of self-management), but the direction of some scales has been reversed here for comparative purposes and simplicity of presentation.


to modify demonstrated barrierswould prove beneficial, this is notnecessarily the case. Some barriersmay prove resistant to change, andalteration of others may not produceimproved outcomes—especially ifsuch an intervention is not related topatient values and goals.37,38 Clearly,more barriers-based interventionresearch is needed.

The Phase 1 psychosocial barriersstudies have done a good job ofincluding different ethnic and culturalgroups and have identified both com-mon and unique barriers to self-man-agement across different groups.However, later-phase studies generallyhave yet to apply these findings or toinclude diverse or representativepatient samples.

Based on the research to date, thegeneral psychosocial barriers thatseem most strongly and consistentlyrelated to low levels of self-manage-ment and diabetes-related quality oflife are low self-efficacy and low levelsof family social support. There arealso emerging literatures, includingmultiple prospective studies, support-ing the impact of both depression andPersonal Models of Diabetes on dia-betes outcomes. Finally, two morespecific measures of barriers, fear ofhypoglycemia and diabetes-relateddistress, appear to be clinically useful.

Methodological and InterpretiveIssuesAssessment of diabetes barriers pre-sents a variety of methodological chal-lenges. Foremost among these aredecisions regarding how barriers ques-tions will be phrased and what criteri-on variables will be used. Standardquestionnaire development issues ofpotential response bias, ensuring anadequate range of responses, andinvestigating and/or controlling forpotential social desirability andmethod variance issues apply. In addi-tion, there are issues specific to barri-ers assessment that raise interpretivecomplexities.

One important issue concerns thetype of ratings that respondents areasked to provide and the time frameof the questions (e.g., “over the past 6months” vs. “currently” vs. “thinkingahead over the coming 3 months”).Many barriers questions have askedhow difficult barriers items have beenover a previous period of time.Having done this ourselves, we under-stand that this is intuitively appealingand seems straightforward.

Interpretive difficulties arise, however,when attempting to differentiate self-reports of self-management behaviors(“How often did you do X?”) fromthis type of barriers assessment(“How difficult was barrier A in pre-venting you from doing X?”).

Many respondents likely have diffi-culty understanding the differencebetween these two questions. If theydo make the distinction, it is likelythat respondents will base their diffi-culty ratings on their self-managementbehavior outcomes. This is problemat-ic and somewhat circular if barriersare to be considered as predictors ofself-management. Methodologicallycleaner ways to state barriers ques-tions are a) to ask about the frequencyof barrier occurrence (and to separatethis from assessment of self-manage-ment behaviors), or b) to ask aboutconfidence in overcoming barriersover a future interval (and to prospec-tively assess self-management).

This brings up the related issue ofwhat should be used as criterion vari-ables in studies of diabetes barriers. Inour opinion, self-management—espe-cially if it is possible to assess self-management via different modali-ties—and diabetes-related quality oflife are the most direct and appropri-ate consequences of barriers. As dis-cussed above, the most commonlyused outcome in the literature hasbeen HbA1c, however. This practicecan create interpretive difficulties,especially when only cross-sectionalanalyses are conducted. In the mostextreme (but not unusual) case, whenbarriers are assessed “at present” andHbA1c is collected concurrently(reflecting glycemic control over thepast 6–8 weeks) it is impossible todetermine directionality of effects. Themost logical conclusion if a relation-ship is found in this type of studywould be that poor glycemic controlproduced barriers, rather than viceversa, given the temporal parameters.

This is why prospective designs area necessity in barriers research. Even ifusing a prospective design, it is strong-ly recommended that investigationsalso include measures of self-manage-ment and quality of life when usingHbA1c as an outcome. This strategythen permits investigation of directversus indirect effects of barriers. Afinal interpretive issue concernswhether one should expect barriersratings to be stable or to change overtime, especially as a result of interven-tion. In our view, this depends on

how barriers questions are worded. Ifquestions are phrased concerning thefrequency of experiencing differentbarriers, one would not necessarilyexpect intervention to impact the fre-quency of different barriers. On theother hand, if barriers questions arephrased as self-efficacy type questions,asking about confidence that one canovercome different barriers, it wouldbe expected that such a measurewould be responsive to intervention.

Future DirectionsWe conclude with three recommenda-tions for future research.1. Move beyond correlational stud-

ies to research designs that allowcausal inferences. Either longitu-dinal designs that permit causalmodeling or, preferably, interven-tion studies that tailor interven-tions based on reported barriersare needed to advance the field.Such designs will allow investiga-tion of the extent to whichchanges in barriers versus otherprocess factors are related toimprovements in study outcomes.

2. Identify similarities and differ-ences in both individual barriersand in types of barriers across dif-ferent ethnic and cultural groupsand across different regimenbehaviors. Few direct compar-isons have been made of psy-chosocial barriers across differentgroups. A recent exception to thisconclusion is a cross-culturalstudy of the PAID instrumentamong Dutch and U.S. patients.39

Given the well-established findingof low intercorrelations amongdifferent self-management behav-iors, it is likely that different bar-riers may predict different self-management behaviors. Few stud-ies have addressed this level ofcomplexity.

3. Develop practical assessment andintervention approaches that canbe implemented in primary careor similar settings and are capableof reaching large and representa-tive segments of the population.There is still important basicresearch to be conducted regard-ing psychosocial diabetes barriers.However, more efforts need to bedevoted to developing methodsthat can be integrated broadlyinto practice settings (as evi-denced by the dearth of Phase 4and Phase 5 studies). We need“translational research” to test


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methods for adapting barriersassessments and barrier-basedinterventions that are feasible toconduct in real-world settings.19

AcknowledgmentsPreparation of this report was sup-ported by National Institutes ofHealth grants HL62156 and DK35524 and by grant 030103 from theRobert Wood Johnson Foundation.

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