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Health Education Research Advance Access originally published online on November 30, 2004
Health Education Research 2005 20(4):402-409; doi:10.1093/her/cyg140
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Health Education Research Vol.20 no.4, © Oxford University Press 2004; All rights reserved

The Chronic Illness Resources Survey: cross-validation and sensitivity to intervention

Russell E. Glasgow1,4, Deborah J. Toobert2, Manuel Barrera, Jr3 and Lisa A. Strycker2

1 Kaiser Permanente, Denver, CO 80237-8066, 2 Oregon Research Institute, Eugene, OR 97403 and 3 Arizona State University, Tempe, AZ 85287, USA

4 Correspondence to: R. E. Glasgow, 335 Road Runner Road, Penrose, CO 81240, USA; E-mail: russg{at}ris.net


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Appendix: Chronic Illness...
 References
 
There is great interest in, but few instruments to assess, multiple levels of support and community resources from a social–ecological perspective. This study evaluated the psychometric characteristics of the Chronic Illness Resources Survey (CIRS) and its sensitivity to a multifaceted social–ecological intervention to enhance personally relevant community resources supportive of healthful lifestyles. Participants were 293 post-menopausal women having type 2 diabetes who were part of a multiple-behavior lifestyle change program. Key measures included the CIRS, a validated Food Frequency Questionnaire, the Kristal Fat and Fiber Behavior Questionnaire, the CHAMPS Activities Questionnaire for Older Adults, and other measures of social support. Results revealed that the CIRS displayed good psychometric characteristics in this new sample, was significantly correlated as predicted with established measures of social support, was sensitive to intervention, and partially mediated the effects of intervention on both dietary and physical activity outcomes. The 22-item CIRS scale appears useful for assessing multilevel support resources, predicting successful behavior change and detecting social–ecological intervention effects.


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Appendix: Chronic Illness...
 References
 
Research evidence on both social–ecologic theory (Stokols et al., 1996Go; Krieger, 2001Go) and social support (Cohen et al., 2000Go) confirm the importance of multiple types and multiple levels of support. Emerging research on social capital (Kawachi et al., 1997Go) also points to the importance of community connections and social ties. Although there are numerous measures of interpersonal support from family and friends, there are very few multilevel assessments of community support that span proximal to distal levels of supportive resources. The Chronic Illness Resources Survey (CIRS) evaluates support for healthful lifestyle behaviors and chronic illness self-management from multiple sources, including family and friends, the neighborhood, community, media, and health policies. It is based on a multilevel, social–ecological model of support resources (Figure 1).



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  		Fig. 1. Pyramid model of social–ecological support resources.

 
An initial study of the CIRS (Glasgow et al., 2000Go) found it to be applicable across multiple illnesses, to correlate with predicted convergent measures and to be relatively stable over time. Both a full-length 64-item scale and a briefer 22-item scale predicted illness self-management better than several other social support scales (Glasgow et al., 2000Go).

Subsequent research found that the CIRS could be used to tailor intervention to enhance community resources (Riley et al., 2001Go), and translated and validated the CIRS in Spanish (Eakin et al., submitted). There are, however, no studies of its sensitivity to intervention effects or the extent to which changes in multilevel resources mediate treatment outcomes.

The purposes of this report are to (1) provide a more comprehensive validation of the CIRS by assessing its reliability and relationship to other support measures, (2) provide data on sensitivity to multilevel community resources enhancement interventions, and (3) determine whether the CIRS mediates intervention effects.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Appendix: Chronic Illness...
 References
 
Participants
Participants were 293 post-menopausal women with type 2 diabetes who were patients of collaborating primary care clinics. Fifty-one percent of eligible women contacted agreed to participate, and were similar to non-participants and to the diabetes population in Oregon. Participant characteristics are summarized in Table I. Representativeness data are presented in Toobert et al. (Toobert et al., 2002Go). Enrollees were representative of type 2 patients in participating primary care offices and the diabetes population of the state.


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  		Table I. Descriptive statistics and baseline characteristics of participants by treatment condition

 
Design and intervention
Participants were randomized to usual care and/or to the Mediterranean Lifestyle Program. This project investigated whether an ongoing behavior change intervention and support group sessions enhanced the practice and maintenance of healthful lifestyle behaviors relative to usual care. Intervention participants took part in weekly meetings for 6 months, which consisted of 1 hour each of physical activity, practice of stress management, Mediterranean-style potluck and tips for adhering to the Mediterranean diet, and support groups. Frequency of contact was faded over the next 6 months, and meetings focused on enhancing social and community resources to support lifestyle changes. Design and intervention details are presented in Toobert et al. (Toobert et al., 2004Go).

Measures
The brief CIRS (Glasgow et al., 2000Go) (Appendix) provided a profile of an individual's support for disease management-related behaviors, ranging from proximal support (e.g. family and friends) to more distal factors (e.g. neighborhood or community). Respondents rated the extent to which each of the 22 items was used over the past 6 months on a 1 (not at all) to 5 (a great deal) Likert scale. For analyses, we used newly developed CIRS scales for support for diet ({alpha} = 0.63) and physical activity ({alpha} = 0.67), as well as an overall summary score and subscales previously developed on personal, family/friend, neighborhood, work, community/organization, health care team and media resources.

Social desirability
To evaluate the relationship of social desirability to the CIRS, the Balanced Inventory of Desirable Responding (Paulhus, 1984Go) was used.

Convergent validity measures
A 7-day self-monitoring log of supportive resources from friends, family, health care provider and neighborhood was developed for this project: {alpha}s ranged from 0.91 to 0.96. Four scales from the UCLA Social Support Inventory (Schwarzer et al., 1994Go) were used. These scales documented support from support group ({alpha} = 0.97), friends ({alpha} = 0.91), health care provider ({alpha} = 0.81) and spouse/partner/other family members ({alpha} = 0.87).

Outcome measures
We selected a priori one key dietary change measure and one key physical activity measure to evaluate the potential mediating effects of CIRS scales. We used the total score from the Fat and Fiber Behavior Questionnaire (Kristal et al., 1990Go) to measure behaviors related to low-fat and high-fiber eating patterns. The CHAMPS Activities Questionnaire for Older Adults (Stewart et al., 1997Go) provided an estimate of total kilocalories expended. The 3-month stability coefficient for expended calories per week was 0.84.

Analyses
Pearson product–moment correlation coefficients were used to evaluate relationships among scales that were approximately normally distributed. Point-biserial coefficients or Spearman's correlation coefficient for ranked data, as appropriate, were used to assess relations among non-normal variables. To evaluate within-group sensitivity to change from baseline to 12-month follow-up, paired t-tests were used. Between-condition sensitivity to change was assessed via analyses of covariance (ANCOVAs), using baseline scores on the dependent variable and the Socially Desirable Responding Scale as covariates.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Appendix: Chronic Illness...
 References
 
The overall CIRS scale and most of the subscales displayed good variability and distributions (Table II). Six of the nine subscales showed good internal consistency ({alpha} > 0.60) for very brief scales and the {alpha} for the summary score was 0.82. These {alpha}s are not exceedingly high, but most subscales were only three or four items long and some contained items from different domains (e.g. neighborhood/community).


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  		Table II. Psychometric characteristics of CIRS scales

 
Most subscales were reasonably stable over the 6-month test–re-test interval (usual care patients only). Test–re-test correlations ranged from 0.55 to 0.66 for the various subscales and the summary score coefficient was 0.70. The various subscales were moderately intercorrelated (r = 0.11–0.48, median = 0.25).

Social desirability was not significantly correlated with the CIRS scale. Of nine demographic and medical characteristic measures, only age, weight, years diagnosed and hemoglobin A1c were correlated with the CIRS summary scale. The overall range of correlations was 0.02–0.21 (median = 0.10) and the magnitude of the correlations that were significant was quite modest (absolute values of r = 0.14–0.21, P < 0.05), although each was in the expected direction.

Most CIRS scales were significantly correlated as predicted with the other support measures (Table II). Four of 10 correlations with other established support measures were ≥0.40 and all were significant at P < 0.05. All subscales except for work were also significantly related to self-monitoring data for that respective dimension.

Sensitivity to change
The overall CIRS score was sensitive to change, producing both significant within-group change from baseline for intervention patients and significantly greater change among intervention than usual care patients (Table III). The family/friend, neighborhood/community and work subscales, as well as the new dietary and physical activity scales, were similarly sensitive to intervention. The health care team and media/policy subscales did not show improvement.


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  		Table III. Sensitivity to change of CIRS scales for intervention condition

 
Mediational analyses
Following Baron and Kenny (Baron and Kenny, 1986Go), multiple regression analyses were conducted to determine if changes in CIRS diet, exercise and total scores mediated intervention effects on eating (Fat and Fiber Behavior scale) and physical activity (CHAMPS). There were significant intervention effects at the 12-month follow-up on the CIRS total score as well as the CIRS diet and physical activity subscales, satisfying one of the conditions for mediation. A second condition was met as significant intervention effects were found for the dietary, F(1,218) = 49.87, P < 0.001, and physical activity outcomes, F (1, 199) = 11.61, P < 0.001. The final step was to show that the effects of the intervention on outcomes were eliminated (total mediation) or significantly reduced (partial mediation) with the simultaneous entry of the CIRS scores in the regression, following procedures described by Sobel (Sobel, 1982Go).

When change in the Fat and Fiber Behavior scale was the outcome, change in CIRS total scores from baseline to the 12 months were z = 2.64, P = 0.02 and changes in CIRS diet subscale scores were z = 2.54, P = 0.01, which indicate partial mediation of intervention effects (Table IV). Also, change in CIRS physical activity subscale partially mediated intervention effects on changes in CHAMPS, z = 2.32, P = 0. 02. Not shown is a marginal, but non-significant, mediation effect for CIRS total scores on the CHAMPS, z = 1.87, P = 0.06.


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  		Table IV. Hierarchical multiple regression results to test CIRS scores as mediators of intervention effects

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Appendix: Chronic Illness...
 References
 
This replication and extension of our initial study of the CIRS considerably expands the information available on its psychometric characteristics. This diabetes study had a larger sample size (N = 293) than the original study (N = 123) and replicates the results of earlier multi-disease studies (Glasgow et al., 2000Go; Eakin et al., submitted). The CIRS and its subscales have reasonably good internal consistency and test–re-test reliability, especially for brief measures. With the exception of the work subscale, all CIRS scales demonstrated construct validity by being significantly related to other measures assessing similar constructs and to 7-day, self-monitoring records of community support received.

The CIRS generally did not relate to demographic or medical history characteristics and the signs of the few significant modest associations were in the expected direction. Given concerns about self-report measures being influenced by socially desirable responding, it was especially encouraging that the CIRS was not significantly associated with social desirability.

Possibly most relevant to health educators, the CIRS summary score and five of its subscales were sensitive to change: they showed significantly greater improvement over time in the intervention than the usual care condition. This study also expands validity data to the new diet and physical activity subscales. The mediation analyses revealed that the intervention produced significant improvements on key behavior change outcomes, that the CIRS was significantly related to those outcomes and that it partially mediated intervention effects.

The CIRS predicted both dietary and physical activity outcomes, and is one of the few social support instruments to produce indicants at the multiple social–ecological levels relevant for community interventions. It appears relevant for Latino as well as Anglo patients (Eakin et al., submitted). It may be of value to state diabetes control programs and community health centers that seek to enhance community support. A strength of this study is the relatively wide range of education and income among participants within the age, race/ethnicity, gender and post-menopausal status studied. Design strengths include the longitudinal design including measures of stability and sensitivity to change, and the mediational analyses.

Limitations of the study include the restriction to one community setting and one multi-faceted intervention (other studies have shown the applicability of the CIRS to different interventions and samples), and to post-menopausal women having type 2 diabetes. The CIRS subscales may be too brief to adequately capture or represent some of the different levels of resources. Also, the work and media subscales do not appear to perform as well as the others and may need to be modified or dropped. Further work is needed with samples having other chronic illnesses, and to assess the sensitivity of the CIRS to different multilevel lifestyle, healthcare, community interventions and ethnicities. Its applicability for adolescents and young adults has not been determined.

The CIRS is in the public domain, and may be reprinted and used without charge or permission (Appendix). We encourage other researchers and health educators to use the scale, improve upon it and adapt it for their purposes. We recommend further work with the CIRS, especially with diverse populations, as a tool to help evaluate community-based interventions. The new diet and physical activity subscales, as well as the original CIRS subscales, should also be appropriate for, but need to be evaluated with, non-diseased populations and preventive interventions.


   Appendix: Chronic Illness Resources Survey (CIRS)
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix: Chronic Illness...
References
 

The following questions ask about a variety of different resources that people may use to manage their illness. For each item, select the number that best indicates your experience over the past 6 months.

Over the past 6 months, to what extent:
Not at all
A little
A moderate amount
Quite a bit
A great deal
1. Has your doctor involved you as an equal partner in making decisions about illness management strategies and goals? (HC) {square}1 {square}2 {square}3 {square}4 {square}5
2. Has your doctor or other health care advisor listened carefully to what you had to say about your illness? (HC) {square}1 {square}2 {square}3 {square}4 {square}5
3. Has your doctor or other health care provider thoroughly explained the results of tests you had done (e.g. cholesterol, blood pressure or other laboratory tests? (HC) {square}1 {square}2 {square}3 {square}4 {square}5
4. Have family or friends exercised with you? (FF, E) {square}1 {square}2 {square}3 {square}4 {square}5
5. Have you shared healthy low-fat recipes with friends or family members? (FF, D) {square}1 {square}2 {square}3 {square}4 {square}5
6. Family or friends bought food or prepared food for you that were especially healthy or recommended? (FF, D) {square}1 {square}2 {square}3 {square}4 {square}5
7. Have you focused on the things you did well to manage your illness instead of those you did not? (P) {square}1 {square}2 {square}3 {square}4 {square}5
8. Have you thought about or reviewed how you were doing in accomplishing your disease management goals? (P) {square}1 {square}2 {square}3 {square}4 {square}5
9. Have you arranged your schedule so that you could more easily do the things you needed to do for your illness? (P) {square}1 {square}2 {square}3 {square}4 {square}5
10. Have you walked or exercised outdoors in your neighborhood? (N, E) {square}1 {square}2 {square}3 {square}4 {square}5
11. Have you walked or done other exercise activities with neighbors? (N, E) {square}1 {square}2 {square}3 {square}4 {square}5
12. Have you eaten at a restaurant that offered a variety of tasty, low-fat food choices? (N, D) {square}1 {square}2 {square}3 {square}4 {square}5
13. Have you gone to parks for picnics, walks or other outings? (N) {square}1 {square}2 {square}3 {square}4 {square}5
14. Have you read articles in newspapers or magazines about people who were successfully managing a chronic illness? (M) {square}1 {square}2 {square}3 {square}4 {square}5
15. Have you had health insurance that covered most of the costs of your medical needs including medicine? (MP) {square}1 {square}2 {square}3 {square}4 {square}5
16. Have you seen billboards or other advertisements that encouraged not smoking, low-fat eating or regular exercise? (MP) {square}1 {square}2 {square}3 {square}4 {square}5
17. Have you attended free or low-cost meetings (e.g. Weight Watchers, church groups, hospital programs) that supported you in managing your illness? (O) {square}1 {square}2 {square}3 {square}4 {square}5
18. Have you volunteered your time for local organizations or causes? (O) {square}1 {square}2 {square}3 {square}4 {square}5
19. Have you attended wellness programs or fitness facilities? (O) {square}1 {square}2 {square}3 {square}4 {square}5
20. Have you had a flexible work schedule that you could adjust to meet your needs? (Leave blank if you don't work.) (W) {square}1 {square}2 {square}3 {square}4 {square}5
21. Has your workplace had rules or policies that made it easier for you to manage your illness (such as no smoking rules or time off work to exercise)? (Leave blank if you don't work.) (W) {square}1 {square}2 {square}3 {square}4 {square}5
22. Have you had control over your job in terms of making decisions and setting priorities? (Leave blank if you don't work.) (W)
 {square}1
 {square}2
 {square}3
 {square}4
 {square}5

HC = health care; FF = friends/family; D = Dietary; P = Personal; N = Neighborhood; E = Exercise; MP = Media/Policy; O = Organizational; W = Work or Volunteer Subscales. Use mean of contributing items to create scales.


   Acknowledgments
 
The work reported here was supported by grant R01 HL62156 from the National Heart, Lung and Blood Institute. The authors thank Kate Bennett, SuAn Carey, Melda DeSalvo, Katie Geiser, Nancy Hopps, Sally Huck, Molly Kennedy, Tamberly Koorndyk, Katie Marcotte, Donna O'Neill, Janice Radcliffe and Serge Renaud for their contributions during the development and intervention phases of this project. We are deeply indebted to the wonderful women who participated in this study.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix: Chronic Illness...
 References
 
Baron, R. and Kenny, D. (1986) The moderator–mediator variable distinction in social psychological research: conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.[CrossRef][ISI][Medline]

Cohen, S., Underwood, L.G. and Gottlieb, B.H. (2000) Social Support Measurement and Intervention. Oxford University Press, New York.

Glasgow, R.E., Strycker, L.A., Toobert, D.J. and Eakin, E.G. (2000) The Chronic Illness Resources Survey: a social–ecologic approach to assessing support for disease self-management. Journal of Behavioral Medicine, 23, 559–583.[CrossRef][ISI][Medline]

Kawachi, I., Kennedy, B.P., Lochner, K. and Prothrow-Stith, D. (1997) Social capital, income inequality and mortality. American Journal of Public Health, 87, 1491–1498.[Abstract/Free Full Text]

Krieger, N. (2001) Theories for social epidemiology in the 21st century: an ecosocial perspective. International Journal of Epidemiology, 30, 668–677.[Free Full Text]

Kristal, A.R., Shattuck, A.L. and Henry, H.J. (1990) Patterns of dietary behavior associated with selecting diets low in fat: reliability and validity of a behavioral approach to dietary assessment. Journal of the American Dietetic Association, 90, 214–220.[ISI][Medline]

Paulhus, D.L. (1984) Two-component models of social desirable responding. Journal of Personality and Social Psychology, 46, 598–609.[CrossRef][ISI]

Riley, K.M., Glasgow, R.E. and Eakin, E.G. (2001) Resources for health: a social–ecological intervention for supporting self-management of chronic conditions. Journal of Health Psychology, 6, 693–705.[Abstract]

Schwarzer, R., Dunkel-Schetter, C. and Kemeny, M.E. (1994) The multidimensional nature of received social support in gay men at risk of HIV infection and AIDS. American Journal of Community Psychology, 22, 319–339.[ISI][Medline]

Sobel, M.E. (1982) Asymptotic confidence intervals for indirect effects in structural equation models. In: Leinhard, S. (ed.), Sociological Methodology. American Sociological Association, Washington, DC, pp. 290–312.

Stewart, A.L., Sepsis, P.G., King, A.C., McLelland, B.Y., Roitz, K. and Ritter, P.L. (1997) Evaluation of CHAMPS, a physical activity promotion program for older adults. Annals of Behavioral Medicine, 19, 353–361.[ISI][Medline]

Stokols, D., Allen, J. and Bellingham, R.L. (1996) The social ecology of health promotion: implications for research and practice. American Journal of Health Promotion, 10, 247–251.[ISI][Medline]

Toobert, D.J., Strycker, L.A., Glasgow, R.E. and Bagdade, J.D. (2002) If you build it, will they come? Reach and adoption associated with a comprehensive lifestyle management program for women with type 2 diabetes. Patient Education and Counseling, 48, 99–105.[CrossRef][ISI][Medline]

Toobert, D.J., Glasgow, R., Strycker, L.A., Barrera, M., Jr, Radcliffe, J.L., Wander, R.C. and Bagdade, J.D. (2003) Biologic and quality of life outcomes from the Mediterranean Lifestyle Program: a Randomized Clinical Trial. Diabetes Care, 26, 2288–2293.[Abstract/Free Full Text]

Toobert, D.J., Strycker, L.A., Glasgow, R.E., Barrera, M. and Angell, K. (2004) Effects of the Mediterranean Lifestyle Program on multiple risk behaviors and psychosocial outcomes among women at risk for heart disease. Annals of Behavioral Medicine, in press.

Received on March 30, 2004; accepted on October 21, 2004


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