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Epidemiology |

Tailored and Targeted Interventions to Encourage Dilated Fundus Examinations in Older African Americans FREE

Nancy J. Ellish, DrPH; Renee Royak-Schaler, PhD; Eve J. Higginbotham, MD
[+] Author Affiliations

Author Affiliations: Departments of Ophthalmology and Visual Sciences (Dr Ellish) and Epidemiology and Public Health (Dr Royak-Schaler), University of Maryland School of Medicine, Baltimore; and Health Sciences, Howard University, Washington, DC (Dr Higginbotham). Dr Ellish is now with the Department of Epidemiology and Public Health, University of Maryland School of Medicine.

†Deceased.


Arch Ophthalmol. 2011;129(12):1592-1598. doi:10.1001/archophthalmol.2011.190.
Text Size: A A A
Published online

Objectives To compare the effects of a tailored (individualized) and targeted (designed for a subgroup) print intervention in promoting dilated fundus examinations (DFEs) in older African Americans and to determine whether other factors (eg, demographics, preventive health practices, health literacy score, behavioral intentions, and DFE rates) are associated with getting a DFE.

Methods African Americans aged 65 years or older who had not had a DFE in at least 2 years were recruited from community settings. Participants were randomized to receive either a tailored or targeted newsletter. Telephone follow-up was conducted at 1, 3, and 6 months to ascertain eye examination status. All participant-reported DFEs were confirmed by contacting their eye doctor (optometrist or ophthalmologist) by telephone.

Main Outcome Measure Eye doctor–confirmed DFE at 6 months.

Results Of the 329 participants enrolled, 128 (38.9%) had an eye doctor–confirmed DFE. No significant difference was noted in this measure by intervention group (relative risk, 1.07; 95% confidence interval, 0.82-1.40), with 66 participants in the tailored group (40.2%) and 62 participants in the targeted group (37.6%) having an eye doctor–confirmed DFE. Based on logistic regression analysis, reading the newsletter (odds ratio, 1.76; 95% confidence interval, 1.08-2.87) and planning to make an appointment for a DFE (odds ratio, 2.46; 95% confidence interval, 1.42-4.26) were significant predictors for DFE.

Conclusions The tailored and targeted interventions were equally effective in promoting eye doctor–confirmed DFEs at 6 months. Given the increased cost and effort associated with tailoring, our results suggest that well-designed targeted print messages can motivate older African Americans to get DFEs.

Trial Registration clinicaltrials.gov Identifier: NCT00649766

Figures in this Article

Dilated fundus examinations (DFEs) have been demonstrated to be an important strategy to diagnose treatable causes of visual impairment, such as glaucoma and diabetic retinopathy. However, the reluctance of people to undergo such examinations continues to be an important public health problem.1 This problem will grow as the US population ages, with a resultant increase in age-related eye disease. Despite public and professional educational programs to increase awareness about eye health,13 there is still a lack of knowledge as well as misconceptions about DFEs and eye disease.4 Interventions to increase the proportion of people who undergo DFEs at the recommended schedule can have a major influence on public health and affect people's vision-related and general health-related quality of life.

This article describes the results of a randomized controlled trial comparing tailored and targeted print interventions designed to promote eye examination behavior in older African Americans. Generic, targeted, and tailored health education material can be considered as being on a continuum with increasing degrees of customization and audience segmentation.5 Generic messages provide the same information to all people, targeted messages are designed for a subgroup, and tailored messages are based on each person's characteristics.6 Information processing theory postulates that tailoring provides information that is more personally relevant than other messages; people therefore pay more attention to the information, which eventually leads to behavior change.7 Although tailored messages have been shown to influence the adoption and maintenance of various health-related behaviors, 710 this health communication strategy has had limited application for eye examination behavior.1113

We investigated the hypothesis that the rate of DFEs confirmed by an eye doctor (optometrist or ophthalmologist) would be higher in the tailored compared with the targeted intervention group. Constructs from the Health Belief Model,14 Transtheoretical Model,15 and Precaution Adoption Process Model16 guided intervention development. To better understand the reasons why people undergo DFEs, we examined the associations between demographics, preventive health practices, health literacy score, behavioral intentions, and DFE rates.

Community-dwelling African Americans aged 65 years or older who had not had a DFE in at least 2 years were recruited for the study. We conducted presentations at sites frequented by older African Americans, such as senior centers and church groups, networked with community organizations, placed advertisements on the radio and in newspapers, and attended health fairs. Details of the recruitment process have been previously described.17 This research was approved by the Institutional Review Board of the University of Maryland School of Medicine.

Interested participants were screened for eligibility based on self-report; those included in the study signed consent forms and provided the research team with contact information. The health literacy skills of our participants were assessed by trained interviewers who administered the revised Rapid Estimate of Adult Literacy in Medicine (REALM-R),18 a shortened version of the REALM word recognition test. We administered a questionnaire to collect baseline demographic information and information about behavioral intention, perceived barriers to and benefits of DFEs, and preventive health behaviors. We adapted questions from the mammography,9,19 ophthalmology,2022 and health behavior theory literature,16,23 as well as information from focus groups,4 to design the questionnaire. Participants were paid $25 to compensate them for their time.

Participants were randomly assigned to the tailored or targeted intervention groups, with both groups receiving a 4-page newsletter designed to have a Flesch-Kincaid reading level of less than sixth grade. As demonstrated in Table 1, the newsletter consisted of 6 sections, including a testimonial designed to model eye examination behavior and a barrier table to convey specific ideas to overcome barriers. Each section of the tailored newsletter was tailored on specific variables. We also included information about Medicare coverage of eye examinations.

Table Graphic Jump LocationTable 1. Tailoring Variables Based on Participants' Responses by Newsletter Section

All participants randomized to the targeted group received the same newsletter with the same messages. Each participant randomized to the tailored group received a unique newsletter, with the same sections and pictures as the targeted newsletter but with specific messages based on his or her responses to selected questions from the baseline questionnaire. For example, the “ask the doctor” section included 5 different messages in the message library, with a predetermined ranking to determine the order in which the messages would be used. People in the tailored arm received 1 of these 5 messages, depending on their questionnaire responses. One of these messages was preselected for the targeted newsletter. Newsletters were mailed within 3 weeks of study enrollment.

Participants were contacted by telephone at 1, 3, and 6 months. We ascertained whether they made an appointment for or had an eye examination during the previous period or what their intentions were regarding undergoing a DFE in the future. At the 1-month follow-up, participants were asked whether they read the newsletter. If a participant reported having a DFE, no subsequent calls were made. If a person could not be reached by telephone after 8 attempts at different times and days, we called their contacts. If they could still not be reached, we mailed a brief questionnaire requesting that it be completed and returned by mail. Lastly, we sent a certified letter asking the participant to contact us. If we were unable to contact a participant at a follow-up period, we attempted to contact them at subsequent follow-up periods.

Participants' reports of DFEs, as well as reports of making an appointment at the 6-month follow-up, were confirmed by contacting their eye doctor by telephone. Information about the date of the appointment and whether the appointment included dilation was reported by office staff. Participants who withdrew before follow-up or could not be contacted were considered as not having had a DFE.

MEASURES

We asked several questions to categorize participants according to their intention to undergo a DFE, based on the Transtheoretical Model and Precaution Adoption Process Model. For our analysis, participants who never thought about having a DFE were classified as being in stage 1. Respondents who decided that they did not want a DFE were in stage 2, and those who had not yet decided were in stage 3. Respondents who wanted a DFE were asked about their plans. Those intending to make an appointment within the next 6 months were in stage 4, and those planning to make an appointment within the next month were in stage 5. Participants planning to make an appointment someday but not very soon were considered as still deciding and were in stage 3. On the basis of these definitions, stages 1 through 3 were considered the precontemplation stage of the Transtheoretical Model, stage 4 corresponded to the contemplation stage, and stage 5 corresponded to the preparation stage.

The questionnaire asked when participants most recently had a routine medical examination, had their eyes checked to determine their need for eyeglasses, and had a mammogram for women or prostate-specific antigen blood test for men. We created a variable by assigning 1 point for each of the preventive care activities a participant engaged in during the last 2 years, with scores ranging from 0 to 3.

To calculate a health literary score, 1 point was given for each of the 8 test words on the REALM-R that was pronounced correctly. Scores between 0 and 6 were considered poor literacy and scores of 7 or 8 were considered adequate literacy.18

We classified participants as having read the newsletter if they reported having read some or all of it. Some participants were not asked the newsletter questions because they had died, withdrawn before their first follow-up telephone call, were lost to follow-up, or received a version of the follow-up questionnaire that did not have the newsletter questions. Participants were considered as not having read the newsletter if they were not asked the newsletter questions, did not remember receiving it, had not read any of it, or did not remember whether they read it.

There were 4 times that interaction with the study participants could affect eye examination behavior: after enrollment, after the newsletter was sent, and after the first and second follow-up telephone calls. Timing of the DFE was first calculated by determining where on the timeline the DFE occurred. Timing was then classified as being before receipt of the intervention, before the first telephone call, after the first telephone call, or after the second telephone call. If a DFE occurred within 3 days of the newsletter being mailed, we adjusted the timing to before receipt of the intervention. If, at the first or second telephone call, a person reported having an appointment scheduled and the DFE occurred within 6 weeks of the telephone call date, we reasoned that the telephone call did not affect the decision to have an eye examination and adjusted the timing. If the DFE occurred before the first follow-up or after the third telephone call, no adjustments were made.

SAMPLE SIZE AND STATISTICAL METHODS

On the basis of sample size calculations, we needed 130 people in the tailored and targeted groups to give us 90% power to detect a minimum difference in eye examination rates of 20% between the groups. We believed that differences less than 20% would not justify the added time, expense, and complexity of using tailored rather than targeted messages. Assuming an 80% follow-up rate, our goal was to enroll 165 people in each group, for a total of 330 people enrolled in the study. Because our follow-up rates were higher than 80%, our final sample size yielded 95% power to detect a 20% difference between groups.

For the analysis, we first compared participants by intervention group for key baseline demographic variables. Also selected were independent variables from the baseline questionnaire that could have an effect on eye examination behavior, including health-related variables, literacy score, and behavioral intention. To examine our primary outcome measure, we compared eye doctor–confirmed DFE rates at 6 months between those randomized to the tailored and targeted groups, based on intent-to-treat analysis. We also compared demographic variables and DFE status for participants with complete, partial, and no follow-up. To determine whether other factors were associated with getting a DFE, we compared those who did with those who did not have an eye doctor–confirmed DFE by demographic and other variables.

The Pearson χ2 test was used to compare dichotomous variables and Mantel-Haenszel χ2 test was used for ordinal response variables. For small cell size, the Fisher exact test was used. Relative risks and 95% confidence intervals were calculated to assess the strength of the association between covariates and getting a DFE. Analyses were performed using commercial software (SAS, version 9.1; SAS Institute, Inc, Cary, North Carolina).

We conducted logistic regression analyses, using an eye doctor–confirmed DFE as the outcome variable, and calculated odds ratios and 95% confidence intervals. Covariates considered in the logistic regression analysis included variables that were statistically significant or approached significance (P < .25) in the univariate analysis. The intervention group was also included in the model.

We enrolled 329 participants between June 27, 2006, and September 21, 2007, with 164 randomized to receive the tailored newsletter and 165 randomized to receive the targeted newsletter, as shown in the Figure. One person signed a consent form but did not complete the questionnaire and was not randomized. Of the 329 participants randomized, 279 (84.8%) completed all follow-up telephone calls for which they were eligible, 14 participants (4.3%) had no follow-up information, and 36 participants (10.9%) had partial follow-up. Among those with partial follow-up, 30 completed the last follow-up for which they were eligible. There was no significant difference in follow-up rates between the 2 intervention groups (χ2 = 5.1, P = .08).

Place holder to copy figure label and caption
Graphic Jump Location

Figure. Follow-up status of 329 study participants.

As reported in Table 2, at baseline the intervention groups were comparable for demographic and other variables. We then compared these variables by follow-up status and found a statistically significant association between follow-up status and age, sex, health status, and preventive care. Participants who were 80 years or older, male, reported poor health, and received no preventive care in the previous 2 years were less likely to have complete follow-up.

Table Graphic Jump LocationTable 2. Comparison of Demographic and Other Variables by Intervention Groupsa

We compared participants who reported reading the newsletter with those who did not and found statistically significant differences for age (χ2 = 15.98, P < .001 by Mantel-Haenszel χ2 test), income (χ2 = 12.97, P < .001 by Mantel-Haenszel χ2 test), and literacy score (χ2 = 4.47, P = .03 by Mantel-Haenszel χ2 test). Participants 80 years or older and those with annual income less than $20 000 were less likely to read the newsletter. Among those with adequate literacy scores, 57.0% read the newsletter compared with 44.9% of those with poor literacy. Although we found a statistically significant association between behavioral intention and last DFE (χ2 = 19.2, P = .01), no clear pattern emerged.

DILATED FUNDUS EXAMINATIONS

By the completion of the 6-month telephone call, 141 participants reported having had a DFE and an additional 14 of 329 people reported making an appointment. Of these 155 self-reports, DFE was confirmed by the eye doctor for 128 participants (82.6%). The examinations were reported to not include dilation in 9 participants (5.8%), 2 people (1.3%) did not show up for their appointment, and 16 people (10.3%) had no record of having undergone an examination. To assess the accuracy of the examination dates, we compared the month and year reported by the participant with the date given by the confirming eye doctor's office. Of the 135 records with both dates, 79.3% agreed and 95.6% were separated by 1 month.

Based on 128 eye doctor–confirmed DFEs, 38.9% of the study participants had a DFE. As listed in Table 3, no significant difference was noted in this measure by intervention group (relative risk, 1.07; 95% confidence interval, 0.82-1.40), with 40.2% of participants in the tailored group and 37.6% of those in the targeted group having an eye doctor–confirmed DFE. Excluding the 20 people without the final follow-up or any follow-up did not change the results. We further explored DFE rates by examining the timing of the examination in relation to the follow-up telephone calls. As given in Table 4, there was no significant difference in timing of DFEs by intervention group (χ2 = 1.2, P = .76).

Table Graphic Jump LocationTable 3. Association Between Demographic and Other Variables and Eye Doctor–Confirmed DFE
Table Graphic Jump LocationTable 4. Timing of DFEs by Intervention Group

To determine whether other factors were associated with undergoing a DFE, we calculated the relative risk for select variables, as listed in Table 3. We found no association between any of the demographic variables and DFE. However, a statistically significant association was noted between undergoing a DFE during the study period and the timing of previous DFE, literacy score, reading the newsletter, and behavioral intention. Compared with participants who never had a DFE, those whose most recent DFE was 2 to 5 years before the study were 1.5 times as likely to have an eye doctor–confirmed DFE. Participants with an adequate health literacy score were 1.5 times as likely to have a DFE compared with participants with poor literacy, and participants who read the newsletter were 1.5 times as likely to have a DFE compared with those who did not read it. We also found that those planning to make an appointment for a DFE within the next month or 6 months were twice as likely to undergo the test compared with participants who had not yet decided. We found similar results after excluding participants who withdrew or were lost to follow-up.

Table 5 summarizes the results of logistic regression analysis for predictors of DFE. Reading the newsletter and planning to make an appointment for a DFE remained significant predictors.

Our hypothesis that older African Americans receiving the tailored intervention would have higher rates of eye doctor–confirmed DFEs than participants receiving the targeted newsletter was not confirmed. The approaches were equally effective in promoting DFEs at 6 months; 40.2% of participants who received the tailored newsletter and 37.6% of those who received the targeted newsletter underwent DFEs. Although these rates may not seem high, they are comparable to a recent study comparing a tailored telephone intervention to a generic print intervention in a population with diabetes, where 34% of participants receiving tailored phone messages had a confirmed DFE within 6 months.13

The effect of the intervention on DFE rates is complicated by the additional contacts we had with participants, although this should not have affected our primary comparison because follow-up procedures were the same for both groups and project staff were unaware of group assignment. Although 38.3% of the DFEs occurred before the first telephone call and after the intervention was received, an additional 53.9% occurred after at least 1 telephone call. For the 7.8% of DFEs occurring before receipt of the newsletter, the enrollment process may have been a motivating factor. Although some of our study participants may have undergone a DFE without any intervention, it is unlikely that we would have achieved a 38.9% DFE rate in these previously nonadherent individuals.

Most studies examining the effectiveness of tailored messages have shown small but significant changes in health behavior when compared with nontailored messages, although there have been other studies24,25 in which no significant differences were observed. As the degree of customization of health messages increases and as audience segmentation increases, cost and effort also increase. Our study confirms that targeted messages that meet the needs of their audience can be as effective as tailored messages for promoting eye examination behavior and questions whether the added cost of tailored interventions is justified.26,27 Therefore, these results suggest that well-designed targeted print messages delivered in a community setting with a moderate degree of customization and segmentation can motivate older African Americans to undergo DFEs.

In our study population, behavioral intention was the strongest predictor of DFE. Participants in the contemplation or preparation stage were 2.5 times more likely to undergo a DFE than were participants in the precontemplation stage, with 46.1% having an eye doctor–confirmed DFE (100 of 217 participants). The rates of DFE were higher among participants who had never thought about having one done compared with the rates among those still deciding or those who had decided not to have the test. This finding suggests that an intervention may be more successful among people who never thought about the behavior than among those who had given the behavior some thought but had not taken any action.

Reading the newsletter, which measures adherence to the intervention, was also a predictor of a DFE. The odds of having a DFE were 1.8 times higher among those who read the newsletter than among those who did not. Participants with poor health literacy were significantly less likely to read the newsletter compared with those with adequate literacy. Even an intervention written at a sixth grade reading level may be inappropriate for all members of the intended audience, suggesting the need to identify other intervention approaches that may be more effective with individuals having poor health literacy.

Our finding that this older African American population was actively engaged in preventive health practices, as evidenced by 93.9% having some contact with the health care system in the past 2 years, suggests the need for the eye care community to work more closely with primary care physicians and other health professionals to encourage discussions about eye care with their patients who are at increased risk of eye disease. Recommendations by primary care professionals for patients to receive eye examinations may be important to augment the delivery of the message to undergo DFEs.

Study participants were relatively reliable in reporting DFEs, with 82.6% of self-reports confirmed and 95.6% of the dates given for the examination within 1 month of the actual date. Although confirmation of self-reported DFEs remains the standard, it is reassuring that self-reports can provide a reasonable estimate if they cannot be confirmed because of cost or time limitations that arise in both research and clinical settings.

Our study had several limitations. First, we conservatively estimated that only half the participants read the newsletter. Second, although we had some loss to follow-up, we completed all follow-up telephone calls or the final follow-up telephone call for 93.9% of our study population. Third, the use of self-reported prior DFEs as an eligibility criterion may have resulted in misclassification error, resulting in people being included who were ineligible or excluded although they were eligible. Reporting errors may have resulted in people being included who were ineligible or excluded although they were eligible. Fourth, confirmation of eye examination status was performed by office staff rather than medical record abstraction. To encourage office staff to access the medical information rather than simply agree or disagree with our information, we asked them to report the date and type of examination. Twenty-seven (17.4%) of the self-reported DFEs that were not confirmed were excluded from the analysis.

Although results from this study may not be generalizable to the US population aged 65 years or older, the proportion of participants who graduated from high school or had some college was similar to that of the US general population.28 Our population was similar to the US population of African Americans aged 65 years or older in terms of self-reported health, with 34.6% in our study reporting fair or poor health compared with 39% in the United States overall. Perhaps as a reflection of self-selection, our study participants were more likely to report having trouble seeing even with eyeglasses, with 35.0% of men and 30.6% of women reporting this compared with 14% and 19%, respectively, in the US population aged 65 years and older.29

Our findings support the effectiveness of targeted and tailored health messages for promoting DFEs among individuals 65 years or older who are at increased risk of glaucoma. Given the additional time and cost needed to develop tailored messages, our results have important implications for developing health communication strategies to promote eye-care–seeking behavior in adults at increased risk of eye disease that can be disseminated in a variety of settings.

Correspondence: Nancy J. Ellish, DrPH, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 W Redwood St, Ste 100, Baltimore, MD 21201 (nellish@epi.umaryland.edu).

Submitted for Publication: March 24, 2010; final revision received October 21, 2010; accepted November 23, 2010.

Financial Disclosure: None reported.

Funding/Support: This study was supported by grant R01EY15899, National Institutes of Health (Dr Ellish [principal investigator], Drs Royak-Scaler and Higginbotham [coinvestigators]).

US Department of Health and Human Services.  Healthy People 2010: Understanding and Improving Health. 2nd ed. Washington, DC: US Dept of Health and Human Services; 2000
Communication Plan.  A Diabetic Eye Disease Education Program for People With Diabetes. Bethesda, MD: National Health Education Program, National Eye Institute; 1991
Communication Plan.  A Glaucoma Public Education Program. Bethesda, MD: National Health Education Program, National Eye Institute; 1994
Ellish NJ, Royak-Schaler R, Passmore SR, Higginbotham EJ. Knowledge, attitudes, and beliefs about dilated eye examinations among African-Americans.  Invest Ophthalmol Vis Sci. 2007;48(5):1989-1994
PubMed   |  Link to Article
Hawkins RP, Kreuter M, Resnicow K, Fishbein M, Dijkstra A. Understanding tailoring in communicating about health.  Health Educ Res. 2008;23(3):454-466
PubMed   |  Link to Article
Kreuter MW, Farrell D, Olevitch L, Brennan L. Tailoring Health Messages: Customizing Communication With Computer Technology. Mahway, NJ: Lawrence Erlbaum Associates; 2000
Kreuter MW, Bull FC, Clark EM, Oswald DL. Understanding how people process health information: a comparison of tailored and nontailored weight-loss materials.  Health Psychol. 1999;18(5):487-494
PubMed   |  Link to Article
Brug J, Campbell M, van Assema P. The application and impact of computer-generated personalized nutrition education: a review of the literature.  Patient Educ Couns. 1999;36(2):145-156
PubMed   |  Link to Article
Skinner CS, Strecher VJ, Hospers H. Physicians' recommendations for mammography: do tailored messages make a difference?  Am J Public Health. 1994;84(1):43-49
PubMed   |  Link to Article
Strecher VJ. Computer-tailored smoking cessation materials: a review and discussion.  Patient Educ Couns. 1999;36(2):107-117
PubMed   |  Link to Article
Basch CE, Walker EA, Howard CJ, Shamoon H, Zybert P. The effect of health education on the rate of ophthalmic examinations among African Americans with diabetes mellitus.  Am J Public Health. 1999;89(12):1878-1882
PubMed   |  Link to Article
Legorreta AP, Hasan MM, Peters AL, Pelletier KR, Leung KM. An intervention for enhancing compliance with screening recommendations for diabetic retinopathy. A bicoastal experience.  Diabetes Care. 1997;20(4):520-523
PubMed   |  Link to Article
Walker EA, Schechter CB, Caban A, Basch CE. Telephone intervention to promote diabetic retinopathy screening among the urban poor.  Am J Prev Med. 2008;34(3):185-191
PubMed   |  Link to Article
Strecher VJ, Rosenstock IM. The health belief model. In: Glanz K, Lewis FM, Rimer BK, eds. Health Behavior and Health Education. Theory, Research, and Practice. 2nd ed. San Francisco: Jossey-Bass Publishers; 1997:41-59
Prochaska JO, Velicer WF. The transtheoretical model of health behavior change.  Am J Health Promot. 1997;12(1):38-48
PubMed   |  Link to Article
Weinstein ND, Sandman PM. A model of the precaution adoption process: evidence from home radon testing.  Health Psychol. 1992;11(3):170-180
PubMed   |  Link to Article
Ellish NJ, Scott D, Royak-Schaler R, Higginbotham EJ. Community-based strategies for recruiting older, African Americans into a behavioral intervention study.  J Natl Med Assoc. 2009;101(11):1104-1111
PubMed
Bass PF III, Wilson JF, Griffith CH. A shortened instrument for literacy screening.  J Gen Intern Med. 2003;18(12):1036-1038
PubMed   |  Link to Article
Rakowski W, Ehrich B, Dubé CE,  et al.  Screening mammography and constructs from the transtheoretical model: associations using two definitions of the stages-of-adoption.  Ann Behav Med. 1996;18:91-100Link to Article
Link to Article
 Glaucoma Eye-Q test. National Eye Institute Website 2010. http://www.nei.nih.gov/health/glaucoma_quiz/eyeqtest2006.pdf. Accessed May 31, 2011 
 Diabetic eye disease Eye-Q test. National Eye Institute Website, 2010. http://www.nei.nih.gov/health/ded_quiz/dedqtest.pdf. Accessed June 14, 2011 
Pasagian-Macaulay A, Basch CE, Zybert P, Wylie-Rosett J. Ophthalmic knowledge and beliefs among women with diabetes.  Diabetes Educ. 1997;23(4):433-437
PubMed   |  Link to Article
At A Glance T. A Guide for Health Promotion Practice. Bethesda, MD: National Institutes of Health, National Cancer Institute; 1997
Noar SM, Benac CN, Harris MS. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions.  Psychol Bull. 2007;133(4):673-693
PubMed   |  Link to Article
Sohl SJ, Moyer A. Tailored interventions to promote mammography screening: a meta-analytic review.  Prev Med. 2007;45(4):252-261
PubMed   |  Link to Article
Kreuter MW, Oswald DL, Bull FC, Clark EM. Are tailored health education materials always more effective than non-tailored materials?  Health Educ Res. 2000;15(3):305-315
PubMed   |  Link to Article
Lairson DR, DiCarlo M, Myers RE,  et al.  Cost-effectiveness of targeted and tailored interventions on colorectal cancer screening use.  Cancer. 2008;112(4):779-788
PubMed   |  Link to Article
US Census Bureau.  Census 2000, Summary File 1. http://factfinder2.census.gov. Accessed July 20, 2006
Federal Agency Forum on Aging-Related Statistics. Older Americans Update 2006: Key Indicators of Well-being. Washington, DC: Federal Interagency Forum on Aging-Related Statistics; May 2006

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure. Follow-up status of 329 study participants.

Tables

Table Graphic Jump LocationTable 1. Tailoring Variables Based on Participants' Responses by Newsletter Section
Table Graphic Jump LocationTable 2. Comparison of Demographic and Other Variables by Intervention Groupsa
Table Graphic Jump LocationTable 3. Association Between Demographic and Other Variables and Eye Doctor–Confirmed DFE
Table Graphic Jump LocationTable 4. Timing of DFEs by Intervention Group

References

US Department of Health and Human Services.  Healthy People 2010: Understanding and Improving Health. 2nd ed. Washington, DC: US Dept of Health and Human Services; 2000
Communication Plan.  A Diabetic Eye Disease Education Program for People With Diabetes. Bethesda, MD: National Health Education Program, National Eye Institute; 1991
Communication Plan.  A Glaucoma Public Education Program. Bethesda, MD: National Health Education Program, National Eye Institute; 1994
Ellish NJ, Royak-Schaler R, Passmore SR, Higginbotham EJ. Knowledge, attitudes, and beliefs about dilated eye examinations among African-Americans.  Invest Ophthalmol Vis Sci. 2007;48(5):1989-1994
PubMed   |  Link to Article
Hawkins RP, Kreuter M, Resnicow K, Fishbein M, Dijkstra A. Understanding tailoring in communicating about health.  Health Educ Res. 2008;23(3):454-466
PubMed   |  Link to Article
Kreuter MW, Farrell D, Olevitch L, Brennan L. Tailoring Health Messages: Customizing Communication With Computer Technology. Mahway, NJ: Lawrence Erlbaum Associates; 2000
Kreuter MW, Bull FC, Clark EM, Oswald DL. Understanding how people process health information: a comparison of tailored and nontailored weight-loss materials.  Health Psychol. 1999;18(5):487-494
PubMed   |  Link to Article
Brug J, Campbell M, van Assema P. The application and impact of computer-generated personalized nutrition education: a review of the literature.  Patient Educ Couns. 1999;36(2):145-156
PubMed   |  Link to Article
Skinner CS, Strecher VJ, Hospers H. Physicians' recommendations for mammography: do tailored messages make a difference?  Am J Public Health. 1994;84(1):43-49
PubMed   |  Link to Article
Strecher VJ. Computer-tailored smoking cessation materials: a review and discussion.  Patient Educ Couns. 1999;36(2):107-117
PubMed   |  Link to Article
Basch CE, Walker EA, Howard CJ, Shamoon H, Zybert P. The effect of health education on the rate of ophthalmic examinations among African Americans with diabetes mellitus.  Am J Public Health. 1999;89(12):1878-1882
PubMed   |  Link to Article
Legorreta AP, Hasan MM, Peters AL, Pelletier KR, Leung KM. An intervention for enhancing compliance with screening recommendations for diabetic retinopathy. A bicoastal experience.  Diabetes Care. 1997;20(4):520-523
PubMed   |  Link to Article
Walker EA, Schechter CB, Caban A, Basch CE. Telephone intervention to promote diabetic retinopathy screening among the urban poor.  Am J Prev Med. 2008;34(3):185-191
PubMed   |  Link to Article
Strecher VJ, Rosenstock IM. The health belief model. In: Glanz K, Lewis FM, Rimer BK, eds. Health Behavior and Health Education. Theory, Research, and Practice. 2nd ed. San Francisco: Jossey-Bass Publishers; 1997:41-59
Prochaska JO, Velicer WF. The transtheoretical model of health behavior change.  Am J Health Promot. 1997;12(1):38-48
PubMed   |  Link to Article
Weinstein ND, Sandman PM. A model of the precaution adoption process: evidence from home radon testing.  Health Psychol. 1992;11(3):170-180
PubMed   |  Link to Article
Ellish NJ, Scott D, Royak-Schaler R, Higginbotham EJ. Community-based strategies for recruiting older, African Americans into a behavioral intervention study.  J Natl Med Assoc. 2009;101(11):1104-1111
PubMed
Bass PF III, Wilson JF, Griffith CH. A shortened instrument for literacy screening.  J Gen Intern Med. 2003;18(12):1036-1038
PubMed   |  Link to Article
Rakowski W, Ehrich B, Dubé CE,  et al.  Screening mammography and constructs from the transtheoretical model: associations using two definitions of the stages-of-adoption.  Ann Behav Med. 1996;18:91-100Link to Article
Link to Article
 Glaucoma Eye-Q test. National Eye Institute Website 2010. http://www.nei.nih.gov/health/glaucoma_quiz/eyeqtest2006.pdf. Accessed May 31, 2011 
 Diabetic eye disease Eye-Q test. National Eye Institute Website, 2010. http://www.nei.nih.gov/health/ded_quiz/dedqtest.pdf. Accessed June 14, 2011 
Pasagian-Macaulay A, Basch CE, Zybert P, Wylie-Rosett J. Ophthalmic knowledge and beliefs among women with diabetes.  Diabetes Educ. 1997;23(4):433-437
PubMed   |  Link to Article
At A Glance T. A Guide for Health Promotion Practice. Bethesda, MD: National Institutes of Health, National Cancer Institute; 1997
Noar SM, Benac CN, Harris MS. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions.  Psychol Bull. 2007;133(4):673-693
PubMed   |  Link to Article
Sohl SJ, Moyer A. Tailored interventions to promote mammography screening: a meta-analytic review.  Prev Med. 2007;45(4):252-261
PubMed   |  Link to Article
Kreuter MW, Oswald DL, Bull FC, Clark EM. Are tailored health education materials always more effective than non-tailored materials?  Health Educ Res. 2000;15(3):305-315
PubMed   |  Link to Article
Lairson DR, DiCarlo M, Myers RE,  et al.  Cost-effectiveness of targeted and tailored interventions on colorectal cancer screening use.  Cancer. 2008;112(4):779-788
PubMed   |  Link to Article
US Census Bureau.  Census 2000, Summary File 1. http://factfinder2.census.gov. Accessed July 20, 2006
Federal Agency Forum on Aging-Related Statistics. Older Americans Update 2006: Key Indicators of Well-being. Washington, DC: Federal Interagency Forum on Aging-Related Statistics; May 2006

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