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Original Investigation |

A Randomized Comparison of Print and Web Communication on Colorectal Cancer Screening FREE

David S. Weinberg, MD, MSc; Eileen Keenan, MA, CCRP; Karen Ruth, MS; Karthik Devarajan, PhD; Michelle Rodoletz, PhD; Eric J. Bieber, MD
[+] Author Affiliations

Author Affiliations: Fox Chase Cancer Center, Philadelphia, Pennsylvania (Drs Weinberg, Devarajan, and Rodoletz and Mss Keenan and Ruth), and Department of Reproductive Biology, Case Western Reserve University, Shaker Heights, Ohio (Dr Bieber).


JAMA Intern Med. 2013;173(2):122-129. doi:10.1001/2013.jamainternmed.1017.
Text Size: A A A
Published online

Background New methods to enhance colorectal cancer (CRC) screening rates are needed. The web offers novel possibilities to educate patients and to improve health behaviors, such as cancer screening. Evidence supports the efficacy of health communications that are targeted and tailored to improve the uptake of recommendations.

Methods We identified unscreened women at average risk for CRC from the scheduling databases of obstetrics and gynecology practices in 2 large health care systems. Participants consented to a randomized controlled trial that compared CRC screening uptake after receipt of CRC screening information delivered via the web or in print form. Participants could also be assigned to a control (usual care) group. Women in the interventional arms received tailored information in a high- or low-monitoring Cognitive Social Information Processing model–defined attentional style. The primary outcome was CRC screening participation at 4 months.

Results A total of 904 women were randomized to the interventional or control group. At 4 months, CRC screening uptake was not significantly different in the web (12.2%), print (12.0%), or control (12.9%) group. Attentional style had no effect on screening uptake for any group. Some baseline participant factors were associated with greater screening, including higher income (P = .03), stage of change (P < .001), and physician recommendation to screen (P < .001).

Conclusions A web-based educational intervention was no more effective than a print-based one or control (no educational intervention) in increasing CRC screening rates in women at average risk of CRC. Risk messages tailored to attentional style had no effect on screening uptake. In average-risk populations, use of the Internet for health communication without additional enhancement is unlikely to improve screening participation.

Trial Registration clinicaltrials.gov Identifier: NCT00459030

Figures in this Article

Colorectal cancer (CRC) is the second leading cause of cancer death in the United States.1 However, less than 60% of the average-risk population currently adheres to CRC screening recommendations.2 Healthy People 2020 endorses efforts to enhance screening participation.3 To date, most educational intervention studies have used written materials, although videos, computer programs, and group education sessions are described.4 Although such methods are modestly effective (typically 10%-20% absolute effect relative to no intervention), electronic communication capitalizing on the proliferation of web-based applications has the potential to improve message delivery. Although some web-based approaches have been successful in modifying health behaviors,5 many have been complex and costly, limiting dissemination.6 Few electronic intervention studies focus on cancer prevention and screening.79

We conducted a prospective randomized controlled trial comparing the effect on CRC screening of health communications delivered via web or print channels. A usual care arm was included to control for secular screening trends. This design permits a comparison of the communication channels to each other and to a control. Both interventions contained identical educational content. We studied women at average risk for CRC not adherent with screening recommendations. Potential participants were approached at a routine obstetrics and gynecology visit. This visit represented an opportunity to link CRC risk–related attitudes and behavior with extant breast and cervical cancer risk perceptions and screening behaviors, including mammogram and Papanicolaou smear.

Accumulating evidence supports the efficacy of health communications targeted to group level and tailored to individual factors.10 For this project, we used the Cognitive Social Information Processing (C-SHIP) model to develop tailored health communications.11 According to C-SHIP, individuals are characterized by 2 distinct attentional styles: high monitoring (information seeking) vs low monitoring (information distracting). Each style is associated with different but stable emotional, behavioral, and cognitive reactions to stressors at cognitive-affective and decision-making levels.12 Under threat, these attentional styles appear to mediate behavior (eg, uptake of or adherence to screening); therefore, they may have important implications for the content and amount of information in health communications.10,13

Participants randomized to the web or print arms were assigned with equal likelihood to receive communications reflecting high- or low-monitoring C-SHIP content. The participant's attentional style was measured at study entry, allowing a test of the hypothesis that concordance between attentional style and tailored communication form would result in greater screening uptake.

Study participants for this institutional review board–approved trial were drawn from the obstetrics and gynecology practices at Geisinger Health System, Danville, Pennsylvania, and Emory University, Atlanta, Georgia. We searched each institution's scheduling database for routine appointments 4 to 6 weeks in advance, applying electronic filters matching eligibility requirements. Eligibility criteria included the following: (1) female sex; (2) age of 50 years or older; (3) average risk for CRC defined as no personal history of colorectal polyps or adenocarcinoma, inflammatory bowel disease, or CRC in more than 1 first-degree relative; (4) nonadherence with CRC screening recommendations at the time of index appointment (meaning no at-home fecal occult blood test in the past 12 months and no barium enema, flexible sigmoidoscopy, or colonoscopy in the past 5 years); and (5) web access at home and/or work.

We telephoned potential participants before their index appointment to confirm eligibility and to obtain consent. As previously described,14 all participants completed by telephone a baseline survey containing demographic items; medical history information, including mammography; and a series of psychometric scales, including one assessing C-SHIP attentional style.11 Participants also responded to items regarding their knowledge and expectations about CRC screening and their intention to screen.

After survey completion, participants were randomized to 1 of 5 arms: an intervention arm with web or print interventions and either high- or low-monitoring attentional style information (4 possible intervention arms) or control. The educational content of the interventions was identical, covering essential information about CRC screening, including rationale; description of available techniques (fecal immunochemical test or fecal occult blood test, flexible sigmoidoscopy, barium enema, or colonoscopy); benefits, risk, and timing of each technique; and information about where to look for additional resources about CRC and screening (eg, the American Cancer Society). Two versions of each intervention were prepared: one tailored to high-monitoring and one to low-monitoring attentional style. The high-monitoring version was lengthier and contained extensively detailed messages pertaining to CRC risk status. Descriptions of CRC screening methods were more substantial and the benefits of adherence to preventive behaviors were emphasized. All messages were positively framed to underline the potential for gain. In contrast, the low-monitoring version was briefer, was less detailed, and included messages that were negatively framed to highlight the costs to health if recommended behaviors were not pursued.

The development of the web intervention included a systematic approach to pretesting messages, layout, and usability. We pretested the website on 50 eligible women not enrolled in this study. The website was refined before study initiation to establish literacy level (seventh grade). On the basis of pretesting, we anticipated that website review would require approximately 5 to 7 minutes.14 Our goal was to develop a simple, visually appealing website that did not require high-speed Internet connections or powerful computing capabilities to load and function adequately.

After completing the baseline survey, web participants received instruction on how to acquire credentials to access the study website. Study materials were placed securely on the Fox Chase Cancer Center web portal. After logging on, participants could view the website from any location as often and as long as they wished. Web participants also received a follow-up letter by standard post containing a review of web use instructions and username and password information.

Participants were contacted to complete telephone surveys 4 and 12 months after randomization. Up to 15 attempts were made to collect information about use of print or web materials. In addition to querying self-reported use of intervention materials, we electronically tracked web use by participants. We collected information about if, when, and how often each participant accessed the study website and how much time was spent viewing information.14

Uptake of CRC screening was calculated based on electronic and hand review of participant medical records. Electronic reviews were conducted first, searching for completion date of any sanctioned CRC screening test. If the online review was unrevealing, paper records were then reviewed to look for reports not recorded electronically. Because of the potential delay in scheduling tests or recording test results, screening rates were calculated at 4 and 12 months relative to study entry.

A target sample size of 1200 participants was selected to allow the detection of a clinically relevant 10-percentage-point absolute difference between intervention groups in the proportion of participants using CRC screening (80% power, 2-sided comparison-wise P = .05). However, a planned interim analysis performed by the study's data and safety monitoring board revealed that the effect of the study interventions was smaller than hypothesized. Because further enrollment was unlikely to yield tangible benefit, the data and safety monitoring board mandated that enrollment be discontinued.

We used intent-to-treat analysis using the randomized groups without adjustment for self-reported or actual use of print or web materials. Participants who missed their index appointment, were screened before the index appointment (based on later record review), or did not have a record review were excluded from the analyses. Baseline characteristics by randomization arms were compared using χ2 tests.

To compare screening rates by communication channel, we used Fisher exact tests to compare CRC screening rates across the 5 study arms, separately for screening by 4 months and 12 months. We also looked at screening rates by communication channel, where we combined the high- and low-monitoring participants within the print and web arms and then compared print, web, and control groups using the Fisher exact tests. We examined the influence of demographic characteristics, medical history, CRC-related knowledge, and motivational readiness on CRC screening uptake using χ2 tests or Fisher exact tests. Trend was assessed with the Cochran-Armitage test for predictors with ordered categories. The interaction of communication channel and baseline characteristics on CRC screening was evaluated using χ2 tests for the selected characteristic. Agreement of self-reported and actual web use was evaluated using the McNemar test.

Participants were identified as having high- or low-monitoring attentional styles from their baseline C-SHIP score. They were classified as being matched or mismatched to the intervention attentional style based on the concordance of their attentional style and randomization arm. For example, a high-monitor participant would be classified as a match if she were randomized to either the print high-monitoring or web high-monitoring arm. The effect of receiving an intervention tailored to attentional style was assessed by comparing the screening rates for those matched vs mismatched using χ2 tests, within the print and web arms. In these analyses, control arm participants were excluded.

A total of 904 women were randomized to participate in the study (Figure). This number represented 26.7% of eligible women successfully contacted in advance of the index appointment.

Place holder to copy figure label and caption
Graphic Jump Location

Figure. Participant recruitment and subsequent randomization by communication channel and attentional style.

For the primary end point of CRC screening uptake within 4 months of enrollment, 865 women were included for analysis. Thirty-nine women were not included: 5 because they did not present for their index visit, 10 because they underwent colonoscopy within the previous 5 years, and 24 because no record was available to determine screening status. Baseline demographic and selected medical history information is presented in Table 1. Most participants were white, 59 years or younger, and married and had at least some college education. For those providing an answer, the range of annual incomes was broad. Across study arms, no significant differences were found in age, race, marital status, educational level, employment status, or income. Regardless of the randomization arm, participants were similar in terms of previous cancer history. Mammography use was similar across groups. A total of 96.1% of participants reported ever having a mammogram (data not shown), with 73.0% reporting having a mammogram within the past year.

Table Graphic Jump LocationTable 1. Demographic and Medical History Characteristics by Randomization Arm

Table 2 indicates that CRC screening rates at 4 months were similar across intervention arms (12.3%). Colonoscopy was preferred, with 75.5% of those screened using this method. However, no difference was found in screening use by intervention arm. We also looked at screening rates at 12 months because of concern about long wait times for screening colonoscopy in some locales (Table 3). Although screening rates were higher (21.0% at 12 months vs 12.3% at 4 months), when stratified by intervention, no differences were seen for control vs print or web or for web vs print. Attentional style had no effect on screening outcome at either time point (Tables 2 and 3).

Table Graphic Jump LocationTable 2. CRC Screening Within 4 Months by Intervention Arm
Table Graphic Jump LocationTable 3. CRC Screening Within 12 Months, by Intervention Arm

Because no effect was seen by intervention, we collapsed the study groups to identify whether any participant factors were associated with greater screening at 4 or 12 months (Table 4). A significant, positive relationship was found between increasing income and likelihood of screening (P = .03 at 4 months and P < .001 at 12 months). Stage of change at baseline tended to predict subsequent actual screening participation. Those participants who stated they planned screening within the next 1 to 6 months were more likely to pursue it than participants who had no plan or were not thinking about it (P < .001). Participants whose baseline knowledge about screening was greater demonstrated a strong trend toward more screening at 4 months than those with lower knowledge scores (P = .054). Finally, midway through study enrollment, we added a baseline question asking whether the participant's physician had recommended screening. For the 499 participants who answered, those who said yes (53.3%) were 1.4 times more likely to be screened at 4 months (P < .001).

Table Graphic Jump LocationTable 4. CRC Screening at 4 and 12 Months by Selected Variables

Attentional style was characterized as either high monitoring or low monitoring.13 When dichotomized around the C-SHIP score, 47.6% of participants were in the high-monitoring group and 52.4% were in the low-monitoring group (data not shown). Regardless of the score, participants were randomly assigned to one of the intervention arms or to the control group. Table 5 gives the screening rates stratified by attentional style match or mismatch. There was no apparent moderating interaction between attentional style and the receipt of concordant or discordant tailored information.

Table Graphic Jump LocationTable 5. CRC Screening Within 4 Months and Attentional Style Match or Mismatch, Stratified by Intervention Arm

As previously reported, self-reported and actual use of the web was low in this study.14 Of 345 women randomized to the web intervention, we could not contact 105 (30.4%) to complete the 4-month follow-up survey, which included items about web use. Thirty-one participants were excluded because of miscellaneous technical problems leading to an inability to access the website or missing survey data. The remaining 201 participants had screening results (Table 6). Their demographics and other characteristics were similar to the entire cohort. On the basis of tracking data, 49 (24.4%) actually logged onto the website, whereas 33 (16.4%) recalled and then reported using the web intervention. These values are similar to a previous report.14

Table Graphic Jump LocationTable 6. CRC Screening by Self-reported Web Use vs Actual Web Use

The results of CRC screening in the self-report and actual use groups are given inTable 6. Screening rates at 4 months were similar between those who logged onto the web (12.2%) and those who did not (11.2%). In addition, screening rates in either group were similar to the control group. For the self-report group, screening rates were also similar regardless of reporting (15.2%) or not reporting web use (10.7%).

This prospective study compared the effect of tailored information, delivered through print or web-based communications, on CRC screening uptake in average-risk women. At the 4-month end point, screening rates of approximately 12% in the 2 intervention arms were not significantly different from each other or from the control group. Furthermore, no moderating effect was seen for participants characterized as having high- vs low-monitoring attentional styles.

The Internet is widely viewed as an important channel of health communication.15 Nearly 75% of adults in the United States use the Internet, and more than 60% use it to obtain health information.16 Recent results from the National Health Information Survey found that 51% of adults (58% women and 43% men) used the Internet in the preceding 12 months to seek health information.17

This broad diffusion of Internet access suggests a method to remedy health knowledge and information gaps and to facilitate healthy behaviors, such as periodic cancer screening. A recent meta-analysis of studies comparing behavioral change outcomes after web-based or non–web-based interventions concluded that knowledge improvement and clinical target outcomes were greater with web-based interventions, although study heterogeneity precluded a precise effect size estimate.15

Our report is one of the few web-based studies of cancer screening behavior change in average-risk populations. Chan and Vernon18 published a feasibility study of 97 participants who received an e-mail invitation to access a website displaying video decision aids about CRC screening. Website use was limited based on electronic tracking, and no data about effect on screening uptake were reported.

The absence of effect on screening use is disappointing. Inexpensive, easily disseminated mechanisms to increase screening uptake are needed. Study participants were regular users of mammography for breast cancer screening. Exposure to study interventions increased knowledge about CRC and CRC screening, suggesting that the content effectively remedied knowledge gaps.14 Although individuals willingly participated in a study exploring the effect of web-based interventions, a precondition of which was web access, only approximately 25% actually logged on. It is premature to conclude that web-based interventions are ineffective. However, it can be confidently predicted that future interventions will need to be coupled with aggressive efforts to facilitate and verify use of the website because spontaneous uptake was limited in this average-risk population.

A recent National Institutes of Health State of the Science Conference on CRC screening emphasized research on the effectiveness of tailoring to meet specific population needs.19 In our study, we examined the role of tailoring to attentional preferences for cancer risk–related information. Delivering information matched or mismatched to attentional style had no effect on screening uptake. Leveraging attentional preferences has proved effective in some clinical settings, particularly screening in higher-risk groups, such as women with BRCA mutations or individuals at risk for Lynch syndrome–related CRC and other cancers.2022 However, little is known about average-risk groups or how change in behavior instead of change in affect can be elicited. If our results are duplicated, especially when exposure to the intervention is more widespread, they suggest that attentional status is not a powerful tailoring force in all settings.

The limited rates of actual web use may provide insight into the modest effects on screening rates after exposure to print interventions. Unlike the web, the ability to objectively track attendance to, and use of, print media is not available. Print interventions have not typically resulted in substantial increases in screening rates. One explanation is that some interventions were neither targeted nor tailored to the recipient. Our negative results suggest that an additional contributor may simply be lack of attention to the intervention materials.

This study has several limitations. All participants were women. Although data are conflicting about whether women use the Internet differently than men, no conclusions can be drawn about this intervention's effect in a male population. We specifically limited our participants to persons overdue, at study entry, to receive standard CRC screening. Although 39.9% of study participants were 55 years or older, it is impossible to know which participants would have screened spontaneously in the future. In addition, current colonoscopy screening guidelines advocate a 10-year interval between normal study results; therefore, some participants may have been misclassified as nonadherent if more than 5 but less than 10 years had elapsed. Finally, we opted to take a “low-tech” approach by constructing a website that required modest computing power to load. Although this decision may have reduced inattention because of slow website function, future interventions may need to contain additional components to engage website entrance.

Despite its potential advantages, our results suggest that the web-based efforts to promote screening do not represent a guaranteed improvement over other methods. This large randomized trial failed to demonstrate that communication channel or information tailored to attentional style had a significant influence on screening uptake. Although knowledge levels increased, screening rates did not, casting some doubt on the effectiveness of simple web interventions as a tool to alter screening behavior. Future efforts, regardless of communication channel, must combine appropriate content and appealing interfaces with new strategies to increase engagement. Passive diffusion is unlikely to render web-based interventions any more effective than nonelectronic predecessors.

Correspondence: David S. Weinberg, MD, MSc, Fox Chase Cancer Center, 333 Cottman Ave, Philadelphia, PA 19111 (david.weinberg@fccc.edu).

Accepted for Publication: August 21, 2012.

Published Online: December 17, 2012. doi:10.1001/2013.jamainternmed.1017

Author Contributions:Study concept and design: Weinberg, Rodoletz, and Bieber. Acquisition of data: Weinberg and Keenan. Analysis and interpretation of data: Weinberg, Keenan, Ruth, Devarajan, and Rodoletz. Drafting of the manuscript: Weinberg, Ruth, Devarajan, and Rodoletz. Critical revision of the manuscript for important intellectual content: Weinberg, Keenan, Ruth, Devarajan, Rodoletz, and Bieber. Statistical analysis: Ruth, Devarajan, and Rodoletz. Obtained funding: Weinberg. Administrative, technical, and material support: Keenan and Bieber. Study supervision: Weinberg.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported in part by grant R01CA102695 from the National Institutes of Health (Dr Weinberg).

Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012.  CA Cancer J Clin. 2012;62(1):10-29
PubMed   |  Link to Article
Centers for Disease Control and Prevention.  Cancer screening—United States, 2010.  MMWR Morb Mortal Wkly Rep. 2012;61(3):41-45
PubMed
Koh HK. A 2020 vision for healthy people.  N Engl J Med. 2010;362(18):1653-1656
PubMed   |  Link to Article
Rawl SM, Menon U, Burness A, Breslau ES. Interventions to promote colorectal cancer screening: an integrative review. Nursing Outlook. 2012;60:172-181, e13
Brouwer W, Kroeze W, Crutzen R,  et al.  Which intervention characteristics are related to more exposure to Internet-delivered healthy lifestyle promotion interventions? a systematic review.  J Med Internet Res. 2011;13(1):e2
PubMed  |  Link to Article   |  Link to Article
Institute of Medicine.  Speaking of Health: Assessing Health Communication Strategies for Diverse Populations. Washington, DC: Institute of Medicine; 2002
Fotheringham MJ, Owies D, Leslie E, Owen N. Interactive health communication in preventive medicine: Internet-based strategies in teaching and research.  Am J Prev Med. 2000;19(2):113-120
PubMed   |  Link to Article
Oenema A, Brug J, Lechner L. Web-based tailored nutrition education: results of a randomized controlled trial.  Health Educ Res. 2001;16(6):647-660
PubMed   |  Link to Article
Kukafka R, Lussier YA, Patel VL, Cimino JJ. Developing tailored theory-based educational content for WEB applications: illustrations from the MI-HEART project.  Stud Health Technol Inform. 2001;84(pt 2):1474-1478
PubMed
Miller SM, Shoda Y, Hurley K. Applying cognitive-social theory to health-protective behavior: breast self-examination in cancer screening.  Psychol Bull. 1996;119(1):70-94
PubMed   |  Link to Article
Miller SM. Monitoring versus blunting styles of coping with cancer influence the information patients want and need about their disease: implications for cancer screening and management.  Cancer. 1995;76(2):167-177
PubMed   |  Link to Article
Miller SM, Brody DS, Summerton J. Styles of coping with threat: implications for health.  J Pers Soc Psychol. 1988;54(1):142-148
PubMed   |  Link to Article
Miller S, Diefenbach M. C-SHIP: a cognitive-social health information processing approach to cancer. In: Krantz D, ed. Perspectives in Behavioral Medicine. Mahwah, NJ: Lawrence Erlbaum; 1998
Fleisher L, Kandadai V, Keenan E,  et al.  Build it, and will they come? unexpected findings from a study on a web-based intervention to improve colorectal cancer screening.  J Health Commun. 2012;17(1):41-53
PubMed   |  Link to Article
Webb TL, Joseph J, Yardley L, Michie S. Using the Internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy.  J Med Internet Res. 2010;12(1):e4
PubMed  |  Link to Article   |  Link to Article
Zulman DM, Kirch M, Zheng K, An LC. Trust in the Internet as a health resource among older adults: analysis of data from a nationally representative survey.  J Med Internet Res. 2011;13(1):e19
PubMed  |  Link to Article   |  Link to Article
Cohen RA, Sussman B. Health Information Technology Use Among Men and Women Aged 18-64: Early Release of Estimates From the National Health Interview Survey, January-June 2009. Washington, DC: National Center for Health Statistics; February 2010
Chan EC, Vernon SW. Implementing an intervention to promote colon cancer screening through e-mail over the Internet: lessons learned from a pilot study.  Med Care. 2008;46(9):(suppl 1)  S117-S122
PubMed   |  Link to Article
Steinwachs D, Allen JD, Barlow WE,  et al.  National Institutes of Health state-of-the-science conference statement: enhancing use and quality of colorectal cancer screening.  Ann Intern Med. 2010;152(10):663-667
PubMed
Williams-Piehota P, Pizarro J, Schneider TR, Mowad L, Salovey P. Matching health messages to monitor-blunter coping styles to motivate screening mammography.  Health Psychol. 2005;24(1):58-67
PubMed   |  Link to Article
Shiloh S, Koehly L, Jenkins J, Martin J, Hadley D. Monitoring coping style moderates emotional reactions to genetic testing for hereditary nonpolyposis colorectal cancer: a longitudinal study.  Psychooncology. 2008;17(8):746-755
PubMed   |  Link to Article
Bartle-Haring S, Toviessi P, Katafiasz H. Predicting the use of individualized risk assessment for breast cancer.  Womens Health Issues. 2008;18(2):100-109
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure. Participant recruitment and subsequent randomization by communication channel and attentional style.

Tables

Table Graphic Jump LocationTable 1. Demographic and Medical History Characteristics by Randomization Arm
Table Graphic Jump LocationTable 2. CRC Screening Within 4 Months by Intervention Arm
Table Graphic Jump LocationTable 3. CRC Screening Within 12 Months, by Intervention Arm
Table Graphic Jump LocationTable 4. CRC Screening at 4 and 12 Months by Selected Variables
Table Graphic Jump LocationTable 5. CRC Screening Within 4 Months and Attentional Style Match or Mismatch, Stratified by Intervention Arm
Table Graphic Jump LocationTable 6. CRC Screening by Self-reported Web Use vs Actual Web Use

References

Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012.  CA Cancer J Clin. 2012;62(1):10-29
PubMed   |  Link to Article
Centers for Disease Control and Prevention.  Cancer screening—United States, 2010.  MMWR Morb Mortal Wkly Rep. 2012;61(3):41-45
PubMed
Koh HK. A 2020 vision for healthy people.  N Engl J Med. 2010;362(18):1653-1656
PubMed   |  Link to Article
Rawl SM, Menon U, Burness A, Breslau ES. Interventions to promote colorectal cancer screening: an integrative review. Nursing Outlook. 2012;60:172-181, e13
Brouwer W, Kroeze W, Crutzen R,  et al.  Which intervention characteristics are related to more exposure to Internet-delivered healthy lifestyle promotion interventions? a systematic review.  J Med Internet Res. 2011;13(1):e2
PubMed  |  Link to Article   |  Link to Article
Institute of Medicine.  Speaking of Health: Assessing Health Communication Strategies for Diverse Populations. Washington, DC: Institute of Medicine; 2002
Fotheringham MJ, Owies D, Leslie E, Owen N. Interactive health communication in preventive medicine: Internet-based strategies in teaching and research.  Am J Prev Med. 2000;19(2):113-120
PubMed   |  Link to Article
Oenema A, Brug J, Lechner L. Web-based tailored nutrition education: results of a randomized controlled trial.  Health Educ Res. 2001;16(6):647-660
PubMed   |  Link to Article
Kukafka R, Lussier YA, Patel VL, Cimino JJ. Developing tailored theory-based educational content for WEB applications: illustrations from the MI-HEART project.  Stud Health Technol Inform. 2001;84(pt 2):1474-1478
PubMed
Miller SM, Shoda Y, Hurley K. Applying cognitive-social theory to health-protective behavior: breast self-examination in cancer screening.  Psychol Bull. 1996;119(1):70-94
PubMed   |  Link to Article
Miller SM. Monitoring versus blunting styles of coping with cancer influence the information patients want and need about their disease: implications for cancer screening and management.  Cancer. 1995;76(2):167-177
PubMed   |  Link to Article
Miller SM, Brody DS, Summerton J. Styles of coping with threat: implications for health.  J Pers Soc Psychol. 1988;54(1):142-148
PubMed   |  Link to Article
Miller S, Diefenbach M. C-SHIP: a cognitive-social health information processing approach to cancer. In: Krantz D, ed. Perspectives in Behavioral Medicine. Mahwah, NJ: Lawrence Erlbaum; 1998
Fleisher L, Kandadai V, Keenan E,  et al.  Build it, and will they come? unexpected findings from a study on a web-based intervention to improve colorectal cancer screening.  J Health Commun. 2012;17(1):41-53
PubMed   |  Link to Article
Webb TL, Joseph J, Yardley L, Michie S. Using the Internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy.  J Med Internet Res. 2010;12(1):e4
PubMed  |  Link to Article   |  Link to Article
Zulman DM, Kirch M, Zheng K, An LC. Trust in the Internet as a health resource among older adults: analysis of data from a nationally representative survey.  J Med Internet Res. 2011;13(1):e19
PubMed  |  Link to Article   |  Link to Article
Cohen RA, Sussman B. Health Information Technology Use Among Men and Women Aged 18-64: Early Release of Estimates From the National Health Interview Survey, January-June 2009. Washington, DC: National Center for Health Statistics; February 2010
Chan EC, Vernon SW. Implementing an intervention to promote colon cancer screening through e-mail over the Internet: lessons learned from a pilot study.  Med Care. 2008;46(9):(suppl 1)  S117-S122
PubMed   |  Link to Article
Steinwachs D, Allen JD, Barlow WE,  et al.  National Institutes of Health state-of-the-science conference statement: enhancing use and quality of colorectal cancer screening.  Ann Intern Med. 2010;152(10):663-667
PubMed
Williams-Piehota P, Pizarro J, Schneider TR, Mowad L, Salovey P. Matching health messages to monitor-blunter coping styles to motivate screening mammography.  Health Psychol. 2005;24(1):58-67
PubMed   |  Link to Article
Shiloh S, Koehly L, Jenkins J, Martin J, Hadley D. Monitoring coping style moderates emotional reactions to genetic testing for hereditary nonpolyposis colorectal cancer: a longitudinal study.  Psychooncology. 2008;17(8):746-755
PubMed   |  Link to Article
Bartle-Haring S, Toviessi P, Katafiasz H. Predicting the use of individualized risk assessment for breast cancer.  Womens Health Issues. 2008;18(2):100-109
PubMed   |  Link to Article

Correspondence

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The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
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For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
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