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Making Behavior Change Interventions Available to Young African American Women: Development and Feasibility of an eHealth Lifestyle Program

Staffileno, Beth A. PhD, FAHA; Tangney, Christy C. PhD, CNS, FACN; Fogg, Louis PhD; Darmoc, Rebecca BS

The Journal of Cardiovascular Nursing: November/December 2015 - Volume 30 - Issue 6 - p 497–505
doi: 10.1097/JCN.0000000000000197

Background: Less is known about young African American (AA) women, largely because the young are hard to reach. Traditional approaches to behavior changes interventions impose several challenges, especially for AA women at risk for developing hypertension.

Purpose: This feasibility study describes the process of transforming a face-to-face lifestyle change intervention into a Web-based platform (eHealth) accessible by iPads, iPhones, smartphones, and personal computers.

Methods: Four sequential phases were conducted using elements of formative evaluation and quantitative analysis. A convenience sample of AA women, aged 18 to 45 years, with self-reported prehypertension and regular access to the Internet were eligible to participate.

Results: Eleven women involved in phase 1 expressed that they (1) currently use the Internet to retrieve health-related information, (2) prefer to use the Internet rather than face-to-face contact for nonserious conditions, (3) need convenience and easily accessible health-related interventions, and (4) are amenable to the idea of an eHealth lifestyle modification program. During phase 2, learning modules derived from printed manuals were adapted and compressed for a Web audience. The modules were designed to present evidence-based content but allowed for tailoring and individualization according to the needs of the target population. During phase 3, 8 women provided formative information concerning appeal and usability of the eHealth program in relation to delivery, visual quality, interactivity, and engagement. Phase 4 involved 8 women beta testing the 12-week program, with a 63% completion rate. Most of the women agreed that the program and screens opened with ease, the functions on the screens did what they were supposed to do, and the discussion board was easy to access. Program completion was greater for physical activity compared with dietary content.

Conclusion: This study outlines a step-by-step process for transforming face-to-face content into a Web-based platform, which, importantly, can serve as a template for promoting other health behaviors.

Beth A. Staffileno, PhD, FAHA Associate Professor, Department of Adult Health and Gerontological Nursing, Medical Center, Rush University, Chicago, Illinois.

Christy C. Tangney, PhD, CNS, FACN Professor, Department of Clinical Nutrition, Medical Center, Rush University, Chicago, Illinois.

Louis Fogg, PhD Associate Professor, Department of Community Systems and Mental Health Nursing, Medical Center, Rush University, Chicago, Illinois.

Rebecca Darmoc, BS Director of Marketing, College of Nursing, Medical Center, Rush University, Chicago, Illinois.

Funding for this study was provided by the College of Nursing Research Fund.

The authors have no conflicts of interest to disclose.

Correspondence Beth A. Staffileno, PhD, FAHA, Medical Center, Rush University, 600 S Paulina St 1060D AAC, Chicago, IL 60612 (

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Although there are guidelines for promoting lifestyle changes and cardiovascular disease risk reduction, greater emphasis is needed on how to translate these guidelines into practical messages that are individualized, meaningful, and accessible for high-risk groups.1 Traditional approaches to behavioral change interventions rely on face-to-face individual, group, or community-based delivery methods that often impose several challenges for participants, such as scheduling visits, travel to and from the intervention site, release time from work, and/or family responsibilities.2–4 An alternative approach to disseminating health information and behavioral interventions is online learning, or eHealth learning.5–9 New delivery modes (eg, Internet, mobile applications) are being used as part of health promotion strategies to reach large numbers of the population.5,6 eHealth learning has the potential to educate one-on-one, at a convenient time, place, and pace, allowing young, busy individuals to learn anytime or anywhere provided they can access the Internet.10,11 This delivery approach may be particularly important for young African American (AA) women who are at a greater risk for developing hypertension, because (1) the prevalence of hypertension is greatest among AAs compared with whites and Hispanics, particularly AA women,12,13 (2) hypertension develops at younger ages among AAs, thereby increasing the rate of pressure-related complications such as stroke and kidney disease,12–15 and (3) AA women havethe highest prevalence of physical inactivity and obesity.12,16–19 Internet technology offers the ability of tailoring messages to meet the needs of participants and can personalize the intervention, making it more culturally specific.21,22 There is nearly universal Internet use among younger adults aged 18 to 49 years, regardless of race.23 More AA women than men use the Internet and own a smartphone. Interestingly, AAs have higher rates of social networking than whites do, especially among the younger age users (18–29 years).23 These data suggest a strong level of adoption of e-technology among young AA women.

The rising Internet use suggests that eHealth is a plausible medium for delivering behavior change interventions, especially when targeting a younger population. Several recent systematic reviews using Web-based programs provide favorable evidence with respect to changing physical activity (PA) and dietary behaviors. However, the intended reach of eHealth interventions is varied.5,7–9,22,24 Although the intent of Internet interventions is to reach diverse populations, most published studies are homogenous involving female, white, higher socioeconomic level, and low-risk populations.5,8,9,22 To the best of our knowledge, few eHealth interventions have been conducted among young, at-risk AA women.5,7–9,22 There is strong evidence supporting the benefits of adopting dietary and lifestyle behaviors for preventing incident hypertension,25 yet less information is available for women in general17,26 and for young AA women at risk for developing hypertension in partucular.27–29 Although multicomponent lifestyle interventions have been tested in several recent clinical trials, these have been directed at middle-aged AA men and women30–34 and in those already with hypertension or on antihypertensive medication.35 Less is known about young AA women, largely because the young are hard to reach. Despite the success with our previous healthy lifestyle change interventions,36–38 younger women do not always have the flexibility to attend face-to-face behavioral change interventions.2,39 Therefore, the purpose of this study was to transform the delivery of a healthy lifestyle change intervention using the interactive, computerized technology of eHealth (accessible by iPads, iPhones, smartphones, and personal computers). Specific phases of this effort were to (1) assess Internet use and preferences for seeking health information among our target population, (2) convert previously used PA and dietary behavior change content into Web-based learning modules, (3) assess appeal and usability through formative evaluation, and (4) beta test the learning modules for further refinement.

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Study Design, Sample, and Setting

This study included elements of formative evaluation and quantitative analysis, as outlined in Table 1. Formative evaluation was used to provide evaluative information to develop and improve the delivery of health-related information.40–42 A convenience sample of AA women, aged 18 to 45 years, with self-reported prehypertension and regular access to a computer either at home or at work and who gave informed consent were eligible for study participation. This age group was targeted because (1) they are prone to obesity and physical inactivity and at risk for developing definite hypertension, (2) the Web-based content is conducive to a healthy lifestyle and should be encouraged by all young women, and (3) they rely heavily on the Internet for information. Optimal sample size was not calculated as this was a feasibility study.43 This study was conducted at Rush University Medical and received institutional review board approval.



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During phase 1, formative evaluation was conducted to better understand how young AA women obtain health-related information. In other words, what are the preferences of young AA women when seeking health-related information (speak directly with the health professional face to face, speak by telephone, use the Internet, request information via e-mail, or receive print material)? Eleven women participated in a 1-hour discussion that was held in a small conference room located in the Academic Building of the Medical Center. Participants were also asked to respond to 12 survey questions about preferences for seeking health-related information.

The second phase consisted of converting previously used face-to-face content into 24 eHealth learning modules using the Web-based platform WordPress. WordPress is a self-hosted blogging tool and content management system used by millions of people on a daily basis ( This system is customizable with plugins, widgets, and themes. A plugin is a collection of files to extend the functionality of the Web site, such as enhancing specific services of the Weblog. Widgets add content and features to the Web site sidebar, like search and navigation features. A theme is a graphical presentation and allows greater options for site design and content ( The learning content contained 12 modules focusing on Dietary Approaches to Stop Hypertension (DASH) eating plan and 12 modules focusing on Lifestyle PA and used interactive and situational learning technology. This approach requires the learner to participate actively in the experience using technology, such as computers or mobile devices, and the learning activities involve real-life situations that encourage problem solving and a culture of practice.44 The eHealth learning modules incorporated Social Cognitive Theory,45,46 self-directed behavior change (behavioral self-management),47 and motivational coaching techniques48–51 to enhance participant knowledge and to develop social support strategies to foster behavior changes. These approaches have been implemented in numerous lifestyle change trials.52–56 Strategies were used to (1) set realistic expectations, (2) recognize and modify environmental and personal barriers, (3) maintain changes, and (4) prevent relapse.

The third phase used formative evaluation to assess the appeal and usability of the eHealth learning modules and fine tune the program.40–42 Eight women attended a 1-hour interactive session that was located in a computer laboratory of the Academic Building to ensure Internet access for each participant. This session was designed to assess delivery of the module content in terms of use, navigation, timing, and pacing; visual quality; interactivity and engagement; and module content (organization, relevance, comprehension).

The fourth phase was the beta testing period of 12 weeks, with 1 module to be completed each week, if desired. Beta testing is commonly used during program/software development to evaluate functionality by end users. Eight young, prehypertensive AA women were randomly assigned to either the Lifestyle PA content (12 modules) or the DASH content (12 modules). Beta testing participants attended a 30-minute session in a conference room with computer access located in the Academic Building. This session was designed to provide each participant with log-in access and familiarity with navigating the eHealth program. Participants were instructed to go through the eHealth learning modules (over a 12-week period) and assess whether these modules (1) met the requirements that guided its design and development and (2) worked as expected. At the end of the 12 modules, participants were asked to respond to 7 survey questions about the functionality of the program. Log-in rates were identified as a function of program utilization and determined by the number of times participants logged in to the program. Program dose was determined by how many participants completed the program materials.7 Participants reviewing the Lifestyle PA content were given a pedometer (Digiwalker, Yamax SW-200, New Lifestyles Inc, Lee’s Summit, Missouri). Pedometers (step counters) were used as a self-management and goal setting tool and as an objective indicator of habitual PA. Lifestyle PA participants were asked to wear the pedometer at all times except while sleeping or bathing and to record the number of total daily steps taken and reset the device for use the next day. A weekly log was embedded within the Lifestyle PA modules for participants to conveniently record daily steps and PA minutes. Participants reviewing the DASH content were asked to record dietary intake using SuperTracker, an online tool developed in effort to translate and implement the national dietary guidelines (

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Participants were identified via strategies we have previously used with young AA women, such as advertising through the Internet, print materials, and at blood pressure screenings.36 Participants were compensated for travel expenses, $50 for formative evaluation (phase 1 and 3) and $75 for beta testing (phase 4).

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Data Management and Analyses

For phase 1, a database was created with descriptive information from the formative evaluation. A summary table was created and examined with participants’ narrative relating to preferences for seeking health-related information.42 The focus of this approach was to describe personal and social experiences and categorize aspects of the accounts as told by the participants. Data were reviewed for patterns and themes.57 Semantic differential was used to rate the connotative meaning of the quantitative data.58 This approach attempts to calibrate meaning to participant responses and thereby derive the attitude toward a given object, event or concept. For example, reactions toward a particular object can be measured in terms of ratings on bipolar scales with contrasting adjectives, such as like or dislike.59 No data management was used for converting the eHealth learning modules into the Web-based WordPress platform (phase 2). For phase 3, a database of descriptive information and participant comments relating to the appeal and usability of the eHealth learning modules was created. For phase 4, quantitative data from beta testing was examined using frequency and distributions of responses to each question. Log-in, program dose, pedometer steps, and PA minutes were tabulated to calculate an average weekly rate.

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With respect to phase 1, formative evaluation, 11 women, aged 35 ± 10.4 years, participated in a 1-hour discussion about their preferences for acquiring health-related information. These women indicated that they (1) currently use the Internet to retrieve health-related information, (2) prefer to use the Internet rather than face-to-face contact for nonserious conditions, (3) need convenience and easily accessible health-related interventions, (4) are amenable to the idea of an eHealth lifestyle modification program, and interestingly, (5) find it more challenging to adopt healthy dietary habits than to increase levels of PA. Women were comfortable with using the Internet for health-related information and would like to try an Internet program for improving nutrition and increasing PA. Most women felt that changing eating habits rather than increasing PA was the bigger challenge to tackle. In particular, women expressed that eating on a budget, eating out, feeding the kids snacks after school while rushing to extracurricular activities, and eating traditional “soul food” represent difficult daily challenges. Women suggested using support groups and coaches to help with making better healthy choices. Table 2 displays responses to 12 quantitative questions that suggest that these young AA women are amenable to retrieving health-related information via the Internet; however, some ambivalence was evident. The questionnaire was administered before formative discussions started, which may have impacted participant responses. Some of this is because of the fact that items 9 and 10 require a very strong preference for electronic communication. In addition, the last question is negatively phrased and conditional in nature, which may have been confusing. Participants may have only been responding to the primary clause (“I am currently too busy…”) and ignored the reference to face-to-face content in the subclause.



In phase 2, face-to-face content was converted into a Web-based platform. A Web designer created 24 eHealth learning modules using WordPress. WordPress is a free, Open Source software that functions as a blogging and content management system. This software was selected to create and manage the eHealth Web site because of its accessibility, ease of use, and customizable templates with plugins, widgets, and themes ( The learning content was derived from the printed manuals and adapted slightly for a Web audience, including condensing the information to adhere to best practices in Web site content development. The modules were designed to present evidence-based content but allowed for tailoring and individualization according to the needs of the target population. For example, a target number of servings for fruits and vegetables was provided that was contingent upon individual calorie needs and whether weight loss was desired. An additional effort was made to acknowledge participant’s lifestyle routines/constraints and barriers to meeting recommended dietary and PA guidelines. The eHealth learning modules required active engagement from the participant, such as weekly logs for diet and PA, interactive activities, group support and discussions, and simulations.

With respect to phase 3, 8 women participated in a 1-hour interactive discussion to assess the appeal and usability of the eHealth learning modules. The computer laboratory afforded each woman Internet access and the opportunity to review aspects of the eHealth learning modules. Overall, the participants found the weekly modules and links easy to navigate. Although the content was initially compressed from the original printed manuals, this evaluation uncovered that the organization of content required additional revisions appropriate for online participation (Table 3). Women indicated that (1) the delivery of the content was adequate but greater instruction was needed in relation to the dietary food tracker; (2) visual quality could be enhanced by using less text, improving relatable visual materials in the modules, and providing discussion sections at the top of the lesson; (3) embedding videos into specific lessons would increase interactivity, and providing feedback on food choices would provide greater engagement; and (4) module content (organization, relevance, comprehension) should have less text that minimizes scrolling through too many pages of information. These formative suggestions were used and incorporated into the eHealth learning modules by (1) reducing the amount of text, (2) providing more participant instruction for navigating module functions, (3) embedding video links highlighting cooking demonstrations, healthy recipes, and PA routines, (4) enhancing graphics, and (5) improving discussion capability.



Phase 4 involved 8 women, aged 43 ± 5.6 years, beta testing the eHealth program (3 tested the DASH program and 5 tested the Lifestyle PA program). Overall, there was a 63% completion rate for the beta testing phase. Of the 5 women in the Lifestyle PA program, 4 completed (80%) the 12 weekly modules, with the fifth woman completing up to week 8. The 3 women enrolled in the DASH program did not complete the 12 weekly modules. At the end of the 12-week program, women were asked to complete a 7-item survey to assess functionality. A total of 7 of 8 women completed the survey. As shown in Table 4, most of the women agreed that the program and screens opened with ease, the functions on the screens did what they were supposed to do, and the discussion board was easy to access. The 5 women in the Lifestyle PA program strongly agreed that the PA log (tracking steps and minutes) was easy to navigate and enter information. The 2 women in the DASH program who completed the survey disagreed that the dietary log was easy to navigate or enter information. The log-in rates for the program averaged 1 to 2 times a week for most of the women. For the 5 women in the Lifestyle PA program, the average pedometer steps were 5686 per week and minutes of accumulated PA was 51 per week.



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The intent of our study was to transform the delivery of a healthy lifestyle change intervention to reach more women at risk for developing hypertension. The transformation process involved 4 phases: (1) assessing Internet use and information seeking preferences, (2) converting content into a Web-based platform, (3) assessing appeal and usability, and (4) beta testing the program. Although there is a growing body of evidence supporting Internet-based behavior change interventions, very little has been reported on the actual process of transforming content into a Web-based platform using formative evaluation.60 This is particularly the case with respect to interventions designed for young AA women.21 We observed several findings in relation to the use and reach of eHealth and its potential impact for targeting healthy behavior change among young AA women.

Our first formative discussion (phase 1) confirmed that this sample of young AA women use the Internet and text messaging, a finding similar to nationally reported data from the Pew Organization.23 Most of our sample preferred seeking health-related information on the Internet for nonserious conditions, and they expressed interest in using eHealth technology for receiving healthy lifestyle information. These findings are consistent with other studies identifying AA parents as active users of the Internet and mobile technology and their interests in seeking health-related information on the Internet.61,62

The process of converting our face-to-face content into a Web-based platform was successful (phase 2). We used several strategies for promoting eHealth learning, such as educational modules, self-navigation, and Social Cognitive Theory techniques, which are components similar to other Internet-based interventions.9,62 Participants responded to content questions that were embedded in each module, and individual, tailored feedback from study staff was provided within 12 hours. This individualized approach is slightly different from other studies that report using automated feedback with computer algorithms.9,10,63 Finally, we embedded a mechanism within the eHealth program for participants to track their progress with lifestyle changes, which is similar to many Internet-based interventions.63–65

A number of suggestions were identified during our second formative discussion (phase 3). We incorporated these into the program to enhance and improve the eHealth learning modules. Women requested less text, relatable visuals, enhanced graphics, more videos with demonstrations, and easier navigation for discussion opportunities. These recommendations are similar to those identified by Durant and colleagues.21

The final phase of our study involved beta testing the eHealth program to assess use, dose, and functionality in our target population. Log-in rates for this sample of women averaged 1 to 2 times per week, which is similar to other reports.5,65,66 Eighty percent of the PA participants completed the 12-week learning modules and weekly PA logs. With respect to program dose, none of the DASH participants completed the 12 eHealth learning modules or weekly logs, suggesting challenges with adhering to dietary behavior change.67,68 Both log-in frequency and program completion have been associated with improved behavior change outcomes.5,7,65,66

We used several factors to stimulate use of the eHealth program that may have contributed to the high completion rate among the PA participants, such as sending personal reminders, incorporating professional support, providing tailored feedback to meet participant’s behavior change goals, and recommending strategies for overcoming barriers. Most of this sample found that the eHealth program functioned with ease in terms of displays, buttons, discussion boards, and tracking logs. This finding suggests that the program operated very efficiently. These factors to stimulate use of the program, along with intervention characteristics related to program delivery and multiple exposures, have been reported to enhance participant engagement5,7,22 and may have contributed to the high completion rate among the PA participants. Failure of the DASH participants to complete the program modules/materials may be related to the complexity of changing dietary behaviors.68 This is also confirmed by participant comments that dietary behaviors are more difficult to change.

We used tracking logs for PA and dietary habits as a tool for goal setting and self-monitoring. The tracking logs worked well for PA participants but not for DASH participants. All of the PA participants tracked their PA outcomes and submitted weekly logs. Our sample of women averaged fewer pedometer steps per day per week and minutes of weekly PA per compared with the recommended 10 000 steps per day and 150 PA min/wk,69 as well as other PA Internet-based studies.20,64,70

Finally, this study contributes to the field because it outlines a step-by-step process for transforming face-to-face content into a Web-based platform, which, importantly, can serve as a template for promoting other health behaviors. In addition, we incorporated formative information and gathered input from participants to purposely design a program that is relevant, accessible, and culturally desirable for the target population. This eHealth program that resulted from this process is a dynamic tool for getting young women to adopt healthy behaviors and afforded opportunities for tailored feedback and social support. Offering eHealth programs is a health promotion delivery approach important at the individual and community level for disseminating health-related information, especially for hard-to-reach individuals.

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First, this was a feasibility study that used small sample sizes for formative evaluation and beta testing.71 Future studies designed to evaluate effectiveness should use robust methodology, adequate sample size, and power to afford generalizability. Second, we did not assess exposure to the intervention in terms of time spent on the eHealth learning modules. Measuring time spent on the Web site can serve as an indicator of participant engagement in eHealth and can help explain the effectiveness of an intervention.72 Finally, aside from difficulty navigating the dietary tracking log, there is no definitive information as why the 3 DASH participants did not complete the 12 eHealth learning modules. Obtaining formative information after the beta testing phase would have provided greater insight.

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What’s New and Important

  • Greater emphasis is needed on how to translate evidence-based guidelines into practical and accessible programs that are accessible for high-risk groups.
  • eHealth provides an alternative approach for offering health promotion programs for hard-to-reach individuals.
  • Young, AA women are amenable to lifestyle change programs that are offered using eHealth technology.
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Although evidence supports eHealth for dietary and PA behavior change, the feasibility of tailored eHealth interventions targeting young AA women at risk for developing hypertension has not been fully determined from earlier studies. This study used formative information from our target population during the process of transforming our face-to-face behavioral change content into a delivery platform allowing greater accessibility, convenience, and fewer program barriers for young AA women. Overall, the delivery of our healthy lifestyle program was successfully transformed. However, the higher completion rate for the PA intervention suggests that the delivery of the program was more effective for PA but not for dietary change.

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1. Lloyd-Jones DM, Hong Y, Labarthe D, et al.; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010; 121: 586–613.
2. Moreno JP, Johnston CA. Barriers to physical activity in women. Am J Lifestyle Med. 2014; 8 (3): 164–166.
3. Scisney-Matlock M, Glazewki L, McCkerking C, Kachorek L. Development and evaluation of DASH diet tailored messages for hypertension treatment. Appl Nurs Res. 2006; 19: 78–97.
4. Atkinson NL, Gold RS. The promise and challenge of eHealth interventions. Am J Health Behav. 2002; 26: 494–503.
5. Kohl LFM, Crutaen R, de Vries NK. Online prevention aimed at lifestyle behaviors: a systematic review of reviews. J Med Internet Res. 2013; 15 (7): e146.
6. Cotter AP, Durant N, Agne AA, Cherrington AL. Internet interventions to support lifestyle modification for diabetes management: a systematic review of the evidence. J Diabetes Complications. 2014; 28 (2): 243–251.
7. Norman GJ, Zabinski MF, Adams MA, Rosenberg DE, Yaroch AL, Atienza AA. A review of eHealth interventions for physical activity and dietary behavior change. Am J Prev Med. 2007; 33 (4): 336–345.
8. LaPlante C, Peng W. A systematic review of e-health interventions for physical activity: an analysis of study design, intervention characteristics, and outcomes. Telemed e-Health. 2011; 17 (7): 509–523.
9. Joseph RP, Durant H, Benitez TJ, Pekmezi DW. Internet-based physical activity interventions. Am J Lifestyle Med. 2014; 8 (1): 42–67.
10. Marcus B, Lewis BA, Williams DM A comparison of Internet and print-based physical activity interventions. Arch Intern Med. 2007; 167: 944–949.
11. World Health Organization. Health Topics—e-Health. WHO. 2014. Accessed April 25, 2014.
12. Go AS, Mozaffarian D, Roger VL Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation. 2014; 129: e28–e292. doi:10.1161/01.cir.0000441139.02102.80.
13. Nwankwo T, Yoon SS, Burt V, Gu Q. Hypertension among adults in the United States: national health and nutrition examination survey, 2011–2012. National Center for Health Statistics Data Brief. 2013. Accessed April 28, 2014.
14. Glasser SP, Judd S, Basile J Prehypertension, racial prevalence and its association with risk factors: analysis of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Am J Hypertens. 2011; 24 (2): 194–199.
15. Guo F, Zhang W, Walton G. Trends in prevalence, awareness, management, and control of hypertension among United States adults, 1999–2010. J Am Coll Cardiol. 2012; 60: 599–606.
16. Flegal KM, Caroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 199–2010. JAMA. 2012; 307 (5): 491–497.
17. Forman JP, Stampfer MJ, Curhan GC. Diet and lifestyle risk factors associated with incident hypertension in women. JAMA. 2009; 302 (4): 401–411.
18. Churilla JR, Ford ES. Comparing physical activity patterns of hypertensive and nonhypertensive US adults. Am J Hypertens. 2010; 23: 987–993.
19. Toprak A, Wang H, Chen W Prehypertension and black-white contrasts in cardiovascular risk in young adults: Bogalusa Heart Study. J Hypertens. 2009; 27: 243–250.
20. Pekmezi DW, Brooke BL, Bodenlos JS, Jones GN, Brantley PJ. Promoting physical activity in low income African Americans: project LAPS. J Health Disparities Res Pract. 2009; 3 (2): 82–91.
21. Durant NH, Joseph RP, Cherrington A Recommendations for a culturally relevant Internet-based tool to promote physical activity among overweight young African-American women, Alabama 2010–2011. Prev Chronic Dis. 2014; 11: 130–169.
22. Neville LM, O’Hara B, Milat A. Computer-tailored physical activity behavior change interventions targeting adults: a systematic review. Int J Behav Nutr Physical Activity. 2009; 6: 30. doi:10.1186/1479-5868-6-30.
23. Pew Research Internet Project. African-Americans and technology use. Accessed April 26, 2014.
24. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight. J Cardiovasc Nurs. 2013; 28 (4): 320–329.
25. Bavikati VV, Sperling LS, Salmon RD Effect of comprehensive therapeutic lifestyle changes on prehypertension. Am J Cardiol. 2008; 102: 1677–1680.
26. Bassuk SS, Manson JE. Physical activity and health in women. Am J Lifestyle Med. 2014; 8 (3): 144–158.
27. Paynter NP, Cook NR, Everett BM, Sesso HD, Buring JE, Ridker PM. Prediction of incident hypertension risk in women with currently normal blood pressure. Am J Med. 2009; 122: 464–471.
28. Lopez L, Cook EF, Horng MS, Hicks LS. Lifestyle modification counseling for hypertensive patients: results from the national health and nutrition examination survey 1999–2004. Am J Hypertension. 2009; 22 (3): 325–331.
29. Gu Q, Burt V, Paulose-Ram R, Dillion CF. Gender differences in hypertension treatment, drug utilization patterns, and blood pressure control among US adults with hypertension: data from the national health and nutrition examination survey 1999–2004. Am J Hypertens. 2008; 21: 789–798.
30. Whelton SP, Chin A, Xin X, He J. Effect of aerobic exercise on blood pressure: a meta-analysis of randomized, controlled trials. Ann Intern Med. 2002; 136: 493–503.
31. Bacon SL, Sherwood A, Hinderliter A, Blumenthal JA. Effects of exercise, diet and weight loss on high blood pressure. Sports Med. 2004; 34 (5): 307–316.
32. Dickinson HO, Mason JM, Nicolson DJ Lifestyle interventions to reduce blood pressure: a systematic review of randomized controlled trials. J Hypertens. 2006; 24: 215–233.
33. Svetkey LP, Erlinger TP, Vollmer WM Effect of lifestyle modifications on blood pressure by race, sex, hypertension status, and age. J Hum Hypertens. 2005; 19: 21–31.
34. Blumental JA, Babyak MA, Hinderliter A Effects of the DASH diet along and in combination with exercise and weight loss on blood pressure and cardiovascular biomarkers in men and women with high blood pressure. Arch Intern Med. 2010; 170 (2): 126–135.
35. Bosworth HB, Olsen MK, Neary A Take control of your blood pressure (TCYB) study: a multifactorial tailored behavioral and education intervention for achieving blood pressure control. Patient Educ Couns. 2008; 70: 338–347.
36. Staffileno BA, Coke L. Recruiting and retaining young, sedentary, hypertension-prone African-American women for a physical activity intervention study. J Cardiovasc Nurs. 2006; 21 (3): 208–216.
37. Staffileno BA, Minnick A, Coke LA, Hollenberg SM. Blood pressure responses to lifestyle physical activity among young, hypertension-prone African-American women. J Cardiovasc Nurs. 2007; 22 (2): 107–117.
38. Tangney CC, Ventrelle J, Morris LR, Oleske DM. Six month changes in dietary behaviors, physical and emotional health of recent breast cancer survivors. Poster Presentation at Experimental Biology 2008. April 2008; San Diego, CA. Abstract no. 9412.
39. Barnett J, Aguilar S, Brittner M, Bonuck KM. Recruiting and retaining low-income, multi-ethnic women into randomized controlled trials: successful strategies and staffing. Contemp Clin Trials. 2012; 33: 925–932.
40. Dunn KE, Mulvenon SW. A critical review of research on formative assessment: the limited scientific evidence of the impact of formative assessment in education. Pract Assess Res Eval. 2009; 14 (7). Accessed April 2, 2014.
41. Formative evaluation: a practical guide. ELearn Magazine. Accessed June 10, 2009.
42. Miles MB, Huberman AM. Qualitative Data Analysis. 2nd ed. Thousand Oaks CA: Sage Publications, Inc; 1994.
43. Arain M, Campbell MJ, Cooper CL, Lancaster GA. What is a pilot or feasibility study? A review of current practice and editorial policy. BMC Med Res Methodol. 2010; 10: 67. Accessed July 7, 2014.
44. Oliver R, Herrington J. Exploring technology-mediated learning from a pedagogical perspective. Interact Learn Environ. 2003; 11 (2): 111–126. doi:10.1076/ilee.
45. Bandura A. Self-efficacy: The Exercise of Control. New York: Freeman; 1997.
46. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall; 1986.
47. Watson DL, Tharp RG. Self-directed Behavior: Self-modification for Personal Adjustment. 5th ed. Pacific Grove, CA: Brooks/Cole; 1989.
48. Miller WR, Rollnick S. Motivational Interviewing: Preparing People to Change Addictive Behavior. New York: The Guilford Press; 1991.
49. Rollnick S, Miller W. What is motivational interviewing? Behav Cogn Psychother. 1995; 23: 325–334.
50. Rollnick S, Mason P, Butler C. Health Behavior Change: A Guide for Practitioner’s. New York: Churchill Livingstone; 1999.
51. Lane C, Huws-Thomas M, Hood K Measuring adaptations of motivational interviewing: the development and validation of the Behavior Change Counseling Index (BECCI). Patient Educ Couns. 2005; 56 (2): 166–173.
52. The Diabetes Prevention Program (DPP) Research Group. Description of lifestyle intervention. Diabetes Care. 2002; 25 (12): 2165–2171.
53. Howard BV, Van Horn L, Hsia J Low-fat dietary pattern and risk of cardiovascular disease. The women’s health initiative randomized controlled dietary modification trial. JAMA. 2006; 295: 655–666.
54. Qi Q, Bray GA, Smith SR, Hu FB, Sacks FM, Qi L. Insulin receptor substrate 1 gene variation modifies insulin resistance response to weight-loss diets in a 2-year randomized trial. The Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial. Circulation. 2011; 124: 563–571.
55. The Look AHEAD Research Group. Long-term effects of lifestyle intervention and cardiovascular risk factors in individuals with type 2 diabetes mellitus. Four year results of the Look AHEAD trial. Arch Intern Med. 2010; 170 (17): 1566–1575.
56. Ogedegbe G, Chaplin W, Schoenthaler A A practice-based trial of motivational interviewing and adherence in hypertensive African Americans. Am J Hypertens. 2008; 21: 1137–1143.
57. Reissman CK. Narrative Methods for the Human Sciences. London, England: Sage Publications; 2008.
58. Tzeng O, Landis D, Tzeng DY, Charles E. Osgood’s continuing contributions to intercultural communication and far beyond! Int J Intercultural Relat. 2012; 36 (6): 832–842.
59. Bradley MM, Lang PJ. Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psychiatr. 1994; 25 (1): 49–59.
60. Carr LJ, Dunsiger SI, Lewis B Randomized controlled trial testing an Internet physical activity intervention for sedentary adults. Health Psychol. 2013; 32 (3): 328–336.
61. Mitchell SJ, Godoy L, Shabazz K, Horn IB. Internet and mobile technology use among urban African-American parents: survey study of clinical population. J Med Internet Res. 2014; 16 (1): e9. doi:10.2196/jmir.2673.
62. Demartini TL, Beck AF, Klein MD, Kahn RS. Access to digital technology among families coming to urban pediatric primary care clinics. Pediatrics. 2013; 132 (1): e142–e148. doi:10.1542/peds.2013-0594.
63. Watson A, Bickmore T, Cange A, Kulshreshtha A, Kvedar J. An Internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. J Med Internet Res. 2012; 14 (1): el.
64. Booth AO, Nowson CA, Matters H. Evaluation of an interactive, Internet-based weight loss program: a pilot study. Health Education Res. 2008; 23 (3): 371–381.
65. Tate DF, Wing RR, Winett RA. Using Internet technology to deliver a behavioral weight loss program. JAMA. 2001; 285: 1172–1177.
66. Tate DF, Jackvony EH, Wing RR. A randomized trial comparing human e-mail counseling, computer-automated tailored counseling, and no counseling in an Internet weight loss program. Arch Intern Med. 2006; 166: 1620–1625.
67. Ogedegbe G, Tobin JN, Fernandez S Counseling African Americans to control hypertension cluster-randomized clinical trial main effects. Circulation. 2014; 129: 2044–2051.
68. Epstein DE, Sherwood A, Smith PJ Determinants and consequences of adherence to the Dietary Approaches to Stop Hypertension diet in African-American and White adults with high blood pressure: results from the ENCORE trial. J Acad Nutr Dietetics. 2012; 112 (11): 1763–1773.
69. US Department of Health and Human Services. 2008 Physical activity guidelines for Americans. Accessed May 2, 2014
70. Pekmezi DW, Marcus B, Meneses K Developing an intervention to address physical activity barriers for African-American women in the deep south (USA). Women’s Health. 2013; 9 (3): 301–312.
71. Thabane L, Ma J, Chu R A tutorial on pilot studies: the what, why and how. BMC Med Res Methodol. 2010; 10: 1 Accessed March 30, 2014
72. Schultz DN, Kremers SPJ, Vandelanotte C Effects of a Web-based tailored multiple-lifestyle intervention for adults: two-year randomized controlled trial comparing sequential and simultaneous delivery modes. J Med Internet Res. 2014; 16 (1): e26. Accessed April 2, 2014

African American women; eHealth; healthy lifestyle changes

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