African American (AA) women are at risk for developing hypertension and cardiovascular disease (CVD) because (1) the prevalence of hypertension is greater among AA compared with white and Hispanic women,1,2 (2) hypertension develops at younger ages among AAs, attributing to the cumulative effects of pressure-related complications,3 and (3) AA women have high rates of physical inactivity and poor dietary habits, both contributing factors to obesity.1,4–6 Individuals with blood pressure (BP) in the prehypertensive range are at risk of definite hypertension,7 and a higher BP trajectory in young adulthood is associated with CVD and target organ damage.8,9 Because young AA women are particularly vulnerable to hypertension, greater efforts are needed for primary prevention. National guidelines recommend incorporating healthy lifestyle behaviors into daily living,10–13 yet most of the US population does not meet these physical activity (PA) and dietary recommendations.1,11,14 Alternative and accessible approaches promoting healthy lifestyles are needed, especially among individuals at risk and hard to reach.
One strategy for promoting healthy lifestyle behaviors is through digital health (referred to as eHealth), which affords greater accessibility and reach compared with traditional strategies. eHealth is the use of information and communications technology to improve health and healthcare, largely delivered through the Internet and mobile devices.15,16 There is considerable evidence suggesting that Internet and mobile device interventions are effective in primary and secondary CVD prevention that extend to a variety of individuals and settings who may otherwise be hard to reach.16–19 eHealth has the potential to educate one-on-one, at a convenient time, place, and pace, allowing young, busy women to learn anytime and anywhere as long as they can access the Internet or mobile devices (including iPhones, iPads, Smartphones, etc). eHealth is an attractive approach particularly for young AA women who are at risk for developing hypertension and who many prefer healthy lifestyle behavior change strategies that are accessible and convenient.20 Historically, young AA women have been a group that is hard to reach because of time constraints, fear of research participation, and cultural norms, and traditional approaches for behavior changes have not necessarily been designed to address the lifestyle needs of younger women.21,22 Although there is strong evidence supporting the benefits of adopting lifestyle behaviors for preventing incident hypertension, less information is available for women, in general, and particularly young AA women who are underrepresented in behavioral change interventions.23,24 Further widening this gap is the lack of studies evaluating lifestyle interventions using eHealth strategies that are designed and culturally relevant for young AA women.22,25,26 Therefore, the purpose of this study was to evaluate a healthy lifestyle intervention delivered using an eHealth platform, targeting young AA women at risk for developing hypertension. The primary aim of the study was to promote a healthy lifestyle through increased PA and improved nutrition to
- decrease the risk of developing hypertension as measured by the change in BP, weight, and body mass index (BMI);
- improve healthy nutrition behaviors as measured by change in a 6-item Dietary Approaches to Stop Hypertension (DASH) screener;
- increase healthy PA behaviors as measured by change in pedometer steps; and
- leverage eHealth technologies as a tool to decrease health risk behaviors, increase healthy behaviors, and decrease the risk of hypertension as measured by program engagement.
Study Design, Sample, and Setting
A randomized, pre-post design was used to assess an eHealth behavior change intervention. A convenience sample of AA women, aged 18 to 45 years, with untreated prehypertension (120–139 mm Hg and/or 80–89 mm Hg) and regular access to the Internet were eligible to participate in a 12-week digital healthy lifestyle intervention (referred to as the eHealth study). Aspects of the eHealth study have been previously reported,22,27 but briefly, the 12-week study was Web based and accessible via the Internet and mobile devices. Two in-person visits were required and conducted either at the University Medical Center or at 1 of the designated community clinics. All other study-related activities were conducted using the eHealth platform. Participants were compensated with a $20 gift card at each in-person visit. This study received institutional review board approval.
Recruitment and Randomization
Participants were identified using both traditional (flyers, tabletop cards, BP screenings, in-services, health fairs, and presence at university-run and community clinics) and online-related (Facebook, Craigslist, and the university Web site and intranet) strategies. The recruitment duration was 18 consecutive months and specific details have been reported elsewhere.27 A computer-generated, random numbers table was created and women were randomly assigned to either lifestyle PA content (12 online education modules) or DASH content (12 online education modules). Randomization occurred at the first in-person visit, after informed consent and baseline study measures (BP and weight) were obtained.
The online education modules were developed using evidence-based guidelines11,13,28 and contained 12 modules focusing on the DASH eating plan and 12 modules focusing on lifestyle PA20 (Table 1). The modules incorporated social cognitive theory,20,29,30 self-directed behavior change (behavioral self-management),31 and motivational coaching techniques32–34 to enhance participant knowledge and to develop social support strategies to foster behavior changes. Strategies were used to (1) set realistic expectations, (2) recognize and modify environmental and personal barriers, (3) maintain changes, and (4) prevent relapse. The intervention provided a unique template for women by emphasizing healthy nutrition or PA behaviors, as previously described.20 Behavioral change strategies focused on teaching women how to navigate life’s stressors and illuminate processes for lifestyle change by empowering them to find a balance between their “self-care” and caring for others, while preserving one’s ethnic identity. An emphasis was placed on promoting PA and nutritious eating rather than restrictive behaviors for lifestyle changes within women’s cultural and social context.
The modules presented standardized content but allowed for tailoring and individualization of messaging to address participant’s needs. For example, identifying target number of servings for fruits and vegetables depended on the individual’s needs to meet the DASH eating plan recommendations, calorie requirements, and taste preferences. The modules required active engagement. Each week, there was 1 module for participants to complete. Each module contained (1) content on 1 topic, (2) links to Web-based resources, (3) related videos, (4) activities that were suggested to do for that week, and (5) 2 to 3 questions participants needed to respond to that were related to the week’s topic. For example, each participant set individual goals for increasing PA or improving dietary behaviors. Participants randomized to lifestyle PA were given a pedometer and gathered a baseline level of PA during the first week of the study (determined by steps accumulated). This information was used to develop an individualized PA plan and to set goals for changes in PA behaviors. The recommendation of 10,000 steps per day was encouraged to align with national PA guidelines.35,36 Participants were encouraged to engage in lifestyle activities, such as walking rather than driving, taking stairs rather than waiting for the elevator, and getting up to move with family and friends. Weekly activity logs served as a self-monitoring guide and enabled individualized feedback and reinforcement. For participants randomized to DASH, a 6-item DASH screener was used to track participant dietary intake. This information was reviewed with the participant and used to identify a dietary plan and set goals for changes in individual eating behaviors that follow the DASH eating plan. The DASH eating plan promotes increased consumption of fruits, vegetables, and low-fat dairy products while encouraging foods low in total fat, saturated fat, and cholesterol and sugar-containing beverages. It also includes grains, especially whole grains; lean meats, fish, and poultry; nuts; and legumes.37 For instance, if a participant struggled with juggling work and school responsibilities, rarely ate fruits and vegetables, and used the convenience of fast foods, an individualized feedback was provided based on the participant’s needs. If this participant identified eating fruit as a goal, strategies would be developed to incorporate fruits into her daily routine, such as carrying an apple in her backpack for a snack at school instead of going to the vending machine for chips, adding blueberries as part of breakfast, or freezing bananas for a treat instead of eating ice cream.
The eHealth Platform
The eHealth intervention was delivered using the Web-based platform Wix. Wix is a self-hosted Web site builder and content management system with more than 90 million users (http://www.wix.com). The cloud-based Web development platform is customizable with drag and drop features and includes apps, graphics, image galleries, fonts, and responsive design to adjust the site for mobile viewing. A unique Web site domain URL was purchased to avoid pop-up advertising from Wix. To prevent the general public from accessing the site, a required password login was added to secure the proprietary information and the responses from participants. Details on how the Web site was developed and beta tested have been reported elsewhere.20
Delivery of the Intervention
After a participant was randomized, the research assistant would e-mail the principal investigator with the participants’ randomization number and group (DASH or PA). The principal investigator was responsible for delivering the intervention and all intervention-related communications were directly between the participant and principal investigator. Depending upon group, the respective Web site link and password were e-mailed to the participant welcoming them to the eHealth study. A spreadsheet tracked participant enrollment dates and weekly completion of modules. This enabled the principal investigator to follow each participant in a systematic manner. For example, the principal investigator sent out a reminder e-mail to participants when weekly modules were due. When a participant completed a weekly module, an e-mail was sent directly to the principal investigator and an individualized response was sent back to the participant, typically within a few hour timeframe. Similarly, when a PA log or DASH screener was completed by a participant, this too was sent directly to the principal investigator who then incorporated that information into the tailored response to the participant for that week.
Measurements and Outcomes
Blood Pressure, Weight, and Body Mass Index
To minimize inherent BP measurement variability, the Omron IntelliSense Digital BP Monitor (Omron Health Care, Inc, Vernon Hill, Illinois) recorded the average of 3 pressure readings for each visit. This monitor allows blinded, automatic measures and has reliability and reproducibility of mercury sphygmomanometer measurements.38 Blood pressure measurements followed the American Heart Association Recommendations for Human Blood Pressure Determination,39 with participants seated in an upright chair and feet flat on the floor and confirmed no ingestion of caffeine or smoking within 30 minutes. Body mass index was calculated as weight (pounds) divided by height squared (inches). Weight was measured with a balance beam (Health O Meter) calibrated with standard weights and reported to the quarter pound. Height was measured using a stadiometer and to the 1/16 inch. Participants stood in light clothing and without shoes for measurement. A research assistant measured BP and weight at just before randomization, during the first in-person visit (week 1) and again at the second in-person visit (week 12).
Dietary Approaches to Stop Hypertension Screener
To monitor and assess changes in dietary behaviors, a 6-item DASH screener was used as defined by Toledo and colleagues.40 For this scoring system, a value of 0 or 1 was assigned to each of 6 components. Daily consumption of fruits (≥5 servings), vegetables (≥4 servings), low-fat or nonfat dairy (2–3 servings), whole grains (≥1 servings), sweets (≤1/2 serving), and 1 to 3 servings of lean meat, fish, or poultry was assigned a value of 1 because these amounts were consistent with the DASH eating plan.41,42 The DASH screener was embedded in the module at weeks 2, 6, and 12 with a hyperlink directing participants to complete the 6-item survey. The week 6 DASH screener was used to assess participant dietary adherence, reinforce goals, and set new goals as needed.
Pedometers were used as an objective measure of habitual PA and as a self-management/goal-setting tool. The Digiwalker (Yamax SW-200, Yamax, Inc) was used because of strong evidence of reliability and convergent and discriminative validity in adults,43–45 specifically in women.46 Lifestyle PA participants were asked to wear a pedometer at all times, except while sleeping or bathing, and to self-record the number of total daily steps taken into a PA log. The PA log was embedded in the weekly online module with a hyperlink connecting directly to Survey Monkey, where daily steps for the week were recorded. Weekly PA logs were used to assess participant adherence, reinforce goals, and set new goals as needed.
To assess adherence to the intervention, program engagement was determined by tracking the number of completed program activities (ie, weekly modules, PA logs, or DASH screener) so participation and activity could be directly recorded. For example, participants assigned to lifestyle PA were requested to complete the 12 weekly modules and 12 corresponding PA logs (totaling 24 program activities). Participants assigned to DASH were requested to complete the 12 weekly modules and the 3 DASH screeners at weeks 2, 6, and 12 (totaling 15 program activities).
Data Management and Analyses
This study was designed to assess the feasibility of an eHealth behavior change intervention and determine effect sizes for a sample of young AA women. Descriptive statistics were used to summarize demographic and clinical characteristics. Data were examined to ensure normality, and independent-samples t tests were used to assess differences between groups at baseline. Similarly, tests for differences in these baseline attributes were examined between participants who dropped out and those who completed the 12-week study. To determine whether changes differed across treatment groups, general linear model was run with BP, weight, and BMI. Given the ordinal nature of the DASH data, change in total DASH score from week 2 to week 12 was examined using a Sign test. Change in daily average pedometer steps was examined using a paired t test. Program engagement was calculated as the number of completed study materials divided by the number of expected study materials and expressed as a percentage. Effect sizes calculated as Cohen d47 were determined for weight, BMI, total DASH score, and each individual DASH item and for program engagement. Analyses were performed using data from participants who provided both baseline and 12-week follow-up, and differences were deemed significant at the .05 level. All analyses were performed using SPSS version 22 (SPSS, Chicago, Illinois).
There were 176 inquiries during the 18-month recruiting campaign, with 35 (20%) women meeting BP criteria and enrolled in the eHealth Study, as previously reported.27 The DASH and PA groups were similar at baseline, with no appreciative differences noted. Although these women were relatively young and prehypertensive, they were overweight or obese according current national guidelines.48,49 The Consort diagram illustrates the flow of participants (Figure 1). Nine participants were lost to follow-up (1 DASH and 8 PA), 4 of whom did not complete any program activities and were lost after randomization. There were no differences between those who dropped out and those who remained in the study with respect to age, BP, weight, or BMI (P > .05).
With respect to changes in clinical outcomes, systolic BP, diastolic BP, weight, and BMI did not differ across treatment groups. However, on average, there was a −1.2- and −5.6-lb weight loss in the DASH and PA groups, respectively (Table 2). Upon further examination, we noted 0.18 and 0.84 within-group effect sizes for weight in the DASH and PA groups, respectively. Among DASH participants, total DASH scores improved (P = .001). Of clinical importance were the notable effect sizes for individual DASH components and total DASH score, as noted in Table 3. The largest effects noted were associated with increases in vegetables (0.84), nonfat dairy (0.71), and fruits (0.62), which contributed to a very large total DASH score effect (1.68). With regard to PA participants, the change in daily average steps was trending significance (P = .055) and corresponded to a favorable (+39%) change in daily steps. Average daily steps are shown by week in Figure 2. Lastly, with respect to program engagement, 71% of DASH and 48% of PA participants completed program activities. The difference in engagement between the groups corresponds to a moderate effect size (0.58) (Table 3).
The intent of our study was to evaluate a healthy lifestyle intervention delivered using an eHealth platform targeting young AA women at risk for developing hypertension. We observed several notable findings. First, both groups were similar at baseline. Furthermore, both groups had average BMIs of 35 or greater, therefore classifying participants as obese, further substantiating the need for health promotion and lifestyle interventions among this high-risk population.
Second, although a treatment effect was not observed for BP and BMI, a large effect was discerned for weight change in PA, with a smaller effect noted in DASH participants. With respect to BP, our findings are somewhat inconclusive and may be related to the small sample size and relatively short duration of the intervention.50 In addition, the inconclusive BP effects could be attributed to the subadditivity of the intervention, as noted in other studies in which nonsignificant BP differences were observed.51,52 Participants have experienced challenges adhering to more than 1 lifestyle change.53 With respect to BMI, Joseph and colleagues54 conducted an Internet-enhanced PA intervention among overweight/obese college-aged AA women. The 3-month intervention resulted in a significant decrease in sedentary screen time although no changes in moderate-vigorous intensity PA or BMI were observed. Similar to our study, their sample size was small and the duration of the intervention was relatively short. However, it is important to note that compared with our study, their participants were, on average, 13 years younger and had smaller average BMI (by −4) and BP was not measured. Furthermore, in a systematic review in which 19 intervention studies were designed for improving nutrition and PA behaviors among AA adults, the authors found that for an average 3.0% decrease of initial body weight, BMI still remained in the same obese category.23 These studies, however, were relatively short in duration (2–6 months).
A third notable finding is the change in diet among the DASH participants. The improvement in total DASH scores, as well as large effects among several individual DASH components, suggests that this group of young AA women were successful at modifying dietary habits. The increase in fruit and vegetable intake among our participants is similar to other mobile-based studies, although these were not targeted to young AA women.55,56 In another 12-week DASH intervention study involving 25 middle-aged AAs (88% women), participants achieved significant increases in fruit and vegetable intakes.57 Although this study did not use an eHealth approach, it was similar to ours in terms of the tailoring and problem-solving strategies used. In addition, in a slightly older group of 144 men and women (40% AA) enrolled in a 16-week program with a DASH intervention, all participants improved DASH scores and showed increases of an additional serving of fruit and vegetable servings per day.58 On the basis of the National Health and Nutrition Examination Survey data during 2007–2010, half of the total US population consumed less than 1 cup of fruit and less than 1.5 cups of vegetables daily; 87% did not meet vegetable intake recommendations.59 Therefore, in the context of the National Health and Nutrition Examination Survey data, such observed dietary changes are favorable. The individual tailoring afforded in our eHealth study, along with the streamlined DASH scoring approach, likely contributed to the successful outcomes for these younger AA women.
Fourth, PA participants had a favorable increase in pedometer steps over time, suggesting receptivity to the eHealth intervention. Several recent systematic reviews provide evidence of using eHealth interventions for increasing PA18,60; however, the intended reach of these interventions is varied, making direct comparisons to our study difficult. Many of the published studies are homogenous, involving middle age, white, female, and low-risk populations.17,18,61,62 To the best of our knowledge, few studies have been conducted with young AA women or with participants at risk for hypertension and CVD.20,54,63,64 Furthermore, many of the studies used self-reported questionnaires to assess PA rather than an objective measure like pedometers. For example, a community-based program to promote PA among young to middle-aged AA women reported a significant increase in weekly minutes of moderate-intensity PA assessed using a self-reported questionnaire.65 In a recent systematic review of Internet and mobile device lifestyle interventions, 33 studies specifically evaluated PA, of which 19 studies used pedometers. Of these studies, 15 (79%) reported significant increases in step count, averaging 900 to 4500 steps per day, which is comparable with our findings.66 However, most of these studies involved women with a mean age of between 30 and 60 years. Neither BP status nor race was reported in this review, thereby limiting generalizability to our study. Penn46 conducted a PA study involving middle-aged AA women (52.4 ± 6.7 years) with BP at prehypertensive levels. An increase in daily average steps (1961) was observed, although not statistically significant, which is likely a result of the very small sample (n = 5) and short duration (5 weeks). In our study, we observed a 2191 increase in daily average steps. Even though our participants did not, on average, meet the daily 10,000 step recommendation, the increase we observed is clinically important given that sedentary behavior is associated with CVD morbidity and mortality.67,68
The increase in pedometer steps we observed may, in part, be related to the tailored responses and individual feedback that participants received on a weekly basis. Scott and colleagues69 also found counseling regarding goal setting, identifying social supports, and strategies for overcoming barriers beneficial among middle-aged AA women participating in regular PA. A systematic review conducted by McEwan and colleagues62 identified multicomponent goal setting to be effective for changing PA behaviors among a range of populations and settings.
Lastly, in terms of program engagement, the percentage of completed program activities was higher among DASH compared with PA participants. This finding was somewhat unexpected as data from our previous focus groups and beta testing indicated that participants were more amenable to changing PA behaviors than dietary habits.20 The DASH participants were asked to complete 15 versus the 24 program activities, which may have contributed to the lower engagement we observed among PA participants. The PA participants had a weekly log to complete in addition to the weekly module, suggesting that, perhaps, the number of expected program activities was too time consuming and resulted in a ceiling effect. The level of program engagement in our study was similar to that in Joseph and colleagues,54 who reported 65% completed sessions in an Internet-based PA intervention in young AA women. In addition, Spring and colleagues70 reported 74% of recommended coaching calls in a mobile technology intervention involving middle-aged adults.
Random treatment assignment, blinded clinical outcomes, accessibility, and tailoring the intervention to meet participant needs underscore strengths of our study; however, there are limitations that should also be addressed. First, the sample size is small and limited to young AA women at risk for hypertension. Although generalizability of our findings may be tempered, it is possible to use the eHealth platform as a template for other at-risk populations. Second, we did not assess program engagement using the indicator of time spent logged into the Web site/program. Time spent on the Web site by participants may be a contributing factor to the overall success of the intervention. Third, we did not capture the amount of time spent by the principal investigator in terms of responding to individual participants and tailoring messages. This information would have been helpful for planning follow-up studies and estimation for personal resources and costs effectiveness of the intervention. Fourth, although the pedometer we used has strong evidence of reliability and validity, downloadable data were not a function of this particular device; therefore, pedometer steps were self-report in nature. Fifth, the low engagement among PA participants may relate to attrition bias and should be acknowledged; however, no baseline characteristics differed between those who left the study and those who remained. Last, we offered participants a small compensation to offset their time and parking; however, we do not know whether this had an impact on study participation.
This study explored the effects of implementing a healthy lifestyle intervention using an eHealth platform targeting young AA women at risk for hypertension. The intervention was easy to administer and accessible for young women with access to the Internet or mobile device. We observed relatively large effect sizes in relation to fruit, vegetable, and low-fat dairy intakes and for the total DASH score. In addition, we observed a favorable trend in relation to pedometer steps. Although we did not observe changes in BP, there were small effect sizes for change in weight and BMI. Overall, participant engagement was successful. These findings, in totality, suggest that our tailored approach for delivering a lifestyle change intervention for young AA women provides positive health benefits. Simple strategies that can easily be incorporated into daily living appear amenable for young, busy women.
What’s New and Important
- A Web-based, culturally relevant lifestyle change intervention provides an alternative approach for reaching young AA women at risk for hypertension.
- Increases in fruit, vegetable, and low-fat dairy intake and pedometer stepswere favorable lifestyle changes outcomes.
- Simple strategies that can easily be incorporated into daily living appear amenable for young, busy women.
- Internet and mobile devices have the potential as a powerful resource for changing health behaviors.
1. Mozaffarian D, Benjamin EJ, Go AS, et al. Heart disease and stroke statistics—2015 update: a report from the American Heart Association. Circulation
2. Nwankwo T, Yoon SS, Burt V, Gu Q. Hypertension among adults in the United States: National Health and Nutrition Examination Survey, 2011–2012. NCHS Data Brief
3. Glasser SP, Judd S, Basile J, et al. 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
4. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA
5. Churilla JR, Ford ES. Comparing physical activity patterns of hypertensive and nonhypertensive US adults. Am J Hypertens
7. Huang Y, Wang S, Cai X, et al. Prehypertension and incidence of cardiovascular disease: a meta-analysis. BMC Med
8. Allen NB, Siddique J, Wilkins JT, et al. Blood pressure trajectories in early adulthood and subclinical atherosclerosis in middle age. JAMA
9. Kishi S, Teixido-Tura G, Ning H, et al. Cumulative blood pressure in early adulthood and cardiac dysfunction in middle age: the CARDIA Study. J Am Coll Cardiol
10. US Department of Health and Human Services. Healthy People 2020
. Washington, DC: US Department of Health and Human Services; 2010. https://www.healthypeople.gov/
. Accessed April 1, 2016.
12. Lloyd-Jones DM, Hong Y, Labarthe D, et al. 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
13. Kraus WE, Bittner V, Appel L, et al. The National Physical Activity Plan: a call to action from the American Heart Association: a science advisory from the American Heart Association. Circulation
14. Rehm CD, Peñalvo JL, Afshin A, Mozaffarian D. Dietary Intake Among US Adults, 1999–2012. JAMA
16. Burke LE, Ma J, Azar KM, et al. Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation
17. Kohl LF, Crutaen R, de Vries NK. Online prevention aimed at lifestyle behaviors: a systematic review of reviews. J Med Internet Res
18. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs
19. Widmer RJ, Collins NM, Collins CS, West CP, Lerman LO, Lerman A. Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. Mayo Clin Proc
20. Staffileno BA, Tangney CC, Fogg L, Darmoc R. Making behavior change interventions available to young African American women: development and feasibility of an eHealth lifestyle program. J Cardiovasc Nurs
21. Stallings DT. Illness perceptions and health behaviors of black women. J Cardiovasc Nurs
. 2016;31(6):492–499. doi:10.1097/JCN.000000000000276.
22. Joseph RP, Keller C, Affuso O, Ainsworth BE. Designing culturally relevant physical activity programs for African-American women: a framework for intervention development [published online ahead of print May 13, 2016]. J Racial Ethn Health Disparities
23. Lemacks J, Wells BA, Ilich JZ, Ralston PA. Interventions for improving nutrition and physical activity behaviors in adult African American populations: a systematic review, January 2000 through December 2011. Prev Chronic Dis
. 2013;10:E99. doi:http://dx.doi.org/10.5888/pcd10.120256.
24. Jenkins F, Jenkins C, Gregoski MJ, Magwood GS. Interventions promoting physical activity in African American women: an integrative review. J Cardiovasc Nurs
25. Durant NH, Joseph RP, Cherrington A, et al. Recommendations for a culturally relevant Internet-based tool to promote physical activity among overweight young African American women, Alabama, 2010–2011. Prev Chronic Dis
26. Joseph RP, Ainsworth BE, Keller C, Dodgson JE. Barriers to physical activity among African American women: an integrative review of the literature. Women Health
27. Staffileno BA, Zschunke J, Weber M, Gross LE, Fogg L, Tangney CC. The feasibility of using Facebook, Craigslist, and other online strategies to recruit young African American women for a Web-based healthy lifestyle behavior change intervention [published online ahead of print July 13, 2016]. J Cardiovasc Nurs
28. James PA, Oparil S, Carter BL, et al. 2014 Evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA
29. Bandura A. Social Foundations of Thought and Action
: A Social Cognitive Theory
. Englewood Cliffs, NJ: Prentice-Hall; 1986.
30. Bandura A. Self-efficacy: The Exercise of Control
. New York: Freeman; 1997.
31. Watson DL, Tharp RG. Self-directed Behavior: Self-modification for Personal Adjustment
. 5th ed. Pacific Grove, CA: Brooks/Cole ; 1989.
32. Rollnick S, Miller W. What is motivational interviewing? Behav Cogn Psychother
33. Rollnick S, Mason P, Butler C. Health Behavior Change: A Guide for Practitioner’s
. New York: Churchill Livingstone; 1999.
34. Butler CC, Simpson SA, Hood K, et al. Training practitioners to deliver opportunistic multiple behaviour change counselling in primary care: a cluster randomised trial. BMJ
36. Tudor-Locke C, Craig CL, Aoyagi Y, et al. How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act
38. Ostchega Y, Nwankwo T, Sorlie PD, Wolz M, Zipf G. Assessing the validity of the Omron HEM-907XL oscillometric blood pressure measurement device in a National Survey environment. J Clin Hypertens (Greenwich)
39. Pickering TG, Hall JE, Appel LJ, et al. Recommendations for blood pressure measurement in humans and experimental animals: part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation
40. Toledo E, de A Carmona-Torre F, Alonso A, et al. Hypothesis-oriented food patterns and incidence of hypertension: 6-year follow-up of the SUN (Seguimiento Universidad de Navarra) prospective cohort. Public Health Nutr
41. Appel LJ, Moore TJ, Obarzanek E, et al. A clinical trial of the effects of dietary patterns on blood pressure. N Engl J Med
43. Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA
44. Kooiman TJ, Dontje ML, Sprenger SR, Krijnen WP, van der Schans CP, de Groot M. Reliability and validity of ten consumer activity trackers. BMC Sports Sci Med Rehabil
45. Beevi FH, Miranda J, Pedersen CF, Wagner S. An evaluation of commercial pedometers for monitoring slow walking speed populations. Telemed J E Health
46. Penn WR. Use of Pedometers to Increase Physical Activity in African American Females
[thesis]. Jonesboro, AR: Arkansas State University ; 2010.
47. Cohen J. Statistical Power Analysis for the Behavioral Sciences
. Hillsdale, NJ: Lawrence Erlbaum; 1988.
48. Apovian CM, Aronne LJ, Bessesen DH, et al. Pharmacological management of obesity: an endocrine society clinical practice guideline. J Clin Endocrinol Metab
49. Wadden TA, Webb VL, Moran CH, Bailer BA. Lifestyle modification for obesity new developments in diet, physical activity, and behavior therapy. Circulation
50. Pocock SJ, Stone GW. The primary outcome fails—what next? N Engl J Med
. 2016;375:861–870. doi:10.1056/NEJMra1510064.
51. Ogedegbe G, Tobin JN, Fernandez S, et al. Counseling African Americans to Control Hypertension (CAATCH): cluster randomized clinical trial main effects. Circulation
. 2014;129(20):2044–2051. CIRCULATIONAHA-113.
52. Appel LJ, Champagne CM, Harsha DW, et al. Effects of comprehensive lifestyle modification on blood pressure control: main results of the PREMIER clinical trial. JAMA
53. Schulz DN, Kremers SP, Vandelanotte C, et al. Effects of a Web-based tailored multiple-lifestyle intervention for adults: a two-year randomized controlled trial comparing sequential and simultaneous delivery modes. J Med Internet Res
54. Joseph RP, Pekmezi D, Dutton GR, et al. Results of a culturally adapted Internet-enhanced physical activity pilot intervention for overweight and obese young adult African American women. J Transcult Nurs
. 2016;27(2):136–146. doi:10.1177/1043659614539176.
55. Norman GJ, Kolodziejczyk JK, Adams MA, Patrick K, Marshall SJ. Fruit and vegetable intake and eating behaviors mediate the effect of a randomized text-message based weight loss program. Prev Med
56. Spring B, Schneider K, McFadden HG, et al. Multiple behavior changes in diet and activity: a randomized controlled trial using mobile technology. Arch Intern Med
57. Whitt-Glover MC, Hunter JC, Foy CG, et al. Translating the Dietary Approaches to Stop Hypertension (DASH) diet for use in underresourced, urban African American communities, 2010. Prev Chronic Dis
. 2013;10:120088. doi:http://dx.doi.org/10.5888/pcd10.120088.
58. Epstein DE, Sherwood A, Smith PJ, et al. 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 Diet
60. Laplante C, Peng W. A systematic review of e-health interventions for physical activity: an analysis of study design, intervention characteristics, and outcomes. Telemed J E Health
61. Joseph RP, Durant NH, Benitez TJ, Pekmezi DW. Internet-based physical activity interventions. Am J Lifestyle Med
62. McEwan D, Harden SM, Zumbo BD, et al. The effectiveness of multi-component goal setting interventions for changing physical activity behaviour: a systematic review and meta-analysis. Health Psychol Rev
63. Pekmezi D, Jennings E. Interventions to promote physical activity among African Americans. Am J Lifestyle Med
64. Pekmezi DW, Williams DM, Dunsiger S, et al. Feasibility of using computer-tailored and internet-based interventions to promote physical activity in underserved populations. Telemed J E Health
65. Adams T, Burns D, Forehand JW, Spurlock A. A community-based walking program to promote physical activity among African American women. Nurs Womens Health
66. Afshin A, Babalola D, Mclean M, et al. Information technology and lifestyle: a systematic evaluation of Internet and mobile interventions for improving diet, physical activity, obesity, tobacco, and alcohol use. J Am Heart Assoc
67. Pandey A, Salahuddin U, Garg S, et al. Continuous Dose-Response Association Between Sedentary Time and Risk for Cardiovascular Disease: A Meta-analysis. JAMA Cardiol
68. Young DR, Hivert MF, Alhassan S, et al. Sedentary behavior and cardiovascular morbidity and mortality: a science advisory from the American Heart Association. Circulation
69. Scott MS, Oman RF, John R. The benefits and barriers related to regular participation in physical activity by African-American women: implications for intervention development. Open J Prev Med
70. Spring B, Duncan JM, Janke EA, et al. Integrating technology into standard weight loss treatment: a randomized controlled trial. JAMA Intern Med