Participation of African Americans in e-Health and m-Health Studies: A Systematic Review

Abstract

Background: African Americans (AA) experience high levels of health disparities for several diseases, yet remain underrepresented in clinical trials and other types of research. The high ownership of smartphones among AA puts them in a unique position to be recruited into e-Health/m-Health interventions. Objective: This article is a systematic review of the participation of AA in e-Health/m-Health interventions, the diseases/health conditions targeted, and the recruitment and retention strategies used. Materials and Methods: A systematic review was done with PubMed, PsycINFO, Web of Science, EBSCOhost, and CINAHL databases, as well as hand searches of 14 journals. The search was restricted to studies conducted in the United States and that were published between January 2000 and June 2016. Twenty-three distinct search terms were used. Results: After removal of duplicates, 565 studies were screened and assessed for eligibility, and 56 met the inclusion criteria. Eight studies had exclusively AA participants. Eighty percent of the studies had female participants. Participants were recruited primarily from clinics/healthcare facilities. Forty-five percent of the studies provided monetary incentives. Only five studies addressed retention of participants. The diseases/health conditions that were studied included overweight/obesity, diabetes, physical activity, cardiovascular conditions, nutrition, prenatal health, and HIV. Conclusion: There was a low representation of AA in the studies in this review. Opportunities exist to engage AA in e-Health/m-Health research, but researchers must go beyond the traditional one-size-fits-all approach to determine what mixture of incentives and recruitment/retention strategies would work best for a particular health condition, population group, or community.

Introduction

African Americans (AA) experience lower life expectancy and higher rates of chronic disease morbidity and mortality relative to their representation in the U.S. population.¹ Specifically, AA have the highest mortality rates for heart disease, stroke, and colorectal cancer.¹ They also have one of the highest prevalence of obesity, hypertension, and diabetes, and the highest infection rate of HIV infection.¹ The lower life expectancy and high prevalence of chronic disease morbidity and mortality rates have been attributed to factors such as institutional racism, socioeconomic status, limited access to and utilization of quality healthcare, environmental conditions, unhealthy lifestyle factors, and the cumulative effects of these factors over the life course.^1
–3

AA have historically been underrepresented in clinical trials and interventions.^4,5 This trend has continued despite the National Institutes of Health (NIH) Revitalization Act of 1993 mandate for the inclusion of minorities in all federally-funded research.⁶ The inclusion of AA in more quasi-experimental, observational, and randomized clinical trials would generate data that can be translated into more effective programs for health promotion and disease prevention, rehabilitation, chronic disease self-management, and clinical and pharmaceutical treatment.⁷

The use of current and emerging communication technologies to improve or enable health and healthcare (e.g., e-Health) has the potential to increase the scope of research and improve the effectiveness of behavioral interventions aimed at reducing health disparities and improving health equity.^8

–11 In particular, mobile health (m-Health) interventions may minimize many of the common challenges seen in community-based and clinical interventions such as low enrollment, attrition, ease of access, geographic proximity, time limitations, inconvenience, and sociocultural attitudes toward research.^7,12 In addition, m-Health technologies may allow many interventions to be delivered with higher fidelity, lower cost, and minimal time demands on patients, researchers, and clinical personnel.^7,12

Nationally representative data suggest that AA are in a unique position to be potential users of mobile technologies and participate in e-Health/m-Health interventions. While AA are less likely than whites to have broadband Internet at home (49% vs. 66%),¹³ they are adopting broadband services at a faster rate than the general population. They are also more likely than whites to own smartphones and to use smartphones as their main source of Internet access.¹³ Therefore, numerous opportunities exist for researchers to recruit AA to participate in e-Health/m-Health research. This article reviews (1) the participation of AA in e-Health/m-Health interventions, (2) the diseases/health conditions studied, and (3) the recruitment and retention strategies used.

Materials and Methods

Our search, review, and reporting methods (Fig. 1) followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).¹⁴ We also used several other nationally and internationally recognized sources to inform the search strategy.^15

–18

Fig. 1.

PRISMA flow diagram depicting the search strategy.

Information Sources and Searches

We conducted computerized searches on PubMed, PsycINFO, Web of Science, EBSCOhost, and CINAHL electronic literature databases. In addition, we completed hand searches on 14 journals as shown in Table 1. Searches were done on English language studies published between January 2000 and June 2016. Twenty-three distinct search terms (Table 2) were used alone and in combination in logical sequences using the Boolean operators “AND,” “OR,” “NOT,” and wildcards [* ()?] in the database searches. Boolean operators use wildcard symbols associated to a word to provide for all possible spellings or variations inside a word or word stem.

Table 1.

Search Terms, Journal Hand-Searched, and PubMed and Medline Medical Subject Heading (MESH) Terms

SEARCH TERMS	JOURNALS HAND SEARCHED	MESH TERMS
• Intervention	• Annals of Behavioral Medicine	• Informatics, Medical
• eHealth intervention	• American Journal of Behavioral Medicine	• Health Information Technology
• mHealth intervention	• American Journal of Health Promotion	• Health Information Technologies
• Mobile health intervention	• Journal of Medical Internet Research	• Information Technologies, Health
• U health intervention	• Telemedicine and eHealth	• Information Technology, Health
• Telehealth intervention	• PLos Medicine	• Technologies, Health Information
• Telemedicine intervention	• Journal of Health Communication	• Technology, Health Information
• Gaming intervention	• Patient Education and Counseling	• Informatics Applications, Medical
• Text messaging intervention	• Contemporary Clinical Trials	• Application, Medical Informatics
• Short message service	• Journal of Medical Library Association	• Informatics Application, Medical
• SMS intervention	• Journal of Consumer Health on the Internet	• Medical Informatics Application
• Web-based intervention	• Journal of Technology in Human Services	• Applications, Medical Informatics
• Online-based intervention	• Journal of Information Science
• Mobile-based intervention	• Medical Informatics and Internet in Medicine
• Internet-based intervention
• Computer-based intervention
• Health technology interventions
• Health information technology interventions
• Web site intervention
• Social media intervention
• Social Networking intervention
• Apps
• Applications

Table 2.

Details of Included Studies

AUTHOR (YEAR)^*	INTERVENTION	POPULATION (N, GENDER, % AA)	RECRUITMENT/INCENTIVES	HEALTH OUTCOMES
Abel et al.⁶⁷	4-week, email vs. text-based weight management study	N = 246, 62% female, 40% AA	Recruited at a hospital dining facility; $5 gift card.	Text message was significantly more effective in improving caloric knowledge.
Anderson et al.²⁶	8-week, IVR for pain management in breast cancer patients	N = 60, 100% female, 42% AA	Recruited from outpatient oncology clinics. No incentives.	Intervention group reported significantly less moderate to severe pain compared to the control group.
Bantum et al.⁵⁹	6-week, Web-based health education program for cancer survivors.	N = 352, 82% female, 13% AA	Recruited from oncology clinics, a tumor registry, Facebook and the Association of Cancer Online Resources (ACOR), and other online sources; $10 gift card.	Treatment group reported reduced insomnia and greater number of min. exercising and stretching. No change in fruit or vegetable intake.
Bennett et al.⁶⁸	24-month, IVR weight management and hypertension study	N = 365, 69% female, 71% AA	Recruited from chart reviewed patient population of three community health centers. No incentives.	Intervention group had larger mean weight losses vs. control; mean systolic blood pressure not significantly different.
Capozza et al.³⁷	6 months, text-based diabetes study	N = 93, 61% female, 2% AA	Recruited from primary care clinics by mail. No incentives.	No statistically significant differences found in A1C between intervention and control groups at 90 and 180 days.
Choi et al.³⁸	12-week, mHealth physical activity study	N = 30, 100% female, 7% AA	Recruited from prenatal clinics and flyers were posted in public settings. No incentives.	Intervention group increased daily steps more than control group; not statistically significant. Intervention group had significantly lower perceived barrier to being active compared to control group.
Evans et al.³⁰	4 weeks, Text4baby messaging prenatal study	N = 943, 100% female, 8% AA	Recruited at first prenatal visit at a military medical center. No incentives.	Increased agreement with belief in the importance of taking prenatal vitamins, avoiding alcohol while pregnant, and seeing a healthcare provider postpartum. No significant effect on behavioral outcomes.
Green et al.³¹	6-month, Web-based cardiovascular and obesity study.	N = 101, 42% female, 5% AA	Recruited through electronic medical record. No incentives.	Intervention group lost significantly more weight than the control group, but reduction in cardiovascular disease risk and blood pressure was not significantly different.
Gustafson et al.⁴⁷	16-week, Web-based breast cancer education	N = 231, 100% female, 37% AA	Participants recruited from callers to 1-800-4-CANCER service for information on breast cancer. Computers provided for study duration. Peer Advocates received $40 for each participant assisted.	Low income women spent more time using the program than more affluent women in a previous study. Urban AA used more information & analysis services, but less communication services than rural Caucasians.
Ipsen et al.⁷¹	6-month, Web-based study with in-person vs. online vs. motivational interviewing health education	N = 222, 60% female, 15% AA	Persons with disabilities recruited from two state general vocational rehabilitation agencies in WA and NC. No incentives.	No significant differences between-groups based on intervention intensity. Both groups experienced reductions in secondary health issues.
Joseph et al.^42,*	8-week, Facebook + text messaging physical activity study	N = 29, 100% female, 100% AA	Recruited through churches, newspaper, social event Web sites, and sororities. $15 incentive.	Intervention group significantly decreased sedentary time, increased light intensity, and moderate-lifestyle intensity physical activity compared to the control participants.
Joseph et al.^21,*	3-month, Web-based weight management and physical activity study.	N = 31, 100% female, 100% AA	Recruited through flyers, recruitment tables at the university center, word-of-mouth, etc. No incentives.	Study completers reported significant reduction in sedentary screen time, significant increase in self-regulation for PA, and marginally significant increases in social support and outcome expectations. No changes in BMI, PA enjoyment, or exercise self-efficacy reported.
Kalichman et al.⁶⁶	9-month, Web-based HIV study	N = 448, 25.9% female, 89% AA	Recruited from AIDS service organizations, healthcare providers, social service agencies, and clinics. No incentives.	HIV/AIDS-Internet skills group demonstrated greater Internet use for health, information coping, and social support compared with the control group.
Keyserling et al.⁶³	12-month, Web-based heart disease study	N = 385, 48% female, 24% AA	Participants were recruited from five family medicine practices. No incentives reported.	Both intervention formats (counselor-based and Web-based groups) reduced CHD risk throughout the study. Web format was less expensive.
Kim et al.²²	13-week, Web-based gestational diabetes study	N = 49, 100% female, 5% AA	Recruited from a university health system, a large nonprofit managed care plan, and several private practices using targeted mailing strategies. No incentives.	No significant differences observed in behavioral constructs, physical activity, or anthropometrics compared with women in the control group.
Kosma et al.²³	1 month, Web-based leisure-time physical activity motivational study	N = 75, 72% female, 5% AA	Recruited persons with disability using flyers and advertising in disability newsletters. No incentives.	No significant differences observed between groups at posttest.
Krein et al.⁷³	12-month, Web-based physical activity and pain study.	N = 229, 11% female, 11% AA	Recruited from Veterans Administration Hospital. No incentives.	Intervention group reported a greater decrease in back pain-related disability at 6 months. Between-group differences especially prominent for patients reporting greater baseline levels of disability, but did not persist over 12 months.
Krukowski et al.⁴⁹	6-month, Web-based weight management	N = 161, 92% female, 31% AA	Recruited through targeted Web site. No incentives.	Patterns of consistent self-monitoring through Web-based tools emerged early for participants who went on to achieve greater than a 5% weight loss.
Leveille et al.³²	3-month, Web-based health educational study for people with chronic health conditions.	N = 241, 57% female, 10% AA	Recruited patients registered with PatientSite who had routine scheduled appointments. No incentives.	No significant differences in detection or management of screened conditions, symptom ratings, and quality of life between groups.
McTigue et al.³³	12-month, Web-based weight management study	N = 50, 76% female, 4% AA	Recruited from a single academic general internal medicine practice. No incentives reported.	31% of study completers had at least a 5% weight loss, 18% had at least a 7% weight loss at the end of 1 year, and a marginal decrease in blood pressure.
Martin et al.^50,*	6-month computer-based hypertension study	N = 434, 68% female, 95% AA	Recruited from public health clinics. No incentives.	No significant differences between the intervention and control in posttest measures.
Merchant et al.³⁶	24-month Facebook + health coaching weight management study	N = 199, 70.4% female, 3.5% AA [intervention arm]	Recruited on a college campus through websites, student orientations, health fairs, email, listserv, and Facebook. $5 after each completed visit; $20 at baseline and $50 at completion.	Health coach posted 1,816 unique weight loss Facebook posts to the study's Facebook page. Almost 73% of weight loss posts were “liked” at least once by the intervention group. Intervention group was significantly less likely to engage in Facebook posts over time.
Micco et al.³⁴	12-month, Web-based weight management study with or without in-person meetings	N = 123, 83% female, 1% AA	Recruited through newspaper and directed to an online application interface. No incentives reported.	No significant differences in weight losses at 6 and 12 months.
Moussa et al.^51,*	4-week Web-based diabetes study	N = 42, 61% female, 100% AA	Recruited through flyers posted at a free health clinic and a community health center. No incentives.	Intervention was more effective in improving diabetes knowledge than paper-based text-only tutorials.
Napolitano et al.⁵⁶	8 weeks, Facebook, Facebook + text messaging weight management study	N = 52, 87% female, 31% AA	In-person and electronic recruitment strategies. $5 at 4 weeks and $10 at 8 weeks (posttreatment).	Overweight/Obesity/Weight management-Facebook Plus group had significantly greater weight loss vs. other two groups. No significant weight loss difference between Facebook Only and waitlist control.
Nguyen et al.¹⁹	6-month, text messaging + self-monitoring, text messaging + health coaching COPD study	N = 17, 65% female, 18% AA	Recruitment from clinic patients. Incentives were a prepaid cell phone and data plan for the duration of the study.	No difference in forced expiratory volume between groups, but the mobile self-monitored group increased total steps/day whereas mobile-coached group logged fewer steps over 6 months.
Patrick et al.²⁷	16-week, text + multimedia messaging weight management study	N = 65, 80% female, 17% AA	Recruited through newspaper ads, flyers, and announcements on craigslist. No incentives.	Intervention group lost significantly more weight than controls and more intervention participants reported that they would recommend this system to friends and family.
Pellegrini et al.³⁵	6-month, Web and phone-based weight management	N = 51, 86% female, 10% AA	Recruitment strategies not described. $50 gift card after completing the 6-month follow-up.	They produced similar, if not greater, weight losses and changes in physical activity than the standard in-person behavioral program at 6 months.
Pekmezi et al.^41,*	12-month, Web-based physical activity study	N = 38, 100% female, 100% AA	Recruitment using newspaper and radio advertisements. No incentives.	Intervention group showed significant increases in physical activity, as well as cognitive and behavior processes of change.
Richardson et al.⁶⁴	6-week Web-based diabetes and physical activity study	N = 35, 67% female, 18% AA	Recruitment through newspaper and a medical research participant website. $25 gift card.	Intervention group significantly increased number of steps per day and pedometer use.
Saver et al.³⁹	30 days, Web-based medical decision-making study.	N = 463, 100% female, 20% AA	Recruitment through targeted mailings, newspaper, newsletters, and signs posted in the community; $25 cash.	Intervention group had greater increases in decisional satisfaction in the second trial and knowledge in both trials. High dropout rate among women randomized to the Web site in the first trial effectively negated benefits, but not in the second.
Sepah et al.⁷²	24-month, Web-based diabetes program	N = 220, 72.7% female, 29.3% AA	Participants were recruited online through ads on Craigslist. No incentives.	Intervention group lost an average of 4.7% of baseline body weight after 1 year and 4.2% after 2 years. A1C was reduced by mean 0.38% after 1 year and 0.43% after 2 years. No significant differences were found for reduction in body weight or A1C.
Skoyen et al.⁵⁸	180 days, telehealth weight management study	N = 171, 21.6% female, 20.5% AA	No recruitment strategies or incentives reported.	Intervention group lost significantly more weight in the first 90-day cycle of the intervention compared to the second 90 days. Those enrolled in both cycles significantly lost more weight overall.
Somers et al.²⁵	16-month, Web-based cancer pain management study	N = 25, 76% female, 24% AA	Recruited from a medical center. No incentives reported.	Intervention group had significant decrease in pain severity, physical symptoms, psychological distress, and pain catastrophizing post-intervention.
Song et al.⁴⁴	4-week, text messaging prenatal study	N = 20, 100% female, 80% AA	Recruitment from survey respondents from a previous study. $50 gift card and cell phone for the duration of the study.	Participants reported significant reductions in stress and depression and improved mental health after using the system.
Spring et al.⁵⁵	3-week daily fruit and vegetable study using personal digital assistant (PDA)	N = 204, 76.5% female, 47% AA	Recruited through community advertisements. $175 for meeting targeted goals during the treatment phase; additional $30-$80 for uploading data during consecutive follow-ups.	Daily fruit/vegetable intake significantly increased, sedentary leisure time decreased, saturated fat intake decreased, and calorie intake decreased. Differences were maintained through 5-month follow-up.
Steinberg et al.⁴⁵	6-month, Web-based weight management study	N = 91, 75% female, 26% AA	Recruited through a university listserv and flyers. $25 incentive for follow-up assessment.	Intervention group lost significantly more weight vs. control group and a greater percentage achieved 5% and 10% weight loss. On average, the intervention group self-weighed more days/week and consumed fewer calories/day compared to the control group.
Steinberg et al.^48,*	12-month, IVR weight management study	N = 189, 100% female, 100% AA	Recruited from five community health centers. No incentives.	Intervention group with a completion rate ≥80% had significantly greater weight loss compared to those in the intervention group with a completion rate <80%.
Svetkey et al.⁵⁷	24-month, smartphone app vs. personal coaching through phone weight management study	N = 365, 69.6% female, 36% AA	Recruited through mass mailings and advertisement. No incentives.	Intervention group had significantly more weight loss compared to control group. No significant differences were found for participants at 24 months or with the smartphone app group.
Tan et al.⁴⁶	6-week, Web-based PTSD and chronic pain management study	N = 34, 100% female, 26% AA	Recruited from the Veterans Administration. No incentives.	Statistically significant decreases in self-reported pain unpleasantness, pain interference, depressive symptoms, PTSD symptoms, and sleep disturbance. Improvements were maintained at 6-week follow-up.
Taylor et al.²⁹	13-month, Web-based prostate cancer study	N = 1,879, 100% males, 40% AA	Recruited through targeted mailing from two hospitals. $10 gift card for first follow-up; lottery entry for $100 or a $200 gift card drawn after completion of the final assessment.	Intervention group had significant improvement in knowledge and reduced decisional conflict compared with control group. No significant differences in screening rates at 13 months.
Tomita et al.⁶²	12-month, Web-based congestive heart failure study	N = 40, 68% female, 13% AA	Recruited from three hospitals and two health insurance companies. No incentives.	Intervention group had a high adherence and showed significant improvement in knowledge, physical activity, quality of life, and reduction in symptoms.
Turner-McGrievy et al.⁶⁹	6-month podcast weight management study	N = 96, 75% female, 20% AA	Recruited through television advertisements and email listserv. $20 gift card at the 3- and 6-month assessments.	Results that confirm and extend previous findings showing a minimally intensive weight-loss intervention can be delivered through podcast. However, prompting and mobile communication using Twitter and monitoring app without feedback did not enhance weight loss.
Turner-McGrievy et al.⁵⁴	12-week, Web-based podcast weight management study	N = 78, 73% female, 14% AA	Recruited through newspaper and email. $20 gift card at posttest.	Participants in the enhanced podcast group had a greater decrease in weight and a difference in reported vigorous activity, with a greater increase in the number of days participants engaged in vigorous activity.
Vidrine et al.⁶¹	3-month, cell phone smoking cessation counseling study	N = 95, 22% female, 72% AA	Smokers recruited from an HIV clinic. Incentive was a prepaid cell phone.	Intervention group had higher abstinence rate and was significantly more likely to quit compared to participants who received usual care.
Wolin et al.⁵³	24-month, Web-based obesity intervention vs. IVR self-monitoring + printed materials	N = 180, 71% female, 72% AA	Recruited through community health centers. No incentives.	IVR self-monitoring had significantly greater reduction in BMI, weight, and blood pressure.
Wilson et al.^24,*	1 week, Web-based nutrition intervention	N = 47, 96% female, 100% AA	Recruited through community events, churches, schools, radio, newspaper, and health clinics. No incentives.	Significant increase in parents' self-reported daily fruit intake and parent + adolescent fruit and vegetable intake.
Winett et al.²⁸	16 months, Web-based nutrition	N = 1,071, 67% female, 23% AA	Recruited through churches. Churches were paid $1,000 per assessment. Participants $20 at pretest, $30 at posttest, and $40 at follow-up.	Intervention group significantly increased fruit and vegetable and fiber consumption at posttest and follow-up.
Wingood et al.^43,*	3-month, Web-based HIV/AIDS study	N = 135, 100% female, 100% AA	Recruited at Planned Parenthood. $50.	Intervention group was significantly more likely to consistently use condoms for vaginal and oral sex.
Yanez et al.⁴⁰	10-week, Web-based prostate cancer intervention	N = 74, 100% male, 41% AA	Participants were recruited from a local comprehensive cancer center. No incentives.	Intervention group reported significant reductions in depressive symptoms and improvements in relaxation self-efficacy at 6-month follow-up.
Ziemba et al.⁷⁰	15-week, telemedicine intervention for PTSD	N = 18, 10% female, 79% AA	Recruited from the Veterans Affairs hospital. No incentives.	Most participants experienced a clinically significant change in PTSD.
Zullig et al.⁶⁵	3-month, Web-based cardiovascular disease study	N = 96, 77% female, 33% AA	Recruited through electronic medical record. No incentives.	No significant differences found.

AA, African Americans; BMI, body mass index; COPD, Chronic Obstructive Pulmonary Disorder; IVR, Interactive Voice Response; PTSD, Post-traumatic stress disorder.

represents studies with samples consisting of 100% African Americans

Eligibility Criteria

Only full-text, original e-Health, and/or m-Health interventions conducted as randomized control trials, quasi-experimental designs, mixed methods, and embedded nonexperimental designs were considered. The study's inclusion criteria were as follows: (1) published in English, (2) conducted in the United States, (3) AA adults (≥18 years), and (4) specified the type of technology (e.g., online or text message) used in the studies. Studies were excluded if race/ethnicity was not specified (e.g., nonwhites). We extracted data on sample size, gender, recruitment and retention strategies, and illness/disease studied.

Data Collection, Management, and Analysis

Four graduate students independently extracted and coded the data, and the project supervisor resolved any discrepancies. Full texts of the articles were subsequently analyzed to ensure that the studies in the sample fully met inclusion criteria. Duplicate studies were eliminated using DigitalVolcano 4.0, a duplicate cleaner application (www.digitalvolcano.co.uk/duplicatecleaner.html).

Studies that met the inclusion criteria were coded into an electronic database using the evaluation rubric developed for this analysis. After the first coding phase, the researchers used a matrix that denoted participants by race/ethnicity, study purpose, e-Health/m-Health intervention type, study, disease/condition studied, and recruitment/retention strategies.

Results

We identified 1,017 studies from the database searches (n = 691) and the hand searches (n = 330). After duplicate removal, 565 full-text studies were screened and assessed for eligibility. Fifty-six studies met the inclusion criteria (Fig. 1) and these studies made up the final sample (Table 2). Fifty-two percent (n = 29) of the studies in the final sample disclosed funding support from NIH.

Participant Characteristics

The reported sample sizes ranged from n = 17 to n = 2,318.^19,20 Most studies (n = 29, 52%) had a participant sample of 100 or more. The studies with the smallest sample sizes were pilot studies (n = 8).^{19,21

–27} Three studies had sample sizes of n ≥ 1,000.^20,28,29

Only 14% of the studies (n = 8) enrolled exclusively AA participants. Of the remaining (n = 48) studies, more than half (n = 27, 56%) had less than 25% AA participants. Of the 27 studies that had less than 25% AA participation, 12 reported AA participation of 10% or less.^{20,24,25,30

–38} The remaining studies (n = 21, 38%) reported samples with 26% or higher AA participation.

Eighty percent of all the studies (n = 45) consisted mostly of females. Fourteen studies (25%) had only female participants.^{21,22,26,29,30,38

–47} Two studies focused on prostate cancer, and thus only had men.^29,39 More than half of the eight studies (63%) with exclusively AA participants (n = 5) had only female participants.^{21,41
–43,48} Two studies had 96% and 92% female participants.^24,49

Most studies (n = 42, 75%) consisted of adults more than the age of 40, with four samples being exclusively AA. The four studies with exclusively AA included a tailored Web-based physical activity promotion intervention⁴¹; a pilot study on a Web-based intervention to promote fruit and vegetable consumption²⁴; a computer-based behavioral intervention to enhance hypertension medication adherence⁵⁰; and a diabetes e-Health program for AA adults with low diabetes literacy.⁵¹ Two studies had more than 70% AA participation.^52,53 One of which used cell phones for health counseling among smokers with HIV/AIDS and uniquely had 70% male participants, which was one of the few studies in the review with women that had a majority male sample.⁵²

Seven studies had participants between the ages of 30 and 40. Two of those studies had samples that were exclusively AA. One used interactive voice response (IVR) technology for self-monitoring of weight and the other used a combination of Facebook and texting to increase physical activity.^42,48 Five studies included AA aged 30–40 in the sample and included a Web-based pedometer program to prevent gestational diabetes²²; a Web-based intervention to promote physical activity among a disabled population²³; a podcast weight loss intervention⁵⁴; a self-monitoring and remote coaching intervention targeted to multiple behavior change in diet and activity⁵⁵; and a m-Health physical activity intervention.³⁹

Seven studies (13%) had participants in their 20 s, and two of those had exclusively AA participants. Of the two studies with exclusively AA, one study used Web-based strategies to address HIV and sexual health,⁴³ and the other examined physical activity and overweight/obesity.²¹ Of the other five studies with participants in their 20s, two used text messaging for pregnant women³⁰ and prenatal care,⁴⁴ one examined Facebook as a platform for an overweight/obesity intervention,⁵⁶ while another examined Facebook as a platform for weight loss messages,³⁶ and the fifth used smartphone apps, self-monitoring, and personal health coaching for weight loss.⁵⁷

Recruitment and Retention Strategies

Slightly more than half of participants (n = 29, 52%) were recruited in a clinical setting (private medical practices, public health clinics/community health centers, hospitals, or electronic medical records). Several studies recruited through flyers, newspaper, public service announcements, etc. (n = 14, 25%) and through electronic websites, listserv, email, or postal mailings (n = 8, 15%). Three studies actively recruited participants from churches (n = 2) and a college campus (n = 3, 5%). Several studies used multiple recruitment strategies, notably one cancer study recruited from five different sources including oncology clinics, tumor registry, Facebook, online cancer resources, and other online sources.⁵⁸ Two studies did not describe any recruitment strategies.^35,59

Most studies (n = 31, 55%) did not report providing any incentives for recruiting and/or retaining participants. Studies that recruited through medical records did not report using incentives. Forty-five percent of the studies (n = 25) reported giving some type of incentive. Nineteen of those (76%) used monetary incentives (e.g., cash or gift cards). The value of cash/gift card incentives ranged from $10 to $175. One study provided a $175 incentive for meeting the goals for both targeted behaviors during the treatment phase and additional financial incentives ($30–$80) for uploading data during consecutive follow-ups.⁵⁵ Notably, one study offered participating churches $1,000 for each assessment phase plus an additional $500 if 95% of participants returned for follow-up assessments.²⁸ One study reported providing compensation, but did not specify the amount.⁴⁰ Six studies (24%) used nonmonetary incentives. The most common nonmonetary incentive was prepaid no contract cell phones.^21,44,60,61 Notably, one study offered free hypertension medication for the duration of the study,⁵⁰ and another study provided participants with a free gym membership.⁴⁸

Only 10 studies (18%) addressed retention of participants. One study reported higher retention rates (81%) among breast cancer patients in the treatment group that provided a peer advocate in the use of an e-Health application compared to those that did not receive peer support.⁴⁷ A weight loss study reported a retention rate of 87% for the intervention group; however, no analyses were provided about those that dropped out.⁴⁵ One study of an online diabetes prevention program used a unique approach where coaches texted or called participants who did not log in for 21 consecutive days.³³ Nonrespondents were considered “inactive” until they logged in again.³³ One study used a single reminder to utilize the system after a 48-h period with no interaction.⁴⁹ Another study staged incentives as part of the retention strategy by providing incremental incentives for each assessment phase.²⁸ The researchers also leveraged positive social pressure by offering an additional $500 to churches that had ≥95% of their participating congregation return for follow-up assessments.²⁸ Incremental incentives were used by another study, but all the incentives earned were provided at the end of the study.⁵⁵

Finally, although not reporting retention/attrition rates, three studies oversampled in anticipation of higher attrition,^62
–64 whereas another study described that increased human interaction with regards to e-Health studies could reduce attrition.⁶⁵ Only four studies reported attrition or retention rates.^20,35 A HIV/AIDS study lost overall 27% from the intervention group and 17% from the control, however, found no significant effects due to attrition on the baseline measures.⁶⁶ A smoking cessation study reported an attrition rate of 80%,²⁰ and another study reported a significant difference in retention rates between the three study groups: (1) standard treatment (53%), (2) standard treatment + technology (100%), and (3) technology only (77%).³⁵ Another study reported an attrition rate of 37.8% and found that those that completed the study were more likely to be Caucasian, female, and educated compared to noncompleters.⁶⁷

Health Conditions

Most interventions (n = 33, 59%) were delivered online. Other interventions were delivered through text^{19,27,30,37,42,44,56,67} or email messages,⁶⁷ IVR,^26,47,48,68 Facebook,^36,42,56 podcast,^54,69 telemedicine,⁷⁰ and by phone using motivational interviewing⁷¹ as shown in Table 2. The diseases/health conditions represented in the sample are shown in Figure 2. Nine studies (16%) examined two or more health conditions. Studies that focused on multiple health conditions included: HIV prevention behaviors among AA women seeking sexual health services at a Planned Parenthood clinic⁴³; posttraumatic stress disorder (PTSD), depression, and chronic pain among female veterans⁴⁶; physical activity and type 2 diabetes patients²²; diabetes prevention and obesity⁷²; chronic pain and physical inactivity⁷³; physical activity and overweight/obese female college students²¹; decreasing cardiovascular risk among overweight/obese patients³¹; and reducing blood pressure in obese patients taking antihypertensive medication.⁶⁸

Fig. 2.

Frequency of health conditions in study sample.

Discussion

There is a gap in the literature regarding the participation of AA in e-Health/m-Health research. In this systematic literature review, we sought to identify the participation of AA in these types of interventions, diseases/health conditions studied, and recruitment and retention strategies used. Examining AA participation in this accelerating branch of health behavior research is critical to advancing the nation's health disparity research and achieving the letter and spirit of the 1993 NIH Revitalization Act.⁶

There was a low representation of AA in the studies in this review. In addition, several studies were excluded from the review because the racial/ethnic demographic data of the participant pool were either not disclosed or separated only into “white” and “nonwhite” categories. The low representation of AA in research may be more a reflection of ineffective, culturally inappropriate recruitment and retention strategies rather than a lack of interest by AA.⁷⁴

While mistrust and skepticism of research may still exist among many AA, it may be less pervasive than in previous decades. Indeed, results from two recent studies found that most of AA surveyed expressed willingness to participate in e-Health/m-Health research.^75,76

The rapid pace of AA adoption of mobile technology, specifically the use of smartphones as the primary mode for Internet access, suggests a need for more innovative recruitment and retention strategies. In addition to being culturally appropriate, these strategies may also need to be tailored by gender and age group.^75,76 Younger AA are more likely to own smartphones and have different health concerns than their older counterparts. Our results suggest that the lag of AA male participation in traditional public health research has been carried forward into e-Health and m-Health research.

Efforts to engage AA males in research using e-Health/m-Health technology deserve further study. Studies have shown that AA women are generally more interested than their male counterparts in improving their health habits⁷⁷ and have reported willingness to participate in e-Health/m-Health research as a factor of personal interest in the study topic, opportunity to become more educated about the topic, and to generally contribute to the greater good.⁷⁶ More effective recruitment and retention strategies should focus on leveraging the influence of AA women within their families and communities.

A variety of strategies were used to recruit participants into the studies. Most studies were recruited through clinical settings and electronic medical records, which may be a viable approach for healthcare-based research. Studies recruiting from these settings may want to consider using an “opt-out” letter approach, which has been shown in traditional studies to shorten recruitment time and increase the participant pool compared to the “opt-in” letter approach.⁷⁸ Studies were also more likely to use a single recruitment strategy even though research suggests that a multilevel recruitment approach that considers culture, learning styles, and personal engagement may result in a larger more diverse participant pool.^78,79

The efficacy of church-based health promotion programs has been well established in the literature,^80,81 but only two studies in this sample used church congregation recruitment strategies.^24,28 AA churches have well-established social networks that can facilitate the successful recruitment into research studies.^80,81 Furthermore, a community-based recruitment approach fosters trust and identifies individuals who are likely to be interested and motivated to participate in research.⁷⁸ In addition, studies have shown that recruitment strategies that involved some degree of interpersonal engagement or community outreach may increase enrollment rate.^78,79 This fact should not be lost on researchers conducting e-Health/m-Health studies. Whether the intervention exclusively uses Telehealth or mobile technology tools or uses them as a component, it appears that some personal engagement at the recruitment stage may be vital for a high enrollment and retention rate.⁷⁸

Ten studies mentioned retention strategies, with some providing more details than others. The studies that described retention strategies were different in so many ways that comparisons could not be made. Studies that provided incentives periodically or at the end of the study appeared to have a better retention rate. Retention strategies that are usually recommended in the literature with traditional studies include providing accessible locations, frequent reminders, and timely payment of incentives.^78,82 The latter two strategies are especially relevant to e-Health/m-Health studies, but funded research should make more effort to publish the strategies used. Study protocols should also require research staff to record the number of participants approached and the number that refused to participate.

The heterogeneity of the sample in the present study and the variety of incentives used prevented a reliable meaningful comparison of incentive strategies. One systematic review that compared the use of incentives in psychology, economics, and health behavior research reported that behaviors and incentive schemes differ greatly in moderating behavioral characteristics. This study also stated that uncertainty remains about the type and structure of financial incentives that would work for specific groups, communities, and targeted health behaviors, which would lead to sustained behavioral change.⁸³ Thus, researchers must continue to seek innovative ways to incentivize participation in e-Health/m-Health studies. They also must go beyond the traditional one-size-fits-all approach to determine what mixture of incentives and recruitment/retention strategies would work best for a particular health condition, population group, or community.

Most of the studies in this review addressed diseases and conditions that are of public health importance.² Specifically, more than half of the studies focused on overweight/obesity, physical activity, cardiovascular disease, and diabetes. Other opportunities also exist to design and implement more e-Health/m-Health studies that target young tech savvy AA who are engaged in social media (e.g., HIV, smoking cessation, alcohol and drug abuse, unplanned pregnancy, and breastfeeding).^75,76 Recruiting a diverse pool of AA in more e-Health/m-Health studies will provide data that can be translated into more effective programs and treatment that may ultimately impact healthcare cost, health outcomes, healthcare quality, health equity, and overall quality of life.²

Limitations

There are some limitations to this study. We used specific search terms, which may not have captured all the articles on the topic. Publication bias, or selective publication of studies, is an ever-present limitation in the existing world of academic publishing. We did not seek out unpublished studies to include in the literature review. We included randomized control trials (RCTs), quasi-experimental designs, mixed methods, and embedded nonexperimental designs; most of the studies in our sample were RCTs, but no quality appraisal was performed. Five of the studies in the sample were pilot studies, and there was a high degree of variability in the objectives and outcomes. Many of the studies included in the systematic review enrolled relatively small patient populations; future studies should assess the sample size required to have an adequately powered design and allow for a more generalizable population to assess the effectiveness of e-Health and m-Health research among AA.

Conclusion

This systematic review of the literature found a limited representation of AA in e-Health/m-Health research even though they continue to experience high rates of health disparities for many diseases. E-health/m-Health research interventions have great promise for public health impact by delivering evidence-based interventions to a wide audience at relatively low cost. However, if we are to achieve reduced health disparities and achieve health equity through designing and implementing effective e-Health/m-Health clinical and community-based research and interventions, the traditional ineffective methods used to recruit and retain AA in traditional research cannot be used.

Researchers must acknowledge the critical role of culture, attempt to explore the cultural appropriateness of these interventions with AA, and engage community members in recruitment and retention efforts, even while they explore and leverage recent and emerging technological advances. The ubiquity of no-contract mobile phones, unlimited talk/text/data mobile plans, and access to low-cost smartphones has resulted in an increasing democratization of technology and the Internet. By addressing these issues, researchers will be better able to evaluate the efficacy and effectiveness of long-term e-Health/m-Health interventions and translate these findings to improve the health outcomes in ethnic minority populations.

Footnotes

Acknowledgment

The research leading to this publication was supported by an internal seed grant from the University of Florida.

Disclosure Statement

No competing financial interests exist.

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