Abstract
Most African American (AA) men own a smartphone, which positions them to be targeted for a variety of programs, services, and health interventions using mobile devices (mHealth). The goal of this study was to assess AA men’s use of technology and the barriers and motivators to participating in mHealth research. A self-administered survey was completed by 311 men. Multinomial logistic regression examined associations between three age groups (18-29 years, 30-50 years, and 51+ years), technology access, and motivators and barriers to participating in mHealth research. Sixty-five percent of men owned a smartphone and a laptop. Men aged 18 to 29 years were more likely willing to use a health app and smartwatch/wristband monitor than older men (p < .01). Men aged 18 to 29 years were also more likely than older men to be motivated to participate for a free cell phone/upgraded data plan and contribution to the greater good (p < .05). Older men were more likely than younger ones to be motivated to become more educated about the topic (p < .05). Younger men were more likely than older ones to report lack of interest in the topic as a barrier to participating (p < .01), while older men were more likely than younger ones to cite lack of research targeted to minority communities as a barrier (p < .05). This study suggests that culturally tailored mHealth research using smartphones may be of interest to AA men interested in risk reduction and chronic disease self-management. Opportunities also exist to educate AA men about the topic at hand and why minority men are being targeted for the programs and interventions.
The number of Americans who are seeking health information online has increased tremendously over the past decade (Fox, 2013; Zickuhr & Smith, 2012). Americans are going beyond seeking health information on the Internet to using it to improve health and manage disease and conditions. Smartphones have emerged both as essential communication channels as well as tools to reach, teach, prevent, manage, and improve the public’s health (i.e., mHealth). Nationally, 65% of African American men (AA) own a smartphone (Smith & Page, 2015). Opportunities exist to engage AA men in participating in mHealth research that focuses on disease prevention, risk reduction, and self-management of chronic diseases. This is a vital public health issue since AA men have higher morbidity and mortality rates from acute diseases such as HIV/AIDS and chronic conditions such as diabetes, hypertension, heart disease, obesity, and some types of cancer (U.S. Department of Health and Human Services, 2010). AA men remain underrepresented in all types of research despite the mandate of the 1993 National Institutes of Health Revitalization Act to include minorities in all federally funded research (National Institutes of Health, 1993).
Many AA men may be hesitant to participate in education, clinical, and community-based health research for a variety of sociocultural reasons, including mistrust of researchers, institutional racism, discrimination within the health care system, low physician referral rates, privacy concerns, the perception that research has no real value, and interference with work, family, and other responsibilities (Brown et al., 2012; Campbell et al., 2012; Griffith, Gunter, & Allen, 2011; James et al., 2016; Schmotzer, 2012). AA men may have a general lack of understanding of the different types of research studies (James et al., 2016). One comprehensive literature review of studies that reported consent rates in health research studies by race or ethnicity reported very small differences in the willingness of AA to participate in research compared with Whites (Wendler et al., 2006).
mHealth research studies have the potential to minimize many of the challenges that occur in traditional clinical and community-based interventions such as long travel times, transportation, low enrollment, high attrition, geographic access to rural residents, time limitations, inconvenience, and overall sociocultural attitudes toward research. In addition, some mHealth research studies may also allow interventions to be delivered with greater anonymity, lower cost, and with minimal demands placed on patients, researchers, and clinical personnel (Campbell et al., 2012; Chisolm & Sarkar, 2015; Farmer, Jackson, Camacho, & Hall, 2007; Song, Cramer, & McRoy, 2015). Efforts will still need to be put into appropriate targeted recruitment strategies.
While there are few research studies that document the success of long-term mHealth interventions, and the efficacy of short-term ones are still being evaluated, current evidence suggest that mHealth tools can support behavior change by educating, motivating, and empowering, patients (Gurman, Rubin, & Roess, 2012; Krishna, Boren, & Bolas, 2009). A review of the literature identifies that mHealth interventions and education programs that have used smartphone apps to improve self-management of obesity, diabetes, hypertension, and asthma; using social media networks to educate about HIV/AIDS; and to send text messages for cancer education, smoking cessation, and to improve physical activity levels and improve eating habits (Bock et al., 2013; Kirwan, Vandelanotte, Fenning, & Duncan, 2013; Pollak et al., 2014; Young & Jaganath, 2014). mHealth approaches via smartphones may be especially suited for self-monitoring and self-management of diseases and conditions, since smartphones allow for immediate tracking, monitoring, and reporting as well as the ability to receive immediate and tailored feedback (Coons, Roehrig, & Spring, 2011). Furthermore, they offer many advantages over paper-based methods that require participants and patients to remember to physically bring all food, physical activity, and medication logs (Glanz, Murphy, Moylan, Evensen, & Curb, 2006).
Studies reported that AA men, including low-income ones, have adequate access to the Internet, report high ownership of cell phones, possess the skills to text and navigate the Internet, and show a strong preference for interventions using text messages, and use smartphones to access the Internet at high rates compared with Whites (Smith & Page, 2015; Song et al., 2015; Zickuhr & Smith, 2012). AA men are prime targets for mHealth interventions. The aims of this cross-sectional study were to (a) examine AA men’s use of and access to technology; (b) identify barriers and motivators to participating in mHealth research; and (c) determine if AA men’s use, access, motivators, and barriers varied by age group.
Method
Subjects and Procedures
Data were collected from a convenience sample of 311 AA men during a 9-month period from April 2014 to January 2015 in north central Florida. They were recruited primarily from community events, churches, and barbershops. The men completed a self-administered questionnaire and received a $5 gift card for participating. The study received approval from the institutional review board at the investigators’ institution.
The self-administered questionnaire consisted of 60 questions that measured several variables including sociodemographic information, barriers, and motivators to participating in research, willingness to participate in mHealth research and lifestyle and wellness data. Question types included “yes/no,” “choose the answer that best suits you,” and “choose all that apply.” Participants were asked to choose “all that apply” from seven possible locations where they accessed the Internet, 14 motivators to participating, and 12 barriers to participating. The questionnaire took approximately 15 minutes to complete.
Data were analyzed using the Statistical Package for Social Science (SPSS) software (version 21.0 2012, SPSS Inc., Chicago, IL). Surveys were checked for completeness by the research team at initial survey administration and afterward. Listwise deletion was used to remove cases with missing responses. Statistical significance was established at the p < .05 level for all tests. Frequency tables were computed to check for completeness, range, and consistency. Descriptive statistics were computed to summarize the data, and means were calculated with standard deviations. Analyses included odds ratio (OR), independent samples t test, and analysis of variance. Post hoc comparison was performed with Tukey–Kramer HSD. Multinomial logistic regression analyses examined the association between three age groups (18-29, 30-50, and 51+ years) and (a) how Internet was accessed, (b) barriers to participating, and (c) motivators to participating. The dependent variable was age group, with 51+-year-olds as the referent group. All independent variables were dichotomous (1 = yes and 0 = no). The amount of variation in the model was determined using the Cox and Snell R2 and the Nagelkerke R2 statistics. Last, likelihood ratio tests were used to calculate the statistical significance of each predictor variable into the models.
Results
Participant Characteristics
The mean age was 37.08 ± 14.86 years, with the age distribution as follows: 18 to 29 years (n = 132, 43%), 30 to 50 years (n = 99, 32%), and 51+ years (n = 78, 25%). Most men were single and never married (n = 171, 55%), employed (n = 191, 61%), non-homeowners (n = 220, 71%), and did not have a child younger than age 18 years (n = 188, 61%; Table 1).
Demographic Data of African American Men (N = 311).
Note. AA = Associate of Arts; AS = Associate of Science; BA = Bachelor of Arts; BS = Bachelor of Science.
Health and Wellness
The mean body mass index (BMI) was 28.32 ± 1.65. BMI varied by age, with those older than 18 to 29 years having significantly lower BMI (26.25 ± 5.98) than those 30 to 50 years (30.21 ± 7.11) and those 51+ years (29.85 ± 6.11), p < .0001. There were no significant differences in BMI between the other groups.
BMI varied by age, with those 18 to 29 years having significantly lower BMI (27.32 ± 8.00) than those 30 to 50 years (31.90 ± 8.24) and those 51+ years (31.12 ± 6.64), p < .0001. There were no significant differences in BMI between the other groups. Several men reported that they had been diagnosed with hypertension (n = 83, 27%), elevated cholesterol (n = 54, 18%), type 2 diabetes (n = 39, 12%), and obesity (n = 34, 11%). Within the past 12 months, the top five health topics that men searched for on the Internet were general health and wellness (n = 165, 52%), nutrition (n = 118, 38%), medication (n = 69, 22%), diabetes (n = 52, 17%), and stress/depression/anxiety (n = 43, 14%).
Mobile Devices and Internet Access
Most men (n = 224, 72%) rated their computer skills between good to excellent. Men owned smartphones (n = 202, 65%), laptop computers (n = 202, 65%), and tablet computers (n = 89, 27%). A multinomial logistic regression model examined the association between age group and mobile devices (χ2/6 = 37.06, p < .0001). Compared with those 51+ years, those 18 to 29 years were 4 times more likely to own a smartphone (β = 1.39, p < .0001, OR = 4.00), 2 times as likely to own a laptop (β = 0.88, p < .01, OR = 2.41), but 0.49 times as likely to own a tablet computer (β = −0.77, p = .05, OR = 0.49). There were no significant differences in ownership of these devices between men aged 51+ years and those 30 to 50 years (p > .05, for all). Men reported accessing the Internet from a variety of locations: home computers/networks (n = 214, 69%), smartphones (n = 214, 69%), work or school computers/network (n = 144, 46%), libraries (n = 107, 34%), someone else’s home (n = 71, 23%), restaurant wi-fi (n = 66, 21%), and community centers (n = 3, 11%). A multinomial regression model was used to examine if access to the Internet varied by age group (Table 2). There were four significant variables between those 18 to 29 years and 30 to 50 years. Compared with men aged 51+ years, men aged 18 to 29 years were 8 times more likely to access it from someone else’s home (β = 2.14, p < .0001, OR = 8.48), almost 3 times more likely to access it from their smartphones, (β = 0.99, p < .01, OR = 2.69), and twice as likely from someone work (β = 0.70, p < .04, OR = 2.02). Compared with those 51+ years, those 30 to 50 years were twice as likely to access the Internet from their smartphones (β = 0.67, p = .05, OR = 1.95), but 0.46 times as likely from home (β = −0.77, p = .03, OR = 0.46).
Multinominal Logistic Regression of Internet Access and Age Group Among African American Men (N = 311).
Note. SE = standard error. The referent age group is 51+ years. Model fit: −2 log likelihood = 206.377, χ2(14) = 72.912, p < .001*; Pseudo R2: Cox and Snell = .210, Nagelkerke = .238, McFadden = .110.
p < .05.
Willingness to Participate in mHealth Research
Most (n = 221, 72%) reported they had never participated in a research intervention study, yet most (n = 189, 62%) were willing to receive health education text messages as part of a research study. Men reported willingness to participate in mHealth research interventions that used smartwatches and wristband monitors (n = 171, 55%), smartphone/tablet computer apps (n = 160, 51%), websites to log data (n = 102, 33%), and online support/counseling (n = 67, 21%). Compared with those 51+ years, men aged 18 to 29 years were almost 3 times more willing than to use an app (β = 0.96, p = .002, OR = 2.63) and 2 times more willing to use a smartwatch and wristband monitors (β = 0.76, p = .01, OR = 2.13). Compared with those 51+ years, men aged 30 to 50 years were almost 3 times as likely to be willing to use an app (β = 1.0, p = .002, OR = 2.68).
Motivation to Participate
The men were asked to choose “all that apply” from 14 motivators to participating in mHealth research. Motivators for participating were contribution to the greater good (n = 164, 53%), interest in topic (n = 158, 51%), becoming more educated about the topic (n = 143, 46%), financial incentives (n = 123, 40%), research having a positive impact on their life (n = 118, 38%), free medication/checkup (n = 110, 35%), disease management (n = 103, 33%), being diagnosed with a disease (n = 101, 32%), free cell phone/data plan (n = 98, 32%), making a difference in minority communities (n = 93, 30%), encouragement by friends/family members (n = 84, 27%), having minority researchers on the team (n = 64, 21%), gain technical/computer skills (n = 58, 19%), and referral by health care provider (n = 51, 16%).
A multinomial regression model was used to examine if motivation to participate in mHealth research varied by age group (Table 3). Compared with those 51+ years, men aged 18 to 29 years were 2 times more likely to be motivated to contribute to the greater good (β = 0.69, p = .05, OR = 2.00), but were 0.49 times as likely to be motivated to become more educated about the topic (β = −0.71, p = .05, OR = 0.49). Compared with those 51+ years, men aged 30 to 50 years were almost 3 times as likely to be motivated to contribute to the greater good (β = 1.02, p < .01, OR = 2.78).
Multinominal Logistic Regression of Motivators to Participating in eHealth Research and Age Group Among African American Men (N = 311).
Note. The referent age group is 51+ years. Model fit: −2 log likelihood = 488.013, χ2(28) = 39.467, p = .074; Pseudo R2: Cox and Snell = .120, Negelkerke = .136, McFadden = .059.
p < .05.
Barriers to Participation
Participants were asked to choose from “all that apply” from 12 barriers to participating in mHealth research. Barriers to participation were being too busy (n = 138, 44%), no interest in research (n = 117, 38%), no interest in the topic (n = 113, 36%), privacy concerns (n = 86, 28%), mistrust of researchers (n = 84, 27%), no financial incentives offered (n = 49, 16%), a lack of minority researchers (n = 42, 14%), research does not target minorities (n = 40, 13%), no reliable Internet access (n = 39, 13%), concerns about smartphone data plans (n = 31, 10%), a lack of a computer or smartphone (n = 30, 9%), and research has no real value (n = 29, 9%).
A multinomial regression model was used to examine if barriers to participating in mHealth research varied by age (Table 4). Compared with those 51+ years, men aged 18 to 29 years were 0.32 times as likely to report they would not participate if the research did not target minority groups (β = −1.13, p = .02, OR = 0.32) and men aged 30 to 50 years were 0.32 times as likely to report they would not participate if the research did not target minority groups (β = −1.14, p = .03, OR = 0.32).
Multinominal Logistic Regression of Barriers to Participating in eHealth Research Studies and Age Group Among African American Men (N = 311).
Note. The referent age group is 51+ years. Model fit: −2 log likelihood = 352.911, χ2(24) = 43.130, p = .01*; Pseudo R2: Cox and Snell = .130, Nagelkerke = .147, McFadden = .065.
p < .05.
Discussion
AA men have traditionally been underrepresented in research in general and mHealth in particular. Given their disproportionate burden of chronic diseases, poorer health outcomes, and lower life expectancy, it is important to enroll AA men in emerging research that can promote health and prevent disease (U.S. Department of Health and Human Services, 2010). This study fills a gap and deepens the research into understanding the barriers and motivators to mHealth research among AA men. The results are consistent with several studies that indicate that AA men are willing to participate in research studies (Byrd et al., 2011). Furthermore, most men in the present study rated their computer skills between good and excellent, and expressed a willingness to participate in mHealth research using various devices.
Mobile Devices and Internet Access
Most men owned a smartphone and a laptop. The prevalence of smartphone ownership was equal to the national average (65% vs. 64%), but lower than the national sample of AA (65% vs. 70%; Smith & Page, 2015). The ownership rate was also lower than that of AA women (65% vs. 73%) in a local study (James et al., 2016). As with other findings, younger men in this study were more likely to own smartphones than older men (Smith & Page, 2015). This high prevalence of smartphone ownership has the potential to empower AA men to seek and use health information for personal health and wellness and self-management of chronic and acute illnesses. It also can allow greater participation in clinical and community-based research (Coughlin, 2014; Whiteley et al., 2011). Furthermore, mHealth researchers can also use smartphone technologies to minimize health risk. Some studies reported that smartphone apps and social media sites that promote dating and sex partner locating may facilitate risky sexual behaviors among men, especially men who have sex with men (Chng & Geliga-Vargas, 2000; Grosskopf, Harris, Wallace, & Nanin, 2011; Landovitz et al., 2013; Rice et al., 2012). Although younger men had higher ownership of smartphones, some studies have suggested that age per se did not appear to be the sole influence of using technology for health communication (Selkie, Benson, & Moreno, 2011; Young & Jaganath, 2014).
The participants accessed the Internet from a variety of locations, with most men using smartphones and home computers. Nationally, 19% of Americans rely to some extent on a smartphone for Internet access, and 10% of these individuals use them as their only access to the Internet (Smith & Page, 2015). This study did not focus on whether the men lived in urban, suburban, or rural areas. However, researchers who are interested in conducting mHealth research via smartphones must consider geography. Geography will determine which mobile carriers are in the area and their respective signal strength. One will need to consider the cost of data plans, the types of plans carried by participants (e.g., limited vs. unlimited data plans), and other financial constraints. This study did not ask about the number of smartphones in the household, but this is an important consideration since many low-income households may have only one (Smith & Page, 2015). Thus, mHealth researchers may need to provide participants with a smartphone dedicated for research purposes (James et al., 2016).
Researchers should not only be concerned with if participants have access to the Internet but also if they can use and interact with the Internet in ways that can improve their health and well-being (Wyatt, Henwood, Hart, & Smith, 2005). One researcher posits that even if equal access to the Internet exists, there will still be a second-level “digital divide” between those who are able to search, find, and use credible information and those who cannot (Hargittai, 2002). Opportunities still exist to improve both health literacy and eHealth literacy among AA men.
Motivation for Participating
Even though most men never participated in any type of research interventions, most expressed a willingness to participate in mHealth interventions that used text messages, smartphone apps, smartwatches, and wristband monitors. These findings are consistent with other research that indicate that many AA, especially young adults, are willing to participate in mHealth and technology-based studies (Byrd et al., 2011; James et al., 2016; Young & Jaganath, 2014).
Men reported they would be primarily motivated to participate in mHealth research to contribute to the greater good and if they were interested in the topic. Older men who are diagnosed with a disease may be especially motivated if the research allows them to better understand the disease, empower them to self-manage the disease, and add to their overall quality of life. mHealth technologies may also help empower men to improve communication with their health care providers (e.g., physicians, pharmacists, therapist, and registered dietitian). For example, the Ask Me 3™ patient education program can easily become a social media campaign that teaches men to ask their health care providers three simple questions: “What is my main problem?” “What do I need to do?” and “Why is it important for me to do this?” (National Patient Safety Foundation, 1997).
The study also suggests that physicians and other health care providers can play a vital role in referring these individuals to mHealth clinical trials and interventions, especially with older men. AA men may also be more motivated to participate in research studies if men in their social circle also participate (Griffith et al., 2011; Griffith, King, & Allen, 2012).
Health researchers who plan on enrolling participants in long-term interventions must think carefully about research incentives. In addition to obvious incentives such as cash and free medication, they may also want to consider offering free smartphones, smartwatches, digital activity trackers, and unlimited data plans. The age of the men will also help decide on the type of incentives offered.
Barriers to Participating
No single barrier was selected by the majority of men. However, the most common barrier selected was being too busy. This is a common barrier reported by men in many research studies, and could be related to the male gender role strain that often affect AA men. In essence, AA men often experience role strain as a result of time commitment and prioritization of family, work, and community responsibilities (Griffith et al., 2011). mHealth researchers may need to focus some of their recruitment messages on how the participation in the research study can complement rather than conflict with their other priorities and activities.
Several of the other barriers to participating in mHealth research in the present study were similar to those identified in other types of research studies. These included privacy concerns and mistrust of researchers (Blanton et al., 2006; Bolen et al., 2006; Williams, 2004). These issues may be compounded with the historical legacy of the Tuskegee Syphilis study, the frequent media coverage of breach of patient data and computer hacking, and other issues of living in a digital world, especially among men aged 51+ years (James et al., 2016). Although this study did not ask about sexual orientation and lifestyle risk factors, these issues may also be very important when researching sensitive topics such as HIV/AIDS, and when working with populations such as men who have sex with men (Grosskopf, LeVasseur, & Glaser, 2014; Young & Jaganath, 2014). Furthermore, there may be a reluctance to participate if there is no AA male presence (as a researcher and as an advisory board member), and if the research team has no presence or visible ties to the local community (James et al., 2016; Shavers, Lynch, & Burmeister, 2001). The current research suggests that mHealth researchers may need to especially address privacy concerns and why they are being targeted for research.
Limitations
This study used a convenience sample of AA men, which limits generalizability of the findings to AA men in the local geographic area and the general population of AA men. In addition, the results cannot identify a causal relationship between age group and willingness to participate in mHealth research. The sample was also well educated, with only 9% who did not earn a high school diploma or a general equivalency diploma. However, the results may have several practical applications to researchers who plan on targeting and recruiting AA men for clinical or community-based mHealth research.
Conclusion and Implications
This study adds to the literature by providing in-depth information on the willingness of AA men to participate in mHealth research. First, most men owned smartphones and few were concerned about their cell phone data plans, which meant that they may be very receptive to participating in mHealth research that is of interest to them. Second, AA men have a variety of ways to access the Internet and participate in technology-based research (e.g., smartphones, home computers/networks, and work computers/networks). If the aims of the National Broadband Plan are achieved, it will considerably close the “digital divide” between the “haves” and the “have-nots.” This will mean even greater access to the Internet among AA households and better reception and network coverage for smartphones, especially in rural areas (Federal Communications Commission, 2013). Third, several barriers to participating in mHealth research may be age-specific as well as gender-specific. Many older men are willing to participate in research, but may need training in using smartphone apps, wearable devices, and other web-based technology. mHealth research may be of particular interest to AA men who are interested in the topic and want to contribute to the greater good, but who may still have significant privacy concerns. Fourth, careful considerations should be given to the incentives that will be provided. In addition to the typical financial incentives, providing wearable devices, smartphones, and upgrades to mobile plans may be very valuable, especially among low-income men. However, the ability to gain computer and technical skills may be sufficient to others. Fifth, there is still a need to increase the number of AA researchers in academia, increase the number of AA programmers and coders who can develop culturally specific health-related apps, engage physicians in referring AA patients to mHealth research studies, and for researchers to connect meaningfully with the community outside of the present research interest. Sixth, careful consideration should be given to writing and reviewing the institutional review board mHealth research protocol. In addition to clearly explaining the nature of the research and addressing privacy and data concern, focus also should be placed on explaining how the participants’ electronic footprints will be handled. This may be especially important with sensitive topics (e.g., sexually transmitted infection, HIV/AIDS, and substance abuse), with certain populations (e.g., men who have sex with men and transgender individuals), and certain technologies (e.g., online counseling and social networking sites).
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.