African American Women’s Willingness to Participate in Online Health Communities During COVID-19

Introduction

The nation’s relationship with technology continues to deepen as more people rely on online health information and health care services to prevent and treat illnesses.^1,2 Moreover, consumers, patients, and caregivers are embracing all dimensions of “digital” self-care.³ This aligns well with some of the Healthy People 2030 national health objectives to increase the use of and access to health information technologies to improve health outcomes, health care quality, and health equity.⁴ Furthermore, as the health care system moves toward a patient-centered model, more initiatives aim to empower patients/consumers to improve self-management practices and enhance communication with providers.^5,6

Although many individuals get their health information through random online searches or social media, more are accessing information through online health communities (OHCs).^7,8 Potential benefits of joining OHCs include patient empowerment, disease self-management, peer-to-peer social support, access to information/resources about treatment options, tracking of personal health data, enhanced patient–provider communication, and improved health outcomes.^7,9,10 Patient-led OHCs, the most common type of OHCs, focus on peer-to-peer social support and exchange of advice, experiences, and resources. They are often referred to as online support groups.^9,11 Both patient-led and health care-based OHCs are expected to continue growing even after the wane of the COVID-19 pandemic, which escalated the need for telehealth services.¹² They also will play a more significant role in countering the “infodemic” of misinformation during and after the pandemic.¹³

In addition to benefits for patients and caregivers, OHCs also provide forums for practitioners, researchers, entrepreneurs, and tech giants to focus on using current and emerging technologies to improve user experience, solicit feedback, and solve health care system issues.^7,9,10 Research-driven OHCs provide a platform that pools patient feedback about the efficacy and effectiveness of pharmacological and nonpharmacological therapies, disease progression, treatment side effects, and so on.⁹ On the contrary, campaign-based OHCs engage patients and caregivers to influence health policy and funding for their specific conditions.⁹

Historically, research on OHCs has focused on how patients and caregivers cope with the diagnosis of a disease/condition, manage an existing disease/condition, or share information with peers.¹⁴ However, research is emerging to examine barriers and motivators to participating in OHCs. Despite the apparent benefits of different types of m-health research, populations with a high prevalence of health disparities like African American women (AAW) are less likely to participate in them and, thus, benefit from them.^15–17 AAW continue to experience inequities in the health care system, high prevalence of chronic diseases, poor health outcomes, and lower overall quality of life levels than the general population.^4,16,17 The disparity in participating in OHCs is also occurring when the federal government strongly urges health professionals and organizations to put more information online and increase access and participation in telehealth and telemedicine services.^3,18,19

Unique opportunities exist to recruit and engage AAW in OHCs because of their high ownership of smartphones and mobile devices, access to the internet, and social media engagement comparable with the general population.^16,18 The aims of this cross-sectional study were to (1) examine ownership of digital devices, (2) determine self-reported health status and diagnosis of health conditions, and (3) identify motivators and barriers to participating in OHCs among AAW of different age groups.

Methods

SUBJECTS AND PROCEDURES

This descriptive, cross-sectional study used a convenience sample of 985 AAW who completed an online self-administered questionnaire via Qualtrics (Qualtrics, Provost, UT). Data were collected from March 2020 to July 2020. Participants were recruited primarily through local community partners and ResearchMatch (a national health volunteer registry). The survey link was sent via e-mail to potential participants. Nonrespondents received two follow-up e-mails 1 week apart. There was an 87% response rate. The grant was funded by the Robert Wood Johnson Foundation Culture of Health Leaders Program, and the study was approved by the authors’ University Institutional Review Board (IRB). Participants received a $5 online gift card.

The questionnaire was developed based on a literature review on OHCs, digital health interventions, and previously validated instruments from the team’s research.²⁰ The instrument included questions on sociodemographic variables, device ownership, health status, and barriers and motivators to participating in OHCs. Question types included “fill in the blank,” “yes/no,” and “choose all that apply.” The questionnaire was pilot tested with 50 individuals who were not included in the final sample. The survey took ∼15 min to complete.

Data were analyzed with IBM SPSS Statistics for Windows, version 24 (IBM Corp., Armonk, NY). Listwise deletion was used to remove cases with missing responses. Statistical significance was established at the p < 0.05 level for all tests. Frequency tables were used to check for completeness, range, and consistency. Descriptive statistics were calculated to summarize the data; means were calculated with standard deviations. Analyses included odds ratio (OR), independent samples t-test, and analysis of variance. Post hoc comparisons were performed with Tukey–Kramer honest significant difference (HSD). Multinomial logistic regression analyses examined the association between three age groups (18–29, 30–50, and 51+) and devices owned, health status, and motivators and barriers to participating in OHC. Age group was the dependent variable, with those 51+ as the reference 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 Negelkerke R² statistics.

Results

PARTICIPANT CHARACTERISTICS

A total of 985 women completed the survey. The mean age was 36.87 ± 13.0 years, with the age distribution as follows: 18–29 years (34%), 30–50 years (48%), and 51+ years (18%). Most women were single (60%), employed (82%), born in the United States (87%), nonhomeowners (60%), and did not have children younger than 18 years of age (64%) (Table 1).

DEVICE OWNERSHIP AND USE OF TECHNOLOGY

Most women (96%) had internet access at home. Women were asked to choose all that applied from a list of digital/technology devices that they owned. They owned smartphones (95%), laptops (81%), tablets (52%), desktops (40%), smartwatches (32%), and basic (flip) cellphones (6%). Women older than 51+ years were more like than those aged 18–29 years to own a desktop computer (β = 0.77, OR = 2.16, p < 0.001) and tablet (β = 1.03, OR = 2.80, p < 0.001). However, they were less likely to own a smartwatch (β = −0.58, OR = 0.59, p = 0.008). Women older than 51+ years were more likely than those aged 30–50 years to own a laptop (β = 0.50, OR = 1.66, p = 0.04).

HEALTH AND WELLNESS

Most (95%) reported health insurance coverage (employer, government, or self-insured) and an annual checkup in the past 12 months (87%). Women older than 51+ years were more like than those aged 18–29 years to report an annual checkup (β = 0.90, OR = 2.41, p = 0.003). There were no significant differences in getting an annual checkup between those aged 51+ and those aged 30–50 years (p > 0.05).

Participants rated their health on a scale of 1–5, with 5 being excellent. The ratings were as follows: poor (2%), fair (15%), good (40%), very good (32%), and excellent (12%). There were no significant differences by age group and self-rating (p > 0.05). The mean body mass index (BMI) was 29.38 ± 7.46. BMIs were classified as obese (37%), overweight (33%), normal weight (28%), and underweight (2%). BMI varied by age group, with those older than 18–29 years having significantly lower BMI (27.24 ± 6.72) than those aged 30–50 years (30.17 ± 7.91) and those aged 51+ (31.33 ± 6.57), (F_2,22.83, p < 0.0001).

Several participants reported that a physician diagnosed them with: obesity (28%), hypertension (24%), mental health condition (24%), high cholesterol, 16%), diabetes (13%), asthma (14%), heart disease (4%), and cancer (4%). Women older than 51+ years were more like than those aged 18–29 years to have been diagnosed with obesity (β = 0.68, OR = 1.97, p = 0.004), hypertension (β = 2.05, OR = 7.73, p < 0.001), high cholesterol (β = 1.61, OR = 5.00, p < 0.001), and cancer (β = 1.12, OR = 3.08, p = 0.044). However, they were less likely to have been diagnosed with a mental health condition (β = −0.85, OR = 0.43, p = 0.002). Women older than 51+ years were more likely than those aged 30–50 years to be diagnosed with hypertension (β = 0.92, OR = 2.50, p < 0.001) and high cholesterol (β = 1.13, OR = 3.10, p < 0.001). However, they were less likely to have been diagnosed with a mental health condition (β = −0.75, OR = 0.48, p = 0.002).

MOTIVATORS FOR PARTICIPATING IN OHCs

Participants chose “all that apply” from 10 motivators to participating in OHCs. Motivators were: to become more educated about a specific condition (70%), prevent condition/disease (64%), become empowered about self-care (53%), get general advice about health or health care system (51%), support others (48%), better manage condition (47%), share experience with others (43%), get emotional/social support (42%), and if referred by a health professional (34%).

The multinomial regression model was significant (Table 2). Women older than 51+ years would be more likely than those aged 18–29 years to be motivated to manage a specific condition (β = 0.89, OR = 2.43, p < 0.001) and to provide emotional/social support (β = 0.54, OR = 1.70, p = 0.011). However, they would be less likely to be motivated if friends/family asked them to do so (β = −0.50, OR = 0.61, p = 0.025) and to get emotional/social support (β = −0.99, OR = 0.37, p < 0.001). Women older than 51+ years would be more likely than those aged 30–50 years to be motivated to provide emotional/social support to (β = 0.51, OR = 1.67, p = 0.021) but would be less likely to be motivated to get emotional/social support (β = −0.79, OR = 0.45, p < 0.001).

BARRIERS TO PARTICIPATING IN OHCs

Participants chose “all that apply” from 10 barriers to participating in OHCs. Barriers were: being too busy (53%), concerns about privacy/personal information (45%), no interest in the topic (37%), mistrust of people (35%), no interest in sharing one’s experience (20%), no/minimal financial incentives (15%), no need for emotional/social support (10%), no interest in helping/supporting others (n = 8%), no reliable internet access (7%), and concerns about smartphone data plans (5%). The multinomial logistic regression model was significant (Table 3). Women older than 51+ years were more likely than those aged 18–29 years to have privacy concerns (β = 0.91, OR = 2.48, p ≤ 0.001). However, they had less concerns than those aged 18–29 years about being too busy (β = −0.61, OR = 0.52, p < 0.001), mistrust of people (β = −0.59, OR = 0.56, p = 0.003), and not wanting to share experiences (β = −0.64, OR = 0.53, p = 0.019). Women older than 51+ years were more likely than those aged 30–50 years to have privacy concerns (β = 0.87, OR = 2.40, p ≤ 0.001).

Discussion

This study fills a gap in research that seeks to understand motivators and barriers to participating in OHCs among AAW. The women in the study had high ownership of digital devices, rated their health status between good and excellent, and expressed an overall willingness to participate in OHC.

DEVICE OWNERSHIP

Most participants owned smartphones, laptops, and tablets, which are essential communication tools for accessing online health information and participating in OHC and telehealth/telemedicine services. Their ownership of smartphones was higher than a national sample of U.S. adults (95% vs. 85%), Whites (95% vs. 85%), other women (95% vs. 85%), and other African Americans (95% vs. 83%).²¹ The difference in ownership may be because this study used a convenience sample of AAW who were technologically savvy, whereas the national sample was a weighted representative of the adult U.S. population.²¹

Smartwatches have gone beyond fitness tracking to self-monitoring and self-management of various health conditions.²² They also provide a unique platform to connect to the internet of things^23,24 to allow tracking, monitoring, and sharing of health data with clinicians, researchers, OHCs, and other interested third parties. The women’s ownership of smartwatches in this study was higher than a national sample of U.S. adults (32% vs. 21%), Whites (32% vs. 20%), men (32% vs. 18%), other women (32% vs. 25%), and other African Americans (32% vs. 23%).²² As with the national findings, women in the two younger age groups were more likely to own smartwatches than those in the oldest group.²² This suggests that as early adopters, younger AAW may be more willing to participate in technology-based research (including OHC research) than their older counterparts.²⁰

HEALTH STATUS

The study had a high prevalence of women whose BMI classified them as overweight and obese. However, the obesity prevalence was lower than the national prevalence for all adults (37% vs. 42%) and other AAW (37% vs. 57%).²⁵ Obesity increases the risk for lower life expectancy, higher rates of chronic diseases and other adverse social, economic, and psychological outcomes.⁴ Although younger women had significantly lower BMI than the two older groups, their mean BMI was still high enough to be classified as overweight. Thus, opportunities exist to engage with and recruit AAW into OHCs focused on weight management and treatment for obesity-related diseases such as type 2 diabetes, hypertension, and heart disease. However, instead of using a one-size-fits-all approach, OHCs should also be tailored to different age groups.

MOTIVATORS TO PARTICIPATE

Motivating users to participate in OHCs has been reported as the most crucial key to a successful OHC.²⁶ The benefits of participating in OHCs are well documented.^9,10 The findings in this study are consistent with other findings that indicate that many African Americans are willing to participate in technology-based research studies, including OHCs.^1,20

The reasons that would motivate the women in this study to participate in OHCs aligned well with the goals of most OHCs—information and insights about a specific condition, management of the condition/disease, solidarity, and support.⁹ However, the motivators varied significantly by age. Not surprising, younger and middle-aged AAW were less likely than older women to be motivated to manage a health condition such as hypertension and high cholesterol, and cancer. However, the study found that younger and middle-aged AAW were more likely than older women to be diagnosed with a mental health condition. Thus, opportunities exist to recruit younger women and middle-aged women in researched-based OHCs focused on mental health issues. On the contrary, opportunities exist to recruit older women in OHC focused on obesity-related conditions and other chronic diseases.

BARRIERS TO PARTICIPATE

OHCs often languish or fail because of a lack of user activity and contributions to discussions.²⁶ Thus, it is essential to understand the perceived barriers to participating in OHC. Studies report that the main barriers to participating in OHCs are not technological issues or poor usability. Instead, participants value interacting with interesting people, learning about relevant topics, and engaging with good content. Thus, they are likely to stop participating in OHC if one of these is inadequate.²⁶

Most women in this study reported busyness as a barrier to participating. In addition, many were concerned about mistrust and privacy/personal information. Thus, busy individuals might consider participating if the OHC was dedicated to a specific health issue in their roles as patients, consumers, or caregivers. Also, researchers must build trusting relationships with unrepresented communities before recruitment begins. Part of that trust-building should include information about how data and personal information are stored and the notification process for a data breach.¹⁶ Furthermore, privacy/personal information concerns can be handled by having participants use pseudonyms or usernames. Addressing these issues upfront may also improve the retention of participants, regardless of the age group.

LIMITATIONS

This study had several limitations. First, it used a convenience sample of women who were technologically savvy and had a high ownership of digital devices, limiting the generalizability of the data to other samples of AAW. Second, the impact of the COVID-19 pandemic on the results was beyond the scope of the study. Although the IRB approved the study before the pandemic, the rapidly evolving nature of the pandemic required the researchers to adapt quickly to collecting data during the early and middle stages of the pandemic. Third, although this descriptive study provided some rich data, it cannot show a causal relationship between age group and motivators and barriers to participating in OHCs. Despite these limitations, the results have practical applications for researchers, clinicians, and health care organizations who want to create and sustain OHC for AAW.

Conclusions and Implications

The high smartphone ownership and internet access provide a unique opportunity to recruit AAW into research-based OHCs focused on a specific topic or health condition. Although participants would be willing to provide social support to other community members, they would be less inclined to receive such support, suggesting that building trust among community members may take time. There are several implications for future research. First, there is no universal definition of OHC, and there has not been a significant reconceptualization of “online community” in the past decade. Second, there is a paucity in the literature on theory-driven OHC.²⁶ Thus, there is a dire need for theory in the planning, implementation, and evaluation of OHC.

Third, considering the high prevalence of chronic diseases and poor health outcomes among AAW, more OHCs should focus on health promotion and disease prevention, not just treating conditions/diseases and medication adherence. Fourth, in addition to training in group facilitation, OHCs that target AAW should use moderators trained in crosscultural communication, cultural compassion, and cultural competence. Furthermore, training might be needed to work with AAW from different socioeconomic statuses, geographic locations, health literacy levels, immigrant backgrounds, and so on. Fifth, best practices for recruiting and retaining AAW in OHC need to be developed and disseminated.¹

Indeed, some methods used in m-health or other digital health studies may not translate well into OHCs. Sixth, there needs to be a more definitive measurement of “user participation” other than the active/nonactive dichotomy. Specifically, more research is needed on lurkers (i.e., passive observers who do not share information), how they impact the effectiveness of OHCs, and the possible value they bring to OHCs. Seventh, there is a need for longitudinal studies on OHC, the ideal length of the studies, and why users leave over time. Eight, many users are unaware of how their data are stored, used, and shared with third parties.²² Thus, digital health researchers and clinicians should ensure that this protocol is part of the informed consent process.

Authors’ Contributions

D.C.S.J. conceptualized and designed the project; received and administered the grant; coordinated all aspect of the project; conducted data analysis; wrote article drafts; reviewed and edited final version of the article. P.K. conducted data analysis, compiled tables, and reviewed final version of the article. D.M. assisted with developing the codebook, data analysis, literature review, and editing and reviewing the final version of the article. All authors agree to be accountable for all aspects of the work.

Disclosure Statement

No competing financial interests exist.

Funding Information

The Robert Wood Johnson Foundation Culture of Health Leaders Program provided funding for this study as a part of the first author’s strategic initiative. Grant number AWD02943.