Participants included English- and Spanish-speaking patients with a diagnosis of hypertension receiving care from an urban academic safety net system. Participants were recruited from August 2022 through October 2023 from an outpatient registry of patients with hypertension. Patients were called and invited to share information about their use of home BP monitors. Participant interviews were not connected to hospital visits. Patients were excluded if primary care clinicians thought the patients would be unable to provide consent due to cognitive impairment or capacity. Participants were recruited until data saturation was reached, as no new themes emerged during the interviews [18]. This study follows the SRQR (Standards for Reporting Qualitative Research) guidelines [19].

All participants completed a survey and a semistructured interview in their preferred language with a native speaker. Researchers followed an interview guide (Multimedia Appendix 1 and Multimedia Appendix 2), which was updated based on iterative review and feedback from the study team and included participant demonstration of home BP measurement to evaluate baseline SMBP technique. The survey included closed-ended questions about sociodemographic traits, SMBP practices and training, and experience with technology. Participants self-reported demographic information, including gender, race and ethnicity, highest educational attainment, and preferred language. A survey question was added mid-study to further assess participants’ digital literacy. Interviews asked open-ended questions about BP device preferences, experience with BP device mobile apps, and current practices around tracking and sharing BP measurements with their clinicians. Each participant was presented with 2 home BP monitoring devices during the interview: a Bluetooth-enabled device (Device 1) and a cellular-enabled device that syncs data directly (Device 2). Participants were allotted 10 minutes per device to test and record a BP reading. Interviews were audio-recorded, and interviewers simultaneously documented field notes to capture participants’ nonverbal cues, key observations, and immediate reflections on the interview content.

Rapid qualitative data analysis was used to analyze qualitative data [20-23]. Compared to traditional qualitative methods, this is a rigorous, pragmatic approach to analyzing qualitative data on an accelerated timeline. The data collection and analysis goals of rapid qualitative analysis focus on identifying key descriptors to address time-sensitive questions as opposed to developing a theoretically extensive understanding of a concept. As this study sought to inform intervention elements for future home BP monitoring trials [24-26], rapid qualitative analysis with purposeful data reduction activities was used. Rapid qualitative data analysis steps include developing a codebook of domains from interviews, summarizing interviews based on domains, and validating across the study team for consistency.

Analysis involved an iterative and systematic approach to identifying key insights from the interview data. An interview guide (Multimedia Appendices 1 and 2) was initially developed to gather feedback relevant to intervention design, which was iteratively updated to ensure subsequent interviews captured all relevant perspectives and content areas. To standardize interview analyses, a summary template was structured around 8 initial coding domains that were developed according to these emerging topics: (1) Existing general knowledge of and prior training about blood pressure, (2) Impact of device’s physical features on patient preference, (3) Considerations and technique when measuring their blood pressure at home, (4) Patient preferences: Bluetooth versus cellular connection, (5) Logging and sharing BP data, (6) User app experience: Viewing BP results and navigating features, (7) Evaluation of provided training and learning materials, and (8) Ideal training and resource preferences.

Following this initial coding and summarization process, we engaged in a second phase of analysis focused on data reduction and theme generation. Through iterative team discussions, we collapsed the 8 domains into 3 higher-order codes, each representing a major conceptual area of patient experience with SMBP. Specifically, domains 1, 7, and 8 were merged into Code 1: Patient Knowledge about SMBP. Domains 3, 5, and 6 were consolidated into Code 2: Patterns and Challenges in Self-Monitoring Blood Pressure. Domains 2 and 4 formed Code 3: Varied Patient Preferences on Home Blood Pressure Monitoring Device Features. Within each analytic code, we then identified themes, defined as recurrent, explanatory patterns that captured participants’ reported barriers, preferences, and facilitators for SMBP adoption. This multistep process allowed us to move systematically from coded content to broader thematic insights. After this process, we selected representative quotes from the interview transcripts to illustrate key concepts within each code.

Three investigators (JS, VK, and IL) reviewed audio recordings and field notes independently for an initial subset of interviews. Using the template with the set of 8 domains, each interview was summarized to extract key concepts. The process continued for 8 interviews until consensus was reached on the approach and content of the interview summaries. The remaining interviews were then analyzed and summarized by a single investigator using the established framework (IL). After completing summaries from all interviews, the content within each domain was reviewed across all interviews to identify overarching themes. Particular attention was given to differences in responses by language to capture any nuanced variations. Agreement on key themes was reached through multiple collaborative discussions with the entire research team until consensus was achieved. Frequency of participant responses within each theme for these nonsurvey interview data was synthesized according to the following nomenclature: “few” representing approximately 3-5 participants, “many” representing 5-7, “most” representing the majority or at least 8 of the 15 total participants, and “nearly all” representing 13 or 14.

Analytical credibility and trustworthiness were enhanced through multiple strategies [27]. We used investigator triangulation with 3 independent coders (JS, VK, and IL) analyzing initial interviews and coordinated with clinical staff members who had years of experience onboarding patients to SMBP studies. Two senior study investigators (CL and EK) have conducted SMBP research for more than 5 years, providing prolonged engagement with the research topic. Interviews lasted more than 90 minutes and incorporated multiple follow-up questions to ensure depth of data collection and participant discussion. Emerging domains were incorporated into the codebook through iterative discussion and team consensus, allowing the analysis to evolve and reflect new insights gained throughout the process.

To ensure dependability and confirmability, we maintained comprehensive audit trails throughout the research process. This included detailed field notes from each interview, multiple versions of our codebook documenting its evolution, and iterations of the interview guide as it was refined based on emerging insights. Meeting minutes from team discussions captured analytical decisions and rationale for theme development. To enhance confirmability, multiple team members independently reviewed the coding process to ensure findings were grounded in participant data rather than researcher assumptions. At least 2 researchers reviewed each transcript, with discrepancies resolved by a third investigator or the whole research team. This audit trail and multireviewer approach helped ensure consistency of findings over time and minimized individual bias in interpretation [27]. Reflexivity was maintained through field notes documenting the researcher’s observations, reactions, and preliminary interpretations during each interview. Research team meetings included structured self-reflection on how our professional backgrounds as healthcare researchers and clinicians in safety-net settings might influence data interpretation. Team members explicitly discussed potential biases related to our experiences with health disparities and digital health implementation. These discussions were used to ensure interpretations remained grounded in participant data.

All participants provided written informed consent and received a US $50 gift card for participation. Data were deidentified for analysis and reporting. Participants were given the option to withdraw at any time throughout the study and have their data removed. This study was approved by the University of California, San Francisco Institutional Review Board (number 21‐33711).

Table 1 displays the characteristics of 15 total participants, all of whom completed both the survey and interview. Eleven participants were assessed for their SMBP technique against a rubric using the updated interview guide. The median age of participants was 57 (IQR 37-71) years.

From survey responses, all participants had a smartphone, 10 used mobile apps several times a day, and 10 reported no difficulty using their smartphone or installing apps without assistance. Eight participants either did not have internet service at home other than via smartphone, did not know how to connect a device to Wi-Fi, or had not activated their patient portal account.

In the past 12 months, 5 participants measured their BP outside of the clinic, all at home using their own BP monitor. Overall, 3 measured their BP less than once a month, 2 measured at least once a month, and 2 shared these BP measurements with their clinical team. Four participants reported that measuring BP at home and sharing results with their clinician was “extremely helpful.”

Through our analysis, we consolidated the 8 initial coding domains into 3 high-order analytical codes. Within each analytical code, we identified themes that captured the key barriers, preferences, and facilitators for SMBP adoption. All analytical codes, themes, and notable quotes are provided in Table 2.

In our qualitative study of safety net patients with hypertension, the cost of device features, gaps in existing BP knowledge, and lack of training and resources presented challenges to SMBP adoption. Our findings highlight the need to provide affordable, language-concordant resources and comprehensive training to leverage SMBP for hypertension management in safety net patients.

Our findings of participant motivation for SMBP and barriers related to cost and health literacy among low-income and minority communities were overall consistent with prior studies [11,14-16]. Our participants valued tracking their BP and are motivated to engage in SMBP practices and share results with their providers [11]. Moreover, participants preferred having their readings automatically shared with their provider without requiring pairing to their own device, citing it as extremely helpful. However, nearly all participants were not willing to pay for this feature. Plug-in devices were also strongly preferred to avoid the cost of batteries. These findings suggest that SMBP adoption in lower-income and uninsured and Medicaid populations is impacted by affordability or payor coverage of SMBP devices that meet patients’ needs. In addition, nearly all patients demonstrated inconsistencies in BP education, displayed incorrect BP measurement technique, and had not received formal training. These findings reinforce the notion that barriers to successful SMBP adoption stem from external care factors, such as cost and gaps in available training and resources, rather than patient motivation. Providers should evaluate patients’ barriers and preferences when recommending SMBP.

In addition to cost, digital accessibility and literacy should be assessed as contributors to SMBP non-adoption. Aligned with studies that demonstrate socioeconomic status and Medicaid insurance as risk factors for digital exclusion [29], 8 of 15 participants exhibited some form of digital inaccessibility: lacking internet access, not activating their patient portal, or having difficulty connecting a device to Wi-Fi. Furthermore, most participants required assistance with pairing Bluetooth to Device 1 and with navigating app features on both devices, potentially indicating limited digital literacy. SMBP interventions should be complemented with patient training and resources. Importantly, it appears that cellular SMBP devices that automatically transmit BP readings to reduce digital complexity may promote sharing results with providers, and this should be considered as a focus of future research and implementation.

Moreover, Spanish-speaking participants reported that using apps was challenging because they were presented in English. This further supports the unmet need to have user-friendly, language-concordant digital SMBP tools [11,30]. Spanish-speaking participants also wanted manuals and training to be delivered in Spanish. Addressing language nonconcordance in training and resources for other prominent but less prevalent languages (eg, Arabic) [31] in addition to Spanish may further promote widespread SMBP adoption. Notably, patients who face barriers in digital literacy and language discordance may be especially vulnerable to SMBP adoption challenges.

While a previous study on home BP monitoring enrolled both English and Spanish speakers [15], our study was the first to assess patient preferences between 2 SMBP devices and the impact of language on SMBP practices. Our results further add to knowledge about patient preferences for communication modality of their BP results with their care team and barriers to SMBP among racially diverse, low-income populations.

Our study was limited by a small, convenient sample. However, our sample size aligns with findings from a recent systematic review suggesting that saturation in qualitative research can often be achieved within a narrow sample size of 9-17 interviews [18]. Participants were assessed at only 1 time point; SMBP skills and preferences may differ in real care scenarios. In addition, despite our efforts to ensure credibility through independent coding, team discussions, and participant validation of findings, researcher interpretation may still reflect inherent subject biases common to qualitative research. While we maintained reflexivity through field notes and team discussions about our positionality, as with all qualitative work, the lived experiences and perspectives of the study team, which included individuals with expertise in primary care, health equity, and public health, may have introduced implicit biases in theme identification. Although we described our study setting and participants in detail to support transferability, our single-site study within an urban safety net context may limit applicability to different health care settings. Future studies should explore how support systems (eg, family and caregivers) could impact SMBP adoption. Collaborating directly with patients to refine analytical domains and themes could also improve the transferability of our findings. An ongoing randomized controlled trial is currently underway to longitudinally assess device implementation and BP outcomes, which will help address these limitations [25].

Patients’ values and barriers can inform solutions that facilitate and improve patient self-management of hypertension. Our findings reinforce the importance of affordability, accessibility, and providing robust resources when implementing SMBP in diverse, safety net populations. The effectiveness of cellular-enabled SMBP devices should be further evaluated.