Abstract
This study examined access to, and use of remote patient monitoring (RPM), medical applications, and wearables in a racially diverse, lower-income population. Data were obtained via a cross-sectional survey of adults from low-income communities in Houston, Los Angeles, and New York between April and August 2023. The survey examined access to, and use of RPM, medical applications, and wearables, among respondents. Binary responses to the following questions were examined using logistic regression models: In the past 12 months, have you (i) used RPM, (ii) used a medical app, and (iii) used an electronic wearable device to monitor or track health or activity? A total of 305 surveys were returned, of which 212 were complete (69.5% completion rate). Demographically, 22% self-identified as Hispanic, 41% as non-Hispanic Black individuals, and 33% as non-Hispanic White individuals. Overall, 69% of respondents reported a pre-tax annual household income of less than $35 000 and 96% indicated they own a smart phone. However, only 3 of 10 reported using RPM, 15% reported using a medical app, and 14% reported using wearables. Race was strongly associated with RPM usage, with Black respondents significantly less likely to have used RPM, compared to their white counterparts (OR: 0.31, P = .002). Education (bachelor’s degree or more OR: 4.79, P = .03) and higher income ($35 001 + OR: 4.68, P = .008) were strongly associated with medical app usage. In the wearables model, the same trend was observed with education (bachelor’s degree or more OR: 4.45, P = .04), and higher income ($35 001 + OR: 5.49, P = .01). Compared to earlier studies that have reported utilization rates of between 50% and 60%, our finding of much lower utilization in economically disadvantaged populations that are at greater risks for sub-optimal health outcomes gives cause for greater concern. Considering the ongoing proliferation of digital health technological modalities, this further highlights the need to explore and address equity-based barriers to these health tools.
Keywords
Remote patient monitoring (RPM), medical application, and electronic wearable device usage have shown promising advantages for managing chronic conditions, and historically face equity-related barriers.
We note significant disparities and suboptimal utilization of RPM, medical app, and wearables in a lower-income, racially diverse population despite high rates of smart device ownership and reliable access to internet.
This study provides evidence of continued disparities in RPM, medical application, and electronic wearable device usage among lower-income, racially diverse populations, and highlight the need to decrease equity-related barriers and increased patient education to combat low utilization rates.
Introduction
In recent times, remote patient monitoring (RPM)—the use of digital technologies to collect health data from individuals outside of a clinical environment, such as their home, and electronically transmit them to healthcare providers to facilitate clinical assessment and decision making 1 —has exploded in popularity. Fueled by the access to care policies during the COVID-19 pandemic which precipitated interest in virtual technology, estimates suggest a 1000-fold increase in RPM medical claims between January 2019 and November 2022. 2 In this post-COVID era, studies have reported sustained levels of RPM uptake 3 as individuals continue to seek convenient care options post-pandemic.
Several earlier studies report significant benefits associated with RPM. For example, RPM usage has been linked with improved blood pressure outcomes among patients with hypertension,4 -7 while similar improvements were seen in RPM interventions to promote blood sugar control among patients with diabetes. 8 RPM has also been linked to reduced mortality and readmissions for patients with heart failure and chronic obstructive pulmonary disease.9,10 Despite these benefits, many studies have highlighted a major need to overcome barriers to RPM, most notably equity-related barriers such as the cost of technology for users, poor internet connectivity, and poor health literacy. 11 Efforts to bridge technology gaps include the Affordability Connectivity Program (ACP) that sought to provide low-income households with discounted, high-speed broadband services and lower-cost devices, such as laptops or tablets. 12 As of January 2024, demand for ACP had surged, with over 9 million U.S. households enrolled in the program. 13 Despite these efforts, researchers noted that when given the choice between telephone and app-based monitoring, telephone-based monitoring was preferred among minoritized enrollees and enrollees living in disadvantaged neighborhoods. 14 This suggests that RPM’s potential to improve population health may be eclipsed by equity-related barriers in low-income communities that may benefit from it the most.
RPM employs varied terminology and interventions to “capture the essence” of remote monitoring. 15 One notable intervention includes the use of medical apps, such as calorie counters, medication reminders, and blood sugar documentation, to self-monitor and report health behaviors. Medical apps have been found to enhance disease tracking, boost medication adherence, and encourage positive behavioral and lifestyle changes.16,17 Current literature supports these claims to a certain extent, 18 with the strongest evidence indicating improvements in patient empowerment 19 and medication adherence. 20 Most have highlighted medical app’s unique self-monitoring nature, which may encourage patients to reflect on and change their health behaviors. 21 Despite the widespread ownership of smartphones and tablet computers, 17 medical apps are characterized by underutilization, with work by Krebs and Duncan 22 suggesting a utilization rate of 58%, and strong inequities in patient characteristics, with lower adoption rates among males, older individuals, and individuals having less than a high school education. 23 While there is potential for medical apps to improve chronic disease management 24 this may be overshadowed by equity-related barriers, particularly smartphone and app fees, which may be a challenge for residents of underseved communities.
RPM interventions also include wearable devices, which consist of portable electronic devices such as smart watches and bracelets directly worn on the body to monitor and analyze bodily parameters such as walking speed, respiratory and heart rate, and blood pressure. 25 As an even more active form of health management, wearable devices can play a significant role in precision medicine by measuring clinically relevant parameters and providing healthcare providers with real-time measures of clinical indicators.26,27 Earlier work suggests that this may be useful in risk prevention and 28 chronic disease management.29,30 However, work by Venn et al 31 suggests that commercial wearable devices are still largely underutilized. Although, 95% of non-users indicated that they would use one if it were provided at no cost. 31
Considering the noted gaps among low-income individuals, this study examined access to, and use of RPM, medical applications, and wearables in a racially diverse, lower-income population. Unlike earlier work that focused on the general population, this brief report focuses on medically underserved area residents in three large metropolitan areas in the US.
Methods
Data and Measurement
Data were obtained via a cross-sectional survey of adults from low-income communities in Houston (Third Ward, East End), New York (Bronx, Brooklyn, Queens), and Los Angeles (East Los Angeles, Hyde Park, Huntington Park), between April and August 2023. Inclusion criteria included (1) residence in one of these three low-income communities, (2) aged 18 or older, and (3) have 1 + chronic condition. In addition to obtaining verbal consent, written consent was included on the first page of the survey tool. The survey examined access to, and use of RPM, medical applications, and wearables, among respondents. To reach individuals who might not have access to technology, the research team employed convenience sampling and collaborated with local community-based organizations to distribute paper surveys in the target communities. This study was approved by the University of Houston Institutional Review Board (IRB) in April 2023 (STUDY00004168).
Three health information technology (HIT) measures were examined based on responses to the following questions: (i) Remote patient monitoring (RPM) is technology to remotely monitor patients’ health data and deliver it to providers for additional review and action. Have you used RPM in the past 12 months; (ii) Have you used medical apps for calorie counting, medication reminder, blood sugar documentation, pain diary, etc., in the past 12 months; (iii) Have you used an electronic wearable device to monitor or track your health or activity in the past 12 months? For example, a Fitbit, Apple Watch, or Garmin Vivofit. Responses to these three questions were used to create binary flags depicting three respective dependent variables: (i) used RPM, (ii) used a medical app, (iii) used an electronic wearable device to monitor or track health or activity.
Analysis
Chi-square tests were used to assess independent bivariate associations between survey respondent characteristics and each of the three HIT measures. Three separate logistic regression models examined adjusted relationships between survey respondent characteristics and each of the three HIT measures.
Results
Table 1 shows the summary characteristics of the surveyed sample. A total of 305 surveys were returned, of which 212 were complete (69.5% completion rate). Overall, 40.6% of survey respondents identified as non-Hispanic Black individuals, 33.5% as non-Hispanic White individuals, 22.2% as Hispanic individuals, and less than 4% identified as Middle Eastern/Asian/Native American/Pacific Islander/Other. About half of the respondents were 40 to 64 years old, and over two-thirds (68.9%) reported a pre-tax annual household income of less than $35 000. Fifty-six percent of respondents were female and only 20.7% reported having a bachelor’s degree or more. Thirty-six percent of respondents reported using RPM within the past 12 months, however, only 14.6% of respondents reported using medical apps and 13.7% of respondents reported using wearables to monitor or track their health or activity. When probed for ownership/access to a smart device, 96.2% indicated they owned a smartphone, while 20.8% indicated that they had heard of, and enrolled in the Affordable Connectivity Program.
Characteristics of Survey Respondents (n = 212).
Bivariate Associations
Bivariate associations of independent variables by RPM, medical app, and wearable usage are shown in Table 2. Race/ethnicity was independently associated with RPM usage (49.3% White vs 26.7% Black vs 31.9% Hispanic; P = .02) but not associated with medical app or wearable use. Medical app users were more likely to have higher incomes (37.5% making $35 001 or more vs 6.2% making ≤$35 000; P ≤ .01), hold a bachelor’s degree or more (36.4% bachelor’s degree or more vs 14.7% technical/vocational or some college, vs. 4.3% with high school or less; P ≤ .001), and identify as female (21.7% female vs 5.4% male; P ≤ .01). Wearable device users were also more likely to have higher incomes (33.9% making $35 001 or more vs 4.1% making ≤$35 000, P ≤ .01), hold a bachelor’s degree or more (40.9% bachelor’s degree or more vs 4.3% with high school or less, 9.3% technical/vocational or some college, P ≤ .001), and identify as female (20.0% female vs 5.4% male, P ≤ .01).
Bivariate Associations Between Respondents’ Characteristics and Use of RPM, Medical App, Wearables.
Multivariate Associations
Three separate multivariable logistic regression models examined factors associated with RPM, Medical Apps and Wearables, respectively (Table 3). The RPM multivariable logistic regression model found that compared to white respondents, Black respondents were significantly less likely to have used RPM (OR: 0.31, P = .002), but no significant differences were observed for Hispanic respondents. There were no racial/ethnic differences in the use of medical apps; however, respondents with a bachelor’s degree were more likely to report using a medical app in the past year (OR: 4.79, P = .03), compared to those with a high school degree or less. Respondents with $35 001 or more in annual income were also more likely to use medical apps (OR: 4.68, P = .008) when compared to those with incomes of less than $35,001. In the wearables model, education was strongly associated with wearables use (bachelor’s degree or more OR: 4.45, P = .04), as was higher income ($35,001 + OR: 5.49, P = .01). Females were more likely than males to have used medical apps (OR: 4.68, P = .01) and wearables (OR: 3.77, P = .21) in the past year, but no gender differences were observed for RPM use.
Multivariable Regression Models for (a) RPM, (b) medical apps, and (c) wearables.
Discussion
This study examined RPM, medical app, and wearables utilization in racially diverse, lower-income communities and found sub-optimal utilization rates despite high rates of smart device ownership. Unlike other studies that focus on the general population, this study specifically targeted adults from inner-city low-income communities in the three largest metroplexes in the US. Overall, we found that only 3 in 10 had used RPM in the past year, medical app utilization was 15%, while 14% reported using wearables in the same period. Compared to earlier studies that have reported utilization rates of between 50% and 60%,22,31 our finding of much lower utilization in economically disadvantaged populations that are at greater risks for sub-optimal health outcomes gives cause for greater concern. Considering the ongoing proliferation of digital health technological modalities, this further highlights the need to explore and address equity-based barriers to these health tools.
Despite these low overall patterns, we observed even lower RPM utilization among black individuals, aligning with earlier findings reported in the literature. For example, Mastoris et al 32 examined RPM use in patients with heart failure and found Black patients to be significantly under-enrolled and under-referred for RPM. Kirkland et al 33 noted that Black patients were more likely to have a greater drop in engagement following enrollment in RPM compared to non-black patients. Given the limited dissemination of RPM in black communities 34 and the under-enrollment of urban and socially vulnerable populations, we posit that these disparities may reflect long-standing distrust of clinical research and the US healthcare system generally. 35 Unfortunately, low utilization may contribute to worse clinical outcomes and avoidable hospitalizations.
We also found that medical apps usage was lower among individuals who reported income levels of $35 000 or less, those with education levels below a bachelor’s degree, and those identifying as males. This is consistent with work by Carroll et al, 23 which found lower health app usage among males and those with less than a high school education. Work by Laing et al 36 in low-income communities found similar results, with lower health app usage among respondents earning less than $20 000 per year. Higher medical app utilization rates among females may be explained by their proclivity to seek healthcare when compared to males,37,38 while those with higher income and educational attainment are more likely to have digital health literacy. 39 However, others have reported that patients in diverse, low-income communities show greater interest in medical apps compared to their white, high-income counterparts, especially for chronic disease and overall health management.4,40 This interest combined with high rates of smartphone ownership in this population points toward the need for culturally tailored education 41 to increase familiarity and sustained usage.
Wearable device usage was also found to be lower among individuals who reported income levels of $35 000 or less, those with education levels below a bachelor’s degree, and those identifying as males. This aligns with work42,43 that found lower rates of wearable utilization among males, those with lower income, and individuals with education less than a college degree. Cost-related barriers may contribute to low uptake among low-income residents, 8 however, there is still significant interest in wearable usage with a majority of low-income patients expressing interest in monitoring their health via wearable devices. 44 This interest along with high rates of smartphone ownership points toward a growing need to address equity-based barriers to wearables. Additional considerations should adapt user interfaces for utility among those with lower digital literacy. 45 Finally, considering the evidence of the impact of these technology tools on health outcomes, health insurers may offer these to patients with chronic conditions as a way of supporting patients’ chronic disease management efforts. 46
Limitations
This study has several limitations that should be considered when interpreting the findings. Firstly, the cross-sectional design limits our ability to establish causality or determine the direction of observed associations. Secondly, data were collected through self-reported surveys, which are subject to recall bias and social desirability bias. The survey’s reliance on verbal consent and paper dissemination through community-based organizations might also have influenced the respondent pool, potentially excluding those less engaged with these entities or those experiencing greater health disparities. Furthermore, while we focused on racially diverse, low-income communities in three large metropolitan areas, the findings may not be generalizable to other settings or populations. Due to the pilot nature of the study, sample size/power analysis was not performed for this study. Finally, the measures of HIT usage were binary, potentially overlooking the nuances in frequency and intensity of use.
Conclusion
In conclusion, this study highlights significant disparities in the utilization of RPM, medical apps, and wearable devices among racially diverse, low-income communities in major metropolitan areas. Despite high levels of smartphone ownership, the adoption of these digital health tools remains suboptimal, particularly among Black individuals and those with lower income and educational attainment. These findings underscore the need for targeted interventions to address equity-related barriers, such as affordability, digital literacy, and cultural relevance, to enhance the uptake of digital health technologies. As RPM, medical apps, and wearables continue to evolve and become integral to chronic disease management, it is crucial to ensure that these innovations are accessible and beneficial to all populations, particularly those who stand to gain the most from improved health outcomes. Future research should focus on longitudinal studies to assess causality, as well as qualitative investigations to better understand the specific barriers and facilitators to digital health technology adoption in underserved communities.
Supplemental Material
sj-docx-1-inq-10.1177_00469580241271137 – Supplemental material for Equity in Digital Health: Assessing Access and Utilization of Remote Patient Monitoring, Medical Apps, and Wearables in Underserved Communities
Supplemental material, sj-docx-1-inq-10.1177_00469580241271137 for Equity in Digital Health: Assessing Access and Utilization of Remote Patient Monitoring, Medical Apps, and Wearables in Underserved Communities by Omolola Adepoju, Patrick Dang, Holly Nguyen and Jennifer Mertz in INQUIRY: The Journal of Health Care Organization, Provision, and Financing
Footnotes
Acknowledgements
We acknowledge Ms. Maya Singh for research assistance.
Author Contribution
OA conceptualized the study, supervised the data analysis contributed to the initial draft and subsequent drafts, PD conducted the analysis and wrote the initial draft, HN assisted with literature reviews, JN contributed to interpreting the findings and reviewed multiple versions. All authors approved the final version.
Data Availability
The informed consent states that non-aggregated data will not be shared with third parties; hence, we are unable to share raw data from this study.
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.
Deanonymized Ethical Considerations
Authors only had access to fully anonymized de-identified data for research and review purposes, hence, the ethics committee at the University of Houston waived the requirement for informed consent.
Consent
Written consent language was included on the first page of the survey tool, and all survey respondents consented before completing the survey.
Supplemental Material
Supplemental material for this article is available online.
References
Supplementary Material
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