This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
Racial inequity persists for chronic disease outcomes amid the proliferation of health information technology (HIT) designed to support patients in following recommended chronic disease self-management behaviors (ie, medication behavior, physical activity, and dietary behavior and attending follow-up appointments). Numerous interventions that use consumer-oriented HIT to support self-management have been evaluated, and some of the related literature has focused on racial minorities who experience disparate chronic disease outcomes. However, little is known about the efficacy of these interventions.
This study aims to conduct a systematic review of the literature that describes the efficacy of consumer-oriented HIT interventions designed to support self-management involving African American and Hispanic patients with chronic diseases.
We followed an a priori protocol using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-Equity 2012 Extension guidelines for systematic reviews that focus on health equity. Themes of interest included the inclusion and exclusion criteria. We identified 7 electronic databases, created search strings, and conducted the searches. We initially screened results based on titles and abstracts and then performed full-text screening. We then resolved conflicts and extracted relevant data from the included articles.
In total, there were 27 included articles. The mean sample size was 640 (SD 209.5), and 52% (14/27) of the articles focused on African American participants, 15% (4/27) of the articles focused on Hispanic participants, and 33% (9/27) included both. Most articles addressed 3 of the 4 self-management behaviors: medication (17/27, 63%), physical activity (17/27, 63%), and diet (16/27, 59%). Only 15% (4/27) of the studies focused on follow-up appointment attendance. All the articles investigated HIT for use at home, whereas 7% (2/27) included use in the hospital.
This study addresses a key gap in research that has not sufficiently examined what technology designs and capabilities may be effective for underserved populations in promoting health behavior in concordance with recommendations.
Nearly half of all adults in the United States are living with 1 or more of the
Chronic disease self-management is challenging because the treatment regimens often demand much from the patient and their families; recommended self-management frequently includes regular meal planning, consistent physical activity, monitoring and tracking (eg, fluid intake and blood glucose), and daily medication behavior [
Patients with chronic diseases may use information technology (eg, mobile apps) as sources of health information to help answer questions regarding symptoms and treatment options [
Sociocultural factors also influence individuals from ethnic minority groups’ use of consumer-oriented HIT. Trust, perceived credibility, attitudes, and perceptions predict health technology acceptance and use [
Sociocultural factors present barriers that contribute to intervention-generated inequality [
HIT research describes the potential benefit from the use of technologies designed to track and report health behaviors, along with the acknowledgment of sparse insights to guide researchers concerning specific barriers to use for ethnically diverse populations [
After confirming health equity as the focus of this study, we followed an a priori protocol with equity as the focus, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-Equity 2012 Extension was selected as a guideline for conducting systematic reviews that focus on health equity [
We developed a rationale for eligible study designs and inclusion of outcomes, per the PRISMA-Equity 2012 Extension for systematic reviews [
Next, we crafted themes of interest, again per the PRISMA-Equity 2012 Extension for systematic reviews [
To evaluate and select databases, we again reviewed the 4 foundational articles. We also consulted with a health sciences librarian to evaluate and finalize the databases. We selected seven electronic databases: PubMed, Cumulative Index of Nursing and Allied Health Literature, Web of Science, Cochrane, Compendex, Institute of Electrical and Electronics Engineers, and Computers and Applied Sciences Complete.
We created search strings based on our themes of interest (eg, acceptance, usability, readiness, satisfaction, and preference), according to the specific database format, to locate articles that met our inclusion criteria. We consulted with health science librarians to ensure adherence to the database string format. Information regarding the search strategy (eg, search strings) is given in
Articles were included if they met specific inclusion criteria and excluded if they fulfilled the exclusion criteria (
Rayyan (Rayyan Inc), an internet-based software package, was used to facilitate article screening [
Articles included patients with chronic diseases or caregivers who specified they were of Black or African American, or Hispanic origin.
The patient or caregiver must be the end user or direct benefactor of technology.
Technology gives personalized information to patients and or caregivers.
Technology was designed to support self-management recommended for chronic conditions (ie, medication behavior, physical activity, dietary behavior, and attending follow-up appointments).
The article is in English in a peer-reviewed journal.
The article has been published since 1990.
Intervention targets providers.
No electronic technologies (ie, technology using electricity) examined in the article.
Technology is not designed to support self-management recommended for chronic conditions (ie, medication behavior, physical activity, dietary behavior, and attending follow-up appointments). Technology designed to prevent falls was not included.
A systematic review of technology.
Once conflicts were resolved, we analyzed the included articles and extracted relevant information (
We analyzed the risk of bias in each included article using the Cochrane Collaboration Risk of Bias Tool [
General characteristics (N=27).
Characteristicsa | Values, n (%) | ||
|
|||
|
Medication behavior | 17 (62) | |
|
Follow-up appointment attendance | 4 (14) | |
|
Physical activity | 17 (62) | |
|
Dietary behavior | 16 (59) | |
|
|||
|
Home (capability to access or use from home) | 27 (100) | |
|
Hospitalb | 2 (7) | |
|
|||
|
Computer, laptop, or tabletc | 3 (11) | |
|
Telephone (landline) | 0 (0) | |
|
Mobile phone | 17 (62) | |
|
Mobile app | 1 (3) | |
|
Text | 15 (55) | |
|
Web-based | 8 (29) | |
|
Bluetooth device | 2 (7) | |
|
Specialized telemedicine device | 2 (7) | |
|
Nintendo Wii | 1 (3) | |
|
Voice-enabled device | 1 (3) | |
|
Social media | 1 (3) | |
|
|||
|
Collecting personal health datad | 13 (48) | |
|
Goal setting and tracking | 17 (62) | |
|
Integrated survey and assessment | 19 (70) |
aArticles may be included within multiple categories.
bWe did not include articles in which users could use videos to chat or communicate with providers.
cTelemedicine units or devices were included.
dTracking of patient’s personal health data (data logs) and tracking of patient data by providers were included.
A total of 25 eligible articles involving African American participants and 13 articles with Hispanic participants were identified. Of these, only 27 met our final criteria, as not all articles discussed technology use and design for patients (see PRISMA flowchart in
Each of the 27 included articles was examined for the risk of potential bias according to each of the 6 domains (
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart.
Risk of bias in individual articles (N=27).
Study | Participants, n | Patients or caregivers involved in the design of technology | Incomplete outcome dataa | Blinding of participants or personnelb | Other biasc |
Almeida et al [ |
452 | High | Low | Low | Not reported |
Collins and Champion [ |
15 | Not reported | Low | Low | Not reported |
Davidson et al [ |
50 | Low | Low | Low | Low |
Davis et al [ |
51 | Low | Low | High | Not reported |
Finkelstein et al [ |
30 | Not reported | Low | Low | Not reported |
Finkelstein and Wood [ |
N/Ad | High | High | Not reported | Low |
Fortmann et al [ |
414 | Low | Low | Low | Not reported |
Friedman et al [ |
267 | Not reported | Low | Low | Not reported |
Gerber et al [ |
95 | Not reported | Not reported | High | High |
Green et al [ |
9298 | Low | Low | Low | Low |
Grimes et al [ |
12 | Low | Not reported | High | Not reported |
Heitkemper et al [ |
220 | Low | High | Not reported | Not reported |
Joseph et al [ |
29 | Low | Low | Low | Not reported |
Kline et al [ |
123 | Not reported | Low | High | Not reported |
MacDonell et al [ |
48 | Low | High | Low | Low |
Lin et al [ |
124 | High | Low | Low | High |
Mayberry et al [ |
19 | Low | Low | High | Not reported |
McGillicuddy et al [ |
12 | Low | Low | Low | Not reported |
Newton et al [ |
97 | Not reported | High | Low | High |
Nundy et al [ |
15 | Not reported | Low | Not reported | Not reported |
Reese et al [ |
14 | Low | High | High | Not reported |
Reininger et al [ |
71 | Not reported | Low | High | Not reported |
Rosal et al [ |
89 | Low | Low | Low | Low |
Shea [ |
1665 | High | High | Low | Low |
Skolarus et al [ |
94 | Low | High | Low | Low |
Trief et al [ |
1665 | Low | Low | Low | Not reported |
Weinstock et al [ |
1665 | Low | Low | Low | Not reported |
aOutcome data.
bRandomization or blinding of patients.
cAny other bias identified by the reviewers.
dN/A: not applicable.
Articles that reported technology interventions and included self-management aimed at improving chronic disease outcomes using either clinical or behavioral outcomes were eligible for systematic review inclusion (
Self-management behaviors in the included articles (N=27).
Study | Medication behavior | Follow-up appointment attendance | Physical activity | Dietary behavior |
Almeida et al [ |
No | No | Yes | No |
Collins and Champion [ |
No | No | Yes | Yes |
Davidson et al [ |
Yes | No | No | No |
Davis et al [ |
Yes | No | Yes | Yes |
Finkelstein et al [ |
No | No | Yes | No |
Finkelstein and Wood [ |
No | No | Yes | No |
Fortmann et al [ |
Yes | No | Yes | Yes |
Friedman et al [ |
Yes | No | No | No |
Gerber et al [ |
No | No | Yes | Yes |
Green et al [ |
Yes | Yes | No | No |
Grimes et al [ |
No | No | No | Yes |
Heitkemper et al [ |
Yes | No | Yes | Yes |
Joseph et al [ |
No | No | Yes | No |
Kline et al [ |
Yes | No | Yes | Yes |
MacDonell et al [ |
Yes | No | No | No |
Lin et al [ |
No | No | Yes | Yes |
Mayberry et al [ |
Yes | No | Yes | Yes |
McGillicuddy et al [ |
Yes | No | No | No |
Newton et al [ |
No | No | Yes | Yes |
Nundy et al [ |
Yes | Yes | No | Yes |
Reese et al [ |
No | No | Yes | No |
Reininger et al [ |
Yes | No | Yes | Yes |
Rosal et al [ |
Yes | No | Yes | Yes |
Shea [ |
Yes | No | No | No |
Skolarus et al [ |
Yes | No | Yes | Yes |
Trief et al [ |
Yes | Yes | No | Yes |
Weinstock et al [ |
Yes | Yes | No | Yes |
Other data recorded from the articles included the technology functions (
Technology functions in the included articles (N=27).
Study | Tracking by a patient or caregiver using technology | Tracking or viewing patient data by a patient or caregiver | Tracking of patient data by providers | Goal setting or tracking | Integrated surveys or assessments |
Almeida et al [ |
No | No | Yes | Yes | No |
Collins and Champion [ |
No | No | No | No | Yes |
Davidson et al [ |
Yes | Yes | Yes | Yes | Yes |
Davis et al [ |
No | No | No | Yes | Yes |
Finkelstein et al [ |
No | Yes | Yes | Yes | No |
Finkelstein and Wood [ |
No | Yes | Yes | Yes | No |
Fortmann et al [ |
No | No | No | Yes | No |
Friedman et al [ |
No | No | No | Yes | Yes |
Gerber et al [ |
No | No | No | No | No |
Green et al [ |
Yes | Yes | Yes | No | Yes |
Grimes et al [ |
No | No | No | Yes | No |
Heitkemper et al [ |
No | No | No | Yes | Yes |
Joseph et al [ |
No | Yes | Yes | Yes | Yes |
Kline et al [ |
No | No | No | No | Yes |
MacDonell et al [ |
No | No | No | No | Yes |
Lin et al [ |
No | Yes | Yes | Yes | No |
Mayberry et al [ |
No | Yesa | No | Yes | Yes |
McGillicuddy et al [ |
No | Yes | Yes | No | No |
Newton et al [ |
No | Yes | Yes | No | Yes |
Nundy et al [ |
Yes | No | Yes | No | Yes |
Reese et al [ |
No | No | No | Yes | Yes |
Reininger et al [ |
No | No | No | No | Yes |
Rosal et al [ |
No | Yes | Yes | Yes | Yes |
Shea [ |
No | Yes | Yes | No | Yes |
Skolarus et al [ |
No | Yes | No | Yes | Yes |
Trief et al [ |
No | Yes | Yes | Yes | Yes |
Weinstock et al [ |
No | Yes | Yes | Yes | Yes |
Total, n (%) | 4 (14) | 14 (51) | 15 (55) | 17 (62) | 19 (70) |
aCoaching of family members via phone was also conducted.
Diabetes, hypertension, and heart failure were the three chronic conditions included in the resultant studies (N=27). Diabetes was the most common chronic disease among these studies. Of the total number of studies, 8 specifically tracked hemoglobin A1c (HbA1c) and blood pressure (BP) levels [
Hypertension was the next most common condition specified (ie, they focused on hypertension vs BP reporting). Three studies specified the goals of reducing hypertension [
Heart failure was the third chronic disease that was the focus of one of the resultant studies. A study by Finkelstein and Wood [
Given the development of HIT apps and considerable research in this area, a relatively small number of resultant articles (N=27) investigated associations between the use of HIT and chronic disease outcomes among African American and Hispanic patients. This is a vital gap because of persistent inequity in chronic disease outcomes for racial minority populations and because intention to use HIT designed for chronic disease self-management is most predicted by performance expectancy, followed by social influence [
All the articles investigated HIT designed with capabilities to access or use at the patient’s home, whereas only 2 articles also included use in the hospital. This is concordant with the movement of developing HIT for use in patients’ homes versus hospitals [
Various technologies are included in the resultant articles, except for the landline telephone, in which none of the articles were investigated. This follows the broad trend that more than half of US households are reliant on mobile phones and do not have landlines. In addition, Hispanic and Black adults are more likely than White adults to live in households with only mobile phones [
The collecting and tracking of personal health data, which over 10% of users are doing on behalf of someone else (eg, caregivers), and goal setting and evaluation are pertinent capabilities that are closely related to self-management behavior [
Insights derived from this study of the 27 resultant articles reveal the potential for future development and evaluation of HIT tools in two distinct areas—known barriers faced by members of ethnic minority groups in using HIT and the unique barriers they may face in following self-management recommendations. For example, in a limited sample size, a mobile phone–based intervention that combined SMS text messaging with nursing care showed improvement in following recommended self-management behavior (ie, medication behavior, glucose monitoring, foot care, physical activity, and dietary behavior) for Black adults with diabetes [
Despite these important findings, more specific research is needed to elucidate the sociocultural factors that in particular are known to impact HIT acceptance and use [
This study has a key limitation. We only examined articles that specified Black and Hispanic users. Specific cultural factors may emerge from a broader examination, given that various cultural factors influence both technology acceptance and use (eg, practices, customs, language, and communication) [
The proliferation of technology-enabled tools designed to support people in following recommendations for chronic disease self-management has outpaced the research describing the degree to which the Black and Hispanic populations use this technology to support self-management behavior. Although factors driving the general use among the Black and Hispanic populations continue to be investigated, little is known about their impact on health outcomes because of their use. In this paper, we have helped to address this important gap because various technology skills are required to use consumer-oriented HIT designed to support recommended self-management and doing so may require considerable effort from the patients [
A list of search strings and their corresponding databases, the care setting and self-management behavior for each included article, and technology effectiveness in managing chronic disease for each included article.
blood pressure
hemoglobin A1c
health information technology
information and communication technology
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
The authors would like to acknowledge the health sciences librarians from Rutgers University who assisted in refining our search strategy, specifically Lilyana Todorinova.
None declared.