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As consumer health information technology (IT) becomes more thoroughly integrated into patient care, it is critical that these tools are appropriate for the diverse patient populations whom they are intended to serve. Cultural differences associated with ethnicity are one aspect of diversity that may play a role in user-technology interactions.
Our aim was to evaluate the current scope of consumer health IT interventions targeted to the US Spanish-speaking Latino population and to characterize these interventions in terms of technological attributes, health domains, cultural tailoring, and evaluation metrics.
A narrative synthesis was conducted of existing Spanish-language consumer health IT interventions indexed within health and computer science databases. Database searches were limited to English-language articles published between January 1990 and September 2015. Studies were included if they detailed an assessment of a patient-centered electronic technology intervention targeting health within the US Spanish-speaking Latino population. Included studies were required to have a majority Latino population sample. The following were extracted from articles: first author’s last name, publication year, population characteristics, journal domain, health domain, technology platform and functionality, available languages of intervention, US region, cultural tailoring, intervention delivery location, study design, and evaluation metrics.
We included 42 studies in the review. Most of the studies were published between 2009 and 2015 and had a majority percentage of female study participants. The mean age of participants ranged from 15 to 68. Interventions most commonly focused on urban population centers and within the western region of the United States. Of articles specifying a technology domain, computer was found to be most common; however, a fairly even distribution across all technologies was noted. Cancer, diabetes, and child, infant, or maternal health were the most common health domains targeted by consumer health IT interventions. More than half of the interventions were culturally tailored. The most frequently used evaluation metric was behavior/attitude change, followed by usability and knowledge retention.
This study characterizes the existing body of research exploring consumer health IT interventions for the US Spanish-speaking Latino population. In doing so, it reveals three primary needs within the field. First, while the increase in studies targeting the Latino population in the last decade is a promising advancement, future research is needed that focuses on Latino subpopulations previously overlooked. Second, preliminary steps have been taken to culturally tailor consumer health IT interventions for the US Spanish-speaking Latino population; however, focus must expand beyond intervention content. Finally, the field should work to promote long-term evaluation of technology efficacy, moving beyond intermediary measures toward measures of health outcomes.
Patients are at the heart of the health care system. As primary stakeholders, they are not only affected by national and local policy, medical services, and the health care workforce, but also have the ability to affect health care cost, quality, and access through individual and community engagement. Patient engagement is a broadly defined term used to describe patient acquisition of knowledge, skills, ability, and motivation to participate in positive health behaviors and the interventions increasing these attributes [
Consumer health information technology (IT) is increasingly being used to engage patients in shared decision making, self-management, and disease prevention through facilitation of health information access, social and clinical support, and electronic communication [
Critical to the design of technologies that facilitate health and health care management is the consideration of population needs and characteristics [
As consumer health IT becomes more thoroughly integrated into patient care, it is critical that these tools are appropriate for the diverse patient populations whom they are intended to serve [
Research within this field is both timely and important because of existing health disparities faced by ethnic minority populations and the national priority to decrease these disparities [
This paper focuses on a single ethnic group: the US Spanish-speaking Latino population, a heterogeneous group consisting of numerous subpopulations. This ethnic population was chosen for its current and increasing prominence within the United States. The Latino population represents the nation’s largest ethnic minority, numbering over 54 million [
A narrative synthesis [
Searches were first conducted in August 2014 within four health sciences (ie, PubMed, Web of Science, CINAHL, Cochrane Central Register of Controlled Trials [Cochrane]) and three computer sciences and engineering databases (Compendex, IEEE Xplore, and the Computers and Applied Sciences Complete [CASC]). A second search was run in September 2015 within these databases to capture additional articles published during the screening process. All databases were accessed via the University of Virginia libraries. A third search was run in June 2016 to expand the search to a 25-year time span from 1990-2015. Search terms were divided into three clusters referencing technology, ethnicity, and patient-centeredness (see
Search terms for PubMed (terms were adapted for each database).
Technology | Ethnicity | Patient-centeredness |
cellular phonea | Hispanic Americans | consumer health information |
mobile phone | Hispanic | health educationa |
mobile computing | Spanish Americans | health promotiona |
mobile health | Latino(a) | health care quality, access, and evaluationa |
text messaging | Spanish-speaking | patient compliance |
interneta | patient participation | |
ehealth | patient satisfaction | |
blogging | patient preference | |
social media | patient education | |
preventive health servicesa | ||
telemedicinea | ||
audio player | ||
audiovisual aidsa | ||
multimedia | ||
health records, personal | ||
computer systemsa | ||
tablet computer | ||
computer/utilizationa | ||
user-computer interfacea | ||
computer user | ||
televisiona | ||
radioa | ||
soap opera | ||
reminder system | ||
educational technologya | ||
medical informatics | ||
health information technology |
aMedical Subject Headings (MeSH) term.
The combined electronic searches identified 2742 records. Records were divided as follows: PubMed (1798 citations), Web of Science (42 citations), CINAHL (717 citations), Cochrane (87 citations), Compendex (6 citations), CASC (136 citations), and IEEE Xplore (0 citations). After removal of duplicates, a combined total of 2626 unique records was compiled for preliminary abstract review.
The search was limited to full-text, English language articles published between January 1990 and September 2015, with additional inclusion and exclusion criteria described in
Article characteristics:
Article must be published between January 1990 and September 2015.
Article must be in the English language.
Population characteristics:
Participants must live within the United States, defined by the 48 contiguous states, Alaska, and Hawaii.
If participants lived in both the United States and abroad, article must analyze US participants as cohesive population subset.
Intervention characteristics:
Intervention must involve electronic technology (technology using electricity). This includes radio, television, mobile phone, computer, tablet, MP3 player, etc.
Information delivered through the intervention must be available in Spanish.
Intervention must target health and include topics pertaining to one or more of the following:
disease treatment
disease prevention
health education
personal safety
access to care
personal wellness
mental health
well-being
care of dependents
Patient and/or the patient’s legal guardian must be the end user and direct benefactor of the device.
Interventions targeting providers are excluded.
Interventions consisting only of phone calls were excluded.
Study selection consisted of two steps: abstract screening and full-text review. Abstracts were independently screened by authors AC and BM and compared at intervals of 50-100 articles until a Cohen’s kappa score of .95, indicating near-perfect agreement [
In total, 240 articles were returned for full-text review. AC reviewed all full-text articles using inclusion criteria. Reasons for article exclusion are detailed in the
PRISMA schematic of study selection.
We identified 42 articles for full-text extraction. AC and BM independently reviewed and extracted data from all 42 articles (see
Categories for journal domain [
Technology functionality framework [
Functionality subcategory | Definition |
Inform | Provide information in a variety of formats (text, photo, video) |
Instruct | Provide instructions to the user |
Record | Capture user-entered data |
Display | Graphically display user-entered data/ output user-entered data |
Guide | Provide guidance based on user-entered information (eg, recommend a physician consultation or course of treatment) |
Remind/Alert | Provide reminders to the user |
Communicate | Provide communication with health care provider/ patients and/or provide links to social networks |
Culturally-informed design framework [
Cultural tailoring category | Description | Examples of cultural considerations |
Content | Message being delivered through the technology | Origins and consequences of health conditions |
Norms related to diet, religion, and division of labor | ||
Alternative medicine | ||
Functionality | Array of actions performed by the technology | Culturally specific health management behaviors |
Perception of privacy and health care decision making | ||
Preferences for information delivery and communication (eg, voice communication) | ||
Technology platform | Technology hardware used to deliver the health intervention | Access and exposure to technology |
Use of hardware within target population | ||
Role of Internet | ||
User interface | Presentation and organization of the content and functionality | Cultural symbols |
Language and dialect | ||
Spatial orientation | ||
Colors |
Evaluation metrics categories.
Category | Description |
Behavior/ attitude change | Changes in the lifestyle, disease management, or attitude toward a health topic or behavior. These include measurements such as changes in disease screening rates, treatment compliance, medical care utilization, performance of self-care tasks, attitude toward organ donation, attitude toward breast cancer screening, and attitude toward alcohol use. |
Knowledge retention | Any measurement of information taught through technology intervention. These include measurements such as knowledge of diabetes care, knowledge of disease prevention techniques, or knowledge of vaccination schedules. |
Self-reported health marker | Self-reported health measures including depression scale rankings, pain rankings, self-efficacy, psychosocial functioning, or quality of life. |
Biometric health marker | Quantitative measures of body function including HbA1c levels, blood pressure, glycemic control, and body mass index. Both clinic-generated and self-reported biometric health markers were included within this category. |
Usability | Specific feedback regarding physical characteristics of technology, user interface, acceptability of technology, and perceived utility. These include measures such as ease of use, readability, ability of patient to relate to video characters, acceptability of video length, emotional appeal, and satisfaction with device. |
Using the data abstracted from the individual studies, the research team inductively derived categories for the following variables: technology platform, population density, health domains, evaluation results, and intervention delivery location (see
Technology platform: When it was not possible to infer the specific technology platform, the article was coded as “unspecified.” This was most common for video interventions that did not specify whether the video was delivered through a computer, digital video disc (DVD) player, videocassette recorder (VCR), or tablet.
Population density: If it was possible to infer an urban or rural location from article language or study location, the article was coded accordingly. For example, an intervention conducted within a “city center” was coded as “urban.”
Health domain: Given the numerous health domains investigated by studies, we engaged in a second round of coding to reduce the number of health domain categories. For example, “infant immunization” was coded as “infant, child health, or maternal health.”
Intervention delivery location: Similarly, some intervention delivery locations were coded at a higher level of abstraction. For example, “mass media” and “internet” interventions were ultimately coded as “ubiquitous environment.”
Evaluation results: Results were drawn directly from the text of each article. If multiple outcomes were reported, only primary outcomes were included.
Inductively derived categories.
Variable | Description |
Population density | Article was categorized as “rural” only if authors specified a rural community. If authors did not use the term “urban” but specified a city or county that was predominantly urban, the article was classified as urban. If technology use occurred within an urban hospital center, the article was classified as urban. Articles that were unclear or did not specify any location were classified as “Did Not Specify (DNS).” |
Intervention delivery |
Clinic: Intervention delivery within a clinic, hospital, or medical center. Includes clinic waiting room or medical encounter. |
Ubiquitous environment: Intervention delivery could occur in multiple physical environments. Technologies accessed by patients through personal devices such as mobile phones, desktop computers, or radio, or through public mass media. This includes all interventions accessed through the Internet. | |
Community center: Intervention delivery in any public gathering space that does not formally provide medical care (ie, not a clinic). This includes churches, schools, pharmacies, cafes, libraries, and other community centers. |
Data were coded manually into numerical categories, and basic statistics were computed using Microsoft Excel Version 14.1. All percentages were calculated out of the total number of included studies (N=42). If a study did not report on a given category, the study was coded as “did not specify.” Studies could be categorized in multiple subcategories for the following: cultural tailoring category, evaluation metrics, technology platform and functionality, population density, intervention delivery location, and journal domain.
All studies were published between 1990 and 2015, with the majority of studies (30/42, 71%) published between 2009 and 2015. All 42 articles detailed distinct consumer health IT interventions [
A wide variety of consumer health IT interventions have been used to target health within the Latino population. These interventions have focused most commonly on chronic diseases and included some degree of cultural tailoring. Computer, radio, and television were the most commonly used technology platforms; however, a fairly even distribution across all technologies was noted. Nearly all interventions had the functionality of informing the end user (38/42, 91%), and nearly one half of studies employed more than one functionality (19/42, 45%). The large majority of technology interventions (32/42, 76%) specified availability in English in addition to Spanish. Cancer (10/42, 24%), diabetes (9/42, 21%), and child, infant, or maternal health (9/42, 21%) were the most commonly addressed health domains. As shown in
Frequencies of selected intervention characteristics of included studies (for all domains, articles may be included within multiple subcategories).
Frequency, n | Percentage, % | ||
Computer | 8 | 19 | |
Radio | 8 | 19 | |
Television | 8 | 19 | |
Kiosk | 7 | 17 | |
Unspecified | 5 | 12 | |
Mobile phone ‒ text message | 5 | 12 | |
VCR | 3 | 7 | |
DVD | 2 | 4 | |
Tablet | 2 | 4 | |
Not reported | 0 | 0 | |
Ubiquitous environment | 19 | 45 | |
Clinic setting | 17 | 40 | |
Community center | 5 | 12 | |
Not reported | 2 | 5 | |
Urban | 35 | 83 | |
Rural | 5 | 12 | |
Not reported | 4 | 10 | |
Content | 21 | 50 | |
User interphase | 6 | 14 | |
Functionality | 2 | 5 | |
Technology platform | 1 | 2 | |
Not reported | 17 | 40 | |
Inform | 38 | 91 | |
Communicate | 6 | 14 | |
Guide | 5 | 12 | |
Instruct | 4 | 10 | |
Record | 4 | 10 | |
Remind/Alert | 4 | 10 | |
Display | 3 | 7 | |
Not reported | 0 | 0 | |
Cancer | 10 | 24 | |
Child, infant, or maternal health | 9 | 21 | |
Diabetes | 9 | 21 | |
Cardiovascular disease | 3 | 7 | |
Organ donation | 3 | 7 | |
Physical activity | 2 | 5 | |
General adult health | 2 | 5 | |
Sexual health | 1 | 2 | |
Anesthesia | 1 | 2 | |
Appointment reminder | 1 | 2 | |
Driving under influence recidivism | 1 | 2 | |
Pain | 1 | 2 | |
Health care utilization | 1 | 2 | |
Patient safety | 1 | 2 | |
Not reported | 0 | 0 |
Evaluation metrics included both intermediate health-related measures and measures of technology usability. Most articles (28/42, 67%) used more than one evaluation metric. The most commonly used evaluation metric subcategory was behavior/attitude change (31/42, 74%), followed by usability (20/42, 48%), and knowledge retention (22/42, 52%). Behavior/attitude change included metrics such as medication adherence [
In summary, the Spanish language consumer health IT interventions targeting US Spanish-speaking Latinos published within the past 25 years (1990-2015) are characterized by a great amount of diversity with regards to technology platform and study design (eg, sample size and evaluation metrics); however, similarities can be seen in the technology functionality, specific populations, and health domains addressed by these interventions. Interventions most commonly focused on urban population centers, the western United States, and chronic health domains including cancer and diabetes. While sample size varied tremendously across studies, study samples were largely female. Behavior/attitude change, knowledge retention, and usability were the most commonly used evaluation metrics. Just over half of studies detailed some type of cultural tailoring, with content tailoring being the most common.
A wide distribution of technology platforms to engage Spanish-speaking patients in their health and health care is seen within the literature. Computer, radio, and television were the most common platforms used across interventions. As we continue to explore newer technologies, it is important to understand what aspects of these intervention designs are applicable to other technology modalities. Moreover, as the technology platforms used to engage consumers evolve, researchers should be cognizant of disparities in technology exposure and access across subpopulations. For example, within the Latino population computer ownership as well as Internet usage, mobile phone ownership, smartphone ownership, and social media use vary significantly across age, socioeconomic status, and language dominancy [
Nearly all interventions served to inform or educate the end users while fewer interventions incorporated other functionalities such as delivering a direct service or treatment to patients. This is not surprising given that health education aims to equip patients with the knowledge and skills they need to manage their disease while promoting behavior change [
This study reveals an initial movement toward integrating culturally tailored features into consumer health IT for the US Spanish-speaking Latino population. More than half of the articles mentioned some form of cultural tailoring, suggesting awareness of the interaction between culture and user interactions with technology. Intervention content was the predominant mechanism of tailoring, with fewer articles tailoring functionality, technology platform, or user interface. While there is abundant literature on culturally competent health care for Latinos in the past two decades [
This study reveals a focus within the literature on chronic diseases and a need for future consumer health IT interventions to target two areas: underrepresented health domains within Latino subpopulations and challenges faced by Latinos in health care access and utilization. Given the disproportionate prevalence of diabetes, obesity, and cancer within the Latino population [
It is important to note that the majority of participants across studies were female. This may be influenced by the fact that a large number of interventions focused within the category of “Child, Infant, or Maternal Health,” targeting health concerns such as breast cancer [
This study offers two principle findings regarding the location of intervention delivery. First, the majority of studies were conducted in urban settings, likely reflecting the location of academic institutions. Given that nearly 12% of the Latino population lives within a rural area [
The majority of included studies focused on intermediate measures, or those that are conceptualized as precursors to predicting health outcomes, namely, knowledge retention and behavior/attitude change. Although studies have shown that positive behaviors can affect significant changes in chronic disease outcomes, these behaviors must be sustained in the long term for significant changes in health status [
Given the interdisciplinary nature of studies focused on consumer health IT interventions for the US Spanish-speaking Latino population, diversity was seen both in types of evaluation metrics and combinations of metrics used by studies. Although this approach allows for multiple perspectives on effective intervention development, a key limitation is the ability to conduct meta-analyses and to compare findings across studies. Future research should synthesize various perspectives from relevant disciplines to create a framework for evaluation of consumer health IT for the US Spanish-speaking Latino population. A crosswalk approach might then be used to identify connections between various evaluation metrics [
Characterizing studies was challenging because of lack of detail and vague or incomplete descriptions in study reporting. Lack of detail was evident in the large number of articles that did not specify the technology platform used. This required an “unspecified” category for technology platform to be made. Vague or incomplete intervention descriptions were evident in our classification of cultural tailoring. Future studies should explicitly detail cultural tailoring processes and should cite feasibility studies or other evidence-based rationales to substantiate these tailoring choices. Ultimately, our ability to report frequency statistics was limited by a lack of standardized reporting methods across studies.
A consensus statement for reporting consumer health IT studies would improve the prospects for valuable meta-analyses to be conducted in the future. Consensus statements have been developed for many other types of studies, such as RCTs [
Several study limitations warrant mention. Given restrictions in database access and time, a limited number of databases was chosen and additional mechanisms for hand searching were not undertaken. Furthermore, only English language articles were included. This likely contributed to selection bias and limitations in the scope of studies compiled for screening. Nonetheless, the authors used a wide range of databases from both health and computer sciences to capture a rich body of articles. Limitations were additionally faced in the classification of cultural tailoring categories. Some studies justified selecting a particular technology platform; in other cases, it was unclear whether selection of a technology platform was founded upon population-specific needs assessments or usage statistics. In these latter cases, the intervention was not considered to be culturally tailored in terms of technology platform. This approach likely led to a conservative estimate of cultural tailoring and reveals a need for more explicit descriptions of decision-making approaches used to design and develop consumer health IT interventions. Finally, the inductively derived categories were representative only of characteristics present within the studies. The scope of these categories does not give a sense of characteristics that should ideally be included but were not represented.
In this study, we have characterized the growing body of consumer health IT interventions targeted toward the US Spanish-speaking Latino population. In doing so, three primary needs have been identified within this field. First, while the increase in studies targeting the Latino population in the last decade is a promising advancement, future research is needed that focuses on subpopulations previously overlooked in designing interventions within this space. For example, the Latino migrant farmworker community faces acute health conditions such as pesticide exposure, which may pose a more immediate health threat than the chronic diseases plaguing the statistical majority of the Latino demographic. Second, preliminary steps have been taken to culturally tailor consumer health IT interventions for the US Spanish-speaking Latino population; however, focus has remained predominantly on intervention content. Interdisciplinary fieldwork between the health sciences and engineering is needed to understand how to create technology culturally tailored in terms of platform, user interface, and functionality preferences. Finally, the majority of studies used intermediary measures such as knowledge retention and behavior/attitude change to evaluate technology efficacy. Given the immense financial investment and potential social benefits of consumer health IT, it is critical that research within the field engages patients long enough to begin measuring health outcomes.
Boolean search strings across databases.
Full-text exclusion justification breakdown.
List of excluded studies.
General study characteristics of included studies.
Participant demographics of included studies.
Intervention characteristics of included studies.
Outcome metrics, study design, and evaluation results of included studies.
Cumulative Index of Nursing and Allied Health Literature
Institute of Electrical and Electronics Engineers
information technology
Medical Subject Headings
Patient Activation Measure
randomized controlled trial
Research reported in this publication was supported by National Cancer Institute of the National Institutes of Health under Award Number R21CA167418. Author AC was supported by a Diversity Training Branch Supplement under this parent grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We would also like to thank Voto Latino for the funding support provided for this publication through their Innovators Challenge. We would like to thank the University of Virginia librarians for their immense help in database selection and refinement of our search strategy.
None declared.