Do we need to understand the technology to get to the science? A systematic review of the concept of computer literacy in preventive health programs

BackgroundMental health conditions are prevalent among US veterans and pose a number of self-management and health care navigation challenges. Post-Traumatic Stress Disorder (PTSD) with comorbid chronic medical conditions (CMCs) is especially common, in both returning Iraq or Afghanistan and earlier war-era veterans. Patient-facing electronic health (eHealth) technology may offer innovative strategies to support these individuals’ needs.ObjectiveThis study was designed to identify the types of eHealth tools that veterans with PTSD and comorbid CMCs use, understand how they currently use eHealth technology to self-manage their unique health care needs, and identify new eHealth resources that veterans feel would empower them to better manage their health care.MethodsA total of 119 veterans with PTSD and at least one CMC who have used the electronic personal health record system of the US Department of Veterans Affairs (VA) responded to a mailed survey about their chronic conditions and preferences related to the use of technology. After the survey, 2 focus groups, stratified by sex, were conducted with a subgroup of patients to explore how veterans with PTSD and comorbid CMCs use eHealth technology to support their complex health care needs. Focus groups were transcribed verbatim and analyzed using standard content analysis methods for coding textual data, guided by the “Fit between Individual, Task, and Technology” framework.ResultsSurvey respondents had a mean age of 64.0 (SD 12.0) years, 85.1% (97/114) were male, 72.4% (84/116) were white, and 63.1% (70/111) had an annual household income of < US $50,000. Mean score on a measure of eHealth literacy was 27.7 (SD 9.8). Of the respondents, 44.6% (50/112) used health-related technology 1 to 3 times per month and 21.4% (24/112) used technology less than once per month. Veterans reported using technology most often to search for health information (78.9%, 90/114), communicate with providers (71.1%, 81/114), and track medications (64.9%, 74/114). Five major themes emerged that describe how eHealth technology influences veterans with PTSD and comorbid CMCs: (1) interactions with social support, (2) condition management, (3) access to and communication with providers, (4) information access, and (5) coordination of care.ConclusionsThe “Fit between Individual, Task, and Technology” model provided a useful framework to examine the clinical tasks that arose for veterans and their resourceful adoption of eHealth tools. This study suggests that veterans who use the Web are eager to incorporate eHealth technology into their care and self-management activities. Findings illustrate a number of ways in which the VA and eHealth technology developers can refine existing applications, develop new resources, and better promote tools that address challenges experienced by veterans with PTSD and comorbid CMCs.

Section: Methodsmentioning

confidence: 99%

How Veterans With Post-Traumatic Stress Disorder and Comorbid Health Conditions Utilize eHealth to Manage Their Health Care Needs: A Mixed-Methods Analysis

Whealin¹,

Jenchura²,

Wong³

et al. 2016

“…Because the Internet and its impact keep growing, computer and Internet literacy are becoming an important addition to traditional health literacy skills [ 17 ]. Therefore, to get a complete overview of people’s skills to obtain and use health information, we should measure eHealth literacy [ 11 , 14 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Does the eHealth Literacy Scale (eHEALS) Measure What it Intends to Measure? Validation of a Dutch Version of the eHEALS in Two Adult Populations

Vaart¹,

Deursen²,

Drossaert³

et al. 2011

338

363

BackgroundThe Internet increases the availability of health information, which consequently expands the amount of skills that health care consumers must have to obtain and evaluate health information. Norman and Skinner in 2006 developed an 8-item self-report eHealth literacy scale to measure these skills: the eHealth Literacy Scale (eHEALS). This instrument has been available only in English and there are no data on its validity.ObjectivesThe objective of our study was to assess the internal consistency and the construct and predictive validity of a Dutch translation of the eHEALS in two populations.MethodsWe examined the translated scale in a sample of patients with rheumatic diseases (n = 189; study 1) and in a stratified sample of the Dutch population (n = 88; study 2). We determined Cronbach alpha coefficients and analyzed the principal components. Convergent validity was determined by studying correlations with age, education, and current (health-related) Internet use. Furthermore, in study 2 we assessed the predictive validity of the instrument by comparing scores on the eHEALS with an actual performance test.ResultsThe internal consistency of the scale was sufficient: alpha = .93 in study 1 and alpha = .92 in study 2. In both studies the 8 items loaded on 1 single component (respectively 67% and 63% of variance). Correlations between eHEALS and age and education were not found. Significant, though weak, correlations were found between the eHEALS and quantity of Internet use (r = .24, P = .001 and r = .24, P = .02, respectively). Contrary to expectations, correlations between the eHEALS and successfully completed tasks on a performance test were weak and nonsignificant: r = .18 (P = .09). The t tests showed no significant differences in scores on the eHEALS between participants who scored below and above median scores of the performance test.ConclusionsThe eHEALS was assessed as unidimensional in a principal component analysis and the internal consistency of the scale was high, which makes the reliability adequate. However, findings suggest that the validity of the eHEALS instrument requires further study, since the relationship with Internet use was weak and expected relationships with age, education, and actual performance were not significant. Further research to develop a self-report instrument with high correlations with people’s actual eHealth literacy skills is warranted.

“…Several systematic reviews have analyzed the relationship between health literacy in mHealth apps and interventions [5,7,8,25,26], generally reporting positive associations among health literacy, digital literacy, and health outcomes. Other reviews have specifically examined how technology can affect health literacy in health programs [27][28][29].…”

Section: The Ehealth Literacy Modelmentioning

confidence: 99%

Application of the eHealth Literacy Model in Digital Health Interventions: Scoping Review

Benny¹,

Kabakian‐Khasholian²,

El‐Jardali³

et al. 2021

Background Digital health interventions (DHIs) are increasingly being adopted globally to address various public health issues. DHIs can be categorized according to four main types of technology: mobile based, web based, telehealth, and electronic health records. In 2006, Norman and Skinner introduced the eHealth literacy model, encompassing six domains of skills and abilities (basic, health, information, scientific, media, and computer) needed to effectively understand, process, and act on health-related information. Little is known about whether these domains are assessed or accounted for in DHIs. Objective This study aims to explore how DHIs assess and evaluate the eHealth literacy model, describe which health conditions are addressed, and which technologies are used. Methods We conducted a scoping review of the literature on DHIs, based on randomized controlled trial design and reporting the assessment of any domain of the eHealth literacy model. MEDLINE, CINAHL, Embase, and Cochrane Library were searched. A duplicate selection and data extraction process was performed; we charted the results according to the country of origin, health condition, technology used, and eHealth literacy domain. Results We identified 131 unique DHIs conducted in 26 different countries between 2001 and 2020. Most DHIs were conducted in English-speaking countries (n=81, 61.8%), delivered via the web (n=68, 51.9%), and addressed issues related to noncommunicable diseases (n=57, 43.5%) or mental health (n=26, 19.8%). None of the interventions assessed all six domains of the eHealth literacy model. Most studies focused on the domain of health literacy (n=96, 73.2%), followed by digital (n=19, 14.5%), basic and media (n=4, 3%), and information and scientific literacy (n=1, 0.7%). Of the 131 studies, 7 (5.3%) studies covered both health and digital literacy. Conclusions Although many selected DHIs assessed health or digital literacy, no studies comprehensively evaluated all domains of the eHealth literacy model; this evidence might be overlooking important factors that can mediate or moderate the effects of these interventions. Future DHIs should comprehensively assess the eHealth literacy model while developing or evaluating interventions to understand how and why interventions can be effective.