While the field of digital inequality continues to expand in many directions, the relationship between digital inequalities and other forms of inequality has yet to be fully appreciated. This article invites social scientists in and outside the field of digital media studies to attend to digital inequality, both as a substantive problem and as a methodological concern. The authors present current research on multiple aspects of digital inequality, defined expansively in terms of access, usage, skills, and self-perceptions, as well as future lines of research. Each of the contributions makes the case that digital inequality deserves a place alongside more traditional forms of inequality in the twenty-first century pantheon of inequalities. Digital inequality should not be only the preserve of specialists but should make its way into the work of social scientists concerned with a broad range of outcomes connected to life chances and life trajectories. As we argue, the significance of digital inequalities is clear across a broad range of individual-level and macro-level domains, including life course, gender, race, and class, as well as health care, politics, economic activity, and social capital.

Our dynamic probit model indicates that for retired older adults in the United States, Internet use was found to reduce the probability of a depressed state by about 33%. Number of people in the household partially mediates this relationship, with the reduction in depression largest for people living alone. This provides some evidence that the mechanism linking Internet use to depression is the remediation of social isolation and loneliness. Encouraging older adults to use the Internet may help decrease isolation and depression.

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Literature has shown that people living in rural areas are less likely to have access to the Internet for demographic and technological reasons; however, less information is available regarding rural-urban differences in online health-information seeking. Data from the National Cancer Institute's nationally representative 2005 Health Information National Trends Survey (N = 5,586) are used to examine these relationships. Logistic regression results show that those in rural areas use the Internet less than those who live in urban areas. Among individuals who have used the Internet, those in rural areas are less likely to use the Internet for health purposes. The persistence of a digital divide between rural and urban residents in online health searching is attributable to factors such as educational level, income, and diffusion of broadband. The article discusses the impact of these differences.This article examines rural-urban differences in factors associated with using the Internet for general and health-related purposes. First, we examine factors associated with general Internet use. Second, we limit our analysis to Internet users to examine what factors are associated with types and number of health-related Internet uses during the past year. We pay particular attention to rural-urban differences, because

BackgroundHeart failure (HF) is a chronic condition affecting nearly 5.7 million Americans and is a leading cause of morbidity and mortality. With an aging population, the cost associated with managing HF is expected to more than double from US $31 billion in 2012 to US $70 billion by 2030. Readmission rates for HF patients are high—25% are readmitted at 30 days and nearly 50% at 6 months. Low medication adherence contributes to poor HF management and higher readmission rates. Remote telehealth monitoring programs aimed at improved medication management and adherence may improve HF management and reduce readmissions.ObjectiveThe primary goal of this randomized controlled pilot study is to compare the MedSentry remote medication monitoring system versus usual care in older HF adult patients who recently completed a HF telemonitoring program. We hypothesized that remote medication monitoring would be associated with fewer unplanned hospitalizations and emergency department (ED) visits, increased medication adherence, and improved health-related quality of life (HRQoL) compared to usual care.MethodsParticipants were randomized to usual care or use of the remote medication monitoring system for 90 days. Twenty-nine participants were enrolled and the final analytic sample consisted of 25 participants. Participants completed questionnaires at enrollment and closeout to gather data on medication adherence, health status, and HRQoL. Electronic medical records were reviewed for data on baseline classification of heart function and the number of unplanned hospitalizations and ED visits during the study period.ResultsUse of the medication monitoring system was associated with an 80% reduction in the risk of all-cause hospitalization and a significant decrease in the number of all-cause hospitalization length of stay in the intervention arm compared to usual care. Objective device data indicated high adherence rates (95%-99%) among intervention group participants despite finding no significant difference in self-reported adherence between study arms. The intervention group had poorer heart function and HRQoL at baseline, and HRQoL declined significantly in the intervention group compared to controls.ConclusionsThe MedSentry medication monitoring system is a promising technology that merits continued development and evaluation. The MedSentry medication monitoring system may be useful both as a standalone system for patients with complex medication regimens or used to complement existing HF telemonitoring interventions. We found significant reductions in risk of all-cause hospitalization and the number of all-cause length of stay in the intervention group compared to controls. Although HRQoL deteriorated significantly in the intervention group, this may have been due to the poorer HF-functioning at baseline in the intervention group compared to controls. Telehealth medication adherence technologies, such as the MedSentry medication monitoring system, are a promising method to improve patient self-management,the quality of pati...

BackgroundA sizable majority of adult Internet users report looking for health information online. Social networking sites (SNS) like Facebook represent a common place to seek information, but very little is known about the representation and use of health content on SNS.ObjectiveOur goal in this study was to understand the role of SNS in health information seeking. More specifically, we aimed to describe how health conditions are represented on Facebook Pages and how users interact with these different conditions.MethodsWe used Google Insights to identify the 20 most searched for health conditions on Google and then searched each of the resulting terms on Facebook. We compiled a list of the first 50 Facebook “Pages” results for each health condition. After filtering results to identify pages relevant to our research, we categorized pages into one of seven categories based on the page’s primary purpose. We then measured user engagement by evaluating the number of “Likes” for different conditions and types of pages.ResultsThe search returned 50 pages for 18 of the health conditions, but only 48 pages were found for “anemia” and 5 pages were found for “flu symptoms”, yielding a total of 953 pages. A large number of pages (29.4%, 280/953) were irrelevant to the health condition searched. Of the 673 relevant pages, 151 were not in English or originated outside the United States, leaving 522 pages to be coded for content. The most common type of page was marketing/promotion (32.2%, 168/522) followed by information/awareness (20.7%, 108/522), Wikipedia-type pages (15.5%, 81/522), patient support (9.4%, 49/522), and general support (3.6%, 19/522). Health conditions varied greatly by the primary page type. All health conditions had some marketing/promotion pages and this made up 76% (29/38) of pages on acquired immunodeficiency syndrome (AIDS). The largest percentage of general support pages were cancer (19%, 6/32) and stomach (16%, 4/25). For patient support, stroke (67%, 4/6), lupus (33%, 10/30), breast cancer (19%, 6/31), arthritis (16%, 6/36), and diabetes (16%, 6/37) ranked the highest. Six health conditions were not represented by any type of support pages (ie, human papillomavirus, diarrhea, flu symptoms, pneumonia, spine, human immunodeficiency virus). Marketing/promotion pages accounted for 46.73% (10,371,169/22,191,633) of all Likes, followed by support pages (40.66%, 9,023,234/22,191,633). Cancer and breast cancer accounted for 86.90% (19,284,066/22,191,633) of all page Likes.ConclusionsThis research represents the first attempts to comprehensively describe publicly available health content and user engagement with health conditions on Facebook pages. Public health interventions using Facebook will need to be designed to ensure relevant information is easy to find and with an understanding that stigma associated with some health conditions may limit the users’ engagement with Facebook pages. This line of research merits further investigation as Facebook and other SNS continue to evolve over the coming years.

2020 marks the 25th anniversary of the “digital divide.” Although a quarter century has passed, legacy digital inequalities continue, and emergent digital inequalities are proliferating. Many of the initial schisms identified in 1995 are still relevant today. Twenty-five years later, foundational access inequalities continue to separate the digital haves and the digital have-nots within and across countries. In addition, even ubiquitous-access populations are riven with skill inequalities and differentiated usage. Indeed, legacy digital inequalities persist vis-à-vis economic class, gender, sexuality, race and ethnicity, aging, disability, healthcare, education, rural residency, networks, and global geographies. At the same time, emergent forms of inequality now appear alongside legacy inequalities such that notions of digital inequalities must be continually expanded to become more nuanced. We capture the increasingly complex and interrelated nature of digital inequalities by introducing the concept of the “digital inequality stack.” The concept of the digital inequality stack encompasses access to connectivity networks, devices, and software, as well as collective access to network infrastructure. Other layers of the digital inequality stack include differentiated use and consumption, literacies and skills, production and programming, etc. When inequality exists at foundational layers of the digital inequality stack, this often translates into inequalities at higher levels. As we show across these many thematic foci, layers in the digital inequality stack may move in tandem with one another such that all layers of the digital inequality stack reinforce disadvantage.

BackgroundCurrently, the increasing interest in telehealth and significant technological breakthroughs of the past decade create favorable conditions for the widespread adoption of telehealth services. Therefore, expectations are high that telehealth can help alleviate prevailing challenges in health care delivery. However, in order to translate current research to policy and facilitate adoption by patients and health care providers, there is need for compelling evidence of the effectiveness of telehealth interventions. Such evidence is gathered from rigorously designed research studies, which may not always be practical in many real-world settings.ObjectiveOur aim was to summarize current telehealth evaluation strategies and challenges and to outline practical approaches to conduct evaluation in real-world settings using one of our previously reported telehealth initiatives, the Diabetes Connect program, as a case study.MethodsWe reviewed commonly used current evaluation frameworks and strategies, as well as best practices based on successful evaluative efforts to date to address commonly encountered challenges in telehealth evaluation. These challenges in telehealth evaluation and commonly used frameworks are described relevant to the evaluation of Diabetes Connect, a 12-month Web-based blood glucose monitoring program.ResultsDesigners of telehealth evaluation frameworks must give careful consideration to the elements of planning, implementation, and impact assessment of interventions. Evaluating performance at each of these phases is critical to the overall success of an intervention. Although impact assessment occurs at the end of a program, our review shows that it should begin at the point of problem definition. Critical to the success of an evaluative strategy is early planning that involves all stakeholders to identify the overall goals of the program and key measures of success at each phase of the program life cycle. This strategy should enable selection of an appropriate evaluation strategy and measures to aid in the ongoing development and implementation of telehealth and provide better evidence of program impact.ConclusionsWe recommend a pragmatic, multi-method, multi-phase approach to telehealth evaluation that is flexible and can be adapted to the characteristics and challenges unique to each telehealth program.