Continued Use of Contact-Tracing Apps in the United States and the United Kingdom: Insights From a Comparative Study Through the Lens of the Health Belief Model.

BACKGROUND: To contain the spread of SARS-CoV-2, contact-tracing (CT) mobile apps were developed and deployed to identify and notify individuals who have exposure to the virus. However, the effectiveness of these apps depends not only on their adoption by the general population but also on their continued use in the long term. Limited research has investigated the facilitators of and barriers to the continued use of CT apps. OBJECTIVE: In this study, we aimed to examine factors influencing the continued use intentions of CT apps based on the health belief model. In addition, we investigated the differences between users and nonusers and between the US and UK populations. METHODS: We administered a survey in the United States and the United Kingdom. Respondents included individuals who had previously used CT technologies and those without experience. We used the structural equation modeling technique to validate the proposed research model and hypotheses. RESULTS: Analysis of data collected from 362 individuals showed that perceived benefits, self-efficacy, perceived severity, perceived susceptibility, and cues to action positively predicted the continued use intentions of CT apps, while perceived barriers could reduce them. We observed few differences between the US and UK groups; the only exception was the effect of COVID-19 threat susceptibility, which was significant for the UK group but not for the US group. Finally, we found that the only significant difference between users and nonusers was related to perceived barriers, which may not influence nonusers' continued use intentions but significantly reduce experienced users' intentions. CONCLUSIONS: Our findings have implications for technological design and policy. These insights can potentially help governments, technology companies, and media outlets to create strategies and policies to promote app adoption for new users and sustain continued use for existing users in the long run.

The Continued Use of Mobile Health Apps: Insights From a Longitudinal Study.

BACKGROUND: Mobile health (mHealth) apps that support individuals pursuing health and wellness goals, such as weight management, stress management, smoking cessation, and self-management of chronic conditions have been on the rise. Despite their potential benefits, the use of these tools has been limited, as most users stop using them just after a few times of use. Under this circumstance, achieving the positive outcomes of mHealth apps is less likely. OBJECTIVE: The objective of this study was to understand continued use of mHealth apps and individuals' decisions related to this behavior. METHODS: We conducted a qualitative longitudinal study on continued use of mHealth apps. We collected data through 34 pre- and postuse interviews and 193 diaries from 17 participants over two weeks. RESULTS: We identified 2 dimensions that help explain continued use decisions of users of mHealth apps: users' assessment of mHealth app and its capabilities (user experience) and their persistence at their health goals (intent). We present the key factors that influence users' assessment of an mHealth app (interface design, navigation, notifications, data collection methods and tools, goal management, depth of knowledge, system rules, actionable recommendations, and user system fit) and relate these factors to previous literature on behavior change technology design. Using these 2 dimensions, we developed a framework that illustrated 4 decisions users might make after initial interaction with mHealth apps (to abandon use, limit use, switch app, and continue use). We put forth propositions to be explored in future research on mHealth app use. CONCLUSIONS: This study provides insight into the factors that shape users' decisions to continue using mHealth apps, as well as other likely decision scenarios after the initial use experience. The findings contribute to extant knowledge of mHealth use and provide important implications for design of mHealth apps to increase long-term engagement of the users.

Understanding the Impact of Electronic Medical Record Use on Practice-Based Population Health Management: A Mixed-Method Study.

BACKGROUND: Practice-based population health (PBPH) management is the proactive management of patients by their primary care clinical team. The ability of clinics to engage in PBPH and the means by which they incorporate it in a clinical setting remain unknown. OBJECTIVE: We conducted the Canadian Population Health Management Challenge to determine the capacity and preparedness of primary care settings to engage in PBPH using their existing medical record systems and to understand the complexities that may exist in PBPH implementation. METHODS: We recruited a sample of electronic medical record (EMR) -enabled and paper-based clinics from across Canada to participate in the challenge. The challenge required clinic staff and physicians to complete time-controlled, evidence-based practice reviews of their patients who may benefit from evidence-informed care, treatment, or interventions across five different areas (immunization, postmyocardial infarction care, cancer screening, diabetes management, and medication recall). We formulated a preparedness index to measure the capacity of clinics to engage in PBPH management. Finally, we conducted follow-up qualitative interviews to provide richer understanding of PBPH implementation and related issues (ie, challenges and facilitators). RESULTS: A total of 11 primary care clinics participated, representing 21 clinician practices. EMR-enabled clinics completed a full review of charts in an average of 1.37 hours. On the contrary, paper-based clinics reviewed nearly 10% of their charts in an average of 3.9 hours, hinting that they would have required an estimated 40 hours to complete a review of charts in their practice. Furthermore, the index revealed a major gap in preparedness between the EMR and paper-based clinics (0.86-3.78 vs 0.05-0.12), as well as a broad range among the EMR clinics. Finally, building on the results of the qualitative analysis, we identified factors facilitating the integration of PBPH. CONCLUSIONS: Our results suggest that EMR usage is pivotal in setting the foundation to support PBPH. The wide range of performance variation among EMR-enabled clinics suggests that EMR functionality and optimization, its support of clinical practice workflow, and policy issues to ensure adoption of standards are critical issues to facilitate PBPH.

Health information technologies in geriatrics and gerontology: a mixed systematic review.

OBJECTIVE: To review, categorize, and synthesize findings from the literature about the application of health information technologies in geriatrics and gerontology (GGHIT). MATERIALS AND METHODS: This mixed-method systematic review is based on a comprehensive search of Medline, Embase, PsychInfo and ABI/Inform Global. Study selection and coding were performed independently by two researchers and were followed by a narrative synthesis. To move beyond a simple description of the technologies, we employed and adapted the diffusion of innovation theory (DOI). RESULTS: 112 papers were included. Analysis revealed five main types of GGHIT: (1) telecare technologies (representing half of the studies); (2) electronic health records; (3) decision support systems; (4) web-based packages for patients and/or family caregivers; and (5) assistive information technologies. On aggregate, the most consistent finding proves to be the positive outcomes of GGHIT in terms of clinical processes. Although less frequently studied, positive impacts were found on patients' health, productivity, efficiency and costs, clinicians' satisfaction, patients' satisfaction and patients' empowerment. DISCUSSION: Further efforts should focus on improving the characteristics of such technologies in terms of compatibility and simplicity. Implementation strategies also should be improved as trialability and observability are insufficient. CONCLUSIONS: Our results will help organizations in making decisions regarding the choice, planning and diffusion of GGHIT implemented for the care of older adults.