Objective With age, older adults experience a greater number of chronic diseases and medical visits, and an increased need to manage their health information. Technological advances in consumer health information technologies (HITs) help patients gather, track, and organize their health information within and outside of clinical settings. However, HITs have not focused on the needs of older adults and their caregivers. The goal of the SOARING (Studying Older Adults and Researching their Information Needs and Goals) Project was to understand older adult personal health information management (PHIM) needs and practices to inform the design of HITs that support older adults. Materials and Methods Drawing on the Work System Model, we took an ecological approach to investigate PHIM needs and practices of older adults in different residential settings. We conducted in-depth interviews and surveys with adults 60 years of age and older. Results We performed on-site in-person interview sessions with 88 generally healthy older adults in various settings including independent housing, retirement communities, assisted living, and homelessness. Our analysis revealed 5 key PHIM activities that older adults engage in: seeking, tracking, organizing, sharing health information, and emergency planning. We identified 3 major themes influencing older adults’ practice of PHIM: (1) older adults are most concerned with maintaining health and preventing illness, (2) older adults frequently involve others in PHIM activities, and (3) older adults’ approach to PHIM is situational and context-dependent. Discussion Older adults’ approaches to PHIM are dynamic and sensitive to changes in health, social networks, personal habits, motivations, and goals. Conclusions PHIM tools that meet the needs of older adults should accommodate the dynamic nature of aging and variations in individual, organizational, and social contexts.

Background: Usability—the extent to which an intervention can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction—may be a key determinant of implementation success. However, few instruments have been developed to measure the design quality of complex health interventions (i.e., those with several interacting components). This study evaluated the structural validity of the Intervention Usability Scale (IUS), an adapted version of the well-established System Usability Scale (SUS) for digital technologies, to measure the usability of a leading complex psychosocial intervention, Motivational Interviewing (MI), for behavioral health service delivery in primary care. Prior SUS studies have found both one- and two-factor solutions, both of which were examined in this study of the IUS. Method: A survey administered to 136 medical professionals from 11 primary-care sites collected demographic information and IUS ratings for MI, the evidence-based psychosocial intervention that primary-care providers reported using most often for behavioral health service delivery. Factor analyses replicated procedures used in prior research on the SUS. Results: Analyses indicated that a two-factor solution (with “usable” and “learnable” subscales) best fit the data, accounting for 54.1% of the variance. Inter-item reliabilities for the total score, usable subscale, and learnable subscale were α = .83, α = .84, and α = .67, respectively. Conclusion: This study provides evidence for a two-factor IUS structure consistent with some prior research, as well as acceptable reliability. Implications for implementation research evaluating the usability of complex health interventions are discussed, including the potential for future comparisons across multiple interventions and provider types, as well as the use of the IUS to evaluate the relationship between usability and implementation outcomes such as feasibility. Plain language abstract: The ease with which evidence-based psychosocial interventions (EBPIs) can be readily adopted and used by service providers is a key predictor of implementation success, but very little implementation research has attended to intervention usability. No quantitative instruments exist to evaluate the usability of complex health interventions, such as the EBPIs that are commonly used to integrate mental and behavioral health services into primary care. This article describes the evaluation of the first quantitative instrument for assessing the usability of complex health interventions and found that its factor structure replicated some research with the original version of the instrument, a scale developed to assess the usability of digital systems.

Telemedicine (TM) enabled by digital health technologies to provide medical services has been considered a key solution to increasing health care access in rural communities. With the immediate need for remote care due to the COVID-19 pandemic, many health care systems have rapidly incorporated digital technologies to support the delivery of remote care options, including medication treatment for individuals with opioid use disorder (OUD). In responding to the opioid crisis and the COVID-19 pandemic, public health officials and scientific communities strongly support and advocate for greater use of TM-based medication treatment for opioid use disorder (MOUD) to improve access to care and have suggested that broad use of TM during the pandemic should be sustained. Nevertheless, research on the implementation and effectiveness of TM-based MOUD has been limited. To address this knowledge gap, the National Drug Abuse Treatment Clinical Trials Network (CTN) funded (via the NIH HEAL Initiative) a study on Rural Expansion of Medication Treatment for Opioid Use Disorder (Rural MOUD; CTN-0102) to investigate the implementation and effectiveness of adding TM-based MOUD to rural primary care for expanding access to MOUD. In preparation for this large-scale, randomized controlled trial incorporating TM in rural primary care, a feasibility study is being conducted to develop and pilot test implementation procedures. In this commentary, we share some of our experiences, which include several challenges, during the initial two-month period of the feasibility study phase. While these challenges could be due, at least in part, to adjusting to the COVID-19 pandemic and new workflows to accommodate the study, they are notable and could have a substantial impact on the larger, planned pragmatic trial and on TM-based MOUD more broadly. Challenges include low rates of identification of risk for OUD from screening, low rates of referral to TM, digital device and internet access issues, workflow and capacity barriers, and insurance coverage. These challenges also highlight the lack of empirical guidance for best TM practice and quality remote care models. With TM expanding rapidly, understanding implementation and demonstrating what TM approaches are effective are critical for ensuring the best care for persons with OUD.

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