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Sleep's impact on quality of life and health among the general population is well documented. Meanwhile, there is a growing number of policies across the United States that criminalize homelessness by outlawing sleeping or sheltering in public, but there is little research about sleep quality among people experiencing homelessness within the context of larger systems. Using data (N = 484) collected to explore public health implications of criminalization of homelessness, this study describes sleep among people experiencing homelessness in Denver, Colorado through an ecological systems perspective. Participants reported an average of 3.6 hours of sleep in a 24-hour period and participants, on average, slept 2.87 hours without being interrupted. Race, disability status, and military status have statistically significant correlations with interrupted sleep, suggesting the systems and structures associated with these demographics may be related to poor sleep. Furthermore, there is a statistically significant correlation between amount of sleep and uninterrupted sleep and feelings of depression, experiencing images or hallucinations, and feeling sick. In building upon previous research which shows that people experiencing homelessness typically get less than the recommended amount of sleep, this study illuminates both the systemic and self-reported health and well-being implications that poor sleep has among people experiencing homelessness. [ABSTRACT FROM AUTHOR]

People with serious mental illness (SMI) have lower rates of use of preventative medical services and higher rates of mortality compared to the general population. Research shows that specialized primary care medical homes improve the health care of patients with SMI and are feasible to implement, safe, and more effective than usual care. However, specialized medical homes remain uncommon and model dissemination limited. As part of a controlled trial assessing an SMI-specialized medical home, we examined clinician and administrator perspectives regarding specialized versus mainstream primary care and identified ways to enhance the scale-up of a specialized primary care model for future dissemination. We conducted semistructured interviews with clinicians and administrators at three sites prior to the implementation of an SMI-specialized primary care medical home ( n = 26) and at 1-year follow-up ( n = 24); one site implemented the intervention, and two sites served as controls. Interviews captured service design features that affected the quality of care provided; contextual factors that supported or impeded medical home implementation; and knowledge, attitudes, and behaviors regarding the care of patients with SMI. Interviews were transcribed and coded. Clinicians and administrators described SMI-specialized primary care medical homes as advancing care coordination and outcomes for patients with SMI. Stakeholders identified elements of a specialized medical home that they viewed as superior to usual care, including having a holistic picture of patients' needs and greater care coordination. However, to enable scale-up, efforts are needed to increase staffing on care teams, develop robust clinician onboarding or training, and ensure close coordination with mental health care providers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background: Early intervention in mental health crises can prevent negative outcomes. A promising new direction is remote mental health monitoring using smartphone technology to passively collect data from individuals to rapidly detect the worsening of serious mental illness (SMI). This technology may benefit patients with SMI, but little is known about health IT acceptability among this population or their mental health clinicians., Objective: We used the Health Information Technology Acceptability Model to analyze the acceptability and usability of passive mobile monitoring and self-tracking among patients with serious mental illness and their mental health clinicians., Methods: Data collection took place between December 2020 and June 2021 in 1 Veterans Administration health care system. Interviews with mental health clinicians (n=16) assessed the acceptability of mobile sensing, its usefulness as a tool to improve clinical assessment and care, and recommendations for program refinements. Focus groups with patients with SMI (n=3 groups) and individual usability tests (n=8) elucidated patient attitudes about engaging in health IT and perceptions of its usefulness as a tool for self-tracking and improving mental health assessments., Results: Clinicians discussed the utility of web-based data dashboards to monitor patients with SMI health behaviors and receiving alerts about their worsening health. Potential benefits included improving clinical care, capturing behaviors patients do not self-report, watching trends, and receiving alerts. Clinicians' concerns included increased workloads tied to dashboard data review, lack of experience using health IT in clinical care, and how SMI patients' associated paranoia and financial instability would impact patient uptake. Despite concerns, all mental health clinicians stated that they would recommend it. Almost all patients with SMI were receptive to using smartphone dashboards for self-monitoring and having behavioral change alerts sent to their mental health clinicians. They found the mobile app easy to navigate and dashboards easy to find and understand. Patient concerns centered on privacy and "government tracking," and their phone's battery life and data plans. Despite concerns, most reported that they would use it., Conclusions: Many people with SMI would like to have mobile informatics tools that can support their illness and recovery. Similar to other populations (eg, older adults, people experiencing homelessness) this population presents challenges to adoption and implementation. Health care organizations will need to provide resources to address these and support successful illness management. Clinicians are supportive of technological approaches, with adapting informatics data into their workflow as the primary challenge. Despite clear challenges, technological developments are increasingly designed to be acceptable to patients. The research development-clinical deployment gap must be addressed by health care systems, similar to computerized cognitive training. It will ensure clinicians operate at the top of their skill set and are not overwhelmed by administrative tasks, data summarization, or reviewing data that do not indicate a need for intervention., International Registered Report Identifier (irrid): RR2-10.2196/39010., (©Melissa Medich, Shay L Cannedy, Lauren C Hoffmann, Melissa Y Chinchilla, Jose M Pila, Stephanie A Chassman, Ronald A Calderon, Alexander S Young. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 24.10.2023.)