Applying User-Centered Design in the Development of a Supportive mHealth App for Women in Substance Use Recovery.

PURPOSE AND APPROACH: Women in recovery describe stigma, negative treatment, and limited support as barriers to achieving their health and parenting goals. Mobile health technologies carefully tailored to support the unique needs of recovery communities can provide less burdensome alternatives to in-person services for women transitioning out of substance use treatment. An iterative design process integrated women's interests into the structure, content, and interaction flow of a mobile health (mHealth) app. SETTING AND PARTICIPANTS: Participants included women in recovery from opioid, alcohol, and polysubstance use disorders in a comprehensive housing program in urban Arizona. METHODS: Five focus groups with 3-7 participants each (n = 27 total) informed creation of the mHealth app. Informed by theoretical models of usability and person-centered design, development involved an iterative series of focus groups in which we asked women to comment on interest in using each feature. This provided a qualitative priority framework for feature development. We then modified the app and repeated the process to gauge consensus and continually refine our prototype. RESULTS: Women were interested in access to resources, such as housing, counseling, and parenting advice in settings known to treat women in recovery with respect. They also asked for positive messages, chatting with peers, and access to expert answers. They were less interested in points-based learning modules and "scored" activities, leading us to develop a "daily challenges" concept that builds good habits, but does not feel like "classwork". Women's recommendations shaped an mHealth app tailored to maximize utility, access, and safety for this at-risk population. CONCLUSION: Integration of user-centered design with applied ethnographic techniques guided the development of a custom-tailored mHealth app responsive to lived experiences and needs of women in recovery. Future research should evaluate the potential for user-centered apps to increase self-efficacy, perceived social support, and to reduce risk of relapse.

Possibilities and constraints of rapid online ethnography: Lessons from a rapid assessment of COVID-19 policy for people who use drugs.

During the COVID-19 Pandemic, health care provision changed rapidly and funding became available to assess pandemic-related policy change. Research activities, however, were limited to contactless, online delivery. It was clear early on that some elements of online rapid ethnography were feasible and effective, while others would not approach traditional ethnographic depth. We conducted an online Rapid Assessment, Response, and Evaluation (RARE) project from August 2020 to September 2021 to understand how COVID-19 policy impacted people who use drugs. Our interdisciplinary research team conducted online ethnographic interviews and focus groups with 45 providers and community stakeholders, and 19 clients from rural and urban areas throughout Arizona. In addition, 26 webinars, online trainings, and virtual conferences focused on opioid policy and medication for opioid use disorders (MOUD) were opportunities to observe conversations among providers and program representatives about how best to implement policy changes, how to reach people in recovery, and what aspects of the changes should carry forward into better all-around opioid services in the future. Our RARE project was successful in collecting a range of providers' perspectives on both rural and urban implementation of take-home MOUDs as well as a wide view of national conversations, but client perspectives were limited to those who were not impacted by the policies and continued to attend in-person daily clinic visits. We describe challenges to online rapid ethnography and how online research may have allowed for an in-depth, but incomplete picture of how policy changes during COVID-19 policy affected people with opioid use disorders.

SPARSEMODr: Rapidly simulate spatially explicit and stochastic models of COVID-19 and other infectious diseases.

Building realistically complex models of infectious disease transmission that are relevant for informing public health is conceptually challenging and requires knowledge of coding architecture that can implement key modeling conventions. For example, many of the models built to understand COVID-19 dynamics have included stochasticity, transmission dynamics that change throughout the epidemic due to changes in host behavior or public health interventions, and spatial structures that account for important spatio-temporal heterogeneities. Here we introduce an R package, SPARSEMODr, that allows users to simulate disease models that are stochastic and spatially explicit, including a model for COVID-19 that was useful in the early phases of the epidemic. SPARSEMOD stands for SPAtial Resolution-SEnsitive Models of Outbreak Dynamics, and our goal is to demonstrate particular conventions for rapidly simulating the dynamics of more complex, spatial models of infectious disease. In this report, we outline the features and workflows of our software package that allow for user-customized simulations. We believe the example models provided in our package will be useful in educational settings, as the coding conventions are adaptable, and will help new modelers to better understand important assumptions that were built into sophisticated COVID-19 models.

A sliding window approach to optimize the time-varying parameters of a spatially-explicit and stochastic model of COVID-19.

The implementation of non-pharmaceutical public health interventions can have simultaneous impacts on pathogen transmission rates as well as host mobility rates. For instance, with SARS-CoV-2, masking can influence host-to-host transmission, while stay-at-home orders can influence mobility. Importantly, variations in transmission rates and mobility patterns can influence pathogen-induced hospitalization rates. This poses a significant challenge for the use of mathematical models of disease dynamics in forecasting the spread of a pathogen; to create accurate forecasts in spatial models of disease spread, we must simultaneously account for time-varying rates of transmission and host movement. In this study, we develop a statistical model-fitting algorithm to estimate dynamic rates of SARS-CoV-2 transmission and host movement from geo-referenced hospitalization data. Using simulated data sets, we then test whether our method can accurately estimate these time-varying rates simultaneously, and how this accuracy is influenced by the spatial population structure. Our model-fitting method relies on a highly parallelized process of grid search and a sliding window technique that allows us to estimate time-varying transmission rates with high accuracy and precision, as well as movement rates with somewhat lower precision. Estimated parameters also had lower precision in more rural data sets, due to lower hospitalization rates (i.e., these areas are less data-rich). This model-fitting routine could easily be generalized to any stochastic, spatially-explicit modeling framework, offering a flexible and efficient method to estimate time-varying parameters from geo-referenced data sets.