The use of mobile applications or “apps” is beginning to be identified as a potential cost-effective tool for treating depression. While the use of mobile apps for health management appears promising, little is known on how to incorporate these tools into integrated primary care settings—especially from the viewpoints of patients and the clinic personnel. The purpose of this study was to explore patient- and clinic-level perceptions of the use of depression self-management apps within an integrated primary care setting. Patients (n = 17), healthcare providers, and staff (n = 15) completed focus groups or semi-structured interviews in-person or via Zoom between January and July 2020. Participants were asked about barriers and facilitators to app use, how to best integrate it into care, and reviewed pre-selected mental health apps. Data were analyzed using a directed content analysis approach. From a patient perspective, features within the app such as notifications, the provision of information, easy navigation, and a chat/support function as well as an ability to share data with their doctor were desirable. Providers and staff identified integration of app data into electronic health records to be able to share data with patients and the healthcare team as well as clear evidence of effectiveness as factors that could facilitate implementation. All participants who reviewed apps identified at least one of them they would be interested in continuing to use. Overall, patients, healthcare providers, and staff believed depression apps could be beneficial for both patients and the clinic.

It is with deep appreciation that we read the research by Yu et al. 1 investigating the risk factors for mortality among 1,663 patients hospitalized with the novel coronavirus disease 2019 (COVID-19) in a Wuhan (China) hospital that utilized clinical characteristics in the development of a statistical model that predicts death risk. 1 Given that the ongoing pandemic has placed great pressure on healthcare systems worldwide, we consider prediction models to be of high utility in assessing which patients with COVID-19 have higher mortality risk to optimize healthcare resources. To better apply this prediction model to clinical care around the world, future validation studies are warranted. Considering the variability in health service capacities and policies across different settings, we feel that such a model deserves to be further validated-or even refined-to generalize it to patients in other countries and regions (e.g., non-epicenters in China, the U.S., Australia). As has been noted, 2 all confirmed and suspected patients with COVID-19 in Wuhan are hospitalized regardless of illness severity; such practice may not be necessary/feasible in other countries/regions where patients with COVID-19 with asymptomatic or mild illness could be routinely discharged and isolated at home. In light of the different conditions between inpatient and outpatient care, we call for future prediction studies for all confirmed cases, hospitalized and nonhospitalized patients with COVID-19, to understand how to design and apply this model effectively. Furthermore, social determinants of health, in addition to clinical risk factors, vary both among and within populations (e.g., Chinese versus American). For instance, differences in illness severity have been observed between racial groups in the U.S. 3 In addition to further validation and development of the prediction model, we hope for more studies on the characteristics and risk/prognostic factors for important outcomes (e.g., hospitalization, 4 critical illness 4,5) related to COVID-19 in different national or subnational regions as well as among general COVID-19 cohorts or specific COVID-19 cohorts such as pediatric 6 or obstetric patients. 7 Successful efforts to address these matters will enable a better understanding of the coronavirus, helping to develop accurate clinical predictions that can maximize efficiency in healthcare delivery.

Background: Recent surveys have revealed many adults have basic or below basic health literacy, which is linked to medical errors, increased illness, and compromised public health. Health literacy as a concept is multi-faceted extending beyond the individual to include social structures and the context in which health information is being accessed. Delivering health information via mobile devices (mHealth) expands the amount of information available while presenting challenges to ensuring these materials are suitable for a variety of literacy needs. The aims of this study are to discover how health literacy is addressed and evaluated in mHealth app development.Methods: A scoping review of 5 peer-reviewed databases was conducted. Eligible articles were written in English, addressed general literacy or mHealth/digital/eHealth literacy, and collected literacy information in order to incorporate literacy into the design and/or modification of an app or collected literacy information to describe the population being studied. The "Health Literacy Online" (HLO) United States (U.S.) government guide was used as a framework.Results: Thirty-two articles were reviewed. Articles included health literacy recommendations for all HLO categories and some recommendations not aligned with these categories. Most articles addressed health literacy using specific HLO categories though none incorporated every HLO category. The most common categories addressed engagement and testing of mHealth content. Though several studies addressed health literacy through a formal assessment tool, most did not. Evaluation of health literacy in mHealth was enduser focused and did not extensively evaluate content for fit to a variety of individuals with limited health literacy. Conclusions:The recommendations seen consistently in our results in conjunction with formal HLO categories can act as beginning steps towards development of a health literacy evaluation tool for mHealth apps themselves. It is clear efforts are being made to reduce barriers to using mHealth for those with literacy deficits, however, it was also clear that this space has room to be more pragmatic in evaluation of mHealth tools for literacy. End user engagement in design and testing is necessary in future mHealth literacy tool development.

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Depression is the leading cause of disability worldwide and is one of the most common mental health issues being addressed within primary care settings. Mobile apps, which can be used to help people manage their depressive symptoms, are rapidly developing. However, many challenges exist for clinicians and providers to simply select an appropriate app for use within target populations. The objectives of this article are as follows: (1) to describe the search processes that were used to identify depressionrelated mobile apps and (2) to describe the review process that was implemented to inform and evaluate the identified depressionrelated mobile health apps for use with our target population. A research team consisting of information technology researchers, primary and psychiatric care providers, and health care researchers completed two mobile app searches to identify depressionrelated apps which could be used for further exploration within an underserved integrated primary care setting. Sixteen mobile apps were narrowed down to 4 mobile apps, through a series of steps involving screening, collaboration of the interprofessional team, information technology expertise input, and mobile app evaluation tools. This article described the steps a research team used to search, screen, and assess mental health mobile apps for integrated primary care patients with depression. This step-bystep guide focused on depression-related apps; however, similar steps and principles identified in this guide can be applied to other health apps.