Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students.

Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results: In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions: Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.

Cross cultural and global uses of a digital mental health app: results of focus groups with clinicians, patients and family members in India and the United States.

BACKGROUND: Despite significant advancements in healthcare technology, digital health solutions - especially those for serious mental illnesses - continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India. METHODS: Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality. RESULTS: Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement. CONCLUSION: People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary - not substitutive - of therapeutic care from a clinician.

Investigating Associations Between Screen Time and Symptomatology in Individuals With Serious Mental Illness: Longitudinal Observational Study.

BACKGROUND: Increasing screen time exposure from digital devices like smartphones has shown a variety of mixed associations with cognition, behavior, and well-being in adults and children but little is known about its associations with symptomatology in individuals with serious mental illness. OBJECTIVE: To determine the range of associations between screen time and symptoms of individuals with mental illness, we utilized a method called specification curve analysis. METHODS: In this observational study, we recruited smartphone-owning adults (≥18 years old) with schizophrenia and healthy controls. We installed 2 research-source smartphone apps, mindLAMP and Beiwe, to collect survey results, cognitive test results, and screen time metrics over a period of 3 months. Surveys were scheduled for twice a week, but participants were instructed to take the surveys naturally as much or as little as they wanted. Screen time was collected continuously in the background. A total of 140 participants was recruited from the outpatient clinic population as well as through general public advertising. Age-matched, smartphone-owning healthy controls were also part of the recruitment pool. A specification curve analysis was a priori designed to explore the relationship between every combination of independent variable and dependent variable in order to demonstrate to what degree screen time relates to symptoms in individuals with serious mental illness. RESULTS: The sample consisted of 88 participants (54 with schizophrenia and 34 healthy controls) who completed both the initial and follow-up visits, completed at least one self-reported survey, and had a minimum passive data cutoff of 5 consecutive days. While we found an association between smartphone screen time metrics and cognition (adjusted R2=0.107, P<.001), specification curve analysis revealed a wide range of heterogenous associations with screen time from very negative to very positive. The effects differed based on diagnostic group, age bracket, type of regression model used, and the specific independent and dependent variables selected for analysis. CONCLUSIONS: The associations between screen time and mental health in patients with schizophrenia are heterogenous when examined with methods that reduce analytical bias. The heterogeneity in associations suggests that complex and personalized potential effects must be understood in the greater context of an individual. This analysis of longitudinally collected screen time data shows potential for future research that could benefit from high resolution metrics on smartphone use.

Psychiatric rehabilitation through teaching smartphone skills to improve functional outcomes in serious mental illness.

This study measured the impact of a digital competencies and skills course on participants with serious mental illness. Close to 75% of participants reported an improvement in a smartphone related skill, and the majority of participants that reported improvement in one skill reported improvement in at least one other. Qualitative feedback from participants suggests how digital competencies acquired were used to immediately support functional outcomes. OBJECTIVE: To improve functional outcomes in patients with serious mental illness through a multi-session curriculum designed to improve smartphone skills and engage participants in group learning and problem solving, targeting negative and cognitive symptoms of illness. METHODS: An eight-week smartphone digital competencies and skills course was offered to two distinct groups of youth with serious mental illness. Pre and post self-report measurements were captured for each participant for each session. RESULTS: Group participation varied by session, but overall 28 unique patients attended. From survey results, 75% reported improvement in smartphone related skills because of the groups. Qualitative feedback suggests how skills acquired by patients were immediately utilized to gain insight into health and support functional outcomes. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Smartphone skills groups are a means to provide practical psychiatric rehabilitation that may enable some patients to compensate for cognitive and social deficits due to illness. While ensuring groups are responsive to patients with varying degrees of skills remains a challenge, adapting lesson structures and mediums, as well as creating new measurement tools, offers a means to modify the course with the clinical need.

Smartphone Health Assessment for Relapse Prevention (SHARP): a digital solution toward global mental health.

BACKGROUND: Predicting and preventing relapse presents a crucial opportunity and first step to improve outcomes and reduce the care gap for persons living with schizophrenia. Using commercially available smartphones and smartwatches, technology now affords opportunities to capture real-time and longitudinal profiles of patients' symptoms, cognition, physiology and social patterns. This novel data makes it possible to explore relationships between behaviours, physiology and symptoms, which may yield personalised relapse signals. AIMS: Smartphone Health Assessment for Relapse Prevention (SHARP), an international mental health research study supported by the Wellcome Trust, will inform the development of a scalable and sharable digital health solution to monitor personal risk of relapse. The resulting technology will be studied toward predicting and preventing relapse among individuals diagnosed with serious mental illness. METHOD: SHARP is a two-phase study with research sites in Boston, Massachusetts, and Bangalore and Bhopal, India. During phase 1, focus groups will be conducted at each study site to collect feedback on the design and features available on mindLAMP, a digital health platform. Individuals with serious mental illness will use mindLAMP for the duration of a year during phase 2. RESULTS: The results of the research outlined in this protocol will guide the development of technology and digital tools to help address pervasive challenges in global mental health. CONCLUSIONS: The digital tools developed as a result of this study, and participants' experiences using them, may offer insight into opportunities to expand digital mental health resources and optimize their utilisation around the world.

Making mental health more accessible in light of COVID-19: Scalable digital health with digital navigators in low and middle-income countries.

The rapid spread of COVID-19 and the devastating consequences to economies and healthcare systems around the world has highlighted the exigent need for accessible mental health support. Increasing use of mobile devices in Lower Middle-Income Countries (LMIC) such as India offers novel opportunity to expand treatment options and reach underserved populations. Prior efforts have utilized technology to redistribute or supplement clinical care but measurable outcomes of this research are limited. In this paper, we explain the structural barriers that prevent access to care and build on prior research to demonstrate how technology can be utilized to offer treatment if it is aided by education and technical support.

Health Information Technology Resources to Support Measurement-Based Care.

To increase utilization and convert health information technology into measurement-based care, this article highlights 4 criteria and actions that help distinguish successful mobile mental health interventions: first, interest: draw on pervading interest in mobile mental health; second, engagement: offer personalized design features and recommendations; third, specificity and simplicity: provide simple, specific, and timely feedback; fourth, support: incorporate support from peers, family and friends, and caregivers.

The digital clinic: Implementing technology and augmenting care for mental health.

Increasing both access to and quality of mental healthcare is a global priority. One solution is to integrate technologies such as smartphone apps and sensors directly into care. Acknowledging many prior attempts and barriers, we introduce the Digital Clinic which is an already functioning clinic using smartphone apps to augment and extend care today at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts. In this piece, we outline the theoretical foundation of the Digital Clinic and its emphasis on the therapeutic alliance, measurement-based care, and shared decision making. We explore both workflow and engagement challenges as well as solutions including a new care team member, the Digital Navigator, and the customization of technology. Acknowledging that the Digital Clinic is an evolving program, we offer details on our implementation in order to allow others to replicate, expand on, and improve these initial efforts.

Technology Evaluation and Assessment Criteria for Health Apps (TEACH-Apps): Pilot Study.

BACKGROUND: Despite the emergence of app evaluation tools, there remains no well-defined process receptive to diverse local needs, rigorous standards, and current content. The need for such a process to assist in the implementation of app evaluation across all medical fields is evident. Such a process has the potential to increase stakeholder engagement and catalyze interest and engagement with present-day app evaluation models. OBJECTIVE: This study aimed to develop and pilot test the Technology Evaluation and Assessment Criteria for Health apps (TEACH-apps). METHODS: Tailoring a well-known implementation framework, Replicating Effective Programs, we present a new process to approach the challenges faced in implementing app evaluation tools today. As a culmination of our experience implementing this process and feedback from stakeholders, we present the four-part process to aid the implementation of mobile health technology. This paper outlines the theory, evidence, and initial versions of the process. RESULTS: The TEACH-apps process is designed to be broadly usable and widely applicable across all fields of health. The process comprises four parts: (1) preconditions (eg, gathering apps and considering local needs), (2) preimplementation (eg, customizing criteria and offering digital skills training), (3) implementation (eg, evaluating apps and creating educational handouts), and (4) maintenance and evolution (eg, repeating the process every 90 days and updating content). TEACH-apps has been tested internally at our hospital, and there is growing interest in partnering health care facilities to test the system at their sites. CONCLUSIONS: This implementation framework introduces a process that equips stakeholders, clinicians, and users with the foundational tools to make informed decisions around app use and increase app evaluation engagement. The application of this process may lead to the selection of more culturally appropriate and clinically relevant tools in health care.

Digital technology for management of severe mental disorders in low-income and middle-income countries.

PURPOSE OF REVIEW: Increasingly, digital technologies, especially mobile telecommunications and smartphone apps, are seen as a novel tool for managing severe mental disorders (SMDs) in low-income and middle-income countries (LMICs). However, there is a need to identify best practices in the use of digital technologies to effectively reach, support, and manage care for patients living with SMDs. In this review, we summarize recent studies using digital technology to manage symptoms and support clinical care for this patient population and discuss new opportunities to advance digital psychiatry research and practice in LMICs. RECENT FINDINGS: Studies evaluating digital interventions for clinical populations living with SMDs in LMICs are limited. Yet, across recent articles surveyed, digital technology appears to yield diverse benefits for this at-risk patient population. These benefits include improved medication adherence, appointment adherence, reduced instances of relapse, and fewer re-hospitalizations. SUMMARY: Continued rigorous research evaluating effectiveness and cost-effectiveness of digital technologies in reaching, treating, and managing symptoms and supporting clinical care for patients with SMDs in LMICs is vital. The urgency for remote approaches for delivering specialized psychiatric care is particularly pronounced because of the immediate and long-term impact of the coronavirus (COVID-19) pandemic on access to in-person services. Future research should emphasize participatory approaches rooted in a process of codesign with target users, in order to achieve clinically effective remotely delivered digital mental health interventions.

Regulating digital health technologies with transparency: the case for dynamic and multi-stakeholder evaluation.

BACKGROUND: The prevalence of smartphones today, paired with the increasing precision and therapeutic potential of digital capabilities, offers unprecedented opportunity in the field of digital medicine. Smartphones offer novel accessibility, unique insights into physical and cognitive behavior, and diverse resources designed to aid health. Many of these digital resources, however, are developed and shared at a faster rate than they can be assessed for efficacy, safety, and security-presenting patients and clinicians with the challenge of distinguishing helpful tools from harmful ones. MAIN TEXT: Leading regulators, such as the FDA in the USA and the NHS in the UK, are working to evaluate the influx of mobile health applications entering the market. Efforts to regulate, however, are challenged by the need for more transparency. They require real-world data on the actual use, effects, benefits, and harms of these digital health tools. Given rapid product cycles and frequent updates, even the most thorough evaluation is only as accurate as the data it is based on. CONCLUSIONS: In this debate piece, we propose a complementary approach to ongoing efforts via a dynamic self-certification checklist. We outline how simple self-certification, validated or challenged by app users, would enhance transparency, engage diverse stakeholders in meaningful education and learning, and incentivize the design of safe and secure medical apps.