Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial.

As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations.

Digital phenotyping correlates of mobile cognitive measures in schizophrenia: A multisite global mental health feasibility trial.

Traditional cognitive assessments in schizophrenia are time-consuming and necessitate specialized training, making routine evaluation challenging. To overcome these limitations, this study investigates the feasibility and advantages of utilizing smartphone-based assessments to capture both cognitive functioning and digital phenotyping data and compare these results to gold standard measures. We conducted a secondary analysis of data from 76 individuals with schizophrenia, who were recruited across three sites (one in Boston, two in India) was conducted.

Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample.

Introduction: Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g.

Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study.

Background: There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables.

Objective: This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.

Applying machine learning to smartphone based cognitive and sleep assessments in schizophrenia.

Background: Cognitive impairment in schizophrenia remains a chief source of functional disability and impairment, despite the potential for effective interventions. This is in part related to a lack of practical and easy to administer screening strategies that can identify and help triage cognitive impairment. This study explores how smartphone-based assessments may help address this need.

Assessing mental health apps marketplaces with objective metrics from 29,190 data points from 278 apps.

Objective: Utilizing a standard framework that may help clinicians and patients to identify relevant mental health apps, we sought to gain a comprehensive picture of the space by searching for, downloading, and reviewing 278 mental health apps from both the iOS and Android stores.

Methods: 278 mental health apps from the Apple iOS store and Google Play store were downloaded and reviewed in a standardized manner by trained app raters using a validated framework. Apps were evaluated with this framework comprising 105 questions and covering app origin and accessibility, privacy and security, inputs and outputs, clinical foundation, features and engagement style, and interoperability.

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

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 were 100 students enrolled in 4-year universities. 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.

Anomaly detection to predict relapse risk in schizophrenia.

The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital phenotyping, or the use of integrated sensors to identify patterns in behavior and symptomatology, has shown potential in detecting subtle moment-to-moment changes. These changes, often referred to as anomalies, represent significant deviations from an individual's baseline, may be useful in informing the risk of relapse in serious mental illness.

Changes to the Psychiatric Chatbot Landscape: A Systematic Review of Conversational Agents in Serious Mental Illness: Changements du paysage psychiatrique des chatbots: une revue systématique des agents conversationnels dans la maladie mentale sérieuse.

Objective: The need for digital tools in mental health is clear, with insufficient access to mental health services. Conversational agents, also known as chatbots or voice assistants, are digital tools capable of holding natural language conversations. Since our last review in 2018, many new conversational agents and research have emerged, and we aimed to reassess the conversational agent landscape in this updated systematic review.

Patient Innovation in Investigating the Effects of Environmental Pollution in Schizophrenia: Case Report of Digital Phenotyping Beyond Apps.

This patient perspective highlights the role of patients in the innovation and codesign of digital mental health technology. Though digital mental health apps have evolved and become highly functional, many still act as data collection silos without adequate support for patients to understand and investigate potentially meaningful inferences in their own data. Few digital health platforms respect the patient's agency and curiosity, allowing the individual to wear the hat of researcher and data scientist and share their experiences and insight with their clinicians.

Digital Opportunities for Outcomes in Recovery Services (DOORS): A Pragmatic Hands-On Group Approach Toward Increasing Digital Health and Smartphone Competencies, Autonomy, Relatedness, and Alliance for Those With Serious Mental Illness.

Digital health technologies such as smartphones present the potential for increased access to care and on-demand services. However, many patients with serious mental illnesses (eg, schizophrenia) have not been offered the digital health training necessary to fully utilize these innovative approaches. To bridge this digital divide in knowledge and skills, we created a hands-on and interactive training program grounded in self-determination theory, technology use cases, and the therapeutic alliance.

Multiple uses of app instead of using multiple apps - a case for rethinking the digital health technology toolbox.

There are tens of thousands of mental health-related apps available today - representing extreme duplication in this digital age. Instead of a plethora of apps, there is a need for a few that meet the needs of many. Focusing on transparency and free sharing of software, we argue that a collaborative approach towards apps can advance care through creating customisable and future proofed digital tools that allow all stakeholders to engage in their design and use.

Assessing Cognition Outside of the Clinic: Smartphones and Sensors for Cognitive Assessment Across Diverse Psychiatric Disorders.

Traditionally, the assessment of cognition and the administration of cognitive therapies has been performed in the clinic, but with modern technology, this clinic-centric view is changing. This article explores the landscape of digital devices used to measure cognition in settings outside the clinic. These devices range in mobility from user-friendly mobile devices to setting-specific devices able to provide powerful, robust cognitive therapy and living assistance in the comfort of a patient's home.

Proteomic Analysis of 3T3-L1 Adipocytes Treated with Insulin and TNF-α.

Insulin resistance is an indication of early stage Type 2 diabetes (T2D). Insulin resistant adipose tissues contain higher levels of insulin than the physiological level, as well as higher amounts of intracellular tumor necrosis factor-α (TNF-α) and other cytokines. However, the mechanism of insulin resistance remains poorly understood.

Actionable digital phenotyping: a framework for the delivery of just-in-time and longitudinal interventions in clinical healthcare.

Designed to improve health, today numerous wearables and smartphone apps are used by millions across the world. Yet the wealth of data generated from the many sensors on these wearables and smartwatches has not yet transformed routine clinical care. One central reason for this gap between data and clinical insights is the lack of transparency and standards around data generated from mobile device that hinders interoperability and reproducibility.

Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape.

Objective: The aim of this review was to explore the current evidence for conversational agents or chatbots in the field of psychiatry and their role in screening, diagnosis, and treatment of mental illnesses.

Methods: A systematic literature search in June 2018 was conducted in PubMed, EmBase, PsycINFO, Cochrane, Web of Science, and IEEE Xplore. Studies were included that involved a chatbot in a mental health setting focusing on populations with or at high risk of developing depression, anxiety, schizophrenia, bipolar, and substance abuse disorders.

Understanding the quality, effectiveness and attributes of top-rated smartphone health apps.

Objective: This study aimed to understand the attributes of popular apps for mental health and comorbid medical conditions, and how these qualities relate to consumer ratings, app quality and classification by the WHO health app classification framework.

Methods: We selected the 10 apps from the Apple iTunes store and the US Android Google Play store on 20 July 2018 from six disease states: depression, anxiety, schizophrenia, addiction, diabetes and hypertension. Each app was downloaded by two authors who provided information on the apps' attributes, functionality, interventions, popularity, scientific backing and WHO app classification rating.