Remote Digital Psychiatry for Mobile Mental Health Assessment and Therapy: MindLogger Platform Development Study.

BACKGROUND: Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health. OBJECTIVE: This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. METHODS: We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform's configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study. RESULTS: Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users' data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser-based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. CONCLUSIONS: We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health-related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population.

Measuring strengths and weaknesses in dimensional psychiatry.

BACKGROUND: The Extended Strengths and Weaknesses Assessment of Normal Behavior (E-SWAN) reconceptualizes each diagnostic criterion for selected DSM-5 disorders as a behavior, which can range from high (strengths) to low (weaknesses). Initial development focused on Panic Disorder, Social Anxiety, Major Depression, and Disruptive Mood Dysregulation Disorder. METHODS: Data were collected from 523 participants (ages 6-17). Parents completed each of the four E-SWAN scales and traditional unidirectional scales addressing the same disorders. Distributional properties, Item Response Theory Analysis (IRT), and Receiver Operating Characteristic (ROC) curves were used to assess and compare the performance of E-SWAN and traditional scales. RESULTS: In contrast to the traditional scales, which exhibited truncated distributions, all four E-SWAN scales had symmetric distributions. IRT analyses indicate the E-SWAN subscales provided reliable information about respondents throughout the population distribution; traditional scales only provided reliable information about respondents at the high end of the distribution. Predictive value for DSM-5 diagnoses was comparable to prior scales. CONCLUSIONS: E-SWAN bidirectional scales can capture the full spectrum of the population distribution of behavior underlying DSM disorders. The additional information provided can better inform examination of inter-individual variation in population studies, as well as facilitate the identification of factors related to resiliency in clinical samples.

Parent-Perceived Benefits and Harms Associated With Internet Use by Adolescent Offspring.

Importance: Limited systematic information on familial factors and perception of the benefits and harms of internet use by youths is available. Much of the current research has been hampered by small nondiverse samples and limited information on key familial and offspring characteristics. Objective: To characterize parental perceptions and concerns about internet use associated with adolescent development, well-being, safety, family connectedness, and potential for problematic internet use. Design, Setting, and Participants: A 20-minute, English-language survey was developed with expert stakeholders using previously validated questionnaires and was conducted online between June 17 and July 5, 2022. Participants included 1005 parents of children and adolescents aged 9 to 15 years drawn from an online digital survey platform and calibrated for representation with post hoc weightings. Main Outcomes and Measures: Parent survey responses about family characteristics and internet use were used to compute Internet Addiction Test scores for parents and their offspring, Alabama Parenting Questionnaire scores, and an aggregate family connectedness score. Results: The survey cohort of 1005 parents included 568 women (56.5%) and 437 men (43.5%) with a mean age (SD) of 39.5 (6.4) years. In terms of race and ethnicity, the most common categories included Black or African American (95 [9.5%]), Latinx or Hispanic (100 [10.0%]), White (602 [59.9%]), and 2 or more races or ethnicities (122 [12.1%]). Respondents endorsed parental concerns that included exposure to harmful content (646 [64.3%]) and online bullying (533 [53.0%]). Two hundred twenty-five parents (22.4%) had specific concerns about internet addiction in their adolescent offspring, and twice as many parents reported specific concerns about internet addiction than substance addiction. However, parents also indicated that internet use improved family connectedness among immediate families (468 [46.6%]) and extended families (568 [56.5%]). Internet Addiction Test scores in adolescent offspring were correlated with parent scores (ß = 0.62 [SE = 0.02]; P < .001) and Alabama Parenting Questionnaire-Inconsistent Discipline scores (ß = 0.23 [SE = 0.11]; P = .04). Conclusions and Relevance: In this survey study of parent perceptions of internet use among adolescent offspring, parents believed the internet brought families closer yet also expressed concerns. Problematic internet use among youths was correlated with negative parenting styles and parent internet use. This research adds to the literature by suggesting that families, their communities, and industry may have common ground to collaborate on reducing the negative effects of internet use.

A resource for assessing information processing in the developing brain using EEG and eye tracking.

We present a dataset combining electrophysiology and eye tracking intended as a resource for the investigation of information processing in the developing brain. The dataset includes high-density task-based and task-free EEG, eye tracking, and cognitive and behavioral data collected from 126 individuals (ages: 6-44). The task battery spans both the simple/complex and passive/active dimensions to cover a range of approaches prevalent in modern cognitive neuroscience. The active task paradigms facilitate principled deconstruction of core components of task performance in the developing brain, whereas the passive paradigms permit the examination of intrinsic functional network activity during varying amounts of external stimulation. Alongside these neurophysiological data, we include an abbreviated cognitive test battery and questionnaire-based measures of psychiatric functioning. We hope that this dataset will lead to the development of novel assays of neural processes fundamental to information processing, which can be used to index healthy brain development as well as detect pathologic processes.

An open resource for transdiagnostic research in pediatric mental health and learning disorders.

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).