Smartphone digital phenotyping, surveys, and cognitive assessments for global mental health: Initial data and clinical correlations from an international first episode psychosis study

The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact

Digital phenotyping collects health data digitally, supporting early disease diagnosis and health management. This paper systematically reviews the diversity of research methods in digital phenotyping and its clinical benefits, while also focusing on its importance within the P4 medicine paradigm and its core role in advancing its application in biobanks. Furthermore, the paper envisions the continued clinical benefits of digital phenotyping, driven by technological innovation, global collaboration, and policy support.

A systematic review of passive data for remote monitoring in psychosis and schizophrenia

There is increasing use of digital tools to monitor people with psychosis and schizophrenia remotely, but using this type of data is challenging. This systematic review aimed to summarise how studies processed and analysed data collected through digital devices. In total, 203 articles collecting passive data through smartphones or wearable devices, from participants with psychosis or schizophrenia were included in the review. Accelerometers were the most common device (n = 115 studies), followed by smartphones (n = 46). The most commonly derived features were sleep duration (n = 50) and time spent sedentary (n = 41). Thirty studies assessed data quality and another 69 applied data quantity thresholds. Mixed effects models were used in 21 studies and time-series and machine-learning methods were used in 18 studies. Reporting of methods to process and analyse data was inconsistent, highlighting a need to improve the standardisation of methods and reporting in this area of research.

Biomarker discovery using machine learning in the psychosis spectrum

The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches.

Integration of sex/gender and utilization of ecological Momentary assessment of cognition in clinical populations: A scoping review

Objectives: We aimed to describe the methods of smartphone-based cognitive ecological momentary assessment designs in clinical populations, with an intention to evaluate how the role of sex and/or gender has been considered in the design and analyses, particularly including female-specific physiology. Methods: This scoping review was conducted based on JBI scoping review methodology. On March 2nd, 2023, we searched for literature across four databases. Screening of the results and data extraction were conducted in duplicate according to the a priori methods in the pre-registered protocol. Results: 31 articles were included in this review. Participants ranged in age from 15-85 years old with various clinical disorders. Prompts were given between 1-7 times per day for 7-84 days. Executive function was the most frequently assessed cognitive domain. Over half the studies (n = 17, 55%) did not investigate the effects of sex and/or gender, and only one study considered the impact of hormonal therapy. Many studies (n = 14, 45%) used sex and gender interchangeably or incorrectly. Conclusions: Studies varied in design, with heterogeneity in the reporting of methodological information. The lack of attention to sex/gender on neuropsychological outcomes can lead to confusion and contradiction regarding its potential impact on cognition in clinical populations. This may hinder the identification of effective interventions for those assigned female at birth who have been overlooked or considered indistinguishable from their male counterparts. Given the well-documented impact of sex/gender on cognition, it is essential that future neuropsychological research, especially EMA-based studies, prioritize investigating sex/gender to ensure better outcomes for all.

Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health

The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.

Artificial intelligence in mental healthcare: an overview and future perspectives

Artificial intelligence is disrupting the field of mental healthcare through applications in computational psychiatry, which leverages quantitative techniques to inform our understanding, detection, and treatment of mental illnesses. This paper provides an overview of artificial intelligence technologies in modern mental healthcare and surveys recent advances made by researchers, focusing on the nascent field of digital psychiatry. We also consider the ethical implications of artificial intelligence playing a greater role in mental healthcare.

Schizophrenia Assessment, Referral and Awareness Training for Health Auxiliaries (SARATHA): Protocol for a Mixed-Methods Pilot Study in Rural India

BACKGROUND

Workforce shortages pose major obstacles to the timely detection and treatment of schizophrenia, particularly in low-income and middle-income countries. The SARATHA (Schizophrenia Assessment, Referral, and Awareness Training for Health Auxiliaries) project involves the systematic development, iterative refinement, and pilot testing of a digital program for training community health workers in the early detection and referral of schizophrenia in primary care settings in rural India.

METHODS

SARATHA is a three-phase study. Phase 1 involves consulting with experts and clinicians, and drawing from existing evidence to inform the development of a curriculum for training community health workers. Phase 2 consists of designing and digitizing the training content for delivery on a smartphone app. Design workshops and focus group discussions will be conducted to seek input from community health workers and service users living with schizophrenia to guide revisions and refinements to the program content. Lastly, Phase 3 entails piloting the training program with a target sample of 20 community health workers to assess feasibility and acceptability. Preliminary effectiveness will be explored, as measured by community health workers' changes in knowledge about schizophrenia and the program content after completing the training.

DISCUSSION

If successful, this digital training program will offer a potentially scalable approach for building capacity of frontline community health workers towards reducing delays in early detection of schizophrenia in primary care settings in rural India. This study can inform efforts to improve treatment outcomes for persons living with schizophrenia in low-resource settings.