Large language models for the mental health community: framework for translating code to care.

Large language models (LLMs) offer promising applications in mental health care to address gaps in treatment and research. By leveraging clinical notes and transcripts as data, LLMs could improve diagnostics, monitoring, prevention, and treatment of mental health conditions. However, several challenges persist, including technical costs, literacy gaps, risk of biases, and inequalities in data representation.

Translation Effectiveness of Offset Heart Rate Biofeedback as a Mindless Intervention for Alcohol Craving Among Risky Drinkers: Controlled Experiment.

Background: Digital and wearable intervention systems promise to improve how people manage their behavioral health conditions by making interventions available when the user can best benefit from them. However, existing interventions are obtrusive because they require attention and motivation to engage in, limiting the effectiveness of such systems in demanding contexts, such as when the user experiences alcohol craving. Mindless interventions, developed by the human-computer interaction community, offer an opportunity to intervene unobtrusively.

A translationally informed approach to vital signs for psychiatry: a preliminary proof of concept.

The nature of data obtainable from the commercial smartphone - bolstered by a translational model emphasizing the impact of social and physical zeitgebers on circadian rhythms and mood - offers the possibility of scalable and objective vital signs for major depression. Our objective was to explore associations between passively sensed behavioral smartphone data and repeatedly measured depressive symptoms to suggest which features could eventually lead towards vital signs for depression. We collected continuous behavioral data and bi-weekly depressive symptoms (PHQ-8) from 131 psychiatric outpatients with a lifetime DSM-5 diagnosis of depression and/or anxiety over a 16-week period.

Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.

Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent work suggests that detection tools do not generalize well when trained and tested in more heterogeneous samples. In this work, we contribute a narrative review and findings from two studies with 41 mental health clinicians to understand these generalization challenges.

Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling Study.

Background: Stressors for health care workers (HCWs) during the COVID-19 pandemic have been manifold, with high levels of depression and anxiety alongside gaps in care. Identifying the factors most tied to HCWs' psychological challenges is crucial to addressing HCWs' mental health needs effectively, now and for future large-scale events.

Objective: In this study, we used natural language processing methods to examine deidentified psychotherapy transcripts from telemedicine treatment during the initial wave of COVID-19 in the United States.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups.

Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study.

Background: Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used.

Objective: We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making.

Corrigendum: Personalized digital intervention for depression based on social rhythm principles adds significantly to outpatient treatment.

[This corrects the article DOI: 10.3389/fdgth.2022.

Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians.

Recent research has explored computational tools to manage workplace stress via personal sensing, a measurement paradigm in which behavioral data streams are collected from technologies including smartphones, wearables, and personal computers. As these tools develop, they invite inquiry into how they can be appropriately implemented towards improving workers' well-being. In this study, we explored this proposition through formative interviews followed by a design provocation centered around measuring burnout in a U.

Personalized digital intervention for depression based on social rhythm principles adds significantly to outpatient treatment.

We conducted a 16-week randomized controlled trial in psychiatric outpatients with a lifetime diagnosis of a mood and/or anxiety disorder to measure the impact of a first-of-its-kind precision digital intervention software solution based on social rhythm regulation principles. The full intent-to-treat (ITT) sample consisted of 133 individuals, aged 18-65. An exploratory sub-sample of interest was those individuals who presented with moderately severe to severe depression at study entry (baseline PHQ-8 score ≥15;  = 28).

Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies.

Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients' lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short time periods, using a single data collection platform or mobile application. The generalizability of model performance across studies has not been assessed.

Equity-Driven Sensing System for Measuring Skin Tone-Calibrated Peripheral Blood Oxygen Saturation (OptoBeat): Development, Design, and Evaluation Study.

Background: Many commodity pulse oximeters are insufficiently calibrated for patients with darker skin. We demonstrate a quantitative measurement of this disparity in peripheral blood oxygen saturation (SpO) with a controlled experiment. To mitigate this, we present OptoBeat, an ultra-low-cost smartphone-based optical sensing system that captures SpO and heart rate while calibrating for differences in skin tone.

Identifying Mobile Sensing Indicators of Stress-Resilience.

Resident physicians (residents) experiencing prolonged workplace stress are at risk of developing mental health symptoms. Creating novel, unobtrusive measures of resilience would provide an accessible approach to evaluate symptom susceptibility without the perceived stigma of formal mental health assessments. In this work, we created a system to find indicators of resilience using passive wearable sensors and smartphone-delivered ecological momentary assessment (EMA).

A call for open data to develop mental health digital biomarkers.

Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions ('model equity') across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms.

mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia.

Theoretical views and a growing body of empirical evidence suggest that psychiatric relapses in schizophrenia-spectrum disorders (SSDs) have measurable warning signs. However, because they are time- and resource-intensive, existing assessment approaches are not well-suited to detect these warning signs in a timely, scalable fashion. Mobile technologies deploying frequent measurements-i.

Digital biomarker of mental fatigue.

Mental fatigue is an important aspect of alertness and wellbeing. Existing fatigue tests are subjective and/or time-consuming. Here, we show that smartphone-based gaze is significantly impaired with mental fatigue, and tracks the onset and progression of fatigue.

Banbury Forum Consensus Statement on the Path Forward for Digital Mental Health Treatment.

A major obstacle to mental health treatment for many Americans is accessibility: the United States faces a shortage of mental health providers, resulting in federally designated shortage areas. Although digital mental health treatments (DMHTs) are effective interventions for common mental disorders, they have not been widely adopted by the U.S.

Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study.

Background: Inhibitory control, or inhibition, is one of the core executive functions of humans. It contributes to our attention, performance, and physical and mental well-being. Our inhibitory control is modulated by various factors and therefore fluctuates over time.

Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability.

Increased stability in one's daily routine is associated with well-being in the general population and often a goal of behavioral interventions for people with serious mental illnesses like schizophrenia. Assessing behavioral stability has been limited in clinical research by the use of retrospective scales, which are susceptible to reporting biases and memory inaccuracies. Mobile passive sensors, which are less susceptible to these sources of error, have emerged as tools to assess behavioral patterns in a range of populations.

Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia.

Schizophrenia is a severe and complex psychiatric disorder with heterogeneous and dynamic multi-dimensional symptoms. Behavioral rhythms, such as sleep rhythm, are usually disrupted in people with schizophrenia. As such, behavioral rhythm sensing with smartphones and machine learning can help better understand and predict their symptoms.

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

Background: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early warning signs of impending symptomatic relapses would allow clinicians to intervene before the patient's condition worsens.

Objective: In this study, we aim to create the first models, exclusively using passive sensing data from a smartphone, to predict behavioral anomalies that could indicate early warning signs of a psychotic relapse.

Capturing behavioral indicators of persecutory ideation using mobile technology.

Most existing measures of persecutory ideation (PI) rely on infrequent in-person visits, and this limits their ability to assess rapid changes or real-world functioning. Mobile health (mHealth) technology may address these limitations. Little is known about passively sensed behavioral indicators associated with PI.

Relationships between smartphone social behavior and relapse in schizophrenia: A preliminary report.

Social dysfunction is a hallmark of schizophrenia. Social isolation may increase individuals' risk for psychotic symptom exacerbation and relapse. Monitoring and timely detection of shifts in social functioning are hampered by the limitations of traditional clinic-based assessment strategies.