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Background:Mental health apps offer scalable care, yet clinical adoption is hindered by low user engagement and integration challenges into clinic workflows. Human support staff called digital navigators, trained in mental health technology, could enhance care access and patient adherence and remove workflow burdens from clinicians. While the potential of this role is clear, training staff to become digital navigators and assessing their impact are primary challenges. Methods:We present a detailed manual/framework for implementation of the Digital Navigator within a short-term, cognitive-behavioral therapy-focused hybrid clinic. We analyze patient engagement, satisfaction, and digital phenotyping data quality outcomes. Data from 83 patients, for the period spanning September 2022 to September 2023, included Digital Navigator satisfaction, correlated with demographics, mindLAMP app satisfaction, engagement, and passive data quality. Additionally, average passive data across 33 clinic patients from November 2023 to January 2024 were assessed for missingness. Results:Digital Navigator satisfaction averaged 18.8/20. Satisfaction was not influenced by sex, race, gender, or education. Average passive data quality across 33 clinic patients was 0.82 at the time this article was written. Digital Navigator satisfaction scores had significant positive correlation with both clinic app engagement and perception of that app. Conclusions:Results demonstrate preliminary support and patient endorsement for the Digital Navigator role and positive outcomes around digital engagement and digital phenotyping data quality. Through sharing training resources and standardizing the role, we aim to enable clinicians and researchers to adapt and utilize the Digital Navigator for their own needs. [ABSTRACT FROM AUTHOR]

Digital mental health interventions have shown promise for alleviating various forms of psychopathology, including depression and anxiety. However, the mechanisms of such interventions remain largely unexplored. The purpose of this study was to investigate a potential mechanistic process through which one hybrid digital mental health intervention (i.e., the Digital Clinic) might operate. We hypothesized that emotion regulation (ER) self-efficacy at the treatment midpoint may mediate the relationship between alliance (i.e., therapeutic alliance and digital alliance) and outcome (i.e., co-morbid symptoms of depression and anxiety) at the treatment endpoint.Data used in this study came from the Digital Clinic, a brief transdiagnostic telehealth treatment program augmented by a dual-purpose digital phenotyping and intervention smartphone app. Recruited primarily from primary care, participants were 82 adults (73% White, 64% cisgender women, mean age 41) receiving outpatient treatment in the northeastern United States. All constructs were measured with validated scales, including The Working Alliance Inventory—Short Revised (WAI-SR) for therapeutic alliance, the Digital Working Alliance Inventory (DWAI) for digital alliance, the PROMIS Self-Efficacy for Managing Emotions Short Form scale for ER self-efficacy, and the Patient Health Questionnaire Anxiety-Depression Scale (PHQ-ADS) for co-morbid symptoms of depression and anxiety.Significant reductions in co-morbid symptoms of depression and anxiety and significant increases in ER self-efficacy were found from baseline to treatment endpoint. Therapeutic and digital alliance at the midpoint each predicted reductions in co-morbid symptoms of depression and anxiety at the endpoint through ER self-efficacy, controlling for baseline scores.Findings suggest that ER self-efficacy may be a proximal predictor of clinical improvement that may be enhanced by therapeutic and digital alliance. Future controlled research is essential to improve knowledge of the mechanisms of digital mental health interventions and to enhance their effectiveness. [ABSTRACT FROM AUTHOR]

Suicide is the second leading cause of death for young adults in the United States. One of the many risk factors for suicide includes exposure to early life trauma. The present study examined whether rumination and impulsivity play a role in the relationship between early life trauma and increased risk for suicidal behavior (i.e., suicide ideation and suicide attempts) among 426 young adults. Early life trauma was associated with brooding, reflective rumination, and impulsivity in the form of negative urgency. Current or recent suicide ideators self-reported greater early life trauma, ruminative thinking, and impulsivity than non-ideators and non-attempters. Further, a multinomial logistic regression found that early life trauma, reflection, lack of premeditation, and lack of perseverance were associated with higher odds of reporting suicide ideation in the previous 6 months. We also found indirect relationships between early life trauma and suicide ideation through brooding or reflection and lack of perseverance in serial mediation analyses. These findings suggest that early life trauma may increase risk of suicide ideation to the degree that it leads to ruminative thinking and lack of follow-through with difficult tasks. How early life trauma might increase risk for ruminative thinking, impulsivity, and subsequent suicidal behavior is discussed. [ABSTRACT FROM AUTHOR]

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. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology., (©James Burns, Kelly Chen, Matthew Flathers, Danielle Currey, Natalia Macrynikola, Aditya Vaidyam, Carsten Langholm, Ian Barnett, Andrew (Jin Soo) Byun, Erlend Lane, John Torous. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.08.2024.)

Background: Mental health apps offer scalable care, yet clinical adoption is hindered by low user engagement and integration challenges into clinic workflows. Human support staff called digital navigators, trained in mental health technology, could enhance care access and patient adherence and remove workflow burdens from clinicians. While the potential of this role is clear, training staff to become digital navigators and assessing their impact are primary challenges. Methods: We present a detailed manual/framework for implementation of the Digital Navigator within a short-term, cognitive-behavioral therapy-focused hybrid clinic. We analyze patient engagement, satisfaction, and digital phenotyping data quality outcomes. Data from 83 patients, for the period spanning September 2022 to September 2023, included Digital Navigator satisfaction, correlated with demographics, mindLAMP app satisfaction, engagement, and passive data quality. Additionally, average passive data across 33 clinic patients from November 2023 to January 2024 were assessed for missingness. Results: Digital Navigator satisfaction averaged 18.8/20. Satisfaction was not influenced by sex, race, gender, or education. Average passive data quality across 33 clinic patients was 0.82 at the time this article was written. Digital Navigator satisfaction scores had significant positive correlation with both clinic app engagement and perception of that app. Conclusions: Results demonstrate preliminary support and patient endorsement for the Digital Navigator role and positive outcomes around digital engagement and digital phenotyping data quality. Through sharing training resources and standardizing the role, we aim to enable clinicians and researchers to adapt and utilize the Digital Navigator for their own needs.