Inclusion of Individuals With Lived Experiences in the Development of a Digital Intervention for Co-Occurring Depression and Cannabis Use: Mixed Methods Investigation.

BACKGROUND: Existing interventions for co-occurring depression and cannabis use often do not treat both disorders simultaneously and can result in higher rates of symptom relapse. Traditional in-person interventions are often difficult to obtain due to financial and time limitations, which may further prevent individuals with co-occurring depression and cannabis use from receiving adequate treatment. Digital interventions can increase the scalability and accessibility for these individuals, but few digital interventions exist to treat both disorders simultaneously. Targeting transdiagnostic processes of these disorders with a digital intervention-specifically positive valence system dysfunction-may yield improved access and outcomes. OBJECTIVE: Recent research has highlighted a need for the inclusion of individuals with lived experiences to assist in the co-design of interventions to enhance scalability and relevance of an intervention. Thus, the purpose of this study is to describe the process of eliciting feedback from individuals with elevated depressed symptoms and cannabis use and co-designing a digital intervention, Amplification of Positivity-Cannabis Use Disorder (AMP-C), focused on improving positive valence system dysfunction in these disorders. METHODS: Ten individuals who endorsed moderate to severe depressive symptoms and regular cannabis use (2-3×/week) were recruited online via Meta ads. Using a mixed methods approach, participants completed a 1-hour mixed methods interview over Zoom (Zoom Technologies Inc) where they gave their feedback and suggestions for the development of a mental health app, based on an existing treatment targeting positive valence system dysfunction, for depressive symptoms and cannabis use. The qualitative approach allowed for a broader investigation of participants' wants and needs regarding the engagement and scalability of AMP-C, and the quantitative approach allowed for specific ratings of intervention components to be potentially included. RESULTS: Participants perceived the 13 different components of AMP-C as overall helpful (mean 3.9-4.4, SD 0.5-1.1) and interesting (mean 4.0-4.9, SD 0.3-1.1) on a scale from 1 (not at all) to 5 (extremely). They gave qualitative feedback for increasing engagement in the app, including adding a social component, using notifications, and being able to track their symptoms and progress over time. CONCLUSIONS: This study highlights the importance of including individuals with lived experiences in the development of interventions, including digital interventions. This inclusion resulted in valuable feedback and suggestions for improving the proposed digital intervention targeting the positive valence system, AMP-C, to better match the wants and needs of individuals with depressive symptoms and cannabis use.

Use of passively collected actigraphy data to detect individual depressive symptoms in a clinical subpopulation and a general population.

The presentation of major depressive disorder (MDD) can vary widely due to its heterogeneity, including inter- and intraindividual symptom variability, making MDD difficult to diagnose with standard measures in clinical settings. Prior work has demonstrated that passively collected actigraphy can be used to detect MDD at a disorder level; however, given the heterogeneous nature of MDD, comprising multiple distinct symptoms, it is important to measure the degree to which various MDD symptoms may be captured by such passive data. The current study investigated whether individual depressive symptoms could be detected from passively collected actigraphy data in a (a) clinical subpopulation (i.e., moderate depressive symptoms or greater) and (b) general population. Using data from the National Health and Nutrition Examination Survey, a large nationally representative sample (N = 8,378), we employed a convolutional neural network to determine which depressive symptoms in each population could be detected by wrist-worn, minute-level actigraphy data. Findings indicated a small-moderate correspondence between the predictions and observed outcomes for mood, psychomotor, and suicide items (area under the receiver operating characteristic curve [AUCs] = 0.58-0.61); a moderate-large correspondence for anhedonia (AUC = 0.64); and a large correspondence for fatigue (AUC = 0.74) in the clinical subpopulation (n = 766); and a small-moderate correspondence for sleep, appetite, psychomotor, and suicide items (AUCs = 0.56-0.60) in the general population (n = 8,378). Thus, individual depressive symptoms can be detected in individuals who likely meet the criteria for MDD, suggesting that wrist-worn actigraphy may be suitable for passively assessing these symptoms, providing important clinical implications for the diagnosis and treatment of MDD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evaluating a mobile app's effects on depression and anxiety in medication-treated opioid use disorder.

Depression and anxiety frequently co-occur with opioid use disorder (OUD) yet are often overlooked in standard OUD treatments. This study evaluated the feasibility, acceptability, and preliminary effectiveness of a mobile application designed to address these symptoms in individuals receiving medications for OUD (MOUD). A randomized controlled trial recruited N = 63 adults with OUD who received MOUD and screened positive for moderate depression or generalized anxiety. Participants were randomized to an app-based digital intervention or treatment-as-usual for 4 weeks, and completed follow-ups at 4 and 8 weeks. Primary outcomes were self-reported severity measures for depression and generalized anxiety, and urine drug screens (UDS). Secondary outcomes included self-reported OUD severity, craving intensity, and digital biomarkers derived from passive smartphone sensors. The application was well-received (median app rating = 4/5 stars). The intervention group showed significant reductions in depressive and generalized anxiety symptoms post-intervention and at 8 weeks follow-up (d > 0.70), with large (d = 0.78) and moderate (d = 0.38) effect sizes, respectively, compared to controls. Both groups exhibited substantial decreases in self-reported severity of opioid use symptoms (d > 2.50). UDS suggested similar between-group adherence to MOUD, with a marginal decrease in opioid (MOP) use in the intervention group and increase in controls, yielding medium between group effect sizes (d = 0.44). Passive sensor data suggested significant increases in social connectedness in the intervention group, evidenced by a significant rise in incoming and outgoing calls and text connections. Initial evidence supports the feasibility and acceptability of a digital intervention for treating anxiety and depressive symptoms in persons receiving MOUD. While underpowered to confidently determine statistical significance beyond directionality, the intervention showed promise in reducing depressive and anxiety symptoms, suggesting its potential as a cost-effective and scalable adjunctive therapy alongside standard OUD treatment. Due to the preliminary nature of this pilot study, further research with sample sizes permitting greater statistical power is needed to confirm findings and explore long-term effects.

A large-scale observational comparison of antidepressants and their effects.

BACKGROUND: Selective Serotonin Reuptake Inhibitors (SSRIs) represent a diverse class of medications widely prescribed for depression and anxiety. Despite their common use, there is an absence of large-scale, real-world evidence capturing the heterogeneity in their effects on individuals. This study addresses this gap by utilizing naturalistic search data to explore the varied impact of six different SSRIs on user behavior. METHODS: The study sample included ∼508 thousand Bing users with searches for one of six SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline) from April-December 2022, comprising 510 million queries. Cox proportional hazard models were employed to examine 30 topics (e.g., shopping, tourism, health) and 195 health symptoms (e.g., anxiety, weight gain, impotence), using each SSRI as a reference. We assessed the relative hazard ratios between drugs and, where feasible, ranked the SSRIs based on their observed effects. We used Cox proportional hazard models in order to account for both the likelihood of users searching for a particular topic or symptom and the associated time to that search. The temporal aspect aided in distinguishing between potential symptoms of the disorder, short-term medication side effects, and later appearing side effects. RESULTS: Differences were found in search behaviors associated with each SSRI. E.g., fluvoxamine was associated with a significantly higher likelihood of searching weight gain compared to all other SSRIs (HRs 1.85-2.93). Searches following citalopram were associated with significantly higher rates of later impotence queries compared to all other SSRIs (HRs 5.11-7.76), except fluvoxamine. Fluvoxamine was associated with a significantly higher rate of health related searches than all other SSRIs (HRs 2.11-2.36). CONCLUSIONS: Our study reveals new insights into the varying SSRI impacts, suggesting distinct symptom profiles. This novel use of large-scale, naturalistic search data contributes to pharmacovigilance efforts, enhancing our understanding of intra-class variation among SSRIs, potentially uncovering previously unidentified drug effects.

MoodCapture: Depression Detection Using In-the-Wild Smartphone Images.

MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: "I have felt down, depressed, or hopeless". Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.

A template and tutorial for preregistering studies using passive smartphone measures.

Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to determine the (social) context of a person, and accelerometers to measure movement. However, utilizing passive smartphone measures presents methodological challenges during data collection and analysis. Researchers must make multiple decisions when working with such measures, which can result in different conclusions. Unfortunately, the transparency of these decision-making processes is often lacking. The implementation of open science practices is only beginning to emerge in digital phenotyping studies and varies widely across studies. Well-intentioned researchers may fail to report on some decisions due to the variety of choices that must be made. To address this issue and enhance reproducibility in digital phenotyping studies, we propose the adoption of preregistration as a way forward. Although there have been some attempts to preregister digital phenotyping studies, a template for registering such studies is currently missing. This could be problematic due to the high level of complexity that requires a well-structured template. Therefore, our objective was to develop a preregistration template that is easy to use and understandable for researchers. Additionally, we explain this template and provide resources to assist researchers in making informed decisions regarding data collection, cleaning, and analysis. Overall, we aim to make researchers' choices explicit, enhance transparency, and elevate the standards for studies utilizing passive smartphone measures.

Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning.

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of social interaction and engagement within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N = 192 individuals, n = 48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained, validated, and tested to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and highlight social phenomena. The model achieved a test AUC = 0.73 ([0.61, 0.85], 95% CI), suggesting that network-based socio-behavioral patterns of online interaction can signal for heightened suicide risk. Transitivity, density, and in-degree centrality were among the most important features driving this performance. Moreover, predicted HRUs tended to be targets of social exchanges with lesser frequency and possessed egocentric networks with "small world" network properties. Through the implementation of an underutilized method on an unlikely data source, findings support future incorporation of network-based social interaction features in descriptive, predictive, and preventative STB research.

From mood to use: Using ecological momentary assessments to examine how anhedonia and depressed mood impact cannabis use in a depressed sample.

Anhedonia and depressed mood are two cardinal symptoms of major depressive disorder (MDD). Prior work has demonstrated that cannabis consumers often endorse anhedonia and depressed mood, which may contribute to greater cannabis use (CU) over time. However, it is unclear (1) how the unique influence of anhedonia and depressed mood affect CU and (2) how these symptoms predict CU over more proximal periods of time, including the next day or week (rather than proceeding weeks or months). The current study used data collected from ecological momentary assessment (EMA) in a sample with MDD (N = 55) and employed mixed effects models to detect and predict weekly and daily CU from anhedonia and depressed mood over 90 days. Results indicated that anhedonia and depressed mood were significantly associated with CU, yet varied at daily and weekly scales. Moreover, these associations varied in both strength and directionality. In weekly models, less anhedonia and greater depressed mood were associated with greater CU, and directionality of associations were reversed in the models looking at any CU (compared to none). Findings provide evidence that anhedonia and depressed mood demonstrate complex associations with CU and emphasize leveraging EMA-based studies to understand these associations with more fine-grained detail.

The role of borderline personality disorder traits in predicting longitudinal variability of major depressive symptoms among a sample of depressed adults.

BACKGROUND: Major depressive disorder (MDD) and borderline personality disorder (BPD) often co-occur, with 20 % of adults with MDD meeting criteria for BPD. While MDD is typically diagnosed by symptoms persisting for several weeks, research suggests a dynamic pattern of symptom changes occurring over shorter durations. Given the diagnostic focus on affective states in MDD and BPD, with BPD characterized by instability, we expected heightened instability of MDD symptoms among depressed adults with BPD traits. The current study examined whether BPD symptoms predicted instability in depression symptoms, measured by ecological momentary assessments (EMAs). METHODS: The sample included 207 adults with MDD (76 % White, 82 % women) recruited from across the United States. At the start of the study, participants completed a battery of mental health screens including BPD severity and neuroticism. Participants completed EMAs tracking their depression symptoms three times a day over a 90-day period. RESULTS: Using self-report scores assessing borderline personality disorder (BPD) traits along with neuroticism scores and sociodemographic data, Bayesian and frequentist linear regression models consistently indicated that BPD severity was not associated with depression symptom change through time. LIMITATIONS: Diagnostic sensitivity and specificity may be restricted by use of a self-report screening tool for capturing BPD severity. Additionally, this clinical sample of depressed adults lacks a comparison group to determine whether subclinical depressive symptoms present differently among individuals with BPD only. CONCLUSIONS: The unexpected findings shed light on the interplay between these disorders, emphasizing the need for further research to understand their association.

Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App.

MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.

Predicting Individual Response to a Web-Based Positive Psychology Intervention: A Machine Learning Approach.

Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self-guided web-based interventions, but not all persons benefit from web-based interventions. Therefore, it is important to identify whether someone is likely to benefit from web-based PPIs, in order to triage persons who may not benefit from other interventions. In the current study, we used machine learning to predict individual response to a web-based PPI, in order to investigate baseline prognostic indicators of likelihood of response (N = 120). Our models demonstrated moderate correlations (happiness: r Test = 0.30 ± 0.09; depressive symptoms: r Test = 0.39 ± 0.06), indicating that baseline features can predict changes in happiness and depressive symptoms at a 6-month follow-up. Thus, machine learning can be used to predict outcome changes from a web-based PPI and has important clinical implications for matching individuals to PPIs based on their individual characteristics.

A mouse model of TB-associated lung fibrosis reveals persistent inflammatory macrophage populations during treatment.

Post-TB lung disease (PTLD) causes a significant burden of global disease. Fibrosis is a central component of many clinical features of PTLD. To date, we have a limited understanding of the mechanisms of TB-associated fibrosis and how these mechanisms are similar to or dissimilar from other fibrotic lung pathologies. We have adapted a mouse model of TB infection to facilitate the mechanistic study of TB-associated lung fibrosis. We find that the morphologies of fibrosis that develop in the mouse model are similar to the morphologies of fibrosis observed in human tissue samples. Using Second Harmonic Generation (SHG) microscopy, we are able to quantify a major component of fibrosis, fibrillar collagen, over time and with treatment. Inflammatory macrophage subpopulations persist during treatment; matrix remodeling enzymes and inflammatory gene signatures remain elevated. Our mouse model suggests that there is a therapeutic window during which adjunctive therapies could change matrix remodeling or inflammatory drivers of tissue pathology to improve functional outcomes after treatment for TB infection.

The Trajectories of Online Mental Health Information Seeking: Modeling Search Behavior Before and After Completion of Self-report Screens.

There is an appreciable mental health treatment gap in the United States. Efforts to bridge this gap and improve resource accessibility have led to the provision of online, clinically-validated tools for mental health self-assessment. In theory, these screens serve as an invaluable component of information-seeking, representing the preparative and action-oriented stages of this process while altering or reinforcing the search content and language of individuals as they engage with information online. Accordingly, this work investigated the association of screen completion with mental health-related search behaviors. Three-year internet search histories from N=7,572 Microsoft Bing users were paired with their respective depression, anxiety, bipolar disorder, or psychosis online screen completion and sociodemographic data available through Mental Health America. Data was transformed into network representations to model queries as discrete steps with probabilities and times-to-transition from one search type to another. Search data subsequent to screen completion was also modeled using Markov chains to simulate likelihood trajectories of different search types through time. Differences in querying dynamics relative to screen completion were observed, with searches involving treatment, diagnosis, suicidal ideation, and suicidal intent commonly emerging as the highest probability behavioral information seeking endpoints. Moreover, results pointed to the association of low risk states of psychopathology with transitions to extreme clinical outcomes (i.e., active suicidal intent). Future research is required to draw definitive conclusions regarding causal relationships between screens and search behavior.

Comparing Transdiagnostic Risk Factors: Predicting Emergence of Significant Depressive, Anxiety, and Substance Abuse Symptoms Among Juvenile Delinquents.

In a 7-year 11-wave study of low-SES adolescents (N = 856, age = 15.98), we compared multiple well-established transdiagnostic risk factors as predictors of first incidence of significant depressive, anxiety, and substance abuse symptoms across the transition from adolescence to adulthood. Risk factors included negative emotionality, emotion regulation ability, social support, gender, history of trauma, parental histories of substance abuse, parental mental health, and socioeconomic status. Machine learning models revealed that negative emotionality was the most important predictor of both depression and anxiety, and emotion regulation ability was the most important predictor of future significant substance abuse. These findings highlight the critical role that dysregulated emotion may play in the development of some of the most prevalent forms of mental illness.

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample.

Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.

Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Creation and Validation of a Massive Hemoptysis Simulator.

BACKGROUND: Simulation for the management of massive hemoptysis is limited by the absence of a commercially available simulator to practice procedural skills necessary for management. RESEARCH QUESTION: Is it feasible to create and validate a hemoptysis simulator with high functional task alignment? STUDY DESIGN AND METHODS: Pulmonary and critical care medicine (PCCM) attending physicians from four academic institutions in the Denver, Colorado, area and internal medicine residents from the University of Colorado participated in this mixed-methods study. A hemoptysis simulator was constructed by connecting a 3-D-printed airway model to a manikin that may be intubated. Attending PCCM physicians evaluated the simulator through surveys and qualitative interviews. Attendings were surveyed to determine simulation content and appropriate assessment criteria for a hemoptysis simulation. Based on these criteria, expert and novice performance on the simulator was assessed. RESULTS: The manikin-based hemoptysis simulator demonstrated adequate physical resemblance, high functional alignment, and strong affective fidelity. It was universally preferred over a virtual reality simulator by 10 PCCM attendings. Twenty-seven attendings provided input on assessment criteria and established that assessing management priorities (eg, airway protection) was preferred to a skills checklist for hemoptysis management. Three experts outperformed six novices in hemoptysis management on the manikin-based simulator in all management categories assessed, supporting construct validity of the simulation. INTERPRETATION: Creation of a hemoptysis simulator with appropriate content, high functional task alignment, and strong affective fidelity was successful using 3-D-printed airway models and existing manikins. This approach can overcome barriers of cost and availability for simulation of high-acuity, low-occurrence procedures.

Reliability (or lack thereof) of smartphone ecological momentary assessment of visual dot probe attention bias toward threat indices.

BACKGROUND AND OBJECTIVES: Cognitive bias theories posit that generalized anxiety disorder (GAD) and social anxiety disorder (SAD) are entwined with attention bias toward threats, commonly indexed by faster response time (RT) on threat-congruent (vs. threat-incongruent) trials on the visual dot probe. Moreover, although smartphone ecological momentary assessment (EMA) of the visual dot probe has been developed, their psychometric properties are understudied. This study thus aimed to assess the reliability of 8 smartphone-delivered visual dot probe attention bias and related indices in persons with and without GAD and SAD. METHODS: Community-dwelling adults (n = 819; GAD: 64%; SAD: 49%; Mixed GAD and SAD: 37%; Non-GAD/SAD Controls: 24%) completed a five-trial smartphone-delivered visual dot probe for a median of 60 trials (12 sessions x 5 trials/session) and an average of 100 trials (20 sessions x 5 trials/session). RESULTS: As hypothesized, Global Attention Bias Index, Disengagement Effect, and Facilitation Bias had low-reliability estimates. However, retest-reliability and internal reliability were good for Trial-Level Bias Scores (TLBS) (Bias Toward Treat: intra-class correlation coefficients (ICCs) = 0.626-0.644; split-half r = 0.640-0.670; Attention Bias Variability: ICCs = 0.507-0.567; split-half r = 0.520-0.580) and (In)congruent RTs. Poor retest-reliability and internal reliability estimates were consistently observed for all traditional attention bias and related indices but not TLBS. LIMITATIONS: Our visual dot probe EMA should have administered ≥320 trials to match best-practice guidelines based on similar laboratory studies. CONCLUSIONS: Future research should strive to examine attention bias paradigms beyond the dot-probe task that evidenced meaningful test-retest reliability properties in laboratory and real-world naturalistic settings.