Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study.

BACKGROUND: Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population. OBJECTIVE: Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity. METHODS: We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home). RESULTS: Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy. CONCLUSIONS: This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field's move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53857.

Vocally-Encoded Emotional Arousal as a Marker of Callous-Unemotional Traits in a Sample of Justice-Involved Adolescents.

Callous-unemotional (CU) traits are associated with severe and persistent juvenile offending. CU traits are also associated with dampened emotional arousal, which suggests that fundamental frequency (f0), a measure of vocally-encoded emotional arousal, may serve as an accessible psychophysiological marker of CU traits in youth. This study investigated the associations between f0 range measured during an emotionally evocative task, CU traits, and emotion dysregulation in a mixed-gender sample of 168 justice-involved youth. For boys, after controlling for covariates, wider f0 range-indicating greater emotional arousal-was negatively associated with CU traits and positively associated with emotion dysregulation. For girls, no significant associations with f0 range emerged; however, CU traits were positively associated with emotion dysregulation. Findings suggest that f0 range may serve as a valid indicator of CU traits in JJ-involved boys, and that detained boys and girls with high CU traits are characterized by different profiles of emotion dysregulation.

Passive Sensor Data for Characterizing States of Increased Risk for Eating Disorder Behaviors in the Digital Phenotyping Arm of the Binge Eating Genetics Initiative: Protocol for an Observational Study.

BACKGROUND: Data that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge eating disorder (BED) and bulimia nervosa (BN), conditions that lack highly efficacious, enduring, and accessible treatments. These data are easy to collect digitally but are highly complex and present unique methodological challenges that invite innovative solutions. OBJECTIVE: We describe the digital phenotyping component of the Binge Eating Genetics Initiative, which uses personal digital device data to capture dynamic patterns of risk for binge and purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathologies and just-in-time interventions to reduce risk for related behaviors. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application. METHODS: The University of North Carolina Biomedical Institutional Review Board approved all study procedures. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and the Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches over 30 days. Data were cleaned to account for user and device recording errors, including duplicate entries and unreliable heart rate and step values. Across participants, the proportion of data points removed during cleaning ranged from <0.1% to 2.4%, depending on the data source. To prepare the data for multivariate time series analysis, we used a novel data handling approach to address variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feeling ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge and purge episodes. RESULTS: Data collection and cleaning are complete. Between August 2017 and May 2021, 1019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8274 binge or purge events, and 85,200 feeling observations. Analysis will begin in spring 2022. CONCLUSIONS: We provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. The results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data. TRIAL REGISTRATION: ClinicalTrials.gov NCT04162574; https://clinicaltrials.gov/ct2/show/NCT04162574. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38294.

Retention, engagement, and binge-eating outcomes: Evaluating feasibility of the Binge-Eating Genetics Initiative study.

OBJECTIVE: Using preliminary data from the Binge-Eating Genetics Initiative (BEGIN), we evaluated the feasibility of delivering an eating disorder digital app, Recovery Record, through smartphone and wearable technology for individuals with binge-type eating disorders. METHODS: Participants (n = 170; 96% female) between 18 and 45 years old with lived experience of binge-eating disorder or bulimia nervosa and current binge-eating episodes were recruited through the Recovery Record app. They were randomized into a Watch (first-generation Apple Watch + iPhone) or iPhone group; they engaged with the app over 30 days and completed baseline and endpoint surveys. Retention, engagement, and associations between severity of illness and engagement were evaluated. RESULTS: Significantly more participants in the Watch group completed the study (p = .045); this group had greater engagement than the iPhone group (p's < .05; pseudo-R2 McFadden effect size = .01-.34). Overall, binge-eating episodes, reported for the previous 28 days, were significantly reduced from baseline (mean = 12.3) to endpoint (mean = 6.4): most participants in the Watch (60%) and iPhone (66%) groups reported reduced binge-eating episodes from baseline to endpoint. There were no significant group differences across measures of binge eating. In the Watch group, participants with fewer episodes of binge eating at baseline were more engaged (p's < .05; pseudo-R2 McFadden  = .01-.02). Engagement did not significantly predict binge eating at endpoint nor change in binge-eating episodes from baseline to endpoint for both the Watch and iPhone groups. DISCUSSION: Using wearable technology alongside iPhones to deliver an eating disorder app may improve study completion and app engagement compared with using iPhones alone.

Current Posttraumatic Stress Symptoms Mediate the Relationship Between Adverse Childhood Experiences and Executive Functions.

Adverse childhood experiences (ACEs) and posttraumatic stress disorder (PTSD) are both associated with lower performances on executive function tasks. However, few researchers have evaluated ACEs, posttraumatic stress (PTS) symptoms, and executive function difficulties in conjunction. Using an online micropayment service, the current study assessed whether PTS symptoms mediated the relationship between ACEs and executive functions. In total, 83 participants (54.2% female, age: M = 28.86, SD = 7.71) were administered the ACE questionnaire, PTSD Checklist for DSM-5 (PCL-5), and the Executive Function Index (EFI). A higher number of reported ACEs was related to greater PTS symptom severity (ß = .40, p < .001) and worse self-rated executive functions (ß = -.32, p = .002). Controlling for the number of reported ACEs, current PTS symptom severity was related to worse executive functions (ß = -.45, p < .001). A bootstrapped 95% confidence interval (CI) indicated a significant indirect effect, ß = -.18 (95% CI: -.30, -.08), by which current PTS symptoms mediated the relationship between the number of reported ACEs and executive functions. These results suggest that psychological interventions targeting PTS symptoms, in the context of a history of childhood trauma, may concurrently improve executive functions in adult populations.

A Bayesian Structural Equation Modeling Factor Analysis of the UCLA Posttraumatic Stress Disorder Reaction Index for DSM-5 in a Polyvictimized Sample of Adolescents.

The revised criteria for posttraumatic stress disorder (PTSD) in the fifth edition of the Diagnostic and Statistical Manual necessitated the development of new screening tools for youth, one of the most widely used of which is the UCLA Posttraumatic Stress Disorder Reaction Index for DSM-5 (RI-5). Thus far, the few studies that have investigated the RI-5's factor structure have supported a four-factor model. However, to date this research has been limited to youth with histories of exposure to single-event traumatic stressors, a significant limitation as evidence suggests many trauma-exposed youth report exposure to multiple types of traumatic stressors, or polyvictimization. It is imperative to determine the generalizability of previous factor models to specific populations which they are purported to represent. We investigated whether the RI-5's four-factor model replicated in a sample of 455 polyvictimized justice-involved adolescents. Initial confirmatory factor analysis demonstrated that the four-factor model did not converge. Therefore, we utilized Bayesian Structural Equations Modeling (BSEM) to determine why the previously proposed factor structure did not converge. The BSEM model suggested that the global factor structure was acceptable and did not require addition or subtraction of any factor or cross-factor loadings. However, small and moderate residual covariances resulted in model misspecification, suggesting there may be additional associations not captured by the current DSM-5 model for polyvictimized youth. Future work should continue examining the RI-5's factor structure in order to better understand whether the current results are unique and how measurements assessing DSM-5 PTSD symptom criteria perform in diverse trauma-exposed youth populations.

A network analysis of posttraumatic stress disorder and dissociation in trauma-exposed adolescents.

Posttraumatic stress disorder (PTSD) and dissociation have long been recognized to co-occur, leading the DSM-5 to introduce a dissociative subtype of PTSD into its nomenclature. Most research to date on the dissociative subtype has focused on adults. The current study aimed to extend this research to an adolescent sample and to examine symptom-level associations between PTSD and dissociation using network analysis. The analysis was conducted with 448 trauma-exposed detained US adolescents (24.55% female; mean age 15.98 ± 1.25 years). A network consisting of 20 DSM-5 PTSD symptoms was constructed, followed by a network consisting of 20 PTSD symptoms and five dissociative items. Expected influence bridge centrality was estimated to examine items with the most/strongest cross-construct connections (i.e. between PTSD and dissociation). The PTSD symptoms concentration problems, amnesia and recurrent memories and the dissociative items depersonalization, derealisation and can't remember things that happened had the highest bridge centrality values. These symptom-level associations extend our understanding of the PTSD-dissociation relationship by pointing to specific symptoms of PTSD and dissociation that may drive the co-morbidity between the two constructs. These findings may inform future intervention efforts.

Evaluating the evidential value of empirically supported psychological treatments (ESTs): A meta-scientific review.

Empirically supported treatments (or therapies; ESTs) are the gold standard in therapeutic interventions for psychopathology. Based on a set of methodological and statistical criteria, the APA has assigned particular treatment-diagnosis combinations EST status and has further rated their empirical support as Strong, Modest, and/or Controversial. Emerging concerns about the replicability of research findings in clinical psychology highlight the need to critically examine the evidential value of EST research. We therefore conducted a metascientific review of the EST literature, using clinical trials reported in an existing online APA database of ESTs, and a set of novel evidential value metrics (i.e., rates of misreported statistics, statistical power, R-Index, and Bayes Factors). Our analyses indicated that power and replicability estimates were concerningly low across almost all ESTs, and individually, some ESTs scored poorly across multiple metrics, with Strong ESTs failing to continuously outperform their Modest counterparts. Lastly, we found evidence of improvements over time in statistical power within the EST literature, but not for the strength of evidence of EST efficacy. We describe the implications of our findings for practicing psychotherapists and offer recommendations for improving the evidential value of EST research moving forward. (PsycINFO Database Record (c) 2019 APA, all rights reserved).