Risk Factors for Suicidal Behavior among Bhutanese Refugees Resettled in the United States

Suicidal behavior and death by suicide are significant and pressing problems in the Bhutanese refugee community. Currently, Bhutanese refugees are dying by suicide at a rate nearly 2 times higher than the general United States population. Proper identification of risk factors for suicide saves lives and prevents suicides (Mann et al., 2005); however, if suicide risk is underestimated due to culturally inflexible risk assessments, preventable deaths may continue to needlessly grow. In a community sample of Bhutanese refugees resettled in Vermont (N=60), the current study aims to (1) test elements of a comprehensive conceptual model of incremental risk factors for suicide – adapted from the interpersonal psychological theory of suicide (IPTS; Joiner, 2005) – including suicidal desire, suicidal ideation, thwarted belongingness, and perceived burdensomeness and (2) test the relative contributions of suicidal desire and suicidal ideation as risk factors for suicidal behavior. Participants attended a single study visit at which they completed self-report measures administered in an interview format via an interpreter, if needed. Key measures included the Beck Scale for suicidal ideation (BSS; Beck & Steer, 1991), Interpersonal Needs Questionnaire (INQ; Van Orden et al., 2012), Wish to be Dead Scale (WDS; Lester, 2013), Refugee Health Screener – 15 (RHS-15; Hollifield et al., 2013), Postmigration Living Difficulties checklist (PmLD; Laban et al., 2005), Brief Biosocial Gambling Screen (BBGS; Gebauer et al., 2010), basic demographics questions, and qualitative questions about suicide within the Bhutanese refugee community. The analytic approach relied on the use of hurdle models, Fisher’s exact tests, hierarchical logistic regression, and independent samples t-tests to assess the relationships among aspects of our conceptual model. Although endorsement of suicidal ideation (n = 4, 6.7%) and suicidal behavior (n = 2, 3.3%; measured by combining the planning and concealment subscales of the BSS) was low in the sample, a substantial minority (n = 29, 48.3%) endorsed some desire to be dead. Perceived burdensomeness, but not thwarted belongingness, was significantly associated with both suicidal ideation and the desire to be dead. There was no evidence that the desire for death contributed additional risk of suicidal behavior, above and beyond suicidal ideation. Of participants with a history of suicide attempts (n = 4), none reported any suicidal ideation and 3 reported some desire to be dead. Neither desire to be dead nor suicidal ideation was significantly related to suicide attempt history. These findings have implications for suicide detection and prevention among resettled Bhutanese refugees. The cultural responsiveness of suicide screening in this population could be improved by assessing two constructs not typically assessed: desire to be dead (e.g., the WDS) and perceived burdensomeness (e.g., INQ). Explicit evaluation of these two constructs in Bhutanese refugees may increase the sensitivity of risk assessments without sacrificing specificity in comparison to assessments exclusively focused on self-reported suicidal ideation

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains

The Supportive Accountability Inventory: Psychometric properties of a measure of supportive accountability in coached digital interventions

Background: One of the most widely used coaching models is Supportive Accountability (SA) which aims to provide intervention users with clear expectations for intervention use, regular monitoring, and a sense that coaches are trustworthy, benevolent, and have domain expertise. However, few measures exist to study the role of the SA model on coached digital interventions. We developed the Supportive Accountability Inventory (SAI) and evaluated the underlying factor structure and psychometric properties of this brief self-report measure. Method: Using data from a two-arm randomized trial of a remote intervention for major depressive disorder (telephone CBT [tCBT] or a stepped care model of web-based CBT [iCBT] and tCBT), we conducted an Exploratory Factor Analysis on the SAI item pool and explored the final SAI's relationship to iCBT engagement as well as to depression outcomes. Participants in our analyses (n = 52) included those randomized to a receive iCBT, but were not stepped up to tCBT due to insufficient response to iCBT, had not remitted prior to the 10-week assessment point, and completed the pool of 8 potential SAI items. Results: The best fitting EFA model included only 6 items from the original pool of 8 and contained two factors: Monitoring and Expectation. Final model fit was mixed, but acceptable (χ2(4) = 5.24, p = 0.26; RMSR = 0.03; RMSEA = 0.091; TLI = 0.967). Internal consistency was acceptable at α = 0.68. The SAI demonstrated good convergent and divergent validity. The SAI at the 10-week/mid-treatment mark was significantly associated with the number of days of iCBT use (r = 0.29, p = .037), but, contrary to expectations, was not predictive of either PHQ-9 scores (F(2,46) = 0.14, p = .89) or QIDS-C scores (F(2,46) = 0.84, p = .44) at post-treatment. Conclusion: The SAI is a brief measure of the SA framework constructs. Continued development to improve the SAI and expand the constructs it assesses is necessary, but the SAI represents the first step towards a measure of a coaching protocol that can support both coached digital mental health intervention adherence and improved outcomes

APS 2023: EMA, wearable, digital mental health & network slides

A bunch of APS talks on related topics :

Mental Health Self-Tracking Preferences of Young Adults With Depression and Anxiety Not Engaged in Treatment: Qualitative Analysis

BackgroundDespite the high prevalence of anxiety and depression among young adults, many do not seek formal treatment. Some may turn to digital mental health tools for support instead, including to self-track moods, behaviors, and other variables related to mental health. Researchers have sought to understand processes and motivations involved in self-tracking, but few have considered the specific needs and preferences of young adults who are not engaged in treatment and who seek to use self-tracking to support mental health. ObjectiveThis study seeks to assess the types of experiences young adults not engaged in treatment have had with digital self-tracking for mood and other mental health data and to assess how young adults not seeking treatment want to engage in self-tracking to support their mental health. MethodsWe conducted 2 online asynchronous discussion groups with 50 young adults aged 18 years to 25 years who were not engaged in treatment. Participants were recruited after indicating moderate to severe symptoms of depression or anxiety on screening surveys hosted on the website of Mental Health America. Participants who enrolled in the study responded anonymously to discussion prompts on a message board, as well as to each other’s responses, and 3 coders performed a thematic analysis of their responses. ResultsParticipants had mixed experiences with self-tracking in the past, including disliking when tracking highlighted unwanted behaviors and discontinuing tracking for a variety of reasons. They had more positive past experiences tracking behaviors and tasks they wanted to increase, using open-ended journaling, and with gamified elements to increase motivation. Participants highlighted several design considerations they wanted self-tracking tools to address, including building self-understanding; organization, reminders, and structure; and simplifying the self-tracking experience. Participants wanted self-tracking to help them identify their feelings and how their feelings related to other variables like sleep, exercise, and events in their lives. Participants also highlighted self-tracking as useful for motivating and supporting basic activities and tasks of daily living during periods of feeling overwhelmed or low mood and providing a sense of accomplishment and stability. Although self-tracking can be burdensome, participants were interested and provided suggestions for simplifying the process. ConclusionsThese young adults not engaged in treatment reported interest in using self-tracking to build self-understanding as a goal in and of itself or as a first step in contemplating and preparing for behavior change or treatment-seeking. Alexithymia, amotivation, and feeling overwhelmed may serve both as barriers to self-tracking and opportunities for self-tracking to help

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

Abstract 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. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations

A Measure of Cognitions Specific to Seasonal Depression: Development and Validation of the Seasonal Beliefs Questionnaire

We introduce the Seasonal Beliefs Questionnaire (SBQ), a self-report inventory of maladaptive thoughts about the seasons, light availability, and weather conditions, proposed to constitute a unique cognitive vulnerability to winter seasonal affective disorder (SAD; Rohan, Roecklein, & Haaga, 2009). Potential items were derived from a qualitative analysis of self-reported thoughts during SAD-tailored cognitive-behavioral therapy (CBT-SAD) and subsequently refined based on qualitative feedback from 48 SAD patients. In the psychometric study (N = 536 college students), exploratory and confirmatory factor analyses pruned the items to a 26-item scale with a 5-factor solution, demonstrating good internal consistency, convergent and divergent validity, and 2-week test-retest reliability. In a known groups comparison, the SBQ discriminated SAD patients (n = 86) from both nonseasonal major depressive disorder (MDD) patients (n = 30) and healthy controls (n = 110), whereas a generic measure of depressogenic cognitive vulnerability (the Dysfunctional Attitudes Scale [DAS]) discriminated MDD patients from the other groups. In a randomized clinical trial comparing CBT-SAD with light therapy (N = 177), SBQ scores improved at twice the rate in CBT-SAD than in light therapy. Greater change in SBQ scores during CBT-SAD, but not during light therapy, was associated with a lower risk of depression recurrence 2 winters later. In contrast, DAS scores improved comparably during CBT-SAD and light therapy, and DAS change was unrelated to recurrence following either treatment. These results support using the SBQ as a brief assessment tool for a SAD-specific cognitive vulnerability and as a treatment target in CBT-SAD. (PsycINFO Database Record (c) 2019 APA, all rights reserved)

Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety

Background: Prior literature links passively sensed information about a person's location, movement, and communication with social anxiety. These findings hold promise for identifying novel treatment targets, informing clinical care, and personalizing digital mental health interventions. However, social anxiety symptoms are heterogeneous; to identify more precise targets and tailor treatments, there is a need for personal sensing studies aimed at understanding differential predictors of the distinct subdomains of social anxiety. Our objective was to conduct a large-scale smartphone-based sensing study of fear, avoidance, and physiological symptoms in the context of trait social anxiety over time. Methods: Participants (n = 1013; 74.6 % female; M age = 40.9) downloaded the LifeSense app, which collected continuous passive data (e.g., GPS, communication, app and device use) over 16 weeks. We tested a series of multilevel linear regression models to understand within- and between-person associations of 2-week windows of passively sensed smartphone data with fear, avoidance, and physiological distress on the self-reported Social Phobia Inventory (SPIN). A shifting sensor lag was applied to examine how smartphone features related to SPIN subdomains 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Results: A decrease in time visiting novel places was a strong between-person predictor of social avoidance over time (distal β = −0.886, p = .002; medial β = −0.647, p = .029; proximal β = −0.818, p = .007). Reductions in call- and text-based communications were associated with social avoidance at both the between- (distal β = −0.882, p = .002; medial β = −0.932, p = .001; proximal β = −0.918, p = .001) and within- (distal β = −0.191, p = .046; medial β = −0.213, p = .028) person levels, as well as between-person fear of social situations (distal β = −0.860, p < .001; medial β = −0.892, p < .001; proximal β = −0.886, p < .001) over time. There were fewer significant associations of sensed data with physiological distress. Across the three subscales, smartphone data explained 9–12 % of the variance in social anxiety. Conclusion: Findings have implications for understanding how social anxiety manifests in daily life, and for personalizing treatments. For example, a signal that someone is likely to begin avoiding social situations may suggest a need for alternative types of exposure-based interventions compared to a signal that someone is likely to begin experiencing increased physiological distress. Our results suggest that as a prophylactic means of targeting social avoidance, it may be helpful to deploy interventions involving social exposures in response to decreases in time spent visiting novel places