Examining Passively Collected Smartphone-Based Data in the Days Prior to Psychiatric Hospitalization for a Suicidal Crisis: Comparative Case Analysis.

BACKGROUND: Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots. OBJECTIVE: This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research. METHODS: To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots-text-captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one's phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots). RESULTS: Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness. CONCLUSIONS: Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual's psychological processes in the time leading up to a suicidal crisis.

On the Selection of Item Scores or Composite Scores for Clinical Prediction.

Recent shifts to prioritize prediction, rather than explanation, in psychological science have increased applications of predictive modeling methods. However, composite predictors, such as sum scores, are still commonly used in practice. The motivations behind composite test scores are largely intertwined with reducing the influence of measurement error in answering explanatory questions. But this may be detrimental for predictive aims. The present paper examines the impact of utilizing composite or item-level predictors in linear regression. A mathematical examination of the bias-variance decomposition of prediction error in the presence of measurement error is provided. It is shown that prediction bias, which may be exacerbated by composite scoring, drives prediction error for linear regression. This may be particularly salient when composite scores are comprised of heterogeneous items such as in clinical scales where items correspond to symptoms. With sufficiently large training samples, the increased prediction variance associated with item scores becomes negligible even when composite scores are sufficient. Practical implications of predictor scoring are examined in an empirical example predicting suicidal ideation from various depression scales. Results show that item scores can markedly improve prediction particularly for symptom-based scales. Cross-validation methods can be used to empirically justify predictor scoring decisions.

Associations among Drug Acquisition and Use Behaviors, Psychosocial Attributes, and Opioid-Involved Overdoses: A SEM Analysis.

Aims: This study sought to develop and assess an exploratory model of how demographic and psychosocial attributes, and drug use or acquisition behaviors interact to affect opioid-involved overdoses. Methods: We conducted exploratory and confirmatory factor analysis (EFA/CFA) to identify a factor structure for ten drug acquisition and use behaviors. We then evaluated alternative structural equation models incorporating the identified factors, adding demographic and psychosocial attributes as predictors of past-year opioid overdose. We used interview data collected for two studies recruiting opioid-misusing participants receiving services from a community-based syringe service program. The first investigated current attitudes toward drug-checking (N = 150). The second was an RCT assessing a telehealth versus in-person medical appointment for opioid use disorder treatment referral (N = 270). Demographics included gender, age, race/ethnicity, education, and socioeconomic status. Psychosocial measures were homelessness, psychological distress, and trauma. Self-reported drug-related risk behaviors included using alone, having a new supplier, using opioids with benzodiazepines/alcohol, and preferring fentanyl. Past-year opioid-involved overdoses were dichotomized into experiencing none or any. Results: The EFA/CFA revealed a two-factor structure with one factor reflecting drug acquisition and the second drug use behaviors. The selected model (CFI = .984, TLI = .981, RMSEA = .024) accounted for 13.1% of overdose probability variance. A latent variable representing psychosocial attributes was indirectly associated with an increase in past-year overdose probability (ß=.234, p = .001), as mediated by the EFA/CFA identified latent variables: drug acquisition (ß=.683, p < .001) and drug use (ß=.567, p = .001). Drug use behaviors (ß=.287, p = .04) but not drug acquisition (ß=.105, p = .461) also had a significant, positive direct effect on past-year overdose. No demographic attributes were significant direct or indirect overdose predictors. Conclusions: Psychosocial attributes, particularly homelessness, increase the probability of an overdose through associations with risky drug acquisition and drug-using behaviors. To increase effectiveness, prevention efforts might address the interacting overdose risks that span multiple functional domains.

Examining the dynamic relationship between nonsuicidal self-injury and alcohol use experiences.

INTRODUCTION: Nonsuicidal self-injury (NSSI) is a prevalent and concerning behavior and its risk pathways require a greater understanding, particularly in predicting short-term risk. Although the literature has supported a between-person link among NSSI and alcohol use, limited research has directly examined the nuances of this relationship at the within-person level using intensive longitudinal data. METHOD: Utilizing two independent samples (total n = 85), the current study examined bidirectional, concurrent and prospective risk relationships between NSSI and alcohol, considering both urges and behavior engagement, via ecological momentary assessment. RESULTS: Findings demonstrate concurrent, within-person relationships between NSSI urges and alcohol urges, as well as alcohol use. Alternatively, prospective between-person findings demonstrated negative relationships between NSSI urges and alcohol use, as well as alcohol urges and NSSI acts; however, this may represent suppression effects as associations were positive with the removal of autoregressive effects. CONCLUSIONS: Together, findings support proximal risk relationships between NSSI and alcohol experiences that, for urges in particular, is bidirectional.

Measurement invariance and response consistency of single-item assessments for suicidal thoughts and behaviors.

The present study aimed to expand the literature on single-item assessments for suicidal thoughts and behaviors (STBs) by examining measurement invariance of commonly used single-item assessments of suicidal ideation (SI), planning (SP), and attempts (SA) with respect to race and ethnicity. Predictive invariance with respect to depression, and multi-item measures of passive and active SI were also explored. Measurement invariance was examined across (a) Black and White respondents and (b) Hispanic/Latinx and non-Hispanic/-Latinx respondents. Participants (N = 1,624; 51.66% male) were recruited from Mechanical Turk and Prime Panels. Participants were administered four distinct single-item measures each for SI, SP, and SA across three timeframes (past month, past year, lifetime). Items were drawn from well-known large-scale studies (e.g., National Comorbidity Survey) and common suicide risk assessments. Multiple group confirmatory factor analysis was used to examine measurement invariance; regression with group by measure interactions were used to evaluate predictive invariance. Measurement invariance was observed for both Black (N = 534) and White (N = 1,089) respondents as well as Hispanic/Latinx (N = 335) and non-Hispanic/-Latinx (N = 1,288) respondents across single-item outcomes. Thus, SI, SP, and SA rates can be defensibly compared between Black and White and Hispanic/Latinx and non-Hispanic/-Latinx respondents within studies; however, comparison of SI and SP rates across studies with differing assessment prompts should be met with caution. Multiple single-item STB measures demonstrated predictive bias across race and ethnicity suggesting potential differential screening capabilities. Elevated SI, SP, and SA rates for Hispanic/Latinx individuals were also observed. Findings reiterate the importance of minor language differences in single-item STB assessments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The impact of social connection on near-term suicidal ideation.

While predominant suicide theories emphasize the role of social connectedness in suicidal thinking, there is a need to better understand (a) how specific aspects of social connection relate to suicidal ideation and (b) the timeframe over which these relationships persist. The current study examined ecological momentary assessment data over a 30-day period from 35 participants with past-year suicidal thoughts or behaviors (mean age = 25.88; 62.9% women; 68.6% White) to address these questions. Results demonstrated that absence of social contact was associated with next timepoint suicidal ideation, even after considering the suicidal ideation autoregressive effect (i.e., concurrent), with effects strongest in the short-term. Findings provide preliminary evidence of the need to assess for the presence of social contact, and for assessments to occur in close proximity (i.e., a few hours), to capture the true dynamics of risk for suicidal ideation. Although needing replication, results suggest importance of just-in-time interventions targeting suicidal ideation.

Supervised latent Dirichlet allocation with covariates: A Bayesian structural and measurement model of text and covariates.

Text is a burgeoning data source for psychological researchers, but little methodological research has focused on adapting popular modeling approaches for text to the context of psychological research. One popular measurement model for text, topic modeling, uses a latent mixture model to represent topics underlying a body of documents. Recently, psychologists have studied relationships between these topics and other psychological measures by using estimates of the topics as regression predictors along with other manifest variables. While similar two-stage approaches involving estimated latent variables are known to yield biased estimates and incorrect standard errors, two-stage topic modeling approaches have received limited statistical study and, as we show, are subject to the same problems. To address these problems, we proposed a novel statistical model-supervised latent Dirichlet allocation with covariates (SLDAX)-that jointly incorporates a latent variable measurement model of text and a structural regression model to allow the latent topics and other manifest variables to serve as predictors of an outcome. Using a simulation study with data characteristics consistent with psychological text data, we found that SLDAX estimates were generally more accurate and more efficient. To illustrate the application of SLDAX and a two-stage approach, we provide an empirical clinical application to compare the application of both the two-stage and SLDAX approaches. Finally, we implemented the SLDAX model in an open-source R package to facilitate its use and further study. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Comparing the role of perceived burdensomeness and thwarted belongingness in prospectively predicting active suicidal ideation.

OBJECTIVE: The Interpersonal Theory of Suicide has been foundational in guiding current suicide literature. Despite recent research underscoring fluctuations of suicidal ideation within hours, there have been few studies examining the key constructs of perceived burdensomeness and thwarted belongingness within an intensive framework. Thus, the current study aimed to add cumulative knowledge regarding the within-person relationship between perceived burdensomeness, thwarted belongingness, and active suicidal ideation as assessed within an ecological momentary assessment design. METHOD: A final sample of 35 individuals with a past-year history of suicidal thoughts or behaviors completed brief surveys four times per day for 30 days. RESULTS: Findings highlighted that the addition of covariates may offer small improvements in modeling subsequent suicidal ideation, while controlling for SI at the prior time. Further, both thwarted belongingness and perceived burdensomeness were associated with next timepoint suicidal ideation, and their interaction added little incremental value. CONCLUSION: Findings demonstrate the potential importance of thwarted belongingness in predicting suicidal ideation. Further, results highlight that the main effects of thwarted belongingness and perceived burdensomeness, rather than their interaction, may be more important to consider in relation to active suicidal ideation.

A critique of using the labels confirmatory and exploratory in modern psychological research.

Psychological science is experiencing a rise in the application of complex statistical models and, simultaneously, a renewed focus on applying research in a confirmatory manner. This presents a fundamental conflict for psychological researchers as more complex forms of modeling necessarily eschew as stringent of theoretical constraints. In this paper, I argue that this is less of a conflict, and more a result of a continued adherence to applying the overly simplistic labels of exploratory and confirmatory. These terms mask a distinction between exploratory/confirmatory research practices and modeling. Further, while many researchers recognize that this dichotomous distinction is better represented as a continuum, this only creates additional problems. Finally, I argue that while a focus on preregistration helps clarify the distinction, psychological research would be better off replacing the terms exploratory and confirmatory with additional levels of detail regarding the goals of the study, modeling details, and scientific method.

Evaluating the item-level factor structure of anhedonia.

BACKGROUND: Anhedonia has long been theorized to be a multidimensional construct, focusing on domains of reward stimuli and temporal relationship to reward. However, little empirical work has directly examined whether there is support for this assertion. METHODS: The study used data from young adults from four independent samples (n = 2098). Participants completed multiple measures of anhedonia. RESULTS: We used rigorous conducted exploratory and confirmatory factor analyses on items from six commonly used anhedonia measures to examine dimensions underlying anhedonia. Results suggested a four-factor solution with factors reflecting social reward, social disinterest, status/achievement, and physical/natural reward. The identified factors reflected broad content domains of pleasure, but not specific reward processes. The four factors were modestly associated with one another, suggesting a weak common underlying anhedonia trait that manifests across multiple dimensions. Factor scores were associated with personality measures, reward-related indices, and depression symptoms, supporting the validity of the factors. LIMITATIONS: Participants were all young adults and we assessed anhedonia only at the level of self-report. CONCLUSION: Anhedonia is a multidimensional construct. However, the dimensions of anhedonia only distinguish domains of, but not temporal processes of anhedonia. Future work should continue to refine the structures underlying the construct of anhedonia through iterative theory- and data-driven research and examine associations between anhedonia and clinical outcomes.

Empirically determined severity levels for binge-eating disorder outperform existing severity classification schemes.

BACKGROUND: Eating-disorder severity indicators should theoretically index symptom intensity, impairment, and level of needed treatment. Two severity indicators for binge-eating disorder (BED) have been proposed (categories of binge-eating frequency and shape/weight overvaluation) but have mixed empirical support including modest clinical utility. This project uses structural equation model (SEM) trees - a form of exploratory data mining - to empirically determine the precise levels of binge-eating frequency and/or shape/weight overvaluation that most significantly differentiate BED severities. METHODS: Participants were 788 adults with BED enrolled in BED treatment studies. Participants completed interviews and self-report measures assessing eating-disorder and comorbid symptoms. SEM Tree analyses were performed by specifying an outcome model of BED severity and then recursively partitioning the outcome model into subgroups. Subgroups were split based on empirically determined values of binge-eating frequency and/or shape/weight overvaluation. SEM Forests also quantified which variable contributed more improvement in model fit. RESULTS: SEM Tree analyses yielded five subgroups, presented in ascending order of severity: overvaluation <1.25, overvaluation = 1.25-2.74, overvaluation = 2.75-4.24, overvaluation ⩾4.25 with weekly binge-eating frequency <4.875, and overvaluation ⩾4.25 with weekly binge-eating frequency ⩾4.875. SEM Forest analyses revealed that splits that occurred on shape/weight overvaluation resulted in much more improvement in model fit than splits that occurred on binge-eating frequency. CONCLUSIONS: Shape/weight overvaluation differentiated BED severity more strongly than binge-eating frequency. Findings indicate a nuanced potential BED severity indicator scheme, based on a combination of cognitive and behavioral eating-disorder symptoms. These results inform BED classification and may allow for the provision of more specific and need-matched treatment formulations.

Regularized structural equation modeling with stability selection.

Regularization methods such as the least absolute shrinkage and selection operator (LASSO) are commonly used in high dimensional data to achieve sparser solutions. Recently, methods such as regularized structural equation modeling (SEM) and penalized likelihood SEM have been proposed, trying to transfer the benefits of regularization to models commonly used in social and behavioral research. These methods allow researchers to estimate large models even in the presence of small sample sizes. However, some drawbacks of the LASSO, such as high false positive rates (FPRs) and inconsistency in selection results, persist at the same time. We propose the application of stability selection, a method based on repeated resampling of the data to select stable coefficients, to regularized SEM as a mechanism to overcome these limitations. Across 2 simulation studies, we find that stability selection greatly improves upon the LASSO in selecting the correct paths, specifically through reducing the number of false positives. We close the article by demonstrating the application of stability selection in 2 empirical examples and presenting several future research directions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Examining momentary associations between behavioral approach system indices and nonsuicidal self-injury urges.

BACKGROUND: The current study aimed to examine the concurrent and prospective relationships between the three hypothesized components of behavioral approach system (BAS) sensitivity: drive, reflecting the motivation to pursue one's desired goals; reward responsiveness, reflecting sensitivity to reward or reinforcement; and fun-seeking, reflecting the motivation for pursuing novel rewards in a spontaneous manner, and NSSI urge severity. METHODS: A sample of 64 undergraduates with a history of repetitive NSSI completed an ecological momentary assessment protocol. During this period of time, participants reported on the BAS-constructs of drive, reward responsiveness, and fun-seeking, as well as NSSI urge severity on a momentary basis at three random intervals each day for a period of ten-days. RESULTS: Drive and reward responsiveness, but not fun-seeking, were concurrently positively associated with NSSI urge severity. However, no associations between BAS facets and prospective NSSI urges were found. LIMITATIONS: This study was limited by its use of single items to assess the BAS-constructs of drive, reward responsiveness, and fun-seeking. CONCLUSIONS: Our findings indicate that feeling strongly impacted by rewards and having a strong sense of drive toward goal attainment may represent cognitive risk states that are associated with increased within-person NSSI risk. However, their lack of prospective prediction may suggest that these cognitive states are associated only on a momentary basis with NSSI urges and may not confer risk.

Explorations of Individual Change Processes and Their Determinants: A Novel Approach and Remaining Challenges.

Over the past 40 years there have been great advances in the analysis of individual change and the analyses of between-person differences in change. While conditional growth models are the dominant approach, exploratory models, such as growth mixture models and structural equation modeling trees, allow for greater flexibility in the modeling of between-person differences in change. We continue to push for greater flexibility in the modeling of individual change and its determinants by combining growth mixture modeling with structural equation modeling trees to evaluate how measured covariates predict class membership using a recursive partitioning algorithm. This approach, referred to as growth mixture modeling with membership trees, is illustrated with longitudinal reading data from the Early Childhood Longitudinal Study with the MplusTrees package in R.

How we ask matters: The impact of question wording in single-item measurement of suicidal thoughts and behaviors.

The present study aimed to extend prior literature on single-item assessment by examining response consistency (1) between several commonly used single-item assessments of suicidal ideation, planning, and attempts, and (2) across three timeframes (past month, past year, and lifetime) commonly employed in the literature. Participants (N = 613) were recruited from an online community, Amazon Mechanical Turk (mTurk). Participants were administered three sets of four distinct single-items assessing suicidal ideation, suicidal planning, and suicide attempt history, respecitvely. Items were drawn from well-known large-scale studies (e.g., National Comorbidity Survey; World Health Organization Mental Health Survey Initiative, Youth Risk Behavior Survey) and commonly used suicide risk assessments (i.e., Self-Injurious Thoughts and Behaviors Interview). Through examinations of intraclass correlations and confirmatory factor analyses, findings suggested mixed response agreement across most outcomes and timeframes. Response inconsistency among items assessing suicidal ideation and among items assessing suicidal planning were partly attributed to minor, yet important, language differences. Given findings that even minor language changes in suicidal ideation and planning items may inflate or restrict prevalence estimates in a meaningful way, it will be important for researchers and clinicians alike to pay close attention to the wording of single items in designing research studies, interpreting findings, and assessing patient risk.

Using shape and weight overvaluation to empirically differentiate severity of other specified feeding or eating disorder.

BACKGROUND: Although no severity specifiers are noted in the Diagnostic and Statistical Manual of Mental Disorders - 5 for other specified feeding or eating disorder (OSFED), shape/weight overvaluation is a proposed eating disorder (ED) severity specifier. We used structural equation modeling (SEM) Trees to empirically determine values of shape/weight overvaluation that differentiate OSFED severity. We additionally tested whether the SEM Tree-defined thresholds or a clinical cutoff for shape/weight overvaluation differentiated severity more meaningfully. METHODS: Participants were 690 females with OSFED presenting to residential ED treatment. SEM Tree analyses specified an outcome model of OSFED severity and then recursively partitioning the outcome model into severity groups. The SEM Tree-defined and clinical cutoff severity groups were compared on clinical characteristics. RESULTS: SEM Trees identified one split that occurred at value 5.12 on our shape/weight overvaluation items from the Eating Disorder Examination Questionnaire. The subgroup with higher overvaluation had significantly greater intensity of ED and depressive symptoms and longer lengths of stay. The subgroups created from the shape/weight overvaluation clinical-cut off value of 4 differed on the same clinical characteristics as the SEM Tree-derived groups, with the exception of laxative use frequency. Effect sizes were larger for the clinical cutoff as compared to the SEM Tree severity specification scheme. LIMITATIONS: These cross-sectional data were used from a predominately white and female residential treatment sample; this likely skewed the subgroups and may limit generalizability. CONCLUSIONS: Shape/weight overvaluation can meaningfully differentiate OSFED severity. The clinical cutoff slightly outperformed the empirically determined thresholds for shape/weight overvaluation.

A prospective examination of COVID-19-related social distancing practices on suicidal ideation.

BACKGROUND: The COVID-19 pandemic has spurred the implementation of several public safety measures to contain virus spread, most notably socially distancing policies. Prior research has linked similar public safety measures (i.e., quarantine) with suicide risk, in addition to supporting the role of social connection in suicidal thoughts and behaviors; consequently, there is a need to better understand the relationship between widespread social distancing policies and suicide risk. The current study aimed to examine the prospective association between COVID-19-related social distancing practices and suicidal ideation. METHODS: Participants (N = 472) completed measures of suicidal ideation and impacts of social distancing practices at baseline and two weeks later. RESULTS: After controlling for general psychosocial distress (i.e., depression, social connectedness), cross-lagged regression models identified prospective, bidirectional relationships between perceived impacts of social distancing on one's mental health and both passive and active suicidal ideation. The impact of social distancing on work/social routine was not associated with suicidal ideation. CONCLUSIONS: Overall, findings suggest the importance of an individual's perception regarding the effect of social distancing on their mental health, rather than the disruption to work or social routine, in suicide risk. Findings highlight potential targets for suicide risk prevention and intervention.

Using ordinal regression for advancing the understanding of distinct suicide outcomes.

OBJECTIVE: As recent advances in suicide research have underscored the importance of studying distinct suicide outcomes (i.e., suicidal thinking vs. behavior), there is a need to consider the theoretical meaningfulness of our statistical approach(es). As an alternative to more popular statistical methods, we introduce ordinal regression, detailing specific forms that are well-aligned to examine outcomes specific to suicide research. METHOD: Ordinal regression models allow for assessment of the influences of covariates on the experience of lower (i.e., suicidal ideation) to higher (i.e., suicidal planning) suicide risk outcomes. RESULTS: As an empirical application, we fit a sequential ordinal regression model with 17 theoretically selected covariates and modeled category specific effects for each covariate. CONCLUSIONS: Results detailed from depression and presence of nonsuicidal self-injury demonstrate the utility of ordinal regression in consideration of transitions across suicide outcomes. Ordinal regression models may be particularly informative in identifying risk factors unique to each suicide outcome, which has the potential to meaningfully inform theoretical models of suicide and suicide risk prediction.

The use of text-based responses to improve our understanding and prediction of suicide risk.

OBJECTIVE: Text-based responses may provide significant contributions to suicide risk prediction, yet research including text data is limited. This may be due to a lack of exposure and familiarity with statistical analyses for this data structure. METHOD: The current study provides an overview of data processing and statistical algorithms for text data, guided by an empirical example of 947 online participants who completed both open-ended items and traditional self-report measures. We give an introduction to a number of text-based statistical approaches, including dictionary-based methods, topic modeling, word embeddings, and deep learning. RESULTS: We analyze responses from the open-ended question "How do you feel today?", detailing characteristics of the responses, as well as predicting past-year suicidal ideation. CONCLUSIONS: We see the analysis of text from social media, open-ended questions, and other text sources (i.e., medical records) as an important form of complementary assessment to traditional scales, shedding insight on what we are missing in our current set of questionnaires, which may ultimately serve to improve both our understanding and prediction of suicide.