Person-specific dynamics between negative emotions and suicidal thoughts.

INTRODUCTION: Recent technology has enabled researchers to collect ecological momentary assessments (EMA) to examine within-person correlates of suicidal thoughts. Prior studies examined generalized temporal dynamics of emotions and suicidal thinking over brief periods, but it is not yet known how variable these processes are across people. METHOD: We use data EMA data delivered over two weeks with youth/young adults (N = 60) who reported past year self-injurious thoughts/behaviors. We used group iterative multiple model estimation (GIMME) to model group- and person-specific associations of negative emotions (i.e., fear, sadness, shame, guilt, and anger) and suicidal thoughts. RESULTS: 29 participants (48.33%) reported at least one instance of a suicidal thought and were included in GIMME models. In group level models, we consistently observed autoregressive effects for suicidal thoughts (e.g., earlier thoughts predicting later thoughts), although the magnitude and direction of this link varied from person-to-person. Among emotions, sadness was most frequently associated with contemporaneous suicidal thoughts, but this was evident for less than half of the sample, while other emotional correlates of suicidal thoughts broadly differed across people. No emotion variable was linked to future suicidal thoughts in >14% of the sample, CONCLUSIONS: Emotion-based correlates of suicidal thoughts are heterogeneous across people. Better understanding of the individual-level pathways maintaining suicidal thoughts/behaviors may lead to more effective, personalized interventions.

The link between daily affective complexity and anxiety is altered by oral contraceptive use.

Affective complexity - the unique ways in which individuals' emotions covary and differentiate - is an important aspect of internalizing problems. For instance, daily affective complexity has been linked to anxiety increases in women and to decreases in men. The mechanisms underlying this gender difference have not been widely investigated, but a role for ovarian hormones is likely. Research on oral contraceptives (OCs) provides promising insights into such mechanisms, as OCs suppress endogenous ovarian hormone production and vary in exogenous hormone formulations. Thus, the goal of this study was to examine links between daily affective complexity and internalizing problems in OC users (n = 84), focusing on dimensionally-assessed anxiety, and to investigate whether the links varied by pill formulation. Affective complexity was operationalized as number of factors for each person, as estimated by p-technique (i.e., person-specific factor analysis) of 75-day intensive longitudinal data. There was not a relation between affective complexity and anxiety in OC users, and this did not depend on OC pill formulation (i.e., estrogenic, progestational, or androgenic activities). Thus, OC use may blunt the relation between affective complexity and anxiety, as OC users had a relation in between the established positive relation for naturally cycling women and the inverse for men (despite a similar range of factors). Findings are consistent with a growing literature showing that OC use modulates stress and anxiety-linked processes, and suggest that gendered mechanisms underlying the relation between affective complexity and anxiety may be suppressed along with ovarian hormones in OC users.

Using the experience sampling method to support clinical practice: An illustration with problematic cannabis use.

The experience sampling method (ESM) has been frequently used in clinical research; however, there is low translational uptake in clinical practice. This may be due to challenges with interpreting individual-level data at granular intervals. We provide an illustrative example of how ESM can be leveraged to generate personalized cognitive-behavioral strategies for problematic cannabis use.We conducted a descriptive case series analysis using ESM data from 30 individuals reporting on problematic cannabis use, craving, affect, and coping four times daily for 16-days (t = 64, T = 1,920).Analyzing ESM data using descriptive statistics and visualizations from individuals with similar clinical and demographic profiles supported a diverse array of personalized clinical insights and recommendations for each case. These recommendations included psychoeducation regarding affect- and boredom-regulation strategies, functional analyses of occasions during which cannabis was not used, and discussions on how cannabis use intersects with one's personal values.While many clinicians utilize measurement-based care, barriers have limited the incorporation of ESM towards personalized, data-informed approaches to treatment. We provide an illustrative example of how ESM data can be used to generate actionable treatment strategies for problematic cannabis use and highlight continued challenges with interpreting time-series data.

The daily association between affect and alcohol use: A meta-analysis of individual participant data.

Influential psychological theories hypothesize that people consume alcohol in response to the experience of both negative and positive emotions. Despite two decades of daily diary and ecological momentary assessment research, it remains unclear whether people consume more alcohol on days they experience higher negative and positive affect in everyday life. In this preregistered meta-analysis, we synthesized the evidence for these daily associations between affect and alcohol use. We included individual participant data from 69 studies (N = 12,394), which used daily and momentary surveys to assess affect and the number of alcoholic drinks consumed. Results indicate that people are not more likely to drink on days they experience high negative affect, but are more likely to drink and drink heavily on days high in positive affect. People self-reporting a motivational tendency to drink-to-cope and drink-to-enhance consumed more alcohol, but not on days they experienced higher negative and positive affect. Results were robust across different operationalizations of affect, study designs, study populations, and individual characteristics. These findings challenge the long-held belief that people drink more alcohol following increases in negative affect. Integrating these findings under different theoretical models and limitations of this field of research, we collectively propose an agenda for future research to explore open questions surrounding affect and alcohol use.

Adapting a transdiagnostic digital mental health intervention for use among immigrant and refugee youth in Seattle: a human-centered design approach.

Digital mental health interventions show promise in addressing mental health needs, especially among youth and marginalized communities. This study adapted the World Health Organization -developed STARS (Sustainable Technology for Adolescents to Reduce Stress) digital mental health intervention for use among youth and young adults aged 14-25 from immigrant and refugee communities in Seattle, Washington. Human-centered design methods centered around qualitative semi-structured interviews were used to contextually and culturally adapt the intervention and prioritize the needs and preferences of the intended end user. Intervention prototypes were modified and then presented to the target groups in iterative cycles until saturation was achieved. Qualitative interviews occurred in three iterations of five participants each. Modifications were documented according to the Framework for Reporting Adaptations and Modifications-Expanded (FRAME) implementation science framework. Modifications aligned with the FRAME process elements: (a) tailoring/refining, which included adapting language to less resemble digital phishing scams; (b) changes in packaging or materials, which included naming the chatbot and adopting a corresponding avatar; (c) adding/removing, which included changing existing emojis and adding additional media types including graphics interchange format images, pictures, and voice memos; (d) shortening/condensing, which included shortening the length of individual text sections as well as deleting redundant language; (e) lengthening/extending, which included allowing the user to choose to receive content catered to teenagers or to adults; and (f) loosening structure, including giving users options to skip parts of modules or to engage with additional material. The modified STARS intervention shows promise for engagement with immigrant and refugee youth in Seattle and can be examined for clinical effectiveness. Adaptations increased the relevance of content to the intended end user, expanded options for personalization and customization of the user experience, and utilized language that was age appropriate, engaging, and did not invoke feelings of stigma or distrust. Adaptations of digital mental health interventions should focus on modifications that maximize acceptability and appropriateness to intended audiences.

Digital mental health interventions like apps and online mental health tools show promise in addressing mental health needs. This study adapted the STARS (Sustainable Technology for Adolescents to Reduce Stress) digital mental health intervention for use among youth and young adults from immigrant and refugee communities in Seattle, Washington. In our study, we adapted the intervention in a way that prioritizes the preferences of the intended end user. Modifications occurred in cycles: each time modifications resulted in a new version, the version was presented to a group of participants for their feedback and further modifications. Modifications in the final version included adapting language to less resemble digital phishing scams; naming the chatbot and adopting a corresponding avatar; changing existing emojis and adding additional media types; shortening the length of individual text sections as well as deleting redundant language; allowing the user to choose content catered to teenagers or to adults; and giving users options to skip parts of modules or to engage with additional material. The modified STARS intervention shows promise for engagement with immigrant and refugee youth in Seattle and can be examined for clinical effectiveness.

Sexual Offense Disclosure Assessment in Youth Referred to Community Treatment: A Latent Class Analysis.

This study seeks to extend research evaluating tools to assess the disclosure of sexually abusive behavior. The subjects were 239 male youth (ages 10-20 years) who were court-ordered to participate in a community-based collaborative intervention for sexual offending that includes outpatient and probationary services. All youth participated in an interview to capture referral incident details about admission, responsibility, empathy, and remorse at intake, during intervention, and at discharge. Intake, treatment, discharge, and recidivism measures were also collected from multiple sources. Latent class analysis identified three classes based on the intake interview: Empathetic Admitters (22%), Unempathetic Admitters (38%), and Unempathetic Deniers (40%). Significant class differences were found on intake (e.g., use of physical force, caregiver denial of youth responsibility), treatment (e.g., any sanctions/violations), and discharge measures (e.g., successful treatment, probation officer ratings), but not in recidivism rates. The findings extend efforts to identify and target different disclosure patterns whose clinical monitoring may support a comprehensive intervention.

A meta-analysis on the affect regulation function of real-time self-injurious thoughts and behaviours.

Prominent theories suggest that self-injurious thoughts and behaviours are negatively reinforced by decreased negative affect. The present meta-analysis quantifies effects from intensive longitudinal studies measuring negative affect and self-injurious thoughts and behaviours. We obtained data from 38 of the 79 studies (48%, 22 unique datasets) involving N = 1,644 participants (80% female, 75% white). Individual-participant data meta-analyses revealed changes in affect pre/post self-injurious thoughts and behaviours. In antecedent models, results supported increased negative affect before nonsuicidal self-injurious behaviour (k = 14, 95% CI 0.09 to 0.31) and suicidal thoughts (k = 14, 95% CI 0.03 to 0.19). For consequence models, negative affect was reduced following nonsuicidal self-injurious thoughts (k = 6, 95% CI -0.79 to -0.44), nonsuicidal self-injurious behaviours (k = 14, 95% CI -0.73 to -0.19) and suicidal thoughts (k = 13, 95% CI -0.79 to -0.23). Findings, which were not moderated by sampling strategies or sample composition, support the affect regulation function of self-injurious thoughts and behaviours.

Identifying person-specific coping responses to suicidal urges: A case series analysis and illustration of the idiographic method.

INTRODUCTION: Suicide is a leading cause of death. One challenge to prevention efforts is the wide phenomenological heterogeneity in suicidal urges, thoughts, and behaviors across individuals at risk. Despite this heterogeneity, most suicide research estimates group-level effects by averaging across people as if they were the same, preventing detection of person-specific factors that may modulate risk and be key to effective prevention. The goal of the present study is to illustrate the idiographic (i.e., person-specific) approach and highlight its utility for suicide research. METHODS: We implemented a case series approach using three cases from a subset of psychiatrically hospitalized adolescents who provided intensive longitudinal data on daily urges and coping behavior after discharge following a suicide attempt. For illustration, person-specific, bidirectional links between suicidal urges and coping behavior were modeled across a series of cases using a vector autoregression approach. RESULTS: The relationship between suicidal urges and coping differed across the three individuals, who were presented to exhibit the range of this variability in the presence/absence and magnitude of effects. CONCLUSIONS: Individuals who report similar suicidal risk levels likely respond in individualized ways to suicidal urges (e.g., use different coping strategies), necessitating personalized assessment and treatment. We discuss implications for future suicide research.

Limitations of cross-lagged panel models in addiction research and alternative models: An empirical example using project MATCH.

OBJECTIVE: Models of addiction often posit bidirectional and dynamic associations between constructs relevant to the etiology and maintenance of addictive behaviors. The cross-lagged panel model (CLPM) is commonly used in addiction research but has been critiqued for not appropriately adjusting for between-person variance. Alternatives to the CLPM have been suggested but remain underutilized. The primary purpose of this article is to highlight interpretational limitations of the CLPM and to provide examples of alternative models. METHOD: We specified CLPM, Random-Intercept CLPM, and a Latent Curve Model with Structured Residuals using four waves of data from Project MATCH (n = 1,201). We modeled prospective relations among depression symptoms and temptation to drink. Substantive inferences and assumptions across models were compared. RESULTS: The CLPM provided the most evidence of significant cross-lagged paths but the poorest fit to the data compared to other models. Alternative models found little evidence of prospective within-person associations, and more evidence for between-person associations and wave-specific within-person relations between depression symptoms and temptation to drink. CONCLUSIONS: This study highlights shortcomings of the CLPM and details alternative models to consider. Addiction researchers should consider alternatives to the CLPM to more optimally delineate relations among constructs across time. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Heterogeneity in affective complexity among men and women.

Affective phenomena have noteworthy complexity and heterogeneity-shared experiences and emotions evoke distinct responses and risk for affective problems across individuals (e.g., higher rates in women than men). Yet by averaging across individuals, affective science research traditionally treats affect as homogenous. Directly modeling person-specific heterogeneity in affective complexity (AC)-like the granularity and covariation of affective experiences-is paramount for identifying shared (i.e., common; nomothetic) and/or unshared (i.e., personal; idiographic) features of AC. The present study applied a person-specific technique to capture heterogeneity in daily affect and risk for affective problems in men and women and leveraged personalized results to improve general understanding of AC. Young adults (n = 56; 25 female) reported affect on each of 75 days of an intensive longitudinal study. AC was modeled using p-technique (i.e., person-specific factor analysis), and its utility over traditional, between-person models of affect (i.e., bivariate positive and negative affect) was compared for prediction of risk for affective problems in women compared to men. A community detection network algorithm was then applied to estimate person-specific AC to develop an idiographically informed nomothetic model of AC. Person-specific analyses detected wide variation in AC across individuals (i.e., range of 2-8 factors). Relative to the traditional bivariate model, idiographic models had incremental utility for differentiating risk for affective problems by gender. Nomothetic review of idiographic results (via community detection) revealed distinct dynamics in positive and negative affect networks. Person-specific science holds particular promise for mapping heterogeneity in AC and uncovering risk pathways for affective problems. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Differences in child and adult biopsychosocial characteristics associated with regular cannabis use in individuals with and without cannabis use disorder.

BACKGROUND: Regular cannabis use, even without cannabis use disorder (CUD), is associated with numerous biopsychosocial problems. Biopsychosocial risk factors that precede regular use and CUD might reflect broader pre-existing risk factors rather than the consequence of cannabis use. We aimed to (1) replicate prior work differentiating psychosocial problems associated with regular cannabis use with or without CUD relative to no-use in adulthood, and (2) test if these use groups differed in biopsychosocial functioning in early and middle childhood. METHODS: Biopsychosocial characteristics of individuals at-risk for substance use problems (n = 402) reporting no-use, regular use without CUD, and regular use with CUD by young adulthood were prospectively compared during early childhood (ages 3-5), middle childhood (ages 9-11) and young adulthood (ages 18-25). RESULTS: Regular use (vs. no-use) was associated with more health problems (mean d = |0.57|), psychopathology (mean d = |0.72|), social and family environment risk (mean d = |0.88|) in childhood and adulthood and comorbid substance use in adulthood (mean d = |1.25|). Regular use with and without CUD was linked to similar, developmentally-persistent patterns of problems across domains. CONCLUSIONS: We found that childhood risk factors present many years prior to cannabis initiation (as early as age 3) differentiated patterns of adult cannabis use and CUD status in adulthood. Therefore, biopsychosocial impairments associated with regular cannabis use in adulthood is not solely attributable to cannabis exposure but can be traced back to early and persistent biopsychosocial risk that may benefit from early behavioral intervention, irrespective of CUD diagnosis.

Evidence on Digital Mental Health Interventions for Adolescents and Young People: Systematic Overview.

BACKGROUND: An estimated 1 in 5 adolescents experience a mental health disorder each year; yet because of barriers to accessing and seeking care, most remain undiagnosed and untreated. Furthermore, the early emergence of psychopathology contributes to a lifelong course of challenges across a broad set of functional domains, so addressing this early in the life course is essential. With increasing digital connectivity, including in low- and middle-income countries, digital health technologies are considered promising for addressing mental health among adolescents and young people. In recent years, a growing number of digital health interventions, including more than 2 million web-based mental health apps, have been developed to address a range of mental health issues. OBJECTIVE: This review aims to synthesize the current evidence on digital health interventions targeting adolescents and young people with mental health conditions, aged between 10-24 years, with a focus on effectiveness, cost-effectiveness, and generalizability to low-resource settings (eg, low- and middle-income countries). METHODS: We searched MEDLINE, PubMed, PsycINFO, and Cochrane databases between January 2010 and June 2020 for systematic reviews and meta-analyses on digital mental health interventions targeting adolescents and young people aged between 10-24 years. Two authors independently screened the studies, extracted data, and assessed the quality of the reviews. RESULTS: In this systematic overview, we included 18 systematic reviews and meta-analyses. We found evidence on the effectiveness of computerized cognitive behavioral therapy on anxiety and depression, whereas the effectiveness of other digital mental health interventions remains inconclusive. Interventions with an in-person element with a professional, peer, or parent were associated with greater effectiveness, adherence, and lower dropout than fully automatized or self-administered interventions. Despite the proposed utility of digital interventions for increasing accessibility of treatment across settings, no study has reported sample-specific metrics of social context (eg, socioeconomic background) or focused on low-resource settings. CONCLUSIONS: Although digital interventions for mental health can be effective for both supplementing and supplanting traditional mental health treatment, only a small proportion of existing digital platforms are evidence based. Furthermore, their cost-effectiveness and effectiveness, including in low- and middle-income countries, have been understudied. Widespread adoption and scale-up of digital mental health interventions, especially in settings with limited resources for health, will require more rigorous and consistent demonstrations of effectiveness and cost-effectiveness vis-à-vis the type of service provided, target population, and the current standard of care.

Personalized models of personality disorders: using a temporal network method to understand symptomatology and daily functioning in a clinical sample.

BACKGROUND: An ongoing challenge in understanding and treating personality disorders (PDs) is a significant heterogeneity in disorder expression, stemming from variability in underlying dynamic processes. These processes are commonly discussed in clinical settings, but are rarely empirically studied due to their personalized, temporal nature. The goal of the current study was to combine intensive longitudinal data collection with person-specific temporal network models to produce individualized symptom-level structures of personality pathology. These structures were then linked to traditional PD diagnoses and stress (to index daily functioning). METHODS: Using about 100 daily assessments of internalizing and externalizing domains underlying PDs (i.e. negative affect, detachment, impulsivity, hostility), a temporal network mapping approach (i.e. group iterative multiple model estimation) was used to create person-specific networks of the temporal relations among domains for 91 individuals (62.6% female) with a PD. Network characteristics were then associated with traditional PD symptomatology (controlling for mean domain levels) and with daily variation in clinically-relevant phenomena (i.e. stress). RESULTS: Features of the person-specific networks predicted paranoid, borderline, narcissistic, and obsessive-PD symptom counts above average levels of the domains, in ways that align with clinical conceptualizations. They also predicted between-person variation in stress across days. CONCLUSIONS: Relations among behavioral domains thought to underlie heterogeneity in PDs were indeed associated with traditional diagnostic constructs and with daily functioning (i.e. stress) in person-specific networks. Findings highlight the importance of leveraging data and models that capture person-specific, dynamic processes, and suggest that person-specific networks may have implications for precision medicine.

Sex Differences in Subjective and Behavioral Responses to Stressful and Smoking Cues Presented in the Natural Environment of Smokers.

INTRODUCTION: Some evidence suggests that female smokers may show more context-dependent smoking and that males may show more stereotyped smoking (regardless of stress or cue exposure). The goal of this study was to characterize sex differences in response to stressful and smoking cues ecologically presented in daily life and variability in day-to-day smoking behavior. METHODS: Adult smokers (N = 177) provided ratings of mood and cigarette craving before and after stress and smoking cues were presented four times daily for 14 days via a mobile device. Linear mixed models tested whether (1) female smokers exhibited greater reactivity to stressful cues than male smokers; (2) pre-cue negative affect increased reactivity to smoking cues more in female smokers than male smokers; (3) across both sexes, greater reactivity to stressful and smoking cues correlated with greater quantity of smoking within a day; and (4) female smokers exhibited greater variability in cigarettes per day (CPD) relative to males. RESULTS: Relative to male smokers, female smokers reported greater negative affect, stress, and craving in response to stressful cues, but not smoking cues, after accounting for time since last cigarette and pre-cue responding. No sex differences in CPD or variability in CPD were detected. Days with higher subjective reactivity to cues were not associated with increased smoking, in either males or females. CONCLUSIONS: Sex differences were observed in response to stress but not smoking cues in the natural environment of regular cigarette smokers. Further research is necessary to evaluate whether stress reactivity in female smokers is associated with reduced latency to smoke following stress exposure in daily life. IMPLICATIONS: This study provides naturalistic evidence that female smokers may not be more reactive to smoking cues than males, but experience heightened stress and craving following stress exposure. There was no evidence to support the hypothesis that amount smoked per day varied more for females, relative to males, as a result of more context-driven smoking for females.

Higher average potency across the United States is associated with progression to first cannabis use disorder symptom.

OBJECTIVE: To determine if higher potency cannabis is associated with earlier progression to regular cannabis use, daily cannabis use, and cannabis use disorder symptom onset. METHODS: Data sources were the Michigan Longitudinal Study, an ongoing prospective, high-risk family study investigating the course and predictors for substance use disorders among youth beginning prior to school entry and time-parallel national average trends in delta-9-tetrahydrocannabinol (i.e., psychoactive compound in cannabis). The national average trends in delta-9-tetrahydrocannabinol were used to estimate potency level for the individual. Only cannabis users were included in analyses (n = 527). RESULTS: Cox regression showed an increased risk of progression from cannabis initiation to cannabis use disorder symptom onset by 1.41 times (p < .001) for each unit increase in national average delta-9-tetrahydrocannabinol as compared to those not endorsing CUD symptom onset, adjusting for sex, regular use, and cohort effects. Accounting for regular use, individuals initiating cannabis at national average 4.9% delta-9-tetrahydrocannabinol were at 1.88 times (p = .012) higher risk for cannabis use disorder symptom onset within one year compared to those who did not endorse CUD symptom onset, while those initiating cannabis at national average 12.3% delta-9-tetrahydrocannabinol were at 4.85 times (p = .012) higher risk within one year. CONCLUSIONS: This study provides prospective evidence suggesting higher potency cannabis, on average in the U.S., increases risk for onset of first cannabis use disorder symptom. Development of guidelines regarding cannabis potency is critical for reducing the costs associated with negative health outcomes.

Positive and negative effects of internalizing on alcohol use problems from childhood to young adulthood: The mediating and suppressing role of externalizing.

A longstanding hypothesis is that some alcohol use problems (AUP) develop and are maintained through the "self-medication" of internalizing (INT; depression and anxiety) problems. However, their high rate of co-occurrence with one another and with externalizing (EXT; antisocial behavior and impulse control) problems obscures any causal association because EXT may account for the INT-AUP link. Using a large community sample, we estimated prospective effects of INT and EXT on AUP via latent cross-lagged mediation panel spanning 14 years from childhood (ages 9-11) to young adulthood (ages 21-23). After adjusting for the cross-lagged, concurrent, and stability effects across factors, INT decreased AUP risk through its direct and indirect effects and increased AUP risk through shared variance with EXT. Between childhood and young adulthood, unique aspects of INT reduced risk for AUP while aspects of INT shared with EXT increased risk for AUP. (PsycINFO Database Record

Advancing statistical analysis of ambulatory assessment data in the study of addictive behavior: A primer on three person-oriented techniques.

Ambulatory assessment (AA) methodologies have the potential to increase understanding and treatment of addictive behavior in seemingly unprecedented ways, due in part, to their emphasis on intensive repeated assessments of an individual's addictive behavior in context. But, many analytic techniques traditionally applied to AA data - techniques that average across people and time - do not fully leverage this potential. In an effort to take advantage of the individualized, temporal nature of AA data on addictive behavior, the current paper considers three underutilized person-oriented analytic techniques: multilevel modeling, p-technique, and group iterative multiple model estimation. After reviewing prevailing analytic techniques, each person-oriented technique is presented, AA data specifications are mentioned, an example analysis using generated data is provided, and advantages and limitations are discussed; the paper closes with a brief comparison across techniques. Increasing use of person-oriented techniques will substantially enhance inferences that can be drawn from AA data on addictive behavior and has implications for the development of individualized interventions.