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

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.

Commentary on Stull et al.: Considering person-specific heterogeneity when modeling time-varying trends

TVEM represents an important statistical method for modeling non-linear, dynamic processes. However, TVEM models the average -level process for the focal effect. As addiction- and recovery-related processes demonstrate person-specific heterogeneity, we encourage researchers to model non-parametric and non-ergodic change to advance our understanding of how, when, and for whom change occurs.

Examining Associations Between Negative Affect and Substance Use in Treatment-Seeking Samples: A Review of Studies Using Intensive Longitudinal Methods

Purpose of Review

Understanding dynamic relationships between negative affect and substance use disorder (SUD) outcomes, including craving, may help inform adaptive and personalized interventions. Recent studies using intensive longitudinal methods were reviewed to examine relationships between negative affect and the outcomes of either craving or substance use during and following SUD treatment.

Recent Findings

Results on associations between negative affect and craving/substance use were mixed and difficult to synthesize, given methodological differences across studies. The strength and direction of these relationships varied across outcomes, subgroups, contexts, and time course.

Summary

The current literature is mixed concerning negative affect and craving/substance use associations during and following SUD treatment. Researchers should increasingly recruit diverse individuals, for example, samples of varying racial and ethnic backgrounds and those reporting co-occurring disorders and polysubstance use. Experimental, qualitative, and person-specific methods will improve our understanding of relationships between negative affect and substance-related outcomes during SUD treatment.

Hormonal contraceptives, stress, and the brain: The critical need for animal models

Hormonal contraceptives are among the most important health and economic developments in the 20thCentury, providing unprecedented reproductive control and a range of health benefits including decreased premenstrual symptoms and protections against various cancers. Hormonal contraceptives modulate neural function and stress responsivity. These changes are usually innocuous or even beneficial, including their effects onmood. However, in approximately 4-10% of users, or up to 30 million people at any given time, hormonal contraceptives trigger depression or anxiety symptoms. How hormonal contraceptives contribute to these responses and who is at risk for adverse outcomes remain unknown. In this paper, we discussstudies of hormonal contraceptive use in humans and describe the ways in which laboratory animal models of contraceptive hormone exposure will be an essential tool for expanding findings to understand the precise mechanisms by which hormonal contraceptives influence the brain, stress responses, and depression risk.

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.

Individualized learning potential in stressful times: How to leverage intensive longitudinal data to inform online learning

Societal events - such as natural disasters, political shifts, or economic downturns - are time-varying and impact the learning potential of students in unique ways. These impacts are likely accentuated during the COVID-19 pandemic, which precipitated an abrupt and wholesale transition to online education. Unfortunately, the individual-level consequences of these events are difficult to determine because the extant literature focuses on single-occasion surveys that produce only group-level inferences. To better understand individual-level variability in stress and learning, intensive longitudinal data can be leveraged. The goal of this paper is to illustrate this by discussing three different techniques for the analysis of intensive longitudinal data: (1) regression analyses; (2) multilevel models; and (3) person-specific network models, (e.g., group iterative multiple model estimation; GIMME). For each technique, a brief background in the context of education research is provided, an illustrative analysis is presented using data from college students who completed a 75-day intensive longitudinal study of cognition, somatic symptoms, anxiety, and intellectual interests during the 2016 U.S. Presidential election - a period of heightened sociopolitical stress - and strengths and limitations are considered. The paper ends with recommendations for future research, especially for intensive longitudinal studies of online education during COVID-19.

Methodological Advances in Leveraging Neuroimaging Datasets in Adolescent Substance Use Research

Purpose of Review

Recent innovations in the statistical analysis of neuroimaging data related to adolescent substance use are highlighted. Going beyond assumptions of homogeneity in small studies of regional localization, the focus is on novel approaches that integrate across regions of the brain and levels of analysis in order to detect individual differences in use along with antecedents and consequences.

Recent Findings

Three analysis approaches are considered. Multimodal approaches like the construct-network framework combine neural, behavioral (including cognitive), and self-report indicators to create comprehensive representations of risk factors for adolescent substance use. Machine learning approaches link adolescent substance use to complex patterns of brain activity detected using prediction-focused algorithms. Person-specific approaches reflect heterogeneity in functional brain connectivity associated with adolescent substance use.

Summary

When applied to specialized datasets, multimodal, machine learning, and person-specific approaches have significant potential to provide unique insights into the neural processes underlying adolescent substance use.