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Improvements in substance use disorder recovery may be achieved by recognizing that effective interventions do not work equally well for all individuals. Heterogeneity of intervention effects is traditionally examined as a function of a single variable, such as gender or baseline severity. However, responsiveness to an intervention is likely a result of multiple, intersecting factors. Latent class moderation enables the examination of heterogeneity in intervention effects across subgroups characterized by profiles of characteristics. This study analyzed data from adolescents (aged 13 to 18 years old) who needed treatment for cannabis use (n = 14,854) and participated in the Global Appraisal of Individual Needs to evaluate differential effects of substance use services on cannabis use outcomes. We demonstrate an adjusted three-step approach using weights that account for measurement error; sample codes in Mplus and Latent Gold are provided and data are publicly available. Indicators of the latent class moderator comprised six contextual (e.g., recovery environment risk) and individual (e.g., internal mental distress) risk factors. The latent class moderator comprised four subgroups: low risk (21.1%), social risk (21.1%), environmental risk (12.5%), and mixed risk (45.2%). Limited moderation of associations between level of care and any past 90-day cannabis use were observed. In predicting number of cannabis use-days, compared to individuals with low risk, those with environmental risk showed improved outcomes from intensive outpatient care whereas individuals with social risk and mixed risk showed improved outcomes from residential care (all compared to early intervention/outpatient care). Latent class moderation holds potential to elucidate heterogeneity in intervention effectiveness that otherwise may go undetected.

The Affective Symptoms Scale (ASRS) is a unique instrument designed to separately measure depressive and manic symptoms in mood disorders. We validated the ASRS against the Patient Health Questionnaire (PHQ-9) and the Quick Inventory of Depressive Symptomatology (QIDS-16).A retrospective study of 258 patients who completed the PHQ-9, QIDS-16 and ASRS as part of routine clinical care. To establish meaningful clinical thresholds for the depression subscale of the ASRS, it was equated with the QIDS and the PHQ-9.The depression subscale of the ASRS had significant positive correlations with the QIDS-16 and the PHQ-9 (respectively, r= 0.8, t[253] = 19.8, p 0.001, and r= 0.8, t[245] = 28.2, p 0.001). The equipercentile equating method with the PHQ-9 indicated that the thresholds corresponded to ASRS depression subscale scores of 5.4, 10.6, 16.1, and 23. Equating with the QIDS indicated that thresholds corresponded to ASRS depression subscale scores of 5.1, 11, 18.4, and 27.5.Limitations include a small sample size that did not allow more detailed statistical analysis, such as Item Response Theory. The population is a heterogenous population at a university outpatient setting.The ASRS depression subscale significantly correlated with the PHQ-9 and QIDS-16. Our proposed threshold scores for the ASRS are 5, 11, 16 and 23 to indicated mild, moderate, severe and very severe depression respectively.

To identify patient risk factors associated with prescription opioid misuse and abuse as well as groupings of misuse and abuse behaviors as measured by the Prescription Opioid Misuse and Abuse Questionnaire (POMAQ). Adults with chronic pain requiring long-term treatment with opioids completed the POMAQ and other study questionnaires. Latent class analysis (LCA) was used to examine underlying subgroups exhibiting particular risk profiles. Patient demographic and clinical characteristics were examined as covariates and the concordance between the identified latent classes at-risk classifications and the POMAQ clinical scoring algorithm was assessed. Analysis of data from 809 patients revealed four classes: “chronic pain, low risk” (n = 473, low to no prevalence of POMAQ behaviors), “chronic pain, comorbid condition” (n = 152, high prevalence of anti-anxiety, sleeping pill, and antihistamine use), “at risk” (n = 154, taking more opioids than prescribed and drinking alcohol with opioids more frequently than other groups), and “high risk” (n = 30, highest prevalence of each behavior). The “high risk” group was associated with being younger, less educated, and unemployed compared to other groups. When examining the LCA classes by groups defined by the original POMAQ clinical scoring algorithm, the “high risk” class had the highest proportion of participants identified with abuse behaviors (46.7%), compared to just 4.7% in the “chronic pain, low risk” group. Findings suggest there are four distinct subgroups of patients defined by chronic opioid misuse and abuse behaviors and support the use of the POMAQ to identify risk factors associated with prescription opioid misuse and abuse.

Among young adults, subjective feelings of alcohol's effects often guide risky decision making. The majority of studies measuring subjective effects have used singular indices ("How drunk do you feel?") which limits our understanding of young adults' full range of subjective states and their individual differences in subjective effects language. Toward a more in-depth understanding of the heterogeneity among alcohol users based on their subjective experiences of alcohol's effects, we identified latent classes of individuals based on their self-generated language describing feelings after drinking and compared these classes across demographic and drinking characteristics. Participants (N = 323, 54% women, 68% White, Ages 18-25 years) were recruited using Amazon's Mechanical Turk (MTurk). Participants listed words they would use to describe how they feel after drinking low, moderate, and heavy amounts of alcohol. Four latent classes of young adults emerged: "happy drinkers" (31%) primarily reported feeling "happy" when drinking; "relaxed drinkers" (24%) reported feeling happy, relaxed, and buzzed; "buzzed drinkers" (18%) reported feeling buzzed and dizzy; and "multiexperience drinkers" (27%) reported feeling buzzed, tipsy, drunk, and wasted. Relaxed drinkers indicated heavier alcohol use and buzzed drinkers reported lower drinking frequency. Classes did not differ by demographic characteristics. Young adult alcohol users can be distinguished based on the language they use to describe their feelings of intoxication. To continue to advance our understanding of subjective effects, it is necessary to take into account the full range of language used and how this language differs by young adult drinking behavior. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

At the population level, use of multiple substances (or "co-use") is prevalent in young adulthood and linked with increased risk for experiencing substance-related harms. Less understood is the heterogeneity of substance use behaviors within individuals and across days, as well as the proximal predictors of these daily use patterns. The present study applied latent class analysis to daily diary data to identify daily substance use patterns and compare day-level class membership based on day-level stress and positive and negative affect among a higher-risk sample of young adult substance users. Participants (n = 152) completed up to 13 daily assessments of stress, affect, and substance use behavior. Among substance use days, five classes of days were identified: cannabis (some alcohol; 43% of days), alcohol-only (26%), vaping (some alcohol, cannabis; 24%), stimulant + alcohol (some cannabis, vaping; 4%), and cigarette-only (3%) days. Days with lower levels of perceived stress were significantly more likely to be alcohol-only Days relative to being days characterized by cigarette use, cannabis use, or multiple drug combinations. Days with higher levels of stress and negative affect were more likely to be cigarette-only days relative to cannabis and vaping days. Study findings document the wide range of substance use and co-use behaviors exhibited among young adults in daily life and highlight the importance of considering risk factors that correspond to days of problematic use patterns.

Parent-youth intimacy protects adolescents from adjustment problems, including weight concerns, low self-esteem, and depressive symptoms. This study aimed to identify when in development parent-youth intimacy emerges as a protective factor, how this protective effect changes in its strength across adolescence, and whether there are differences in intimacy-adjustment linkages depending on parent and youth gender. The sample was 388 predominately White American adolescents (50.8% female; ages 12-20 years, M = 15.2, SD = 1.63 at Time 1) from 202 families. Time-varying effect models revealed that associations between intimacy and adjustment problems were dynamic and differed by parent and youth gender and across adjustment problems. Father-youth intimacy was associated with fewer weight concerns across most of adolescence for girls and boys, and these effects were strongest in mid- and late adolescence, respectively. Mother-youth intimacy was associated with boys' but not girls' weight concerns, and only in early adolescence. Father-youth intimacy was associated with fewer depressive symptoms for boys and girls across most of adolescence, whereas mother-youth intimacy was associated with fewer depressive symptoms in mid-adolescence. However, the association between intimacy and depressive symptoms was strongest during mid-adolescence for mother- and father-youth intimacy. Finally, father-youth intimacy was associated with higher self-esteem from early through mid-adolescence for boys and girls, whereas mother-youth intimacy was associated with higher self-esteem across most of adolescence for girls and during early and late adolescence for boys. Findings have implications for the timing and targets of family interventions and highlight parents' unique and shared roles in adolescents' adjustment. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

OBJECTIVE: This study aimed to illuminate developmental changes and gender differences in the link between weight concerns and cigarette use across adolescence. Specifically, we examined whether and how the strength of the association between weight concerns and cigarette use changed across adolescence, and whether patterns of association differed between boys and girls. METHOD: Participants were 397 predominately White adolescents ages 11–18 years (50.5% female) from a longitudinal observational study conducted in the United States. RESULTS: Time-varying effect modeling revealed that even after adjusting for BMI, the association between weight concerns and cigarette use was positive and significant for girls from age 11.3 to 15.9, with the strongest association at 12.7 years. For boys, this association was non-significant throughout adolescence. DISCUSSION: Results suggest a sensitive period in early to mid-adolescence during which girls with weight concerns may be at heightened risk for cigarette use. Findings have implications for the developmental timing of interventions to prevent cigarette use and weight concerns, and suggest that tailored interventions for girls may be warranted.

In intensive longitudinal studies using ecological momentary assessment, mood is typically assessed by repeatedly obtaining ratings for a large set of adjectives. Summarizing and analyzing these mood data can be problematic because the reliability and factor structure of such measures have rarely been evaluated in this context, which-unlike cross-sectional studies-captures between- and within-person processes. Our study examined how mood ratings (obtained thrice daily for 8 weeks

The prevalence of nicotine vaping is increasing among adolescents and emerging evidence suggests weight concerns may promote risk for vaping. The aims of this study were to investigate whether there is an association between attempting to lose weight and nicotine vaping during adolescence, when this association emerges and is strongest, and whether there are sex differences in this link.This study used time-varying effect modeling, an analytic method that estimates regression coefficients as a continuous function of age, to model dynamic associations between weight loss behavior and nicotine vaping across adolescence and sex differences in these links. Data were from 13,677 adolescents (aged 14-18 years, 49.4% female) who participated in the 2019 National Youth Risk Behavior Survey, a nationally representative U.S.Results revealed an age-varying association that differed by sex. Girls who were trying to lose weight were at a higher risk for past 30-day vaping from ages 14.2-15.9 years, with the strongest association at 14.8 years (odds ratio [OR] = 1.92, 95% confidence interval [CI] [1.25, 2.95]). For boys, this association was nonsignificant across age (OR = 1.76, 95% CI [0.91, 3.41] at age 14.0 years). Girls who were trying to lose weight were at a higher risk for frequent vaping (vaping on 20-30 of the past 30 days) from ages 15.2-17.1 years, with the strongest association at 16.1 years (OR = 2.59, 95% CI [1.45, 4.62]). This link was not meaningfully significant at any age for boys (OR = 3.26, 95% CI [0.86, 12.33] at age 14.0 years).Girls, but not boys, who are trying to lose weight appear vulnerable to nicotine vaping during adolescence.

Supportive relationships with parents and friends reduce adolescent risk for depression; however, whether and how the strength of these associations changes across adolescence remains less clear. Age-varying associations of mother-adolescent and father-adolescent closeness and friend support with depressive symptoms were examined across ages 12.5-19.5 using data from the National Longitudinal Study of Adolescent to Adult Health (N = 4,819). Positive relationships with mothers, fathers, and friends were associated with lower depressive symptoms across adolescence, and the associations were generally stable across age. The association between father-adolescent closeness and depressive symptoms was stronger for girls than for boys during mid-adolescence. Mother-adolescent closeness was more strongly negatively associated with depressive symptoms in the context of higher friend support during mid-adolescence.

BACKGROUND: Individuals who engage in alcohol and cannabis co-occurring use are at heightened risk for negative outcomes than individuals who only use alcohol or only use cannabis, but far less is known about implications of alcohol and cannabis co-occurring use for prescription drug misuse (PDM). OBJECTIVES: This study aimed to (1) identify whether co-occurring use was linked with greater risk for PDM across ages 18-60 and to determine ages at which this association was strongest, (2) determine whether associations remain after controlling for pain severity, and (3) test for gender differences. METHODS: Cross-sectional data were from Wave 1 of the Population Assessment of Tobacco and Health (PATH) study. The analytic sample included 20,120 (50.6%(wt) men) individuals aged 18-60 reporting past-year alcohol and/or cannabis use. RESULTS: Time-varying effect models indicated that individuals reporting co-occurring use were at increased risk for PDM than individuals reporting single-substance use across all ages, with odds ratios peaking at >3 at age 34. After controlling for pain severity, associations were only slightly weakened. Associations were slightly stronger for men than women from ages 28 to 35. CONCLUSION: Co-occurring use was linked with heightened risk for PDM compared to individuals who used only one substance; co-occurring use may be important to target for PDM prevention. Identifying factors underlying study associations, beyond pain, and how these factors evolve with age are important avenues for prevention work.

To develop efficient just-in-time personalised treatments, dynamical models are needed that provide a description of how an individual responds to treatment. However, available system identificatio...

Background: Substance use disorders (SUDs) and mental health disorders may change and co-occur in complex patterns across adult ages, but these processes can be difficult to capture with traditional statistical approaches. Objective: To elucidate disorder prevalence and comorbidities across adult ages by using time-varying effect models (TVEMs), latent class analysis (LCA), and modeling latent class prevalences as complex functions of age. Methods: Data were drawn from participants who are 18–65 years old in the National Epidemiologic Survey on Alcohol and Related Conditions III (n = 30,999; 51% women) and a subsample who reported a past-year post-traumatic stress disorder (PTSD), mood, anxiety, or SUD based on DSM-5 diagnoses (n = 11,279). TVEM and LCA were used to examine age trends and comorbidity patterns across ages. Results: SUD prevalence peaked at age 23 (31%) and decreased thereafter, while mental health disorder prevalence was stable (20%–26% across all ages). The prevalence of five classes of individuals based on specific combinations of mental health and SUDs varied by age: the Alcohol Use Disorder class had the highest prevalence at age 26, whereas the Mood and Anxiety Disorder classes peaked around age 63. Interestingly, the Poly-Disorder class prevalence was greatest at age 18 but decreased sharply across young adulthood; however, the prevalence of the other high comorbidity class, PTSD with Mood or Anxiety Disorder, remained fairly constant across age, peaking at age 44. Conclusions: Multimorbid mental health disorders (excluding SUDs) persist in prevalence across adult ages. LCA, TVEM, and their integration together hold substantial potential to advance addiction research.

Objectives Anhedonia can accompany substance use disorders (SUDs); its severity may vary by substance type, severity of SUD symptoms, or psychiatric comorbidity. The goal of this study was to clarify the contribution of each. Methods Data were from participants aged 18 to 65 years in the National Epidemiologic Survey on Alcohol and Related Conditions III (n = 30,999; 51% women), a cross-sectional, nationally representative sample reporting lifetime DSM-5 symptoms and lifetime anhedonia. We used logistic regression to test how anhedonia was associated with specific SUDs and psychiatric disorders in respondents with one lifetime diagnosis. We used latent class analysis to assess the association of anhedonia with patterns of comorbidity in all respondents. Results Opioid use disorder (OUD) had the greatest odds of anhedonia relative to other SUDs (ORs [95% CIs]): mild alcohol use disorder (AUD) (3.33 [1.74, 6.38]), moderate/severe AUD (2.73 [1.41, 5.30]), and cannabis use disorder (3.21 [1.43, 7.19]), though not significantly greater than stimulant use disorder (2.44 [.88, 6.73]). Anhedonia was more likely in mood disorders and post-traumatic stress disorder (PTSD) than in any SUD, except for PTSD versus OUD (OR [95% CIs] = .98 [.47, 2.02]). In latent class analysis analyses, the poly disorder class, which included SUDs and other diagnoses, had greater odds of anhedonia than the Poly SUD (ORs [95% CIs] = 1.62 [1.25, 2.09] and AUD 2.89 [2.40, 3.48]) classes. Conclusions People with OUD or a lifetime history of mood disorder or PTSD may be most likely to present to SUD treatment with anhedonia.

Background and aims The simultaneous use of alcohol and cannabis (“simultaneous alcohol and marijuana [SAM] use”) is common among young adults and associated with negative substance-related consequences. SAM use may be tied to fluctuating mood states such as negative affect and individual characteristics including trait level of anxiety and sex. However, little is understood about their collective role. In this study, we sought to understand the daily link between SAM use and negative affect and whether this link might differ by both trait anxiety and sex. Method Participants were 154 young adults (57.8% female, 72.7% White, M age = 20.2) who completed baseline surveys on trait anxiety symptoms and up to 14 consecutive daily surveys on their substance use and affective states. Results Multilevel models tested for associations of type of substance use day (i.e., alcohol-only days, cannabis-only days, and no use days relative to SAM use days) with next-day negative affect. Three-way and lower order interactions were tested for substance use day type, anxiety, and sex. Two three-way interactions between cannabis-only days, anxiety, and sex and between alcohol-only days, anxiety, and sex emerged such that SAM use was associated with greater next-day negative affect relative to single substance use days particularly among female participants with elevated anxiety. Conclusions Anxiety and sex are salient factors in the link between SAM use relative to single-substance use and daily negative affect. Study findings reinforce the need to account for all of these factors in order to develop maximally efficacious substance use interventions.

Information criteria (ICs) based on penalized likelihood, such as Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. They may not realize that the criteria may disagree. Others try to compare models using multiple criteria but encounter ambiguity when different criteria lead to substantively different answers, leading to questions about which criterion is best. In this paper we present an alternative perspective on these criteria that can help in interpreting their practical implications. Specifically, in some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC. This perspective may lead to insights about how to interpret the ICs in more complex situations. For example, AIC or BIC could be preferable, depending on the relative importance one assigns to sensitivity versus specificity. Understanding the differences and similarities among the ICs can make it easier to compare their results and to use them to make informed decisions.

Purpose Sexual minorities experience significant health disparities across a variety of mental, behavioral, and physical health indicators. Yet, an understanding of the etiology and progression of sexual minority health disparities across the lifespan is limited. Methods We used the U.S. National Epidemiologic Survey of Alcohol and Related Conditions III to evaluate the association between sexual minority status and seven past-year health outcomes (alcohol use disorder, tobacco use disorder, drug use disorder, major depressive episode, generalized anxiety disorder, sexually transmitted infection, and cardiovascular conditions). To do this, we used unadjusted and adjusted logistic regression among our study sample (n = 30,999; aged 18–65 years) and time-varying effect models to evaluate how sexual orientation differences in these outcomes vary across adulthood. Results Relative to heterosexuals, sexual minorities had elevated odds of past-year alcohol use disorder and drug use disorder across all ages (18–65 years) although the magnitude of the disparity varies by age. Sexual minorities were also more likely to experience major depressive episode, generalized anxiety disorder, tobacco use disorder, sexually transmitted infection, and cardiovascular disease, but only at specific ages. Conclusions Sexual minority health disparities vary appreciably across the adult lifespan, thus elucidating critical periods for focused prevention efforts.

Objective To extend the sparse literature on moderators, we used time-varying effect modeling (TVEM; Tan, Shiyko, Li, Li, & Dierker, 2012) to examine how depressive symptoms and duration of generalized anxiety disorder (GAD) moderated effects of 3 treatments for GAD (applied relaxation [AR], cognitive-behavioral therapy [CBT], and nondirective therapy [ND]) over time using intensive repeated measures. Method In a secondary analysis of Borkovec and Costello (1993), 66 GAD clients were randomly assigned to AR (n = 23), CBT (n = 23), or ND (n = 20). Clients received 12 therapy sessions over 6 weeks, and after 2 weeks of posttreatment assessment, had 2 additional weekly fading sessions. They completed thrice daily anxiety ratings during this 10-week period. GAD duration (Anxiety Disorders Interview Schedule-Revised) and depressive symptoms (Hamilton Depression Rating Scale) were assessed at baseline. Results Longer GAD duration predicted less anxiety reduction in CBT and ND relative to AR. These effects were pronounced in the later phase of treatment, suggesting benefits of focused relaxation practice for clients with longer duration. Higher depression predicted better response to CBT than AR and ND. The moderation effects were also more noticeable in the later phase. In multilevel analyses, a similar moderation pattern held at 1-year follow-up on clinician-rated measures. Conclusion GAD clients with long-standing symptoms may benefit more from repeatedly practicing fewer skills than learning multiple skills. On the other hand, clients with comorbid depression may respond better to CBT than AR, perhaps because CBT includes cognitive interventions that can generalize to depression. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Objectives The conceptual models underlying physical activity interventions have been based largely on differences between more and less active people. Yet physical activity is a dynamic behavior, and such models are not sensitive to factors that regulate behavior at a momentary level or how people respond to individual attempts at intervening. We demonstrate how a control systems engineering approach can be applied to develop personalized models of behavioral responses to an intensive text message-based intervention. Design & method To establish proof-of-concept for this approach, 10 adults wore activity monitors for 16 weeks and received five text messages daily at random times. Message content was randomly selected from three types of messages designed to target (1) social-cognitive processes associated with increasing physical activity, (2) social-cognitive processes associated with reducing sedentary behavior, or (3) general facts unrelated to either physical activity or sedentary behavior. A dynamical systems model was estimated for each participant to examine the magnitude and timing of responses to each type of text message. Results Models revealed heterogeneous responses to different message types that varied between people and between weekdays and weekends. Conclusions This proof-of-concept demonstration suggests that parameters from this model can be used to develop personalized algorithms for intervention delivery. More generally, these results demonstrate the potential utility of control systems engineering models for optimizing physical activity interventions.

Although stress is a common experience in everyday life, a clear understanding of how often an individual experiences and reports stress is lacking. Notably, there is little information regarding factors that may influence how frequently stress is reported, including which stress dimension is measured (i.e., stressors-did an event happen, subjective stress-how stressed do you feel, conditional stress-how stressful a stressor was) and the temporal features of that assessment (i.e., time of day, day of study, weekday vs. weekend day). The purpose of the present study was to conduct a coordinated analysis of five independent ecological momentary assessment studies utilizing varied stress reporting dimensions and temporal features. Results indicated that, within days, stress was reported at different frequencies depending on the stress dimension. Stressors were reported on 15-32% of momentary reports made within a day; across days, the frequency ranged from 42 to 76% of days. Depending on the cutoff, subjective stress was reported more frequently ranging about 8-56% of all moments within days, and 40-90% of days. Likewise, conditional stress ranged from just 3% of moments to 22%, and 11-69% of days. For the temporal features, stress was reported more frequently on weekdays (compared to weekend days) and on days earlier in the study (relative to days later in the study); time of day was inconsistently related to stress reports. In sum, stress report frequency depends in part on how stress is assessed. As such, researchers may wish to measure stress in multiple ways and, in the case of subjective and conditional stress with multiple operational definitions, to thoroughly characterize the frequency of stress reporting.

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted--weekly, daily, or even many times a day. The micro-randomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise., Comment: arXiv admin note: substantial text overlap with arXiv:2005.05880, arXiv:2004.10241

We begin with a conceptual overview of time-varying effect modeling (TVEM), focusing on definitions of “time” and how this modeling framework extends the commonly used linear regression framework. In this first chapter, we describe the concept and promise of TVEM and convey the utility of TVEM findings for researchers (e.g., epidemiologists, prevention scientists), administrators (e.g., policy-makers, public school administrators), and practitioners (e.g., clinicians). This discussion presents a wide range of research questions TVEM could be used to address. To help more concretely illustrate the conceptual approach of TVEM, we walk the reader through a recently published empirical example assessing the risk for suicidal behavior across ages 18–60 for sexual minority versus heterosexual adults. We conclude with a brief roadmap of the subsequent chapters of the book.

In this chapter, we guide the reader through model specification and interpretation in time-varying effect modeling (TVEM) with a continuous dependent variable. We begin by discussing selecting data that meet the requirements for TVEM and structuring the dataset. Next, we discuss technical details, including two variants of spline regression to estimate the coefficient functions, weighted estimation, nested structures in repeated-measures data, model selection, and significance testing. We then walk readers through a progression of four hypothetical models: an intercept-only TVEM, a model with a single independent variable (i.e., main effect model with a single covariate), an extended model that incorporates a statistical control variable, and a model to examine time-varying moderation. For each, we describe the mathematical model, the variables included in the analysis, specification of the model in SAS, and interpretation of the resultant coefficient functions. Finally, we present an empirical example examining depressive symptoms across age using data from a longitudinal panel study to facilitate a concrete understanding of the concepts presented earlier in this chapter. In this section, we walk the reader through the various decisions an analyst must make when using TVEM. A sample results section based on this empirical example, similar to what one might include in an applied paper, is included.

In this chapter, we discuss the use of time-varying effect modeling (TVEM) for examining time-varying associations in intensive longitudinal data (ILD). We will demonstrate the application of TVEM to examine associations across real-time using data from a 21-day daily diary study assessing interparental and parent-child conflict. Particular focus will be given to the meaning of the variable used to indicate time and types of research questions that TVEM is poised to address when applied to ILD. Additionally, we compare and contrast TVEM with multilevel modeling.

In this chapter, we introduce the use of time-varying effect modeling (TVEM) to examine historical change in the prevalence of health behaviors, investigate how these historical trends vary across population subgroups, and estimate the time-varying associations between risk factors and health behaviors. The empirical example focuses on the historical trends in the prevalence of cigarette smoking and the perceived risk of smoking among high school seniors from 1990 to 2017, the time-varying association between cigarette use and the perceived risk of smoking, and the time-varying moderation effect of sex. We use the publicly available repeated (annual) cross-sectional data from the Monitoring the Future study. The national sample analyzed here consists of n = 68,422 high school seniors who were assessed during this 28-year period. This chapter presents a roadmap for using TVEM to address research questions about historical shifts in (1) prevalence of outcomes and of corresponding risk factors, (2) associations between risk factors and outcomes, and (3) moderation effects that unfold over historical time.

In this chapter, we introduce readers to the use of time-varying effect modeling (TVEM) to estimate prevalences and associations that vary as a function of continuous age. Tests of age-varying associations can include either (a) tests of how associations change throughout development as people age in longitudinal studies or (b) tests of how associations differ between people of different ages in a cross-sectional study. We present an empirical example of the latter, estimating age-varying associations in a large, nationally representative cross-sectional sample of adults aged 18–65. The overall goal of this study is to examine generalized anxiety disorder (GAD) across adults aged 18–65, its links to major depressive disorder (MDD), and the moderating role of sex across this age range. Specifically, analyzing data from a subset of 30,999 adults from the National Epidemiologic Survey of Alcohol and Related Conditions-III (NESARC-III), we examine (a) prevalence of past-year GAD and past-year MDD across ages 18–65, (b) associations between MDD and GAD across age, (c) associations between sex and GAD across age, and (d) sex as a moderator of the relationship between MDD and GAD across age.

In this chapter, we discuss extensions of time-varying effect modeling (TVEM) for binary and count outcomes. Specifically, this chapter will present model specification and interpretation of (a) logistic TVEM for binary dependent variables and (b) Poisson TVEM for count dependent variables. Interpretation of logistic models will cover time-varying logistic regression coefficients, odds ratios, and predicted probabilities. We will guide the reader through an empirical example that estimates the age-varying prevalence of past-year hypertension and estimates the age-varying effects of sex, racial/ethnic group, and their interaction on past-year hypertension. Interpretation of Poisson models will cover time-varying regression coefficients, incidence rate ratios, and predicted counts. An empirical example is included that estimates the age-varying mean number of drinks during one’s typical drinking occasion during the past year. The age-varying effects of sex, racial/ethnic group, and their interaction are similarly examined.

INTRODUCTION AND AIMS: Simultaneous alcohol and marijuana (SAM) use is reported by roughly 30% of young adult drinkers. Among SAM users, SAM use days have more negative substance-related consequences than single-substance days. Little information is available about contextual factors contributing to the likelihood of SAM use on a particular day. This study compared days on which individuals reported SAM use relative to days on which they reported alcohol but not marijuana use in terms of physical location, engagement in risky activities, and social context of use. DESIGN AND METHOD: Participants were 148 young adults (57% female) reporting past-month SAM use and past two-week binge drinking. Participants completed up to 14 daily surveys assessing substance use behaviour and socio-environmental characteristics of use. RESULTS: For those

RATIONALE: Given that many patients being treated for opioid-use disorder continue to use drugs, identifying clusters of patients who share similar patterns of use might provide insight into the disorder, the processes that affect it, and ways that treatment can be personalized. OBJECTIVES AND METHODS: We applied hierarchical clustering to identify patterns of opioid and cocaine use in 309 participants being treated with methadone or buprenorphine (in a buprenorphine-naloxone formulation) for up to 16 weeks. A smartphone app was used to assess stress and craving at three random times per day over the course of the study. RESULTS: Five basic patterns of use were identified: frequent opioid use, frequent cocaine use, frequent dual use (opioids and cocaine), sporadic use, and infrequent use. These patterns were differentially associated with medication (methadone vs. buprenorphine), race, age, drug-use history, drug-related problems prior to the study, stress-coping strategies, specific triggers of use events, and levels of cue exposure, craving, and negative mood. Craving tended to increase before use in all except those who used sporadically. Craving was sharply higher during the 90 minutes following moderate-to-severe stress in those with frequent use, but only moderately higher in those with infrequent or sporadic use. CONCLUSIONS. People who share similar patterns of drug-use during treatment also tend to share similarities with respect to psychological processes that surround instances of use, such as stress-induced craving. Cluster analysis combined with smartphone-based experience sampling provides an effective strategy for studying how drug use is related to personal and environmental factors.

We examined the prevalence of psychological outcomes (i.e., symptoms of depression and anxiety) by age and age-varying associations between physical activity and psychological outcomes among rural cancer survivors. Participants (N=219; ages 22–93) completed sociodemographic, psychological, and physical activity questionnaires. Time-varying effect models (TVEM) estimated the prevalence of psychological outcomes and assessed associations between physical activity and psychological outcomes as a flexible function of age. Depression and anxiety symptoms decreased with age among cancer survivors aged 22–40 years and were relatively stable across age among those >40 years. Positive associations between vigorous physical activity and psychological outcomes in those aged 22–40 years were identified. In those >70–80 years, there were negative associations between vigorous physical activity and psychological outcomes. Results suggest there is variation across age in the associations between physical activity and psychological outcomes among rural survivors. Future research should further explore these age-varying relationships to identify important intervention targets.

Prevalence of heavy alcohol use remain high, and daily marijuana use is at an all-time high in young adults. As perceptions of drug effects may guide risky decision making, understanding subjective feelings for alcohol and marijuana use is critical. Existing laboratory-based and diary metrics (0-100 rating of "how drunk/high do you feel?") may be problematic in differentiating levels of subjective effects. Measures incorporating contemporary language may better capture subjective feelings in experimental and ambulatory assessment designs. We developed 2 sliding scales based on crowd-sourced and rank-ordered feelings of subjective alcohol and marijuana effects. Two U.S. young adult (18-25 years) samples were drawn from Amazon's Mechanical Turk (MTurk). In the first study, 323 (53.6% women, 68.4% White, M age = 23.0 years) alcohol and marijuana users provided words to describe subjective effects from alcohol use and from marijuana use. In a second study, 289 (46.4% female, 66.4% White, M age = 23.0 years) users rank-ordered the most common terms to correspond with subjective levels. The sliding scale for alcohol effects resulted in 4 anchors ranging from 0 to 100: slightly buzzed, tipsy/"happy," drunk, and wasted. The sliding scale for marijuana effects also had 4 anchors: relaxed, calm/chill, high, and stoned/baked. By incorporating broader and more contemporary subjective effects language, our metrics may better capture variability in young adults' perceived subjective effects from alcohol and marijuana use. Future work could build upon these findings by pairing sliding scales with measures of actual intoxication to understand within- and between-person covariation in subjective effects, actual impairment, and consequences. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Affect regulation models state that affect both motivates and reinforces alcohol use. We aimed to examine whether affect levels and rates of change differed across drinking versus nondrinking days in a manner consistent with affect regulation models. Four hundred four regularly drinking adults, aged 18-70 years, completed ecological momentary assessments over 3 weeks. Participants provided positive affect (PA; enthusiastic, excited, happy) and negative affect (NA; distressed, sad) reports during all prompts; alcohol consumption reports were also provided. Multilevel spline models revealed that on drinking days, PA was higher and NA was lower both before and after drinking compared to matched times on nondrinking days. PA and NA were also higher and lower, respectively, both before and after drinking, when heavy drinking days were compared to moderate drinking days. Examination of affect rates of change revealed that (a) accelerating increases in PA and accelerating decreases in NA preceded drinking initiation, (b) PA increases and NA decreases were seen up to 2 hr after drinking initiation, and (c) pre- and postdrinking PA increases were larger on heavy versus moderate drinking days, whereas only postdrinking NA decreases were larger on heavy drinking days. Results supported affect regulation models while adding nuance, showing accelerating changes in predrinking affect on drinking days and pre- and postdrinking differences in affect levels and rates of change across days of varying drinking intensity. Beyond theory, our results suggest that accelerating changes in affect may provide a clue to future commencement of heavy drinking, which may aid momentary intervention development. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Simultaneous alcohol and marijuana (SAM) use is prevalent among young adult drinkers and associated with increased risk for harms. Less understood about SAM use is whether increased risk is incurred on SAM use occasions relative to occasions in which individuals used only 1 substance. From a sample of young adult SAM users, we compared occasions in which individuals simultaneously used alcohol and marijuana so that the effects overlapped ("SAM days"), occasions involving only alcohol ("alcohol days"), and occasions involving only marijuana ("marijuana days") on level of use, level of subjective intoxication, and negative and positive consequences. Eligible participants (N = 154; 57.8% female, 72.7% non-Hispanic/Latinx White) were 18-25 years old, reported past-month SAM use, and reported past 2-week binge drinking. Participants completed up to 14 daily assessments on their substance use behavior. Multilevel models indicated that, relative to alcohol days, SAM days had more negative consequences. SAM and alcohol days were not statistically different on level of use, level of subjective alcohol intoxication, or positive consequences. Relative to marijuana days, SAM days had more negative consequences and more positive consequences. Level of use (number of hits) and level of marijuana intoxication were not statistically different. In addition to SAM users being a more at-risk group, SAM occasions are associated with greater risk within this vulnerable population. Fine-grained ecological momentary assessments are needed to better understand perceptions and behavior throughout a SAM use episode. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Introduction Subjective ratings of cannabis effects are important predictors of use-related consequences. However, psychometric research is fairly limited, particularly for measures to capture variability in daily life when diverse modes of cannabis administration and co-substance use are common. Methods This study evaluated the predictive utility of a revised item to assess perceived cannabis effects and examined modes of cannabis administration and alcohol and nicotine co-use as moderators. Participants were 106 young adults (18-25 years; 51% female) who completed up to 14 consecutive daily reports of substance use (n = 1,405 person-days). Two measures of subjective effects were examined: a standard item (0-100 rating of “how high do you feel?”) and a revised item that uses four crowd-sourced anchor points ranging from relaxed (0), calm/chill (33), high (67), and stoned/baked (100). The items shared substantial variance (Pseudo-R2 = 59.5%), however, the revised item showed greater within-person variability (77.0% vs. 68.8%) and stronger day-level associations with consumption levels (Pseudo-R2 = 25.0% vs. 16.7%). Results The cannabis consumption-subjective effects link was weaker on blunt-only days compared to vape-only days. Subjective cannabis effects were higher on nicotine co-use days after controlling for cannabis consumption; neither alcohol nor nicotine co-use moderated the cannabis consumption-subjective effects link. Discussion The revised subjective cannabis effects item is a viable alternative to the standard item among young adults who engage in simultaneous alcohol and cannabis use. Conclusions Future research focused on characterizing the variability in cannabis effects is needed.

AIMS: Alcohol use disorders (AUDs) are linked with numerous severe detrimental outcomes. Evidence suggests that there is a typology of individuals with an AUD based on the symptoms they report. Scant research has identified how these groups may vary in prevalence by age, which could highlight aspects of problematic drinking behavior that are particularly salient at different ages. Our study aimed to (a) identify latent classes of drinkers with AUD that differ based on symptoms of AUD and (b) examine prevalences of latent classes by age. SHORT SUMMARY: Our findings advocate for personalized treatment approaches for AUD and highlight the need for carefully considering the role of age in prevention and intervention efforts. METHODS: We used data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III). Current drinkers aged 18–64 who met criteria for a past-year AUD were included (n = 5402). RESULTS: Latent class analysis (LCA) based on 11 AUD criteria revealed 5 classes: ‘Alcohol-Induced Injury’ (25%), ‘Highly Problematic, Low Perceived Life Interference’ (21%), ‘Adverse Effects Only’ (34%), ‘Difficulty Cutting Back’ (13%) and ‘Highly Problematic’ (7%). Using time-varying effect modeling (TVEM), each class was found to vary in prevalence across age. The Adverse Effects Only and Highly Problematic, Low Perceived Life Interference classes were particularly prevalent among younger adults, and the Difficulty Cutting Back and Alcohol-Induced Injury classes were more prevalent as age increased. CONCLUSIONS: Findings suggest that experience of AUD is not only heterogeneous in nature but also that the prevalence of these subgroups vary across age.

Purpose The goal of this study was to examine changes in contemporary patterns of marijuana use and attitudes over time. Given shifts in state-by-state marijuana legislation, there is a reason to believe that the patterns of high school seniors’ marijuana use behavior and attitudes about use have changed from 2010 to 2016. Methods Data are from high school seniors in the 2010–2016 waves of Monitoring the Future, a nationally representative, cross-sectional survey in the United States. Latent class analysis was used to determine underlying patterns of marijuana use and related attitudes. Differences in latent class membership were examined across years, and gender and race/ethnicity were also examined. Results Five latent classes were identified: Intolerant Nonusers (49% of the sample), Tolerant Nonusers (12%), Disapproving Users (7%), Experimenters (6%), and Marijuana Enthusiasts (26%). Class prevalences remained relatively stable from 2010 to 2013; beginning in 2014 a significant decrease in prevalence was observed for Intolerant Nonusers and significant increases in prevalence were observed for Tolerant Nonusers and Marijuana Enthusiasts. Membership in the Marijuana Enthusiasts class was consistently more likely for males; Hispanic and black adolescents were more likely to be Disapproving Users. Conclusions Contemporary patterns of marijuana use and attitudes have remained consistent over time, however, significant shifts in class prevalence emerged in 2014. Findings also suggest that a substantial subgroup of adolescents would benefit from prevention and intervention efforts targeting marijuana use and attitudes.

Despite widespread interest in variance in affect, basic questions remain pertaining to the relative proportions of between-person and within-person variance, the contribution of days and moments, and the reliability of these estimates. We addressed these questions by decomposing negative affect and positive affect variance across three levels (person, day, moment), and calculating reliability using a coordinated analysis of seven daily diary, ecological momentary assessment (EMA), and diary-EMA hybrid studies (across studies age = 18-84 years, total Npersons = 2,103, total Nobservations = 45,065). Across studies, within-person variance was sizeable (negative affect: 45% to 66%, positive affect: 25% to 74%); in EMA more within-person variance was attributable to momentary rather than daily level. Reliability was adequate to high at all levels of analysis (within-person: .73-.91; between-person: .96-1.00) despite different items and designs. We discuss the implications of these results for the design of future intensive studies of affect variance.

BACKGROUND: Young adults report the heaviest drinking of any age group, and many are at risk for experiencing an alcohol use disorder. Most research investigating young adult drinking has focused on single indicators of use. Using multiple dimensions of consumption, such as federal guidelines for daily/weekly drinking and engagement in drinking at twice the binge threshold (“high-intensity drinking”) to characterize drinking behavior could illuminate drinking patterns linked with harms. OBJECTIVES: We used a person-centered approach to examine latent classes of drinkers from a national sample of young adults. Further, we compared classes on college status. METHODS: We used 2012–2013 data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)-III. We included past-year drinkers aged 18–22 years (n = 2213). Latent classes were estimated based on drinking frequency, daily/weekly drinking, frequency of heavy episodic drinking (4+/5+ drinks for women/men), frequency of high-intensity drinking (8+/10+ drinks), and intoxication frequency. RESULTS: Five latent classes were identified: Occasional, Light Drinkers (30%), Regular Drinkers (6%), Infrequent Drinkers with Occasional Binging (10%), Frequent Drinkers with Occasional Binging (22%), and High-Intensity Drinkers (32%). Although membership in the two riskiest classes were more common among college-attenders, odds of being a High-Intensity Drinker relative to the second riskiest class was not significantly different for college- and non-college-attending young adults. CONCLUSIONS/IMPORTANCE: As high-intensity drinking does not appear to be a drinking pattern unique to college-attenders and non-college-attenders are less likely to mature out of heavy drinking patterns, intervention efforts are needed for this at-risk age group.

Introduction Vaping has recently increased in popularity among adolescents. Little is known about heterogeneity of vapers, particularly in terms of why they vape. Identifying major subgroups of adolescent vapers by reasons for vaping is important to understand adolescent vaping behavior and to identify those most at risk for other substance use. Methods Monitoring Future data from 2015 and 2016 were used in a latent class analysis to identify subgroups of 12th graders based on their endorsement of 10 potential reasons for vaping. Multinomial regression with a latent class outcome was used to predict class membership. Results Three distinct classes of vapers were identified: adolescents who were (1) Vaping to Experiment (29.4%), (2) Vaping to Replace Cigarettes (7.3%), and (3) Vaping for Taste + Entertainment (63.4%). Vaping only flavors was associated with lower odds of membership and cigarette use was associated with higher odds of membership in the Vaping to Replace Cigarettes subgroup, and marijuana was associated with lower odds of membership in the Vaping to Experiment subgroup, compared with the Vaping for Taste + Entertainment subgroup. Conclusions This study identified multiple subgroups of vapers based on reasons for vaping. Whereas a small subgroup vaped for reasons related to cigarette use, most adolescent vapers reported vaping for reasons unrelated to cigarette use. There were considerable differences in primary reasons for vaping and risk for traditional cigarette and other substance use, suggesting different intervention strategies may be needed for different subgroups of vapers.