Matison W. McCool’s research while affiliated with University of New Mexico and other places

Objective: Due to little knowledge regarding the contextual factors related to cannabis use, we aimed to provide descriptive statistics regarding contextual factors related to use and examine the predictive ability of contextual factors. Method: We included college student participants (n = 5700; male = 2893, female = 3702, other gender identity = 48, missing = 57) from three multi-site studies in our analyses. We examined the means and standard deviations of contextual factors related to cannabis use (social context/setting, form of cannabis, route of administration, source of purchase, and proxies of use). Additionally, we tested the predictive ability of the contextual factors on cannabis use consequences, protective behavioral strategies, and severity of cannabis use disorder, via an exploratory machine learning model (random forest). Results: Descriptive statistics and the correlations between the contextual factors and the three outcomes are provided. Exploratory random forests indicated that contextual factors may be helpful in predicting consequences and protective behavioral strategies and especially useful in predicting the severity of cannabis use disorder. Conclusions: Contextual factors of cannabis use warrants further exploration, especially considering the difficulty in assessing dosage when individuals are likely to consume in a group context. We propose considering measuring contextual factors along with use in the past 30 days and consequences of use.

Background Individuals with a substance use disorder complete ecological momentary assessments (EMA) at lower rates than community samples. Previous research in tobacco users indicates that early log‐in counts to smoking cessation websites predicted subsequent smoking cessation website usage. We extended this line of research to examine individuals who are seeking to change their drinking behaviors through mutual support groups. We examined whether adherence in the first 7 days (1487 observations) of an intensive longitudinal study design could predict subsequent EMA protocol adherence (50% and 80% adherence separately) at 30 (5700 observations) and 60 days (10,750 observations). Methods Participants (n = 132) attending mutual‐help groups for alcohol use completed two assessments per day for 6 months. We trained four classification models (logistic regression, recursive partitioning, support vector machines, and neural networks) using a training dataset (80% of the data) with each of the first 7 days' cumulative EMA assessment completion. We then tested these models to predict the remaining 20% of the data and evaluated model classification accuracy. We also used univariate receiver operating characteristic curves to examine the minimal combination of days and completion percentage to best predict subsequent adherence. Results Different modeling techniques can be used with early assessment completion as predictors to accurately classify individuals that will meet minimal and optimal adherence rates later in the study. Models ranged in their performance from poor to outstanding classification, with no single model clearly outperforming other models. Conclusions Traditional and machine learning approaches can be used concurrently to examine several methods of predicting EMA adherence based on early assessment completion. Future studies could investigate the use of several algorithms in real time to help improve participant adherence rates by monitoring early adherence and using early assessment completion as features in predictive modeling.

Objective: Predicting the presence and severity of suicidal ideation in college students is important, as deaths by suicide amongst young adults have increased in the past 20 years. Participants: We recruited college students (N = 5494) from ten universities across eight states. Method: Participants answered three questionnaires related to lifetime and past month suicidal ideation, and an indicator of suicidal ideation in a DSM-5 symptom measure. We used recursive partitioning to predict the presence, absence, and severity, of suicidal ideation. Results: Recursive partitioning models varied in their accuracy and performance. The best-performing model consisted of predictors and outcomes measured by the DSM-5 Level 1 Cross-Cutting Symptom Measure. Sexual orientation was also an important predictor in most models. Conclusions: A single measure of DSM-5 symptom severity may help universities understand suicide severity to promote targeted interventions. Though further work is needed, as similar scaling amongst predictors could have influenced the model.

Background: Skills learned in Dialectical Behavior Therapy (DBT) are a proposed mechanism that prompts behavior change. Few studies have examined the effects of DBT skills on treatment outcomes. No published studies have examined the effects of DBT skills on alcohol and substance use outcomes. Objectives: This study examined 48 individuals in a community mental health facility that delivers DBT-adherent treatment. Utilizing intake data and diary cards, multilevel model analyses were conducted to examine the effects each DBT skills domain had on urges for participants that entered treatment with varying frequencies of alcohol and substance use. Results: Emotion regulation and mindfulness skills domains were related to decreased urges for individuals that entered treatment with high frequencies of alcohol and substance use. Previous-day distress tolerance skills were associated with decreased urges and previous-day interpersonal effectiveness skills were associated with decreased urges for individuals that entered treatment with high frequencies of substance use. Conclusions: DBT skills may be a helpful mechanism to decrease urges for individuals that use alcohol and other substances. However, more research on why certain skills domains may be more effective is needed.

Communities commonly warn against heavy alcohol and other substance use during natural disasters like hurricanes, because such use may produce risk for individuals and communities, with studies showing deleterious effects persisting months or even years. Examining patterns and emotional correlates of use in the immediate presence of hurricanes may identify useful risk prevention targets. We assessed self‐reported substance use and emotions in a university community (faculty, staff, and students) having the unlucky fate of experiencing hurricanes in early September 2 years in a row. Participants (403 in 2018, 76.0% female; M age 28.82; SD = 12.36 and 292 in 2019, 72.6% female; M age 30.63; SD = 13.96) reported typical weekly substance use and emotions and then the same data during each hurricane day. Results showed elevated use of alcohol, caffeine and tobacco before and during each hurricane, but a rapid drop‐off of alcohol and caffeine (but not tobacco) use immediately after—although anxiety remained high. Findings are interpreted using both tension‐reduction and stress‐coping models and suggestions are made for future risk mitigation.