Background
The rate of participant attrition in alcohol clinical trials is often substantial and can cause significant issues with regard to the handling of missing data in statistical analyses of ...treatment effects. It is common for researchers to assume that missing data is indicative of participant relapse, and under that assumption, many researchers have relied on setting all missing values to the worst‐case scenario for the outcome (e.g., missing = heavy drinking). This sort of single‐imputation method has been criticized for producing biased results in other areas of clinical research, but has not been evaluated within the context of alcohol clinical trials, and many alcohol researchers continue to use the missing = heavy drinking assumption.
Methods
Data from the COMBINE study, a multisite randomized clinical trial, were used to generate simulated situations of missing data under a variety of conditions and assumptions. We manipulated the sample size (n = 200, 500, and 1,000) and dropout rate (5, 10, 25, 30%) under 3 missing data assumptions (missing completely at random, missing at random, and missing not at random). We then examined the association between receiving naltrexone and heavy drinking during the first 10 weeks following treatment using 5 methods for treating missing data (complete case analysis CCA, last observation carried forward LOCF, missing = heavy drinking, multiple imputation MI, and full information maximum likelihood FIML).
Results
CCA, LOCF, and missing = heavy drinking produced the most biased naltrexone effect estimates and standard errors under conditions that are likely to exist in randomized clinical trials. MI and FIML produced the least biased naltrexone effect estimates and standard errors.
Conclusions
Assuming that missing = heavy drinking produces biased results of the treatment effect and should not be used to evaluate treatment effects in alcohol clinical trials.
Mobile apps for mental health have the potential to overcome access barriers to mental health care, but there is little information on whether patients use the interventions as intended and the ...impact they have on mental health outcomes.
The objective of our study was to document and compare use patterns and clinical outcomes across the United States between 3 different self-guided mobile apps for depression.
Participants were recruited through Web-based advertisements and social media and were randomly assigned to 1 of 3 mood apps. Treatment and assessment were conducted remotely on each participant's smartphone or tablet with minimal contact with study staff. We enrolled 626 English-speaking adults (≥18 years old) with mild to moderate depression as determined by a 9-item Patient Health Questionnaire (PHQ-9) score ≥5, or if their score on item 10 was ≥2. The apps were (1) Project: EVO, a cognitive training app theorized to mitigate depressive symptoms by improving cognitive control, (2) iPST, an app based on an evidence-based psychotherapy for depression, and (3) Health Tips, a treatment control. Outcomes were scores on the PHQ-9 and the Sheehan Disability Scale. Adherence to treatment was measured as number of times participants opened and used the apps as instructed.
We randomly assigned 211 participants to iPST, 209 to Project: EVO, and 206 to Health Tips. Among the participants, 77.0% (482/626) had a PHQ-9 score >10 (moderately depressed). Among the participants using the 2 active apps, 57.9% (243/420) did not download their assigned intervention app but did not differ demographically from those who did. Differential treatment effects were present in participants with baseline PHQ-9 score >10, with the cognitive training and problem-solving apps resulting in greater effects on mood than the information control app (χ22=6.46, P=.04).
Mobile apps for depression appear to have their greatest impact on people with more moderate levels of depression. In particular, an app that is designed to engage cognitive correlates of depression had the strongest effect on depressed mood in this sample. This study suggests that mobile apps reach many people and are useful for more moderate levels of depression.
Clinicaltrials.gov NCT00540865; https://www.clinicaltrials.gov/ct2/show/NCT00540865 (Archived by WebCite at http://www.webcitation.org/6mj8IPqQr).
Background
Alcohol use disorder (AUD) is a highly prevalent public health problem associated with considerable individual and societal costs. Abstinence from alcohol is the most widely accepted ...target of treatment for AUD, but it severely limits treatment options and could deter individuals who prefer to reduce their drinking from seeking treatment. Clinical validation of reduced alcohol consumption as the primary outcome of alcohol clinical trials is critical for expanding treatment options. One potentially useful measure of alcohol treatment outcome is a reduction in the World Health Organization (WHO, International Guide for Monitoring Alcohol Consumption and Related Harm. Geneva, Switzerland, 2000) risk levels of alcohol use (very high risk, high risk, moderate risk, and low risk). For example, a 2‐shift reduction in WHO risk levels (e.g., high risk to low risk) has been used by the European Medicines Agency (2010, Guideline on the Development of Medicinal Products for the Treatment of Alcohol Dependence. UK) to evaluate nalmefene as a treatment for alcohol dependence (AD; Mann et al. 2013, Biol Psychiatry 73, 706–13).
Methods
The current study was a secondary data analysis of the COMBINE study (n = 1,383; Anton et al., ) to examine the association between reductions in WHO risk levels and reductions in alcohol‐related consequences and mental health symptoms during and following treatment in patients with AD.
Results
Any reduction in WHO risk drinking level during treatment was associated with significantly fewer alcohol‐related consequences and improved mental health at the end of treatment and for up to 1 year posttreatment. A greater reduction in WHO risk drinking level predicted a greater reduction in consequences and greater improvements in mental health.
Conclusions
Changes in WHO risk levels appear to be a valid end point for alcohol clinical trials. Based on the current findings, reductions in WHO risk drinking levels during treatment reflect meaningful reductions in alcohol‐related consequences and improved functioning.
As a chronic disorder, the optimal management of alcohol dependence should include reductions in alcohol consumption. The World Health Organization (WHO) risk levels provide targets for alcohol risk reduction that are strongly associated with meaningful reductions in alcohol‐related consequences. This figure shows the average Drinker Inventory of Consequences (DrInC) total scores by change in WHO risk level from baseline to end of treatment (left figure) and one year posttreatment (right figure).
Background
Research shows that improvements in coping strategies, abstinence self-efficacy, craving, and depression are potential mechanisms of behavioral change (MOBC) in treatments for substance ...use disorders (SUDs). However, little is known about how these insights regarding MOBC can be applied to SUD treatment settings. One way to facilitate MOBC-informed care in frontline settings could be to measure and monitor changes in MOBC throughout treatment using brief, frequent questionnaires that patients complete by using mobile technologies (eg, smartphones). The results derived from these questionnaires could potentially be used for clinical monitoring (ie, measurement-based care) to better understand whether individual patients are experiencing treatment-related improvements on key clinical targets.
Objective
This study evaluated whether brief, weekly MOBC questionnaires completed by patients remotely can potentially provide clinically meaningful information about changes in MOBC in the context of real-world, community-based SUD treatment.
Methods
A total of 30 patients (14/30, 47% female; 13/30, 43% racial or ethnic minority) in a community SUD treatment clinic participated in a pilot study where they were invited to complete brief, weekly questionnaires that assessed various MOBC, including coping strategies, abstinence self-efficacy, craving, depression, and therapeutic alliance. Questionnaires were typically completed remotely via smartphone for up to 6 months; 618 questionnaires were completed in total. Participants also completed longer, psychometrically validated measures of the same MOBC at baseline and 6-month research appointments. Statistical analyses tested whether brief, weekly, remotely completed MOBC questionnaires exhibited characteristics that would be desirable for real-world longitudinal clinical monitoring, including a tendency to detect within-person changes in MOBC over time; cross-sectional and longitudinal associations with longer, psychometrically validated measures completed at research appointments; and similar patterns of associations with 6-month percentage of days abstinent as longer, psychometrically validated MOBC measures completed at research appointments.
Results
The results of this study indicated that the brief, weekly, remotely completed MOBC measures exhibited characteristics that are desirable for clinical monitoring, including a tendency to vary longitudinally (within patients over time) more often than measures of alcohol and drug consumption, generally having medium to large cross-sectional and longitudinal correlations with longer psychometrically validated measures of MOBC completed at research appointments, and generally having similar patterns of association with 6-month percentage of days abstinent from alcohol and drugs as longer psychometrically validated MOBC measures completed at research appointments.
Conclusions
The results of this pilot study provide initial evidence that incorporating brief, weekly, and remotely completed MOBC questionnaires into community SUD treatment may be a viable approach for facilitating MOBC-informed care. Such questionnaires can potentially support measurement-based care by providing meaningful information about within-patient changes in clinical domains that are often directly targeted in SUD treatments and predict long-term substance use outcomes.
Abstract Motivational interviewing (MI) is an efficacious treatment for substance use disorders and other problem behaviors. Studies on MI fidelity and mechanisms of change typically use human raters ...to code therapy sessions, which requires considerable time, training, and financial costs. Natural language processing techniques have recently been utilized for coding MI sessions using machine learning techniques, rather than human coders, and preliminary results have suggested these methods hold promise. The current study extends this previous work by introducing two natural language processing models for automatically coding MI sessions via computer. The two models differ in the way they semantically represent session content, utilizing either 1) simple discrete sentence features (DSF model) and 2) more complex recursive neural networks (RNN model). Utterance- and session-level predictions from these models were compared to ratings provided by human coders using a large sample of MI sessions ( N = 341 sessions; 78,977 clinician and client talk turns) from 6 MI studies. Results show that the DSF model generally had slightly better performance compared to the RNN model. The DSF model had “good” or higher utterance-level agreement with human coders (Cohen's kappa > 0.60) for open and closed questions, affirm, giving information, and follow/neutral (all therapist codes); considerably higher agreement was obtained for session-level indices, and many estimates were competitive with human-to-human agreement. However, there was poor agreement for client change talk, client sustain talk, and therapist MI-inconsistent behaviors. Natural language processing methods provide accurate representations of human derived behavioral codes and could offer substantial improvements to the efficiency and scale in which MI mechanisms of change research and fidelity monitoring are conducted.
Although alcohol use disorder can complicate depression management, there is no standard process for assessing AUD symptoms (i.e., AUD diagnostic criteria) in primary care for patients who screen ...positive for depression. This study characterizes the association between depressive symptoms and high-risk drinking reported by primary care patients on screening measures in routine care. Then, using data from a novel clinical program, this study characterizes the association between depressive symptoms and AUD symptoms reported by primary care patients with high-risk drinking via an Alcohol Symptom Checklist.
In this cross-sectional study, electronic health record data were obtained from patients who visited 33 Kaiser Permanente Washington primary care clinics between 03/2018 and 02/2020 and completed depression (PHQ-2) and alcohol consumption (AUDIT-C) screening measures as part of routine care (N = 369,943). Patients who reported high-risk drinking (AUDIT-C scores 7-12) also completed an Alcohol Symptom Checklist where they reported the presence or absence of 11 AUD criteria as defined by the DSM-5 (N = 8,184). Generalized linear models estimated and compared the prevalence of high-risk drinking (AUDIT-C scores 7-12) and probable AUD (2-11 AUD symptoms on Alcohol Symptom Checklists) for patients with and without positive depression screens.
Patients who screened positive for depression had a 131% higher prevalence of high-risk drinking than those who screened negative (5.2% vs. 2.2%; p < 0.001). Among patients with high-risk drinking, positive depression screens were associated with a significantly higher prevalence of probable AUD (69.8% vs. 48.0%; p < 0.001), with large differences in the prevalence of probable AUD observed with increasing PHQ-2 scores (e.g., probable AUD prevalence of 37.6%, 55.3% and 65.2%, for PHQ-2 scores of 0, 1, and 2, respectively). Although the overall prevalence of high-risk drinking was higher for male patients, similar patterns of association between depression screens, high-risk drinking, and AUD symptoms were observed for male and female patients.
Patients with positive depression screens are more likely to have high-risk drinking. Large percentages of patients with positive depression screens and high-risk drinking report symptoms consistent with AUD to healthcare providers when given the opportunity to do so using an Alcohol Symptom Checklist.
Racial discrimination, including microaggressions, contributes to health inequities, yet research on discrimination and microaggressions has focused on single measures without adequate psychometric ...evaluation. To address this gap, we examined the psychometric performance of three discrimination/microaggression measures among American Indian and Alaska Native (AI/AN) college students in a large Southwestern city.
Students (N = 347; 65% female; ages 18-65) completed the revised-Everyday Discrimination Scale, Microaggressions Distress Scale, and Experiences of Discrimination measure. The psychometric performance of these measures was evaluated using item response theory and confirmatory factor analyses. Associations of these measures with age, gender, household income, substance use, and self-rated physical health were examined.
Discrimination and microaggression items varied from infrequently to almost universally endorsed and each measure was unidimensional and moderately correlated with the other two measures. Most items contributed information about the overall severity of discrimination and collectively provided information across a continuum from everyday microaggressions to physical assault. Greater exposure to discrimination on each measure had small but significant associations with more substance use, lower income, and poorer self-rated physical health. The Experiences of Discrimination measure included more severe forms of discrimination, while the revised-Everyday Discrimination Scale and the Microaggressions Distress Scale represented a wider range of severity.
In clinical practice, these measures can index varying levels of discrimination for AI/ANs, particularly for those in higher educational settings. This study also informs the measurement of racial discrimination and microaggressions more broadly.
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