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).
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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.
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.
•We reviewed the evidence on how MI intervention may result in client change.•MI skills are related to client mechanisms of change (i.e. change and sustain talk).•Client sustain talk is more ...predictive of behavioral outcome than change talk.•Research on relational and conflict resolution processes is needed.
This work provides an overview of Motivational Interviewing (MI) theory, the nature of the evidence for its mechanisms of action, and considers future directions. There are three hypotheses purported to explain how MI works: The Technical Hypothesis, the Relational Hypothesis, and the Conflict Resolution Hypothesis. In contrast to the latter two hypotheses, the Technical Hypothesis has received the most empirical attention in the MI process literature. Research shows that clinician technical skills in MI are well-defined, they relate to the intended client mechanisms (i.e. change talk and sustain talk), but the evidence supporting client mechanisms as predictors of subsequent changes to behavior is less conclusive. Future research and clinical implications are briefly considered.
Background and Aims
Recovery from alcohol use disorder (AUD) is often narrowly defined by abstinence from alcohol and improvements in functioning (e.g. mental health, social functioning, employment). ...This study used latent profile analysis to examine variability in recovery outcomes, defined by alcohol use, alcohol‐related problems and psychosocial functioning at 3 years following treatment. Secondary analysis investigated pre‐treatment, post‐treatment and 1‐ and 3‐year post‐treatment covariate predictors of the latent profiles.
Design
Secondary analysis of data from a randomized clinical trial.
Setting
United States.
Participants
We used data from the out‐patient arm of Project MATCH (n = 806; 29.7% female, 22.2% non‐white).
Measurements
Recovery was defined by latent profile analyses including measures of psychosocial functioning and life satisfaction (Psychosocial Functioning Inventory), unemployment and mental health (Addiction Severity Index), alcohol and other drug use (Form 90) and alcohol‐related consequences (Drinker Inventory of Consequences) 3 years following treatment. Mixture modeling was used to examine correlates of profiles.
Findings
We identified four profiles at 3 years following treatment: (1) poor functioning frequent heavy drinkers, (2) poor functioning infrequent heavy drinkers, (3) high functioning occasional heavy drinkers and (4) high‐functioning infrequent non‐heavy drinkers. There were relatively few differences on indicators of functioning and treatment‐related variables between the high functioning infrequent non‐heavy drinkers and the high‐functioning occasional heavy drinkers, other than high‐functioning occasional heavy drinkers having lower alcohol dependence severity odds ratio (OR) = 0.94, 95% confidence interval (CI) = 0.90, 0.98, fewer post‐treatment coping skills (OR = 0.54, 95% CI = 0.32, 0.90) and lower 3‐year post‐treatment abstinence self‐efficacy (OR = 0.37, 95% CI = 0.28, 0.49) and Alcoholics Anonymous (AA) involvement (OR = 0.87, 95% CI = 0.85, 0.99). The two high‐functioning profiles showed the greatest improvements in functioning from baseline through the 3‐year follow‐up, whereas the low‐functioning profiles showed the least amount of improvement. High‐functioning occasional heavy drinkers had higher purpose in life than the poor‐functioning profiles.
Conclusions
Some individuals who engage in heavy drinking following treatment for alcohol use disorder may function as well as those who are mostly abstinent with respect to psychosocial functioning, employment, life satisfaction and mental health.
•Post-treatment change in alcohol use is a heterogeneous process.•Individuals variably transition in and out of “relapse” and “remission” statuses.•“Any heavy drinking” following treatment (tx) is ...not necessarily a sign of tx failure.•Preventing heavy drinking and intervening quickly if it occurs seems most crucial.•Evaluating “time to first heavy drinking episode” may not capture behavior change.
We sought to understand alcohol behavior change as a process over time by identifying patterns of relapse and remission after outpatient treatment and evaluating how these patterns predict longer-term clinical outcomes.
We conducted latent profile analyses using data from the outpatient arm in Project MATCH. Relapse and remission episodes were defined by the number of consecutive 14-day periods that included any heavy drinking days and no heavy drinking days. Indicators of each profile were: initial 2-week post-treatment remission/relapse status, number of remission/relapse transitions in the first year after treatment, duration of remission episodes, and duration of relapse episodes.
We identified 6 profiles: 1) “remission,” 2) “transition to remission”, 3) “few long transitions,” 4) “many short transitions,” 5) “transition to relapse,” and 6) “relapse.” Profile 1 had the best long-term outcomes. Long-term outcomes were not uniform among individuals with at least some heavy drinking (profiles 2 through 6; ∼75% of the sample). Individuals who transitioned back to and sustained periods of remission (profiles 2–4) had better long-term outcomes than those who failed to transition out of relapse (profiles 5–6) following treatment.
Post-treatment change in alcohol use is a process in which individuals variably transition in and out of “relapse” and “remission” statuses. “Any heavy drinking” following treatment is not necessarily a sign of treatment failure. A more nuanced look at the process of AUD change by considering whether individuals are able to transition to and sustain periods of remission seems warranted.
Objective:This study aimed to evaluate the feasibility and clinical utility of training intensive psychiatric community care team members to serve as “mobile interventionists” who engage patients in ...recovery-oriented texting exchanges.Methods:A 3-month pilot randomized controlled trial was conducted to compare the mobile interventionist approach as an add-on to assertive community treatment (ACT) versus ACT alone. Participants were 49 individuals with serious mental illness (62% with schizophrenia/schizoaffective disorder, 24% with bipolar disorder, and 14% with depression). Clinical outcomes were evaluated at baseline, posttreatment, and 6-month follow-up, and satisfaction was evaluated posttreatment.Results:The intervention appeared feasible (95% of participants assigned to the mobile interventionist arm initiated the intervention, texting on 69% of possible days and averaging four messages per day), acceptable (91% reported satisfaction), and safe (no adverse events reported). Exploratory posttreatment clinical effect estimations suggested greater reductions in the severity of paranoid thoughts (Cohen’s d=–0.61) and depression (d=–0.59) and improved illness management (d=0.31) and recovery (d=0.23) in the mobile interventionist group.Conclusions:Augmentation of care with a texting mobile interventionist proved to be feasible, acceptable, safe, and clinically promising. The findings are encouraging given the relative ease of training practitioners to serve as mobile interventionists, the low burden placed on patients and practitioners, and the simplicity of the technology. The technical resources are widely accessible to patients and practitioners, boding well for potential intervention scalability. When pandemics such as COVID-19 block the possibility of in-person patient-provider contact, evidence-based texting interventions can serve a crucial role in supporting continuity of care.
Background and Aims
The Opioid Use Disorder (OUD) Cascade of Care is a public health model that has been used to measure population‐level OUD risk, treatment engagement, retention, service and ...outcome indicators. However, no studies have examined its relevance for American Indian and Alaska Native (AI/AN) communities. Thus, we aimed to understand (1) the utility of existing stages and (2) the relative ‘fit’ of the OUD Cascade of Care from a tribal perspective.
Design, Setting, Participants and Measurements
Qualitative analysis of in‐depth interviews with 20 individuals who were knowledgeable regarding the treatment of OUD in an Anishinaabe tribal setting in Minnesota, USA. Community member roles included clinicians, peer support specialists and cultural practitioners, among others. Thematic analysis was used to analyze the data.
Findings
Participants identified the key transition points of prevention, assessment, inpatient/outpatient pathways and recovery as relevant to their community. They re‐imagined an Aanji'bide (Changing our Paths) model of opioid recovery and change that was non‐linear; included developmental stage and individual pathways; and demonstrated resilience through connection to culture/spirituality, community and others.
Conclusions
Community members living/working in a rural tribal nation in Minnesota, USA identified non‐linearity and cultural connection as key elements to include in an Anishinaabe‐centered model of opioid recovery and change.
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