A mediator effect size in randomized clinical trials

Effectiveness of a universal personalized intervention for the prevention of anxiety disorders: Protocol of a randomized controlled trial (the prevANS project)

Background

To date, all preventive anxiety disorders interventions are one-fit-all and none of them are based on individual level and risk profile. The aim of this project is to design, develop and evaluate an online personalized intervention based on a risk algorithm for the universal prevention of anxiety disorders in the general population.

Methods

A randomized controlled trial (RCT) with two parallel arms (prevANS vs usual care) and 1-year follow-up including 2000 participants without anxiety disorders from Spain and Portugal will be conducted.The prevANS intervention will be self-guided and can be implemented from the prevANS web or from the participants' Smartphone (through an App). The prevANS intervention will have different intensities depending on the risk level of the population, evaluated from the risk algorithm for anxiety: predictA. Both low and moderate-high risk participants will receive information on their level and profile (risk factors) of anxiety disorders, will have access to stress management tools and psychoeducational information periodically. In addition, participants with a moderate-high risk of anxiety disorders will also have access to cognitive-behavioral training (problem-solving, decision-making, communication skills, and working with thoughts). The control group will not receive any intervention, but they will fill out the same questionnaires as the intervention group.Assessments will be completed at baseline, 6 and 12-month follow-up. The primary outcome is the cumulative incidence of anxiety disorders. Secondary outcomes include depressive and anxiety symptoms, risk probability of anxiety disorders (predictA algorithm) and depression (predictD algorithm), improvement in physical and mental quality of life, and acceptability and satisfaction with the intervention. In addition, cost-effectiveness and cost-utility analyses will also be carried out from two perspectives, societal and health system, and analyses of mediators and moderators will also be performed.

Discussion

To the best of our knowledge, prevANS study will be the first to evaluate the effectiveness and cost-effectiveness of a personalized online intervention based on a risk predictive algorithm for the universal prevention of anxiety disorders.

Trial registration

ClinicalTrials.gov: NCT05682365.

A digital health game to prevent opioid misuse and promote mental health in adolescents in school-based health settings: Protocol for the PlaySmart game randomized controlled trial

Adolescents who engage in non-opioid substance misuse and/or experience mental health symptoms are at greater risk of misusing opioids and/or developing opioid use disorder. Adolescence is a critical developmental period to both prevent the initiation of opioid misuse and target mental health. To date, there are no digital health games targeting both conditions. We describe the protocol for a randomized controlled trial designed to assess the efficacy of an original digital health game, PlaySmart. Five hundred and thirty-two adolescents aged 16-19 years old, who are at greater risk for initiating opioid misuse are recruited from 10 Connecticut school-based health sites. Participants are randomized to PlaySmart or a set of time/attention control videogames. Randomization was stratified by sex at birth and school grade. Participants play their assigned game or games for up to six weeks (300 minutes) and complete assessment questions over a 12-month period (baseline, post-gameplay, 3, 6, and 12 months). The primary outcome is perception of risk of harm of opioid misuse at 3 months. Secondary outcome measures specific to opioid misuse include intentions, self-efficacy, attitudes, knowledge, and perceived norms. Mental health outcomes include measures of depression (Patient Health Questionnaire-8), anxiety (Generalized Anxiety Disorder-7), help-seeking behaviors, stigma, measures of self-regulation, self-efficacy to seek professional help for mental health, and knowledge around coping skills. PlaySmart has the potential to significantly reduce the risk of initiation of opioid misuse, improve mental health outcomes, and given its high levels of engagement and accessibility, holds the promise for extensive reach, scale, and impact for adolescents. Trial registration: ClinicalTrials.gov: NCT04941950. Registered on 23 June 2021.

A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study

The predictD is an intervention implemented by general practitioners (GPs) to prevent depression, which reduced the incidence of depression-anxiety and was cost-effective. The e-predictD study aims to design, develop, and evaluate an evolved predictD intervention to prevent the onset of major depression in primary care based on Information and Communication Technologies, predictive risk algorithms, decision support systems (DSSs), and personalized prevention plans (PPPs). A multicenter cluster randomized trial with GPs randomly assigned to the e-predictD intervention + care-as-usual (CAU) group or the active-control + CAU group and 1-year follow-up is being conducted. The required sample size is 720 non-depressed patients (aged 18-55 years), with moderate-to-high depression risk, under the care of 72 GPs in six Spanish cities. The GPs assigned to the e-predictD-intervention group receive brief training, and those assigned to the control group do not. Recruited patients of the GPs allocated to the e-predictD group download the e-predictD app, which incorporates validated risk algorithms to predict depression, monitoring systems, and DSSs. Integrating all inputs, the DSS automatically proposes to the patients a PPP for depression based on eight intervention modules: physical exercise, social relationships, improving sleep, problem-solving, communication skills, decision-making, assertiveness, and working with thoughts. This PPP is discussed in a 15-min semi-structured GP-patient interview. Patients then choose one or more of the intervention modules proposed by the DSS to be self-implemented over the next 3 months. This process will be reformulated at 3, 6, and 9 months but without the GP-patient interview. Recruited patients of the GPs allocated to the control-group+CAU download another version of the e-predictD app, but the only intervention that they receive via the app is weekly brief psychoeducational messages (active-control group). The primary outcome is the cumulative incidence of major depression measured by the Composite International Diagnostic Interview at 6 and 12 months. Other outcomes include depressive symptoms (PHQ-9) and anxiety symptoms (GAD-7), depression risk (predictD risk algorithm), mental and physical quality of life (SF-12), and acceptability and satisfaction ('e-Health Impact' questionnaire) with the intervention. Patients are evaluated at baseline and 3, 6, 9, and 12 months. An economic evaluation will also be performed (cost-effectiveness and cost-utility analysis) from two perspectives, societal and health systems.

Trial registration

ClinicalTrials.gov, identifier: NCT03990792.

Can Translational Social Neuroscience Research Offer Insights to Mitigate Structural Racism in the United States?

Social isolation and conflict due to structural racism may result in human suffering and loneliness across the life span. Given the rising prevalence of these problems in the United States, combined with disruptions experienced during the COVID-19 pandemic, the neurobiology of affiliative behaviors may offer practical solutions to the pressing challenges associated with structural racism. Controlled experiments across species demonstrate that social connections are critical to survival, although strengthening individual resilience is insufficient to address the magnitude and impact of structural racism. In contrast, the multilevel construct of social resilience, defined by the power of groups to cultivate, engage in, and sustain positive relationships that endure and recuperate from social adversities, offers unique insights that may have greater impact, reach, and durability than individual-level interventions. Here, we review putative social resilience-enhancing interventions and, when available, their biological mediators, with the hope to stimulate discovery of novel approaches to mitigate structural racism. We first explore the social neuroscience principles underlying psychotherapy and other psychiatric interventions. Then, we explore translational efforts across species to tailor treatments that increase social resilience, with context and cultural sensitivity in mind. Finally, we conclude with some practical future directions for understudied areas that may be essential for progress in biological psychiatry, including ethical ways to increase representation in research and developing social paradigms that inform dynamics toward or away from socially resilient outcomes.

Dynamic change meets mechanisms of change: Examining mediators in the latent change score framework

Researchers in behavioral sciences are often interested in longitudinal behavior change outcomes and the mechanisms that influence changes in these outcomes over time. The statistical models that are typically implemented to address these research questions do not allow for investigation of mechanisms of dynamic change over time. However, latent change score models allow for dynamic change (not just linear or exponential change) over time and have flexibility in parameter constraints that other longitudinal models do not have. Developmental researchers also frequently utilize mediation analyses to investigate mechanisms of influence in longitudinal research implemented in path analytic or latent growth curve models. In this article, we provide three examples of how mediation can be tested in the latent change score framework by combining aspects of traditional mediation models with latent change score models of repeated measures outcomes (and mediators and predictors) with more than two timepoints. We also provide the Mplus syntax to complete these analyses and practical considerations of latent change score mediation (LCSM) models.

Separation anxiety in PTSD: A pilot study of mechanisms in patients undergoing IPT

INTRODUCTION

Separation anxiety disorder (SAD) comprises one aspect of attachment dysregulation or insecurity. Although SAD aggravates posttraumatic stress disorder (PTSD) risk, no clinical research has tracked how many patients with PTSD have SAD, its clinical associations, or its response to PTSD treatment. Our open trial of interpersonal psychotherapy (IPT) for veterans with PTSD assessed these SAD domains.

METHODS

Twenty-nine veterans diagnosed with chronic PTSD on the Clinician-Administered PTSD Scale were assessed for SAD using the Structured Clinical Interview for Separation Anxiety Symptoms (SCI-SAS), and for Symptom-Specific Reflective Function (SSRF), another dysregulated-attachment marker capturing patients' emotional understanding of their symptoms. Patients received 14 IPT sessions for PTSD with assessments at baseline, Week 4 (SCI-SAS and SSRF), and termination for SAD, PTSD, and depression.

RESULTS

At baseline, 69% of patients met SAD criteria. Separation anxiety did not correlate with baseline PTSD severity, depressive severity, or age when traumatized; patients with and without SAD had comparable PTSD and depression severity. Patients with baseline comorbid SAD who completed IPT (N = 17) reported significantly improved adult separation anxiety (p = .009). Adult SAD improvements predicted depressive improvement (p = .049). Patients with SAD showed a stronger relationship between early SSRF gains and subsequent adult SAD improvement (p = .021) compared with patients without SAD.

DISCUSSION

This first exploration of dysregulated/insecure attachment features among patients with PTSD found high SAD comorbidity and adult SAD improvement among patients with SAD following IPT. Highly impaired attachment patients normalized attachment posttreatment: 14-session IPT improved attachment dysregulation. This small study requires replication but begins to broaden clinical understanding of separation anxiety, attachment dysregulation, and PTSD.

Sample Size Planning for Detecting Mediation Effects: A Power Analysis Procedure Considering Uncertainty in Effect Size Estimates

When planning mediation studies, researchers are often interested in the sample size needed to achieve adequate power for testing mediation. Power depends on population effect sizes, which are unknown in practice. In conventional power analysis, effect size estimates, however, are often used as population values, which could result in underpowered studies. Uncertainty in effect size estimates has been considered in other sample size planning contexts (e.g., t-test, ANOVA), but has not been handled properly for planning mediation studies. In the current study, we proposed an easy-to-use sample size planning method for testing mediation with uncertainty in effect size estimates considered. We conducted simulation studies to demonstrate the impact of uncertainty in effect size estimates on power of testing mediation, and to provide sample size suggestions under different levels of uncertainty. Empirical examples were provided to illustrate the application of our method. R functions and a web application were developed to facilitate implementation.

Statistical properties of four effect-size measures for mediation models

This project examined the performance of classical and Bayesian estimators of four effect size measures for the indirect effect in a single-mediator model and a two-mediator model. Compared to the proportion and ratio mediation effect sizes, standardized mediation effect-size measures were relatively unbiased and efficient in the single-mediator model and the two-mediator model. Percentile and bias-corrected bootstrap interval estimates of ab/s_Y, and ab(s_X)/s_Y in the single-mediator model outperformed interval estimates of the proportion and ratio effect sizes in terms of power, Type I error rate, coverage, imbalance, and interval width. For the two-mediator model, standardized effect-size measures were superior to the proportion and ratio effect-size measures. Furthermore, it was found that Bayesian point and interval summaries of posterior distributions of standardized effect-size measures reduced excessive relative bias for certain parameter combinations. The standardized effect-size measures are the best effect-size measures for quantifying mediated effects.

Toward an Emerging Role for Motivational Interviewing in Primary Care

PURPOSE OF REVIEW

Implementing Motivational Interviewing (MI) in primary care settings has been problematic due in part to persistent gaps in knowledge. Examples include poor understanding of how to effectively train persons to conduct MI, or of which aspects of MI-related communication are associated with better outcomes for patients. This review describes how recent research findings addressing the knowledge gaps support a growing role for MI in primary care.

RECENT FINDINGS

Two trials of MI training combined classroom time with ongoing coaching and feedback, resulting in enhanced MI ability relative to a control arm where PCPs received minimal or no MI training. A third MI training trial excluded coaching and feedback, failing to increase use of MI. Adding to a growing list of behavioral health-related problems for which MI training has shown some effectiveness, a trial of training PCPs to use MI with depressed patients was associated with significantly improved depressive symptoms. Moreover, aspects of the PCPs' MI-related language and patients' arguments for positive behavior changes, "change talk," appeared to explain the positive effects of MI training on depression outcome. MI-training approaches have improved such that PCPs and possibly other clinic staff may want to consider MI training as a way to more effectively support their patients as they address behavioral health-related problems (e.g., tobacco use). MI training should focus on eliciting "change talk" from patients. Researchers and funding agencies might collaborate to continue closing knowledge gaps in the MI literature.

Sedation mediates part of Citalopram's effect on agitation in Alzheimer's disease

BACKGROUND

We found a benefit of citalopram for agitation in the Citalopram for Agitation in Alzheimer's Disease study (CitAD), and wondered if this was mediated by a sedative effect. CitAD was a randomized, placebo-controlled, double-blind, parallel group trial conducted at 8 academic centers in the United States and Canada from August 2009 to January 2013. One hundred sixty-two participants with probable Alzheimer's disease (AD) and clinically significant agitation were analyzed in this study. Participants received a psychosocial intervention and were randomized to receive either citalopram or placebo (approximately half assigned to each group). Participants were rated on the Neurobehavioral Rating Scale Agitation subscale and measures of sedation (i.e., fatigue and somnolence).

METHODS

Using the MacArthur Foundation procedures for documenting a mediator effect, we performed a secondary analysis examining whether sedation mediates the effect of treatment on agitation outcome.

RESULTS

We found a statistically significant mediating effect of sedation on agitation outcomes, but the magnitude of the effect was small, only explaining 11% of the variance in agitation, with a significant, but modest effect size of 0.16 (95% CI: 0.08 to 0.22).

CONCLUSIONS

The benefit of citalopram was partly due to sedation but largely due to other mechanisms of action.

A mediator effect size in randomized clinical trials

To understand the process by which a treatment (T) achieves an effect on outcome (O) and thus to improve the effect of T on O, it is vital to detect mediators, to compare the impact of different mediators, and to develop hypotheses about the causal factors (all mediators) linking T and O. An index is needed to facilitate interpretation of the potential clinical importance of a mediator (M) of choice of T on treatment O in randomized clinical trials (RCTs). Ideally such a mediator effect size should (1) be invariant under any rescaling of M and O consistent with the model used, and (2) reflect the difference between the overall observed effect of T on O and what the maximal effect of T on O could be were the association between T and M broken. A mediator effect size is derived first for the traditional linear model, and then more generally for any categorical (ordered or non-ordered) potential mediator. Issues such as the problem of multiple treatments, outcomes and mediators, and of causal inferences, and the correspondence between this approach and earlier ones, are discussed. Illustrations are given of the application of the approach.