Estimating causal effects of internet interventions in the context of nonadherence

Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data

BACKGROUND

Shame and stigma often prevent individuals with social anxiety disorder (SAD) from seeking and attending costly and time-intensive psychotherapies, highlighting the importance of brief, low-cost, and scalable treatments. Creating prescriptive outcome prediction models is thus crucial for identifying which clients with SAD might gain the most from a unique scalable treatment option. Nevertheless, widely used classical regression methods might not optimally capture complex nonlinear associations and interactions.

OBJECTIVE

Precision medicine approaches were thus harnessed to examine prescriptive predictors of optimization to a 14-day fully self-guided mindfulness ecological momentary intervention (MEMI) over a self-monitoring app (SM).

METHODS

This study involved 191 participants who had probable SAD. Participants were randomly assigned to MEMI (n=96) or SM (n=95). They completed self-reports of symptoms, risk factors, treatment, and sociodemographics at baseline, posttreatment, and 1-month follow-up (1MFU). Machine learning (ML) models with 17 predictors of optimization to MEMI over SM, defined as a higher probability of SAD remission from MEMI at posttreatment and 1MFU, were evaluated. The Social Phobia Diagnostic Questionnaire, structurally equivalent to the Diagnostic and Statistical Manual SAD criteria, was used to define remission. These ML models included random forest and support vector machines (radial basis function kernel) and 10-fold nested cross-validation that separated model training, minimal tuning in inner folds, and model testing in outer folds.

RESULTS

ML models outperformed logistic regression. The multivariable ML models using the 10 most important predictors achieved good performance, with the area under the receiver operating characteristic curve (AU-ROC) values ranging from .71 to .72 at posttreatment and 1MFU. These prerandomization and early-stage prescriptive predictors consistently identified which participants had the highest probability of optimization of MEMI over SM after 14 days and 6 weeks from baseline. Significant predictors included 4 strengths (higher trait mindfulness, lower SAD severity, presence of university education, no current psychotropic medication use), 2 weaknesses (higher generalized anxiety severity and clinician-diagnosed depression or anxiety disorder), and 1 sociodemographic variable (Chinese ethnicity). Emotion dysregulation and current psychotherapy predicted remission with inconsistent signs across time points.

CONCLUSIONS

The AU-ROC values indicated moderately meaningful effect sizes in identifying prescriptive predictors within multivariable models for clients with SAD. Focusing on the identified notable client strengths, weaknesses, and Chinese ethnicity may enhance our ability to predict future responses to scalable treatments. Estimating the likelihood of SAD remission with a "prescriptive predictor calculator" for each client may help clinicians and policymakers allocate scarce treatment resources effectively. Clients with high remission probability may benefit from receiving the MEMI as a vigilant waitlist strategy before intensive therapist-led psychotherapy. These efforts may aid in creating actionable treatment selection tools to optimize care for clients with SAD in routine health care settings that use stratified care principles.

TRIAL REGISTRATION

OSF Registries 10.17605/OSF.IO/M3KXZ; https://osf.io/m3kxz.

Trunk rotation, spinal deformity and appearance, health-related quality of life, and treatment adherence: Secondary outcomes in a randomized controlled trial on conservative treatment for adolescent idiopathic scoliosis

OBJECTIVE

To explore secondary outcomes at endpoint comparing treatments with adequate self-mediated physical activity combined with either night-time brace (NB), scoliosis-specific exercise (SSE), or adequate self-mediated physical activity alone (PA) in Adolescent Idiopathic Scoliosis (AIS).

METHODS

A longitudinal, prospective, multicenter RCT was conducted including 135 girls/boys, Cobb angle 25-40°, 9-17 years, and ≥1-year remaining growth were randomly allocated into NB, SSE, or PA group. Endpoint was curve progression of ≤6° (success) at skeletal maturity or >6° (failure). Outcomes included angle of trunk rotation (ATR), major curve Cobb angle, Spinal Appearance Questionnaire (pSAQ), Scoliosis Research Society-22r (SRS-22r), EQ-5Dimensions Youth 3Levels (EQ-5D-Y-3L), and EQ-Visual-Analogue-Scale (EQ-VAS), adherence to treatment and International Physical Activity Questionnaire (IPAQ-SF).

RESULTS

At endpoint, 122 patients were analyzed per protocol, mean age 12.7 (±1.4) years, and mean Cobb angle 31° (±4.3). A significant difference in change for ATR favored NB group compared to SSE group -2.0º (95% CI -3.7 to -0.3). EQ-5D-Y-3L dimensions showed a significant difference in change with decrease in mobility (p=0.031), and usual activities (p=0.003) for SSE compared to NB and PA groups. Treatment adherence was adequate but slightly better in NB and PA groups compared to SSE on self-report (p=0.012), and health care provider (HCP) report was better in PA compared to SSE group (p=0.013). Higher motivation and capability explained 53% of the variability and gave better odds for higher adherence (OR = 11.12, 95% CI = 1.5 to 34.4; OR = 7.23, 95% CI = 2.9 to 43.3), respectively.

CONCLUSIONS

Night-time brace, scoliosis-specific exercise or physical activity interventions for adolescents with idiopathic scoliosis showed small differences between groups in trunk rotation, spinal deformity and appearance, health-related quality of life, and treatment adherence but not likely reaching clinical relevance.

What are end-users' needs and preferences for a comprehensive e-health program for type 2 diabetes? - A qualitative user preference study

INTRODUCTION

Type 2 diabetes (T2D) prevalence is rising, which imposes a significant burden on individuals, healthcare systems, and economies worldwide. Lifestyle factors contribute significantly to the escalating incidence of T2D. Consequently, there is an increasing need for interventions that not only target at-risk populations for prevention but also empower individuals with T2D to achieve better self-management and possibly attain remission through sustained lifestyle modifications. Technological tools may improve health outcomes compared to traditional in-person care, and can include registration of important health parameters, provide follow-up and support, and enhance self-management. The aim of this study was to receive feedback from end-users to inform the development of a comprehensive e-health program focusing on lifestyle modification in pre-diabetes and T2D.

METHODS

During eight focus group meetings, sixteen adults with pre-diabetes or T2D from all over Norway informed the study about needs and preferences for an e-health program, including essential functionalities and design choices. A questionnaire and paper prototyping were used to complement the discussions in the focus group meetings.

RESULTS

Lack of necessary diabetes knowledge was common, and education was considered essential for improved self-management. Essential functionalities included registration and overview of several health parameters, long-term follow-up and coaching through communication platforms within the program, automatic data transfer from different devices such as blood glucose monitors and smartwatches, and educational courses. To ensure end-users' satisfaction with the program and increase motivation for long-term usage, the participants rendered tailoring of desired functionalities and content as crucial.

CONCLUSION

Based on the findings, a list of recommendations was created, containing the most crucial functionalities and features to include when developing e-health and/or m-health tools for people with pre-diabetes and T2D. Future work should include health care personnel to explore their needs and preferences, and ways such an e-health program may enhance patient interaction without increasing workload and resource use.

Statistical methods to adjust for the effects on intervention compliance in randomized clinical trials where precision treatment rules are being developed

BACKGROUND

Heterogeneity of treatment effects (HTEs) can occur because of either differential treatment compliance or differential treatment effectiveness. This distinction is important, as it has action implications, but it is unclear how to distinguish these two possibilities statistically in precision treatment analysis given that compliance is not observed until after randomization. We review available statistical methods and illustrate a recommended method in secondary analysis in a trial focused on HTE.

METHODS

The trial randomized n = 880 anxious and/or depressed university students to guided internet-delivered cognitive behavioral therapy (i-CBT) or treatment-as-usual (TAU) and evaluated joint remission. Previously reported analyses documented superiority of i-CBT but significant HTE. In the reanalysis reported here, we used baseline (i.e., pre-randomization) covariates to predict compliance among participants randomized to guided i-CBT, generated a cross-validated within-person expected compliance score based on this model in both intervention groups, and then used this expected composite score as a predictor in an expanded HTE analysis.

RESULTS

The significant intervention effect was limited to participants with high expected compliance. Residual HTE was nonsignificant.

CONCLUSIONS

Future psychotherapy HTE trials should routinely develop and include expected compliance composite scores to distinguish the effects of differential treatment compliance from the effects of differential treatment effectiveness.

The Effect of Predicted Compliance With a Web-Based Intervention for Anxiety and Depression Among Latin American University Students: Randomized Controlled Trial

BACKGROUND

Web-based cognitive behavioral therapy (wb-CBT) is a scalable way to reach distressed university students. Guided wb-CBT is typically superior to self-guided wb-CBT over short follow-up periods, but evidence is less clear over longer periods.

OBJECTIVE

This study aimed to compare short-term (3 months) and longer-term (12 months) aggregate effects of guided and self-guided wb-CBT versus treatment as usual (TAU) in a randomized controlled trial of Colombian and Mexican university students and carry out an initially unplanned secondary analysis of the role of differential predicted compliance in explaining these differences.

METHODS

The 1319 participants, recruited either through email and social media outreach invitations or from waiting lists of campus mental health clinics, were undergraduates (1038/1319, 78.7% female) with clinically significant baseline anxiety (Generalized Anxiety Disorder-7 score≥10) or depression (Patient Health Questionnaire-9 score≥10). The intervention arms comprised guided wb-CBT with weekly asynchronous written human feedback, self-guided wb-CBT with the same content as the guided modality, and TAU as provided at each university. The prespecified primary outcome was joint remission (Generalized Anxiety Disorder-7 score=0-4 and Patient Health Questionnaire-9 score=0-4). The secondary outcome was joint symptom reduction (mean scores on the Patient Health Questionnaire Anxiety and Depression Scale) at 3 and 12 months after randomization.

RESULTS

As reported previously, 3-month outcomes were significantly better with guided wb-CBT than self-guided wb-CBT (P=.02) or TAU (P=.02). However, subsequent follow-up showed that 12-month joint remission (adjusted risk differences=6.0-6.5, SE 0.4-0.5, and P<.001 to P=.007; adjusted mean differences=2.70-2.69, SE 0.7-0.8, and P<.001 to P=.001) was significantly better with self-guided wb-CBT than with the other interventions. Participants randomly assigned to the guided wb-CBT arm spent twice as many minutes logged on as those in the self-guided wb-CBT arm in the first 12 weeks (mean 12.5, SD 36.9 vs 5.9, SD 27.7; χ21=107.1, P<.001), whereas participants in the self-guided wb-CBT arm spent twice as many minutes logged on as those in the guided wb-CBT arm in weeks 13 to 52 (mean 0.4, SD 7.5 vs 0.2, SD 4.4; χ21=10.5, P=.001). Subgroup analysis showed that this longer-term superiority of self-guided wb-CBT was confined to the 40% (528/1319) of participants with high predicted self-guided wb-CBT compliance beyond 3 months based on a counterfactual nested cross-validated machine learning model. The 12-month outcome differences were nonsignificant across arms among other participants (all P>.05).

CONCLUSIONS

The results have important practical implications for precision intervention targeting to maximize longer-term wb-CBT benefits. Future research needs to investigate strategies to increase sustained guided wb-CBT use once guidance ends.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04780542; https://www.clinicaltrials.gov/study/NCT04780542.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s13063-022-06255-3.

The Co-Piloting Model for Using Artificial Intelligence Systems in Medicine: Implementing the Constrained-Disorder-Principle-Based Second-Generation System

The development of artificial intelligence (AI) and machine learning (ML)-based systems in medicine is growing, and these systems are being used for disease diagnosis, drug development, and treatment personalization. Some of these systems are designed to perform activities that demand human cognitive function. However, use of these systems in routine care by patients and caregivers lags behind expectations. This paper reviews several challenges that healthcare systems face and the obstacles of integrating digital systems into routine care. This paper focuses on integrating digital systems with human physicians. It describes second-generation AI systems designed to move closer to biology and reduce complexity, augmenting but not replacing physicians to improve patient outcomes. The constrained disorder principle (CDP) defines complex biological systems by their degree of regulated variability. This paper describes the CDP-based second-generation AI platform, which is the basis for the Digital Pill that is humanizing AI by moving closer to human biology via using the inherent variability of biological systems for improving outcomes. This system augments physicians, assisting them in decision-making to improve patients' responses and adherence but not replacing healthcare providers. It restores the efficacy of chronic drugs and improves adherence while generating data-driven therapeutic regimens. While AI can substitute for many medical activities, it is unlikely to replace human physicians. Human doctors will continue serving patients with capabilities augmented by AI. The described co-piloting model better reflects biological pathways and provides assistance to physicians for better care.

Unlocking human-like conversations: Scoping review of automation techniques for personalized healthcare interventions using conversational agents

BACKGROUND

Conversational agents (CAs) offer a sustainable approach to deliver personalized interventions and improve health outcomes.

OBJECTIVES

To review how human-like communication and automation techniques of CAs in personalized healthcare interventions have been implemented. It is intended for designers and developers, computational scientists, behavior scientists, and biomedical engineers who aim at developing CAs for healthcare interventions.

METHODOLOGY

A scoping review was conducted in accordance with PRISMA Extension for Scoping Review. A search was performed in May 2023 in Web of Science, Pubmed, Scopus and IEEE databases. Search results were extracted, duplicates removed, and the remaining results were screened. Studies that contained personalized and automated CAs within the healthcare domain were included. Information regarding study characterization, and human-like communication and automation techniques was extracted from articles that met the eligibility criteria.

RESULTS

Twenty-three studies were selected. These articles described the development of CAs designed for patients to either self-manage their diseases (such as diabetes, mental health issues, cancer, asthma, COVID-19, and other chronic conditions) or to enhance healthy habits. The human-like communication characteristics studied encompassed aspects like system flexibility, personalization, and affective characteristics. Seven studies used rule-based models, eleven applied retrieval-based techniques for content delivery, five used AI models, and six integrated affective computing.

CONCLUSIONS

The increasing interest in employing CAs for personalized healthcare interventions is noteworthy. The adaptability of dialogue structures and personalization features is still limited. Unlocking human-like conversations may encompass the use of affective computing and generative AI to help improve user engagement. Future research should focus on the integration of holistic methods to describe the end-user, and the safe use of generative models.

Offering an auto-play feature likely increases total gambling activity at online slot-machines: preliminary evidence from an interrupted time series experiment at a real-life online casino

Auto-play is a ubiquitous feature in online casino gambling and virtual slot machines especially, allowing gamblers to initiate spin sequences of pre-set length and value. While theoretical accounts diverge on the hypothesized causal effect on gambling behavior of using the auto-play feature, observational findings show that this feature is used to a higher degree by problem and/or high-intensity gamblers, suggesting that banning this feature may constitute a global responsible gambling measure. Direct, experimental research on causal effects of offering auto-play at online casinos is however lacking. Here, we report the findings of an interrupted time series experiment, conducted at a real-life online casino in Sweden, in which the auto-play feature was made available during a pre-set duration on 40 online slot machines, with 40 matched slots serving as control. Aggregated time series on daily betted amount, spins and net losses were analyzed using a structural Bayesian framework that compared observed developments during the peri-intervention period to modeled counterfactual estimates. Results suggest that offering an auto-play feature on online casinos likely increases total gambling activity in terms of betted amount (approx.+ 7-9%) and (perhaps) number of spins (approx. +3%) but has no effect on net losses. Limitations of studying auto-play effects on a population-level, as well as the complexities of banning this feature within a complex ecosystem of non-perfect channelization to licensed providers, are discussed, including suggestions for future research.

Is combined antidepressant medication (ADM) and psychotherapy better than either monotherapy at preventing suicide attempts and other psychiatric serious adverse events for depressed patients? A rare events meta-analysis

Antidepressant medication (ADM)-only, psychotherapy-only, and their combination are the first-line treatment options for major depressive disorder (MDD). Previous meta-analyses of randomized controlled trials (RCTs) established that psychotherapy and combined treatment were superior to ADM-only for MDD treatment remission or response. The current meta-analysis extended previous ones by determining the comparative efficacy of ADM-only, psychotherapy-only, and combined treatment on suicide attempts and other serious psychiatric adverse events (i.e. psychiatric emergency department [ED] visit, psychiatric hospitalization, and/or suicide death; SAEs). Peto odds ratios (ORs) and their 95% confidence intervals were computed from the present random-effects meta-analysis. Thirty-four relevant RCTs were included. Psychotherapy-only was stronger than combined treatment (1.9% v. 3.7%; OR 1.96 [1.20-3.20], p = 0.012) and ADM-only (3.0% v. 5.6%; OR 0.45 [0.30-0.67], p = 0.001) in decreasing the likelihood of SAEs in the primary and trim-and-fill sensitivity analyses. Combined treatment was better than ADM-only in reducing the probability of SAEs (6.0% v. 8.7%; OR 0.74 [0.56-0.96], p = 0.029), but this comparative efficacy finding was non-significant in the sensitivity analyses. Subgroup analyses revealed the advantage of psychotherapy-only over combined treatment and ADM-only for reducing SAE risk among children and adolescents and the benefit of combined treatment over ADM-only among adults. Overall, psychotherapy and combined treatment outperformed ADM-only in reducing the likelihood of SAEs, perhaps by conferring strategies to enhance reasons for living. Plausibly, psychotherapy should be prioritized for high-risk youths and combined treatment for high-risk adults with MDD.

Mind-body internet and mobile-based interventions for depression and anxiety in adults with chronic physical conditions: A systematic review of RCTs

This review summarizes the effectiveness of scalable mind-body internet and mobile-based interventions (IMIs) on depression and anxiety symptoms in adults living with chronic physical conditions. Six databases (MEDLINE, PsycINFO, SCOPUS, EMBASE, CINAHL, and CENTRAL) were searched for randomized controlled trials published from database inception to March 2023. Mind-body IMIs included cognitive behavioral therapy, breathwork, meditation, mindfulness, yoga or Tai-chi. To focus on interventions with a greater potential for scale, the intervention delivery needed to be online with no or limited facilitation by study personnel. The primary outcome was mean change scores for anxiety and depression (Hedges' g). In subgroup analyses, random-effects models were used to calculate pooled effect size estimates based on personnel support level, intervention techniques, chronic physical condition, and survey type. Meta-regression was conducted on age and intervention length. Fifty-six studies met inclusion criteria (sample size 7691, mean age of participants 43 years, 58% female): 30% (n = 17) neurological conditions, 12% (n = 7) cardiovascular conditions, 11% cancer (n = 6), 43% other chronic physical conditions (n = 24), and 4% (n = 2) multiple chronic conditions. Mind-body IMIs demonstrated statistically significant pooled reductions in depression (SMD = -0.33 [-0.40, -0.26], p<0.001) and anxiety (SMD = -0.26 [-0.36, -0.17], p<0.001). Heterogeneity was moderate. Scalable mind-body IMIs hold promise as interventions for managing anxiety and depression symptoms in adults with chronic physical conditions without differences seen with age or intervention length. While modest, the effect sizes are comparable to those seen with pharmacological therapy. The field would benefit from detailed reporting of participant demographics including those related to technological proficiency, as well as further evaluation of non-CBT interventions. Registration: The study is registered with PROSPERO ID #CRD42022375606.

Internet-delivered cognitive behavior therapy versus treatment as usual for anxiety and depression among Latin American university students: A randomized clinical trial

OBJECTIVE

Untreated mental disorders are important among low- and middle-income country (LMIC) university students in Latin America, where barriers to treatment are high. Scalable interventions are needed. This study compared transdiagnostic self-guided and guided internet-delivered cognitive behavioral therapy (i-CBT) with treatment as usual (TAU) for clinically significant anxiety and depression among undergraduates in Colombia and Mexico.

METHOD

1,319 anxious, as determined by the Generalized Anxiety Disorder-7 (GAD-7) = 10+ and/or depressed, as determined by the Patient Health Questionnaire-9 (PHQ-9) = 10+, undergraduates (mean [SD] age = 21.4 [3.2]); 78.7% female; 55.9% first-generation university student) from seven universities in Colombia and Mexico were randomized to culturally adapted versions of self-guided i-CBT (n = 439), guided i-CBT (n = 445), or treatment as usual (TAU; n = 435). All randomized participants were reassessed 3 months after randomization. The primary outcome was remission of both anxiety (GAD-7 = 0-4) and depression (PHQ-9 = 0-4). We hypothesized that remission would be higher with guided i-CBT than with the other interventions.

RESULTS

Intent-to-treat analysis found significantly higher adjusted (for university and loss to follow-up) remission rates (ARD) among participants randomized to guided i-CBT than either self-guided i-CBT (ARD = 13.1%, χ12 = 10.4, p = .001) or TAU (ARD = 11.2%, χ12 = 8.4, p = .004), but no significant difference between self-guided i-CBT and TAU (ARD = -1.9%, χ12 = 0.2, p = .63). Per-protocol sensitivity analyses and analyses of dimensional outcomes yielded similar results.

CONCLUSIONS

Significant reductions in anxiety and depression among LMIC university students could be achieved with guided i-CBT, although further research is needed to determine which students would most likely benefit from this intervention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Effects of a Metacognitive Smartphone Intervention With Weekly Mentoring Sessions for Individuals With Schizophrenia: A Quasi-Experimental Study

Application (app)-based interventions using smartphones could provide effective alternatives to traditional treatment programs during and beyond the coronavirus disease 2019 pandemic. The current quasi-experimental study with a non-equivalent comparison group tested the effects of a smartphone app-based metacognitive intervention program with weekly mentoring sessions on the meta-cognitive beliefs, psychotic symptoms, and social functioning of individuals with schizophrenia from community psychosocial rehabilitation centers. The study was conducted with 20 participants with severe psychotic symptoms and low social functioning and 24 participants with relatively light psychotic symptoms and good social functioning as a comparison group. For the experimental group, the app-based intervention was combined with weekly contact mentoring sessions over 10 weeks. The comparison group received only the app-based intervention over 10 weekly sessions. No differences were observed between groups' total scores; however, the experimental group showed a tendency toward improved psychotic symptoms and social functioning over time, unlike the comparison group. These findings provide an empirical basis for managing schizophrenia symptoms with smartphone apps. [Journal of Psychosocial Nursing and Mental Health Services, 61(2), 27-37.].

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review

BACKGROUND

Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions.

OBJECTIVE

This review aimed to help stakeholders develop more effective digital health interventions by identifying factors influencing the continued use of mHealth apps targeting NCDs. We further derived quantified adherence scores for various health domains to validate the qualitative findings and explore adherence benchmarks.

METHODS

A comprehensive systematic literature search (January 2007 to December 2020) was conducted on MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains of NCD self-management, mental health, substance use, nutrition, physical activity, weight loss, multicomponent lifestyle interventions, mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between the estimated intended use and actual use, were derived for each study and compared with the qualitative findings.

RESULTS

The literature search yielded 2862 potentially relevant articles, of which 99 (3.46%) were included as part of the inclusion criteria. A total of 4 intervention-related factors indicated positive effects on adherence across all health domains: personalization or tailoring of the content of mHealth apps to the individual needs of the user, reminders in the form of individualized push notifications, user-friendly and technically stable app design, and personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors such as user characteristics or recruitment channels further affects adherence. The derived adherence scores of the included mHealth apps averaged 56.0% (SD 24.4%).

CONCLUSIONS

This study contributes to the scarce scientific evidence on factors that positively or negatively influence adherence to mHealth apps and is the first to quantitatively compare adherence relative to the intended use of various health domains. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app's intended use; report objective data on actual use relative to the intended use; and, ideally, provide long-term use and retention data.

Identifying causal mechanisms in psychotherapy: What can we learn from causal mediation analysis?

Despite widespread interest in the development of process-based psychotherapies, little is still known about the underlying processes that underpin our most effective therapies. Statistical mediation analysis is a commonly used analytical method to evaluate how, or by which processes, a therapy causes change in an outcome. Causal mediation analysis (CMA) represents a new advancement in mediation analysis that employs causally defined direct and indirect effects based on potential outcomes. These novel ideas and analytical techniques have been characterized as revolutionary in epidemiology and biostatistics, although they are not (yet) widely known among researchers in clinical psychology. In this paper, I outline the fundamental concepts underlying CMA, clarify the differences between the CMA approach and the traditional approach to mediation, and identify two important data analytical aspects that have been emphasized as a result of these recent advancements. To illustrate the key ideas, assumptions, and mathematical definitions intuitively, an applied clinical example from a previously published randomized controlled trial is used. CMA's main contributions are discussed, as well as some of the key challenges. Finally, it is argued that the most significant contribution of CMA is the formalization of mediation in a unified causal framework with clear assumptions.

Is Continued Improvement After Automated Virtual Reality Exposure Therapy for Spider Phobia Explained by Subsequent in-vivo Exposure? A First Test of the Lowered Threshold Hypothesis

Consumer Virtual Reality (VR) technology offers a powerful, immersive medium for scalable dissemination of mental health interventions. Decades of research has shown VR exposure therapy to be efficacious in the treatment of anxiety disorders and that the fear reduction generalizes to real-world stimuli. Many studies also report continued improvement over time, after discontinuing VR use. The lowered threshold hypothesis states that this continued improvement is moderated by lowering the threshold to conduct subsequent in-vivo exposure. The current study is the first to formally test this hypothesis, using data from a recent trial on automated VR exposure therapy for spider phobia, in which participants (n = 49) were followed for 1 year, completing assessments 1 week, 3 and 12 months post-treatment. The assessment included validated self-report of phobia symptoms, a standardized behavioral approach test featuring a real spider, and a questionnaire for self-reporting frequency of in-vivo exposures since last assessment. Number of in-vivo exposures was found to be independently associated with greater symptom decrease in longitudinal outcome models. In sequential structural equation models, immediate post-treatment symptom reduction was associated with subsequent in-vivo exposures, which in turn was associated with continued symptom reduction. However, this applied only to self-reported phobia symptoms (not behavioral avoidance) and no associations were found past 3 months. Our findings offer preliminary, partial support for the lowered threshold hypothesis, suggesting that VR exposure interventions may benefit from including explicit in-virtuo to in-vivo transitioning components.