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

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:JMIR mental health Jg. 12; S. e67210
Hauptverfasser: Zainal, Nur Hani, Tan, Hui Han, Hong, Ryan Yee Shiun, Newman, Michelle Gayle
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Canada JMIR Publications 13.05.2025
Schlagworte:
Adult
Anxiety disorders
Compensation
Ecological Momentary Assessment
Fear & phobias
Female
Generalized anxiety disorder
Humans
Hypotheses
Machine Learning
Male
Mental depression
Middle Aged
Mindfulness
Mindfulness - methods
Original Paper
Patient-centered care
Phobia, Social - psychology
Phobia, Social - therapy
Precision medicine
Psychotherapy
Psychotropic drugs
Remission (Medicine)
Smartphones
Social anxiety
Treatment Outcome
Variables
Young Adult
ecological
mindfulness
depression severity
digital mental health
machine learning
randomized controlled trial
anxiety
ecological momentary intervention
mobile phone
social anxiety disorder
digital mental health interventions
precision medicine
predictor
intervention
longitudinal
ISSN:2368-7959, 2368-7959
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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. 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). 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. 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. 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. OSF Registries 10.17605/OSF.IO/M3KXZ; https://osf.io/m3kxz.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
content type line 14
ObjectType-Feature-3
ObjectType-Evidence Based Healthcare-1
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ObjectType-Undefined-3
ISSN:2368-7959
2368-7959
DOI:10.2196/67210