CBT for Childhood Anxiety: Reviewing the State of Personalised Intervention Research

Predicting remission following CBT for childhood anxiety disorders: a machine learning approach

BACKGROUND

The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models.

METHODS

A machine learning approach was applied to predict anxiety disorder remission in a large sample of 2114 anxious youth (5-18 years). Potential predictors included demographic, clinical, parental, and treatment variables with data obtained pre-treatment, post-treatment, and at least one follow-up.

RESULTS

All machine learning models performed similarly for remission outcomes, with AUC between 0.67 and 0.69. There was significant alignment between the factors that contributed to the models predicting two target outcomes: remission of all anxiety disorders and the primary anxiety disorder. Children who were older, had multiple anxiety disorders, comorbid depression, comorbid externalising disorders, received group treatment and therapy delivered by a more experienced therapist, and who had a parent with higher anxiety and depression symptoms, were more likely than other children to still meet criteria for anxiety disorders at the completion of therapy. In both models, the absence of a social anxiety disorder and being treated by a therapist with less experience contributed to the model predicting a higher likelihood of remission.

CONCLUSIONS

These findings underscore the utility of prediction models that may indicate which children are more likely to remit or are more at risk of non-remission following CBT for childhood anxiety.

Intensive treatments for children and adolescents with anxiety or obsessive-compulsive disorders: A systematic review and meta-analysis

Anxiety and obsessive-compulsive disorders have a significant negative impact on youth. Cognitive behavioural therapy (CBT) is recommended and established as effective first-step treatment, but persistent symptoms and non-response are common. Intensive psychological treatments deliver more or longer sessions over a shorter time span, with fewer session gaps. However, an understanding of their effectiveness, characteristics, acceptability, and feasibility is lacking. Systematically searching five databases yielded four controlled and 36 uncontrolled studies (N=2707) involving youth with primary anxiety or obsessive-compulsive disorders, many of whom received prior treatments. Intensive treatments were acceptable and feasible, with low drop-out rates. Between-group analyses compared intensive treatment with standard treatment (k = 2) or waitlist (k = 2), revealing no significant post-treatment differences in symptom severity or remission. Uncontrolled within-group analyses of intensive treatments showed large improvements from pre- to post-treatment in symptoms (k = 47), remission (k = 17), impairment (k = 22), functioning (k = 5), and quality of life (k = 2), with larger effects at follow-ups. Intensive treatments show promise for youth with anxiety or obsessive-compulsive disorders by potentially offering high treatment completion rates and comparable outcomes to standard CBT, aiding earlier recovery and reducing overall suffering. This systematic review/meta-analysis provides a comprehensive overview of intensive treatments, their theoretical considerations, and empirical findings. Future RCTs should compare the effectiveness of standard and intensive treatments and identify optimal populations for their use.

Editorial Perspective: Extending IPDMA methodology to drive treatment personalisation in child mental health

To improve outcomes for youth who do not respond optimally to existing treatments, we need to identify robust predictors, moderators, and mediators that are ideal targets for personalisation in mental health care. We propose a solution to leverage the Individual Patient Data Meta-analysis (IPDMA) approach to allow broader access to individual-level data while maintaining methodological rigour. Such a resource has the potential to answer questions that are unable to be addressed by single studies, reduce researcher burden, and enable the application of newer statistical techniques, all to provide data-driven strategies for clinical decision-making. Using childhood anxiety as the worked example, the editorial perspective outlines the rationale for leveraging IPDMA methodology to build a data repository, the Platform for Anxiety Disorder Data in Youth. We also include recommendations to address the methods and challenges inherent in this endeavour.

Defining and operationalizing personalized psychological treatment - a systematic literature review

This systematic literature review aimed to propose a definition of personalized psychological treatment and to suggest how the definition can be operationalized. PsycINFO, Cochrane Library, MEDLINE and EMBASE were searched up to 11 December 2023 for studies in which a definition of personalized psychological treatment was included or a systematic operationalization of personalized psychological treatment was described. Based on a narrative synthesis of the collected definitions, summary categories were developed that informed the proposed definition. Operationalizations were described according to what aspect of treatment, how and when treatment was personalized. The extent to which the operationalizations deviated from the proposed definition was assessed. Thirty-four studies with definitions and 200 with operationalizations were included. The following definition was proposed: personalized psychological treatment aims to optimize treatment outcome for the individual patient by tailoring treatment to unique or specific needs, preferences or other characteristics and includes a systematic adaptation of treatment or a differentiation between treatment strategies. Based on the operationalizations, timing of personalization, specification of the systematic approach and treatment elements that could be personalized were added to the proposed definition. Evidence-based personalization of psychological treatments can be enhanced by clear operationalization based on a comprehensive definition of personalization.

Stepped-care versus therapist-guided, internet-based cognitive behaviour therapy for childhood and adolescent anxiety: A non-inferiority trial

Objective

This preregistered randomized trial examined whether a stepped-care approach to internet-delivered cognitive behaviour therapy (ICBT-SC) is non-inferior to therapist-guided ICBT (ICBT-TG) for child and adolescent anxiety.

Method

Participants were 137 Australians, aged 8-17 years (56 male), with a primary anxiety disorder. This randomized, non-inferiority trial compared ICBT-SC to an evidence-based, ICBT-TG program with assessments conducted at baseline, 12 weeks and 9-months after treatment commencement. All ICBT-SC participants completed the first 5 online sessions without therapist guidance. If they responded to treatment in the first 5 sessions (defined as reductions of anxiety symptoms into non-clinical range), they continued without therapist guidance for the final 5 sessions. If they did not respond to treatment in the first 5 sessions, the final five sessions were supplemented with therapist-guidance (through email). All ICBT-TG participants received therapist guidance (email) after each session, for all 10 sessions. Measures included clinical diagnostic interview (severity rating as primary outcome), as well as parent and child reported anxiety and anxiety-related interference (secondary outcomes).

Results

ICBT-SC was found to be non-inferior to ICBT-TG on primary and secondary outcomes, according to clinician, parent and young person report at 12-weeks and 9-months. Treatment satisfaction was moderate to high for both conditions. Significant clinical benefits were evident for participants in both treatments. Of participants who remained in the study, 77 % (50.7 % ITT) of ICBT-SC and 77 % (57.1 % ITT) of ICBT-TG were free of their primary anxiety diagnosis by 9-month follow-up, with no differences between conditions.

Conclusion

A stepped-care ICBT approach for clinically anxious children and adolescents may offer an acceptable treatment model that can increase access to evidence-based treatment.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.