eHealth Interventions for Anxiety Management Targeting Young Children and Adolescents: Exploratory Review
Bibliographic record
Abstract
BACKGROUND: Advances in technology are progressively more relevant to the clinical practice of psychology and mental health services generally. Studies indicate that technology facilitates the delivery of interventions, such as cognitive behavioral therapy, in the treatment of psychological disorders in adults, such as depression, anxiety, obsessive-compulsive disorder, panic symptoms, and eating disorders. Fewer data exist for computer-based (stand-alone, self-help) and computer-assisted (in combination with face-to-face therapy, or therapist guided) programs for youth. OBJECTIVE: Our objective was to summarize and critically review the literature evaluating the acceptability and efficacy of using technology with treatment and prevention programs for anxiety in young children and adolescents. The aim was to improve the understanding of what would be critical for future development of effective technology-based interventions. METHODS: We conducted an exploratory review of the literature through searches in 3 scientific electronic databases (PsycINFO, ScienceDirect, and PubMed). We used keywords in various combinations: child or children, adolescent, preschool children, anxiety, intervention or treatment or program, smartphone applications or apps, online or Web-based tool, computer-based tool, internet-based tool, serious games, cognitive behavioral therapy or CBT, biofeedback, and mindfulness. For inclusion, articles had to (1) employ a technological therapeutic tool with or without the guidance of a therapist; (2) be specific for treatment or prevention of anxiety disorders in children or adolescents; (3) be published between 2000 and 2018; and (4) be published in English and in scientific peer-reviewed journals. RESULTS: We identified and examined 197 articles deemed to be relevant. Of these, we excluded 164 because they did not satisfy 1 or more of the requirements. The final review comprised 19 programs. Published studies demonstrated promising results in reducing anxiety, especially relative to the application of cognitive behavioral therapy with technology. For those programs demonstrating efficacy, no difference was noted when compared with traditional interventions. Other approaches have been applied to technology-based interventions with inconclusive results. Most programs were developed to be used concurrently with traditional treatments and lacked long-term evaluation. Very little has been done in terms of prevention interventions. CONCLUSIONS: Future development of eHealth programs for anxiety management in children will have to address several unmet needs and overcome key challenges. Although developmental stages may limit the applicability to preschool children, prevention should start in early ages. Self-help formats and personalization are highly relevant for large-scale dissemination. Automated data collection should be built in for program evaluation and effectiveness assessment. And finally, a strategy to stimulate motivation to play and maintain high adherence should be carefully considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".