An In Situ, Child-Led Intervention to Promote Emotion Regulation Competence in Middle Childhood: Protocol for an Exploratory Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Emotion regulation is a key transdiagnostic risk factor for a range of psychopathologies, making it a prime target for both prevention and treatment interventions in childhood. Existing interventions predominantly rely on workshops or in-person therapy-based approaches, limiting the ability to promote emotion regulation competence for children in everyday settings and at scale. Purrble is a newly developed, inexpensive, socially assistive robot-in the form of an interactive plush toy-that uses haptic feedback to support in-the-moment emotion regulation. It is accessible to children as needed in their daily lives, without the need for a priori training. Although qualitative data from previous studies show high engagement in situ and anecdotal evidence of the robot being incorporated into children's emotion regulation routines, there is no quantitative evidence of the intervention's impact on child outcomes. OBJECTIVE: The aim of this study is to examine the efficacy of a new intervention model for child-led emotion regulation-Purrble-that can be deployed across prevention and treatment contexts. METHODS: Overall, 134 children aged 8 to 10 years will be selected from an enriched nonclinical North American population; for inclusion, the cutoff for the parents' rating of child dysregulation will be ≥10 points in the total difficulties score on the Strengths and Difficulties Questionnaire. This cutoff was selected to obtain a measurable, but not necessarily clinical, level of the child's emotion regulatory difficulties. The selected families will be randomly assigned with .5 probability to receive either a Purrble or an active control (noninteractive plush toy). The primary outcome will be a daily ecological momentary assessment measure of child emotion regulation capability (as reported by parents) over a period of 4 weeks. Exploratory analyses will investigate the intervention impact on secondary outcomes of child emotion regulation, collected weekly over the same 4-week period, with follow-ups at 1 month and 6 months postdeployment. Quantitative data will be analyzed on an intent-to-treat basis. A proportion of families (approximately 30% of the sample) will be interviewed after deployment as part of the process analysis. RESULTS: The study is funded by the UKRI Future Leaders Fellowship (MR/T041897/1) and an in-kind contribution from the Committee for Children. This study received ethical approval from the Pearl institutional review board (#18-CFC-101). Participant recruitment started in February 2021, with the 1-month deployment in April-May 2021. The results of this analysis will be published in 2022. CONCLUSIONS: This study will be the first quantitative evaluation of the efficacy of an innovative, proof-of-concept intervention model for an in situ, child-led emotion regulation intervention. Insights into the trajectory of daily changes, complemented with weekly questionnaire batteries and postdeployment interviews, will result in an in-depth understanding of whether and how the hypothesized intervention logic model works, leading to further intervention optimization. TRIAL REGISTRATION: ClinicalTrials.gov NCT04810455; http://clinicaltrials.gov/ct2/show/NCT04810455. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/28914.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.011 |
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".