A Compressive Armchair (OTO) to Perform Deep Pressure Therapy in Children With Autism Spectrum Disorder: User-Centered Design and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Deep pressure therapy (DPT) is widely used to reduce anxiety in children with autism spectrum disorder (ASD), but evidence of its efficacy is limited. OBJECTIVE: This study aims to design a usable, nonstigmatizing compressive armchair that can be easily controlled, electronically, by the user. METHODS: A user-centered approach was used to assess the usability of the device. Testing was carried out in a day hospital for children with ASD in France, with a convenience sample of children with severe forms of ASD and intellectual deficiency (N=39). The Witteman design guideline was used. The System Usability Scale and time of use were reported. RESULTS: The final product is a compressive armchair designed to be user centered, with 4 different cells that can be inflated to induce tailored pressure on the body. The pressure level is recorded electronically. Usability was between good and excellent. The device was used by 39 children, once or twice weekly, over a period of 31 months. Each session lasted between 3 and 20 minutes. The armchair takes up less space than a hug machine. Performing sessions with the chair is feasible. CONCLUSIONS: First clinical impressions show a decrease in anxiety, improved emotional regulation, and improved attention. DPT is widely used in occupational therapy and frequently requested by parents, but efficacy studies are too scarce to make evidence-based recommendations for its use. The results presented here support further controlled efficacy studies of DPT in the treatment of anxiety in children with ASD.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".