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Billet publié sur Twitter/X le 23/04/2022 06:34

Twitter/X Essai clinique clinicaltrials.gov Extrait cité dans le billet Question posée par Dawson dans le billet Lien intégré au billet Termes sur l’autisme

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Essai clinique Récupéré Publication clinicaltrials.gov

Ariel University (2022). Development and Validation of a System for the Anticipation of Challenging Behaviors. ClinicalTrials.gov. Ariel University.

Date de publication
01/06/2022
Identifiant
NCT05340608
Auteurs
Ariel University
Source
ClinicalTrials.gov
Type de référence
clinical_trial
Éditeur
Ariel University
Source de métadonnées
clinicaltrials.gov

Résumé

Most people with autism spectrum disorder (ASD) present at least one form of challenging behavior (CB). Self-injurious, aggressive, and disruptive CBs linked with social interaction, community-based service exclusion, and a life quality reduction for people with ASD, their caregivers, and health professionals. The current study has three objectives: 1) to assess the differences in the physiological reaction of high-functioning adults with ASD and typically developed peers, using bio-signal measurements such as heart rate derived from wearable Smart Shirt (SS), 2) to learn which physiological parameters can best predict the imminent onset of a CB, and 3) to develop a system able to predict the incoming occurrence of a CB in real-time and inform the caregiver through an alert notification sent on a smartphone application. Methods and analysis: comparison between physiological parameters will carry out with two groups of 20 participants with and without ASD. Each participant will be asked to watch two five-minute videos while wearing the SS: one showing relaxing images and the other impressive human body deformities. To identify the matching between the physiological parameters variation collected by the SS and the CBs, ten participants with ASD and aggressive or disruptive CBs will be recruited. Each of these participants will wear the SS for seven consecutive days during waking hours, performing their usual daily activities. During the same seven days, the caregivers who care for the participant will fill a behavioral diary with the participant's status, reporting the times of the day in which he is quiet, agitated and the occurrence of CBs. A learning algorithm capable of predicting immediate CBs occurrence based on physiological parameter variations will be developed together with an ad hoc smartphone application. If the algorithm detects the possibility of an incoming CB, a notification will be sent to the caregiver's smartphone to inform of the possible advent of a CB, therefore enabling the implementation of the selected intervention strategy. After developing the algorithm and related smartphone application, a system efficiency proof of concept (POC) will be carried out with one participant with ASD and CB for seven days in a special school setting with healthcare professionals and teachers. A focus group including health professionals will be conducted after the POC to identify the strengths and weaknesses of the developed system.