PROJECT LEAPP (LEARNING TO EAT APP): DEVELOPING AN IPAD-BASED VIDEO MODELING INTERVENTION TO INCREASE FOOD VARIETY IN CHILDREN WITH AUTISM SPECTRUM DISORDER (ASD)
Notice bibliographique
Résumé
BACKGROUND: Feeding difficulties affect up to 80% of children with ASD starting as early as 6 months of age. Food selectivity (FS) is the most commonly reported phenomenon by families and can lead to parental stress, strained parent-child interactions, and long-term health consequences such as nutrient deficiency, diabetes, and cardiovascular disease. FS is often chronic and resistant to treatment, but behavioural interventions for FS are supported. While successful, such interventions can be costly and resource intensive, and results may not be maintained over time. Video modeling intervention (VMI) is a promising new approach that incorporates video modeling (VM), where a child is expected to imitate the behaviour of interest after viewing a video recording of it. VM has been successful in teaching a variety of skills to children with ASD, including, play, communication, and daily living skills, but to our knowledge has not been attempted as a VM strategy targeting feeding behaviours in ASD. OBJECTIVES: This project serves as the starting point in investigating whether a VMI will increase food variety in preschoolers with ASD and a history of food refusal. Our objective is to develop a novel VM tool incorporating applied behavioural analysis strategies to deliver feeding intervention to preschoolers with ASD. DESIGN/METHODS: An iPad application with an animated model will be developed based on operant conditioning and systematic desensitization. Input was collected from a developmental panel (behavioural therapist, occupational therapist, speech language pathologist, engineer, animator, family team leader) to design the initial prototype. Themes generated from two focus groups consisting of clinicians with expertise in ASD and parents of preschoolers with ASD will address LEApp’s core design. Initial user testing and feedback regarding the application will be collected to revise LEApp. RESULTS: Our preliminary prototype (Figure 1 and 2) was created based on an initial literature review and with concepts derived from feeding intervention outlined by our developmental panel. The application will be modified pending the results of the focus group discussions. CONCLUSION: Project LEApp represents the first step in the creation and exploration of a novel tool that has the potential to impact an essential skill early in the lives of children with ASD. By involving children, their parents and multidisciplinary specialists throughout the process, LEApp has the potential not only to impact feeding outcomes, but can also be shared and utilized universally by families in any setting, thus filling a need in existing feeding intervention.(Figs 2, 3)
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».