PROJECT LEAPP (LEARNING TO EAT APP): DEVELOPING AN IPAD-BASED VIDEO MODELING INTERVENTION TO INCREASE FOOD VARIETY IN CHILDREN WITH AUTISM SPECTRUM DISORDER (ASD)
Bibliographic record
Abstract
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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".