Digital interventions using mobile technologies for life skills development of learners with autism spectrum disorder: a scoping review
Bibliographic record
Abstract
Providing essential information for digital health human-computer interaction designers, this scoping review identified the most utilized features of digital evidence-based interventions in prototyped mobile technologies for autism spectrum disorder (ASD), to lay the groundwork for designing user-focused mobile assistive technologies for learners with ASD. The review systematically categorized the features and capabilities of mobile applications that teach, support, and maintain life skills. Synthesizing and analysing 42 studies using thematic analysis, it was found that mobile applications hold the potential to support the uniqueness and varying needs of learners with ASD. This is achieved through personalization in the user interface and user experience, coupled with customizations on learning content. The scoping review also found that mobile applications can assist learners with ASD by breaking down tasks into smaller steps, incorporating pictures, illustrations, videos, sounds, pre-recorded instructions, and just-in-time prompting. In a broader context, mobile technologies can not only enhance life skills learning but also contribute to routine-building for individuals with ASD. This is possible if interventions taught in school are continued at home and integrated with daily home activities, aiming to enhance learning, daily practice, and maintenance of life skills. Future work focuses on making evidence-based interventions readily available and accessible to learners, parents, caregivers, and educators to support the overall well-being of learners 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".