Mobile App for Parental Empowerment for Caregivers of Children With Autism Spectrum Disorders: Prospective Open Trial
Bibliographic record
Abstract
BACKGROUND: Conflicting data emerge from literature regarding the actual use of smartphone apps in medicine; some considered the introduction of smartphone apps in medicine to be a breakthrough, while others suggested that, in real-life, the use of smartphone apps in medicine is disappointingly low. Yet, digital tools become more present in medicine daily. To empower parents of a child with autism spectrum disorder, we developed the Smartautism smartphone app, which asks questions and provides feedback, using a screen with simple curves. OBJECTIVE: The purpose of this study was to evaluate usage of the app by caregivers of individuals with autism spectrum disorders. METHODS: We conducted a prospective longitudinal exploratory open study with families that have a child with autism spectrum disorder. Data were recorded over a period of 6 months, and the outcome criteria were (1) overall response rates for a feedback screen and qualitative questionnaires, and (2) response rates by degree of completion and by user interest, based on attrition. RESULTS: Participants (n=65) had a very high intent to use the app during the 6-month period (3698/3900 instances, 94.8%); however, secondary analysis showed that only 46% of participants (30/65) had constant response rates over 50%. Interestingly, these users were characterized by higher use and satisfaction with the feedback screen when compared to low (P<.001) and moderate (P=.007) users. CONCLUSIONS: We found that real or perceived utility is an important incentive for parents who use empowerment smartphone apps. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2016-012135.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".