Neurobehavioral Assessment of Sensorimotor Function in Autism Using Smartphone Technology
Bibliographic record
Abstract
Abstract Differences in sensorimotor processing represent an important, yet underrecognized, feature of autism; typically assessed through subjective observations, which are susceptible to biases. A more objective approach to quantify sensorimotor function may be possible through reflex- based neurobehavioral evaluations. The clinical application of these assessments has, however, been confined largely to laboratory settings. Thus, small sample sizes and inconsistent findings have made it challenging to understand how sensorimotor function differs in autism and whether it can be used as an objective biomarker for diagnostics. Here we present a novel smartphone-based platform to conduct neurobehavioral evaluations by measuring facial and behavioral responses in at-home environments. Through a multi-centre study, we explored the platform’s ability to distinguish between children with and without autism. We enrolled 536 children aged 3–12 years. BlinkLab smartphone-based assessments were successfully completed in 431 children (80.4%), including 275 with autism and 156 neurotypical children. We found that autistic children showed altered sensorimotor responses across multiple domains. These included reduced prepulse inhibition (PPI), stronger habituation over the course of a PPI test, more variable eyeblink responses to auditory stimuli and significant sensitization. Additionally, children with autism displayed more screen avoidance, postural instability, head movements, mouth openings, non-syllabic vocalizations, horizontal pupil shifts, "side-eyeing", and variation in baseline eyelid opening. Exploratory analyses showed that these effects were largely independent of co-occurring ADHD or intellectual disability. Notably, co-occurrence did influence certain subdomains (e.g., PPI, mouth openings). These findings illustrate that smartphone-based assessments can capture distinct sensorimotor profiles associated with autism in real-world environments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".