Autism Digital Phenotyping in Preschool- and School-Age Children.
An iPad game validly tracks autism-related gaze, face, and hand movements in 3- to 8-year-olds and matches standard clinical scores.
01Research in Context
What this study did
Researchers tested an iPad app called SenseToKnow. The app watches how kids look, move their face, and use their hands.
They gave the tablet to autistic and non-autistic children aged 3–8. The game recorded tiny behaviors while the child played.
What they found
The app scores lined up with standard tests like the Vineland and SRS. Autistic kids showed clear digital fingerprints that differed from typical peers.
Gaze patterns, facial movements, and visual-motor taps all helped tell the groups apart.
How this fits with other research
Older tools did the same job with paper. Green et al. (1987) and Repp et al. (1992) used short checklists to separate autistic from non-autistic kids with intellectual disability. SenseToNow replaces the pencil with a camera and still gets clean group splits.
Auyeung et al. (2009) and Eussen et al. (2016) also validated new measures for preschoolers. Their surveys asked parents; the iPad simply watches. All studies report positive validity, showing the field keeps making autism screening faster and easier.
Gaines et al. (2025) looked at sensory sex differences the same year. While SenseToKnow captures visual-motor behavior, that study warns that autistic girls may show stronger hearing and balance issues—useful context when you interpret any child’s app profile.
Why it matters
You can try the SenseToKnow app during intake or re-eval. It needs no extra clinician time; the child plays for a few minutes while you collect gaze and motor data that line up with Vineland scores. If you serve busy clinics or telehealth, this quick digital read may flag kids who need deeper assessment or adjust your treatment plan based on real-time behavioral phenotypes.
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02At a glance
03Original abstract
There is a critical need for scalable and objective tools for autism screening and outcome monitoring, which can be used alongside traditional caregiver and clinical measures. To address this need, we developed SenseToKnow, a tablet- or smartphone-based digital phenotyping application (app), which uses computer vision and touch data to measure several autism-related behavioral features, such as social attention, facial and head movements, and visual-motor skills. Our previous work demonstrated that the SenseToKnow app can accurately detect and quantify behavioral signs of autism in 18-40-month-old toddlers. In the present study, we administered the SenseToKnow app on an iPad to 149 preschool- and school-age children (45 neurotypical and 104 autistic) between 3 and 8 years of age. Results revealed significant group differences between autistic and neurotypical children in terms of several behavioral features, which remained after controlling for sex and age. Repeat administration with a subgroup demonstrated stability in the individual digital phenotypes. Examining correlations between the Vineland Adaptive Behavior Scales and individual digital phenotypes, we found that autistic children with higher levels of communication, daily living, socialization, motor, and adaptive skills exhibited higher levels of social attention and coordinated gaze with speech, less frequent head movements, higher complexity of facial movements, higher overall attention, lower blink rates, and higher visual motor skills, demonstrating convergent validity between app features and clinical measures. App features were also significantly correlated with ratings on the Social Responsiveness Scale. These results suggest that the SenseToKnow app can be used as an accessible, scalable, and objective digital tool to measure autism-related behaviors in preschool- and school-age children.
Autism research : official journal of the International Society for Autism Research, 2025 · doi:10.2139/ssrn.4100664