Autism Digital Phenotyping in Preschool- and School-Age Children / Kimberly L. H. CARPENTER ; Pradeep Raj Krishnappa BABU ; J. Matias DI MARTINO ; Steven ESPINOSA ; Scott COMPTON ; Naomi DAVIS ; Lauren FRANZ ; Marina SPANOS ; Guillermo SAPIRO ; Geraldine DAWSON in Autism Research, 18-6 (June 2025)  

Public ISBD [article]  inAutism Research > 18-6 (June 2025) . - p.1217-1233 Titre : Autism Digital Phenotyping in Preschool- and School-Age Children Type de document : Texte imprimé et/ou numérique Auteurs : Kimberly L. H. CARPENTER, Auteur ; Pradeep Raj Krishnappa BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Steven ESPINOSA, Auteur ; Scott COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Marina SPANOS, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur Article en page(s) : p.1217-1233 Langues : Anglais (eng) Mots-clés : autism  computer vision  digital phenotyping  preschool- and school-age Index. décimale : PER Périodiques Résumé : 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. En ligne : https://doi.org/10.1002/aur.70032 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=558 [article] Autism Digital Phenotyping in Preschool- and School-Age Children [Texte imprimé et/ou numérique] / Kimberly L. H. CARPENTER, Auteur ; Pradeep Raj Krishnappa BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Steven ESPINOSA, Auteur ; Scott COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Marina SPANOS, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur . - p.1217-1233. Langues : Anglais (eng) in Autism Research > 18-6 (June 2025) . - p.1217-1233 Mots-clés : autism  computer vision  digital phenotyping  preschool- and school-age Index. décimale : PER Périodiques Résumé : 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. En ligne : https://doi.org/10.1002/aur.70032 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=558

Complexity analysis of head movements in autistic toddlers / Pradeep Raj KRISHNAPPA BABU in Journal of Child Psychology and Psychiatry, 64-1 (January 2023)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 64-1 (January 2023) . - p.156-166 Titre : Complexity analysis of head movements in autistic toddlers Type de document : Texte imprimé et/ou numérique Auteurs : Pradeep Raj KRISHNAPPA BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Zhuoqing CHANG, Auteur ; Sam PEROCHON, Auteur ; Rachel AIELLO, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Scott COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Steven ESPINOSA, Auteur ; Jacqueline FLOWERS, Auteur ; Geraldine DAWSON, Auteur ; Guillermo SAPIRO, Auteur Article en page(s) : p.156-166 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Background Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. Methods Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. Results Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. Conclusions Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism. En ligne : https://doi.org/10.1111/jcpp.13681 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490 [article] Complexity analysis of head movements in autistic toddlers [Texte imprimé et/ou numérique] / Pradeep Raj KRISHNAPPA BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Zhuoqing CHANG, Auteur ; Sam PEROCHON, Auteur ; Rachel AIELLO, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Scott COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Steven ESPINOSA, Auteur ; Jacqueline FLOWERS, Auteur ; Geraldine DAWSON, Auteur ; Guillermo SAPIRO, Auteur . - p.156-166. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 64-1 (January 2023) . - p.156-166 Index. décimale : PER Périodiques Résumé : Background Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. Methods Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. Results Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. Conclusions Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism. En ligne : https://doi.org/10.1111/jcpp.13681 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490

Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders: A Preliminary Report / Dmitry Yu ISAEV in Journal of Autism and Developmental Disorders, 54-6 (June 2024)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 54-6 (June 2024) . - p.2286-2297 Titre : Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders: A Preliminary Report Type de document : Texte imprimé et/ou numérique Auteurs : Dmitry Yu ISAEV, Auteur ; Maura SABATOS-DEVITO, Auteur ; J. Matias DI MARTINO, Auteur ; Kimberly CARPENTER, Auteur ; Rachel AIELLO, Auteur ; Scott COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Connor SULLIVAN, Auteur ; Geraldine DAWSON, Auteur ; Guillermo SAPIRO, Auteur Article en page(s) : p.2286-2297 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : We report preliminary results of computer vision analysis of caregiver-child interactions during free play with children diagnosed with autism (N = 29, 41-91 months), attention-deficit/hyperactivity disorder (ADHD, N = 22, 48-100 months), or combined autism?+?ADHD (N = 20, 56-98 months), and neurotypical children (NT, N = 7, 55-95 months). We conducted micro-analytic analysis of 'reaching to a toy,' as a proxy for initiating or responding to a toy play bout. Dyadic analysis revealed two clusters of interaction patterns, which differed in frequency of 'reaching to a toy' and caregivers' contingent responding to the child?s reach for a toy by also reaching for a toy. Children in dyads with higher caregiver responsiveness had less developed language, communication, and socialization skills. Clusters were not associated with diagnostic groups. These results hold promise for automated methods of characterizing caregiver responsiveness in dyadic interactions for assessment and outcome monitoring in clinical trials. En ligne : https://doi.org/10.1007/s10803-023-05973-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=530 [article] Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders: A Preliminary Report [Texte imprimé et/ou numérique] / Dmitry Yu ISAEV, Auteur ; Maura SABATOS-DEVITO, Auteur ; J. Matias DI MARTINO, Auteur ; Kimberly CARPENTER, Auteur ; Rachel AIELLO, Auteur ; Scott COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Connor SULLIVAN, Auteur ; Geraldine DAWSON, Auteur ; Guillermo SAPIRO, Auteur . - p.2286-2297. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 54-6 (June 2024) . - p.2286-2297 Index. décimale : PER Périodiques Résumé : We report preliminary results of computer vision analysis of caregiver-child interactions during free play with children diagnosed with autism (N = 29, 41-91 months), attention-deficit/hyperactivity disorder (ADHD, N = 22, 48-100 months), or combined autism?+?ADHD (N = 20, 56-98 months), and neurotypical children (NT, N = 7, 55-95 months). We conducted micro-analytic analysis of 'reaching to a toy,' as a proxy for initiating or responding to a toy play bout. Dyadic analysis revealed two clusters of interaction patterns, which differed in frequency of 'reaching to a toy' and caregivers' contingent responding to the child?s reach for a toy by also reaching for a toy. Children in dyads with higher caregiver responsiveness had less developed language, communication, and socialization skills. Clusters were not associated with diagnostic groups. These results hold promise for automated methods of characterizing caregiver responsiveness in dyadic interactions for assessment and outcome monitoring in clinical trials. En ligne : https://doi.org/10.1007/s10803-023-05973-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=530

Relationship between quantitative digital behavioral features and clinical profiles in young autistic children / Marika COFFMAN in Autism Research, 16-7 (July 2023)  

Public ISBD [article]  inAutism Research > 16-7 (July 2023) . - p.1360-1374 Titre : Relationship between quantitative digital behavioral features and clinical profiles in young autistic children Type de document : Texte imprimé et/ou numérique Auteurs : Marika COFFMAN, Auteur ; J. Matias DI MARTINO, Auteur ; Rachel AIELLO, Auteur ; Kimberly L. H. CARPENTER, Auteur ; Zhuoqing CHANG, Auteur ; Scott COMPTON, Auteur ; Brian EICHNER, Auteur ; Steve ESPINOSA, Auteur ; Jacqueline FLOWERS, Auteur ; Lauren FRANZ, Auteur ; Sam PEROCHON, Auteur ; Pradeep Raj KRISHNAPPA BABU, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur Article en page(s) : p.1360-1374 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Abstract Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism-related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver-report and clinician administered measures of autism-related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA-based gaze variables related to social attention were associated with the level of autism-related behaviors. Two language-related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism-related behaviors, and measure changes in autism-related behaviors over time. En ligne : https://doi.org/10.1002/aur.2955 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=510 [article] Relationship between quantitative digital behavioral features and clinical profiles in young autistic children [Texte imprimé et/ou numérique] / Marika COFFMAN, Auteur ; J. Matias DI MARTINO, Auteur ; Rachel AIELLO, Auteur ; Kimberly L. H. CARPENTER, Auteur ; Zhuoqing CHANG, Auteur ; Scott COMPTON, Auteur ; Brian EICHNER, Auteur ; Steve ESPINOSA, Auteur ; Jacqueline FLOWERS, Auteur ; Lauren FRANZ, Auteur ; Sam PEROCHON, Auteur ; Pradeep Raj KRISHNAPPA BABU, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur . - p.1360-1374. Langues : Anglais (eng) in Autism Research > 16-7 (July 2023) . - p.1360-1374 Index. décimale : PER Périodiques Résumé : Abstract Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism-related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver-report and clinician administered measures of autism-related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA-based gaze variables related to social attention were associated with the level of autism-related behaviors. Two language-related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism-related behaviors, and measure changes in autism-related behaviors over time. En ligne : https://doi.org/10.1002/aur.2955 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=510

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