Relationship between quantitative digital behavioral features and clinical profiles in young autistic children / Marika C. 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 Auteurs : Marika C. COFFMAN, Auteur ; J. Matias DI MARTINO, Auteur ; Rachel AIELLO, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Zhuoqing CHANG, Auteur ; Scott N. 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] / Marika C. COFFMAN, Auteur ; J. Matias DI MARTINO, Auteur ; Rachel AIELLO, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Zhuoqing CHANG, Auteur ; Scott N. 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

A scalable computational approach to assessing response to name in toddlers with autism / Sam PEROCHON in Journal of Child Psychology and Psychiatry, 62-9 (September 2021)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 62-9 (September 2021) . - p.1120-1131 Titre : A scalable computational approach to assessing response to name in toddlers with autism Type de document : texte imprimé Auteurs : Sam PEROCHON, Auteur ; Matias DI MARTINO, Auteur ; Rachel AIELLO, Auteur ; Jeffrey BAKER, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Zhuoqing CHANG, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Brian EICHNER, Auteur ; Steven ESPINOSA, Auteur ; Jacqueline FLOWERS, Auteur ; Lauren FRANZ, Auteur ; Martha GAGLIANO, Auteur ; Adrianne HARRIS, Auteur ; Jill HOWARD, Auteur ; Scott H KOLLINS, Auteur ; Eliana M. PERRIN, Auteur ; Pradeep RAJ, Auteur ; Marina SPANOS, Auteur ; Barbara WALTER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur Article en page(s) : p.1120-1131 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis  Autistic Disorder/diagnosis  Child  Child, Preschool  Humans  Infant  Autism spectrum disorders  assessment  behavioral measures  screening. Index. décimale : PER Périodiques Résumé : BACKGROUND: This study is part of a larger research program focused on developing objective, scalable tools for digital behavioral phenotyping. We evaluated whether a digital app delivered on a smartphone or tablet using computer vision analysis (CVA) can elicit and accurately measure one of the most common early autism symptoms, namely failure to respond to a name call. METHODS: During a pediatric primary care well-child visit, 910 toddlers, 17-37 months old, were administered an app on an iPhone or iPad consisting of brief movies during which the child's name was called three times by an examiner standing behind them. Thirty-seven toddlers were subsequently diagnosed with autism spectrum disorder (ASD). Name calls and children's behavior were recorded by the camera embedded in the device, and children's head turns were coded by both CVA and a human. RESULTS: CVA coding of response to name was found to be comparable to human coding. Based on CVA, children with ASD responded to their name significantly less frequently than children without ASD. CVA also revealed that children with ASD who did orient to their name exhibited a longer latency before turning their head. Combining information about both the frequency and the delay in response to name improved the ability to distinguish toddlers with and without ASD. CONCLUSIONS: A digital app delivered on an iPhone or iPad in real-world settings using computer vision analysis to quantify behavior can reliably detect a key early autism symptom-failure to respond to name. Moreover, the higher resolution offered by CVA identified a delay in head turn in toddlers with ASD who did respond to their name. Digital phenotyping is a promising methodology for early assessment of ASD symptoms. En ligne : http://dx.doi.org/10.1111/jcpp.13381 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456 [article] A scalable computational approach to assessing response to name in toddlers with autism [texte imprimé] / Sam PEROCHON, Auteur ; Matias DI MARTINO, Auteur ; Rachel AIELLO, Auteur ; Jeffrey BAKER, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Zhuoqing CHANG, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Brian EICHNER, Auteur ; Steven ESPINOSA, Auteur ; Jacqueline FLOWERS, Auteur ; Lauren FRANZ, Auteur ; Martha GAGLIANO, Auteur ; Adrianne HARRIS, Auteur ; Jill HOWARD, Auteur ; Scott H KOLLINS, Auteur ; Eliana M. PERRIN, Auteur ; Pradeep RAJ, Auteur ; Marina SPANOS, Auteur ; Barbara WALTER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur . - p.1120-1131. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 62-9 (September 2021) . - p.1120-1131 Mots-clés : Autism Spectrum Disorder/diagnosis  Autistic Disorder/diagnosis  Child  Child, Preschool  Humans  Infant  Autism spectrum disorders  assessment  behavioral measures  screening. Index. décimale : PER Périodiques Résumé : BACKGROUND: This study is part of a larger research program focused on developing objective, scalable tools for digital behavioral phenotyping. We evaluated whether a digital app delivered on a smartphone or tablet using computer vision analysis (CVA) can elicit and accurately measure one of the most common early autism symptoms, namely failure to respond to a name call. METHODS: During a pediatric primary care well-child visit, 910 toddlers, 17-37 months old, were administered an app on an iPhone or iPad consisting of brief movies during which the child's name was called three times by an examiner standing behind them. Thirty-seven toddlers were subsequently diagnosed with autism spectrum disorder (ASD). Name calls and children's behavior were recorded by the camera embedded in the device, and children's head turns were coded by both CVA and a human. RESULTS: CVA coding of response to name was found to be comparable to human coding. Based on CVA, children with ASD responded to their name significantly less frequently than children without ASD. CVA also revealed that children with ASD who did orient to their name exhibited a longer latency before turning their head. Combining information about both the frequency and the delay in response to name improved the ability to distinguish toddlers with and without ASD. CONCLUSIONS: A digital app delivered on an iPhone or iPad in real-world settings using computer vision analysis to quantify behavior can reliably detect a key early autism symptom-failure to respond to name. Moreover, the higher resolution offered by CVA identified a delay in head turn in toddlers with ASD who did respond to their name. Digital phenotyping is a promising methodology for early assessment of ASD symptoms. En ligne : http://dx.doi.org/10.1111/jcpp.13381 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456

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