2 recherche sur le mot-clé 'digital phenotyping'

Autism Digital Phenotyping in Preschool- and School-Age Children / Kimberly L.H. CARPENTER ; Pradeep Raj Krishnappa BABU ; J. Matias DI MARTINO ; Steven ESPINOSA ; Scott N. 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 Auteurs : Kimberly L.H. CARPENTER, Auteur ; Pradeep Raj Krishnappa BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Steven ESPINOSA, Auteur ; Scott N. 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] / Kimberly L.H. CARPENTER, Auteur ; Pradeep Raj Krishnappa BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Steven ESPINOSA, Auteur ; Scott N. 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

Smartphone language and resting-state EEG indicators of self-focused attention prospectively predict major depressive disorder risk in adolescents / Lilian Y. LI in Journal of Child Psychology and Psychiatry, 67-6 (June 2026)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 67-6 (June 2026) . - p.941-950 Titre : Smartphone language and resting-state EEG indicators of self-focused attention prospectively predict major depressive disorder risk in adolescents Type de document : texte imprimé Auteurs : Lilian Y. LI, Auteur ; Nayoung KIM, Auteur ; Esha TRIVEDI, Auteur ; Sarah E. SARKAS, Auteur ; Madeline M. MCGREGOR, Auteur ; Aishwarya SRITHARAN, Auteur ; Katherine DURHAM, Auteur ; Ivan ALEKSEICHUK, Auteur ; Allison M. LETKIEWICZ, Auteur ; Vijay A. MITTAL, Auteur ; David PAGLIACCIO, Auteur ; Nicholas B. ALLEN, Auteur ; Randy P. AUERBACH, Auteur ; Stewart A. SHANKMAN, Auteur Article en page(s) : p.941-950 Langues : Anglais (eng) Mots-clés : Depression  adolescence  self-focused attention  digital phenotyping  EEG alpha oscillations Index. décimale : PER Périodiques Résumé : Background Central to major depressive disorder (MDD) onset and maintenance is maladaptive self-focused attention, which can be reliably indexed by greater: (a) usage of first-person singular pronouns (e.g., I) in natural language and (b) alpha oscillations in resting-state EEG. Integrating these largely parallel bodies of research, the present study sought to explicate the associations between, and prospective predictive utility of, linguistic and neural indicators of self-focused attention in adolescents with remitted MDD over 12?months. Methods At baseline, 126 adolescents (ages 13?18) with (n?=?66) and without (n?=?60) remitted MDD completed resting-state EEG. Retrospective interviews determined the occurrence of major depressive episodes (MDEs) during the follow-up period. A total of ~2.3?million messages were passively acquired from adolescents' smartphones, on which the proportion of first-person singular pronouns was derived. Results During the 12?months, 29 (23.0%) participants developed an MDE (28 remitted MDD, 1 control). Cox regression showed that while greater usage of first-person singular pronouns prior to MDE increased the risk for MDE (hazard ratio [HR]?=?2.02, p? En ligne : https://doi.org/10.1111/jcpp.70096 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=587 [article] Smartphone language and resting-state EEG indicators of self-focused attention prospectively predict major depressive disorder risk in adolescents [texte imprimé] / Lilian Y. LI, Auteur ; Nayoung KIM, Auteur ; Esha TRIVEDI, Auteur ; Sarah E. SARKAS, Auteur ; Madeline M. MCGREGOR, Auteur ; Aishwarya SRITHARAN, Auteur ; Katherine DURHAM, Auteur ; Ivan ALEKSEICHUK, Auteur ; Allison M. LETKIEWICZ, Auteur ; Vijay A. MITTAL, Auteur ; David PAGLIACCIO, Auteur ; Nicholas B. ALLEN, Auteur ; Randy P. AUERBACH, Auteur ; Stewart A. SHANKMAN, Auteur . - p.941-950. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 67-6 (June 2026) . - p.941-950 Mots-clés : Depression  adolescence  self-focused attention  digital phenotyping  EEG alpha oscillations Index. décimale : PER Périodiques Résumé : Background Central to major depressive disorder (MDD) onset and maintenance is maladaptive self-focused attention, which can be reliably indexed by greater: (a) usage of first-person singular pronouns (e.g., I) in natural language and (b) alpha oscillations in resting-state EEG. Integrating these largely parallel bodies of research, the present study sought to explicate the associations between, and prospective predictive utility of, linguistic and neural indicators of self-focused attention in adolescents with remitted MDD over 12?months. Methods At baseline, 126 adolescents (ages 13?18) with (n?=?66) and without (n?=?60) remitted MDD completed resting-state EEG. Retrospective interviews determined the occurrence of major depressive episodes (MDEs) during the follow-up period. A total of ~2.3?million messages were passively acquired from adolescents' smartphones, on which the proportion of first-person singular pronouns was derived. Results During the 12?months, 29 (23.0%) participants developed an MDE (28 remitted MDD, 1 control). Cox regression showed that while greater usage of first-person singular pronouns prior to MDE increased the risk for MDE (hazard ratio [HR]?=?2.02, p? En ligne : https://doi.org/10.1111/jcpp.70096 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=587