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Predicting sleep problems in children with autism spectrum disorders.
Shui, Amy M; Katz, Terry; Malow, Beth A; Mazurek, Micah O.
Afiliación
  • Shui AM; Biostatistics Center, Massachusetts General Hospital, 50 Staniford Street, Suite 560, Boston, MA 02114, USA. Electronic address: amy.shui@ucsf.edu.
  • Katz T; Department of Pediatrics, University of Colorado School of Medicine, 13123 E 16th Avenue, Aurora, CO 80045, USA. Electronic address: Terry.Katz@ucdenver.edu.
  • Malow BA; Sleep Disorders Division, Department of Neurology, Vanderbilt University School of Medicine, 1211 Medical Center Drive, Nashville, TN 37232, USA. Electronic address: beth.malow@vanderbilt.edu.
  • Mazurek MO; Department of Human Services, Curry School of Education, University of Virginia, 417 Emmet Street South, Charlottesville, VA 22903, USA. Electronic address: mm5gt@virignia.edu.
Res Dev Disabil ; 83: 270-279, 2018 Dec.
Article en En | MEDLINE | ID: mdl-30393065
BACKGROUND: Sleep difficulties in children with autism spectrum disorders (ASD) have been well-established. AIMS: To develop a model to predict sleep problems in children with ASD. METHODS AND PROCEDURES: A sample of children in the Autism Speaks-Autism Treatment Network (ATN) registry without parent-reported sleep problems at baseline and with sleep problem (yes/no) data at first annual followup was randomly split into training (n = 527) and test (n = 518) samples. Model predictors were selected using the training sample, and a threshold for classifying children at risk was determined. Comparison of the predicted and true sleep problem status of the test sample yielded model performance measures. OUTCOMES AND RESULTS: In a multivariable model aggressive behavior among children with no sleep problems reported at baseline was associated with having more sleep problems at the first annual follow-up visit. This model performed in the test sample with high sensitivity and accurate prediction of low risk. CONCLUSIONS AND IMPLICATIONS: Among children with ASD aggressive behavior independently predicts sleep problems. The model's high sensitivity for identifying children at risk and its accurate prediction of low risk can help with treatment and prevention of sleep problems. Further data collection may provide better prediction through methods requiring larger samples.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trastornos del Sueño-Vigilia / Medición de Riesgo / Agresión / Trastorno del Espectro Autista Tipo de estudio: Clinical_trials / Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Child / Female / Humans / Male Idioma: En Revista: Res Dev Disabil Asunto de la revista: TRANSTORNOS MENTAIS Año: 2018 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trastornos del Sueño-Vigilia / Medición de Riesgo / Agresión / Trastorno del Espectro Autista Tipo de estudio: Clinical_trials / Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Child / Female / Humans / Male Idioma: En Revista: Res Dev Disabil Asunto de la revista: TRANSTORNOS MENTAIS Año: 2018 Tipo del documento: Article Pais de publicación: Estados Unidos