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1.
J Neurodev Disord ; 10(1): 9, 2018 03 01.
Artículo en Inglés | MEDLINE | ID: mdl-29490614

RESUMEN

BACKGROUND: Sleep disturbance is common in children with neurodevelopmental disorders, with high rates identified in children with Smith-Magenis syndrome (SMS), Angelman syndrome (AS), autism spectrum disorder (ASD) and tuberous sclerosis complex (TSC). Phenotypic sleep profiles for these groups may implicate different pathways to sleep disturbance. At present, cross-group comparisons that might elucidate putative phenotypic sleep characteristics are limited by measurement differences between studies. In this study, a standardised questionnaire was administered across groups affording comparison of the prevalence and profile of sleep disturbance between groups and contrast to chronologically age-matched typically developing (TD) peers. METHODS: The modified version of Simonds and Parraga's sleep questionnaire, adapted for use in children with intellectual disabilities, was employed to assess sleep disturbance profiles in children aged 2-15 years with SMS (n = 26), AS (n = 70), ASD (n = 30), TSC (n = 20) and a TD contrast group (n = 47). Associations between sleep disturbance and age, obesity, health conditions and overactivity/impulsivity were explored for each neurodevelopmental disorder group. RESULTS: Children with SMS displayed severe night waking (81%) and early morning waking (73%). In contrast, children with ASD experienced difficulties with sleep onset (30%) and sleep maintenance (43%). Fewer children with ASD (43%) and AS (46%) experienced severe night waking compared to children with SMS (both p < .01). Higher sleep-disordered breathing scores were identified for children with SMS (p < .001) and AS (p < .001) compared to the TD group. Sleep disturbance in children with AS and TSC was associated with poorer health. Children experiencing symptoms indicative of gastro-oesophageal reflux had significantly higher sleep-disordered breathing scores in the AS, SMS and ASD groups (all p < .01). A number of associations between overactivity, impulsivity, gastro-oesophageal reflux, age and sleep disturbance were found for certain groups. CONCLUSIONS: These data reveal syndrome-specific profiles of sleep disturbance. The divergent associations between sleep parameters and person characteristics, specifically symptoms of gastro-oesophageal reflux, overactivity and impulsivity and age, implicate aetiology-specific mechanisms underpinning sleep disturbance. The differences in prevalence, severity and mechanisms implicated in sleep disturbance between groups support a syndrome-sensitive approach to assessment and treatment of sleep disturbance in children with neurodevelopmental disorders.


Asunto(s)
Síndrome de Angelman/epidemiología , Trastorno del Espectro Autista/epidemiología , Trastornos del Sueño-Vigilia/epidemiología , Síndrome de Smith-Magenis/epidemiología , Esclerosis Tuberosa/epidemiología , Síndrome de Angelman/complicaciones , Trastorno del Espectro Autista/complicaciones , Niño , Estudios de Cohortes , Femenino , Humanos , Masculino , Trastornos del Sueño-Vigilia/complicaciones , Síndrome de Smith-Magenis/complicaciones , Encuestas y Cuestionarios , Esclerosis Tuberosa/complicaciones
2.
Front Psychol ; 8: 726, 2017.
Artículo en Inglés | MEDLINE | ID: mdl-28588521

RESUMEN

Extreme and non-extreme response styles (RSs) are prevalent in survey research using Likert-type scales. Their effects on measurement invariance (MI) in the context of confirmatory factor analysis are systematically investigated here via a Monte Carlo simulation study. Using the parameter estimates obtained from analyzing a 2007 Trends in International Mathematics and Science Study data set, a population model was constructed. Original and contaminated data with one of two RSs were generated and analyzed via multi-group confirmatory factor analysis with different constraints of MI. The results indicated that the detrimental effects of response style on MI have been underestimated. More specifically, these two RSs had a substantially negative impact on both model fit and parameter recovery, suggesting that the lack of MI between groups may have been caused by the RSs, not the measured factors of focal interest. Practical implications are provided to help practitioners to detect RSs and determine whether RSs are a serious threat to MI.

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