@article{Nor2019,
title = {Prevalence of overweight and obesity among children and adolescents with autism spectrum disorder and associated risk factors},
author = {N K Nor and A H Ghozali and J Ismail},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064414280&doi=10.3389%2ffped.2019.00038&partnerID=40&md5=4bb61b1df043a4adf79618e223d77f26},
doi = {10.3389/fped.2019.00038},
issn = {22962360},
year = {2019},
date = {2019-01-01},
journal = {Frontiers in Pediatrics},
volume = {7},
number = {FEB},
publisher = {Frontiers Media S.A.},
abstract = {Introduction: Prevalence of obesity in Autism Spectrum Disorder (ASD) has been reported to be higher than in the general population. Determining prevalence may help increase awareness of obesity in ASD and potentially lead to initiatives to reduce obesity. In order to understand obesity in ASD children, common risk factors were assessed including physical activity, feeding problems and sleep disturbances. Methods: This is a cross-sectional study performed at the Child Development Center at Universiti Kebangsaan Malaysia Medical Center on 151 ASD children aged 2-18 years. Anthropometric and demographic information were obtained and parents completed three questionnaires; Children Sleep Habits Questionnaire (CSHQ), Physical Activity for Older Children Questionnaire (PAQ-C) and Brief Autism Mealtime Behavior Questionnaire (BAMBI). Results: For ASD children in our sample, the prevalence of overweight (BMI ≥85th to < 95th percentiles) was 11.3% and the prevalence of obesity (BMI ≥95th percentile) was 21.9%. The overweight/obese ASD children's median age was higher at 8.5 years (IQR 5.81-10.13) compared to the normal/underweight group of 6.33 years (IQR 4.75-7.7) with a p-value of 0.001. The two groups also differed significantly for maternal BMI and paternal age. The median maternal BMI in the overweight/obese group was 26.05 (IQR 23.35-32.25), statistically significantly higher (p = 0.003) than in the non-overweight/obese group, 24.7 (IQR 21-27.9). The median paternal age of 40 years (IQR 37-44) was statistically significantly higher (p = 0.039) in the overweight/obese group, compared to the median paternal age in the non-overweight/obese group of 38 (IQR 35-42). The male overweight/obese children had median PAQ-C score of 2.44 (IQR 2.00-3.00) vs. 2.89 (IQR 2.35-3.53) in the counterpart group with a p-value of 0.01. Using the multiple linear regression stepwise method, three predictors associated with BMI percentiles reached a statistical level of significance; PAQ-C score in males (p < 0.001), the BAMBI domains of Food Refusal (p = 0.001) and Limited Variety of Food (p = 0.001). Conclusions: The prevalence of obesity and overweight is high among Malaysian ASD children and adolescents. Older child age, high maternal BMI, older paternal age, low physical activity, low likelihood of food refusal and high likelihood of food selectivity were found to be risk factors for high BMI in these children. © 2019 Kamal Nor, Ghozali and Ismail.},
note = {cited By 5},
keywords = {Adolescent, Adult, Article, Autism, Body Mass, Brief Autism Mealtime Beahavior Questionnaire, Child Development, Childhood Obesity, Children, Children Sleep Habits Questionnaire, Controlled Study, Cross-Sectional Study, Feeding Difficulty, Female, Food Refusal, Human, Major Clinical Study, Malaysian, Male, Mother, Paternal Age, Physical Activity, Physical Activity for Older Children Questionnaire, Prevalence, Questionnaires, Risk Factor, Sleep Disorder, Underweight},
pubstate = {published},
tppubtype = {article}
}

@article{Al-Hiyali201886,
title = {A review in modification food-intake behavior by brain stimulation: Excess weight cases},
author = {M I Al-Hiyali and A J Ishak and H Harun and S A Ahmad and W A Wan Sulaiman},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062843670&doi=10.14704%2fnq.2018.16.12.1894&partnerID=40&md5=235f66cef05a144be23472641f70bd1d},
doi = {10.14704/nq.2018.16.12.1894},
issn = {13035150},
year = {2018},
date = {2018-01-01},
journal = {NeuroQuantology},
volume = {16},
number = {12},
pages = {86-97},
publisher = {Anka Publishers},
abstract = {Obesity and overweight are frequently prescribed for dysfunction in food-intake behavior. Due to the widely prevalence of obesity in last year’s, there is demand for more studies which are aimed to modify the food-intake behavior. For the past decades many researches has applied in modify food-intake by brain training or stimulation. This review for neuroscience studies in modifying food-intake behavior, it’s involved three sections; The first section explained the role of brain activity in food-intake regulation, general ideas about biomedical devices in food-intake behavior are discussed in second section and third section focused on brain-stimulation systems. Finally, this paper concluded with main points that need to be taken into account when designing experimental study for modification food-intake behavior by brain stimulation according to previous studies recommendation and challenges. © 2018, Anka Publishers. All Rights Reserved.},
note = {cited By 2},
keywords = {Amygdala, Anoxia, Article, Autism, Binge Eating Disorder, Body Mass, Body Weight, Brain Depth Stimulation, Depolarization, Dietary Intake, Drug Craving, Eating Disorder, Electric Current, Electroencephalogram, Electroencephalography, Energy Consumption, Energy Expenditure, Feeding Behavior, Food Intake, Functional Magnetic Resonance Imaging, Gender, Health Status, Homeostasis, Human, Hunger, Lifestyle, Nerve Cell Membrane Steady Potential, Nerve Excitability, Neurofeedback, Neuromodulation, Nutritional Assessment, Outcome Assessment, Questionnaires, Repetitive Transcranial Magnetic Stimulation, Signal Processing, Training, Transcranial Direct Current Stimulation, Transcranial Magnetic Stimulation, Underweight},
pubstate = {published},
tppubtype = {article}
}

@article{Masiran2018b,
title = {Autism and trichotillomania in an adolescent boy},
author = {R Masiran},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053164449&doi=10.1136%2fbcr-2018-226270&partnerID=40&md5=7eed3f6af717df527dce73838feab571},
doi = {10.1136/bcr-2018-226270},
issn = {1757790X},
year = {2018},
date = {2018-01-01},
journal = {BMJ Case Reports},
volume = {2018},
publisher = {BMJ Publishing Group},
abstract = {An adolescent with autism spectrum disorder and improperly treated attention deficit hyperactivity disorder presented with recurrent hair pulling. Treatment with selective serotonin reuptake inhibitor and stimulant improved these conditions. © © BMJ Publishing Group Limited 2018.},
note = {cited By 0},
keywords = {Adolescent, Alopecia, Anxiety, Article, Attention Deficit Disorder, Attention Deficit Hyperactivity Disorder, Autism, Autism Spectrum Disorders, Behaviour Disorder, Body Mass, Case Report, Central Nervous System Stimulants, Child Behaviour Checklist, Clinical Article, Comorbidity, Complication, Diagnosis, Differential, Differential Diagnosis, Drug Dose Titration, Drug Tolerance, DSM-5, Echolalia, Fluvoxamine, Follow Up, Human, Hyperactivity, Intellectual Impairment, Male, Methylphenidate, Obesity, Occupational Therapy, Perceptual Reasoning Index, Priority Journal, Processing Speed Index, Psychiatric Status Rating Scales, Psychological Rating Scale, Rating Scale, Restlessness, Reward, Serotonin Uptake Inhibitor, Serotonin Uptake Inhibitors, Special Education, Speech Delay, Speech Disorder, Speech Therapy, Trichotillomania, Verbal Comprehension Index, Wechsler Intelligence Scale, Working Memory Index},
pubstate = {published},
tppubtype = {article}
}

@article{Hasan201755,
title = {The analysis of three-dimensional ground reaction forces during gait in children with autism spectrum disorders},
author = {C Z C Hasan and R Jailani and N Md Tahir and S Ilias},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015640386&doi=10.1016%2fj.ridd.2017.02.015&partnerID=40&md5=d6a9839cda7f62bcce9bdcca33d3d33b},
doi = {10.1016/j.ridd.2017.02.015},
issn = {08914222},
year = {2017},
date = {2017-01-01},
journal = {Research in Developmental Disabilities},
volume = {66},
pages = {55-63},
publisher = {Elsevier Inc.},
abstract = {Minimal information is known about the three-dimensional (3D) ground reaction forces (GRF) on the gait patterns of individuals with autism spectrum disorders (ASD). The purpose of this study was to investigate whether the 3D GRF components differ significantly between children with ASD and the peer controls. 15 children with ASD and 25 typically developing (TD) children had participated in the study. Two force plates were used to measure the 3D GRF data during walking. Time-series parameterization techniques were employed to extract 17 discrete features from the 3D GRF waveforms. By using independent t-test and Mann-Whitney U test, significant differences (p < 0.05) between the ASD and TD groups were found for four GRF features. Children with ASD demonstrated higher maximum braking force, lower relative time to maximum braking force, and lower relative time to zero force during mid-stance. Children with ASD were also found to have reduced the second peak of vertical GRF in the terminal stance. These major findings suggest that children with ASD experience significant difficulties in supporting their body weight and endure gait instability during the stance phase. The findings of this research are useful to both clinicians and parents who wish to provide these children with appropriate treatments and rehabilitation programs. © 2017 Elsevier Ltd},
note = {cited By 8},
keywords = {Age Distribution, Article, Autism, Autism Spectrum Disorders, Biomechanical Phenomena, Biomechanics, Body Equilibrium, Body Height, Body Mass, Body Weight, Children, Clinical Article, Controlled Study, Disease Assessment, Female, Gait, Gait Analysis, Gait Disorder, Ground Reaction Forces, Human, Imaging, Leg Length, Malaysia, Male, Neurologic Examination, Pathophysiology, Physiology, Postural Balance, Procedures, Psychology, Statistics, Three-Dimensional, Three-Dimensional Imaging, Three-Dimentional Ground Reaction Force, Walking},
pubstate = {published},
tppubtype = {article}
}

@article{Hasan201774,
title = {Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder},
author = {C Z C Hasan and R Jailani and N Md Tahir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043500605&partnerID=40&md5=6f2ffe7c2f5daf9fd02d4456acb94438},
issn = {19984510},
year = {2017},
date = {2017-01-01},
journal = {International Journal of Biology and Biomedical Engineering},
volume = {11},
pages = {74-79},
publisher = {North Atlantic University Union NAUN},
abstract = {Automated differentiation of ASD gait from normal gait patterns is important for early diagnosis as well as ensuring rapid quantitative clinical decision and appropriate treatment planning. This study explores the use of statistical feature selection approaches and artificial neural networks (ANN) for automated identification of gait deviations in children with ASD, on the basis of dominant gait features derived from the three-dimensional (3D) joint kinematic data. The gait data from 30 ASD children and 30 normal healthy children were measured using a state-of-the-art 3D motion analysis system during self-selected speed barefoot walking. Kinematic gait features from the sagittal, frontal and transverse joint angles waveforms at the pelvis, hip, knee, and ankle were extracted using time-series parameterization. Two statistical feature selection techniques, namely the between-group tests (independent samples t-test and Mann-Whitney U test) and the stepwise discriminant analysis (SWDA) were adopted as feature selector to select the meaningful gait features that were then used to train the ANN. The 10-fold cross-validation test results indicate that the selected gait features using SWDA technique are more reliable for ASD gait classification with 91.7% accuracy, 93.3% sensitivity, and 90.0% specificity. The findings of the current study demonstrate that kinematic gait features with the combination of SWDA feature selector and ANN classifier would serve as a potential tool for early diagnosis of gait deviations in children with ASD as well as provide support to clinicians and therapists for making objective, accurate, and rapid clinical decisions that lead to the appropriate targeted treatments. © 2017 North Atlantic University Union NAUN. All Rights Reserved.},
note = {cited By 1},
keywords = {Article, Artificial Neural Network, Autism, Body Height, Body Mass, Children, Clinical Article, Controlled Study, Discriminant Analysis, Early Diagnosis, Female, Gait, Gait Analysis, Gait Disorder, Human, Learning, Male, Pediatrics, School Child, Statistical Analysis, Statistics, Time Series Analysis},
pubstate = {published},
tppubtype = {article}
}