This review article summarises the current available literature on sleep patterns and sleep problems in Singapore children. Co-sleeping is a culturally dependent practice and its prevalence in Singapore has been determined to be 73%. Co-sleeping is not associated with significant sleep problems in Singapore children. Snoring and habitual snoring occur in 28.1% and 6.0% of Singapore children, respectively. Habitual snoring in Singapore children was significantly associated with obesity, allergic rhinitis, atopic dermatitis, maternal smoking and breastfeeding. Atopy was the strongest risk factor for habitual snoring in Singapore, and the effect was cumulative. Children attending psychiatric services in Singapore may also have sleep disorders, the highest prevalence being in children with attention deficit hyperactivity disorder. The knowledge on childhood sleep disorders (including obstructive sleep apnoea) amongst the public, patients, parents and future doctors in Singapore are inadequate and there is an urgent need for increased education in this area given the importance of good sleep in children. There is also a need to change parental attitudes about sleep disorders and encourage early medical consultation.
Child ; Culture ; Health Knowledge, Attitudes, Practice ; Humans ; Quality of Life ; Singapore ; epidemiology ; Sleep ; Sleep Wake Disorders ; epidemiology ; Snoring ; etiology

INTRODUCTIONDuring the 2003 Severe Acute Respiratory Syndrome (SARS) outbreak, all schools in Singapore implemented twice-daily temperature monitoring for students to curtail the spread of the disease. Students were not allowed to attend school if their temperature readings were >37.8 degrees C for students < or =12 years old, or > or =37.5 degrees C for students >12 years old. These values had been arbitrarily determined with professional inputs. The aim of this study is to determine the reference ranges of normal oral temperatures of students in Singapore and recommend the cut-off values for febrile patients. This may be used in another similar outbreak of an infectious disease with fever.

MATERIALS AND METHODSFour co-ed primary schools and 4 co-ed secondary schools were selected for this study. Four thousand and two hundred primary 1 to secondary 3 students responded (96.8%) and participated in this cross-sectional study. The mean ages of the students in the lowest (primary 1) and highest educational levels (secondary 3) were 7.4 years old and 15.3 years old, respectively. Twelve oral temperature readings per student (i.e. measurements taken 4 times a day in 3 consecutive days) were collected. Forty-six thousand seven hundred and eighty-three (92.8%) out of 50,400 temperature readings were used for the analysis as missing data were excluded. A quantile regression model was applied to estimate reference ranges of normal oral temperatures for students with adjustment for potential confounding factors.

RESULTSThe age-specific reference ranges of normal oral temperature from this study for students < or =12 years old and >12 years old were 35.7 degrees C to 37.7 degrees C and 35.6 degrees C to 37.4 degrees C, respectively. Temperatures of 37.8 degrees C and 37.5 degrees C are therefore recommended as the oral temperature cut-offs for those < or =12 years old and >12 years old, respectively.

CONCLUSIONThis study has provided empirical data on normal oral temperature cut-offs which could be used during temperature screening in schools.

Adolescent ; Body Temperature ; Child ; Circadian Rhythm ; Cross-Sectional Studies ; Female ; Humans ; Male ; Reference Values ; Schools ; Singapore ; Students

Objectives: Telemedicine is firmly established in the healthcare landscape of many countries. Acute respiratory infections are the most common reason for telemedicine consultations. A throat examination is important for diagnosing bacterial pharyngitis, but this is challenging for doctors during a telemedicine consultation. A solution could be for patients to upload images of their throat to a web application. This study aimed to develop a deep learning model for the automated diagnosis of exudative pharyngitis. Thereafter, the model will be deployed online. Methods: We used 343 throat images (139 with exudative pharyngitis and 204 without pharyngitis) in the study. ImageDataGenerator was used to augment the training data. The convolutional neural network models of MobileNetV3, ResNet50, and EfficientNetB0 were implemented to train the dataset, with hyperparameter tuning. Results: All three models were trained successfully; with successive epochs, the loss and training loss decreased, and accuracy and training accuracy increased. The EfficientNetB0 model achieved the highest accuracy (95.5%), compared to MobileNetV3 (82.1%) and ResNet50 (88.1%). The EfficientNetB0 model also achieved high precision (1.00), recall (0.89) and F1-score (0.94). Conclusions We trained a deep learning model based on EfficientNetB0 that can diagnose exudative pharyngitis. Our model was able to achieve the highest accuracy, at 95.5%, out of all previous studies that used machine learning for the diagnosis of exudative pharyngitis. We have deployed the model on a web application that can be used to augment the doctor’s diagnosis of exudative pharyngitis.