SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted Reducing Type 1 Childhood Diabetes in Saudi Arabia by Identifying and Modelling Its Key Performance Indicators(Royal Melbourne Institute of Technology, 2024-06) Alazwari, Ahood; Johnstone, Alice; Abdollahain, Mali; Tafakori, LalehThe increasing incidence of type 1 diabetes (T1D) in children is a growing global health concern. Reducing the incidence of diabetes generally is one of the goals in the World Health Organisation’s (WHO) 2030 Agenda for Sustainable Development Goals. With an incidence rate of 31.4 cases per 100,000 children and an estimated 3,800 new cases per year, Saudi Arabia is ranked 8th in the world for number of T1D cases and 5th for incidence rate. Despite the remarkable increase in the incidence of childhood T1D in Saudi Arabia, there is a lack of meticulously carried out research on T1D in children when compared with developed countries. In addition, it is crucial to recognise the critical gaps in current understanding of diabetes in children, adolescents, and young adults, with recent research indicates significant global and sub-national variations in disease incidence. Better knowledge of the development of T1D in children and its associated factors would aid medical practitioners in developing intervention plans to prevent complications and address the incidence of T1D. This study employed statistical, machine learning and classification approaches to analyse and model different aspects of childhood T1D using local case and control data. In this study, secondary data from 1,142 individual medical records (359-377 cases and 765 controls) collected from three cities located in different regions of Saudi Arabia have been used in the analysis to represent the country’s diverse population. Case and control data matched by birth year, gender and location were used to control confounders and create a more robust and clinically relevant model. It is well documented that genetic and environmental factors contribute to childhood T1D so a wide range of potential key performance indicators (KPIs) from the literature were included in this study. The collected data included information on socioeconomic status, potential genetic and environmental factors, and demographic data such as city of residence, gender and birth year. Several techniques, such as cross-validation, hyperparameter tuning and bootstrapping, were used in this study to develop models. Common statistical metrics (coefficient of determination, R-squared, root mean squared error, mean absolute error) were used to evaluate performance for the regression models while for the classification models accuracy, sensitivity, precision, F score and area under the curve were utilised as performance measures. Multiple linear regression (MLR), artificial neural network (ANN) and random forest (RF) models were developed to predict the age at onset of T1D for all children 0-14 years old, as well as for the most common age group for onset, the 5-9 year olds. To improve the performance of the MLR models, interactions between variables were considered. Additionally, risk factors associated with the age at onset of T1D were identified. The results showed that MLR and RF outperformed ANN. The logarithm of age at onset was the most suitable dependent variable. RF outperformed the others for the 5-9 years age group. Birth weight, current weight and current height influenced the age at onset in both age groups. However, preterm birth was significant only in the 0-14 years cohort, while consanguineous parents and gender were significant in the 5-9 age group. Logistic regression (LR), random forest (RF), support vector machine (SVM), Naive Bayes (NB) and artificial neural network (ANN) models were utilised with case and control data to model the development of childhood T1D and to identify its key performance indicators. Full and reduced models were developed to determine the best model. The reduced models were built using the significant factors identified by the individual full model. The study found that full LR had the highest accuracy. Full RF and SVM with a linear kernel also performed well. Significant risk factors identified as being associated with developing childhood T1D include early exposure to cow’s milk, high birth weight, positive family history of T1D and maternal age over 25 years. Poisson regression (PR), RF, SVM and K-nearest neighbor (KNN) were then used to model the incidence of childhood T1D, taking in the identified significant risk factors. The interactions between variables were also considered to enhance the performance of the models. Both full and reduced models were created and compared to find the best models with the minimum number of variables. The full Poisson regression and machine learning models outperformed all other models, but reduced models with a combination of only two out of three independent variables (early exposure to cow’s milk, high birth weight and maternal age over 25 years) also performed relatively well. This study also deployed optimisation procedures with the reduced incidence models to develop upper and lower yearly profile limits for childhood T1D incidence to achieve the United Nations (UN) and Saudi recommended levels of 264 and 339 cases by 2030. The profile limits for childhood T1D then allowed us to model optimal yearly values for the number of children weighing more than 3.5kg at birth, the number of deliveries by older mothers and the number of children introduced early to cow’s milk. The results presented in this thesis will guide healthcare providers to collect data to monitor the most influential KPIs. This would enable the initiation of suitable intervention strategies to reduce the disease burden and potentially slow the incidence rate of childhood T1D in Saudi Arabia. The research outcomes lead to recommendations to establish early intervention strategies, such as educational campaigns and healthy lifestyle programs for mothers along with child health mentoring during and after pregnancy to reduce the incidence of childhood T1D. This thesis has contributed to new knowledge on childhood T1D in Saudi Arabia by: * developing a predictive model for age at onset of childhood T1D using statistical and machine learning models. * predicting the development of T1D in children using matched case-control data and identifying its KPIs using statistical and machine learning approaches. * modeling the incidence of childhood T1D using its associated significant KPIs. * developing three optimal profile limits for monitoring the yearly incidence of childhood T1D and its associated significant KPIs. * providing a list of recommendations to establish early intervention strategies to reduce the incidence of childhood T1D.14 0Item Restricted An evaluation of the Rashaka Initiative: a school-based obesity intervention in Makkah City, Saudi Arabia(University of Technology Sydney, 2024-03) Banany, Mohammed; Sibbritt, David; Gebel, KlausBackground: Childhood overweight and obesity are public health issues worldwide. In Saudi Arabia, in 2016/2017 the Rashaka Initiative, a national school-based, multicomponent, weight-related intervention, was launched to decrease the prevalence of obesity among students by 5% within five years. Neither the development stage of the initiative nor its implementation has been evaluated to explore its processes and outcomes. Aim: This study was aimed at evaluating the implementation of the Rashaka Initiative in intermediate and secondary schools in Makkah City, Saudi Arabia, covering both process and outcome. To this end, the following objectives were pursued: (1) to develop an evaluation framework that can be used to assess the processes and outcomes of the initiative, (2) to determine whether there was a change in students’ body mass indices (BMIs) during the implementation period, and (3) to explore the knowledge and attitudes of the Rashaka stakeholders regarding the perceived barriers and facilitators of implementation in their schools. Methods: This retrospective study, conducted after the implementation of the Rashaka Initiative, was completed in three phases. In phase I, a conceptual framework called the school-based weight-related intervention evaluation framework (SWIEF) was developed by integrating some elements of the program evaluation framework used by the US Centers for Disease Control and Prevention (CDC) with the components of a logic model. In phase II, secondary data from the Rashaka Initiative were analysed. Phase III was a cross-sectional exploration of the Rashaka stakeholders’ knowledge and attitudes as well as what they perceive as facilitators and barriers to implementing the intervention at their schools. Results: The comprehensive literature review yielded a published systematic review (Banany et al. 2024, Systematic Reviews). This systematic review found 11 school-based weight-related intervention studies in the six Gulf Cooperation Council countries (GCC). Despite the methodological limitations of some of these studies, there is preliminary evidence of the possible benefits of school-based interventions on students' weight and associated lifestyle factors in these countries. A review of the literature also facilitated the development of the SWIEF. The analysis of the secondary data revealed a significant reduction in BMI (p<0.001) across schools that participated in the Rashaka Initiative over two school years (2016/17 and 2018/19). However, this reduction was not associated with the school environmental factors attributed to the initiative. The study findings found that students’ BMIs decreased more considerably in girls’ and intermediate schools than in boys’ and secondary schools (p<0.001 and p=0.031, respectively). The cross-sectional study indicated that significantly better knowledge of risk factors and interventions for childhood obesity was exhibited by female Rashaka stakeholders (vs their male counterparts), stakeholders who completed tertiary education (vs those with lower education levels) and stakeholders engaged in the initiative for more than two years (vs participants who joined more recently) (p<0.001, p<0.007 and p<0.033, respectively). School health counsellors had more positive attitudes towards children’s health and weight than principals (p<0.008). Significantly more favourable attitudes towards the Rashaka intervention were also found among female stakeholders (p<0.011) and those with better knowledge of the initiative’s objectives, components, activities, and outcomes (p<0.049). Among the stakeholders, 73% perceived collaboration with different government and private sector institutions as the most common facilitator of the Rashaka implementation at their schools, while 69% perceived a lack of time as the main barrier. Conclusions: Addressing childhood obesity is a public health priority that requires substantial efforts from all relevant key stakeholders in Saudi Arabia. The evidence derived in this thesis revealed that the Rashaka Initiative has yet to satisfy its objectives. Future studies should be more rigorous, theory-based, and holistic to tackle obesity among school students. Evaluations of school-based obesity interventions should use control groups, validated and reliable measures and rigorous data analysis. Long-term monitoring of the implemented interventions is highly recommended for their improvement and sustainability.27 0Item Restricted Multisensory Processing as a Concurrent Contributor to Cognitive and Language Development in School-Aged Children (A Bayesian Approach)(La Trobe University, 2024-04-24) Alhamdan, Areej; Crewther, Sheila; Murphy, MelanieMultisensory processing is fundamental to survival of higher animals, humans included. Rapid and successful integration of visual and auditory information in the brain is necessary to ensure comprehensive understanding of the environment and facilitation of motor responses. Indeed, visual and auditory multisensory processing when measured as Motor Reaction Times (MRTs) in adults has long been known to enhance accuracy and speed of responses, though few have considered how development of motor function per se influences age-related increase in multisensory MRTs and contributes to various cognitive abilities, including working memory (WM), intelligence and language development in primary school children. Thus, the current thesis employed a Bayesian approach to meta-analyze literature up to mid- 2023 to test the association between both motor and verbal measures of multisensory processing and WM development, while also showing that multisensory stimuli contributed more significantly to WM capacity than unisensory visual or auditory stimuli alone. The three experimental studies presented in this thesis employed a simple multisensory MRT task, to explore the interaction of motor development and cognitive abilities in children aged 5-10 years. The first study aimed to examine developmental changes in multisensory MRTs, visuomotor responses and non-motor visual Inspection threshold Time tasks in school children to highlight the more significant contributions of age to motor than sensory function. The second study aimed to investigate the development of visual and auditory WM and visually based nonverbal intelligence, and their relationship to multisensory and visuomotor tasks. Our findings demonstrated that age-related performance on nonverbal intelligence and visual rather than auditory WM were the strongest unique predictors of multisensory MRTs. The final study investigated the association between multisensory and visuomotor processing and the development of receptive and expressive vocabulary abilities, and showed that children with faster MRTs in multisensory and visuomotor processing tasks demonstrated higher complex expressive vocabulary scores (as opposed to simple receptive vocabulary). Overall, the findings of this thesis highlight the interaction of motor development and cognitive abilities and demonstrate that simple, fast, and easily accessible assessment measures of multisensory processing, visuomotor coordination, and nonverbal intelligence as measures of both on-going developmental and neurodevelopmental status in school children.30 0Item Restricted The Potential of iPad Apps to Support Vocabulary Development in Children Learning English as an Additional Language(Saudi Digital Library, 2022-09-22) Aldossary, Norah Saleh; Niland, Amanda; Cairney, Trevor; Curwood, Jen ScottYoung children from various cultural backgrounds are being encouraged to learn English, as this is a widely used language in intercultural settings. Learning a new language involves, in part, developing vocabulary. The aim of this qualitative case study was to explore the role of an Apple iPad application in supporting English vocabulary building. The participants were children aged from four to six years learning English as an Additional Language and Dialect (EAL/D) and their educators in two Australian Early Childhood Education Centres (ECECs). The study was conducted within a theoretical framework of sociocultural learning inspired by Vygotsky’s sociocultural theory, which provides insight into how children learn, particularly through play and interaction with others. Data were collected through observation of small groups of EAL/D children engaged in shared reading and vocabulary activities on an iPad app with an educator. Educators’ perspectives on the use of apps in their curriculum and on their experience of using the selected app for this study with the children were also studied. The app used was Starfall, which was selected after an analytical process conducted by the researcher prior to data collection, drawing on a range of literature. This included research into how young children’s language and vocabulary development, in their first language as well as additional languages, can be supported in early childhood education and care settings through play-based learning, as well as research into the use of digital technology in early childhood education, its potential impact on children’s development, and research into the use of iPad apps in enhancing young children’s language learning and vocabulary development. From this process, a short list of six apps was developed so that educators in the study could select the one they felt was most relevant and engaging for the children in their early childhood centre. Data were collected through children’s observations and educator interviews and thematically analysed, using NVivo software. Findings revealed vi that the selected app provided opportunities for promoting the children’s English language learning, including their vocabulary development. During the iPad sessions in both centres, children were excited to engage in conversations stimulated by the content of the app, both with one another and with the educators. They were also keen to share their life experiences with their friends and educators, which facilitated their English language development and vocabulary building. Further, the findings suggest that the Starfall app’s interactive features supported children’s active engagement and language development. However, in some cases, depending on context, and on educators’ perspectives on app use and teaching strategies, data showed missed opportunities for children’s engagement in rich language interactions. Overall, the study showed that iPad apps have the potential to support the language and vocabulary development of EAL/D children, and they can be used as a language-learning strategy in early childhood classrooms. It also showed that early childhood educators can benefit from learning more about how to utilise iPad apps as resources for language development, as part of their play-based pedagogies. In doing so, they need to start by evaluating the contents and features of apps, to select the apps that fit most closely with their pedagogical aims and teaching strategies. By critically considering how specific apps can enhance multimodal communication and vocabulary development and adopting pedagogical strategies that facilitate children’s interactions and conversations while using the apps, early childhood educators can utilise apps in classrooms that include young EAL/D learners. Educators therefore need access to professional development and resources to support their effective use of iPad and other apps in order to foster children’s multimodal communication in multilingual classrooms.9 0