Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
Browse
6 results
Search Results
Item Restricted Preparedness for Caregiving Among Middle Eastern and North African Arab American Informal Caregivers of People with Dementia: A Multimethod Study(Saudi Digital Library, 2026) Zimami, Sumiyyah; Darwish, HalaBackground: Dementia is increasingly prevalent in the Middle Eastern and North African (MENA) populations. Informal caregivers play an essential role in dementia care; however, their caregiving experiences remain underexamined in the MENA region, and even less is known about these experiences among MENA Arab Americans (MENA-AA). People with dementia require specialized and often complex care, making caregiver preparedness essential for managing caregiving demands, reducing caregiver stress, and improving care outcomes. Caregiver preparedness is the caregiver’s confidence in their knowledge, skills, and abilities to carry out daily caregiving tasks and manage emotional demands over time. Despite its importance, little is known about the factors that predict caregiver preparedness among MENA-AA caregivers or how sociocultural contexts influence their preparedness to provide care. This gap limits the development of culturally responsive interventions and support systems tailored to this underserved population. Aims: To 1) Identify the predictors of caregiver preparedness among MENA-AA informal caregivers of people with dementia, focusing on dementia knowledge, caregiver self-efficacy, caregiving competence, perceived and received social support, sociocultural beliefs and norms; 2) Assess MENA-AA caregivers social support needs and explore discrepancies between the two; and 3) Explore how sociocultural beliefs and norms of caregiving among MENA-AA impact their preparedness to provide care for people with dementia. Methods: This dissertation employed a multimethod design. Manuscripts 1 and 2 employed cross-sectional quantitative designs, with data collected via online surveys. Manuscript 2 also included open-ended responses, which were analyzed using qualitative descriptive content analysis. Quantitative analyses were conducted using Stata 19. Manuscript 3 used a qualitative design, with data collected through semi-structured focus group discussions and analyzed using inductive thematic analysis, supported by Nvivo 15. Results: A total of 150 caregivers participated; 145 were included in inferential analyses due to missing data. In Manuscript 1, caregiver competence emerged as the strongest predictor of preparedness (B = 0.14, p < 0.001), followed by caregiver self-efficacy (B = 0.007, p = 0.045) and received social support (B = 0.005, p = 0.016). Dementia knowledge, perceived social support, and sociocultural beliefs and norms were not significantly associated with preparedness. In Manuscript 2, family (84.9%) and healthcare professionals (81.4%) were the most frequently endorsed sources of support, while emotional support (82.7%) was the most highly rated type of support. Common barriers included distance to support services (46.2%) and lack of available options (42.1%). Perceived and received social support were moderately correlated (ρ = 0.44, p < 0.001). Caregivers reported high perceived social support (57.24% high support); however, perceived support was significantly higher than received support (z = 7.98, p < 0.001), with a median discrepancy of 18.19 points and 76.6% reporting higher perceived than received support. In Manuscript 3, qualitative findings from three focus groups identified five interrelated dimensions: the influence of sociocultural context on caregiver preparedness, mechanisms for enhancing preparedness, existing barriers to caregiver preparedness, caregiving challenges, and rewards and meaning in caregiving roles. Conclusion: Overall, these findings highlight that caregiving preparedness is primarily driven by competence, confidence, and received (enacted) support, while sociocultural factors influence caregiving orientation rather than directly predicting preparedness. This dissertation underscores the need for culturally responsive, skill-based, and accessible interventions to enhance preparedness and better support MENA-AA caregivers, ultimately improving outcomes for both caregivers and people living with dementia.11 0Item Restricted Supportive Environment for Aging Population: Three Approaches to Utilizing the Indoor Environmental Features to Create Psychologically Supportive Environment for Older Adults With Dementia(Saudi Digital Library, 2026) Alrahyani, Mohammed Nasser M; Sharp, Nina; Dongwoo, Jason Yeom; Lesselyong, JuliaThe physical environment is a critical determinant of psychological and physiological well-being in long-term care facilities. However, most memory care facilities are characterized by unfamiliarity, sensory deprivation, and inadequate lighting, which may exacerbate agitation, depression, and sleep disturbances among older adults with dementia. Thus, this dissertation investigates three non-pharmacological environmental approaches, namely biodynamic lighting, a bi-sensory “Arosound” environment (aroma + sound), and affordable multisensory environments, to enhance the quality of life for older adults with dementia living in memory care settings. Three studies were conducted in memory care facilities. Study one evaluated the feasibility and effects of biodynamic lighting, a circadian lighting system that varies in intensity and spectrum throughout the day, using a 7-week counterbalanced crossover design with ten participants (mean age = 75.6). Biodynamic lighting improved total sleep time by 82 minutes compared to Baseline and 57 minutes compared to constant moderate lighting, with trends toward reduced depressive symptoms and lower aggressive behaviors. Study two examined a bi-sensory “Arosound” environment combining aroma and sound, compared to single-sensory aroma and sound exposures. Fifteen residents participated over twelve weeks, with two-week washouts. All interventions reduced evening depressive symptoms, with the strongest effects during aroma exposure. Sound exposure significantly reduced agitation, while Arosound showed balanced improvements across both mood and behavior. Study three tested two affordable, passive multisensory environments, a morning stimulating environment (MSSE) and an evening relaxing environment (MSRE) as well as their combined effect, in eleven participants. The combined MSSE+MSRE condition significantly improved sleep efficiency and reduced depressive symptoms and heart rate compared to Baseline, while MSSE reduced morning non-aggressive behaviors. Collectively, the findings demonstrate that ambient features of the indoor built environment, when strategically designed, can serve as scalable, supportive interventions to improve behavioral and physiological well-being among individuals with dementia. The results provide evidence-based design guidance for architects, designers, and care providers aiming to create supportive, human-centered environments in memory care settings.9 0Item Restricted Ageing, Blood Pressure, and Neurodegeneration: A Lifelong Concern that Dietary Magnesium may Help Address(Australian National University, 2024-06) Alateeq, Khawlah; Cherbuin, NicolasThe growing global burden of neurodegenerative diseases, cognitive decline, and dementia highlights the urgent need to identify modifiable risk factors that promote healthy brain ageing and slow the progression of dementia. This thesis investigates the impact of high blood pressure (BP) – a prevalent risk factor – on brain ageing. It also investigates the role dietary magnesium (Mg) can play in addressing this risk factor and promoting brain health. Five studies were conducted to address the objectives of this thesis. The initial study involved a comprehensive literature review to generate insights into the relationship between BP, magnesium, neurodegeneration, and cognitive decline within the context of ageing. This process involved summarizing the mechanisms and effects to enhance understanding of how these factors potentially interact in the ageing brain. This review generated essential background knowledge that the subsequent studies sought to build upon. In the second study, a systematic review and meta-analysis were performed to quantify the existing literature on the relationship between continuous BP and total/regional brain volumes and white matter lesions (WMLs). The systematic review revealed a significant association between higher BP, lower brain volumes, and larger WMLs across the entire range, extending beyond individuals with hypertension or pre-hypertension. This finding suggests that high BP is associated with poorer brain health in non-hypertensive individuals within a broader population. The third study built upon the findings of the second study, examining the impact of age, gender, and other risk factors on the association between BP and brain health. This investigation quantified the relationships between continuous BP levels, brain volumes, and WMLs while also considering various risk factors such as age, sex, BMI, and use of antihypertensive medication. The findings reveal a consistent association between BP, lower brain volumes, and larger WMLs across the entire BP range, even within the upper normal BP range. This association affects individuals of all ages, particularly younger adults, and exhibits stronger effects in women. Notably, lower BMI and the use of antihypertensive medication appear to exert a protective effect against the detrimental impact of BP on brain health. These findings have substantial implications for population health, suggesting that even minor increases in BP over a lifespan can significantly contribute to the overall disease burden. This underscores the importance of implementing early preventative measures to maintain optimal BP levels. The fourth and fifth studies explored the associations between Mg intake, brain volumes, and WMLs. Additionally, these studies investigated whether BP and inflammation mediate the neuroprotective effect of Mg. The findings from both the fourth and fifth studies demonstrate that high dietary Mg intake is associated with larger brain volumes and lower WMLs. These results imply that a diet rich in Mg could contribute to enhanced brain health and potentially decrease the risk of dementia in the general population. Surprisingly, the neuroprotective impact of Mg is found not to be mediated by BP levels. Instead, the neuroprotective effect of dietary Mg is partially mediated through inflammation. This supports the potential of Mg intake as an effective, scalable, and affordable intervention in reducing inflammation and protecting against premature brain ageing in the general population. In conclusion, this thesis highlights the significant impact of high BP on brain health and finds that dietary Mg intake yields a neuroprotective effect, which can be attributed to its anti- inflammatory properties. Overall, the findings paint an optimistic picture for general population health, emphasizing the potential benefits of early interventions targeting modifiable risk factors. Thus, sufficient support at the health policy, clinical, and community levels can help reduce or prevent certain known risks to brain health.20 0Item Restricted Supervised Machine Learning Assessment of Dementia Using Feature Selection Filter Methods(Spring Nature, 2023-10-30) Rajab, Mohammed Dabash; Wang, DennisThe prevalence of dementia is increasing globally. Due to the massive resources required, this issue is pressuring governments and private healthcare systems. Accurate diagnosis by clinicians on the cause of dementia, such as Alzheimer’s disease (AD), is difficult because of the time and assessments needed like neuropathological. The issue becomes more challenging when considering if various brain lesions contribute to the pathological assessment of dementia, the relationship of these lesions to the various dementia conditions, how they interact, and how to quantify them. Thereby, systematically assessing neuropathological measures by their degree of association with dementia, especially AD, may lead to better diagnostic systems and treatment targets. One promising approach that can answer these challenges is to develop data-driven solutions with core functions of feature evaluation and automatic subject classification based on machine learning (ML). Recent research studies in medical diagnosis, including dementia research, reveal that ML techniques, when used with feature selection, can identify critical features of Alzheimer-related pathologies and their association with the disease’s diagnosis and prognosis. The feature selection removes noisy features from the dementia data to increase the predictive performance and improve interpretability while reducing the dimensionality and computational complexity. However, filter-based feature selection methods can generate dissimilar feature rankings and may be sensitive to the correlations among themselves. This thesis investigates dementia with a focus on AD neuropathological assessments from a data-driven perspective to develop mechanisms to assist pathologists during these clinical assessments. The thesis investigation comprises phases such as feature ranking, feature-feature correlation, and classification. The work determines the impact of neuropathological feature-features correlations on the feature ranking for better biomarker identification. The investigation assesses real datasets related to dementia, the Cognitive Function and Aging Studies (CFAS) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), using filter methods and classification techniques. The results showed that classification models generated from the CFAS and ADNI sets of chosen neuropathological features were strong in terms of sensitivity, accuracy, and other measures when mined by different classification techniques. In the ADNI dataset results, the significant neuropathological features contributing to AD included neocortical neuritic plaques, Braak stage, Thal phase, diffuse plaques, and cerebral amyloid angiopathy (CAA), all of which showed a high correlation with AD’s diagnostic label. In the CFAS dataset, the results were consistent with those derived from the ADNI dataset. Moreover, among the filter methods considered, reliefF had the strongest correlation with feature-feature correlations in both ADNI and CFAS datasets, less sensitive to feature-feature correlations. However, no filter method had clear dominance over ADNI results. More essentially, the results indicated limited consistency in feature rankings between ADNI and CFAS. However, reliefF had the most agreement, while the Gain Ratio method had less consistency in ranking the features in both datasets. In summary, this thesis provided valuable insights into the application of filter methods and neuropathology data for developing classification models for dementia conditions’ diagnosis. The study demonstrated the significance of considering feature-feature correlations when selecting influential features and the impact of different filter methods on feature ranking and classification performance. These findings suggest that the proposed approach could effectively minimise the discrepancy of feature ranking and generate an impactful set of features for classification algorithms. These results had practical implications for pathologists in improving the understanding of AD pathology. Furthermore, the study has highlighted the potential for future research to leverage diverse filter methods to identify more reliable biomarkers and enhance the detection of dementia, particularly for AD.26 0Item Restricted Deep Discourse Analysis for Early Prediction of Multi-Type Dementia(Saudi Digital Library, 2023-06-12) Alkenani, Ahmed Hassan A; Li, YuefengAgeing populations are a worldwide phenomenon. Although it is not an inevitable consequence of biological ageing, dementia is strongly associated with increasing age, and is therefore anticipated to pose enormous future challenges to public health systems and aged care providers. While dementia affects its patients first and foremost, it also has negative associations with caregivers’ mental and physical health. Dementia is characterized by irreversible gradual impairment of nerve cells that control cognitive, behavioural, and language processes, causing speech and language deterioration, even in preclinical stages. Early prediction can significantly alleviate dementia symptoms and could even curtail the cognitive decline in some cases. However, the diagnostic procedure is currently challenging as it is usually initiated with clinical-based traditional screening tests. Typically, such tests are manually interpreted and therefore may entail further tests and physical examinations thus considered timely, expensive, and invasive. Therefore, many researchers have adopted speech and language analysis to facilitate and automate its initial prescreening. Although recent studies have proposed promising methods and models, there is still room for improvement, without which automated pre-screening remains impracticable. There is currently limited empirical literature on the modelling of the discourse ability of people with prodromal dementia stages and types, which is defined as spoken and written conversations and communications. Specifically, few researchers have investigated the nature of lexical and syntactic structures in spontaneous discourse generated by patients with dementia under different conditions for automated diagnostic modelling. In addition, most previous work has focused on modelling and improving the diagnosis of Alzheimer’s disease (AD), as the most common dementia pathology, and neglect other types of dementia. Further, current proposed models suffer from poor performance, a lack of generalizability, and low interpretability. Therefore, this research thesis explores lexical and syntactic presentations in written and spoken narratives of people with different dementia syndromes to develop high-performing diagnostic models using fusions of different lexical and syntactic (i.e., lexicosyntactic) features as well as language models. In this thesis, multiple novel diagnostic frameworks are proposed and developed based on the “wisdom of crowds” theory, in which different mathematical and statistical methods are investigated and properly integrated to establish ensemble approaches for an optimized overall performance and better inferences of the diagnostic models. Firstly, syntactic- and lexical-level components are explored and extracted from the only two disparate data sources available for this study: spoken and written narratives retrieved from the well-known DementiaBank dataset, and a blog-based corpus collected as a part of this research, respectively. Due to their dispersity, each data source was independently analysed and processed for exploratory data analysis and feature extraction. One of the most common problems in this context is how to ensure a proper feature space is generated for machine learning modelling. We solve this problem by proposing multiple innovative ensemble-based feature selection pipelines to reveal optimal lexicosyntactics. Secondly, we explore language vocabulary spaces (i.e., n-grams) given their proven ability to enhance the modelling performance, with an overall aim of establishing two-level feature fusions that combine optimal lexicosyntactics and vocabulary spaces. These fusions are then used with single and ensemble learning algorithms for individual diagnostic modelling of the dementia syndromes in question, including AD, Mild Cognitive Impairment (MCI), Possible AD (PoAD), Frontotemporal Dementia (FTD), Lewy Body Dementia (LBD), and Mixed Dementia (PwD). A comprehensive empirical study and series of experiments were conducted for each of the proposed approaches using these two real-world datasets to verify our frameworks. Evaluation was carried out using multiple classification metrics, returning results that not only show the effectiveness of the proposed frameworks but also outperform current “state-of-the-art” baselines. In summary, this research provides a substantial contribution to the underlying task of effective dementia classification needed for the development of automated initial pre-screenings of multiple dementia syndromes through language analysis. The lexicosyntactics presented and discussed across dementia syndromes may highly contribute to our understanding of language processing in these pathologies. Given the current scarcity of related datasets, it is also hoped that the collected writing-based blog corpus will facilitate future analytical and diagnostic studies. Furthermore, since this study deals with associated problems that have been commonly faced in this research area and that are frequently discussed in the academic literature, its outcomes could potentially assist in the development of better classification models, not only for dementia but also for other linguistic pathologies.31 0Item Restricted CAREGIVER BURDEN AND ADAPTATION OF RELATIVES WITH DEMENTIA DURING COVID-19 PANDEMIC(Proquest, 2023-05-15) Omar, Maryam; Bryant, Sharon AThe incidences of dementia are expected to double every 20 years due to an increasingly elderly population. Globally, 50 million people are living with dementia nowadays. The effects of dementia on patients and their families are profound. Hence, caring for a relative with dementia can be quite taxing on caregivers. While dementia caregiver burdens before the COVID-19 pandemic are well-documented, there is a gap in the literature regarding this phenomenon during the pandemic. Thus, it is urgent to fill that gap and investigate the nature and origin of caregiver load during this unique period. Using the Roy Adaptation model (RAM) framework, this convergent, mixed-methods study aims to explore the burden level among informal caregivers of relatives with dementia during the COVID-19 pandemic and discover their adaptation methods. A total of 104 participants were recruited using convenience sampling. The study reported some critical stimuli that significantly appeared to affect the caregivers’ burden and adaptation, which were gender, relationship to the care receiver, and race. Furthermore, the findings of this study suggested that the caregivers of dementia relatives reported higher burden scores during the COVID-19 pandemic than before. The seven themes emerged as daily activities and recreations of caregivers and their care recipients during the pandemic (food and diet, outdoor exercises, indoor activities, sleep and rest, communication, and prevention), and four themes of positive experience during the COVID-19 pandemic (spending more time together, take active care of relatives, strength of family relationship, gratitude and appreciation). The knowledge gained from this study has implications for nursing practice, education, and policy- making. In addition, it will help advise the best care for this vulnerable population to adapt to their caregiving role at the critical time of similar disease outbreak circumstances.38 0