SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted EVALUATING THE EFFECTIVENESS OF THE SLEEPTRACKER APP FOR DETECTING ANXIETY AND DEPRESSION-RELATED SLEEP DISTURBANCES(University of Bristol, 2025-03-19) Alamoudi, Doaa; Nabney, Ian; Crawley, Esther; Bennett, SarahSleep is necessary for the proper functioning of our bodies and minds because it helps our bodies to recuperate and rebuild while our minds organise and evaluate memories. Poor sleep, on the other hand, can have a severe impact on both physical and emotional well-being, leading to a variety of health problems such as anxiety, depression, heart disease, obesity, dementia, diabetes, and cancer. University students, in particular, are at risk of poor sleep quality and mental health issues, which can exacerbate the risk of anxiety and despair. It is critical to treat sleep disorders and prioritise adequate sleep practises in order to ensure good health and well-being. Many individuals may be unaware they suffer from conditions like insomnia, depression, or anxiety, often because they fail to recognize the symptoms. These issues are typically diagnosed by medical professionals during consultations. While experts understand the connection between poor sleep patterns and mental health, this knowledge gap among the general population highlights the potential for technological intervention. Computer science, particularly through smartphone technology, can bridge this gap by enabling early detection and intervention, helping to preserve mental health. This thesis aims to explore and develop methods using current mobile technology specifically, built-in sensors to diagnose sleep issues and provide interventions for associated mental health problems, focusing on young adults. Existing literature on using mobile sensors to track sleep and mental health reveals several limitations, which this thesis seeks to address. While numerous mobile apps track behavioural signals, such as sleep, and correlate them with mental health, many rely on wearable or non-wearable sensors (e.g., accelerometers, microphones, GPS, light, and screen on/off sensors), but each has limitations in terms of accuracy and usability. This thesis centers on the development of SleepTracker, an app designed to go beyond basic sleep tracking to address mental health concerns like depression and anxiety often associated with insomnia. The SleepTracker app was developed with the goal of accurately detecting sleep duration and disturbances using mobile phone sensors, specifically screen on/off events and accelerometer data. Its development was structured in two distinct phases, each building on the last to progressively enhance the app’s functionality, data accuracy, and user experience. In the first phase, initial development efforts focused on refining the app’s core algorithms through iterative testing. This phase was supported by a Patient and Public Involvement (PPI) session, where users provided feedback on the app’s feasibility and potential benefits. Following this, two field tests were conducted to validate the app’s ability to detect sleep patterns accurately: the first test spanned six nights, while the second test ran for seven nights. The results from these field tests, along with post-test survey feedback, informed the subsequent steps in the app’s development. As the study progressed to the second phase, an enhanced algorithm was introduced to detect sleep disturbances based on screen on/off activity and accelerometer data. Prior to launching this phase, an additional PPI session was held to refine the study’s focus, gathering user insights specifically on the app's aim to investigate links between sleep disturbances and mental health concerns such as anxiety and depression. This 56-day phase involved ongoing data collection and analysis to explore these relationships further, enabling the app to evolve into a tool capable of identifying potential mental health risks associated with sleep issues. To ensure data security and privacy throughout both phases, two separate databases were created: one storing sensitive personal information (e.g., name, email, gender, ethnicity) and another for sleep and sensor data. In the first phase, Firebase was used for data storage; however, due to concerns raised by the ethics committee regarding enhanced data protection, the app transitioned to Amazon Web Services (AWS) in the second phase. This move, along with the separation of databases, was critical in maintaining data integrity and anonymity, providing a strong foundation for ethical compliance and secure data management. At the conclusion of the study, the SleepTracker app’s acceptability was evaluated using both quantitative and qualitative analyses. The quantitative analysis focused on numerical data collected through participant surveys, applying a theoretical framework based on the Technology Acceptance Model (TAM). The qualitative analysis used thematic analysis to explore participants’ experiences with the app, uncovering a range of insights regarding its usability and effectiveness. Together, these analyses provided a holistic understanding of the SleepTracker app’s feasibility and its potential for monitoring mental health. In summary, the SleepTracker app shows promise as a tool for monitoring sleep patterns and identifying potential mental health concerns among young adults. Its integration of advanced technology and data-driven algorithms offers the potential to yield significant insights into the intricate relationship between sleep disturbances and mental health. This research aims to contribute to improved mental well-being and address the challenges posed by sleep-related issues in future health interventions.3 0Item Restricted The interplay between thyroid function and mental disorders(Queen Mary University of London, 2023-12-10) Alsharari, Hussain; Marouli, EiriniThe thyroid gland, central to human development, growth, and metabolism, produces crucial hormones, thyroxine (T4) and triiodothyronine (T3), regulated by thyroid stimulating hormone (TSH) from the pituitary gland. Beyond traditional roles, recent advancements reveal an expanded role of thyroid hormones, influencing nervous system regulation and impacting diverse physiological processes. This scientific investigation endeavors to delve comprehensively into the intricate interplay between thyroid function and human health. The primary objectives include elucidating clinical features, risk factors, disease sub-groups, and responses to conventional therapeutic treatments. Genetic variations exert a significant influence on baseline serum hormone levels, contributing notably to the emergence of subclinical thyroid diseases. The focal points of the study are Hashimoto's and Graves' diseases, distinct autoimmune conditions disrupting thyroid function. The aim is to explore not only known causal associations but also potential mechanistic pathways involved in the pathophysiology of these diseases. The research underscores the pivotal roles of both genetic and environmental factors in the development of Hashimoto's and Graves' diseases, thereby emphasizing the imperative need for identifying and understanding relevant risk factors. Furthermore, the investigation accentuates the intricate association between thyroid function and mental health. The study establishes correlations between thyroid function and various mental health conditions, including depression, bipolar disorder, schizophrenia, and cognitive impairment. In advocating for a comprehensive approach to mental health care, the research underscores the nuanced and interconnected relationships between thyroid function and the spectrum of mental health conditions, highlighting the importance of holistic patient evaluation and targeted interventions.17 0Item Restricted Gender and intersectionality: Understanding and Addressing Women's Mental Health and Mental Health Policy within the Cultural Context of Saudi Arabia(University Of Glasgow, 2024-02-06) Alghamdi, NadiaAhmed Alhamd; Melville, CraigBackground: Intersectionality concerns the interconnected nature of social categories (e.g., race, gender, age, education) and how these ‘intersect’ to produce privilege and oppression. In the current context, this helps to understand women's mental health in socially disadvantaged positions, especially how intersections among gender inequality and factors such as socioeconomic status contribute to women’s mental health inequalities and experiences. Yet this remains an under researched area. This study’s overarching aim concerns understanding Saudi Arabian women’s mental health disorders, risks, challenges, and issues. For this, it has three objectives: to review the effects of intersectionality on this group within extant quantitative literature; to identify and explore the significant interactions among variables relating to this population’s social disadvantage and mental ill-health (e.g., gender and the risk of depression); and to analyse Saudi Arabia’s current mental health policy and gender equality. This study’s more specific aims involve furthering understanding of the effects of content, context, and actors behind mental health policies and programmes on Saudi women to help address their mental health needs. It takes the form of three studies. Study 1. This systematic review investigated quantitative methods used to study the intersectionality of multiple social disadvantages in women with common mental disorders. It reviewed studies on the intersectional effects of gender with multiple social disadvantages from the PROGRESS-Plus inequity framework and examined the quantitative methods these studies employ. The most common and means of studying intersectionality in mental health studies in the included studies was statistical interaction analysis. Other methods such as multilevel modelling and mediation decomposition analysis were also used. These robust statistical methods facilitate research on intersectional effects on mental health and improve understanding of the complex intersection of gender and other social disadvantages concerning women’s risk of common mental disorders. Study 2: This study analysed the National Survey of Saudi Food and Drug Authority dataset, a nationally representative sample of individuals aged 18–88 in Saudi Arabia (3,408 participants: 1,753 males and 1,655 females). Evaluating variable risks of depression using the PHQ-2 screening questionnaire, it found significant correlations between depression risk and the variables of gender, education, family income, and employment status. Although a subsequent multivariate analysis found the only significant predictors of depression risk to be female gender and education below the bachelor level. No interaction effects were observed, implying an additive effect of gender and education on the risk of depression. Study 3: This study analysed Saudi Arabia's mental health policies and gender equality. Using Walt and Gilson's health policy analysis framework, it highlights the need to address gender inequalities in the country's mental health policies. It provides evidence-based mental health policy recommendations relating to women in Saudi Arabia about enhancing their mental health and well-being and establishing an equal health system. Conclusions: Examining women’s mental health through an intersectionality lens can help policymakers address Saudi Arabian women’s mental health issue . To reduce inequalities, advances must be made in women’s education, training, employment, socioeconomic status, access and participation, equality, and overall independence. However, this must take place within a wider targeted and tailored reform agenda (legal, policy, political, PR, cultural, religious, economic, careers, educational) within which women must actively participate. Urgent inclusive, deep, and far-reaching intersectional initiatives, adjustments, research and reforms are needed to elevate Saudi women’s circumstances, experiences, and mental health and thereby address the current issue and ultimately improve society overall.42 0Item Restricted The Effectiveness of Fcial Cues for Automatic Detection of Cognitive Impairment Using In-the-wild Data(Saudi Digital Library, 2023-11-30) Alzahrani, Fatimah; Christensen, Heidi; Maddock, SteveThe development of automatic methods for the early detection of cognitive impairment (CI) has attracted much research interest due to its crucial role in helping people get suitable treatment or care. People with CI may experience various changes in their facial cues, such as eye blink rate and head movement. This thesis aims to investigate the use of facial cues to develop an automatic system for detecting CI using in-the-wild data. Firstly, the 'in-the-wild data' term is defined, and associated challenges are identified by analysing datasets used in previous work. In-the-wild data can affect the reliability of the performance of state-of-the-art approaches. Second, this thesis investigates the automatic detection of neurodegenerative disorder, mild cognitive impairment and functional memory disorder, showing the applicability of detecting health conditions with similar symptoms. Then, a novel multiple thresholds (MTs) approach for detecting an eye blink rate feature is introduced. This approach addresses in-the-wild data challenges by generating multiple thresholds, resulting in a vector of blink rates for each participant. Then, the feasibility of this feature in detecting CI is examined. Other features considered are head turn rate, head turn statistical features, head movement statistical features and low-level features. The results show that these facial features significantly distinguish different health conditions.15 0Item Restricted The Management of New Patients Diagnosed with Major Depressive Disorder in General Practice Surgery: A Clinical Audit(Saudi Digital Library, 2026-10-20) Asiri, Jaber Ali M; Azimi, SafooraIntroduction Major depressive disorder is a mood disorder that may lead to a loss of interest, sadness, or a lack of self-esteem. It is one of the most common cognitive diseases in the United Kingdom, with an estimated incidence of one in six adults. We aimed to conduct a clinical audit to discuss the newly diagnosed depression management guidelines in the National institute for health and care excellence NICE guidelines 2022 in general practice surgery. Methods Based on 22 standards, the clinical audit evaluated GP in conformity with NICE guidelines 2022 [NG222] regarding depression management for new patients. The audit will be compared against the standards and similar audits in the literature review. The data were collected for 77 patients, and statistical analysis was carried out using SPSS Statistics software version 29 (SPSS). Findings The results were significant regarding discussing treatment options and antidepressant preferences according to the patients, such as standards one, three, and five. Also, the pharmacological treatment was statistically significant (p < 0.001). Discussion Pharmacological treatment corresponded significantly to the NICE guidelines 2022, because most patients were on SSRI medications, the first line of treatment recommended for LS and MS depression. However, psychological therapy needed more explanation and adherence, and patient follow-up needed to be improved to ensure that the patient received medications regularly for six months.10 0