EVALUATING THE EFFECTIVENESS OF THE SLEEPTRACKER APP FOR DETECTING ANXIETY AND DEPRESSION-RELATED SLEEP DISTURBANCES

Abstract

Sleep 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.