{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/355833"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/355833","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Development of a Mobile Health-tracking System for Longitudinal Clinical Assessment of People with Impairments in Brain Functioning","abstract":"Individuals with developmental or acquired impairments in brain functioning may present complex needs that affect their quality of life and wellbeing. Clinical assessment in such circumstances would be enhanced if there was a convenient and non-invasive method to collect, integrate, and analyse various health-related measures over time. I propose a novel system that combines electronic observational and self-report data with sensor-based data collected from wearable wrist sensors. The findings focus on people with intellectual disabilities (ID) to illustrate the system’s development, utilisation, and evaluation. The first phase of this thesis provides an overview of the system’s development process. Upon reviewing the relevant literature and web resources, I found that limited attention has been given to the development of integrated assessments encompassing physiological, behavioural, and mental health measures. Mobile applications were created through public and patient involvement (PPI), and various wrist sensors were compared. The final version of the system includes two mobile applications and an external wrist-worn device (Fitbit Versa 2). During the second phase, ten individuals with ID participated in an empirical study. Over the course of two months or more, participants and their informants completed the Daily Assessment Questionnaire at the end of each day, with participants also wearing a wrist sensor on a daily basis. Using the data collected during the empirical phase, the study investigated the feasibility of the system and its inter-rater reliability. Methods of visualising the longitudinal data were explored, and various descriptive and inferential statistical methods were used to examine the relationship between the reported and sensor-based measures. The findings suggest that utilising the proposed method for longitudinal data collection, integration, and analysis could be both acceptable and feasible. The Thesis concludes by discussing the implications of the present work, encompassing ethical and other administrative considerations, and offering suggestions for future research.","abstract_html":"Individuals with developmental or acquired impairments in brain functioning may present complex needs that affect their quality of life and wellbeing. Clinical assessment in such circumstances would be enhanced if there was a convenient and non-invasive method to collect, integrate, and analyse various health-related measures over time. I propose a novel system that combines electronic observational and self-report data with sensor-based data collected from wearable wrist sensors. The findings focus on people with intellectual disabilities (ID) to illustrate the system’s development, utilisation, and evaluation. The first phase of this thesis provides an overview of the system’s development process. Upon reviewing the relevant literature and web resources, I found that limited attention has been given to the development of integrated assessments encompassing physiological, behavioural, and mental health measures. Mobile applications were created through public and patient involvement (PPI), and various wrist sensors were compared. The final version of the system includes two mobile applications and an external wrist-worn device (Fitbit Versa 2). During the second phase, ten individuals with ID participated in an empirical study. Over the course of two months or more, participants and their informants completed the Daily Assessment Questionnaire at the end of each day, with participants also wearing a wrist sensor on a daily basis. Using the data collected during the empirical phase, the study investigated the feasibility of the system and its inter-rater reliability. Methods of visualising the longitudinal data were explored, and various descriptive and inferential statistical methods were used to examine the relationship between the reported and sensor-based measures. The findings suggest that utilising the proposed method for longitudinal data collection, integration, and analysis could be both acceptable and feasible. 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