Giuseppina Gagliardi
Advancements in 'Happy Again', a web app to assess the neurological consequences of Long Covid. A Journey through Enhanced User Interaction, Administration, and Data Analysis.
Rel. Gabriella Olmo, Vito De Feo. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract: |
Long COVID, also known as long-term COVID-19, is a condition characterized by the persistence or onset of symptoms following an infection with COVID-19, even after the initial infection has resolved. These symptoms can vary widely in severity and type, impacting various bodily systems. Long COVID presents a significant challenge to public health, leading to a deterioration in patients' quality of life and necessitating long-term medical care and support. The comprehension of the underlying mechanisms and effective management of Long COVID remain active areas of research and are of paramount importance. This study is dedicated to investigating the neurological consequences associated with Long COVID through the utilization of the website 'Happy Again,' which is accessible at https://happyagain.essex.ac.uk/. Developed as a comprehensive platform for data collection and analysis, 'Happy Again' forms an integral part of a research endeavor undertaken at the University of Essex. My contribution to the project consisted of working on the data collection system, optimizing its structure and utilization, the management of databases and data retrieval. My responsibilities included the maintenance and implementation of new functionalities to enhance the flexibility and efficiency of the website. Among the introduced features is an administrative area enabling the monitoring of user-completed tasks, automated email reminders to prompt task completion within a specific deadline, voucher management via an intuitive administrative interface, and the provision for users to repeat a specific task (Word Categorization Task) after completion. These improvements not only optimize data collection but also contribute to a better understanding of the neurological implications of COVID-19 through the analysis of collected data. |
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Relators: | Gabriella Olmo, Vito De Feo |
Academic year: | 2023/24 |
Publication type: | Electronic |
Number of Pages: | 78 |
Subjects: | |
Corso di laurea: | Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering) |
Classe di laurea: | New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING |
Aziende collaboratrici: | Politecnico di Torino |
URI: | http://webthesis.biblio.polito.it/id/eprint/31031 |
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