SleepVention : a data collection and visualization platform for studying sleep quality
Sadeghi, Mohammadreza (2025-06-12)
M. Sadeghi
12.06.2025
© 2025, Mohammadreza Sadeghi. Tämä Kohde on tekijänoikeuden ja/tai lähioikeuksien suojaama. Voit käyttää Kohdetta käyttöösi sovellettavan tekijänoikeutta ja lähioikeuksia koskevan lainsäädännön sallimilla tavoilla. Muunlaista käyttöä varten tarvitset oikeudenhaltijoiden luvan.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202506124421
https://urn.fi/URN:NBN:fi:oulu-202506124421
Tiivistelmä
Sleep quality plays a vital role in physical health, mental well-being, and cognitive performance. Despite the increasing use of wearable devices and mobile technologies in health monitoring, there remains a lack of digital platforms specifically designed for conducting sleep-related research. This thesis introduces SleepVention, an online platform developed to address this gap by enabling the collection, visualization, and analysis of sleep data for both users and researchers. The platform supports study creation, participant management, and data sharing through integration with Fitbit application programming interfaces (APIs) and a custom-built web application using FastAPI and JavaScript.
To evaluate the platform, two studies were conducted. The first assessed user perceptions regarding the usefulness and importance of a dedicated sleep research platform. The second explored the potential relationship between smartphone addiction and sleep quality using data collected via SleepVention and Smartphone Addiction Inventory (SPAI) and Smartphone Addiction Scale-Short Version (SAS-SV) questionnaires. A total of 31 participants completed the full study process, which included connecting a Fitbit device, submitting sleep data, and responding to questionnaires.
The results revealed strong user support for the platform’s purpose and features, with participants expressing a high willingness to share sleep data, particularly when incentives or personal benefits were offered. However, the findings regarding smartphone addiction and sleep quality were inconclusive; only minor differences were observed between addicted and non-addicted participants in terms of sleep duration, start time, and efficiency.
The study demonstrates the feasibility and value of an online platform for sleep research and suggests directions for future development, including support for additional wearable devices, enhanced data visualizations, improved user experience, anonymized data comparisons, AI-driven chatbot, and extended historical data access. Together, these advancements have the potential to establish SleepVention as a comprehensive and scalable tool for sleep science.
To evaluate the platform, two studies were conducted. The first assessed user perceptions regarding the usefulness and importance of a dedicated sleep research platform. The second explored the potential relationship between smartphone addiction and sleep quality using data collected via SleepVention and Smartphone Addiction Inventory (SPAI) and Smartphone Addiction Scale-Short Version (SAS-SV) questionnaires. A total of 31 participants completed the full study process, which included connecting a Fitbit device, submitting sleep data, and responding to questionnaires.
The results revealed strong user support for the platform’s purpose and features, with participants expressing a high willingness to share sleep data, particularly when incentives or personal benefits were offered. However, the findings regarding smartphone addiction and sleep quality were inconclusive; only minor differences were observed between addicted and non-addicted participants in terms of sleep duration, start time, and efficiency.
The study demonstrates the feasibility and value of an online platform for sleep research and suggests directions for future development, including support for additional wearable devices, enhanced data visualizations, improved user experience, anonymized data comparisons, AI-driven chatbot, and extended historical data access. Together, these advancements have the potential to establish SleepVention as a comprehensive and scalable tool for sleep science.
Kokoelmat
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