Previously submitted to: JMIR mHealth and uHealth (no longer under consideration since Dec 28, 2021)
Date Submitted: Jan 20, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Computational architecture for collecting and integrating sleep data: proof concept
ABSTRACT
Background:
Sleep is a fundamental function for life, and several approaches assist in its study. Ubiquitous monitoring offers parameters that assess sleep in its natural context. A common mobile data collection tool is the smartphone, and studies use the device’s native sensors to record information independently as well as provide users with alternatives to track the data that are captured.
Objective:
This research aims to develop a computational architecture for integration and analysis of data related to sleep, using data from mobile devices and environmental data, categorized by geolocation of the monitored individuals.
Methods:
We divided the architecture into the collection and integration modules. The collection module comprises an application for the Android operating system and was implemented using the Java language. In the integration module, we developed a PHP application responsible for receiving and handling the information from the collection module, storing the data, and orchestrating a series of calls to APIs to obtain data. Inside the server, there is also a Python script for requesting the Twitter API.
Results:
The tests performed in this work show some important information in data integration. Also, it is possible to conduct an in-depth analysis looking for information that addresses the study of sleep through mobile devices.
Conclusions:
The proposed solution integrates data from different databases and generate a representation of sleep data from a macro perspective. Moreover, the proposal explores and analyzes data that previously were not used to represent, approximately, habits and behaviors of society. The results was positive and demonstrated that’s possible expand the data of sleep and generate insight on it.
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Copyright
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