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Comput Biol Med. 2018 Dec 1;103:8-16. doi: 10.1016/j.compbiomed.2018.09.025. Epub 2018 Oct 6.

The mobile sleep lab app: An open-source framework for mobile sleep assessment based on consumer-grade wearable devices.

Author information

1
Department of Medical Informatics, Uniklinik RWTH Aachen, Germany; Cybernetics Lab, RWTH Aachen University, Germany.
2
Department of Medical Informatics, Uniklinik RWTH Aachen, Germany.
3
Department of Medical Informatics, Uniklinik RWTH Aachen, Germany; Faculty of Applied Mathematics, AGH University of Science and Technology, Poland.
4
Cybernetics Lab, RWTH Aachen University, Germany.
5
Exceet Secure Solutions AG, Germany.
6
Department of Medical Informatics, Uniklinik RWTH Aachen, Germany. Electronic address: sjonas@mi.rwth-aachen.de.

Abstract

BACKGROUND:

Sleep disorders have a prevalence of up to 50% and are commonly diagnosed using polysomnography. However, polysomnography requires trained staff and specific equipment in a laboratory setting, which are expensive and limited resources are available. Mobile and wearable devices such as fitness wristbands can perform limited sleep monitoring but are not evaluated well. Here, the development and evaluation of a mobile application to record and synchronize data from consumer-grade sensors suitable for sleep monitoring is presented and evaluated for data collection capability in a clinical trial.

METHODS:

Wearable and ambient consumer-grade sensors were selected to mimic the functionalities of clinical sleep laboratories. Then, a modular application was developed for recording, processing and visualizing the sensor data. A validation was performed in three phases: (1) sensor functionalities were evaluated, (2) self-experiments were performed in full-night experiments, and (3) the application was tested for usability in a clinical trial on primary snoring.

RESULTS:

The evaluation of the sensors indicated their suitability for assessing basic sleep characteristics. Additionally, the application successfully recorded full-night sleep. The collected data was of sufficient quality to detect and measure body movements, cardiac activity, snoring and brightness. The ongoing clinical trial phase showed the successful deployment of the application by medical professionals.

CONCLUSION:

The proposed software demonstrated a strong potential for medical usage. With low costs, it can be proposed for screening, long-term monitoring or in resource-austere environments. However, further validations are needed, in particular the comparison to a clinical sleep laboratory.

KEYWORDS:

Mobile sleep laboratory; Sleep screening; Smartphone; Telemedicine; Wearable technology

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