Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol.

ABSTRACT:

Introduction

Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices.

Methods and analysis

A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief in

SUBMITTER: Tsang KCH 

PROVIDER: S-EPMC9535155 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature