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DIGIPREDICT:哮喘发作的生理、行为和环境预测因子——使用数字标志物和人工智能的前瞻性观察研究——研究方案。

DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.

机构信息

School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand

School of Population Health, University of Auckland, Auckland, New Zealand.

出版信息

BMJ Open Respir Res. 2024 May 22;11(1):e002275. doi: 10.1136/bmjresp-2023-002275.

Abstract

INTRODUCTION

Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks.

METHODS AND ANALYSIS

A prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals.

TRIAL REGISTRATION NUMBER

Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.

摘要

引言

哮喘发作是导致发病率和死亡率的主要原因,但如果能及时发现并治疗,大多数情况下是可以预防的。然而,在发作前几天和几周内发生的生理和行为变化并不总是被识别出来,这突出了技术的潜在作用。本研究“DIGIPREDICT”旨在使用智能设备(包括手表和吸入器)中嵌入的传感器来识别哮喘发作的早期数字标志物,并利用健康和环境数据集和人工智能来开发风险预测模型,以提供哮喘发作的早期个性化预警。

方法和分析

将在新西兰招募 300 名年龄在 12 岁及以上、过去 12 个月内有中度或重度哮喘发作史的前瞻性样本。每个参与者将获得一个智能手表(用于评估生理指标,如心率和呼吸率)、峰值流量计、智能吸入器(用于评估依从性和吸入)和咳嗽监测应用程序,在 6 个月内定期使用,每两周进行一次哮喘控制和幸福感问卷调查。在基线和 6 个月时将收集社会人口统计学、哮喘控制、肺功能、饮食摄入、病史和技术接受度的数据。哮喘发作将通过自我报告和临床记录来测量。收集的数据以及天气和空气质量等环境数据将使用机器学习进行分析,以开发哮喘发作的风险预测模型。

伦理和传播

新西兰健康和残疾伦理委员会已获得伦理批准(2023 年 FULL 13541 号)。招募于 2023 年 8 月开始。结果将在当地、国家和国际会议上公布,包括通过社区团体传播,并提交给同行评议期刊发表。

试验注册编号

澳大利亚新西兰临床试验注册中心 ACTRN12623000764639;澳大利亚新西兰临床试验注册中心。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ca9/11116853/c486685af0cf/bmjresp-2023-002275f01.jpg

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