Suppr超能文献

在一项纵向队列研究中使用移动医疗 COVID-19 数字生物标志物测量结果的参与情况:混合方法评估。

Engagement With mHealth COVID-19 Digital Biomarker Measurements in a Longitudinal Cohort Study: Mixed Methods Evaluation.

机构信息

Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.

Huma Therapeutics Limited, London, United Kingdom.

出版信息

J Med Internet Res. 2023 Jan 13;25:e40602. doi: 10.2196/40602.

Abstract

BACKGROUND

The COVID-19 pandemic accelerated the interest in implementing mobile health (mHealth) in population-based health studies, but evidence is lacking on engagement and adherence in studies. We conducted a fully remote study for ≥6 months tracking COVID-19 digital biomarkers and symptoms using a smartphone app nested within an existing cohort of adults.

OBJECTIVE

We aimed to investigate participant characteristics associated with initial and sustained engagement in digital biomarker collection from a bespoke smartphone app and if engagement changed over time or because of COVID-19 factors and explore participants' reasons for consenting to the smartphone substudy and experiences related to initial and continued engagement.

METHODS

Participants in the Fenland COVID-19 study were invited to the app substudy from August 2020 to October 2020 until study closure (April 30, 2021). Participants were asked to complete digital biomarker modules (oxygen saturation, body temperature, and resting heart rate [RHR]) and possible COVID-19 symptoms in the app 3 times per week. Participants manually entered the measurements, except RHR that was measured using the smartphone camera. Engagement was categorized by median weekly frequency of completing the 3 digital biomarker modules (categories: 0, 1-2, and ≥3 times per week). Sociodemographic and health characteristics of those who did or did not consent to the substudy and by engagement category were explored. Semistructured interviews were conducted with 35 participants who were purposively sampled by sex, age, educational attainment, and engagement category, and data were analyzed thematically; 63% (22/35) of the participants consented to the app substudy, and 37% (13/35) of the participants did not consent.

RESULTS

A total of 62.61% (2524/4031) of Fenland COVID-19 study participants consented to the app substudy. Of those, 90.21% (2277/2524) completed the app onboarding process. Median time in the app substudy was 34.5 weeks (IQR 34-37) with no change in engagement from 0 to 3 months or 3 to 6 months. Completion rates (≥1 per week) across the study between digital biomarkers were similar (RHR: 56,517/77,664, 72.77%; temperature: 56,742/77,664, 73.06%; oxygen saturation: 57,088/77,664, 73.51%). Older age groups and lower managerial and intermediate occupations were associated with higher engagement, whereas working, being a current smoker, being overweight or obese, and high perceived stress were associated with lower engagement. Continued engagement was facilitated through routine and personal motivation, and poor engagement was caused by user error and app or equipment malfunctions preventing data input. From these results, we developed key recommendations to improve engagement in population-based mHealth studies.

CONCLUSIONS

This mixed methods study demonstrated both high initial and sustained engagement in a large mHealth COVID-19 study over a ≥6-month period. Being nested in a known cohort study enabled the identification of participant characteristics and factors associated with engagement to inform future applications in population-based health research.

摘要

背景

新冠疫情加速了人们对基于人群的健康研究中使用移动医疗(mHealth)的兴趣,但缺乏关于参与度和坚持度的证据。我们开展了一项完全远程研究,使用智能手机应用程序对 COVID-19 数字生物标志物和症状进行跟踪,该研究的参与者来自于一个现有的成年人队列,研究时间至少为 6 个月。

目的

我们旨在调查与从定制智能手机应用程序中进行数字生物标志物采集的初始和持续参与相关的参与者特征,以及参与度是否随时间变化或由于 COVID-19 因素而变化,并探讨参与者同意智能手机子研究的原因以及与初始和持续参与相关的体验。

方法

Fenland COVID-19 研究的参与者被邀请参加应用程序子研究,时间从 2020 年 8 月至 2020 年 10 月,直到研究结束(2021 年 4 月 30 日)。参与者被要求每 3 周在应用程序中完成 3 次数字生物标志物模块(血氧饱和度、体温和静息心率 [RHR])和可能的 COVID-19 症状的测量。参与者手动输入测量值,除 RHR 外,该值使用智能手机摄像头进行测量。通过完成 3 个数字生物标志物模块的每周平均频率对参与度进行分类(类别:0、1-2 次/周和≥3 次/周)。探索了同意参与子研究和按参与度类别分类的参与者的社会人口学和健康特征。我们对 35 名参与者进行了半结构式访谈,这些参与者是根据性别、年龄、教育程度和参与度类别进行有针对性抽样的,数据分析采用主题分析法;63%(22/35)的参与者同意参加应用程序子研究,37%(13/35)的参与者不同意。

结果

Fenland COVID-19 研究的 62.61%(2524/4031)的参与者同意参加应用程序子研究。其中,90.21%(2277/2524)完成了应用程序的入门流程。在应用程序子研究中的中位时间为 34.5 周(IQR 34-37),从 0 到 3 个月或 3 到 6 个月,参与度没有变化。整个研究中,数字生物标志物的完成率(每周≥1 次)相似(RHR:56517/77664,72.77%;体温:56742/77664,73.06%;血氧饱和度:57088/77664,73.51%)。年龄较大的年龄组和较低的管理和中级职业与较高的参与度相关,而工作、当前吸烟、超重或肥胖以及较高的感知压力与较低的参与度相关。持续参与是通过常规和个人动机来促进的,而较差的参与则是由于用户错误和应用程序或设备故障导致数据输入困难造成的。根据这些结果,我们制定了一些关键建议,以提高基于人群的移动健康研究中的参与度。

结论

这项混合方法研究表明,在至少 6 个月的时间里,人们对基于人群的大型移动医疗 COVID-19 研究表现出了较高的初始和持续参与度。该研究嵌套在一个已知的队列研究中,使我们能够确定与参与度相关的参与者特征和因素,从而为未来在基于人群的健康研究中的应用提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a27e/9842396/0a7e0500ce51/jmir_v25i1e40602_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验