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利用移动医疗技术促进公共卫生

Leveraging mHealth Technologies for Public Health.

机构信息

School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.

David R Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.

出版信息

JMIR Public Health Surveill. 2024 Sep 12;10:e49719. doi: 10.2196/49719.


DOI:10.2196/49719
PMID:39265164
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11427850/
Abstract

Traditional public health surveillance efforts are generally based on self-reported data. Although well validated, these methods may nevertheless be subjected to limitations such as biases, delays, and costs or logistical challenges. An alternative is the use of smart technologies (eg, smartphones and smartwatches) to complement self-report indicators. Having embedded sensors that provide zero-effort, passive, and continuous monitoring of health variables, these devices generate data that could be leveraged for cases in which the data are related to the same self-report metric of interest. However, some challenges must be considered when discussing the use of mobile health technologies for public health to ensure digital health equity, privacy, and best practices. This paper provides, through a review of major Canadian surveys and mobile health studies, an overview of research involving mobile data for public health, including a mapping of variables currently collected by public health surveys that could be complemented with self-report, challenges to technology adoption, and considerations on digital health equity, with a specific focus on the Canadian context. Population characteristics from major smart technology brands-Apple, Fitbit, and Samsung-and demographic barriers to the use of technology are provided. We conclude with public health implications and present our view that public health agencies and researchers should leverage mobile health data while being mindful of the current barriers and limitations to device use and access. In this manner, data ecosystems that leverage personal smart devices for public health can be put in place as appropriate, as we move toward a future in which barriers to technology adoption are decreasing.

摘要

传统的公共卫生监测工作通常基于自我报告的数据。虽然这些方法经过了很好的验证,但它们可能仍然受到限制,如偏差、延迟、成本或后勤挑战。另一种方法是使用智能技术(例如智能手机和智能手表)来补充自我报告指标。这些设备具有嵌入式传感器,可以对健康变量进行零努力、被动和连续的监测,从而生成数据,可以利用这些数据来处理与同一自我报告指标相关的情况。然而,在讨论使用移动健康技术进行公共卫生时,必须考虑一些挑战,以确保数字健康公平、隐私和最佳实践。本文通过对加拿大主要调查和移动健康研究的回顾,概述了涉及公共卫生移动数据的研究,包括当前公共卫生调查中收集的变量与自我报告的映射、技术采用的挑战以及数字健康公平方面的考虑,特别是针对加拿大的情况。提供了主要智能技术品牌(苹果、Fitbit 和三星)的人口特征以及使用技术的人口统计障碍。最后,我们得出了公共卫生的影响,并提出了我们的观点,即公共卫生机构和研究人员应该利用移动健康数据,同时注意到设备使用和获取的当前障碍和限制。通过这种方式,可以在适当的时候建立利用个人智能设备进行公共卫生的数据生态系统,因为我们正在朝着技术采用障碍不断减少的未来迈进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9042/11427850/d530897acf1a/publichealth_v10i1e49719_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9042/11427850/d530897acf1a/publichealth_v10i1e49719_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9042/11427850/d530897acf1a/publichealth_v10i1e49719_fig1.jpg

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[1]
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[3]
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引用本文的文献

[1]
The Interactive Care Coordination and Navigation mHealth Intervention for People Experiencing Homelessness: Cost Analysis, Exploratory Financial Cost-Benefit Analysis, and Budget Impact Analysis.

JMIR Form Res. 2025-3-18

[2]
Leveraging Administrative Health Databases to Address Health Challenges in Farming Populations: Scoping Review and Bibliometric Analysis (1975-2024).

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本文引用的文献

[1]
Using apple watch ECG data for heart rate variability monitoring and stress prediction: A pilot study.

Front Digit Health. 2022-12-9

[2]
Usability of Smart Home Thermostat to Evaluate the Impact of Weekdays and Seasons on Sleep Patterns and Indoor Stay: Observational Study.

JMIR Mhealth Uhealth. 2022-4-1

[3]
Towards digital health equity - a qualitative study of the challenges experienced by vulnerable groups in using digital health services in the COVID-19 era.

BMC Health Serv Res. 2022-2-12

[4]
Barriers and facilitators to the use of e-health by older adults: a scoping review.

BMC Public Health. 2021-8-17

[5]
Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling.

Int J Environ Res Public Health. 2021-7-22

[6]
Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective.

Front Public Health. 2021

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Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.

J Med Internet Res. 2021-2-22

[8]
A Blockchain-Based Consent Platform for Active Assisted Living: Modeling Study and Conceptual Framework.

J Med Internet Res. 2020-12-4

[9]
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Sleep. 2020-10-13

[10]
Best practices for analyzing large-scale health data from wearables and smartphone apps.

NPJ Digit Med. 2019-6-3

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