Predicting Engagement Patterns With Connected Wearable Devices in a Health System: Survival Analysis.

IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Allistair Clark, Gillian Gresham, Joshua Pevnick, Raymond Duncan, Mitchell Kamrava, Michael Sobolev
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Abstract

Background: The rapid advancement and widespread adoption of wearable devices provide opportunities to collect longitudinal objective activity and health data and integrate the information directly into a patient's electronic health record (EHR). Patterns of engagement and factors associated with the use and nonuse of wearable devices are currently not well understood.

Objective: This study aimed to quantify the number of patients still engaged and using wearable devices at 1 year since each patient's first day of use across a cohort collected over 6 years. We then aimed to identify demographic and behavioral factors that statistically significantly predict the likelihood of staying engaged and using wearable devices within the same 1-year time span since first use.

Methods: We analyzed connected device data from a large, nonprofit academic medical center, which began to incorporate wearable device data into the EHR system in April 2015. We conducted a survival analysis to evaluate time to early disengagement among connected device users and identify factors associated with long-term (1 y) engagement in multivariable Cox proportional hazard regression models.

Results: The analysis included 8616 patients (mean age 45, SD 14.36 y; median 21, IQR 34-55 y; men: n=4489, 52.1%; women: n=4126, 47.9%) with available connected device data (eg, step counts) from the EHR between 2015 and 2022. A total of 5870 (68.13%) patients were engaged with active connected devices in the EHR at 1 year. Multivariable Cox regression models indicated no statistically significant differences between gender groups and race categories. Younger age categories (18-34 y) and lower median daily step counts (<5000) were associated with statistically significant increased hazards for early disengagement at 1 year.

Conclusions: The ongoing development of new sensors and algorithms presents opportunities to expand the capabilities of wearable devices, making them even more integral to health care delivery. It is important to quantify and enhance engagement to maximize the benefits of this technology and inform future use of the technology to improve health outcomes.

预测医疗系统中可穿戴设备的参与模式:生存分析。
背景:可穿戴设备的快速发展和广泛采用为收集纵向客观活动和健康数据提供了机会,并将信息直接集成到患者的电子健康记录(EHR)中。与使用和不使用可穿戴设备相关的参与模式和因素目前还没有得到很好的理解。目的:本研究旨在量化自每位患者第一天使用可穿戴设备1年后仍在使用可穿戴设备的患者数量,该研究收集了6年以上的队列。然后,我们的目标是确定人口统计和行为因素,这些因素在统计上显著预测自首次使用可穿戴设备以来的同一一年时间跨度内保持参与和使用可穿戴设备的可能性。方法:我们分析了一家大型非营利性学术医疗中心的连接设备数据,该中心于2015年4月开始将可穿戴设备数据纳入EHR系统。我们进行了生存分析,以评估连接设备用户早期脱离接触的时间,并在多变量Cox比例风险回归模型中确定与长期(1年)接触相关的因素。结果:分析包括8616例患者(平均年龄45岁,SD 14.36 y;中位数21岁,IQR 34-55 y;男性:n=4489, 52.1%;女性:n=4126, 47.9%), 2015年至2022年期间可从EHR获得连接设备数据(如步数计数)。1年内,共有5870例(68.13%)患者在EHR中使用主动连接设备。多变量Cox回归模型显示性别组和种族类别之间无统计学差异。结论:新型传感器和算法的不断发展为扩展可穿戴设备的功能提供了机会,使其在医疗保健服务中更加不可或缺。重要的是量化和加强参与,以最大限度地发挥这项技术的效益,并为今后使用这项技术改善健康结果提供信息。
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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