Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study.

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2024-11-18 DOI:10.2196/55694
Christopher Slade, Roberto M Benzo, Peter Washington
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引用次数: 0

Abstract

Background: Machine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data collection, challenges in obtaining proper authorization to send notifications, receive background events, and perform background tasks persist.

Objective: We investigated challenges faced by mobile sensing apps in real-world settings in order to develop design guidelines. For active data, we compared 2 prompting strategies: setup prompting, where the app requests authorization during its initial run, and contextual prompting, where authorization is requested when an event or notification occurs. Additionally, we evaluated 2 passive data collection paradigms: collection during scheduled background tasks and persistent reminders that trigger passive data collection. We investigated the following research questions (RQs): (RQ1) how do setup prompting and contextual prompting affect scheduled notification delivery and the response rate of notification-initiated EMA? (RQ2) Which authorization paradigm, setup or contextual prompting, is more successful in leading users to grant authorization to receive background events? and (RQ3) Which polling-based method, persistent reminders or scheduled background tasks, completes more background sessions?

Methods: We developed mobile sensing apps for iOS and Android devices and tested them through a 30-day user study asking college students (n=145) about their stress levels. Participants responded to a daily EMA question to test active data collection. The sensing apps collected background location events, polled for passive data with persistent reminders, and scheduled background tasks to test passive data collection.

Results: For RQ1, setup and contextual prompting yielded no significant difference (ANOVA F1,144=0.0227; P=.88) in EMA compliance, with an average of 23.4 (SD 7.36) out of 30 assessments completed. However, qualitative analysis revealed that contextual prompting on iOS devices resulted in inconsistent notification deliveries. For RQ2, contextual prompting for background events was 55.5% (χ21=4.4; P=.04) more effective in gaining authorization. For RQ3, users demonstrated resistance to installing the persistent reminder, but when installed, the persistent reminder performed 226.5% more background sessions than traditional background tasks.

Conclusions: We developed design guidelines for improving mobile sensing on consumer mobile devices based on our qualitative and quantitative results. Our qualitative results demonstrated that contextual prompts on iOS devices resulted in inconsistent notification deliveries, unlike setup prompting on Android devices. We therefore recommend using setup prompting for EMA when possible. We found that contextual prompting is more efficient for authorizing background events. We therefore recommend using contextual prompting for passive sensing. Finally, we conclude that developing a persistent reminder and requiring participants to install it provides an additional way to poll for sensor and user data and could improve data collection to support adaptive interventions powered by machine learning.

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改进移动传感数据收集的设计指南:前瞻性混合方法研究
背景:机器学习模型通常使用被动记录的传感器数据流作为输入来训练机器学习模型,从而预测通过生态瞬间评估(EMA)捕获的结果。尽管移动数据收集在不断增长,但在获得适当授权以发送通知、接收后台事件和执行后台任务方面仍存在挑战:我们调查了现实世界中移动传感应用所面临的挑战,以制定设计指南。对于主动数据,我们比较了两种提示策略:一种是设置提示,即应用程序在初始运行时请求授权;另一种是上下文提示,即在事件或通知发生时请求授权。此外,我们还评估了两种被动数据收集范例:在预定的后台任务中收集数据和触发被动数据收集的持续提醒。我们对以下研究问题进行了调查:(问题 1)设置提示和情境提示如何影响预定的通知交付和由通知启动的 EMA 的响应率?(问题2)在引导用户授权接收后台事件方面,设置提示和上下文提示哪种授权模式更成功? 问题3)在完成更多后台会话方面,持续提醒和预定后台任务哪种基于轮询的方法更有效?我们为 iOS 和 Android 设备开发了移动感应应用程序,并通过一项为期 30 天的用户研究对其进行了测试,该研究询问了大学生(人数=145)的压力水平。参与者每天回答一个 EMA 问题,以测试主动数据收集情况。传感应用程序收集背景位置事件,通过持续提醒轮询被动数据,并安排背景任务以测试被动数据收集:对于问题 1,设置和情境提示在 EMA 合规性方面没有显著差异(方差分析 F1,144=0.0227; P=.88),30 次评估中平均完成 23.4 次(标准差 7.36)。然而,定性分析显示,iOS 设备上的上下文提示导致通知发送不一致。对于 RQ2,背景事件的上下文提示在获得授权方面的效果为 55.5% (χ21=4.4; P=.04)。对于问题 3,用户对安装持续提醒表现出抵触情绪,但安装后,持续提醒的后台会话比传统后台任务多 226.5%:根据定性和定量结果,我们制定了改进消费类移动设备移动感应的设计指南。定性结果表明,与安卓设备上的设置提示不同,iOS 设备上的上下文提示会导致通知发送不一致。因此,我们建议尽可能在 EMA 中使用设置提示。我们发现,上下文提示在授权后台事件时效率更高。因此,我们建议在被动感应中使用上下文提示。最后,我们得出结论:开发一种持续性提示并要求参与者安装,为传感器和用户数据调查提供了一种额外的方式,可以改进数据收集,从而支持由机器学习驱动的自适应干预。
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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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