Slade Christopher, Sun Yinan, Chao Wei Cheng, Chen Chih-Chun, Benzo Roberto M, Washington Peter
Department of Information and Computer Science, University of Hawaii, Honolulu, HI 96822, United States.
Computer Science Department, Brigham Young University-Hawaii, Laie, HI 96762, United States.
JAMIA Open. 2025 Jul 18;8(4):ooaf025. doi: 10.1093/jamiaopen/ooaf025. eCollection 2025 Aug.
Mobile and ubiquitous devices enable health data collection "in a free-living environment" to support applications such as remote patient monitoring and adaptive digital interventions using machine learning (ML). Despite their potential, significant data collection challenges persist, including issues related to user compliance with reporting data, passive data consistency, and authorization. This scoping review identifies and analyzes these challenges, focusing on barriers to effective data collection.
We searched IEEE, ACM, and Web of Science for papers involving training ML models using both active and passive mobile sensing. We used the following search terms: "mobile OR ubiquitous", "EMA", "health", "passive", and "deep learning OR machine learning". We only included papers that collected both passive and active data and excluded papers that used a pre-existing dataset.
A total of 77 studies met the inclusion criteria. These studies utilized smartphones, smartwatches, wearable devices, and environmental sensors for data collection. Several studies reported challenges with participant compliance in active data collection, while passive data collection faced data consistency and authorization issues. Efforts to address these challenges were documented in some but not all studies. Using this information, we outline current challenges and corresponding opportunities for data collection in mobile sensing studies.
ML techniques can reduce participant burden in active data collection by optimizing prompt timing, auto-filling responses, and minimizing prompt frequency. Simplified interfaces such as user-friendly smartwatch prompts can further improve compliance. For passive data collection, techniques such as optimization of recording times to preserve battery life and motivational techniques to encourage proper device use can increase data consistency.
Mobile sensing offers opportunities for developing intelligent mobile health applications but faces data collection challenges with respect to factors such as compliance, consistency, and authorization. Innovations in ML and user interface design show promise for addressing these barriers.
移动和普及型设备能够在“自由生活环境”中收集健康数据，以支持诸如远程患者监测和使用机器学习（ML）的自适应数字干预等应用。尽管它们具有潜力，但重大的数据收集挑战仍然存在，包括与用户报告数据的合规性、被动数据一致性和授权相关的问题。本范围综述识别并分析了这些挑战，重点关注有效数据收集的障碍。
我们在IEEE、ACM和科学网中搜索涉及使用主动和被动移动传感训练ML模型的论文。我们使用了以下搜索词：“移动或普及型”、“电子移动健康监测”、“健康”、“被动”以及“深度学习或机器学习”。我们只纳入了同时收集被动和主动数据的论文，并排除了使用现有数据集的论文。
共有77项研究符合纳入标准。这些研究利用智能手机、智能手表、可穿戴设备和环境传感器进行数据收集。几项研究报告了主动数据收集过程中参与者合规性方面的挑战，而被动数据收集则面临数据一致性和授权问题。部分但并非所有研究记录了应对这些挑战的努力。利用这些信息，我们概述了移动传感研究中当前的数据收集挑战及相应机遇。
ML技术可以通过优化提示时间、自动填写回复以及最小化提示频率来减轻主动数据收集过程中的参与者负担。诸如用户友好的智能手表提示等简化界面可以进一步提高合规性。对于被动数据收集，诸如优化记录时间以延长电池寿命以及采用激励技术来鼓励正确使用设备等技术可以提高数据一致性。
移动传感为开发智能移动健康应用提供了机遇，但在合规性、一致性和授权等因素方面面临数据收集挑战。ML和用户界面设计方面的创新有望克服这些障碍。
