Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, United States.
College of Gyedang General Education, Sangmyung University, Seoul, Republic of Korea.
Int J Med Inform. 2023 Jun;174:105061. doi: 10.1016/j.ijmedinf.2023.105061. Epub 2023 Mar 30.
Digital phenotyping may detect changes in health outcomes and potentially lead to proactive measures to mitigate health declines and avoid major medical events. While health-related outcomes have traditionally been acquired through self-report measures, those approaches have numerous limitations, such as recall bias, and social desirability bias. Digital phenotyping may offer a potential solution to these limitations.
The purpose of this scoping review was to identify and summarize how passive smartphone data are processed and evaluated analytically, including the relationship between these data and health-related outcomes.
A search of PubMed, Scopus, Compendex, and HTA databases was conducted for all articles in April 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines.
A total of 40 articles were included and went through an analysis based on data collection approaches, feature extraction, data analytics, behavioral markers, and health-related outcomes. This review demonstrated a layer of features derived from raw sensor data that can then be integrated to estimate and predict behaviors, emotions, and health-related outcomes. Most studies collected data from a combination of sensors. GPS was the most used digital phenotyping data. Feature types included physical activity, location, mobility, social activity, sleep, and in-phone activity. Studies involved a broad range of the features used: data preprocessing, analysis approaches, analytic techniques, and algorithms tested. 55% of the studies (n = 22) focused on mental health-related outcomes.
This scoping review catalogued in detail the research to date regarding the approaches to using passive smartphone sensor data to derive behavioral markers to correlate with or predict health-related outcomes. Findings will serve as a central resource for researchers to survey the field of research designs and approaches performed to date and move this emerging domain of research forward towards ultimately providing clinical utility in patient care.
数字表型可能会检测到健康结果的变化，并可能采取主动措施来减轻健康下降和避免重大医疗事件。虽然健康相关的结果传统上是通过自我报告的措施来获得的，但这些方法有许多局限性，如回忆偏倚和社会期望偏倚。数字表型可能是解决这些局限性的一种潜在方法。
本范围综述的目的是确定和总结被动智能手机数据是如何进行处理和分析的，包括这些数据与健康相关结果之间的关系。
我们于 2021 年 4 月按照 PRISMA-ScR 指南对 PubMed、Scopus、Compendex 和 HTA 数据库进行了全面检索，以获取所有文章。
共纳入 40 篇文章，并根据数据收集方法、特征提取、数据分析、行为标志物和健康相关结果进行了分析。本综述展示了从原始传感器数据中提取的特征层，然后可以将这些特征集成到估计和预测行为、情绪和健康相关结果的模型中。大多数研究从多种传感器中收集数据。GPS 是最常用的数字表型数据。特征类型包括身体活动、位置、移动性、社会活动、睡眠和手机内活动。研究涉及广泛使用的特征类型：数据预处理、分析方法、分析技术和测试算法。55%的研究（n=22）关注心理健康相关结果。
本范围综述详细记录了迄今为止使用被动智能手机传感器数据获取行为标志物以关联或预测健康相关结果的研究方法。这些发现将成为研究人员调查迄今为止所进行的研究设计和方法领域的主要资源，并推动这一新兴研究领域向前发展，最终为患者护理提供临床实用价值。
