Using digital phenotyping to understand health-related outcomes: A scoping review
Using digital phenotyping to understand health-related outcomes: A scoping review
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使用数字表型分析来了解健康相关结果：范围界定审查
DOI：
10.1016/j.ijmedinf.2023.105061
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发表时间：
2023
影响因子：
4.9
通讯作者：
Demiris, George
中科院分区：
文献类型：
--
作者：
Lee, Kyungmi;Lee, Tim Cheongho;Yefimova, Maria;Kumar, Sidharth;Puga, Frank;Azuero, Andres;Kamal, Arif;Bakitas, Marie A.;Wright, Alexi A.;Demiris, George
数字表型可以检测健康结果的变化，并可能导致主动措施，以减轻健康下降，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，这些方法有许多局限性，如回忆偏见，社会期望偏差。Digital phenotyping may provide a potential solution to these limitations.ObjectivesThe 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.MethodsA search of PubMed，Scopus，Compendex，2021年4月，使用系统性综述和范围综述荟萃分析的首选报告项目，对所有文章进行了HTA数据库分析（PRISMA-ScR）guidelines.ResultsA共纳入40篇文章，并根据数据收集方法、特征提取、数据分析、行为标记和健康相关结果进行分析。该综述展示了从原始传感器数据中获得的一层特征，然后可以将其整合以估计和预测行为，情绪和健康相关的结果。大多数研究从传感器的组合中收集数据。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、社交活动、睡眠和电话内活动。研究涉及广泛的功能：数据预处理，分析方法，分析技术和算法测试。55%的研究（n = 22）集中在心理健康相关outcome.ConclusionThis范围审查编目详细的研究到目前为止，关于使用被动智能手机传感器数据的方法，以获得行为标记相关或预测健康相关的结果。研究结果将作为研究人员调查研究设计和方法领域的核心资源，并将这一新兴的研究领域推向最终在患者护理中提供临床实用性。
BackgroundDigital 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.ObjectivesThe 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.MethodsA 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.ResultsA 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.ConclusionThis 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.
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影响因子：
2.7
作者：
Gao Y;Li A;Zhu T;Liu X;Liu X
通讯作者：
Liu X
影响因子：
5.4
作者：
He-Yueya J;Buck B;Campbell A;Choudhury T;Kane JM;Ben-Zeev D;Althoff T
通讯作者：
Althoff T
影响因子：
6.7
作者：
Haynes PL;Gengler D;Kelly M
通讯作者：
Kelly M
影响因子：
4
作者：
Stanislaus S;Vinberg M;Melbye S;Frost M;Busk J;Bardram JE;Kessing LV;Faurholt-Jepsen M
通讯作者：
Faurholt-Jepsen M
DOI：
10.1146/annurev-clinpsy-032816-044949
发表时间：
2017-05-08
影响因子：
18.4
作者：
Mohr DC;Zhang M;Schueller SM
通讯作者：
Schueller SM