Laboratory of Intelligent Distributed Systems, Federal University of Maranhão, São Luís, Brazil.
Health Research Institute, University of Limerick, Limerick, Ireland.
J Med Internet Res. 2022 Feb 17;24(2):e28735. doi: 10.2196/28735.
Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients' interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.
This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective.
We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends.
A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal.
This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.
精神障碍通常仅基于症状进行诊断，这些症状是通过对患者访谈和自我报告的经验进行识别的。为了使心理健康诊断和监测更加客观，已经提出了不同的解决方案，例如数字精神健康表型（DPMH），它可以基于人与数字技术的交互扩展识别和监测健康状况的能力。
本文旨在从技术角度确定和描述 DPMH 的传感应用和公共数据集。
我们对科学文献和数据集进行了系统回顾。我们搜索了 8 个数字图书馆和 20 个数据集存储库，以查找符合选择标准的结果。我们从选定的文章和数据集中进行了数据提取过程。为此，设计了一个表格来提取相关信息，从而使我们能够回答研究问题并确定开放问题和研究趋势。
共确定并审查了 31 个传感应用程序和 8 个数据集。传感应用程序探索了不同的上下文数据源（例如定位、惯性、环境），以支持 DPMH 研究。这些应用程序旨在分析和处理收集的数据，以对精神状态/障碍进行分类（n=11）和预测（n=6），并研究上下文数据与精神状态/障碍之间的现有相关性（n=6）。此外，开发了通用传感应用程序仅专注于上下文数据收集（n=9）。所审查的数据集中包含模拟人类行为不同方面的上下文数据，例如社交能力、情绪、身体活动、睡眠，其中一些也是多模态的。
本系统评价提供了关于 DPMH 解决方案的深入分析。结果表明，近年来，DPMH 传感应用程序的提案有所增加，而公共数据集却很少。审查结果表明，智能设备上有可以作为精神状态和幸福感代理的可测量特征；但是，应该注意的是，精神状态的高质量特征的综合证据仍然有限。DPMH 为未来的研究提供了很好的前景，主要是为了达到在临床环境中应用所需的成熟度。
