Zhong Rongrong, Wu XiaoHui, Chen Jun, Fang Yiru
Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Psychiatry & Affective Disorders Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
J Med Internet Res. 2025 May 23;27:e72229. doi: 10.2196/72229.
Differentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression and treatment approaches. Digital phenotyping, which involves using data from smartphones or other digital devices to assess mental health, has emerged as a promising tool for distinguishing between these two disorders.
This systematic review aimed to achieve two goals: (1) to summarize the existing literature on the use of digital phenotyping to directly distinguish between UD and BD and (2) to review studies that use digital phenotyping to classify UD, BD, and healthy control (HC) individuals. Furthermore, the review sought to identify gaps in the current research and propose directions for future studies.
We systematically searched the Scopus, IEEE Xplore, PubMed, Embase, Web of Science, and PsycINFO databases up to March 20, 2025. Studies were included if they used portable or wearable digital tools to directly distinguish between UD and BD, or to classify UD, BD, and HC. Original studies published in English, including both journal and conference papers, were included, while reviews, narrative reviews, systematic reviews, and meta-analyses were excluded. Articles were excluded if the diagnosis was not made through a professional medical evaluation or if they relied on electronic health records or clinical data. For each included study, the following information was extracted: demographic characteristics, diagnostic criteria or psychiatric assessments, details of the technological tools and data types, duration of data collection, data preprocessing methods, selected variables or features, machine learning algorithms or statistical tests, validation, and main findings.
We included 21 studies, of which 11 (52%) focused on directly distinguishing between UD and BD, while 10 (48%) classified UD, BD, and HC. The studies were categorized into 4 groups based on the type of digital tool used: 6 (29%) used smartphone apps, 3 (14%) used wearable devices, 11 (52%) analyzed audiovisual recordings, and 1 (5%) used multimodal technologies. Features such as activity levels from smartphone apps or wearable devices emerged as potential markers for directly distinguishing UD and BD. Patients with BD generally exhibited lower activity levels than those with UD. They also tended to show higher activity in the morning and lower in the evening, while patients with UD showed the opposite pattern. Moreover, speech modalities or the integration of multiple modalities achieved better classification performance across UD, BD, and HC groups, although the specific contributing features remained unclear.
Digital phenotyping shows potential in distinguishing BD from UD, but challenges like data privacy, security concerns, and equitable access must be addressed. Further research should focus on overcoming these challenges and refining digital phenotyping methodologies to ensure broader applicability in clinical settings.
PROSPERO CRD42024624202; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024624202.
区分双相情感障碍（BD）和单相抑郁症（UD）至关重要，因为这两种病症在病程和治疗方法上有很大差异。数字表型分析涉及使用智能手机或其他数字设备的数据来评估心理健康，已成为区分这两种疾病的一种有前景的工具。
本系统评价旨在实现两个目标：（1）总结关于使用数字表型分析直接区分UD和BD的现有文献，（2）综述使用数字表型分析对UD、BD和健康对照（HC）个体进行分类的研究。此外，该综述旨在找出当前研究中的差距，并为未来研究提出方向。
我们系统检索了截至2025年3月20日的Scopus、IEEE Xplore、PubMed、Embase、Web of Science和PsycINFO数据库。如果研究使用便携式或可穿戴数字工具直接区分UD和BD，或对UD、BD和HC进行分类，则纳入研究。纳入以英文发表的原创研究，包括期刊论文和会议论文，而综述、叙述性综述、系统评价和荟萃分析则被排除。如果诊断不是通过专业医学评估做出的，或者研究依赖电子健康记录或临床数据，则排除相关文章。对于每项纳入研究，提取以下信息：人口统计学特征、诊断标准或精神科评估、技术工具和数据类型的详细信息、数据收集持续时间、数据预处理方法、选定的变量或特征、机器学习算法或统计测试、验证以及主要发现。
我们纳入了21项研究，其中11项（52%）专注于直接区分UD和BD，而10项（48%）对UD、BD和HC进行了分类。根据所使用的数字工具类型，这些研究分为4组：6项（29%）使用智能手机应用程序，3项（14%）使用可穿戴设备，11项（52%）分析视听记录，1项（5%）使用多模态技术。来自智能手机应用程序或可穿戴设备的活动水平等特征成为直接区分UD和BD的潜在标志物。BD患者的活动水平通常低于UD患者。他们还倾向于在早晨表现出较高的活动水平，而在晚上较低，而UD患者则表现出相反的模式。此外，语音模式或多种模式的整合在UD、BD和HC组中实现了更好的分类性能，尽管具体的贡献特征尚不清楚。
数字表型分析在区分BD和UD方面显示出潜力，但必须解决数据隐私、安全问题和公平获取等挑战。进一步的研究应专注于克服这些挑战并完善数字表型分析方法，以确保在临床环境中更广泛的适用性。
PROSPERO CRD42024624202；https://www.crd.york.ac.uk/PROSPERO/view/CRD42024624202 。
