Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help—the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students.

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大学生心理健康的数字表型分析：临床评价

由于学生对心理健康服务的需求持续增长，随着 COVID-19 大流行期间课程转向远程虚拟学习，大学正在寻求数字解决方案，以增加获得护理的机会。数字表型分析利用智能手机捕捉与精神疾病相关的实时症状和行为，提供了一种实用工具，帮助大学远程监控和评估心理健康状况，并提供更加定制化和响应性更强的护理。本文对 25 项大学生数字表型研究进行了叙述性回顾，探讨了该方法如何被部署、研究以及如何影响心理健康结果。我们发现研究的平均持续时间为 42 天，平均注册人数为 81 名参与者。最常见的基于传感器的数据流收集包括位置、加速度计和社交信息，这些信息用于通知睡眠、锻炼和社交互动等行为。52% 的研究还以某种形式收集了智能手机调查，这些调查被用来评估情绪、焦虑和压力等许多其他结果。对构建与睡眠、活动和社交互动相关特征的数据的集体关注表明，该领域已经适当关注大学生心理健康问题的主要驱动因素。虽然这些研究方法的异质性没有为移动设备提供自动化帮助提供可靠的目标，但各项研究的可行性表明，今天使用这些数据来实现个性化护理的潜力。随着更加统一的数字表型研究的发展并扩展到更大的样本量，学生心理健康中心可能会考虑将这些数据整合到大学生的临床实践中。