Leimhofer Johannes, Petrovic Milica, Dominik Andreas, Heider Dominik, Hegerl Ulrich
Research Center of German Foundation for Depression and Suicide Prevention, Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe University Frankfurt, Frankfurt am Main, Germany.
Institute of Computer Science, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Interact J Med Res. 2025 Aug 7;14:e69686. doi: 10.2196/69686.
A popular trend in depression forecasting research is the development of machine learning models trained with various types of smartphone sensor data and periodic self-ratings to derive early indications of changes in depression severity. While most works focus on model performance, there is little concern about the universal usability and reliable operation of such systems across smartphone platforms. This review serves as foundational work for the MENTINA clinical trial, which investigates smartphone-based health self-management for depression. The usability and reliability of mobile apps for depression are commonly perceived through the lens of the approaches and interventions offered rather than the reliability of the built-in mobile phone functions to support effortless and exact delivery of intended interventions.
This work aimed to synthesize existing systematic reviews to identify smartphone sensor modalities used in mental health monitoring and, building on this foundation, assess the cross-platform availability of these data streams using PhoneDB to inform the design and implementation of digital depression indication systems.
To identify the already used hardware and software sensors and their purposes in mental health monitoring, an umbrella review was conducted. Three electronic databases, including PubMed, Web of Science Core Collection, and Scopus, were searched using smartphone, sensor data, and depression keyword combination to retrieve relevant literature reviews published within the last 5 years (2019-2024). Once the initial search was completed, the extracted hardware sensors were checked for availability on Android and iOS smartphones by analyzing device specifications in PhoneDB over the last 10 years.
The extracted data streams observed across the 9 included studies covered 16 hardware and 3 software data streams. Hardware data streams included accelerometers, barometers, battery levels, Bluetooth, cameras, cellular networks, GPSs, gyroscopes, humidity, light sensors, magnetometers, proximity sensors, sound sensors, step counters, temperature sensors, and Wi-Fi. Software data streams included app usage, call and message logs, and screen status. Hardware component availability on Android and iOS systems showed the changes in component trends from 2014 to 2024 as of September 2024, with the accelerometers, batteries, cameras, and GPSs remaining consistent on Android and iOS, while components such as gyroscopes, step counters, and barometers gradually increased over the years, particularly on Android.
Multiple data streams identified in the literature review showed a consistent increase in availability over time, enabling improved use of these outputs for depression forecasting and the training of machine learning models with diverse smartphone data, including sensor-derived information. For more precise and reliable data to be used in the mental health field, particularly in critical areas such as tracking and predicting changes in depression severity, further research is required to streamline smartphone data across varying mobile hardware and software configurations to provide reliable output for digital mental health purposes.
抑郁症预测研究中的一个流行趋势是开发机器学习模型，这些模型通过各种类型的智能手机传感器数据和定期的自我评分进行训练，以得出抑郁症严重程度变化的早期迹象。虽然大多数研究都集中在模型性能上，但对于此类系统在不同智能手机平台上的普遍可用性和可靠运行却很少关注。本综述是MENTINA临床试验的基础工作，该试验旨在研究基于智能手机的抑郁症健康自我管理。抑郁症移动应用程序的可用性和可靠性通常是通过所提供的方法和干预措施来衡量的，而不是通过支持轻松、准确地实施预期干预措施的内置手机功能的可靠性来衡量。
本研究旨在综合现有系统评价，以确定心理健康监测中使用的智能手机传感器模式，并在此基础上，使用PhoneDB评估这些数据流的跨平台可用性，为数字抑郁症指示系统的设计和实施提供参考。
为了确定心理健康监测中已经使用的硬件和软件传感器及其用途，我们进行了一项综合评价。我们使用智能手机、传感器数据和抑郁症关键词组合在三个电子数据库中进行搜索，包括PubMed、Web of Science核心合集和Scopus，以检索过去5年（2019 - 2024年）内发表的相关文献综述。初始搜索完成后，通过分析PhoneDB中过去10年的设备规格，检查提取的硬件传感器在安卓和iOS智能手机上的可用性。
在纳入的9项研究中观察到的提取数据流涵盖了16种硬件和3种软件数据流。硬件数据流包括加速度计、气压计、电池电量、蓝牙、摄像头、蜂窝网络、全球定位系统、陀螺仪、湿度、光传感器、磁力计、接近传感器、声音传感器、步数计数器、温度传感器和Wi-Fi。软件数据流包括应用程序使用情况、通话和短信记录以及屏幕状态。截至2024年9月，安卓和iOS系统上硬件组件的可用性显示了2014年至2024年组件趋势的变化，加速度计、电池、摄像头和全球定位系统在安卓和iOS上保持一致，而陀螺仪、步数计数器和气压计等组件多年来逐渐增加，尤其是在安卓系统上。
文献综述中确定的多个数据流的可用性随着时间的推移持续增加，这使得这些输出能够更好地用于抑郁症预测以及使用包括传感器衍生信息在内的各种智能手机数据训练机器学习模型。为了在心理健康领域使用更精确和可靠的数据，特别是在跟踪和预测抑郁症严重程度变化等关键领域，需要进一步研究以简化不同移动硬件和软件配置下的智能手机数据，从而为数字心理健康目的提供可靠输出。
