Amin Rebeka, Schreynemackers Simon, Oppenheimer Hannah, Petrovic Milica, Hegerl Ulrich, Reich Hanna
Research Centre of the German Foundation for Depression and Suicide Prevention, Department for Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University Frankfurt, Frankfurt am Main, Germany.
German Foundation for Depression and Suicide Prevention, Leipzig, Germany.
J Med Internet Res. 2025 Aug 21;27:e57418. doi: 10.2196/57418.
Depression is highly recurrent and heterogeneous. The unobtrusive, continuous collection of mobile sensing data via smartphones and wearable devices offers a promising approach to monitor and predict individual depression trajectories, distinguish illness states, and anticipate changes in symptom severity.
This systematic review evaluates whether objective data from wearable devices and smartphones can (1) monitor and distinguish different states of depression, (2) predict changes in symptom severity, and (3) identify clinically relevant objective features for tracking and forecasting depression within diagnosed individuals.
We searched PubMed and Web of Science databases for English-language studies (published 2012-2022) that used smartphone or wearable device data, included participants aged ≥14 years with a depression diagnosis, and collected continuous data for at least 12 weeks.
Out of 12,997 peer-reviewed articles, 9 original studies met the inclusion criteria, with sample sizes ranging from 45 to 2200 and durations of 12-52 weeks. Of the 9 studies, 3 used smartphone data, 1 used wearable device data, and 5 used both data types. Commonly collected variables were step count, distance moved, smartphone usage, call logs, sleep, heart rate, light exposure, and speech patterns. One study (11%) successfully differentiated between depressive states (worsening, relapse, or recovery). Six studies (67%) showed that mobile sensing data could predict depressive episodes or symptom severity. Four studies reported the predictive accuracy for depression using mobile sensing data from smartphones and wearable devices, ranging from 81% to 91%. Higher accuracy was achieved with personalized models or multimodal data.
Real-time passive monitoring via wearable devices and smartphones holds promise for personalized self-management, but key gaps remain, such as a lack of longitudinal and long-term studies with data collection for 1 year or longer, studies with confirmatory parameters on an individual level, and studies with a strong correlation between parameters in individual patients to support clinical decision-making. Improvements in reporting standards are highly recommended to provide better-informed insights for clinicians. Throughout this process, there is a clear need to address various other issues, such as limited types of collected data, reliability, user adherence, and privacy concerns.
PROSPERO CRD42022355696; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022355696.
抑郁症具有高度复发性且异质性强。通过智能手机和可穿戴设备以不显眼的方式持续收集移动传感数据，为监测和预测个体抑郁轨迹、区分疾病状态以及预测症状严重程度变化提供了一种很有前景的方法。
本系统评价评估来自可穿戴设备和智能手机的客观数据是否能够（1）监测和区分抑郁症的不同状态，（2）预测症状严重程度的变化，以及（3）识别用于跟踪和预测已确诊个体抑郁症的临床相关客观特征。
我们在PubMed和Web of Science数据库中检索了2012年至2022年发表的英文研究，这些研究使用了智能手机或可穿戴设备数据，纳入了年龄≥14岁且患有抑郁症的参与者，并收集了至少12周的连续数据。
在12997篇经同行评审的文章中，9项原创研究符合纳入标准，样本量从45至2200不等，持续时间为12至52周。在这9项研究中，3项使用了智能手机数据，1项使用了可穿戴设备数据，5项同时使用了这两种数据类型。常见收集的变量有步数、移动距离、智能手机使用情况、通话记录、睡眠、心率、光照暴露和语音模式。一项研究（11%）成功区分了抑郁状态（恶化、复发或康复）。六项研究（67%）表明移动传感数据能够预测抑郁发作或症状严重程度。四项研究报告了使用来自智能手机和可穿戴设备的移动传感数据预测抑郁症的准确率，范围为81%至91%。个性化模型或多模态数据实现了更高的准确率。
通过可穿戴设备和智能手机进行实时被动监测对个性化自我管理具有前景，但关键差距仍然存在，例如缺乏为期1年或更长时间的数据收集的纵向和长期研究、个体层面具有确证参数的研究，以及个体患者参数之间具有强相关性以支持临床决策的研究。强烈建议改进报告标准，以便为临床医生提供更有依据的见解。在整个过程中，显然需要解决各种其他问题，如收集数据的类型有限、可靠性、用户依从性和隐私问题。
PROSPERO CRD42022355696；https://www.crd.york.ac.uk/PROSPERO/view/CRD42022355696 。
