John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA; Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA.
Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA.
Schizophr Res. 2022 May;243:64-69. doi: 10.1016/j.schres.2022.02.031. Epub 2022 Mar 1.
Smartphone assessments and sensors offer the ability to easily assess symptoms across environments in a naturalistic and longitudinal manner. However, the value of this new data to make inferences about personal vs population health and the role of environment in moderating symptoms in schizophrenia has not been fully explored in a scalable and reproducible manner.
Eighty-six adults with a diagnosis of schizophrenia were recruited from the Greater Boston Area between August 2019 and May 2021. Using the open-source mindLAMP app in an observational manner, smartphone surveys and sensors (GPS, accelerometer, screen on/off and call and text logs) were collected for up to six months.
Sixty-three participants were analyzed, who had at least completed one survey in the app. App-based self-reported symptom surveys were highly correlated with scores on gold standard clinical assessments (r = 0.80, p = 10 for mood and r = 0.78, p = 10 for anxiety). For these app-based assessments, inter-individual differences account for a larger proportion of the correlations in longitudinal symptoms as compared to intra-individual differences. Mood, sleep, and psychosis symptoms reported on app surveys were more severe when taken at home as determined by the smartphone's GPS sensor.
The intra-individual symptom correlations and the stratification of symptoms by home-time highlight the utility of digital phenotyping methods as a diagnostic tool, as well as the potential for personalized psychiatric treatment building on this data.
智能手机评估和传感器能够以自然和纵向的方式轻松评估跨环境的症状。然而，这种新数据对于推断个人与人群健康以及环境在调节精神分裂症症状方面的作用的价值，尚未以可扩展和可重复的方式得到充分探索。
2019 年 8 月至 2021 年 5 月期间，从大波士顿地区招募了 86 名被诊断为精神分裂症的成年人。以观察的方式使用开源的 mindLAMP 应用程序，收集智能手机调查和传感器（GPS、加速度计、屏幕开/关以及通话和短信记录）长达六个月。
对 63 名参与者进行了分析，他们至少在应用程序中完成了一次调查。基于应用程序的自我报告症状调查与黄金标准临床评估的分数高度相关（r = 0.80，p = 10 用于情绪，r = 0.78，p = 10 用于焦虑）。对于这些基于应用程序的评估，个体间差异在纵向症状中的相关性中占比更大，而个体内差异则占比较小。智能手机 GPS 传感器确定的，在家中进行的基于应用程序的调查中报告的情绪、睡眠和精神病症状更为严重。
个体内症状相关性以及在家时间对症状的分层突出了数字表型方法作为诊断工具的效用，以及基于此数据构建个性化精神科治疗的潜力。
