Background: The COVID-19 pandemic has intensified mental health issues globally, highlighting the urgent need for remote mental health monitoring. Digital phenotyping using smart devices has emerged as a promising approach, but it remains unclear which features are essential for predicting depression and anxiety. Objective: This study aimed to identify the types of features collected through smart packages—integrated systems combining smartphones with wearable devices such as Actiwatches, smart bands, and smartwatches—and to determine which features should be considered essential for mental health monitoring based on the type of device used. Methods: A systematic review was conducted. Searches were performed across Web of Science, PubMed, and Scopus on February 5, 2025. Inclusion criteria comprised quantitative studies involving adults (≥19 years) using smart devices to predict depression or anxiety based on passive data collection. Studies focusing solely on smartphones or qualitative designs were excluded. Risk of bias was assessed using the Mixed Methods Appraisal Tool and the Quality Criteria Checklist. Data were synthesized descriptively, and the relative contribution of each feature was further assessed by calculating coverage (proportion of studies using a feature) and importance among used (proportion identifying it as important when used). These metrics were visualized in quadrant-based scatter plots to identify consistently important features across devices. Results: From 1382 records, 22 studies across 11 countries were included. The overall synthesis identified a core feature package—accelerometer, steps, heart rate (HR), and sleep. Device-specific analyses revealed further nuances: in Actiwatch studies, accelerometer and activity were consistently important, but sleep features were rarely examined. In smart band studies, HR, steps, sleep, and phone usage were essential, while GPS, electrodermal activity (EDA), and skin temperature showed high importance when used, suggesting opportunities for broader adoption. In smartwatch studies, sleep and HR emerged as core features, whereas steps and accelerometer were widely used but often not identified as important. Conclusions: This systematic review identified a core feature package comprising accelerometer, steps, HR, and sleep that consistently contributes to mood disorder prediction across devices. At the same time, device-specific differences were observed: Actiwatch studies mainly emphasized accelerometer and activity but underused sleep features; smart bands highlighted HR, steps, sleep, and phone usage, with EDA, skin temperature, and GPS showing additional promise; and smartwatches most reliably leveraged sleep and HR, while steps and accelerometer were widely used yet less effective. These findings suggest that while a shared core set of features exists, optimizing digital phenotyping requires tailoring feature selection to the characteristics of each device type. To advance this field, improving data accessibility, particularly in smartwatch ecosystems, and adopting standardized reporting frameworks will be essential to enhance comparability, reproducibility, and future meta-analytic integration.

Background: Obsessive–compulsive disorder (OCD) and panic disorder (PD) are debilitating psychiatric conditions, yet their underlying neurobiological differences remain underexplored. This study aimed to directly compare resting-state EEGs in patients with OCD and PD, without a healthy control group, using the eLORETA method. Methods: We collected retrospective EEG data from 24 OCD patients and 22 PD patients who were hospitalized due to significant impairment in daily life functions. eLORETA was used to analyze the EEG data. Results: Heightened theta activity was observed in the anterior cingulate cortex (ACC) of OCD patients compared to PD patients (PD vs. OCD, t = −2.168, p < 0.05). Conversely, higher gamma activity was found in the medial frontal gyrus (MFG) and paracentral lobule (PCL) in PD patients (PD vs. OCD, t = 2.173, p < 0.05). Conclusions: Our findings highlight neurobiological differences between OCD and PD patients. Specifically, the increased theta activity in the ACC for OCD patients and elevated gamma activity in the MFG and PCL for PD patients offer preliminary insights into the neural mechanisms of these disorders. Further studies are essential to validate these results and delve deeper into the neural underpinnings.

Ilju Lee, Jiryun Lee, Myung Ho Lim, Kyoung Min Kim. Clin Psychopharmacol Neurosci 2021;19:739-50. https://doi.org/10.9758/cpn.2021.19.4.739

BACKGROUND The COVID-19 pandemic has intensified mental health issues globally, highlighting the urgent need for remote mental health monitoring. Digital phenotyping using smart devices has emerged as a promising approach, but it remains unclear which features are essential for predicting depression and anxiety. OBJECTIVE This systematic review aimed to identify the types of features collected through smartphones, Actiwatch devices, smartbands, and smartwatches, and to determine which features are essential for mental health monitoring. METHODS A systematic review was conducted following the PRISMA 2020 guidelines. Searches were performed across Web of Science, PubMed, and Scopus on February 5, 2025. Inclusion criteria comprised quantitative studies involving adults (≥19 years) using smart devices to predict depression or anxiety based on passive data collection. Studies focusing solely on smartphones or qualitative designs were excluded. Risk of bias was assessed using the Mixed Methods Appraisal Tool (MMAT) and Quality Criteria Checklist (QCC). The results were synthesized descriptively. RESULTS From 1,382 records, 22 studies were included. These studies were conducted in various countries and involved diverse populations, from clinical patients to community samples. The most frequently utilized features were derived from accelerometer (ACC), such as step counts and activity levels, followed by heart rate (HR) features. Sleep-related features were also important, especially in studies using smartbands and smartwatches. However, features such as peripheral capillary oxygen saturation (SpO₂) and blood volume pulse (BVP) were rarely used. Smartphone-derived features (e.g., call logs, phone usage) were underutilized in smartwatch studies, likely due to data access restrictions. CONCLUSIONS ACC and HR-derived features are essential in digital phenotyping for mood disorder prediction. Sleep features should be emphasized more, particularly in Actiwatch-based studies. Improving data accessibility and establishing standard reporting guidelines are crucial for advancing this field. Even with the various strengths of this study, variability in feature definitions, differences in study designs, and a lack of standardized reporting hindered direct comparisons across studies, making a meta-analysis infeasible. CLINICALTRIAL This review was registered in the Open Science Framework.