Background: The purpose of this study was to investigate the association between the ionotropic and glutamate receptors, N-methyl D-asparate 2A (GRIN2A) and 2B (GRIN2B), and the metabotropic glutamate receptor mGluR7 (GRM7) gene polymorphisms and attention-deficit hyperactivity disorder (ADHD) in Korean population. Methods: We conducted a case–control analysis of 202 ADHD subjects and 159 controls, performed a transmission disequilibrium test (TDT) on 149 trios, and compared scores from the continuous performance test (CPT), the Children’s Depression Inventory (CDI), and the State-Trait Anxiety Inventory for Children (STAIC) according to the genotype of the glutamate receptor genes. Results: There were no significant differences in the genotype or allele frequencies of the GRIN2A rs8049651, GRIN2B rs2284411, or GRM7 rs37952452 polymorphisms between the ADHD and control groups. For 148 ADHD trios, the TDT analysis also showed no preferential transmission of the GRIN2A rs8049651 or GRIN2B rs2284411 polymorphisms. However, the TDT analysis of the GRM7 rs3792452 polymorphism showed biased transmission of the G allele (χ2 = 4.67, p = 0.031). In the ADHD probands, the subjects with GG genotype in the GRM7 rs37952452 polymorphism had higher mean T-scores for omission errors on the CPT than did those with the GA or AA genotype (t = 3.38, p = 0.001). In addition, the ADHD subjects who were homozygous for the G allele in the GRM7 rs37952452 polymorphism had higher STAIC-T (t = 5.52, p < 0.001) and STAIC-S (t = 2.74, p = 0.007) scores than did those with the GA or AA genotype. Conclusions: These results provide preliminary evidence of an association between the GRM7 rs37952452 polymorphism and selective attention deficit and anxiety found within the Korean ADHD population.

Hyun J, et al. J Korean Med Sci. 2020 Apr;35(22):e211. https://doi.org/10.3346/jkms.2020.35.e211

Seo SS, et al. Psychiatry Investig. 2009 Sep;6(3):173-179. http://dx.doi.org/10.4306/pi.2009.6.3.173

Youh J, et al. J Korean Med Sci. 2017 Jul;32(7):1160-1165. https://doi.org/10.3346/jkms.2017.32.7.1160

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.

Han DH, et al. J Korean Med Sci. 2016 Jan;31(1):114-119. https://doi.org/10.3346/jkms.2016.31.1.114