Reliability of Telepsychiatry Assessments Using the Attention-Deficit/Hyperactivity Disorder Rating Scale-IV for Children With Neurodevelopmental Disorders and Their Caregivers: Randomized Feasibility Study.

BACKGROUND: Given the global shortage of child psychiatrists and barriers to specialized care, remote assessment is a promising alternative for diagnosing and managing attention-deficit/hyperactivity disorder (ADHD). However, only a few studies have validated the accuracy and acceptability of these remote methods. OBJECTIVE: This study aimed to test the agreement between remote and face-to-face assessments. METHODS: Patients aged between 6 and 17 years with confirmed Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnoses of ADHD or autism spectrum disorder (ASD) were recruited from multiple institutions. In a randomized order, participants underwent 2 evaluations, face-to-face and remotely, with distinct evaluators administering the ADHD Rating Scale-IV (ADHD-RS-IV). Intraclass correlation coefficient (ICC) was used to assess the reliability of face-to-face and remote assessments. RESULTS: The participants included 74 Japanese children aged between 6 and 16 years who were primarily diagnosed with ADHD (43/74, 58%) or ASD (31/74, 42%). A total of 22 (30%) children were diagnosed with both conditions. The ADHD-RS-IV ICCs between face-to-face and remote assessments showed "substantial" agreement in the total ADHD-RS-IV score (ICC=0.769, 95% CI 0.654-0.849; P<.001) according to the Landis and Koch criteria. The ICC in patients with ADHD showed "almost perfect" agreement (ICC=0.816, 95% CI 0.683-0.897; P<.001), whereas in patients with ASD, it showed "substantial" agreement (ICC=0.674, 95% CI 0.420-0.831; P<.001), indicating the high reliability of both methods across both conditions. CONCLUSIONS: Our study validated the feasibility and reliability of remote ADHD testing, which has potential benefits such as reduced hospital visits and time-saving effects. Our results highlight the potential of telemedicine in resource-limited areas, clinical trials, and treatment evaluations, necessitating further studies to explore its broader application. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000039860; http://tinyurl.com/yp34x6kh.

Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol.

Introduction: Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices. Methods and analysis: Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set. Discussion: Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device. Clinical trial registration: [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].

Long-Acting Injectables Versus Oral Antipsychotics: A Retrospective Bidirectional Mirror-Image Study.

PURPOSE: Mirror-image studies, which compare equal periods of time before and after a new treatment is introduced, may reflect the real-world impact of that treatment. However, most mirror-image studies that have investigated the impact of long-acting injectable antipsychotics (LAIs) were unidirectional in design, for patients switching from oral antipsychotics (OAPs) to LAIs. Therefore, we conducted a bidirectional mirror-image study comparing LAIs and OAPs. METHODS: We included 126 schizophrenia or schizoaffective disorder patients' LAI treatment data from 3 psychiatric hospitals. Patients took OAPs for 6 months or more before initiating LAIs, or the reverse. We obtained data on the number of hospitalizations as a primary outcome, plus the total duration and mean duration of hospitalization as secondary outcomes during the 6 months of the patients' first treatment, and the 6 months after the patients started their second type of treatment. RESULTS: The results indicated that there was no significant difference in any outcomes between LAI and OAP treatment when going from LAIs to OAPs (n = 59). However, when patients started with OAPs and switched to LAIs (n = 67), they were hospitalized a significantly fewer number of times, and the duration of their stays was shorter in the LAI phase than in the OAP phase. When combined with bidirectional data, LAI superiority was still observed. CONCLUSIONS: The findings endorse the relative effectiveness of LAIs over OAPs in the real world, although the inherent flaws of mirror-image studies such as expectation bias and having no parallel comparator should be considered.