What digital health technology types are used in mental health prevention and intervention? Review of systematic reviews for systematization of technologies.

Digital health technology has been widely applied to mental health interventions worldwide. Using digital phenotyping to identify an individual's mental health status has become particularly important. However, many technologies other than digital phenotyping are expected to become more prevalent in the future. The systematization of these technologies is necessary to accurately identify trends in mental health interventions. However, no consensus on the technical classification of digital health technologies for mental health interventions has emerged. Thus, we conducted a review of systematic review articles on the application of digital health technologies in mental health while attempting to systematize the technology using the Delphi method. To identify technologies used in digital phenotyping and other digital technologies, we included 4 systematic review articles that met the inclusion criteria, and an additional 8 review articles, using a snowballing approach, were incorporated into the comprehensive review. Based on the review results, experts from various disciplines participated in the Delphi process and agreed on the following 11 technical categories for mental health interventions: heart rate estimation, exercise or physical activity, sleep estimation, contactless heart rate/pulse wave estimation, voice and emotion analysis, self-care/cognitive behavioral therapy/mindfulness, dietary management, psychological safety, communication robots, avatar/metaverse devices, and brain wave devices. The categories we defined intentionally included technologies that are expected to become widely used in the future. Therefore, we believe these 11 categories are socially implementable and useful for mental health interventions.

A Novel Approach for Investigating Upper Airway Hyperresponsiveness Using Micro-CT in Eosinophilic Upper Airway Inflammation such as Allergic Rhinitis Model.

Airway hyperresponsiveness (AHR) has been proposed as a feature of pathogenesis of eosinophilic upper airway inflammation such as allergic rhinitis (AR). The measurement system for upper AHR (UAHR) in rodents is poorly developed, although measurements of nasal resistance have been reported. Here we assessed UAHR by direct measurement of swelling of the nasal mucosa induced by intranasal methacholine (MCh) using micro-computed tomography (micro-CT). Micro-CT analysis was performed in both naïve and ovalbumin-induced AR mice following intranasal administration of MCh. The nasal cavity was segmented into two-dimensional horizontal and axial planes, and the data for nasal mucosa were acquired for the region of interest threshold. Then, a ratio between the nasal mucosa area and nasal cavity area was calculated as nasal mucosa index. Using our novel method, nasal cavity structure was clearly identified on micro-CT, and dose-dependent increased swelling of the nasal mucosa was observed upon MCh treatment. Moreover, the nasal mucosa index was significantly increased in AR mice compared to controls following MCh treatment, while ovalbumin administration did not affect swelling of the nasal mucosa in either group. This UAHR following MCh treatment was completely reversed by pretreatment with glucocorticoids. This novel approach using micro-CT for investigating UAHR reflects a precise assessment system for swelling of the nasal mucosa following MCh treatment; it not only sheds light on the mechanism of AR but also contributes to the development of new therapeutic drugs in AR patients.