To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.

In 2020, 20% of patients with COVID-19 developed severe complications, including life-threatening pneumonia with systemic inflammatory response syndrome (SIRS). We developed a preliminary SIRS monitor that does not require blood sampling, is noninvasive, and can collect data 24 h per day. The proposed monitor comprises a piezoelectric respiratory sensor located beneath the patient’s mattress and a fingertip pulse sensor that determines ultra-high accuracy respiratory rate (mode of a 40-min frequency distribution of respiratory rates (M40FD-RR)). We assessed the clinical performance of the M40FD-RR preliminary SIRS monitor in 29 patients (12 female, 17 male, aged 15–90 years) hospitalized at Suwa Central Hospital with COVID-19, which was confirmed by a positive polymerase chain reaction test. SIRS was evaluated by logistic regression analysis using M40FD-RR, heart rate, age, and sex as explanatory variables. We compared the results of 109 examinations of 29 COVID-19 inpatients with SIRS against those determined by the proposed monitor. The proposed monitor achieved 75% sensitivity and 83% negative predictive value, making it a promising candidate for future 24 h noninvasive preliminary SIRS tests.

Many patients with major depressive disorder (MDD) feel worse in the morning than in the evening. To clarify the differences in morning physiological characteristics between patients with MDD and healthy participants, a wearable device that measures peripheral wrist skin temperature and heart rate (HR) was adopted. The device incorporates a thermopile sensor to measure peripheral wrist skin temperature using infrared radiation emitted from the skin surface. In total, 30 patients diagnosed with MDD and 24 healthy individuals were recruited. From 00:00 to 12:00, participants wore a wrist-worn device on their non-dominant hand. It was discovered that, at 09:00, the average peripheral wrist skin temperature of patients with MDD was significantly lower (by 0.1% [2.9 °C]) than that of healthy individuals. The dramatic decrease in morning (09:00) peripheral wrist skin temperature in patients with MDD can be attributed to their morning sympathetic surge and peripheral vascular contraction. The average HR of patients with MDD was significantly higher (by 1% [17 beats/min]) than that of healthy controls. Regression analysis, including peripheral wrist skin temperature and HR at 09:00, showed 83.3% sensitivity and a negative predictive value of 76.2%. The potential impact of these results appears promising for future preliminary morning MDD screening.

Background/Objectives: Relapsing polychondritis (RP) is a rare autoimmune disorder marked by recurrent inflammation of cartilaginous tissues, including the airways. Airway involvement, such as subglottic stenosis and airway malacia, significantly impacts prognosis. Although spirometry is the standard for evaluating respiratory function, it may be unfeasible in patients with severe airway narrowing or tracheostomy. This study evaluated the potential of a smartphone-based application, DepthRecorder, which uses the iPhone’s TrueDepth camera to analyze thoracic motion in real time. Methods: Twelve patients with RP were enrolled. All underwent simultaneous respiratory assessment using spirometry and the DepthRecorder application. Thoracic motion data were corrected for height using previously validated regression formulas. Correlation between DepthRecorder and spirometry values was analyzed using Spearman’s rank correlation for forced vital capacity (FVC), forced expiratory volume in one second (FEV₁), and the FEV₁/FVC ratio. Results: Mean age was 53.8 ± 13.3 years, with equal numbers of males and females. Before correction, DepthRecorder showed moderate correlations for FEV₁ (ρ = 0.48, p = 0.003) and FEV₁/FVC (%) (ρ = 0.57, p < 0.001). After correction, stronger correlations were observed for FVC (ρ = 0.76, p < 0.001), FEV₁ (ρ = 0.72, p < 0.001), and FEV₁/FVC (%) (ρ = 0.60, p < 0.001). Conclusions: The DepthRecorder application demonstrated strong correlations with spirometry following height-based correction. This method may offer a practical, non-invasive tool for respiratory assessment in RP patients who cannot undergo conventional lung function testing. Further studies are needed to validate these findings and establish clinical reference standards.