Acute psychological stress has a multifaceted impact on cardiovascular physiology, including increases in chronotropy, inotropy, and vascular tone. Chronic exposure to stress may greatly increase cardiovascular risk. To examine the cardiovascular impact of acute stress comprehensively, new multimodal portable monitoring solutions are needed. We examined the feasibility of using a compact multimodal wearable patch to measure laboratory-based stress-induced cardiovascular responses in a diverse sample with recent myocardial infarction (MI) and healthy participants (N = 37, 28 MI) during a protocol with a public-speaking stressor. Using the electrocardiogram, seismocardiogram, and photoplethysmogram captured from the device, we found several significant (p < 0.05) autonomic changes in the pooled sample suggesting stress activation: increases in heart rate, chest photoplethysmogram amplitude, and perfusion index and decreases in heart rate variability, left ventricular ejection time, pulse arrival time, and pulse transit time. We, thus, demonstrated that a single wearable device can capture stress-induced cardiovascular changes, enabling simultaneous examination of stress-induced inotropic, chronotropic, and vascular effects. This portable, wireless chest patch may be useful in comprehensively and unobtrusively examining stress-induced cardiovascular effects in-lab. Given the public health importance of psychological stress and cardiovascular disease, future studies should assess the device’s full clinical potential in larger groups with longer monitoring periods.

Opioid use disorder (OUD) is a significant global health issue, leading to severe physiological and psychological impacts and substantial societal costs. Current methods for assessing opioid withdrawal, primarily relying on subjective scales, suffer from limitations such as incomplete symptom capture, recall bias, and imprecision. Wearable sensor technologies offer a promising alternative for objective assessment, with previous studies demonstrating their ability to detect opioid use and measure related physiological changes. In this study we investigated the correlation between local cardio-mechanical variability quantified using dynamic time warping (DTW) distances of seismocardiogram (SCG) signals and subjective opioid withdrawal severity (SOWS) scores. In a 7-day in-patient protocol for individuals with OUD$(N=13)$, we found a statistically significant inverse correlation: shorter median DTW distances and reduced variance in SCG signals were associated with higher subjective withdrawal scores with statistically significant differences between the highest withdrawal bin and the two lowest bins ($\mathbf{p}=0.038$and$\mathbf{p}=0.044$, respectively). Our results suggests that local cardio-mechanical variability, as captured by wearable sensors and analyzed with DTW, can serve as a valuable indicator for quantifying opioid withdrawal severity, potentially enabling more timely and effective preventive care.

Seismocardiography is a potent non-invasive cardiovascular monitoring technique whose widespread adoption is currently limited in ambulatory settings due to its susceptibility to corruption from environmental noise. In the absence of a clean concurrently collected electrocardiogram (ECG) signal as a heartbeat reference, template matching paired with windowing methods can serve as a useful method by which to assess seismocardiogram (SCG) signal quality. However, windowing methods can introduce a time-shift in the segmentation of the SCG beats as compared to a template due to persistently adapting heart rate. In this study, we assess the performance of a state-of-the-art SCG signal quality assessment algorithm, dynamic time feature matching (DTFM), in ranking SCG beats by signal-to-noise ratio when introducing an artificial timedelay. We compare this performance against that of a novel methodology based on topological data analysis (TDA) using persistence diagrams. We found no significant difference$(p>0.05)$in ranking performance between topological data analysis (TDA) and dynamic time feature matching (DTFM) when SCG beats were segmented by true R-peak locations. However, we found that TDA significantly outperformed DTFM$(p<0.001)$when SCG beats were segmented 100, 200, or 300 ms earlier than the R-peak locations. These results suggest the potential promise of TDAbased methods for robust ECG-free SCG signal quality analysis. These advancements may facilitate the analysis of longitudinal SCG data taken in out-of-clinic settings in situations where ECG monitoring is not viable.

Cold exposure activates thermoregulatory processes through the autonomic nervous system that, while maintaining homeostasis, reduce peripheral blood flow and dexterity in the extremities. Transcutaneous median nerve stimulation (tMNS) represents a promising method for autonomic regulation through activation of parasympathetic vagal nerve afferents to the brain. However, the effect of this type of non-invasive therapy has not been investigated thoroughly in the context of thermoregulation. We analyzed peripheral skin temperature data in an ancillary study from a cohort of 19 participants who underwent two study visits of a protocol, each involving a dose-response activity, a cold pressor activity, and either tMNS or sham stimulation. Data from each protocol segment was averaged and then normalized as a percent difference from a baseline rest section. Paired t-tests and Pitman-Morgan tests were run on data from the cold pressor and dose-response activities respectively to determine statistical significance. We found that tMNS had a significant$(p=0.036)$effect of blunting peripheral skin temperature drops during cold pressor recovery as compared to sham stimulation. Additionally, tMNS had a general regulatory effect on skin temperature change during the dose-response activity, with significantly less variance than sham stimulation$(p<0.05)$. These results indicate that possible regulation of peripheral skin temperature with tMNS can serve as a therapy for cold exposure. Future work should investigate this mechanism in a larger cohort with a protocol designed to assess reactivity in temperature and dexterity to both cold and heat exposure.

Stress leads to widespread peripheral effects, including manifestations of respiratory distress. The combination of respiratory monitoring and non-invasive Peripheral Nerve Stimulation (PNS) represents a promising technological approach to quantify and reduce the physiological manifestations of stress. Such technologies require non-invasive, reliable respiratory information with adequate signal quality during various conditions. In this work, we computed an established respiratory quality index (RQI) on four respiratory signals derived from three common respiratory sensing modalities (respiratory effort (RSP), electrocardiogram-derived respiration (EDR), and Impedance Pneumography (IP)) in a complex stress protocol wherein 15 subjects underwent three stressors while receiving 1 of 3 active PNS modalities or sham. Our results indicate that the RQI of all signals changed substantially throughout the protocol with a maximal reduction from baseline of 21.28% (p< 0.001) during stressors involving speech. Further, RSP and IP resulted in the highest average quality and both were significantly higher in quality than the two EDR signals (p < 0.001). These results provide unique information that may inform the design of new technologies and studies leveraging respiratory markers for continuous stress detection and mitigation.