Feasibility and acceptability of a Whole-Body hyperthermia (WBH) protocol.

BACKGROUND: Whole-body hyperthermia (WBH) has shown promise as a non-pharmacologic treatment for major depressive disorder (MDD) in prior trials that used a medical (infrared) hyperthermia device. Further evaluation of WBH as a treatment for MDD has, however, been stymied by regulatory challenges. OBJECTIVE: We examined whether a commercially available infrared sauna device without FDA-imposed limitations could produce the degree of core body temperature (101.3 °F) associated with reduced depressive symptoms in prior WBH studies. We also assessed the frequency of adverse events and the amount of time needed to achieve this core body temperature. We explored changes (pre-post WBH) in self-reported mood and affect. METHODS: Twenty-five healthy adults completed a single WBH session lasting up to 110 min in a commercially available sauna dome (Curve Sauna Dome). We assessed core body temperature rectally during WBH, and mood and affect at timepoints before and after WBH. RESULTS: All participants achieved the target core body temperature (101.3 °F). On average, it took participants 82.12 min (SD = 11.3) to achieve this temperature (range: 61-110 min), and WBH ended after a participant maintained 101.3 °F for two consecutive minutes. In exploratory analyses of changes in mood and affect, we found that participants evidenced reductions (t[24] = 2.03, M diff = 1.00, p=.054, 95% CI [-2.02,0.02]) in self-reported depression symptoms from 1 week pre- to 1 week post-WBH, and reductions (t[24]= -2.93, M diff= -1.72, p=.007, 95% CI [-2.93, -0.51]) in self-reported negative affect pre-post-WBH session. CONCLUSION: This novel WBH protocol holds promise in further assessing the utility of WBH in MDD treatment. TRIAL REGISTRATION: This trial was registered at clinicaltrivals.gov (NCT04249700).

Metrics from Wearable Devices as Candidate Predictors of Antibody Response Following Vaccination against COVID-19: Data from the Second TemPredict Study.

There is significant variability in neutralizing antibody responses (which correlate with immune protection) after COVID-19 vaccination, but only limited information is available about predictors of these responses. We investigated whether device-generated summaries of physiological metrics collected by a wearable device correlated with post-vaccination levels of antibodies to the SARS-CoV-2 receptor-binding domain (RBD), the target of neutralizing antibodies generated by existing COVID-19 vaccines. One thousand, one hundred and seventy-nine participants wore an off-the-shelf wearable device (Oura Ring), reported dates of COVID-19 vaccinations, and completed testing for antibodies to the SARS-CoV-2 RBD during the U.S. COVID-19 vaccination rollout. We found that on the night immediately following the second mRNA injection (Moderna-NIAID and Pfizer-BioNTech) increases in dermal temperature deviation and resting heart rate, and decreases in heart rate variability (a measure of sympathetic nervous system activation) and deep sleep were each statistically significantly correlated with greater RBD antibody responses. These associations were stronger in models using metrics adjusted for the pre-vaccination baseline period. Greater temperature deviation emerged as the strongest independent predictor of greater RBD antibody responses in multivariable models. In contrast to data on certain other vaccines, we did not find clear associations between increased sleep surrounding vaccination and antibody responses.

Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.