Feasibility and acceptability of an integrated mind-body intervention for depression: whole-body hyperthermia (WBH) and cognitive behavioral therapy (CBT).

OBJECTIVE: To examine the feasibility of an integrated mind-body MDD treatment combining cognitive behavioral therapy (CBT) and whole-body hyperthermia (WBH). METHODS: In this single-arm trial, 16 adults with MDD initially received 8 weekly CBT sessions and 8 weekly WBH sessions. Outcomes included WBH sessions completed (primary), self-report depression assessments completed (secondary), and pre-post intervention changes in depression symptoms (secondary). We also explored changes in mood and cognitive processes and assessed changes in mood as predictors of overall treatment response. RESULTS: Thirteen participants (81.3%) completed ≥ 4 WBH sessions (primary outcome); midway through the trial, we reduced from 8 weekly to 4 bi-weekly WBH sessions to increase feasibility. The n = 12 participants who attended the final assessment visit completed 100% of administered self-report depression assessments; all enrolled participants (n = 16) completed 89% of these assessments. Among the n = 12 who attended the final assessment visit, the average pre-post-intervention BDI-II reduction was 15.8 points (95% CI: -22.0, -9.70), p = 0.0001, with 11 no longer meeting MDD criteria (secondary outcomes). Pre-post intervention improvements in negative automatic thinking, but not cognitive flexibility, achieved statistical significance. Improved mood from pre-post the initial WBH session predicted pre-post treatment BDI-II change (36.2%; rho = 0.60, p = 0.038); mood changes pre-post the first CBT session did not. LIMITATIONS: Small sample size and single-arm design limit generalizability. CONCLUSION: An integrated mind-body intervention comprising weekly CBT sessions and bi-weekly WBH sessions was feasible. Results warrant future larger controlled clinical trials.Clinivaltrials.gov Registration: NCT05708976.

Elevated body temperature is associated with depressive symptoms: results from the TemPredict Study.

Correlations between altered body temperature and depression have been reported in small samples; greater confidence in these associations would provide a rationale for further examining potential mechanisms of depression related to body temperature regulation. We sought to test the hypotheses that greater depression symptom severity is associated with (1) higher body temperature, (2) smaller differences between body temperature when awake versus asleep, and (3) lower diurnal body temperature amplitude. Data collected included both self-reported body temperature (using standard thermometers), wearable sensor-assessed distal body temperature (using an off-the-shelf wearable sensor that collected minute-level physiological data), and self-reported depressive symptoms from > 20,000 participants over the course of ~ 7 months as part of the TemPredict Study. Higher self-reported and wearable sensor-assessed body temperatures when awake were associated with greater depression symptom severity. Lower diurnal body temperature amplitude, computed using wearable sensor-assessed distal body temperature data, tended to be associated with greater depression symptom severity, though this association did not achieve statistical significance. These findings, drawn from a large sample, replicate and expand upon prior data pointing to body temperature alterations as potentially relevant factors in depression etiology and may hold implications for development of novel approaches to the treatment of major depressive disorder.

Variability of temperature measurements recorded by a wearable device by biological sex.

BACKGROUND: Females have been historically excluded from biomedical research due in part to the documented presumption that results with male subjects will generalize effectively to females. This has been justified in part by the assumption that ovarian rhythms will increase the overall variance of pooled random samples. But not all variance in samples is random. Human biometrics are continuously changing in response to stimuli and biological rhythms; single measurements taken sporadically do not easily support exploration of variance across time scales. Recently we reported that in mice, core body temperature measured longitudinally shows higher variance in males than cycling females, both within and across individuals at multiple time scales. METHODS: Here, we explore longitudinal human distal body temperature, measured by a wearable sensor device (Oura Ring), for 6 months in females and males ranging in age from 20 to 79 years. In this study, we did not limit the comparisons to female versus male, but instead we developed a method for categorizing individuals as cyclic or acyclic depending on the presence of a roughly monthly pattern to their nightly temperature. We then compared structure and variance across time scales using multiple standard instruments. RESULTS: Sex differences exist as expected, but across multiple statistical comparisons and timescales, there was no one group that consistently exceeded the others in variance. When variability was assessed across time, females, whether or not their temperature contained monthly cycles, did not significantly differ from males both on daily and monthly time scales. CONCLUSIONS: These findings contradict the viewpoint that human females are too variable across menstrual cycles to include in biomedical research. Longitudinal temperature of females does not accumulate greater measurement error over time than do males and the majority of unexplained variance is within sex category, not between them.

Women are still excluded from research disproportionately, due in part to documented concerns that menstrual cycles make them more variable and so harder to study. In the past, we have challenged this claim, finding it does not hold for animal physiology, animal behavior, or human behavior. Here we are able to show that it does not hold in human physiology either. We analyzed 6 months of continuously collected temperature data measured by a commercial wearable device, in order to determine if it is true that females are more variable or less predictable than males. We found that temperatures mostly vary as a function of time of day and whether the individual was awake or asleep. Additionally, for some females, nightly maximum temperature contained a cyclical pattern with a period of around 28 days, consistent with menstrual cycles. The variability was different between cycling females, not cycling females, and males, but only cycling female temperature contained a monthly structure, making their changes more predictable than those of non-cycling females and males. We found the majority of unexplained variance to be within each sex/cycling category, not between them. All groups had indistinguishable measurement errors across time. This analysis of temperature suggests data-driven characteristics might be more helpful distinguishing individuals than historical categories such as binary sex. The work also supports the inclusion of females as subjects within biological research, as this inclusion does not weaken statistical comparisons, but does allow more equitable coverage of research results in the world.

Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles.

Background: Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks. Objective: Use data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex. Methods: We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model. Results: Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females. Conclusion: Web-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.

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.

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.

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).

Confirmation of interpersonal expectations is intrinsically rewarding.

People want to interact successfully with other individuals, and they invest significant efforts in attempting to do so. Decades of research have demonstrated that to simplify the dauntingly complex task of interpersonal communication, perceivers predict the responses of individuals in their environment using stereotypes and other sources of prior knowledge. Here, we show that these top-down expectations can also shape the subjective value of expectation-consistent and expectation-violating targets. Specifically, in two neuroimaging experiments (n = 58), we observed increased activation in brain regions associated with reward processing-including the nucleus accumbens-when perceivers observed information consistent with their social expectations. In two additional behavioral experiments (n = 704), we observed that perceivers were willing to forgo money to encounter an expectation-consistent target and avoid an expectation-violating target. Together, these findings suggest that perceivers value having their social expectations confirmed, much like food or monetary rewards.

Feasibility of continuous fever monitoring using wearable devices.

Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.