OBJECTIVE:Despite effective antiretroviral therapy (ART), people with HIV (PWH) continue to experience elevated morbidity, potentially driven by persistent immune dysregulation. This study aimed to define transcriptomic heterogeneity in peripheral CD4+ T cells and identify immune signatures associated with clinical stratification in ART-treated PWH. DESIGN:We analyzed transcriptomic and immunologic (plasma cytokine) data from a well-characterized cohort of ART-suppressed PWH to identify biologically meaningful subgroups. METHODS:Bulk RNA sequencing was performed on peripheral CD4+ T cells from 154 ART-treated PWH. Plasma levels of immune mediators were also quantified. We applied Pairwise Controlled Manifold Approximation (PaCMAP), a novel dimensionality reduction technique, to identify transcriptomic clusters and assessed their associations with clinical and immunological parameters, including CD4:CD8 ratio and cell-associated HIV DNA/RNA. RESULTS:PaCMAP identified three distinct transcriptomic clusters among PWH. These clusters were enriched for differential expression of genes regulated by NF-κB, suggesting a role for chronic immune activation and inflammation. While clustering was not associated with HIV reservoir size, there was a modest association with CD4:CD8 ratio, a key marker of immune recovery. Additionally, plasma levels of IL-1β, TNF-α, and G-CSF differed across clusters, supporting a link between plasma cytokines and CD4+ T cell transcriptomic diversity. CONCLUSIONS:Our findings define transcriptomic subgroups in ART-treated PWH that are characterized by NF-κB-driven gene expression and distinct inflammatory cytokine profiles. These immune signatures may serve as biomarkers for immunological stratification and provide insight into persistent immune dysfunction despite viral suppression.

ABSTRACT Background The HIV reservoir is established within days of infection and persists despite antiretroviral therapy (ART). However, data describing early reservoir decay dynamics and the host immune responses associated with this process remain limited. Methods We analyzed more than 500 longitudinal blood samples from 67 individuals treated during acute HIV infection. Plasma cytokines and HIV reservoir size (intact and defective DNA) were quantified. Associations between immune markers and reservoir decay following ART initiation were assessed using unsupervised clustering, mixed-effects linear spline models, and nonlinear modeling. Results Higher levels of IFN-γ, IL-10, IL-18, and TNF-α during weeks 24-52 of ART were associated with significantly faster decay of both intact and defective HIV DNA. These relationships were independent of ART initiation timing (days since infection), baseline viremia, initial CD4+ T cell count, and longitudinal CD4:CD8 ratio. Among these cytokines, IL-10 demonstrated the strongest association with accelerated reservoir decay, despite prior evidence linking it to larger reservoirs in SIV models. Discussion These findings highlight the pleiotropic and stage-dependent roles of cytokines across acute to later stages of HIV, suggesting that a coordinated balance between immune activation and regulation of inflammation may promote early HIV reservoir decay. Summary In people treated during acute HIV, coordinated immune signals linked to antiviral defense and inflammation predicted faster HIV reservoir decay.

BackgroundThe recent emergence of wearable devices has made feasible the passive gathering of intensive, longitudinal data from large groups of individuals. This form of data is effective at capturing physiological changes between participants (interindividual variability) and changes within participants over time (intraindividual variability). The emergence of longitudinal datasets provides an opportunity to quantify the contribution of such longitudinal data to the control of these sources of variability for applications such as responder analysis, where traditional, sparser sampling methods may hinder the categorization of individuals into these phenotypes. ObjectiveThis study aimed to quantify the gains made in statistical power and effect size among statistical comparisons when controlling for interindividual variability and intraindividual variability compared with controlling for neither. MethodsHere, we test the gains in statistical power from controlling for interindividual and intraindividual variability of resting heart rate, collected in 2020 for over 40,000 individuals as part of the TemPredict study on COVID-19 detection. We compared heart rate on weekends with that on weekdays because weekends predictably change the behavior of most individuals, though not all, and in different ways. Weekends also repeat consistently, making their effects on heart rate feasible to assess with confidence over large populations. We therefore used weekends as a model system to test the impact of different statistical controls on detecting a recurring event with a clear ground truth. We randomly and iteratively sampled heart rate from weekday and weekend nights, controlling for interindividual variability, intraindividual variability, both, or neither. ResultsBetween-participant variability appeared to be a greater source of structured variability than within-participant fluctuations. Accounting for interindividual variability through within-individual sampling required 40× fewer pairs of samples to achieve statistical significance with 4× to 5× greater effect size at significance. Within-individual sampling revealed differential effects of weekends on heart rate, which were obscured by aggregated sampling methods. ConclusionsThis work highlights the leverage provided by longitudinal, within-individual sampling to increase statistical power among populations with heterogeneous effects.

Despite antiretroviral therapy (ART), people with HIV (PWH) on ART experience higher rates of morbidity and mortality vs. age-matched HIV negative controls, which may be driven by chronic inflammation due to persistent virus. We performed bulk RNA sequencing (RNA-seq) on peripheral CD4+ T cells, as well as quantified plasma immune marker levels from 154 PWH on ART to identify host immune signatures associated with immune recovery (CD4:CD8) and HIV persistence (cell-associated HIV DNA and RNA). Using a novel dimension reduction tool - Pairwise Controlled Manifold Approximation (PaCMAP), we defined three distinct participant transcriptomic clusters. We found that these three clusters were largely defined by differential expression of genes regulated by the transcription factor NF-κB. While clustering was not associated with HIV reservoir size, we observed an association with CD4:CD8 ratio, a marker of immune recovery and prognostic factor for mortality in PWH on ART. Furthermore, distinct patterns of plasma IL-1β, TNF-α and GCSF were also strongly associated with the clusters, suggesting that these immune markers play a key role in CD4+ T cell transcriptomic diversity and immune recovery in PWH on ART. These findings reveal novel subgroups of PWH on ART with distinct immunological characteristics, and define a transcriptional signature associated with clinically significant immune parameters for PWH. A deeper understanding of these subgroups could advance clinical strategies to treat HIV-associated immune dysfunction.

BackgroundA substantially lower proportion of female individuals participate in sufficient daily activity compared to male individuals despite the known health benefits of exercise. Investment in female sports and exercise medicine research may help close this gap; however, female individuals are underrepresented in this research. Hesitancy to include female participants is partly due to assumptions that biological rhythms driven by menstrual cycles and occurring on the timescale of approximately 28 days increase intraindividual biological variability and weaken statistical power. An analysis of continuous skin temperature data measured using a commercial wearable device found that temperature cycles indicative of menstrual cycles did not substantially increase variability in female individuals’ skin temperature. In this study, we explore physical activity (PA) data as a variable more related to behavior, whereas temperature is more reflective of physiological changes. ObjectiveWe aimed to determine whether intraindividual variability of PA is affected by biological sex, and if so, whether having menstrual cycles (as indicated by temperature rhythms) contributes to increased female intraindividual PA variability. We then sought to compare the effect of sex and menstrual cycles on PA variability to the effect of PA rhythms on the timescales of days and weeks and to the effect of nonrhythmic temporal structure in PA on the timescale of decades of life (age). MethodsWe used minute-level metabolic equivalent of task data collected using a wearable device across a 206-day study period for each of 596 individuals as an index of PA to assess the magnitudes of variability in PA accounted for by biological sex and temporal structure on different timescales. Intraindividual variability in PA was represented by the consecutive disparity index. ResultsFemale individuals (regardless of whether they had menstrual cycles) demonstrated lower intraindividual variability in PA than male individuals (Kruskal-Wallis H=29.51; P<.001). Furthermore, individuals with menstrual cycles did not have greater intraindividual variability than those without menstrual cycles (Kruskal-Wallis H=0.54; P=.46). PA rhythms differed at the weekly timescale: individuals with increased or decreased PA on weekends had larger intraindividual variability (Kruskal-Wallis H=10.13; P=.001). In addition, intraindividual variability differed by decade of life, with older age groups tending to have less variability in PA (Kruskal-Wallis H=40.55; P<.001; Bonferroni-corrected significance threshold for 15 comparisons: P=.003). A generalized additive model predicting the consecutive disparity index of 24-hour metabolic equivalent of task sums (intraindividual variability of PA) showed that sex, age, and weekly rhythm accounted for only 11% of the population variability in intraindividual PA variability. ConclusionsThe exclusion of people from PA research based on their biological sex, age, the presence of menstrual cycles, or the presence of weekly rhythms in PA is not supported by our analysis.

Objectives Major depressive disorder (MDD) is a critical health issue that is inadequately addressed with currently available treatments, indicating a need for novel treatment approaches. This case report describes a patient’s outcomes after completing an integrated mind-body depression intervention that combined cognitive-behavioral therapy (CBT) administered by a clinician with whole-body hyperthermia (WBH) administered using an infrared sauna device. Methods The patient, a 37-year-old adult male who identified as agender and Asian, completed eligibility screening and a baseline assessment that included a semi-structured clinical interview and self-report measures of mood and emotional health. The intervention included eight weekly 1-hour CBT sessions and eight weekly WBH sessions. Before each CBT session, the patient completed the Beck Depression Inventory-II (BDI-II). After the patient completed all intervention sessions, we re-assessed the patient’s mood and cognitive and emotional health and collected verbal feedback about their treatment experience. Results At baseline, the patient met the criteria for diagnosis of MDD per the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and they were not receiving any form of treatment. From initial contact to the final assessment (14 weeks), the patient evidenced a 25-point reduction on the BDI-II from a score of 28 to 3, reflecting a clinically meaningful decrease from severe depressive symptoms to remission. At the final assessment, the patient no longer met the criteria for MDD. Conclusions For this patient, this relatively brief integrative depression treatment was both feasible and efficacious. The patient achieved depression remission in fewer than 14 weeks. These results warrant greater exploration into the potential benefits of this integrative depression treatment.

Severe SARS-CoV-2 infection has been partially controlled by vaccination, though issues in duration and breadth of protection remain. Few studies investigate factors driving diversity in the cellular immune response to vaccination. Here, we evaluated T-cell immunity in 60 healthy adults and its relationship to vaccine and subject-specific variables.CD4 and CD8 T-cells from COVID-19 vaccinated subjects expressed spike-specific activation-induced markers (AIM+) and cytokines. Overall, AIM+ CD8 T-cells correlated best with neutralizing antibodies and were more strongly expressed by females than males. Unique patterns in CD4 T-regulatory cells, cytokine profiles, and central and effector memory subsets were observed between Moderna, Pfizer, and Janssen vaccinees and by subject-specific factors. Sex differences outweighed differences in BMI and age.Thus, COVID-19 vaccine type and subject sex impacted the magnitude and quality of the spike-specific T-cell response. These results help explain differences in durability and memory between mRNA and adenovirus vector based COVID-19 vaccines and suggest targets for improved vaccine design.

ObjectivesThe main objectives of the current paper were to examine the feasibility, acceptability, and adherence of a remotely delivered intervention consisting of mindfulness-based stress reduction plus prenatal sleep classes (MBSR+PS) compared with treatment as usual (TAU).MethodIn this pilot randomized controlled trial, 52 pregnant women with poor sleep quality were randomized to MBSR+PS or TAU. MBSR was delivered through eight weekly 2.5-hour sessions, and PS was delivered through eight weekly 30-minute sessions. PS content drew material from cognitive behavioral therapy for insomnia tailored for the perinatal period and from a mindfulness- and acceptance-based lens. Participants completed endpoint measures 10-12 weeks after randomization.ResultsWe surpassed all acceptability targets, including the percentage of eligible participants willing to be randomized (96%), percentage of participants who initiated treatment (88%), and satisfaction scores (Client Satisfaction Questionnaire-8 score M = 28.04, SD = 3.6). We surpassed all feasibility targets, including our enrollment target, retention rate (92%), and measure completion (96%). Finally, we surpassed adherence targets, including MBSR and PS session attendance (>= 80%). Though sleep outcomes were exploratory, increases in sleep efficiency were greater in the MBSR+PS group relative to TAU (SMD=.68).ConclusionsPatient-reported poor sleep quality during pregnancy has high public health significance because it is common, consequential, and under-treated. The current feasibility and acceptability data for using remotely delivered MBSR and PS to improve prenatal sleep quality are encouraging and warranting future research that is sufficiently powered and designed to provide efficacy data. In addition, exploratory sleep outcomes offer preliminary evidence that this sleep program may improve sleep efficiency during pregnancy.

There is a substantial body of clinical evidence supporting the beneficial effects of lower-carbohydrate dietary patterns on multiple established risk factors associated with insulin resistance and cardiovascular diseases in adult populations. Nutrition and health researchers, clinical practitioners, and stakeholders gathered for, “The Scientific Forum on Nutrition, Wellness, and Lower-Carbohydrate Diets: An Evidence- and Equity-Based Approach to Dietary Guidance” to discuss the evidence base around lower-carbohydrate diets, health outcomes, and dietary guidance. Consensus statements were agreed upon to identify current areas of scientific agreement and spotlight gaps in research, education, and practice to help define and prioritize future pathways. Given the evidence base and considering that most American adults are living with at least one nutrition-related chronic disease, there was consensus that including a lower-carbohydrate dietary pattern as one part of the Dietary Guidelines for Americans could help promote health equity among the general population.

BACKGROUND:A key research priority for developing an HIV cure strategy is to define the viral dynamics and biomarkers associated with sustained post-treatment control. The ability to predict the likelihood of sustained post-treatment control or non-control could minimize the time off antiretroviral therapy (ART) for those destined to not control and anticipate longer periods off ART for those destined to control. METHODS:Mathematical modeling and machine learning were used to characterize virologic predictors of long-term virologic control using viral kinetics data from several studies in which participants interrupted ART. Predictors of post-ART outcomes were characterized using data accumulated from the time of treatment interruption, replicating real-time data collection in a clinical study, and classifying outcomes as either post-treatment control (plasma viremia ≤400 copies/mL at 2 of 3 time points for ≥24 weeks) or non-control. RESULTS:Potential predictors of virologic control were the time to rebound, the rate of initial rebound, and the peak plasma viremia. We found that people destined to be non-controllers could be identified within 3 weeks of rebound (prediction scores: accuracy, 80%; sensitivity, 82%; specificity, 71%). CONCLUSIONS:Given the widespread use of analytic treatment interruption in cure-related trials, these predictors may be useful to increase the safety of analytic treatment interruption through the early identification of people who are unlikely to become post-treatment controllers.

Background:Prior data suggests the Mindfulness-Based Interventions: (MBI) Teaching Assessment Criteria (MBI:TAC) has good inter-rater reliability, but many raters knew teacher experience level. Objective:We sought to further evaluate the MBI-TAC's inter-rater reliability and obtain preliminary data on predictive validity. Methods:We videorecorded 21 MBSR teachers from academic and community settings. We trained 19 experienced MBI teachers in using the MBI:TAC. MBSR teachers were rated by three assessors; teachers and their assessors did not know one another. To assess predictive validity, MBSR students in courses taught by 18 of the MBSR teachers were invited to complete PROMIS-29 measures before the MBSR course, at the end of the course (month 2), and month 4. Results:Intraclass correlation coefficients (ICCs) representing a single rater ranged from 0.33 to 0.56 on the 6 MBI:TAC domains. Using an average of two raters, ICC estimates ranged from 0.48 to 0.71 and ICCs generalizing to an average of three raters ranged from 0.6 to 0.8. Among n = 152 participating MBSR students, we found improvements from baseline to 2 months and 4 months in PROMIS measures of Anxiety, Depression, Fatigue, Sleep, and Social Role function (range in improvement 2.3 to 6.3, P < 0.0001 for all comparisons except Social Role at 2 months, P = 0.007). Higher MBI:TAC ratings were associated with greater improvements in anxiety among MBSR students from baseline to 2 months, with a -0.31 lower participant anxiety score per 1 unit increase in MBI:TAC composite teaching rating (95% CI -0.58, -0.05, P = 0.019), but we did not find statistically significant relationships with improvements in other PROMIS-29 domains. Conclusions:ICCs indicated good reliability using an average of three ratings, but inter-rater reliability was only fair using a single rater. We found initial validation that higher MBI:TAC ratings predicted greater improvements in anxiety symptoms in MBSR participants.

BACKGROUND:Concern about side effects is a common reason for SARS-CoV-2 vaccine hesitancy. OBJECTIVE:To determine whether short-term side effects of SARS-CoV-2 messenger RNA (mRNA) vaccination are associated with subsequent neutralizing antibody (nAB) response. DESIGN:Prospective cohort study. SETTING:San Francisco Bay Area. PARTICIPANTS:Adults who had not been vaccinated against or exposed to SARS-CoV-2, who then received 2 doses of either BNT162b2 or mRNA-1273. MEASUREMENTS:Serum nAB titer at 1 month and 6 months after the second vaccine dose. Daily symptom surveys and objective biometric measurements at each dose. RESULTS:363 participants were included in symptom-related analyses (65.6% female; mean age, 52.4 years [SD, 11.9]), and 147 were included in biometric-related analyses (66.0% female; mean age, 58.8 years [SD, 5.3]). Chills, tiredness, feeling unwell, and headache after the second dose were each associated with 1.4 to 1.6 fold higher nAB at 1 and 6 months after vaccination. Symptom count and vaccination-induced change in skin temperature and heart rate were all positively associated with nAB across both follow-up time points. Each 1 °C increase in skin temperature after dose 2 was associated with 1.8 fold higher nAB 1 month later and 3.1 fold higher nAB 6 months later. LIMITATIONS:The study was conducted in 2021 in people receiving the primary vaccine series, making generalizability to people with prior SARS-CoV-2 vaccination or exposure unclear. Whether the observed associations would also apply for neutralizing activity against non-ancestral SARS-CoV-2 strains is also unknown. CONCLUSION:Convergent self-report and objective biometric findings indicate that short-term systemic side effects of SARS-CoV-2 mRNA vaccination are associated with greater long-lasting nAB responses. This may be relevant in addressing negative attitudes toward vaccine side effects, which are a barrier to vaccine uptake. PRIMARY FUNDING SOURCE:National Institute on Aging.

Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants’ wearable device data and participants’ responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants’ fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

Background A key research priority for developing a human immunodeficiency virus (HIV) cure strategy is to define the viral dynamics and biomarkers associated with sustained posttreatment control. The ability to predict the likelihood of sustained posttreatment control or noncontrol could minimize the time off antiretroviral therapy (ART) for those destined to be controllers and anticipate longer periods off ART for those destined to be controllers.Methods Mathematical modeling and machine learning were used to characterize virologic predictors of long-term virologic control, using viral kinetics data from several studies in which participants interrupted ART. Predictors of post-ART outcomes were characterized using data accumulated from the time of treatment interruption, replicating real-time data collection in a clinical study, and classifying outcomes as either posttreatment control (plasma viremia, <= 400 copies/mL at 2 of 3 time points for >= 24 weeks) or noncontrol.Results Potential predictors of virologic control were the time to rebound, the rate of initial rebound, and the peak plasma viremia. We found that people destined to be noncontrollers could be identified within 3 weeks of rebound (prediction scores: accuracy, 80%; sensitivity, 82%; specificity, 71%).Conclusions Given the widespread use of analytic treatment interruption in cure-related trials, these predictors may be useful to increase the safety of analytic treatment interruption through early identification of people who are unlikely to become posttreatment controllers.

Large-scale wearable datasets are increasingly being used for biomedical research and to develop machine learning (ML) models for longitudinal health monitoring applications. However, it is largely unknown whether biases in these datasets lead to findings that do not generalize. Here, we present the first comparison of the data underlying multiple longitudinal, wearable-device-based datasets. We examine participant-level resting heart rate (HR) from four studies, each with thousands of wearable device users. We demonstrate that multiple regression, a community standard statistical approach, leads to conflicting conclusions about important demographic variables (age vs resting HR) and significant intra- and inter-dataset differences in HR. We then directly test the cross-dataset generalizability of a commonly used ML model trained for three existing day-level monitoring tasks: prediction of testing positive for a respiratory virus, flu symptoms, and fever symptoms. Regardless of task, most models showed relative performance loss on external datasets; most of this performance change can be attributed to concept shift between datasets. These findings suggest that research using large-scale, pre-existing wearable datasets might face bias and generalizability challenges similar to research in more established biomedical and ML disciplines. We hope that the findings from this study will encourage discussion in the wearable-ML community around standards that anticipate and account for challenges in dataset bias and model generalizability.

Background: There is a pressing need for effective treatments for major depressive disorder (MDD).ObjectiveTo examine the feasibility of an integrated mind-body MDD treatment combining cognitive behavioral therapy (CBT) and whole-body hyperthermia (WBH).MethodsIn 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.ResultsThirteen 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.LimitationsSmall sample size and single-arm design limit generalizability.ConclusionAn 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: NCT05708976ConclusionAn 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

Sleep monitoring has become widespread with the rise of affordable wearable devices. However, converting sleep data into actionable change remains challenging as diverse factors can cause combinations of sleep parameters to differ both between people and within people over time. Researchers have attempted to combine sleep parameters to improve detecting similarities between nights of sleep. The cluster of similar combinations of sleep parameters from a night of sleep defines that night’s sleep phenotype. To date, quantitative models of sleep phenotype made from data collected from large populations have used cross-sectional data, which preclude longitudinal analyses that could better quantify differences within individuals over time. In analyses reported here, we used five million nights of wearable sleep data to test (a) whether an individual’s sleep phenotype changes over time and (b) whether these changes elucidate new information about acute periods of illness (e.g., flu, fever, COVID-19). We found evidence for 13 sleep phenotypes associated with sleep quality and that individuals transition between these phenotypes over time. Patterns of transitions significantly differ (i) between individuals (with vs. without a chronic health condition; chi-square test; p-value < 1e−100) and (ii) within individuals over time (before vs. during an acute condition; Chi-Square test; p-value < 1e−100). Finally, we found that the patterns of transitions carried more information about chronic and acute health conditions than did phenotype membership alone (longitudinal analyses yielded 2–10× as much information as cross-sectional analyses). These results support the use of temporal dynamics in the future development of longitudinal sleep analyses.

Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term “manifestations,” as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.

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.

Abstract Introduction Vaccines remain the primary mitigating strategy to reduce the burden of disease caused by COVID-19. As such, there is a pressing need to identify factors that promote more robust and durable immune responses to vaccination. Sleep and circadian processes, such as the timing of vaccine administration, have been hypothesized to play a meaningful role in predicting durability of antibody responses; however, empirical data supporting links between sleep and timing on COVID-19 vaccine response is limited. Methods We recruited 428 adults (aged 18-88 years old) naive to the COVID-19 vaccination series and SARS-CoV-2 infection who received the mRNA COVID-19 vaccine series (% Pfizer; % Moderna) and underwent blood draws to quantify neutralizing antibody responses (nAB) 1 and 6 months post vaccination series. They completed sleep questionnaires (Pittsburgh Sleep Quality Index) and a week of sleep diaries at three time points. In addition, 198 participants wore a wearable device (Oura Ring) for 2 months to capture behavioral sleep metrics. Time of day of vaccine was obtained by self-report as part of the daily diaries. Results Analyses revealed that independent of age, sex, BMI, smoking status, and vaccine type, poorer global sleep quality was associated with lower nABs 6-months post vaccination (F(1, 424.3)=5.30, p=0.02). We also did detected a trend-level 3-way interaction between vaccine type, time point, and OURA based sleep duration indicating that shorter sleep duration was associated with lower 6-month nAB in those who received the Pfizer vaccine (b=0.17, SE=0.07, p=0.009). In analyses examining the impact of time of day of vaccine administration on nABs, we failed to find any evidence that timing of vaccine administration was associated with nABs at 1 or 6 months post vaccination (Dose 1: F(1, 330.1)=0.01, p=0.91; Dose 2: F(1, 344.5)=0.46, p=0.50). Conclusion Findings suggest that better global sleep quality is associated with greater nAB durability to the COVID-19 vaccine, and among those who received the Pfizer vaccine, longer average sleep duration promoted higher nAB 6-months post-vaccination. However, there was no clear evidence indicating that timing of vaccination administration was relevant to nAB responses. Further research is warranted including investigations into whether sleep interventions can enhance vaccine efficacy. Support (if any) R24AG048024