Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach.

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.

Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint.

The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.

Using Circadian Rhythm Patterns of Continuous Core Body Temperature to Improve Fertility and Pregnancy Planning.

OBJECTIVE: Review relationships among circadian clocks, core body temperature (CBT), and fertility in women. METHODS: Scoping literature review. RESULTS: Circadian clocks are a ubiquitous adaptation to the most predictable environmental events - the daily cycles of light and dark. Core body temperature (CBT) also follows a circadian rhythm. Additionally, CBT is tightly controlled by a combination of neuronal circuits that begin in the hypothalamus and involve many other portions of the brain as well as a wide range of peripheral mechanisms. In women with normal reproductive function, the diurnal temperature pattern for CBT is strongly influenced by the menstrual cycle of reproductive hormones, primarily estradiol and progesterone, which modulate the activity of hypothalamic neural circuits involved in body temperature control, resulting in an infradian CBT rhythm. CONCLUSIONS: Analysis of CBT via continuous recording reveals patterns in the interactions of circadian and infradian CBT rhythms capable of accurately predicting the fertility window and hormonal patterns suggesting oligo-ovulation and subfertility. New wearable technologies can facilitate employment of hormone-associated changes in CBT for pregnancy planning and offer clinical insight to infertility and menopause.

Circadian dysfunction in the Q175 model of Huntington's disease: Network analysis.

Disturbances in sleep/wake cycle are a common complaint of individuals with Huntington's disease (HD) and are displayed by HD mouse models. The underlying mechanisms, including the possible role of the circadian timing system, have been the topic of a number of recent studies. The (z)Q175 mouse is a knock-in model in which the human exon 1 sequence of the huntingtin gene is inserted into the mouse DNA with approximately 190 CAG repeats. Among the numerous models available, the heterozygous Q175 offers strong construct validity with a single copy of the mutation, genetic precision of the insertion and control of mutation copy number. In this review, we will summarize the evidence that this model exhibits disrupted diurnal and circadian rhythms in locomotor activity. We found overwhelming evidence for autonomic dysfunction including blunted daily rhythms in heart rate and core body temperature (CBT), reduced heart rate variability, and almost a complete failure of the sympathetic arm of the autonomic nervous system to function during the baroreceptor reflex. Mechanistically, the Q175 mouse model exhibits deficits in the neural output of the central circadian clock, the suprachiasmatic nucleus along with an enhancement of at least one type of potassium current in these neurons. Finally, we report a novel network analysis examining the phase coherence between activity, CBT, and cardiovascular measures. Such analyses found that even young Q175 mutants (heterozygous or homozygous) show coherence degradation, and suggests that loss of phase coherence is a variable that should be considered as a possible biomarker for HD.

Divergent Circadian Foraging Strategies in Response to Diurnal Predation Versus Persistent Rain in Asian Weaver Ant, Oecophylla smaragdina, Suggest Possible Energetic Trade-offs.

The study of chronobiology of foraging behavior in social insects offers valuable models for the investigation of circadian rhythms. We scored hourly nest entries and exits of Oecophylla smaragdina (Asian weaver ant) workers in 9 active non-polydomous nests on days with and without rain and with and without a primarily diurnal predator present. After determining that Oecophylla display a high nest fidelity, we focused exclusively on analyzing nest entry counts: we found a significant decrease in overall entry counts of individual ants on rainy days compared with non-rainy days (p < 0.0001). They usually maintain a typical diurnal pattern of foraging activity; however, that regularity was often distorted during rainy periods but appeared to quickly revert to typical patterns following rain. This lack of compensatory foraging activity following a period of rain supports the hypothesis that these ants have enough food reserves to withstand a pure masking-induced suppression of foraging activity. Predation through bird anting, too, decreased foraging activity but appeared to cause a reversal in foraging activity timing from diurnal to nocturnal foraging. Daily periodicity of foraging was significantly disrupted in most nests during rain; however, daily foraging periodicity was disrupted in only one nest due to presence of predators. Thus, rain and predation both exert significant impacts on the overall foraging activity of Asian weaver ants, but while persistent pressure from rain seemed to primarily cause masking (diminution) of circadian foraging activity, predation restricted to the daytime resulted in phase-inversion to nocturnal foraging activity, with little diminution. This is consistent with different energetic strategies being used in response to different pressures by this species.

Biometrics of complete human pregnancy recorded by wearable devices.

In the United States, normal-risk pregnancies are monitored with the recommended average of 14 prenatal visits. Check-ins every few weeks are the standard of care. This low time resolution and reliance on subjective feedback instead of direct physiological measurement, could be augmented by remote monitoring. To date, continuous physiological measurements have not been characterized across all of pregnancy, so there is little basis of comparison to support the development of the specific monitoring capabilities. Wearables have been shown to enable the detection and prediction of acute illness, often faster than subjective symptom reporting. Wearables have also been used for years to monitor chronic conditions, such as continuous glucose monitors. Here we perform a retrospective analysis on multimodal wearable device data (Oura Ring) generated across pregnancy within 120 individuals. These data reveal clear trajectories of pregnancy from cycling to conception through postpartum recovery. We assessed individuals in whom pregnancy did not progress past the first trimester, and found associated deviations, corroborating that continuous monitoring adds new information that could support decision-making even in the early stages of pregnancy. By contrast, we did not find significant deviations between full-term pregnancies of people younger than 35 and of people with "advanced maternal age", suggesting that analysis of continuous data within individuals can augment risk assessment beyond standard population comparisons. Our findings demonstrate that low-cost, high-resolution monitoring at all stages of pregnancy in real-world settings is feasible and that many studies into specific demographics, risks, etc., could be carried out using this newer technology.

Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness.

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.

Five million nights: temporal dynamics in human sleep phenotypes.

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.

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.

Assessing Adherence to Multi-Modal Oura Ring Wearables From COVID-19 Detection Among Healthcare Workers.

Background Identifying early signs of a SARS-CoV-2 infection in healthcare workers could be a critical tool in reducing disease transmission. To provide this information, both daily symptom surveys and wearable device monitoring could have utility, assuming there is a sufficiently high level of participant adherence. Purpose The aim of this study is to evaluate adherence to a daily symptom survey and a wearable device (Oura Ring) among healthcare professionals (attending physicians and other clinical staff) and trainees (residents and medical students) in a hospital setting during the early stages of the COVID-19 pandemic. Methods In this mixed-methods observational study, the data were a subset (N=91) of those collected as part of the larger TemPredict Study. Demographic data analyses were conducted with descriptive statistics. Participant adherence to the wearable device protocol was reported as the percentage of days that sleep was recorded, and adherence to the daily survey was reported as the percentage of days with submitted surveys. Comparisons for the primary (wearable and survey adherence of groups) and secondary (adherence patterns among subgroups) outcomes were conducted using descriptive statistics, two-tailed independent t-tests, and Welch's ANOVA with post hoc analysis using Games-Howell. Results Wearable device adherence was significantly higher than the daily symptom survey adherence for most participants. Overall, participants were highly adherent to the wearable device, wearing the device an average of 87.8 ± 11.6% of study nights compared to survey submission, showing an average of 63.8 ± 27.4% of study days. In subgroup analysis, we found that healthcare professionals (HCPs) and medical students had the highest adherence to wearing the wearable device, while medical residents had lower adherence in both wearable adherence and daily symptom survey adherence. Conclusions These results indicated high participant adherence to wearable devices to monitor for impending infection in the course of a research study conducted as part of clinical practice. Subgroup analysis indicated HCPs and medical students maintained high adherence, but residents' adherence was lower, which is likely multifactorial, with differences in work demands and stress contributing to the findings. These results can guide the development of adherence strategies for a wearable device to increase the quality of data collection and assist in disease detection in this and future pandemics.

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.

Feasibility of continuous distal body temperature for passive, early pregnancy detection.

Most American women become aware of pregnancy ~3-7 weeks after conceptive sex, and all must seek testing to confirm their pregnant status. The delay between conceptive sex and pregnancy awareness is often a time in which contraindicated behaviors take place. However, there is long standing evidence that passive, early pregnancy detection may be possible using body temperature. To address this possibility, we analyzed 30 individuals' continuous distal body temperature (DBT) in the 180 days surrounding self-reported conceptive sex in comparison to self-reported pregnancy confirmation. Features of DBT nightly maxima changed rapidly following conceptive sex, reaching uniquely elevated values after a median of 5.5 ± 3.5 days, whereas individuals reported a positive pregnancy test result at a median of 14.5 ± 4.2 days. Together, we were able to generate a retrospective, hypothetical alert a median of 9 ± 3.9 days prior to the date at which individuals received a positive pregnancy test. Continuous temperature-derived features can provide early, passive indication of pregnancy onset. We propose these features for testing and refinement in clinical settings, and for exploration in large, diverse cohorts. The development of pregnancy detection using DBT may reduce the delay from conception to awareness and increase the agency of pregnant individuals.

Female mice exhibit less overall variance, with a higher proportion of structured variance, than males at multiple timescales of continuous body temperature and locomotive activity records.

Despite recent work demonstrating that female rodents and humans do not show greater variance in behavior and physiology than males due to ovulatory cycles, many researchers still default to using males in their investigations. Although government funding agencies now require inclusion of female subjects where applicable, the erroneous belief that the study of males reduces overall data variance continues to result in male subject bias. Recently, we reported the first direct experimental refutation of this belief by examining continuous body temperature and locomotor activity in male and female mice. These findings revealed that males exceeded female variance within and across individuals over time, showing greater variance within a day than females do across an entire estrous cycle. However, the possibility remains that male variance within a day is impacted by ultradian rhythms, analogous to the influence of infradian estrous cycles on female variance, and both sexes show predictable, structured variance across the day. If structures underlying variance can be predicted, then the variance can be statistically accounted for, reducing experimental error and increasing precision of measurements. Here we assess these continuous body temperature and activity data for the contributions of structured and unstructured variance to overall variance within and across individuals at ultradian, circadian, and infradian timescales. In no instance do females exceed male variance, and in most instances male variance exceeds female variance. Additionally, more female variance is accounted for by temporal structure. In conclusion, even when estrous cycles are not controlled for, females show less variability than males, and this advantage can be further capitalized upon by inclusion of known temporal patterns to control for previously unknown but structured sources of variance.

Analysis of wearable time series data in endocrine and metabolic research.

Many hormones in the body oscillate with different frequencies and amplitudes, creating a dynamic environment that is essential to maintain health. In humans, disruptions to these rhythms are strongly associated with increased morbidity and mortality. While mathematical models can help us understand rhythm misalignment, translating this insight into personalised healthcare technologies requires solving additional challenges. Here, we discuss how combining minimally invasive, high-frequency biosampling technologies with wearable devices can assist the development of hormonal surrogates. We review bespoke algorithms that can help analyse multidimensional, noisy, time series data and identify wearable signals that could constitute clinical proxies of endocrine rhythms. These techniques can support the development of computational biomarkers to support the diagnosis and management of endocrine and metabolic conditions.

Modified Wavelet Analyses Permit Quantification of Dynamic Interactions Between Ultradian and Circadian Rhythms.

Circadian rhythms provide daily temporal structure to cellular and organismal biological processes, ranging from gene expression to cognition. Higher-frequency (intradaily) ultradian rhythms are similarly ubiquitous but have garnered far less empirical study, in part because of the properties that define them-multimodal periods, non-stationarity, circadian harmonics, and diurnal modulation-pose challenges to their accurate and precise quantification. Wavelet analyses are ideally suited to address these challenges, but wavelet-based measurement of ultradian rhythms has remained largely idiographic. Here, we describe novel analytical approaches, based on discrete and continuous wavelet transforms, which permit quantification of rhythmic power distribution across a broad ultradian spectrum, as well as precise identification of period within empirically determined ultradian bands. Moreover, the aggregation of normalized wavelet matrices allows group-level analyses of experimental treatments, thereby circumventing limitations of idiographic approaches. The accuracy and precision of these wavelet analyses were validated using in silico and in vivo models with known ultradian features. Experiments in male and female mice yielded robust and repeatable measures of ultradian period and power in home cage locomotor activity, confirming and extending reports of ultradian rhythm modulation by sex, gonadal hormones, and circadian entrainment. Seasonal changes in day length modulated ultradian period and power, and exerted opposite effects in the light and dark phases of the 24 h day, underscoring the importance of evaluating ultradian rhythms with attention to circadian phase. Sex differences in ultradian rhythms were more prominent at night and depended on gonadal hormones in male mice. Thus, relatively straightforward modifications to the wavelet procedure allowed quantification of ultradian rhythms with appropriate time-frequency resolution, generating accurate, and repeatable measures of period and power which are suitable for group-level analyses. These analytical tools may afford deeper understanding of how ultradian rhythms are generated and respond to interoceptive and exteroceptive cues.

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.

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.

Lower variability in female students than male students at multiple timescales supports the use of sex as a biological variable in human studies.

BACKGROUND: Men have been, and still are, included in more studies than women, in large part because of the lingering belief that ovulatory cycles result in women showing too much variability to be economically viable subjects. This belief has scientific and social consequences, and yet, it remains largely untested. Recent work in rodents has shown either that there is no appreciable difference in overall variability across a wealth of traits, or that in fact males may show more variability than females. METHODS: We analyzed learning management system logins associated to gender records spanning 2 years from 13,777 students at Northeastern Illinois University. These data were used to assess variability in daily rhythms in a heterogeneous human population. RESULTS: At the population level, men are more likely than women to show extreme chronotypes (very early or very late phases of activity). Men were also found to be more variable than women across and within individuals. Variance correlated negatively with academic performance, which also showed a gender difference. Whereas a complaint against using female subjects is that their variance is the driver of statistical sex differences, only 6% of the gender performance difference is potentially accounted for by variance, suggesting that variability is not the driver of sex differences here. CONCLUSIONS: Our findings do not support the idea that women are more behaviorally variable than men and may support the opposite. Our findings support including sex as a biological variable and do not support variance-based arguments for the exclusion of women as research subjects.

Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth.

Child sleep disorders are increasingly prevalent and understanding early predictors of sleep problems, starting in utero, may meaningfully guide future prevention efforts. Here, we investigated whether prenatal exposure to maternal psychological stress is associated with increased sleep problems in toddlers. We also examined whether fetal brain connectivity has direct or indirect influence on this putative association. Pregnant women underwent fetal resting-state functional connectivity MRI and completed questionnaires on stress, worry, and negative affect. At 3-year follow-up, 64 mothers reported on child sleep problems, and in the subset that have reached 5-year follow-up, actigraphy data (N = 25) has also been obtained. We observe that higher maternal prenatal stress is associated with increased toddler sleep concerns, with actigraphy sleep metrics, and with decreased fetal cerebellar-insular connectivity. Specific mediating effects were not identified for the fetal brain regions examined. The search for underlying mechanisms of the link between maternal prenatal stress and child sleep problems should be continued and extended to other brain areas.

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.