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Varner, K. J., Bruce, L. K., Soltani, S., Hartogensis, W., Dilchert, S., Hecht, F. M., Chowdhary, A., Pandya, L., Dasgupta, S., Altintas, I., Gupta, A., Mason, A. E., & Smarr, B. L.
Journal of Medical Internet Research, (27)
2025

Background: A 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. Objective: We 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). Methods: We 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. Results: Female 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. Conclusions: The 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.

Burks, J. H., Bruce, L. K., Kasl, P., Soltani, S., Viswanath, V., Hartogensis, W., Dilchert, S., Hecht, F. M., Dasgupta, S., Altintas, I., Gupta, A., Mason, A. E., & Smarr, B. L.
npj Women’s Health, 2(1)
(2024)

In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.

Kasl, P., Soltani, S., Bruce, L. K., Viswanath, V. K., Hartogensis, W., Gupta, A., Altintas, I., Dilchert, S., Hecht, F. M., Mason, A., & Smarr, B. L.
Proceedings of Machine Learning Research, 248
(2024)

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 interdataset 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.

Viswanath, V. K., Hartogensis, W., Dilchert, S., Pandya, L., Hecht, F. M., Mason, A. E., Wang, E. J., & Smarr, B. L.
npj Digital Medicine, 7(150)
(2024)

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.

Jacob M. Ryan, Shreenithi Navaneethan, Natalie Damaso,
Stephan Dilchert, Wendy Hartogensis, Joseph L. Natale,
Frederick M. Hecht, Ashley E. Mason, & Benjamin L. Smarr
Frontiers in Network Physiology
2024

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.

Kasl, P., Bruce, L. K., Hartogensis, W., Dasgupta, S., Pandya, L. S., Dilchert, S., Hecht, F. M., Gupta, A., Altintas, I., Mason, A. E., & Smarr, B. L.
Sensors, 24(6)
(2024)

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.

Mason, A. E., Kasl, P., Soltani, S., Green, A., Hartogensis, W., Dilchert, S., Chowdhary, A., Pandya, L. S., Siwik, C. J., Foster, S. L., Nyer, M., Lowry, C. A., Raison, C. L., Hecht, F. M., & Smarr, B. L.
Scientific Reports, 14(1)
(2024)

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.

Bruce, L. K., Kasl, P., Soltani, S., Viswanath, V. K., Hartogensis, W., Dilchert, S., Hecht, F. M., Chowdhary, A., Anglo, C., Pandya, L., Dasgupta, S., Altintas, I., Gupta, A., Mason, A. E., & Smarr, B.
Biology of Sex Differences, 14(76)
(2023)

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 hanging 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. 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. 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, id not significantly differ from males both on daily and monthly time scales. 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.