Conor Heneghan

Background: Prior studies have observed changes in parameters such as respiration rate, heart rate variability, and resting heart rate over the course of the menstrual cycle. However, prior studies have been limited in scale and have not considered concurrent variations in sleep duration or nocturnal oxygenation. Objectives: To determine the variations in nocturnal respiration rate, heart rate variability, resting heart, main sleep duration and average nocturnal SpO ₂ , associated with underlying menstrual cycle timing. Design: This was a retrospective observational study based on wearable data and self-reported menstrual cycle timing and birth control usage. Methods: Data was collected over 24 months in women using wearable devices (Fitbit and Pixel Watch) who also self-recorded their menstrual cycles and birth control usage. The study considered 1754 women on hormonal birth control (HBC) pills, and 7279 naturally cycling (NC) women. Self-reported menstrual cycle parameters were consistent with normal physiology, and participants recorded wearable data on greater than 70% of the nights. Results: By averaging parameters relative to their day within a menstrual cycle, variations were seen in respiration rate (RR), resting heart rate (RHR) and heart rate variability (HRV), dependent on cycle phase. For the NC cohort, RR varied between the assumed follicular and luteal phase by approximately 0.6 breaths/min (∼4% variation, p p p p 2 (absolute increase of ∼0.1% in assumed late luteal phase, p p 2 being negligible.

Smartphones are essential tools in daily life yet concerns persist about their potential effects on mental health and well-being. Research on these relationships often relies on subjective measures of smartphone use, which can lack validity. These studies are also often limited by small, homogenous samples and the inability to differentiate between types of smartphone activities (e.g., social media vs. other applications). In this study, we analyzed 250,000 d of smartphone usage data from 10,099 adults, using objective measures of smartphone usage collected over 4 wk. We examined normative usage patterns and the temporal relationships between smartphone use and mood. Our findings reveal weak or null associations between smartphone use and mood, both cross-sectionally and longitudinally, and at both individual and group levels. Demographic factors, such as age and gender, were stronger predictors of mood than smartphone use. Statistically significant results that were observed showed that younger adults demonstrated a stronger association between social media use and lower mood cross-sectionally, but not longitudinally. Non-social app use was linked to lower mood in between-subjects analyses, but better mood in within-subjects analyses. However, all effects were small (or null), indicating minimal practical significance. These findings suggest that smartphone use has a limited or negligible impact on mood and well-being over a 4-wk period and do not support claims of a strong or causal relationship between smartphone use and mental well-being across this timeframe.

The autonomic nervous system (ANS) is activated during stress, which can have negative effects on cardiovascular health, sleep, the immune system, and mental health. While there are ways to quantify ANS activity in laboratories, there is a paucity of methods that have been validated in real-world contexts. We present the Fitbit Body Response Algorithm, an approach to continuous remote measurement of ANS activation through widely available remote wrist-based sensors. The design was validated via two experiments, a Trier Social Stress Test (n = 45) and ecological momentary assessments (EMA) of perceived stress (n=87), providing both controlled and ecologically valid test data. Model performance predicting perceived stress when using all available sensor modalities was consistent with expectations (accuracy=0.85) and outperformed models with access to only a subset of the signals. We discuss and address challenges to sensing that arise in real world settings that do not present in conventional lab environments.

Researchers are interested in measuring both objective and subjective assessments of sleep, and associated phenomena such as sleepiness, quality and restoration. Predicting perceived sleep quality accurately from objective measurements remains an unsolved and interesting problem. Previous studies using polysomnograms and actigraphy have shown poor concordance between objective metrics and subjective sleep quality, but were often limited by study duration (e.g., one or two nights of PSG, study population in low 100 s). In this chapter, we consider whether consumer sleep trackers could significantly improve the assessment of subjective sleep quality through longer periods of assessment and larger data scale. We describe a recent study that modeled two subjective sleep quality metrics (PROMIS Sleep-Related Impairment (SI) and Sleep Disturbance (SD) Index) from objective sleep metrics acquired from a consumer wearable device (Fitbit). However, the goodness-of-fit parameter remains relatively low, even with the increased data availability and scale of data provided by consumer wearables. Specifically, for a well-characterized normative population of 2106 adults, we see that a linear multivariate model produces an R² of 0.107 for predicting SI and R² of 0.147 for SR, consistent with prior results using PSG and actigraphy. We conclude that subjective sleep quality remains broadly a psychological construct that cannot be fully modeled solely by objective sleep metrics.

Background: The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. Objective: The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. Methods: In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. Results: A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. Conclusions: The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.

BACKGROUND The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. OBJECTIVE The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. METHODS In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. RESULTS A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. CONCLUSIONS The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.

Respiration rate, heart rate, and heart rate variability are some health metrics that are easily measured by consumer devices and which can potentially provide early signs of illness. Furthermore, mobile applications which accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. We considered two approaches to assessing COVID-19 – a symptom-based approach, and a physiological signs based technique. Firstly, we trained a Logistic Regression classifier to predict the need for hospitalization of COVID-19 patients given the symptoms experienced, age, sex, and BMI. Secondly, we trained a neural network classifier to predict whether a person is sick on any specific day given respiration rate, heart rate, and heart rate variability data for that day and and for the four preceding days. Data on 1,181 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 – July 14, 2020. 11.0% of COVID-19 subjects were asymptomatic, 47.2% of subjects recovered at home by themselves, 33.2% recovered at home with the help of someone else, 8.16% of subjects required hospitalization without ventilation support, and 0.448% required ventilation. Fever was present in 54.8% of subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.77 ± 0.05 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 ± 0.03 for the prediction of illness on a specific day with 4 previous days of history. Respiration rate and heart rate are typically elevated by illness, while heart rate variability is decreased. Measuring these metrics can help in early diagnosis, and in monitoring the progress of the disease.

Resting heart rate is accepted as a valid overall indicator of cardiovascular risk and is a low-cost easy-to-measure metric that can be collected in both home and clinical settings. Wearable devices such as smartwatches and trackers can measure heart rate continuously using optical technology. This study reports initially on the agreement between the resting heart rate reported by a wearable device (Fitbit Charge HR and Fitbit Blaze) and a chest-based consumer electrocardiogram (Polar H7). In 45 subjects, the reported resting heart rates agreed within 0.89 bpm over a resting period of 5 mins. The same device and algorithm was then used in a population of 168 subjects (45.9± 15.6 yrs) who were asked to collect their resting heart rate for four days under seven different conditions and times (at wake sitting/lying down, pre-breakfast, pre-lunch, pre-dinner, and before bed sitting/lying down). The reported values were compared with the Daily Resting Heart Rate (DRHR) reported by the wearable device. The measurement lying down before wake were closest to the DRHR (mean error of 0.0 bpm with a 25-75 ^th percentile spread of ±3.5bpm. There was a significant circadian variation in the reported resting heart rate across subjects, with the highest values reported in the middle of the day. Within individual subjects, the median standard deviation of heart rate at rest was 3-5bpm even measured under nominally similar conditions over four days.

This paper aims to report on the accuracy of estimating sleep stages using a wrist-worn device that measures movement using a 3D accelerometer and an optical pulse photoplethysmograph (PPG). Overnight recordings were obtained from 60 adult participants wearing these devices on their left and right wrist, simultaneously with a Type III home sleep testing device (Embletta MPR) which included EEG channels for sleep staging. The 60 participants were self-reported normal sleepers (36 M: 24 F, age = 34 ± 10, BMI = 28 ± 6). The Embletta recordings were scored for sleep stages using AASM guidelines and were used to develop and validate an automated sleep stage estimation algorithm, which labeled sleep stages as one of Wake, Light (N1 or N2), Deep (N3) and REM (REM). Features were extracted from the accelerometer and PPG sensors, which reflected movement, breathing and heart rate variability. Based on leave-one-out validation, the overall per-epoch accuracy of the automated algorithm was 69%, with a Cohen's kappa of 0.52 ± 0.14. There was no observable bias to under- or over-estimate wake, light, or deep sleep durations. REM sleep duration was slightly over-estimated by the system. The most common misclassifications were light/REM and light/wake mislabeling. The results indicate that a reasonable degree of sleep staging accuracy can be achieved using a wrist-worn device, which may be of utility in longitudinal studies of sleep habits.