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Past studies on how blood glucose levels vary across the menstrual cycle have largely shown inconsistent results based on limited blood draws. In this study, 49 individuals wore a Dexcom G6 continuous glucose monitor and a Fitbit Sense smartwatch while measuring their menstrual hormones and self-reporting characteristics of their menstrual cycles daily. The average duration of participation was 79.3 ± 21.2 days, leading to a total of 149 cycles and 554 phases in our dataset. We use periodic restricted cubic splines to evaluate the relationship between blood glucose and the menstrual cycle, after which we assess phase-based changes in daily median glucose level and associated physiological parameters using mixed-effects models. Results indicate that daily median glucose levels increase and decrease in a biphasic pattern, with maximum levels occurring during the luteal phase and minimum levels occurring during the late-follicular phase. These trends are robust to adjustments for participant characteristics (e.g., age, BMI, weight) and self-reported menstrual experiences (e.g., food cravings, bloating, fatigue). We identify negative associations between each of daily estrogen level, step count, and low degrees of fatigue with higher median glucose levels. Conversely, we find positive associations between higher food cravings and higher median glucose levels. This study suggests that blood glucose could be an important parameter for understanding menstrual health, prompting further investigation into how the menstrual cycle influences glucose fluctuation.

Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO 2 ) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health. Towards this goal, we performed the first clinical development validation on a smartphone camera-based SpO 2 sensing system using a varied fraction of inspired oxygen (FiO 2 ) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG methods on a wider range of SpO 2 values (70–100%) than prior studies (85–100%). We built a deep learning model using this data to demonstrate an overall MAE = 5.00% SpO 2 while identifying positive cases of low SpO 2  < 90% with 81% sensitivity and 79% specificity. We also provide the data in open-source format, so that others may build on this work.

Objective Determine whether data collected from a smartphone camera can be used to detect anemia in a pediatric population. Methods HEMO-AI (Hemoglobin Easy Measurement by Optical Artificial Intelligence), a clinical study carried out from December 2020 to February 2023, recruited patients from the Pediatric Emergency Department, Pediatric Inpatient Department and Pediatric Hematology Unit of the Haemek Medical Center, Afula, Israel. A population-based sample of 823 patients aged 6 months to 18 years who had undergone a venous blood draw for a complete blood count since being admitted to the hospital were enrolled. Patients with total leukonychia, nailbed darkening or discoloration due to medication, nail clubbing, clinically indicated jaundice, subungual hematoma, nailbed lacerations, avulsion injuries, or nail polish applied on fingernails were not eligible for study recruitment. Video and images of the patients’ hand placed in a collection chamber were collected using a smartphone camera. Results 823 samples, 531 from a 12.2 megapixel camera and 256 from a 12.2 megapixel camera, were collected. 26 samples were excluded by the study coordinator for irregularities. 97% of fingernails and 68% of skin samples were successfully identified by a post-trained machine learning model. Separate models built to detect anemia using images taken from the Pixel 3 had an average precision of 0.64 and an average recall of 0.4, whereas models built using the Pixel 6 had an average precision of 0.8 and an average recall of 0.84. Further supplementation of training data with synthetic data boosted the precision of the latter to 0.84 and the average recall to 0.87. Conclusions This study lays the groundwork for the future evolution of non-invasive, pain-free, and accessible anemia screening tools tailored specifically for pediatric patients. It identifies important sample collection parameters and design, provides critical algorithms for the pre-processing of fingernail data, and reports an initial capability to detect anemia with 87% sensitivity and 84% specificity. Trial Registration Prospectively registered on www.clinicaltrials.gov (Identifier: NCT04573244) on 15 September 2020, prior to subject recruitment.

Impedance plethysmography (IPG) and photoplethysmography (PPG) are non-invasive techniques for measuring blood volume changes. This study investigated the differential responses of IPG and PPG to temperature-mediated vasoconstriction induced by localized cooling. Twenty-one participants underwent control and treatment conditions, with fake or real ice cubes applied to the forearm. Blood pressure remained stable, while heart rate decreased. PPG signal amplitude significantly decreased with cooling (p adj = 0.004), indicating sensitivity to superficial blood flow changes. In contrast, IPG signal amplitude remained stable (p adj = 1.0). No statistically significant differences were observed in timing-derived metrics. These findings suggest IPG is less sensitive to superficial changes in blood flow than PPG, and may be more suitable for monitoring deeper blood flow. This study provides insights into the distinct sensitivities of IPG and PPG, with implications for wearable device development and cardiovascular monitoring.

Consumer devices are increasingly used to monitor peripheral body temperature (PBT) for menstrual cycle tracking, but the link between PBT and hormone variations remains underexplored. This study examines the relationship between these variables with a focus on nightly wrist skin temperature (WST). Fifty participants provided physiological and self-reported data, including WST, daily step counts, glucose levels, hormone levels (E3G, LH), and diary entries. Results show a negative correlation between WST and hormone levels when E3G and LH are below average, and this trend was robust to demographics and self-reported stress. Increased variance between mid-cycle hormonal peaks and WST fluctuations may stem from differences between basal body temperature (BBT) and WST. This research suggests that algorithms reliant on body temperature for tracking hormonal changes or other aspects of the menstrual cycle may need to account for increased variance in WST trends if they are meant to be deployed on wearable devices.

Voice communication using an air-conduction microphone in noisy environments suffers from the degradation of speech audibility. Bone-conduction microphones (BCM) are robust against ambient noises but suffer from limited effective bandwidth due to their sensing mechanism. Although existing audio super-resolution algorithms can recover the high-frequency loss to achieve high-fidelity audio, they require considerably more computational resources than is available in low-power hearable devices. This paper proposes the first-ever real-time on-chip speech audio super-resolution system for BCM. To accomplish this, we built and compared a series of lightweight audio super-resolution deep-learning models. Among all these models, ATS-UNet was the most cost-efficient because the proposed novel Audio Temporal Shift Module (ATSM) reduces the network’s dimensionality while maintaining sufficient temporal features from speech audio. Then, we quantized and deployed the ATS-UNet to low-end ARM micro-controller units for a real-time embedded prototype. The evaluation results show that our system achieved real-time inference speed on Cortex-M7 and higher quality compared with the baseline audio super-resolution method. Finally, we conducted a user study with ten experts and ten amateur listeners to evaluate our method’s effectiveness to human ears. Both groups perceived a significantly higher speech quality with our method when compared to the solutions with the original BCM or air-conduction microphone with cutting-edge noise-reduction algorithms.

Developers, designers, and researchers use rapid prototyping methods to project the adoption and acceptability of their health intervention technology (HIT) before the technology becomes mature enough to be deployed. Although these methods are useful for gathering feedback that advances the development of HITs, they rarely provide usable evidence that can contribute to our broader understanding of HITs. In this research, we aim to develop and demonstrate a variation of vignette testing that supports developers and designers in evaluating early-stage HIT designs while generating usable evidence for the broader research community. We proposed a method called health concept surveying for untangling the causal relationships that people develop around conceptual HITs. In health concept surveying, investigators gather reactions to design concepts through a scenario-based survey instrument. As the investigator manipulates characteristics related to their HIT, the survey instrument also measures proximal cognitive factors according to a health behavior change model to project how HIT design decisions may affect the adoption and acceptability of an HIT. Responses to the survey instrument were analyzed using path analysis to untangle the causal effects of these factors on the outcome variables. We demonstrated health concept surveying in 3 case studies of sensor-based health-screening apps. Our first study (N=54) showed that a wait time incentive could influence more people to go see a dermatologist after a positive test for skin cancer. Our second study (N=54), evaluating a similar application design, showed that although visual explanations of algorithmic decisions could increase participant trust in negative test results, the trust would not have been enough to affect people's decision-making. Our third study (N=263) showed that people might prioritize test specificity or sensitivity depending on the nature of the medical condition. Beyond the findings from our 3 case studies, our research uses the framing of the Health Belief Model to elicit and understand the intrinsic and extrinsic factors that may affect the adoption and acceptability of an HIT without having to build a working prototype. We have made our survey instrument publicly available so that others can leverage it for their own investigations.

Deriving personalized insights from popular wearable trackers requires complex numerical reasoning that challenges standard LLMs, necessitating tool-based approaches like code generation. Large language model (LLM) agents present a promising yet largely untapped solution for this analysis at scale. We introduce the Personal Health Insights Agent (PHIA), a system leveraging multistep reasoning with code generation and information retrieval to analyze and interpret behavioral health data. To test its capabilities, we create and share two benchmark datasets with over 4000 health insights questions. A 650-hour human expert evaluation shows that PHIA significantly outperforms a strong code generation baseline, achieving 84% accuracy on objective, numerical questions and, for open-ended ones, earning 83% favorable ratings while being twice as likely to achieve the highest quality rating. This work can advance behavioral health by empowering individuals to understand their data, enabling a new era of accessible, personalized, and data-driven wellness for the wider population. Wearable devices generate vast streams of health data, but making sense of these measurements requires complex numerical reasoning beyond the reach of conventional language models. This study introduces a large language model agent that interprets wearable data to deliver accurate, personalized health insights.

Heart rate (HR) is a crucial physiological signal that can be used to monitor health and fitness. Traditional methods for measuring HR require wearable devices, which can be inconvenient or uncomfortable, especially during sleep and meditation. Noncontact HR detection methods employing microwave radar can be a promising alternative. However, the existing approaches in the literature usually use high-gain antennas and require the sensor to face the user’s chest or back, making them difficult to integrate into a portable device and unsuitable for sleep and meditation tracking applications. This study presents a novel approach for noncontact HR detection using a miniaturized Soli radar chip embedded in a portable device (Google Nest Hub). The chip has a 6.5 mm × 5 mm × 0.9 mm dimension and can be easily integrated into various devices. The proposed approach utilizes advanced signal processing and machine learning techniques to extract HRs from radar signals. The approach is validated on a sleep dataset (62 users, 498 h) and a meditation dataset (114 users, 1131 min). The approach achieves a mean absolute error (MAE) of 1.69 bpm and a mean absolute percentage error (MAPE) of 2.67 % on the sleep dataset. On the meditation dataset, the approach achieves an MAE of 1.05 bpm and a MAPE of 1.56 % . The recall rates for the two datasets are 88.53 % and 98.16 % , respectively. This study represents the first application of the noncontact HR detection technology to sleep and meditation tracking, offering a promising alternative to wearable devices for HR monitoring during sleep and meditation.

BackgroundAxial spondyloarthritis (axSpA) affects equal numbers of females and males, yet females report lower exercise participation and greater functional limitations. Scalable, home-based exercise interventions may help address these differences. The ExerciseRx™ app is a personalized digital health platform that delivers and monitors exercises. This Phase 1 exploratory study evaluated the performance and acceptability in preparation for a future randomized control trial in axSpA.MethodsBaseline demographic and clinical data were collected. Participants completed a one-time supervised in-lab session using the ExerciseRx app to perform recommended exercises, recorded via the smartphone camera. We first evaluated the performance of the machine learning (ML) models built into the ExerciseRx app on two tasks: (1) exercise identification, reported with accuracy (i.e., number of exercises correctly identified); and (2) repetition counting, reported with mean absolute error (i.e., number of repetitions correctly identified). We then investigated the relationship between movement patterns and clinical measures. Finally, we examined acceptability using the Mobile Health App Usability Questionnaire (MAUQ).ResultsWe enrolled 21 females (11 axSpA, 10 controls), mean age 48.1 ± 14.3 (axSpA) and 47.0 ± 13.3 (controls). Most were non-Hispanic White (85.7%). Mean Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) was 4.03 ± 1.68 for axSpA and 1.76 ± 1.30 for controls; mean Bath Ankylosing Spondylitis Functional Index (BASFI) was 2.75 ± 1.84 for axSpA and 0.88 ± 1.31 for controls. Overall mean ML model accuracy in exercise identification was 81.7%, lower in axSpA (72.3%) than controls (92.3%). Higher BASDAI and BASFI scores were not associated with lower accuracy, whereas any abnormal metrology value was. MAUQ scores were high on a Likert scale out of 7 (6.35 ± 1.23 axSpA; 6.45 ± 1.27 controls). Three participants (14%) reported mild, acceptable soreness the following day; no adverse events occurred.ConclusionsThe ExerciseRx ML models showed adequate performance, and the app was accepted by participants, though findings may not be broadly generalizable. Lower accuracy in axSpA highlights the need for ML model refinement prior to larger studies. High acceptability and absence of adverse events suggest that the ExerciseRx app has strong potential to be an effective home-based exercise tool for these patients.