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The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis

Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health–related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs’ application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally.

There exists a stereotype that women are more expressive than men; however, research has almost exclusively focused on a single facial behavior, smiling. A large-scale study examines whether women are consistently more expressive than men or whether the effects are dependent on the emotion expressed. Studies of gender differences in expressivity have been somewhat restricted to data collected in lab settings or which required labor-intensive manual coding. In the present study, we analyze gender differences in facial behaviors as over 2,000 viewers watch a set of video advertisements in their home environments. The facial responses were recorded using participants' own webcams. Using a new automated facial coding technology we coded facial activity. We find that women are not universally more expressive across all facial actions. Nor are they more expressive in all positive valence actions and less expressive in all negative valence actions. It appears that generally women express actions more frequently than men, and in particular express more positive valence actions. However, expressiveness is not greater in women for all negative valence actions and is dependent on the discrete emotional state.

Continued developments of sensor technology including hardware miniaturization and increased sensitivity have enabled the development of less intrusive methods to monitor physiological parameters during daily life. In this work, we present methods to recover cardiac and respiratory parameters using accelerometer and gyroscope sensors on the wrist. We demonstrate accurate measurements in a controlled laboratory study where participants (n = 12) held three different positions (standing up, sitting down and lying down) under relaxed and aroused conditions. In particular, we show it is possible to achieve a mean absolute error of 1.27 beats per minute (STD: 3.37) for heart rate and 0.38 breaths per minute (STD: 1.19) for breathing rate when comparing performance with FDA-cleared sensors. Furthermore, we show comparable performance with a state-of-the-art wrist-worn heart rate monitor, and when monitoring heart rate of three individuals during two consecutive nights of in-situ sleep measurements.

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.

Background While wearables, such as Fitbit devices, are specifically designed to measure physical activity and physiological functioning, they have less consumer uptake than smartphones, which contain many similar sensors that can also measure physical activity (although not cardiac activity). This study’s objective was to assess the agreement between physical and cardiac activity measures collected from Fitbit wearable devices and physical activity measures derived from the naturalistic use of smartphones over 28 days in a large, diverse sample ( n  = 10,085). Agreement between smartphone and Fitbit features was quantified via intraclass correlation coefficients (ICC; two-way random effects, absolute agreement) computed within participants across aligned days. Results Although the level of agreement between smartphone and Fitbit metrics was statistically significant in all comparisons examined, the effect sizes ranged from ‘poor’ to ‘good’ across metrics and individuals. The strongest agreement was observed for measurements of smartphone activity categories with strongly related categories derived from Fitbit exercise data. Some effects were stronger when data was filtered to focus on participants with higher activity levels in both smartphone and Fitbit usage. Conclusions Although variations in data agreement and missingness highlight the need for careful interpretation of results, these findings do provide support for the use of smartphone sensors to measure certain aspects of physical activity at the population level when the scalability of the measurement is critical.

Large language models (LLMs) have emerged as powerful tools for analyzing and interpreting complex datasets. Recent studies demonstrate their potential to generate useful, personalized responses when provided with patient-specific health information that encompasses lifestyle, biomarkers, and context. As LLM-driven health applications are increasingly adopted, rigorous and efficient one-sided evaluation methodologies are crucial to ensure response quality across multiple dimensions, including accuracy, personalization, relevance and safety. However, current evaluation practices, particularly for open-ended text responses, heavily rely on human experts. This approach introduces human factors (perspectives, potential biases, inconsistencies) and is often cost-prohibitive, labor-intensive, and hinders scalability, especially in complex domains like healthcare where response assessment necessitates domain expertise and considers multifaceted patient data, which is often nuanced and diverse. In this work, we introduce Adaptive Precise Boolean rubrics: an evaluation framework that aims to streamline human and automated evaluation of open-ended questions by identifying critical gaps in model responses using a minimal set of targeted rubric questions. Our approach is based on recent work in more general evaluation settings that contrasts a smaller set of complex evaluation targets with a larger set of more precise, granular targets answerable with simple Boolean responses. We validate this approach in metabolic health, a domain encompassing diabetes, cardiovascular disease, and obesity. Our results demonstrate that Adaptive Precise Boolean rubrics yield substantially higher inter-rater agreement among both expert and non-expert human evaluators, as well as in automated assessments, compared to traditional Likert scales, while requiring approximately half the evaluation time of Likert-based methods. This enhanced efficiency and scalability, particularly through automated evaluation and non-expert contributions, paves the way for more extensive and cost-effective evaluation of LLMs in health.

The impact of digital device use on health and well-being is a pressing question. However, the scientific literature on this topic, to date, is marred by small and unrepresentative samples, poor measurement of core constructs, and a limited ability to address the psychological and behavioral mechanisms that may underlie the relationships between device use and well-being. Recent authoritative reviews have made urgent calls for future research projects to address these limitations. The critical role of research is to identify which patterns of use are associated with benefits versus risks and who is more vulnerable to harmful versus beneficial outcomes, so that we can pursue evidence-based product design, education, and regulation aimed at maximizing benefits and minimizing the risks of smartphones and other digital devices. The objective of this study is to provide normative data on objective patterns of smartphone use. We aim to (1) identify how patterns of smartphone use impact well-being and identify groups of individuals who show similar patterns of covariation between smartphone use and well-being measures across time; (2) examine sociodemographic and personality or mental health predictors and which patterns of smartphone use and well-being are associated with pre-post changes in mental health and functioning; (3) discover which nondevice behavior patterns mediate the association between device use and well-being; (4) identify and explore recruitment strategies to increase and improve the representation of traditionally underrepresented populations; and (5) provide a real-world baseline of observed stress, mood, insomnia, physical activity, and sleep across a representative population. This is a prospective, nonrandomized study to investigate the patterns and relationships among digital device use, sensor-based measures (including both behavioral and physiological signals), and self-reported measures of mental health and well-being. The study duration is 4 weeks per participant and includes passive sensing based on smartphone sensors, and optionally a wearable (Fitbit), for the complete study period. The smartphone device will provide activity, location, phone unlocks and app usage, and battery status information. At the time of submission, the study infrastructure and app have been designed and built, the institutional review board of the University of Oregon has approved the study protocol, and data collection is underway. Data from 4182 enrolled and consented participants have been collected as of March 27, 2023. We have made many efforts to sample a study population that matches the general population, and the demographic breakdown we have been able to achieve, to date, is not a perfect match. The impact of digital devices on mental health and well-being raises important questions. The Digital Well-Being Study is designed to help answer questions about the association between patterns of smartphone use and well-being. DERR1-10.2196/49189.

Non-contact heart rate and heart rate variability measurements have applications in healthcare and affective computing. Recently, a system utilizing a five-band camera (RGBCO: red, green, blue, cyan, orange) was proposed, and shown to improve both remote measurement of heart rate and heart rate variability over an RGB camera. In this paper, we propose an improved method for video-based estimation of heart rate variability. We introduce three advancements over previous work utilizing five-band cameras: (1) an adaptive non-rectangular region of interest identified using automatically detected facial feature points, (2) improved peak detection within the blood volume pulse (BVP) signal, and (3) improved HRV calculation using the Welch periodogram. We apply our method to a test dataset of subjects at rest and under cognitive stress and show qualitative improvements in the stability of HRV spectrogram estimation. Although we evaluate our method using a five-band camera, the method could be applied to video recorded with any camera.

BackgroundElectrodermal activity (EDA) is a measure of sympathetic arousal that has been linked to depression in laboratory experiments. However, the inability to measure EDA passively over time and in the real world has limited conclusions that can be drawn about EDA as an indicator of mental health status outside of controlled settings.ObjectiveRecent smartwatches have begun to incorporate wrist-worn continuous EDA sensors that enable longitudinal measurement of sympathetic arousal in everyday life. This work (n=237, 4-week observation period) examines the association between passively collected, diurnal variations in EDA and symptoms of depression, anxiety and perceived stress in a large community sample.MethodsWe conducted a prospective, non-randomised study to investigate patterns and relationships between digital device use patterns, including sensor data from phones and wearables reflecting both behavioural and physiological processes, and self-reported measures of mental health and well-being. We recruited 395 participants who had a Fitbit Sense 2 device with the electrodermal sensor activated. We use a non-linear cosinor fitting method to estimate the difference in mesor, amplitude and phase, between the diurnal rhythms in heart rate (HR), heart rate variability (HRV) root mean square of successive differences, EDA, skin temperature and steps.FindingsSubjects who exhibited elevated depressive and anxiety symptoms had higher tonic EDA, skin temperature and heart rate, despite not engaging in greater physical activity, compared with those that were not depressed or anxious. In contrast, subjects who exhibited elevated stress only exhibited higher skin temperature. Most strikingly, differences in EDA between those with high versus low symptoms were most prominent during the early morning. We did not observe amplitude or phase differences in the diurnal patterns.ConclusionsResults indicate that participants with elevated depressive and anxiety symptoms have different diurnal physiological patterns. Specifically, EDA differences suggest elevated sympathetic activity throughout the day and in particular in the early morning.Clinical implicationsOur work suggests that electrodermal sensors may be practical and useful in measuring the physiological correlates of mental health symptoms in free-living contexts and that recent consumer smartwatches might be a tool for doing so.