Everyday Brain and Systemic Health Wearables: Validity and Equity

Consumer-grade wearables systems incorporating electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and photoplethysmography (PPG) have moved rapidly from research laboratories into everyday life, offering the promise of continuous, passive monitoring of stress, sleep quality, and cognitive focus. Advances in miniaturization, wireless transmission, and machine learning have enabled the instrumentation of daily activities that were previously confined to controlled experimental settings. Yet, a widening gap persists between the conditions under which these devices are developed and validated and the complex, uncontrolled environments in which they are ultimately used. Motion artifacts, ambient noise, interindividual physiological variability, and the inherently lower signal fidelity of consumer hardware all challenge the direct translation of laboratory-grade metrics into meaningful, reliable readouts in daily life. Real-world success depends not only on technical validity but also on whether users can and will wear these devices consistently; long-term comfort, wearability, and sustained adherence are therefore integral to the translational challenge.Alongside these technical limitations, questions of equity have emerged as a critical and underexamined dimension of consumer neurotechnology. The majority of wearable validation studies have been conducted in demographically narrow samples, often skewed toward young, healthy adults of lighter skin tones, limiting the generalizability of device performance claims. PPG-based heart rate and oxygen saturation measurements are known to vary with melanin content; EEG electrode geometries and contact assumptions frequently do not account for the full range of human hair textures and head morphologies. Algorithm training sets rarely reflect the breadth of age, sex, and health status found in intended consumer populations. Transparent reporting of participant demographic characteristics and subgroup-level performance analyses are essential if device performance claims are to be generalised responsibly across populations. Beyond physiological variability, equity considerations also extend to questions of accessibility, affordability, cultural applicability, and digital inclusion, recognising that meaningful access to brain-health monitoring technology remains unevenly distributed across socioeconomic contexts and global deployment settings. These disparities risk compounding existing health inequities if left unaddressed and represent both a scientific problem and an ethical imperative for the field.This Research Topic aims to bring together work that rigorously evaluates the accuracy, personalization, and inclusivity of consumer brain-health wearables across naturalistic settings and diverse populations. Beyond signal-level validation, this collection explicitly asks whether wearable measures produce meaningful improvements in how stress, sleep, and cognitive function are monitored and managed in everyday life. By connecting technical validity to real-world application outcomes, this collection seeks to establish a more robust and equitable evidence base for the responsible development, regulation, and adoption of everyday neurotechnology for stress, sleep, and cognitive monitoring.We welcome submissions spanning device benchmarking, algorithm development, human-factors evaluation, and ethical analysis. Studies comparing consumer wearables against laboratory reference standards in ecologically valid conditions are particularly encouraged, as are contributions that assess whether wearable-derived measures translate into meaningful improvements in real-world stress, sleep, or cognitive monitoring outcomes. Contributions that explicitly address demographic diversity in participant samples, device design, or model training are strongly encouraged; authors are asked to report participant demographic characteristics and present subgroup-level performance analyses where feasible, to support fairness-aware evaluation of device and algorithm performance across populations. Privacy-preserving approaches to continuous monitoring, including federated learning, on-device processing, and edge computing architectures, are in scope and actively encouraged. Methodological papers proposing frameworks for fairness-aware validation, personalization pipelines, or field-deployment protocols are in scope, alongside empirical studies conducted in home, workplace, educational, or clinical adjacent settings. Longitudinal studies examining measurement reliability and calibration drift over time, reproducibility assessments across devices and participant groups, and contributions introducing open datasets or transparent benchmarking pipelines are also strongly encouraged. Studies addressing long-term user adherence, device comfort, and wearability as determinants of sustained real-world deployment are equally welcome.Topics of interest include, but are not limited to:- Validation of consumer EEG, fNIRS, and PPG devices against laboratory reference standards for stress, sleep, and cognitive focus under daily-life conditions, including assessment of whether wearable measures produce meaningful improvements in real-world monitoring outcomes- Signal quality, artifact characterization, and noise-robust processing for wearable neurophysiological and physiological signals in naturalistic settings- Energy-efficient, adaptive, and context-aware sensing architectures for continuous brain-health monitoring- Personalization of classification and regression models to improve within-person accuracy and stability over time- Explainable and interpretable machine learning approaches for consumer neurophysiological monitoring- Multimodal fusion of EEG, fNIRS, PPG, and complementary physiological signals to improve robustness and interpretability in daily use- Longitudinal reliability, calibration drift, and temporal stability of consumer wearable measurements in everyday use- Benchmarking and standardization approaches for translating laboratory-grade cognitive and affective metrics to consumer contexts- Open datasets, reproducible benchmarking pipelines, and transparent evaluation methodologies for wearable neurotechnology- Demographic bias in wearable sensing: the effects of skin tone, hair type, age, sex, and health status on device accuracy and algorithm performance, with explicit reporting of participant diversity and subgroup-level performance breakdowns- Equity-centred design and evaluation frameworks for consumer neurotechnology, including inclusive hardware development and fairness-aware machine learning- Real-world deployment studies examining device acceptability, usability, long-term wearability, comfort, and user adherence across diverse user groups- Privacy-preserving approaches to continuous wearable monitoring, including federated learning, on-device processing, and edge computing architectures- Ethical, regulatory, and privacy considerations in the collection, storage, and commercial application of consumer brain-health data- Applications of everyday wearables to stress management, sleep health, and cognitive performance monitoring in health, occupational, and educational settings Topic editors: Daniel Milej, Carlos Carreto, Jie Cui Source: https://www.frontiersin.org/research-topics/81707/everyday-brain-and-systemic-health-wearables-validity-and-equity