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Wearable technologies are increasingly used in older adult care; however, a coherent framework explaining how these technologies generate value remains lacking. This study aims to develop a clear and ethically grounded framework for understanding how wearable technologies generate value in older adult care, with relevance to nursing practice. Using a formal, non-numerical axiomatic conceptualization approach, we constructed a theory-building model by defining key system components, specifying foundational assumptions and logically deriving their implications for care and design. The framework identifies 10 core system entities and nine governing axioms that describe the necessary conditions under which wearable-generated data become meaningful, actionable and ethically acceptable in older adult care. From these axioms, six logically derived theorems articulate how monitoring can support early detection, safety, adherence and care integration. These theorems are translated into seven practical design principles to guide evaluation and implementation. Value generation is conceptualized as a staged process, from data capture to care outcomes, conditioned by human and system factors such as adherence, usability, trust, safety, relevance and clinical integration. This model provides a common language for researchers, designers, and nurses to evaluate and design wearable technologies that enhance safety, autonomy, and well-being while preserving dignity, trust and relational care in older adult populations.
Enhanced Recovery After Surgery (ERAS) pathways in thoracic oncology emphasize early mobilization and objective discharge readiness, but perioperative functional recovery is often assessed intermittently. Wearable devices may provide continuous, objective recovery metrics. We conducted a PRISMA 2020-based systematic review registered in PROSPERO (CRD420261325339). PubMed, Scopus, and Web of Science Core Collection were searched for English-language studies published between February 2, 1996 and February 2, 2026. Eligible reports included adults undergoing lung cancer surgery or clinically relevant pulmonary resection and evaluated wearable-based activity, physiologic monitoring, or rehabilitation support across the preoperative, in-hospital, or post-discharge phases. Risk of bias was assessed using RoB 2, ROBINS-I, or design-appropriate feasibility and measurement appraisal. Certainty of evidence was qualitatively informed by GRADE principles, and findings were synthesized narratively because of clinical and methodological heterogeneity. Eight reports representing seven independent cohorts were included: two randomized trials, one nonrandomized trial with historical controls, two prospective observational studies, two companion single-arm preoperative feasibility/effectiveness reports, and one development/usability agreement study. In the Move For Surgery RCT, wearable-enhanced preconditioning reduced prolonged hospital stay >5 days from 24% to 7% (12/50 vs 3/45; p=0.021). A digital chest drainage RCT reported shorter postoperative length of stay and chest tube duration in the intervention group, although the cohort was not restricted to lung cancer. Observational studies showed weak but significant associations between perioperative step counts and recovery outcomes. Feasibility studies supported device use and data transmission, while a smartwatch-ePRO study showed close agreement with electronic health record measurements. Wearable-based perioperative monitoring appears feasible and may provide objective recovery signals in lung cancer surgery. However, current evidence remains sparse, heterogeneous, and often indirect. Findings should be interpreted as hypothesis-generating rather than sufficient to support routine clinical implementation. https://www.crd.york.ac.uk/PROSPERO/view/CRD420261325339, identifier CRD420261325339.
The development of wearable biosensors has accelerated due to the combination of nanomaterials, integrative electronics, and miniaturized transduction mechanisms, enabling continuous monitoring of physiological and environmental markers. Electrochemical and photonic modalities have been shown to exhibit complementary capabilities in wearable applications, with each offering distinct advantages in sensitivity, selectivity, miniaturization, and power efficiency. Beyond single-modality functionality, the next generation of wearable diagnostics integrates electrochemical and optical transduction within the same platforms. In this short review, we examine the physical and functional demands of such convergence in wearable systems, highlighting model electrochemical systems, such as textile-integrated wound monitoring and hormone sensors, and how optical modalities provide orthogonal observables of interfacial and biochemical conditions. We further explore new emerging device architectures that leverage electrochemical and optical interrogation to generate robust, information-rich data sets, supporting long-term, real-world deployment of wearable diagnostic technologies for personalized healthcare and continuous monitoring applications.
Many studies have evaluated the use of wearable monitoring systems to improve patient safety in hospital. Although some have demonstrated effects on intensive care admissions, there remains little evidence of impact on patient outcomes such as mortality, hospital length of stay, and time to antibiotic administration. Very few studies have focused on how wearable monitoring systems are used in clinical practice, including how the rate of manual vital sign measurements (MVSMs) is affected. Our primary aim was to describe the physiological pattern of vital signs in hospitalized patients treated for COVID-19 outside of critical care. We also report an exploratory post hoc analysis of the impact of displaying wearable monitoring system data on the frequency of intermittent MVSMs. We conducted a retrospective study during the COVID-19 pandemic following deployment of a wearable monitoring system that continuously displayed heart rate, respiratory rate, and oxygen saturation levels. We included patients treated for COVID-19 in 3 isolation wards in a large UK hospital. Wearable monitoring system data were displayed on a dashboard in the center of each ward. We analyzed the patterns of vital signs in patients monitored using the wearable monitoring system. We compared the time to next observation (led by nursing staff) for routinely collected MVSMs between periods when patients were continuously monitored and those when they were not. In exploratory post hoc analysis, we tested whether the difference varied between stable (early warning score [EWS] above the escalation threshold) and unstable patients. Patients (N=144) had continuous vital signs above the EWS threshold for escalation for 32.7% (2133/6528) of time monitored. The unadjusted median time between MVSMs for continuously monitored periods was 39 minutes (95% CI 29-49; P<.001) longer than for unmonitored periods. When adjusted for EWS category and participant-level clustering, the effect was attenuated but remained significant (14.6 minutes; P<.001). In exploratory post hoc analysis, we found that increases were larger during stable observation periods (51 minutes, 95% CI 39-62; P<.001) than during unstable periods (16 minutes, 95% CI 8-24; P<.001). However, adjusted analyses did not support a significant difference between stable and unstable periods. Patients in this study were at elevated risk of deterioration, spending a third of monitored time at or above the escalation threshold. We found that, by offering additional vital sign data between manual measurements, the time between routine MVSMs increased, which may reflect changes in nursing task prioritization. Although patient safety outcomes were not directly measured, we found no indication that reducing observation frequency adversely affected patient safety.
New technologies, such as wearable sensors, allow the quantitative assessment of gait alterations due to Parkinson's disease (PD) through Digital Mobility Outcomes (DMOs). These DMOs have the potential to complement traditional clinical assessments but must be relevant, reliable, and representative of the patient's overall condition. Real-world monitoring offers a valuable approach for this type of day-to-day evaluation of patients. This systematic review has four primary aims: 1) To identify trends in protocol design for real-world gait monitoring using wearables in patients with PD. 2) To detail the analysis of inertial data and the computation of DMOs. 3) To summarize the clinical scales and symptoms studied. 4) To outline trends in the conclusions and limitations reported by authors in this field. Three databases (MEDLINE via PubMed, Cochrane, and EMBASE) were systematically searched between September 1, 2013, and September 15, 2023. Eligibility criteria included studies involving adults with a PD diagnosis, the use of a wearable device with at least one accelerometer or gyroscope, and gait analysis conducted in real-world settings. Sixty-three studies were selected. Overall, wearables successfully provide clinically meaningful information on gait impairment in patients with PD. Stride speed as a DMO is well-established and clinically meaningful, while other metrics, such as stride length, stride duration, and cadence, show great promise for routine clinical practice and research. However, the lack of consensus on the methods of investigation and the small sample sizes remain significant barriers that must be addressed to facilitate broader adoption in clinical practice and research.
This paper presents a low-profile, lightweight, and flexible antenna to work over three bands used by wearable electronic devices. Wearable applications demand the flexibility of the antenna, and this is achieved by employing a nickel-copper-coated ripstop conductive fabric of the radiative part (patch and ground) and a silicon-based polymer, polydimethylsiloxane (PDMS), for the substrate material. The geometry of the antenna is a triangular patch with two stubs and two slots added to the antenna to enhance the impedance and matching bandwidth. The suggested antenna offers three frequency bands at 3.5, 5.15, and 6.6 GHz, which enable 5G-enabled wearables, Wi-Fi devices, and short-range sensing applications, respectively. The proposed work is validated by studying performance parameters such as S-parameter, gain, and radiation pattern. To make the proposed antenna suitable for wearable devices, conformal analysis, SAR analysis, and gain on the body are studied. The conformal analysis shows a good agreement with the flat antenna analyzed in terms of S11, gain, efficiency, and radiation pattern. The design is also retrieved on a human body phantom to investigate both SAR and gain using the Sim4Life EM simulation tool. Moreover, the antenna's performance is compared with recently published articles. The findings provided by the suggested antenna, together with comprehensive comparisons to existing literature, demonstrate that the antenna exhibits robust performance and is well-suited for incorporation into wearable electronic devices.
Wearable biosensing technologies are advancing sports performance monitoring by enabling the continuous and real-time measurement of physiological and biochemical parameters. Among non-invasive biofluids, sweat has become the most widely studied medium due to its easy accessibility during physical activity and its presence of multiple relevant biomarkers. This review critically examines the recent developments in sweat-based wearable biosensing technologies and identifies the key challenges that hinder their transition from laboratory prototypes to practical sports-monitoring systems. The discussion includes a brief introduction to sweat generation, the important biomarkers present in sweat, and their significance in sports health monitoring. Various electrochemical sensing platforms designed for sweat analysis are reviewed, with an emphasis on their structural designs and operational mechanisms. Major application areas, including lactate monitoring for fatigue detection, electrolyte sensing for hydration assessment, and cortisol measurement for stress evaluation, are discussed. This review also highlights the important challenges, including sensor calibration, motion-related artifacts, variability in sweat composition among individuals, and long-term operational stability. Emerging approaches, including multimodal sensing, machine-learning-assisted data interpretation, nanomaterial-enabled sensors, and closed-loop feedback systems, are also discussed as potential solutions to improve the reliability and real-world applicability of sweat-based wearable biosensors for sports performance monitoring.
After a stroke, wrist and hand dysfunction usually develops from extended non-use and lack of home-based repetitive training, greatly influencing affect daily living and general quality of life. Wearable mobile and traditional electromyography biofeedback devices as biofeedback-based rehabilitation tools offer an intriguing solution for task-specific training, thereby improving user involvement and functional recovery. The main aim of this study was to find out how satisfied people with wrist and hand dysfunction who had recently had a stroke were with using a wearable mobile biofeedback device (Pheezee) compared to traditional electromyography biofeedback. Crossover trial. Thirty subacute stroke (male 16 and female 14) participants with wrist and hand dysfunction participated in the study. All participants received 1 week of training using the Pheezee, then 1 week using traditional electromyography biofeedback. The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0) scale measured user satisfaction. The Mann-Whitney U test applied for between-group comparisons. The wearable mobile biofeedback device demonstrated significantly higher user satisfaction in terms of weight, safety, durability, comfort, simplicity of use, and effectiveness (p < 0.05). However, no significant differences were observed between the two modalities concerning adjustment component (p > 0.05). QUEST 2.0 device subscale total eight items were higher in the Pheezee device group (3.69 ± 0.26) compared to the traditional electromyography biofeedback device group (3.18 ± 0.18) mean and SD respectively, demonstrating a mean difference of 0.52 points (16.3% higher user satisfaction). Wearable mobile biofeedback devices show promise as supportive tools in stroke rehabilitation and can be feasibly integrated into home-based task-specific training programs. Their ability to raise engagement and adherence to rehabilitation could help stroke survivors have better functional results and quality of life.
Hypertension remains a major global public health challenge and a leading risk factor for cardiovascular morbidity and mortality. Effective long-term control of BP largely depends on sustained patient engagement, medication adherence, and lifestyle modification. However, traditional care models often face limitations in delivering continuous monitoring, personalized support, and long-term behavioral interventions. This review provides a comprehensive overview of digital health interventions-including mobile health (mHealth) applications, wearable devices, and artificial intelligence (AI)-driven tools-in the management of hypertension. Current evidence from randomized controlled trials and observational studies suggests that these technologies can significantly improve medication adherence, enhance self-monitoring of BP, and promote healthier behaviors such as increased physical activity and dietary modification. In particular, mHealth applications incorporating reminders, feedback, and educational components have demonstrated measurable improvements in adherence and BP control. Wearable devices enable real-time physiological monitoring, while AI-based systems offer opportunities for personalized risk prediction and adaptive intervention strategies. Despite these promising findings, several challenges remain. The long-term effectiveness and sustainability of digital health interventions are still not well established, with many studies limited to short follow-up periods. In addition, issues related to interoperability between different digital platforms, data privacy and security concerns, and unequal access to technology may hinder widespread implementation. Variability in study design and intervention components also limits the comparability of findings across studies. Future research should focus on conducting large-scale, long-term trials to evaluate clinical outcomes and cost-effectiveness. Efforts are also needed to improve system integration, enhance user engagement, and ensure equitable access to digital health technologies. Overall, digital health interventions represent a promising and scalable approach to improving hypertension self-management and supporting more efficient, patient-centered care.
This systematic review synthesizes current evidence on gait analysis as a diagnostic and prognostic tool for degenerative lumbar spine diseases (DLSD), evaluates the clinical utility of specific gait parameters as biomarkers, and identifies emerging technologies enabling scalable, accessible clinical implementation. A comprehensive literature search was conducted across PubMed, Embase, Cochrane Library, and Web of Science (2010-2026). Studies evaluating gait parameters in patients with lumbar spinal stenosis (LSS), lumbar disc herniation (LDH), or lumbar spondylolisthesis were included. Data extraction focused on spatiotemporal gait metrics, kinematic parameters, and diagnostic performance measures. A total of 47 studies met the inclusion criteria, comprising 2,847 patients with DLSD and 1,892 healthy controls. Disease-specific gait signatures were identified: LSS demonstrated significant increases in gait asymmetry (+ 131%) and variability (+ 436%), while LDH exhibited marked reductions in gait velocity (-76%) and cadence (-67%). Machine learning classifiers achieved diagnostic accuracy up to 93.18% (AUC 0.97) for differentiating lumbar pathologies. Smartphone-based and wearable sensor technologies showed strong agreement with laboratory-based motion capture systems (ICC > 0.85), offering potential for remote clinical applications. Gait analysis provides objective, quantifiable biomarkers that differentiate DLSD subtypes with high diagnostic accuracy. The emergence of smartphone-based video analysis and wearable sensors represents a paradigm shift toward accessible gait assessment. Integration of artificial intelligence with clinical gait databases offers promising directions for decision support systems in spine care.
Obstructive sleep apnea (OSA) is common yet frequently underdiagnosed, partly because overnight polysomnography (PSG) is logistically burdensome and access to specialized testing is limited. We aimed to develop machine-learning models for OSA risk screening using multimodal digital phenotyping from consumer-grade wearable devices, smartphone-based assessments, and clinical scales. We enrolled 338 participants and collected data over four weeks. After preprocessing, 107 features were derived from wearable-derived physiological and activity measures, smartphone-based records, and questionnaire-based clinical risk profiles, and used to classify high- versus low-risk OSA groups defined by the Berlin Questionnaire. Across multiple model configurations, predictive performance was high, with the best-performing model achieving an AUC of up to 0.94 and an F1 score of 0.80 in the internal validation set. Consistently influential predictors included body mass index, Insomnia Severity Index score, Smartphone Overuse Screening Questionnaire score, resting heart rate, and heart rate recovery. These findings suggest that multimodal digital phenotyping from accessible consumer technologies may support scalable pre-screening for OSA risk in real-world settings. Further validation against PSG-confirmed OSA outcomes is needed.Trial Registration: Clinical Research Information Service (CRIS) KCT0009175 (Registration data: Feb-15-2024) (https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&focus=reset_12&search_page=M&pageSize=10&page=undefined&seq=26133&status=5&seq_group=26133).
Workplace stress is a significant concern, as it negatively impacts employee wellbeing and organizational productivity and is a major contributor to burnout and job turnover. Detecting stress in real-world work environments remains challenging; however, recent advances in machine learning and deep learning techniques offer promising solutions. Furthermore, the growing availability of multimodal data and wearable sensor technologies may facilitate individual stress tracking. In this paper, a systematic literature review is presented, focusing on machine learning and deep learning approaches for detecting workplace stress using wearable and multimodal data: (a) suggested machine learning and deep learning techniques are considered, (b) dataset characteristics and the sensor modalities employed are examined, (c) detection performance is reviewed, (d) workplace contexts and professional domains are discussed, and (e) gaps and future research directions are identified. The 20 selected studies show that machine learning and deep learning models applied to physiological, behavioral, and multimodal data can effectively detect workplace stress, particularly in high-risk occupations. However, limitations such as small sample sizes, limited dataset diversity, and minimal use of central nervous system signals (e.g., electroencephalogram) remain.
Wearable flexible sensors have emerged as a cornerstone of next-generation bioelectronics, enabling skin-conformal, continuous, and high-fidelity monitoring of cardiovascular diseases (CVDs). This review elucidates the structure-function relationships that govern sensing performance, highlighting how material innovation, structure engineering, and device architectures synergistically balance sensitivity, mechanical robustness, and biocompatibility. Key cardiovascular physiological signals, including electrical, mechanical, hemodynamic, and biochemical modalities, are systematically summarized and correlated with representative sensing mechanisms such as piezoresistive, capacitive, triboelectric, electrochemical, and optical transduction. The integration of machine learning (ML) and data-driven modeling is further discussed, highlighting its potential to enable personalized diagnostics, multimodal fusion, and adaptive prediction of cardiovascular risks. Despite substantial progress, critical challenges remain in long-term operational stability, scalable manufacturing, cross-population generalizability, and clinical validation. To address these limitations, a unified design paradigm integrating materials engineering, multimodal sensing strategies, and algorithmic intelligence is proposed. This review aims to guide the development of next-generation wearable platforms that are not only mechanically compliant and functionally robust but also algorithmically interpretable and clinically translatable, laying the groundwork for intelligent, reliable, and precision-oriented CVD monitoring systems.
Coronary heart disease (CHD) and carotid artery disease (CAD) often co-occur. However, conventional diagnosis typically involves separate, site-by-site examinations after symptoms appear, leading to delayed intervention. In this work, we developed a wearable ultrasound system that enables synchronous monitoring of cardiac and carotid dynamics for comorbidity assessment. The system combines dual wearable ultrasound patches, a synchronous imaging strategy, artificial intelligence-based image processing algorithms, and human circuitry models to automatically extract and analyze key cardiac-carotid metrics, such as heart rate, pulse rate, cardiac volume, cardiac output, and carotid blood pressure. By evaluating the correlation of these metrics between modeling and measurements, we showed the feasibility of differentiating among healthy participants and patients with CAD, CHD, or CAD-CHD comorbidity. This integrated approach constitutes a promising framework for supporting the proactive assessment of coronary-carotid comorbidity.
We propose a wearable soft robot that assists with individualized scapula adduction and abduction for thoracic stretching in respiratory rehabilitation. Although thoracic stretching is known to be effective for respiratory rehabilitation, the range of motion of older adult patients narrows with age, and long-term external aid by physical therapists is required. The proposed robot consists of a soft and shoulder-wearable brace and cable-pulling mechanism to apply rotational torque on shoulders, resulting in stretching the thorax and scapulae. We designed the pulling mechanism by modeling the humeral head trajectory during stretching by a therapist and reproducing it with two linear actuators pulling the right and left shoulders simultaneously, based on position control aimed at achieving a target tension. The main results of validation experiments with older adults confirmed that the robot-assisted stretching was able to perform scapular stretching similar to that of a physical therapist.
Smartphones and wearables are low-burden tools for assessing real-time mood and behavior. Although these methods have been used with adolescents for behavioral tracking (e.g., activity, sleep), less is known about longer-term use (beyond one week) with adolescents with depression and about mobile sensing for monitoring mood for any adolescent population. This study examined acceptability and feasibility of a one-month EMA, actigraphy, and mobile sensing protocol for adolescents with elevated depressive symptoms. Adolescents aged 12 to 18 (N = 69; Mage = 15.46; 67% assigned female at birth; 42% White; 71% Hispanic or Latine; 38% sexual minority) completed EMA surveys on depressive symptoms, processes, and affect multiple times daily via a smartphone app that also collected passive sensor data (e.g., motion, geolocation). An actigraph measured physical activity and sleep. A feedback interview assessed protocol acceptability. Most participants (91%) completed all components, were willing to participate again (91%), and would recommend participation to peers (93%). EMA response rates improved (mean completion 57% to 66%) after shifting to a semi-personalized schedule with extended response windows. Actigraph wear time was high (> 70%) despite device-related issues. Sensor data availability varied by operating system, and privacy concerns influenced participation. Adherence was correlated within and between modalities, suggesting that individual compliance played a central role in consistent engagement. Findings support the feasibility and acceptability of smartphone and wearable methods for capturing real-world mood and behavior in adolescents, however careful attention to design, engagement, and ethical considerations remains essential.
To evaluate the feasibility of using wearable inertial measurement units (IMUs; small body-worn sensors that capture linear acceleration and angular velocity) combined with machine learning (ML) to identify anterior cruciate ligament (ACL) injuries during clinical knee joint laxity assessments. Prospective exploratory feasibility study using a case-control design. A feed-forward neural network classifier was trained on time-normalised IMU signals recorded during Lachman and Anterior Drawer tests to distinguish ACL-injured from healthy knees. Model performance was assessed using 10-fold participant-level cross-validation and reported at repetition-wise and subject-wise levels. Biomechanics laboratory within a secondary care setting in London, UK. Recruitment occurred through an acute soft-tissue injury management clinic. 50 participants were recruited: 26 healthy controls and 24 individuals with an MRI-confirmed ACL injury (partial or complete) sustained within the current injury episode. Healthy controls contributed 52 uninjured legs and ACL-injured participants contributed 25 injured and 23 contralateral uninjured legs (including 1 bilateral injury). Inclusion criteria for the injured group were acute knee injury, MRI-confirmed ACL tear and ability to bear weight. Controls were ≥18 years with no history of knee ligament injury. Exclusion criteria included age <18 or >45, chronic joint conditions, non-weightbearing status, pregnancy, allergy to adhesives or inability to provide informed consent. All participants completed the study. Not applicable. Participants underwent standardised clinical Lachman and Anterior Drawer tests performed by a specialist physiotherapist while IMUs were mounted on the femur and tibia using a custom three-dimensional-printed rig. The primary outcome was diagnostic accuracy of an ML model classifying ACL injury status (injured vs healthy) based on IMU-derived linear acceleration and angular velocity (three axes per sensor). Secondary outcomes included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUROC) and F1 score. Planned model assessments (repetition-wise and subject-wise) were completed as intended. Hyperparameter adjustments were made post hoc to address overfitting when expanding to the full dataset. Across the full dataset (n=50), the comparative leg model (method 2) achieved a subject-wise diagnostic accuracy of 81%, sensitivity 83%, specificity 79%, PPV 80% and NPV 83% using 10-fold participant-level cross-validation. Repetition-wise accuracy was 71%. Discriminative performance was moderate (AUROC 0.773), with an F1 score of 0.889. The individual leg model (method 1) showed lower performance (subject-wise accuracy 70%; sensitivity 43%; specificity 81%). This study supports the feasibility of wearable sensors to be used in the clinical assessment of ACL injury to assist in capturing movement features associated with joint laxity. More work should be done to increase generalisability and validate findings further.
This paper presents ChromaSense, a reflectance-based wearable photoplethysmography (PPG) sensor designed for robust monitoring of vital signs, including blood oxygen saturation (SpO2), pulse rate, and respiration rate across diverse skin tones. Conventional PPG-based sensors are susceptible to melanin-induced optical attenuation, which can contribute to measurement error, particularly in individuals with darker skin pigmentation. To address this limitation, ChromaSense integrates spectral colorimetric sensing to estimate skin reflectance and adaptively adjust LED drive current. The system was evaluated on a cohort of 50 participants spanning the Fitzpatrick skin tone scale. For SpO2, the device achieved a root-mean-square accuracy of 1.36% relative to a Food and Drug Administration-cleared reference pulse oximeter (Masimo MightySAT), satisfying the ISO 80601-2-61:2013 standard requirement of ≤3.5%. Bland-Altman analysis showed a mean bias of -0.27% with 95% limits of agreement from -2.91% to 2.36%. For pulse rate estimation, 84% of measurements were within ±10% of the reference values, and for respiration rate estimation, 90% of measurements were within ±5 breaths per minute of the reference values. A one-way analysis of variance (ANOVA) showed no statistically significant differences in SpO2 measurement error across Fitzpatrick skin types (p = 0.518). These results suggest that the proposed multimodal sensing strategy mitigates skin-tone-dependent variability within the evaluated cohort and enables consistent physiological measurement performance across diverse users.
The mechanical mismatch between rigid clinical electrodes and soft biological tissues remains a primary bottleneck restricting the stability of long-term electrophysiological monitoring. Printable flexible thin-film electrodes offer a compelling solution by enabling additive, high-throughput patterning on flexible and stretchable substrates, thereby circumventing the reliance on vacuum environments and high-temperature processing typical of conventional microfabrication. This review synthesizes recent advances in functional inks, ranging from metals and carbon nanomaterials to conductive polymers and ionogels, together with high-resolution printing techniques. Addressing the critical challenge of interfacial failure in flexible devices, we explore engineering strategies to enhance adhesion at both electrode-substrate and electrode-tissue interfaces. Specifically, we analyze the pivotal roles of physical interlocking and chemical anchoring mechanisms in suppressing dynamic delamination and maintaining device integrity. Finally, the review highlights representative applications in wearable electronics, implantable systems, and emerging organoid interfaces, and outlines key translational challenges, including long-term stability and manufacturing reproducibility.
The structured day hypothesis posits that the characteristics of a structured school environment play a protective role in adolescents' physical activity. Existing studies predominantly rely on subjective reports or short-term monitoring, lacking longitudinal objective evidence that covers a full academic year cycle for Chinese students who are under high academic pressure. This study employs long-term objective monitoring through wearable devices to systematically compare sleep and physical activity patterns among Chinese middle school students across four phases: study days, weekend days, winter vacation, and summer vacation, while also analyzing gender differences. A longitudinal study was conducted involving 27 first-year middle school students (14 boys and 13 girls) from a middle school in Nanjing. The Huawei Band 6 was utilized to continuously monitor sleep parameters, including sleep onset time, wake-up time, duration of deep, light, and REM sleep, as well as total sleep duration. Additionally, physical activity parameters such as step count, MVPA, and continuous MVPA were recorded during the specified four stages. A linear mixed-effects model was employed to analyze the effects of both stage and gender. On school days, insufficient sleep was observed alongside relatively high levels of physical activity, with only 26.71% of participants meeting the sleep adequacy standard and 34.30% achieving the MVPA target. During holidays, sleep quality improved while physical activity levels decreased, with the MVPA compliance rate among girls dropping to a mere 3.83% on weekend days. In contrast, winter vacation showed significantly higher total sleep duration, deep sleep duration, and step counts compared to summer vacation. Notably, girls exhibited longer deep sleep duration than boys; however, their step counts and MVPA levels were significantly lower than those of boys. Structured school schedules promote the maintenance of physical activity; however, they may also lead to increased sleep deprivation. In contrast, holidays tend to improve sleep quality but are linked to a significant decline in physical activity levels. Therefore, it is imperative to optimize daily routines during school days to ensure adequate sleep. Furthermore, establishing a collaborative intervention mechanism that involves families, schools, and communities during holidays can encourage physical activity and reduce sedentary behavior and screen time, particularly among female students and during summer vacations.