Role of wearable electrochemical biosensors in monitoring renal function biomarkers in sweat: a review.

Real-time detection of renal biomarkers is crucial for immediate and continuous monitoring of kidney function, facilitating early diagnosis and intervention in kidney-related disorders. This proactive approach enables timely adjustments in treatment plans, particularly in critical situations, and enhances overall patient care. Wearable devices emerge as a promising solution, enabling non-invasive and real-time data collection. This comprehensive review investigates numerous types of wearable sensors designed to detect kidney biomarkers in body fluids such as sweat. It critically evaluates the precision, dependability, and user-friendliness of these devices, contemplating their seamless integration into daily life for continuous health tracking. The review highlights the potential influence of wearable technology on individualized renal healthcare and its role in preventative medicine while also addressing challenges and future directions. The review's goal is to provide guidance to academics, healthcare professionals, and technologists working on wearable solutions for renal biomarker detection by compiling the body of current knowledge and advancements.

Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning.

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression to dementia. Wearable devices collecting physiological and behavioral data can help in remote, passive, and continuous monitoring of moods and NPS, overcoming limitations and inconveniences of current assessment methods. In this longitudinal study, we examined the predictive ability of digital biomarkers based on sensor data from a wrist-worn wearable to determine the severity of NPS and mood disorders on a daily basis in older adults with predominant MCI. In addition to conventional physiological biomarkers, such as heart rate variability and skin conductance levels, we leveraged deep-learning features derived from physiological data using a self-supervised convolutional autoencoder. Models combining common digital biomarkers and deep features predicted depression severity scores with a correlation of r = 0.73 on average, total severity of mood disorder symptoms with r = 0.67, and mild behavioral impairment scores with r = 0.69 in the study population. Our findings demonstrated the potential of physiological biomarkers collected from wearables and deep learning methods to be used for the continuous and unobtrusive assessments of mental health symptoms in older adults, including those with MCI. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov (NCT05059353) on September 28, 2021, titled "Effectiveness and Safety of a Digitally Based Multidomain Intervention for Mild Cognitive Impairment".

Skin-attachable Tb-MOF ratio fluorescent sensor for real-time detection of human sweat pH.

The pH of human sweat is highly related with a variety of diseases, whereas the monitoring of sweat pH still remains challenging for ordinary families. In this study, we developed a novel dual-emission Tb-MOF using DPA as the ligand and further designed and constructed a skin-attachable Tb-MOF ratio fluorescent sensor for real-time detection of human sweat pH. With the increased concentration of H+, the interaction of H+ with carbonyl organic ligand leads to the collapse of the Tb-MOF crystal structure, resulting in the interruption of antenna effect, and correspondingly increasing the emission of the ligand at 380 nm and decreasing the emission of the central ion Tb3+ at 544 nm. This Tb-MOF nanoprobe has a good linear response in the pH range of 4.12-7.05 (R2 = 0.9914) with excellent anti-interference ability. Based on the merits of fast pH response and high sensitivity, the nanoprobe was further used to prepare flexible wearable sensor. The wearable sensor can detect pH in the linear range of 3.50-6.70, which covers the pH range of normal human sweat (4.50-6.50). Subsequently, the storage stability and detection accuracy of the sensors were evaluated. Finally, the sensor has been successfully applied for the detection of pH in actual sweat samples from 21 volunteer and the real-time monitoring of pH variation during movement processing. This skin-attachable Tb-MOF sensor, with the advantages of low cost, visible color change and long shelf-life, is appealing for sweat pH monitoring especially for ordinary families.

Wearable defibrillator to improve accuracy in selecting candidates to implantable defibrillator: A real-world experience.

AIMS: The indication for implantable cardioverter defibrillator (ICD) for sudden cardiac death (SCD) prevention relies mostly on left ventricular ejection fraction (LVEF) ≤ 35%. The use of a wearable cardioverter defibrillator (WCD) in the case of dynamic alterations of LVEF may help avoid an improper early ICD implant when a favourable evolution in the post-acute phase is observed and may help reduce costs. METHODS: This parallel cohort retrospective study included patients with heart failure with reduced ejection fraction (HFrEF) at high risk of arrhythmias recruited in the acute phase and divided into an early ICD cohort and a WCD cohort for primary prevention during the waiting period established by European Society of Cardiology guidelines. RESULTS: A total of 41 consecutive patients were enrolled: 26 in the WCD group and 15 in the early ICD group. Age, LVEF at baseline, causes of HFrEF and drug therapy in the two cohorts were similar. During the waiting period after the inclusion, three patients (11.5%) in the WCD cohort and four (26.7%) in the early ICD cohort developed relevant ventricular arrhythmias (P = 0.22); none of them had subsequent LVEF recovery. At the end of the waiting period, 13 patients (50%) in the WCD group and 7 (46.7%) in the early ICD group experienced LVEF recovery (P = 0.84). The average cost per patient at the end of the waiting period was €23 934 in the early ICD cohort versus €19 167 in the WCD cohort (-19.9%). This cost savings from WCD use appears even higher when projected over a 10 year period (-41.2%). CONCLUSIONS: WCD may represent a cost-effective strategy to more accurately select candidates for the primary prevention ICD implant among high-risk patients with HFrEF. ICD use provides effective protection from SCD and reduces costs compared with an extensive early ICD implant.

Emerging Roles of Graphitic Carbon Nitride-based Materials in Biomedical Applications.

Graphite carbon nitride (g-C3N4) is a two-dimensional conjugated polymer with a unique energy band structure similar to graphene. Due to its outstanding analytical advantages, such as relatively small band gap (2.7 eV), low-cost synthesis, high thermal stability, excellent photocatalytic ability, and good biocompatibility, g-C3N4 has attracted the interest of researchers and industry, especially in the medical field. This paper summarizes the latest research on g-C3N4-based composites in various biomedical applications, including therapy, diagnostic imaging, biosensors, antibacterial, and wearable devices. In addition, the application prospects and possible challenges of g-C3N4 in nanomedicine are also discussed in detail. This review is expected to inspire emerging biomedical applications based on g-C3N4.

Synergistic Strategies of Biomolecular Transport Technologies in Transdermal Healthcare Systems.

Transdermal healthcare systems have gained significant attention for their painless and convenient drug administration, as well as their ability to detect biomarkers promptly. However, the skin barrier limits the candidates of biomolecules that can be transported, and reliance on simple diffusion poses a bottleneck for personalized diagnosis and treatment. Consequently, recent advancements in transdermal transport technologies have evolved toward active methods based on external energy sources. Multiple combinations of these technologies have also shown promise for increasing therapeutic effectiveness and diagnostic accuracy as delivery efficiency is maximized. Furthermore, wearable healthcare platforms are being developed in diverse aspects for patient convenience, safety, and on-demand treatment. Herein, a comprehensive overview of active transdermal delivery technologies is provided, highlighting the combination-based diagnostics, therapeutics, and theragnostics, along with the latest trends in platform advancements. This offers insights into the potential applications of next-generation wearable transdermal medical devices for personalized autonomous healthcare.

Highly Stretchable, Tissue-like Ag Nanowire-Enhanced Ionogel Nanocomposites as an Ionogel-Based Wearable Sensor for Body Motion Monitoring.

The development of wearable electronic devices for human health monitoring requires materials with high mechanical performance and sensitivity. In this study, we present a novel transparent tissue-like ionogel-based wearable sensor based on silver nanowire-reinforced ionogel nanocomposites, P(AAm-co-AA) ionogel-Ag NWs composite. The composite exhibits a high stretchability of 605% strain and a moderate fracture stress of about 377 kPa. The sensor also demonstrates a sensitive response to temperature changes and electrostatic adsorption. By encapsulating the nanocomposite in a polyurethane transparent film dressing, we address issues such as skin irritation and enable multidirectional stretching. Measuring resistive changes of the ionogel nanocomposite in response to corresponding strain changes enables its utility as a highly stretchable wearable sensor with excellent performance in sensitivity, stability, and repeatability. The fabricated pressure sensor array exhibits great proficiency in stress distribution, capacitance sensing, and discernment of fluctuations in both external electric fields and stress. Our findings suggest that this material holds promise for applications in wearable and flexible strain sensors, temperature sensors, pressure sensors, and actuators.

Approaches of wearable and implantable biosensor towards of developing in precision medicine.

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.

Examination of a practical method to quantitatively evaluate the rolling over movement in a clinical setting.

[Purpose] We investigated the utility of wearable inertial and magnetic sensing modules for analyzing neck and trunk movements during the rolling over movement. [Participants and Methods] The participants were instructed to roll over from the supine to the side-lying position with three sensor units attached to their forehead, xiphoid process of the sternum, and abdomen. Experiments were conducted on two prescribed patterns: one emphasizing hip joint flexion and adduction, and the other focusing on scapular protraction and horizontal shoulder joint adduction in two healthy participants (one male and one female). The flexion and rotation angles of the neck and trunk were calculated using conventional spreadsheet software with data obtained from the sensors. The obtained values were compared for agreement with those derived from a three-dimensional (3D) motion analysis device. [Results] The cross-correlation coefficient for the flexion and rotation angles of the neck and trunk between the two measurement methods was approximately 0.85, and the root mean square (RMS) angle difference was approximately 5.0°. [Conclusion] Wearable inertial and magnetic sensors can be used to quantitatively evaluate neck and trunk movements during the rolling over movement.

Predicting Workers' Stress: Application of a High-Performance Algorithm Using Working-Style Characteristics.

BACKGROUND: Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual's lifestyle has not been evaluated. OBJECTIVE: This study aims to develop a novel, high-performance algorithm to predict an employee's stress among a group of employees with similar working characteristics. METHODS: This prospective observational study evaluated participants' responses to web­based questionnaires, including attendance records and data collected using a wearable device. Data spanning 12 weeks (between January 17, 2022, and April 10, 2022) were collected from 194 Shionogi Group employees. Participants wore the Fitbit Charge 4 wearable device, which collected data on daily sleep, activity, and heart rate. Daily work shift data included details of working hours. Weekly questionnaire responses included the K6 questionnaire for depression/anxiety, a behavioral questionnaire, and the number of days lunch was missed. The proposed prediction model used a neighborhood cluster (N=20) with working-style characteristics similar to those of the prediction target person. Data from the previous week predicted stress levels the following week. Three models were compared by selecting appropriate training data: (1) single model, (2) proposed method 1, and (3) proposed method 2. Shapley Additive Explanations (SHAP) were calculated for the top 10 extracted features from the Extreme Gradient Boosting (XGBoost) model to evaluate the amount and contribution direction categorized by teleworking rates (mean): low: <0.2 (more than 4 days/week in office), middle: 0.2 to <0.6 (2 to 4 days/week in office), and high: ≥0.6 (less than 2 days/week in office). RESULTS: Data from 190 participants were used, with a teleworking rate ranging from 0% to 79%. The area under the curve (AUC) of the proposed method 2 was 0.84 (true positive vs false positive: 0.77 vs 0.26). Among participants with low teleworking rates, most features extracted were related to sleep, followed by activity and work. Among participants with high teleworking rates, most features were related to activity, followed by sleep and work. SHAP analysis showed that for participants with high teleworking rates, skipping lunch, working more/less than scheduled, higher fluctuations in heart rate, and lower mean sleep duration contributed to stress. In participants with low teleworking rates, coming too early or late to work (before/after 9 AM), a higher/lower than mean heart rate, lower fluctuations in heart rate, and burning more/fewer calories than normal contributed to stress. CONCLUSIONS: Forming a neighborhood cluster with similar working styles based on teleworking rates and using it as training data improved the prediction performance. The validity of the neighborhood cluster approach is indicated by differences in the contributing features and their contribution directions among teleworking levels. TRIAL REGISTRATION: UMIN UMIN000046394; https://www.umin.ac.jp/ctr/index.htm.

Preliminary Validity and Acceptability of Motion Tape for Measuring Low Back Movement: Mixed Methods Study.

BACKGROUND: Low back pain (LBP) is a significant public health problem that can result in physical disability and financial burden for the individual and society. Physical therapy is effective for managing LBP and includes evaluation of posture and movement, interventions directed at modifying posture and movement, and prescription of exercises. However, physical therapists have limited tools for objective evaluation of low back posture and movement and monitoring of exercises, and this evaluation is limited to the time frame of a clinical encounter. There is a need for a valid tool that can be used to evaluate low back posture and movement and monitor exercises outside the clinic. To address this need, a fabric-based, wearable sensor, Motion Tape (MT), was developed and adapted for a low back use case. MT is a low-profile, disposable, self-adhesive, skin-strain sensor developed by spray coating piezoresistive graphene nanocomposites directly onto commercial kinesiology tape. OBJECTIVE: The objectives of this study were to (1) validate MT for measuring low back posture and movement and (2) assess the acceptability of MT for users. METHODS: A total of 10 participants without LBP were tested. A 3D optical motion capture system was used as a reference standard to measure low back kinematics. Retroreflective markers and a matrix of MTs were placed on the low back to measure kinematics (motion capture) and strain (MT) simultaneously during low back movements in the sagittal, frontal, and axial planes. Cross-correlation coefficients were calculated to evaluate the concurrent validity of MT strain in reference motion capture kinematics during each movement. The acceptability of MT was assessed using semistructured interviews conducted with each participant after laboratory testing. Interview data were analyzed using rapid qualitative analysis to identify themes and subthemes of user acceptability. RESULTS: Visual inspection of concurrent MT strain and kinematics of the low back indicated that MT can distinguish between different movement directions. Cross-correlation coefficients between MT strain and motion capture kinematics ranged from -0.915 to 0.983, and the strength of the correlations varied across MT placements and low back movement directions. Regarding user acceptability, participants expressed enthusiasm toward MT and believed that it would be helpful for remote interventions for LBP but provided suggestions for improvement. CONCLUSIONS: MT was able to distinguish between different low back movements, and most MTs demonstrated moderate to high correlation with motion capture kinematics. This preliminary laboratory validation of MT provides a basis for future device improvements, which will also involve testing in a free-living environment. Overall, users found MT acceptable for use in physical therapy for managing LBP.

Synthetic 3D full-body skeletal motion from 2D paths using RNN with LSTM cells and linear networks.

Gait analysis has proven to be a key process in the functional assessment of people involving many fields, such as diagnosis of diseases or rehabilitation, and has increased in relevance lately. Gait analysis often requires gathering data, although this can be very expensive and time consuming. One of the main solutions applied in fields when data acquisition is a problem is augmentation of datasets with artificial data. There are two main approaches for doing that: simulation and synthetic data generation. In this article, we propose a parametrizable generative system of synthetic walking simplified human skeletons. For achieving that, a data gathering experiment with up to 26 individuals was conducted. The system consists of two artificial neural networks: a recurrent neural network for the generation of the movement and a multilayer perceptron for determining the size of the segments of the skeletons. The system has been evaluated through four processes: (i) an observational appraisal by researchers in gait analysis, (ii) a visual representation of the distribution of the generated data, (iii) a numerical analysis using the normalized cross-correlation coefficient, and (iv) an angular evaluation to check the kinematic validity of the data. The evaluation concluded that the system is able to generate realistic and accurate gait data. These results reveal a promising path for this research field, which can be further improved through increasing the variety of movements and the user sample.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements.

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

Physiological Profiling of Agitation in Dementia: Insights From Wearable Sensor Data.

Background and Objectives: The number of people with dementia is expected to triple to 152 million in 2050, with 90% having accompanying behavioral and psychological symptoms (BPSD). Agitation is among the most critical BPSD and can lead to decreased quality of life for people with dementia and their caregivers. This study aims to explore objective quantification of agitation in people with dementia by analyzing the relationships between physiological and movement data from wearables and observational measures of agitation. Research Design and Methods: The data presented here is from 30 people with dementia, each included for 1 week, collected following our previously published multimodal data collection protocol. This observational protocol has a cross-sectional repeated measures design, encompassing data from both wearable and fixed sensors. Generalized linear mixed models were used to quantify the relationship between data from different wearable sensor modalities and agitation, as well as motor and verbal agitation specifically. Results: Several features from wearable data are significantly associated with agitation, at least the p < .05 level (absolute ß: 0.224-0.753). Additionally, different features are informative depending on the agitation type or the patient the data were collected from. Adding context with key confounding variables (time of day, movement, and temperature) allows for a clearer interpretation of feature differences when a person with dementia is agitated. Discussion and Implications: The features shown to be significantly different, across the study population, suggest possible autonomic nervous system activation when agitated. Differences when splitting the data by agitation type point toward a need for future detection models to tailor to the primary type of agitation expressed. Finally, patient-specific differences in features indicate a need for patient- or group-level model personalization. The findings reported in this study both reinforce and add to the fundamental understanding of and can be used to drive the objective quantification of agitation.

Diagnostic Yield and Model Prediction Using Wearable Patch Device in HFpEF.

Heart failure (HF) is a prevalent global health issue projected to escalate, notably in aging populations. The study aimed to identify predictive markers for Heart Failure with preserved Ejection Fraction (HFpEF). We scrutinized vital parameters like age, BMI, eGFR, and comorbidities like atrial fibrillation, coronary artery disease (CAD), diabetes mellites (DM). Evaluating phonocardiogram indicators-third heart sound(S3) and Systolic Dysfunction Index (SDI)-our logistic regression revealed age (≥ 65years), BMI (≥ 25 kg/m2), eGFR (<60 mL/min/1.73m2), CAD, DM, S3 intensity ≥5, and SDI ≥5 as HFpEF predictors, with AUC = 0.816 (p < .001). ROC diagnosis curve showed that the sensitivity, specificity and Youden's index J of the model were 0.755, 0.673 and 0.838, respectively. Nonetheless, further exploration is crucial to delineate the clinical applicability and constraints of these markers.

Patient Generated Health Data Among Older Adults with Cancer in Home Setting: Scoping Review.

This scoping review aimed to identify and synthesize the literature related to patient-generated health data (PGHD) among older adults with cancer in home setting. Of the 1,090 articles extracted through six databases searches, 53 were selected. Studies were published from 2007 to 2022 and the types of devices to generate PGHD included research-grade and consumer-grade wearable devices. PGHD was assessed for physical activity, vital signs, and sleep. PGHD utilization was categorized: 1) identification, monitoring, review, and analysis (100%); 2) feedback and information report (32.1%); 3) motivation (26.4%); and 4) education and coaching (17.0%). Our study reveals that various PGHDs from older adults with cancer are mainly collected passively, with limited use for interaction with healthcare providers. These results may provide valuable insights for healthcare providers into potential PGHD applications in geriatric cancer care.

No beneficial use of the wearable cardioverter defibrillator among patients suffering from inherited and congenital heart disease: data from a European multicenter registry.

Background: Data on the use of the wearable cardioverter defibrillator in patients suffering from inherited and congenital heart disease are limited. Consequently, evidence for guideline recommendations in this patient population is lacking. Methods: In total 1,675 patients were included in a multicenter registry of eight European centers. In the present cohort, we included 18 patients suffering from congenital and inherited heart disease. Results: Nine patients (50%) were male with a mean age of 41.3 ± 16.4 years. Four patients suffered from hypertrophic cardiomyopathy (HCM), four patients suffered from non-compaction cardiomyopathy (NCCM), two patients were diagnosed with arrhythmogenic right ventricular cardiomyopathy (ARVC) and one patient suffered from muscular dystrophy of the limb-girdle type with cardiac involvement, secondary cardiomyopathy. Three patients presented with Brugada syndrome (BrS). One patient suffered from long-QT syndrome type 1 (LQTS1). Furthermore, two patients had congenital heart defects and one patient suffered from cardiac sarcoidosis (CS). There were no appropriate/inappropriate shocks with the WCD in this cohort. One patient had recurrent self-limiting sustained ventricular tachycardia during the wear time, but actively inhibited a shock and was hospitalized. The compliance rate in this cohort was 77.8% with a mean wear time of 45.3 ± 26.9 days with a mean follow-up time of 570 ± 734 days. 55.6% (10/18) of the patients received an ICD after WCD wear time. Conclusions: This retrospective study of patients with inherited and congenital heart disease shows that WCD use is not beneficial in the majority of patients with inherited and congenital heart disease.

Evaluation of a passive wearable arm ExoNET.

Wearable ExoNETs offer a novel, wearable solution to support and facilitate upper extremity gravity compensation in healthy, unimpaired individuals. In this study, we investigated the safety and feasibility of gravity compensating ExoNETs on 10 healthy, unimpaired individuals across a series of tasks, including activities of daily living and resistance exercises. The direct muscle activity and kinematic effects of gravity compensation were compared to a sham control and no device control. Mixed effects analysis revealed significant reductions in muscle activity at the biceps, triceps and medial deltoids with effect sizes of -3.6%, -4.5%, and -7.2% rmsMVC, respectively, during gravity support. There were no significant changes in movement kinematics as evidenced by minimal change in coverage metrics at the wrist. These findings reveal the potential for the ExoNET to serve as an alternative to existing bulky and encumbering devices in post-stroke rehabilitation settings and pave the way for future clinical trials.

DISPEL: A Python Framework for Developing Measures From Digital Health Technologies.

Goal: This paper introduces DISPEL, a Python framework to facilitate development of sensor-derived measures (SDMs) from data collected with digital health technologies in the context of therapeutic development for neurodegenerative diseases. Methods: Modularity, integrability and flexibility were achieved adopting an object-oriented architecture for data modelling and SDM extraction, which also allowed standardizing SDM generation, naming, storage, and documentation. Additionally, a functionality was designed to implement systematic flagging of missing data and unexpected user behaviors, both frequent in unsupervised monitoring. Results: DISPEL is available under MIT license. It already supports formats from different data providers and allows traceable end-to-end processing from raw data collected with wearables and smartphones to structured SDM datasets. Novel and literature-based signal processing approaches currently allow to extract SDMs from 16 structured tests (including six questionnaires), assessing overall disability and quality of life, and measuring performance outcomes of cognition, manual dexterity, and mobility. Conclusion: DISPEL supports SDM development for clinical trials by providing a production-grade Python framework and a large set of already implemented SDMs. While the framework has already been refined based on clinical trials' data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users.

Fabricated advanced textile for personal thermal management, intelligent health monitoring and energy harvesting.

Fabrics are soft against the skin, flexible, easily accessible and able to wick away perspiration, to some extent for local private thermal management. In this review, we classify smart fabrics as passive thermal management fabrics and active thermal management fabrics based on the availability of outside energy consumption in the manipulation of heat generation and dissipation from the human body. The mechanism and research status of various thermal management fabrics are introduced in detail, and the article also analyses the advantages and disadvantages of various smart thermal management fabrics, achieving a better and more comprehensive comprehension of the current state of research on smart thermal management fabrics, which is quite an important reference guide for our future research. In addition, with the progress of science and technology, the social demand for fabrics has shifted from keeping warm to improving health and quality of life. E-textiles have potential value in areas such as remote health monitoring and life signal detection. New e-textiles are designed to mimic the skin, sense biological data and transmit information. At the same time, the ultra-moisturizing properties of the fabric's thermal management allow for applications beyond just the human body to energy. E-textiles hold great promise for energy harvesting and storage. The article also introduces the application of smart fabrics in life forms and energy harvesting. By combining electronic technology with textiles, e-textiles can be manufactured to promote human well-being and quality of life. Although smart textiles are equipped with more intelligent features, wearing comfort must be the first thing to be ensured in the multi-directional application of textiles. Eventually, we discuss the dares and prospects of smart thermal management fabric research.