BACKGROUND: With the advent of personalized medical approaches, precise and tailored treatments are expected to become widely accepted for the prevention and treatment of diabetes. Paper-based colorimetric sensors that function in combination with smartphones have been rapidly developed in recent years because it does not require additional equipment and is inexpensive and easy to perform. In this study, we developed a portable, low-cost, and wearable sweat-glucose detection device for in situ detection.
RESULTS: The sensor adopted an integrated biomimetic nanoenzyme of glucose oxidase (GOx) encapsulated in copper 1, 4-benzenedicarboxylate (CuBDC) (GOx@CuBDC) through a biomimetic mineralization process. CuBDC exhibited a peroxide-like effect, cascade catalytic effect with the encapsulated GOx, and increased the enzyme stability. GOx@CuBDC and 3,3,5,5-tetramethylbenzidine were combined to form a hybrid membrane that achieved single-step paper-based glucose detection.
UNASSIGNED: This GOx@CuBDC-based colorimetric glucose sensor was used to quantitatively analyze the sweat-glucose concentration with smartphone readings. The sensor exhibited a good linear relationship over the concentration range of 40-900 μM and a limit of detection of 20.7 μM (S/N = 3). Moreover, the sensor performed well in situ monitoring and in evaluating variations based on the consumption of foods with different glycemic indices. Therefore, the fabricated wearable sweat-glucose sensors exhibited optimal practical application performance.

The wearable contact lens that continuously monitors intraocular pressure (IOP) facilitates prompt and early-state medical treatments of oculopathies such as glaucoma, postoperative myopia, etc. However, either taking drugs for pre-treatment or delaying the treatment process in the absence of a neural feedback component cannot realize accurate diagnosis or effective treatment. Herein, a neuroprosthetic contact lens enabled sensorimotor system is reported, which consists of a smart contact lens with Ti3C2Tx Wheatstone bridge structured IOP strain sensor, a Ti3C2Tx temperature sensor and an IOP point-of-care monitoring/display system. The point-of-care IOP monitoring and warning can be realized due to the high sensitivity of 12.52 mV mmHg-1 of the neuroprosthetic contact lens. In vivo experiments on rabbit eyes demonstrate the excellent wearability and biocompatibility of the neuroprosthetic contact lens. Further experiments on a living rate in vitro successfully mimic the biological sensorimotor loop. The leg twitching (larger or smaller angles) of the living rat was demonstrated under the command of motor cortex controlled by somatosensory cortex when the IOP is away from the normal range (higher or lower).

OBJECTIVE: To examine the incorporation of wearable electronic devices in the education of undergraduate nursing students.
BACKGROUND: The advancement of technology has influenced nursing education and will continue to do so in the future. Wearable technologies are electronic devices that can be worn as an accessory and expand the possibilities in nursing education with increased engagement in the learning process.
METHODS: A scoping review was conducted following JBI and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines.
METHODS: The search was performed on August 25th, 2023, in the databases: MEDLINE via PubMed, ISI Web of Science, ERIC, EBSCOhost CINAHL, EBSCOhost Academic Search Premier, EBSCOhost Health Source Nursing, EMBASE, Scopus, BVShost LILACS and ProQuest. Literature that examined the application of wearable electronic devices in the education of undergraduate nursing students was included.
RESULTS: This scoping review study included a total of 21 records published between 2014 and 2023. The analysis showed that smart glasses are the most common wearable electronic device used in nursing education, followed by smartwatches. The devices provide an opportunity for competencies development, especially when applied in the simulation environment, such as physical examination and medication administration. Wearable technologies are potentially useful and feasible as learning tools in nursing education, increasing nursing students\' motivation, confidence and satisfaction.
CONCLUSIONS: In the technological revolution, educators must consider the potential of innovative teaching strategies, such as wearable electronic devices, to advance nursing education. Wearables can contribute to developing competencies required for the professionalism of undergraduate nursing students.

BACKGROUND: Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time.
OBJECTIVE: The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures.
METHODS: We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes.
RESULTS: Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness.
CONCLUSIONS: Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study\'s application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin\'s functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological breakthroughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition.

UNASSIGNED: Wearable trunk exoskeletons hold immense potential in fields such as healthcare and industry. Previous research has indicated that intention recognition control plays a crucial role in users\' daily use of exoskeletons.
UNASSIGNED: This review aims to discuss the characteristics of intention recognition control schemes for intelligent trunk exoskeletons under different control objectives over the past decade.
UNASSIGNED: Considering the relatively late development of active trunk exoskeletons, we selected papers published in the last decade (2013 to 2023) from the Web of Science, PubMed, and IEEE Xplore databases. In total, 50 articles were selected and examined based on four control objectives.
UNASSIGNED: In general, we found that researchers focus on trunk exoskeleton devices designed for assistance and motor augmentation, which rely more on body movement signals as a source for intention recognition.
UNASSIGNED: Based on these results, we identify and discuss several promising research directions that may help to attain a widely accepted control methods, thereby advancing further development of trunk exoskeleton technology.

BACKGROUND: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings.
OBJECTIVE: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts.
METHODS: Data were sourced from a large longitudinal mHealth study, wherein participants\' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants\' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score.
RESULTS: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=-93.61, P<.001), increased sleep variability (β=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P=.001; sleep offset: β=1.12, P<.001; M10 onset: β=0.73, P=.003; HR acrophase: β=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = -31.51, P=.002) and summer (β of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer.
CONCLUSIONS: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.

BACKGROUND: The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine screening questionnaires. Passive sensing data have been promoted as a tool for depressive symptoms detection though there is still limited evidence on its usage in the elderly. Therefore, this study aims to review current knowledge on the use of passive sensing data via smartphones and smartwatches in depressive symptom screening for the elderly.
METHODS: The search of literature was performed in PubMed, IEEE Xplore digital library, and PsycINFO. Literature investigating the use of passive sensing data to screen, monitor, and/or predict depressive symptoms in the elderly (aged 60 and above) via smartphones and/or wrist-worn wearables was included for initial screening. Studies in English from international journals published between January 2012 to September 2022 were included. The reviewed studies were further analyzed by a narrative analysis.
RESULTS: The majority of 21 included studies were conducted in Western countries with a few in Asia and Australia. Most studies adopted a cohort study design (n = 12), followed by cross-sectional design (n = 7) and a case-control design (n = 2). The most popular passive sensing data was related to sleep and physical activity using an actigraphy. Sleep characteristics, such as prolonged wakefulness after sleep onset, along with lower levels of physical activity, exhibited a significant association with depression. However, cohort studies expressed concerns regarding data quality stemming from incomplete follow-up and potential confounding effects.
CONCLUSIONS: Passive sensing data, such as sleep, and physical activity parameters should be promoted for depressive symptoms detection. However, the validity, reliability, feasibility, and privacy concerns still need further exploration.

Self-powered wearable piezoelectric sensing devices demand flexibility and high voltage electrical properties to meet personalized health and safety management needs. Aiming at the characteristics of piezoceramics with high piezoelectricity and low flexibility, this study designs a high-performance piezoelectric sensor based on multi-phase barium titanate (BTO) flexible piezoceramic film, namely multi-phase BTO sensor. The substrate-less self-supported multi-phase BTO films had excellent flexibility and could be bent 180° at a thickness of 33 μm, and exhibited good bending fatigue resistance in 1 × 10 4 bending cycles at a thickness of 5 μm. The prepared multi-phase BTO sensor could maintain good piezoelectric stability after 1.2 × 10 4 piezoelectric cycle tests. Based on the flexibility, high piezoelectricity, wearability, portability and battery-free self-powered characteristics of this sensor, the developed smart mask could monitor the respiratory signals of different frequencies and amplitudes in real time. In addition, by mounting the sensor on the hand or shoulder, different gestures and arm movements could also be detected. In summary, the multi-phase BTO sensor developed in this paper is expected to develop convenient and efficient wearable sensing devices for physiological health and behavioral activity monitoring applications.
自供电可穿戴压电传感设备需要柔韧性和高压电性以满足个性化健康安全管理需求。针对压电陶瓷压电性高、柔性差的特点，本文设计了一种基于多相钛酸钡（BTO）柔性压电陶瓷膜的高性能压电传感器，即多相BTO传感器。无衬底自支撑的多相BTO膜具有优异的柔韧性，厚度为33 μm时可实现180°弯曲，厚度为5 μm时可在1 × 10 4次弯曲循环中表现出良好的抗弯曲疲劳性。所制备的多相BTO传感器在经过1.2 × 10 4次压电循环测试后仍能保持良好的压电稳定性。基于此传感器的柔韧性、高压电性、可穿戴性、便携性和无电池自供电特性，开发的智能面罩可以实时监测不同频率和振幅的呼吸信号。此外，将传感器安装在手部或肩部，还可以检测到不同的手势和手臂动作。综上，本文开发的多相BTO传感器有望为生理健康和行为活动监测应用开发出便捷高效的可穿戴传感设备。.

The monitoring of body temperature is a recent addition to the plethora of parameters provided by wellness and fitness wearable devices. Current wearable temperature measurements are made at the skin surface, a measurement that is impacted by the ambient environment of the individual. The use of near-infrared spectroscopy provides the potential for a measurement below the epidermal layer of skin, thereby having the potential advantage of being more reflective of physiological conditions. The feasibility of noninvasive temperature measurements is demonstrated by using an in vitro model designed to mimic the near-infrared spectra of skin. A miniaturizable solid-state laser-diode-based near-infrared spectrometer was used to collect diffuse reflectance spectra for a set of seven tissue phantoms composed of different amounts of water, gelatin, and Intralipid. Temperatures were varied between 20-24 °C while collecting these spectra. Two types of partial least squares (PLS) calibration models were developed to evaluate the analytical utility of this approach. In both cases, the collected spectra were used without pre-processing and the number of latent variables was the only optimized parameter. The first approach involved splitting the whole dataset into separate calibration and prediction subsets for which a single optimized PLS model was developed. For this first case, the coefficient of determination (R2) is 0.95 and the standard error of prediction (SEP) is 0.22 °C for temperature predictions. The second strategy used a leave-one-phantom-out methodology that resulted in seven PLS models, each predicting the temperatures for all spectra in the held-out phantom. For this set of phantom-specific predicted temperatures, R2 and SEP values range from 0.67-0.99 and 0.19-0.65 °C, respectively. The stability and reproducibility of the sample-to-spectrometer interface are identified as major sources of spectral variance within and between phantoms. Overall, results from this in vitro study justify the development of future in vivo measurement technologies for applications as wearables for continuous, real-time monitoring of body temperature for both healthy and ill individuals.