Search Results (2,109)

During the coronavirus 2019 pandemic, sports activities were restricted, raising concerns about their impact on the physical condition of adolescent athletes, which remained largely unquantified. This study was designed with two primary objectives: first, to precisely quantify and elucidate the differences in the physical condition of adolescent athletes before and after activity restrictions due to the pandemic; and second, to innovatively develop and validate a non-contact medical checkup application. Medical checks were conducted on 563 athletes designated for sports enhancement. Participants were junior high school students aged 13 to 15, and the sample consisted of 315 boys and 248 girls. Furthermore, we developed a smartphone application and compared self-checks using the application with in-person checks by orthopedic surgeons to determine the challenges associated with self-checks. Statistical tests were conducted to determine whether there were statistically significant differences in range of motion and flexibility parameters before and after the pandemic. Additionally, items with discrepancies between values self-entered by athletes using the smartphone application and values measured by specialists were detected, and application updates were performed. Student’s t-test was used for continuous variables, whereas the chi-square test was used for other variables. Following the coronavirus 2019 pandemic, athletes were stiffer than during the pre-pandemic period in terms of hip and shoulder joint rotation range of motion and heel–buttock distance. The dominant hip external rotation decreased from 53.8° to 46.8° (p = 0.0062); the non-dominant hip external rotation decreased from 53.5° to 48.0° (p = 0.0252); the dominant shoulder internal rotation decreased from 62.5° to 54.7° (p = 0.0042); external rotation decreased from 97.6° to 93.5° (p = 0.0282), and the heel–buttock distance increased from 4.0 cm to 10.4 cm (p < 0.0001). The heel–buttock distance and straight leg raising angle measurements differed between the self-check and face-to-face check. Although there are items that cannot be accurately evaluated by self-check, physical condition can be improved with less contact by first conducting a face-to-face evaluation under appropriate guidance and then conducting a self-check. These findings successfully address our primary objectives. Specifically, we demonstrated a significant decline in the physical condition of adolescent athletes following pandemic-related activity restrictions, thereby quantifying their impact. Furthermore, our developed non-contact medical checkup application proved to be a viable tool for monitoring physical condition with reduced contact, although careful consideration of measurable parameters is crucial. This study provides critical insights into the long-term effects of activity restrictions on young athletes and offers a practical solution for health monitoring during infectious disease outbreaks, highlighting the potential for hybrid checkup approaches. Full article

Cell-free biosensors harness the selectivity of cellular machinery without living cells’ constraints, offering advantages in environmental monitoring, medical diagnostics, and biotechnological applications. This review examines recent advances in cell-free biosensor development, highlighting their ability to detect diverse analytes including heavy metals, organic pollutants, pathogens, and clinical biomarkers with high sensitivity and specificity. We analyze technological innovations in cell-free protein synthesis optimization, preservation strategies, and field deployment methods that have enhanced sensitivity, and practical applicability. The integration of synthetic biology approaches has enabled complex signal processing, multiplexed detection, and novel sensor designs including riboswitches, split reporter systems, and metabolic sensing modules. Emerging materials such as supported lipid bilayers, hydrogels, and artificial cells are expanding biosensor capabilities through microcompartmentalization and electronic integration. Despite significant progress, challenges remain in standardization, sample interference mitigation, and cost reduction. Future opportunities include smartphone integration, enhanced preservation methods, and hybrid sensing platforms. Cell-free biosensors hold particular promise for point-of-care diagnostics in resource-limited settings, environmental monitoring applications, and food safety testing, representing essential tools for addressing global challenges in healthcare, environmental protection, and biosecurity. Full article

pH is a critical parameter requiring precise monitoring across scientific, industrial, and biological domains. Fluorescent pH probes offer a powerful alternative to traditional methods (e.g., electrodes, indicators), overcoming limitations in miniaturization, long-term stability, and electromagnetic interference. By utilizing photophysical mechanisms—including intramolecular charge transfer (ICT), photoinduced electron transfer (PET), and fluorescence resonance energy transfer (FRET)—these probes enable high-sensitivity, reusable, and biocompatible sensing. This review systematically details recent advances, categorizing probes by operational pH range: strongly acidic (0–3), weakly acidic (3–7), strongly alkaline (>12), weakly alkaline (7–11), near-neutral (6–8), and wide-dynamic range. Innovations such as ratiometric detection, organelle-specific targeting (lysosomes, mitochondria), smartphone colorimetry, and dual-analyte response (e.g., pH + Al³⁺/CN⁻) are highlighted. Applications span real-time cellular imaging (HeLa cells, zebrafish, mice), food quality assessment, environmental monitoring, and industrial diagnostics (e.g., concrete pH). Persistent challenges include extreme-pH sensing (notably alkalinity), photobleaching, dye leakage, and environmental resilience. Future research should prioritize broadening functional pH ranges, enhancing probe stability, and developing wide-range sensing strategies to advance deployment in commercial and industrial online monitoring platforms. Full article

The purpose of the present research is to analyse the negative outcomes associated with the excessive Instagram dependency of those users that access the application through their smartphones. An empirical study was conducted through online interviews using structured questionnaires, resulting in 342 valid responses, with the target population being young users over 18 years old who access Instagram daily. Research shows that dependency on Instagram is primarily driven by individuals’ need for orientation and understanding, with entertainment being a secondary motivation. The results indicate that dependency on the social network is positively associated with excessive use, addiction, and Instastress. Furthermore, excessive use contributes to personal and social problems and increases both stress levels and mindfulness related to the platform. In turn, this excessive use intensifies addiction, which functions as a mediating variable between overuse and Instastress, mindfulness, and emotional exhaustion. This study offers valuable insights for academics, mental health professionals, and marketers by emphasizing the importance of fostering healthier digital habits and developing targeted interventions. Full article

Background: Mobile health applications have emerged as a novel tool to support secondary prevention after myocardial infarction (MI) or percutaneous coronary intervention (PCI). However, the impact of app-based interventions on clinically meaningful outcomes such as hospital readmissions remains uncertain. Objective: To systematically evaluate the effectiveness of smartphone app-based interventions in reducing unplanned hospital readmissions among post-MI/PCI patients. Methods: A systematic search of PubMed was conducted for randomized controlled trials published between January 2020 and April 2025. Eligible studies evaluated smartphone apps designed for secondary cardiovascular prevention and reported on unplanned hospital readmissions. Risk ratios (RRs) and 95% confidence intervals (CIs) were pooled using a random-effects model. Subgroup analyses were performed based on follow-up duration and user adherence. Results: Four trials encompassing 827 patients met inclusion criteria. App-based interventions were associated with a significant reduction in unplanned hospital readmissions compared to standard care (RR 0.45; 95% CI: 0.23–0.89; p = 0.0219). Greater benefits were observed in studies with longer follow-up durations and higher adherence rates. Improvements in patient-reported outcomes, including health-related quality of life, were also documented. Heterogeneity was moderate. Major adverse cardiovascular events (MACEs) were reported in only two studies and were not analyzed due to inconsistent definitions and low event rates. Conclusions: Smartphone applications for post-MI/PCI care are associated with reduced unplanned hospital readmissions and improved patient-reported outcomes. These tools may play a meaningful role in future cardiovascular care models, especially when sustained engagement and personalized features are prioritized. Full article

Developing innovative, low-cost halochromic materials for diagnosing microbial contamination in wounds and burns can effectively facilitate tissue regeneration. Here, we combine the pH-sensing capability of highly colorful red cabbage anthocyanins (RCAs) with their healing potential within a unique cellulose polymer film that mimics the skin matrix. Biological activities of RCA extract in bacterial cellulose (BC) showed no cytotoxicity and skin-sensitizing potential to human cells at concentrations of RCAs similar to those released from BC/RCA dressings (4.0–40.0 µg/mL). A decrease in cell viability and apoptosis was observed in human cancer cells with RCAs. The invisible eye detection of the early color change signal from RCAs in response to pH alteration by bacteria was recorded with a smartphone application. The incorporation of RCAs into BC polymer has altered the morphology of its matrix, resulting in a denser cellulose microfibril network. The complete coincidence of the vibrational modes detected in the absorption spectra of the cellulose/RCA composite with the modes in RCAs most likely indicates that RCAs retain their structure in the BC matrix. Affordable, sensitive halochromic BC/RCA hydrogels can be recommended for online monitoring of microbial contamination, making them accessible to patients. Full article

Background: Smartphones offer a promising tool for monitoring refractive error, especially in underserved areas where there is a shortage of eye-care professionals. We propose a novel method for measuring spherical over-refraction using smartphones. Methods: Specific levels of myopia using positive spherical trial lenses, ranging from 0.00 D to 1.50 D in 0.25 D increments, were induced in 30 young participants (22 ± 5 years). A comparison was conducted between the induced over-refraction and the measurements obtained using a non-commercial mobile application based on the face–device distance measurement using the front camera while the subject was performing a resolution task. Results: Calibrated mobile app over-refraction results showed that 89.5% of the estimates had an error ≤ 0.25 D, and no errors exceeding 0.50 D. Bland–Altman analysis revealed no significant bias between app and clinical over-refraction, with a mean difference of 0.00 D ± 0.44 D (p = 0.981), indicating high accuracy and precision of the method. Conclusions: The methodology used shows high accuracy and precision in the measurement of the spherical over-refraction with only the use of a smartphone, allowing self-monitorization of potential myopia progression. Full article

This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a smartphone equipped with dedicated software and will be used to assess the risk of falling, which is crucial for patients with balance disorders. The authors designed the accelerometer with special attention paid to the specification required in a system, where the acceleration is ±2 g and the frequency is 100 Hz. They investigated the sensor’s behavior in the DC, AC, and time domains, capturing both the mechanical response of the proof mass and the resulting changes in output capacitance due to external acceleration. A key component of the simulation is the implementation of a second-order sigma-delta modulator designed to digitize the small capacitance variations generated by the sensor. The Simulink model includes the complete signal path from analog input to quantization, filtering, decimation, and digital-to-analog reconstruction. By combining MEMS+ modeling with MATLAB-based system-level simulations, the workflow offers a fast and flexible alternative to traditional finite element methods and facilitates early-stage design optimization for MEMS sensor systems intended for real-world deployment. Full article

Background: Despite significant advancements in diabetes care, many individuals with type 2 diabetes (T2D) do not receive optimal care and treatment. Digital interventions promoting behavioral changes have shown promising long-term results in supporting healthier lifestyles but are not implemented in most healthcare offerings, maybe due to lack of general practice support and collaboration. This study evaluates the efficacy of the Digital, Individualized, and Collaborative Treatment of T2D in General Practice Based on Decision Aid (DICTA), a randomized controlled trial integrating a patient-centered smartphone application for lifestyle support in conjunction with a clinical decision support (CDS) tool to assist general practitioners (GPs) in optimizing antidiabetic treatment. Methods: The present randomized controlled trial aims to recruit 400 individuals with T2D from approximately 70 GP clinics (GPCs) in Denmark. The GPCs will be cluster-randomized in a 2:3 ratio to intervention or control groups. The intervention group will receive one year of individualized eHealth lifestyle coaching via a smartphone application, guided by patient-reported outcomes (PROs). Alongside this, the GPCs will have access to the CDS tool to optimize pharmacological decision-making through electronic health records. The control group will receive usual care for one year, followed by the same intervention in the second year. Results: The primary outcome is the one-year change in estimated ten-year cardiovascular risk, assessed by SCORE2-Diabetes calculated from age, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, age at diabetes diagnosis, HbA1c, and eGFR. Conclusions: If effective, DICTA could offer a scalable, digital-first approach for improving T2D management in primary care by combining patient-centered lifestyle coaching with real-time pharmacological clinical decision support. Full article

As a crucial reactive oxygen species, hydrogen peroxide (H₂O₂) serves as both a physiological regulator and a pathological indicator in human systems. Its urinary concentration has emerged as a valuable biomarker for assessing metabolic disorders and renal function. While conventional colorimetric determination methods predominantly employ enzymatic or nanozyme catalysts, we present an innovative non-catalytic approach utilizing the redox-responsive properties of organic neutral radicals. Specifically, we designed and synthesized a novel radical TTM-DMODPA based on the tris (2,4,6-trichlorophenyl) methyl (TTM) scaffold, which exhibits remarkable optical tunability and oxidative sensitivity. This system enables dual-mode H₂O₂ quantification: (1) UV-vis spectrophotometry (linear range: 2.5–250 μmol/L, LOD: 1.275 μmol/L) and (2) smartphone-based visual analysis (linear range: 2.5–250 μmol/L, LOD: 3.633 μmol/L), the latter being particularly suitable for point-of-care testing. Validation studies using urine samples demonstrated excellent recovery rates (96–104%), confirming the method’s reliability for real-sample applications. Our work establishes a portable, instrument-free platform for urinary H₂O₂ determination, with significant potential in clinical diagnostics and environmental monitoring. Full article

The localization of multiple interference sources in Global Navigation Satellite Systems (GNSS) can be achieved using carrier-to-noise ratio (C/N₀) information provided by GNSS receivers, such as those embedded in smartphones. However, in increasingly prevalent complex scenarios—such as the coexistence of multiple directional interferences, increased diversity and density of GNSS interference, and the presence of multiple low-power interference sources—conventional localization methods often fail to provide reliable results, thereby limiting their applicability in real-world environments. This paper presents a multi-interference sources localization method using object detection in GNSS C/N₀ distribution maps. The proposed method first exploits the similarity between C/N₀ data reported by GNSS receivers and image grayscale values to construct C/N₀ distribution maps, thereby transforming the problem of multi-source GNSS interference localization into an object detection and localization task based on image processing techniques. Subsequently, an Oriented Squeeze-and-Excitation-based Faster Region-based Convolutional Neural Network (OSF-RCNN) framework is proposed to process the C/N₀ distribution maps. Building upon the Faster R-CNN framework, the proposed method integrates an Oriented RPN (Region Proposal Network) to regress the orientation angles of directional antennas, effectively addressing their rotational characteristics. Additionally, the Squeeze-and-Excitation (SE) mechanism and the Feature Pyramid Network (FPN) are integrated at key stages of the network to improve sensitivity to small targets, thereby enhancing detection and localization performance for low-power interference sources. The simulation results verify the effectiveness of the proposed method in accurately localizing multiple interference sources under the increasingly prevalent complex scenarios described above. Full article

Indoor navigation poses persistent challenges due to the limitations of traditional positioning systems within buildings. In this study, we propose a novel approach to address this issue—not by continuously tracking the user’s location, but by estimating their position based on how closely their observed behavior matches the expected progression along a predefined route. This concept, while not universally applicable, is well-suited for specific indoor navigation scenarios, such as guiding couriers or delivery personnel through complex residential buildings. We explore this idea in detail in our paper. To implement this behavior-based localization, we introduce an LSTM-based method for classifying user behavior patterns, including standing, walking, and using stairs or elevators, by analyzing velocity sequences derived from smartphone sensors’ data. The developed model achieved 75% accuracy for individual activity type classification within one-second time windows, and 98.6% for full-sequence classification through majority voting. These results confirm the viability of real-time activity recognition as the foundation for a navigation system that aligns live user behavior with pre-recorded patterns, offering a cost-effective alternative to infrastructure-heavy indoor positioning systems. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations. Full article

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans. Full article