Search Results (5,628)

(1) Background: The local divergence exponent (LDE) is a sensitive measure of walking stability deterioration and risk of falling in older adults. We aim to determine the validity the LDE measured using a mobile phone and to assess its ability to discriminate between healthy young and older adults; (2) Methods: 20 older adults (76.4 ± 4.6 years) and 20 young adults (29.1 ± 6.5 yrs) walked for 6 min on a 20-m walkway while wearing a research-grade inertial measurement unit (IMU) and a mobile phone placed on the sternum to record 3D acceleration data. The LDE was calculated using data from both devices for 3D, vertical (VT), mediolateral (ML), anteroposterior (AP), and norm (N) accelerations. ICC (3,1) was used to determine the validity of the mobile phone’s LDE. Mann–Whitney U tests were used to determine age-group discriminability of LDE measures; (3) Results: LDEs demonstrated excellent absolute agreement between the wearable IMU and mobile phone (ICC = 0.844). Mobile phone-derived LDEs demonstrated excellent validity relative to the wearable IMU (ICC > 0.75). No significant age-related differences in LDE were observed; wearable or mobile sensors (both p > 0.05); (4) Conclusions: LDEs measures obtained with a mobile phone are valid. No age group differences were identified. Full article

This paper introduces a compact sub-6 GHz multiple-input multiple-output (MIMO) antenna array developed for 5G smartphone applications. The design employs eight planar inverted-F antenna (PIFA) elements arranged to realize dual-band and dual-polarized operation. The antenna achieves impedance bandwidths of 3.3–3.7 GHz (11.4%) and 5.3–5.8 GHz (10%), covering key sub-6 GHz fifth-generation (5G) bands. To enhance diversity performance, the elements are distributed along the edges of the smartphone mainboard, enabling excitation of orthogonal polarization modes while maintaining an overall board size of 75 mm × 150 mm on an FR4 substrate. Even without the use of dedicated decoupling structures, the closely spaced antenna elements exhibit satisfactory isolation levels, varying between −12 dB and −22 dB across the operating bands. The antenna array achieves wide impedance bandwidths of approximately 400 MHz at 3.5 GHz and more than 500 MHz at 5.5 GHz, supporting high data-rate communication. In addition, the proposed system demonstrates very low correlation and active reflection, with envelope correlation coefficient (ECC) values below 0.002 and total active reflection coefficient (TARC) levels better than −20 dB. User interaction effects are also investigated, and the results confirm acceptable SAR levels and stable radiation behavior in the presence of the human body. Owing to its planar, dual-band/dual-polarization capability and compliance with safety requirements, the proposed antenna represents a promising practical solution for contemporary 5G handheld devices and future multi-band mobile platforms. Full article

Accurately monitoring physical activity, including stationary cycling on an exercise bike, is important in managing chronic diseases and rehabilitation after lower limb surgery. This study aimed to validate a new smartphone-based sensor application (the BeSAFE+) for activity recognition and step counting across five phone placements, using the SENS Motion^® system as a reference standard, and observed activity time as ground truth. In a laboratory-based study, 20 participants performed walking, brisk walking, running, high- and low-intensity cycling, sitting, standing, and lying activities while carrying five smartphones placed in the front and back trouser pockets, a backpack, a running armband, and a fanny pack, and wearing the activity tracker. The front pocket placement had the most accurate classification during cycling activities (89–93%) versus SENS Motion^® (96–98%). For other activities, the highest overall classification accuracy was achieved with the phone in the back pocket. Overall, the SENS Motion^® activity tracker demonstrated higher classification accuracy than most smartphone placements across all activities, except for running. Nevertheless, several smartphone placements and Application Programming Interface (API) approaches achieved activity recognition and step count estimates that were not significantly different from the SENS Motion^® activity tracker, indicating that smartphone-based activity recognition can be valid under specific conditions. Full article

The unchecked and uncontrolled global spread of multidrug-resistant (MDR) bacteria is a serious challenge to healthcare and modern medicine, and demands diagnostic approaches that are rapid, sensitive, multiplexed, and reliable in point-of-care (POC) settings. At the interface of nanomaterials and aptamer-based biosensing, significant advances have been reported. The convergence of portable electrochemical sensing technologies, smartphone-based readout systems, and artificial intelligence (AI)- and machine learning (ML)-based data analysis is also playing a significant role in advancing this area. This perspective reflects on the most recent breakthroughs and translational developments in electrochemical nano-aptasensors for MDR bacterial detection, covering diagnostic targets and translation trends, nanomaterials advancements, aptamer engineering-integration, POC strategies and microfluidics platforms, and novel multimodal strategies that enhance accuracy, reliability, and efficiency in POC testing. Moreover, limitations and knowledge gaps in this area, as well as key considerations for sustainable development, large-scale manufacturing, and deployment of integrated electrochemical nano-aptasensors, are also highlighted. Electrochemical nano-aptasensors can pave the way for the transformation of MDR bacterial diagnosis, but need coordinated translational efforts for POC diagnostic advancements towards real-world applications. Full article

Background: Unwanted loneliness and social isolation in older adults are public health problems with negative effects on physical and mental health. The usual assessment tools, based on self-report questionnaires, have limitations in capturing these phenomena continuously and objectively. Objective: We aimed to critically analyze recent scientific evidence on the use of passive sensor technologies combined with artificial intelligence for the detection of unwanted loneliness and social isolation in older adults. Methods: Studies were reviewed in databases (PubMed, Scopus, Web of Science, and IEEE Xplore) that used wearable devices, environmental sensors in the home, smartphones, and multimodal fusion approaches. This systematic review was conducted following the PRISMA 2020 guidelines. Results: Behavioral variables derived from passive monitoring, such as mobility, time away from home, sleep patterns, and digital interactions, are consistently associated with measures of loneliness and social isolation. Likewise, artificial intelligence models based on the combination of multiple data sources show better predictive performance than unimodal approaches. Conclusions: Sensor-based technologies can complement traditional assessment methods, although their practical application requires overcoming challenges related to methodological validation, user acceptance, and ethical considerations. Full article

Background/Objectives: Nomophobia, the irrational fear of being without a mobile phone, is increasingly prevalent among university students and has emerged as a concerning form of digital dependence. Among nursing students, this condition is particularly relevant due to the emotional demands and cognitive challenges of healthcare education. Nomophobia has been linked with adverse psychological outcomes, sleep disturbances, and impaired academic and clinical performance. However, existing evidence remains fragmented and lacks an integrated conceptual synthesis. This review aimed to synthesize current evidence on the prevalence, correlates, and outcomes of nomophobia among nursing students. Methods: An integrative review was conducted following Whittemore and Knafl’s methodology and PRISMA guidelines. A systematic search was performed in PubMed, CINAHL, PsycINFO, PsycArticles, and Medline (between 2015 and 2025), supplemented by Google Scholar. Cross-sectional studies and literature focusing on nomophobia in nursing students were included. The primary studies and selected review articles were considered when no overlap with the included primary evidence was identified. Methodological quality appraisal was assessed using validated tools (QuADS and JBI). Results: Twenty-two studies were included (19 cross-sectional and 3 reviews). Four thematic areas emerged: prevalence and severity (50–90% moderate to severe); psychological correlates (anxiety, depression, stress, insomnia, alexithymia, fear of missing out); academic and cognitive outcomes (impaired performance, procrastination, reduced decision-making); and behavioural predictors (excessive smartphone use and emotional dysregulation). The Nomophobia Questionnaire (NMP-Q) was the most frequently used instrument. Conclusions: Nomophobia represents a relevant dimension of the mind–technology relationship in nursing education, with implications for students’ mental health, academic engagement, and clinical readiness. Addressing nomophobia may support healthier learning environments and contribute to the development of emotionally competent and safe future healthcare professionals. However, significant gaps remain, particularly regarding longitudinal evidence and intervention-based approaches. Full article

Background: Accurate evaluation of the Near Point of Convergence (NPC) is essential for diagnosing and managing convergence insufficiency (CI). Conventional assessment relies on the patient’s verbal feedback and the examiner’s visual observation, making it subjective and examiner-dependent. The AI-based MobileS platform, previously validated for both diagnosis and home-based therapy of CI, enables smartphone-based measurement and visualisation of NPC through eye tracking, without the need for verbal responses or additional equipment. This study, the third stage of our research programme, examined how ophthalmologists interpret NPC data when presented as videos versus AI-derived graphs. Methods: Twenty-two ophthalmologists completed an online questionnaire with 20 NPC test cases from the validated MobileS database, presented as both silent videos and AI-derived graphs. Accuracy was analysed using mixed-effects logistic regression, and continuous error was assessed using clustered bootstrap. Results: Graph-based interpretation showed higher odds of accurate NPC identification than video-based interpretation at the primary ±5 mm threshold (OR = 19.7, 95% CI: 13.50–28.74; p < 0.0001). Absolute error was lower for graphs than videos (Graphs − Videos: −22.73 mm; 95% CI: −26.88 to −18.59; p < 0.0001). “Uncertain” responses occurred in 28.2% of video-based assessments and 0% of graph-based assessments. Off-target errors decreased from 50.2% (videos) to 3.6% (graphs). Conclusions: AI-derived graphs of eye-movement data were associated with improved NPC estimation, suggesting a potential role in supporting clinical and tele-ophthalmology workflows. Full article

Assessing hip adductor muscle strength is important for identifying weakness or side-to-side imbalances associated with groin injury risk. Although the Copenhagen adductor exercise is widely used to evaluate adductor function, the quantification of strength-related outcomes using inertial sensors integrated in smartphones during this task has not been systematically examined. This study aimed to evaluate the reliability of a smartphone-based Copenhagen adductor field test and its associations with established isometric hip adductor strength assessments. Twenty amateur male football players (21.1 ± 3.2 years) completed two laboratory sessions separated by one week. The reliability of the smartphone-based Copenhagen test was assessed for endurance-related outcome (repetition count) and strength-related outcomes (mean repetition time and peak velocity) using intraclass correlation coefficients (ICC), standard error of measurement (SEM), and minimum detectable change (MDC). Participants also performed unilateral and bilateral isometric hip adductor tests using load cells to obtain isometric peak force (IPF) and rate of force development at 150 ms (RFD150). Associations were examined using Pearson correlation coefficients. The smartphone-based Copenhagen test showed ICC point estimates ranging from 0.63 to 0.83, although several 95% confidence intervals were relatively wide (ICC = 0.63–0.83; SEM = 6.7–18.5%). Endurance-related outcomes were not significantly associated with IPF or RFD150. In contrast, peak velocity showed low-to-moderate correlations with RFD150 (r = 0.48–0.63) and moderate correlations with IPF (r = 0.50–0.64; p < 0.05). These findings suggest that the peak velocity obtained during the Copenhagen adductor test may provide a practical field-based complement to conventional isometric assessments. However, given the moderate strength of the observed associations and the measurement error of peak velocity, these outcomes should be interpreted with caution and warrant further investigation. Full article

Adolescents increasingly engage with digital technologies, yet understanding patterns of smartphone use and fostering reflective awareness remain challenging. Traditional assessments in clinical or school settings may limit participation and self-reflection. This study evaluated the feasibility and impact of a Computer-Assisted Web Interviewing (CAWI) approach to monitor smartphone use, provide immediate individualized feedback, and support peer-mediated dissemination in a public science engagement context. Across three editions of the European Researchers’ Night in Rome (2023–2025), 807 adolescents aged 10–19 completed the SAS-SV questionnaire via on-site tablets or personal devices using QR codes. Smartphone use was categorized into Low Involvement, At-Risk, or Problematic. Participants were encouraged to share the survey link with peers, enabling snowball-mediated recruitment. Participant acceptance was assessed through the Net Promoter Score (NPS). Snowball participation accounted for the majority of responses, highlighting the effectiveness of peer-mediated diffusion. SAS-SV categorization indicated 46% Low Involvement, 39% At-Risk, and 15% Problematic use, with minimal gender differences. NPS values ranged from +69 to +79, with snowball participants reporting slightly higher satisfaction than on-site attendees. These results underscore high engagement, perceived value, and the role of peer networks in promoting reflective digital behavior. Integrating CAWI assessment, immediate feedback, and peer-mediated diffusion created a socially situated environment supporting self-reflection and voluntary dissemination. Peer networks extended both the temporal and social reach of the initiative beyond the public event, demonstrating a scalable and non-stigmatizing model. CAWI-based monitoring combined with feedback and peer-driven diffusion is feasible and effective for adolescent digital wellbeing interventions. This approach fosters reflective digital citizenship, supports self-awareness, and leverages social networks to enhance the reach and impact of youth-centered health promotion initiatives. Full article

Indigenous cultures in Taiwan embody rich ecological knowledge and strong environmental conservation values. However, elementary and secondary education often provides limited exposure to these cultures due to geographic constraints and insufficient instructional resources, relying primarily on textbooks and teacher-centered teaching methods. Such approaches restrict experiential learning, which may diminish students’ motivation and depth of understanding. However, 360-degree virtual reality (VR360) enables immersive simulations of authentic environments, increasing the accessibility of cultural and ecological education through smartphones and low-cost Google Cardboard. In addition, drone technology enhances learning by offering multiple perspectives for environmental exploration and data collection. This study examines the effectiveness of integrating a VR360 and virtual drone system into instruction focused on the ecological context of Sun Moon Lake and Thao Indigenous culture. Learning outcomes for Indigenous and non-Indigenous students were compared in terms of learning effectiveness, motivation, cognitive load, and technology acceptance. Ecological and cultural materials were collected through field investigations and drone photography, enabling students to explore landscapes from a first-person perspective and engage with Thao cultural practices and their relationship with local ecology. The findings indicate that the proposed VR-based system significantly enhances learning experiences and demonstrates strong potential for cultural and ecological education, offering valuable guidance for the design of future immersive instructional strategies and learning materials related to Indigenous cultures. Full article

Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate in infrastructure-less environments. Pedestrian dead reckoning’s primary challenge is maintaining accuracy despite varying smartphone placements (attitudes) and the noisy, low-cost inertial measurements units. In this work, a comprehensive pedestrian dead reckoning framework is presented that integrates advanced step counting and heading estimation techniques. For step detection and counting, we propose a robust step counting algorithm that utilizes the optimum fusion of the raw IMU readings, i.e., accelerometer, linear accelerometer, gyroscope, and magnetometer readings, each broken down into three degrees of freedom for different body placements and walking speeds. Furthermore, to address the critical issue of heading estimation, we propose the heading estimation axis mapping (HEAT-MAP) algorithm, which dynamically adjusts the sensor axes in response to the smartphone’s orientation, ensuring a consistent coordinate frame and reducing heading drift. Moreover, to eliminate cumulative pedestrian dead reckoning errors, the system incorporates an adaptive weighted fusion mechanism with Wi-Fi fingerprinting. Experimental results demonstrate that this integrated system significantly improves the overall trajectory accuracy, providing a high-precision, attitude-unconstrained solution for real-time indoor pedestrian navigation. Full article

Molecular point-of-care testing (POCT) for respiratory infections has undergone remarkable advancement over the past two decades, driven by technological innovation and urgent clinical needs highlighted by the COVID-19 pandemic. This comprehensive systematic review was conducted following PRISMA 2020 guidelines, synthesizing evidence from 254 peer-reviewed studies published between 2006 and 2026, with detailed analysis of the 30 most relevant papers selected through a rigorous four-stage screening process. The review examines the evolution of molecular POCT technologies, including reverse transcription polymerase chain reaction (RT-PCR), loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), and CRISPR-based detection systems. Key findings demonstrate that modern molecular POCT platforms achieve diagnostic performance comparable to laboratory-based testing, with sensitivities ranging from 88% to 100% and specificities from 98% to 100%, while delivering results in 15 to 80 min. These technologies enable rapid, accurate detection of major respiratory pathogens, including SARS-CoV-2, influenza A/B, respiratory syncytial virus (RSV), and atypical bacteria. The integration of microfluidic systems, portable devices, and smartphone-based analysis has expanded access to testing in resource-limited settings, emergency departments, and wearable platforms. This review provides critical insights for clinicians, researchers, and policymakers regarding the current state, clinical applications, and future directions of molecular POCT for respiratory infections. Full article

Cataract diagnosis requires a comprehensive dilated examination by an ophthalmologist using a slit lamp; there is currently no effective means to objectively screen for cataracts in the community using portable devices without dilation. We hypothesized that it would be possible to predict cataract severity using deep learning on images taken using a portable smartphone-based slit lamp prototype, with and without dilation. In this prospective cross-sectional pilot study, slit lamp images were captured from eligible patients with cataracts in a tertiary clinic using a portable slit lamp prototype attached to a smartphone. The Pentacam nuclear staging score (PNS, Pentacam^®, Oculus, Inc., Arlington, WA, USA) was taken from the dilated pupils and served as ground truth. A transformer prototypical network with the Swin transformer on the images was trained to assign the class label corresponding to the highest predicted probability. Heat maps were generated based on attribution masks to identify the anatomical areas of concern. A total of 1900 images from 198 eyes of 99 patients were captured. The average age was 65.3 ± 10.4 years (range, 41.0 to 88.0 years) and the average PNS score was 1.57 ± 0.81 (range, 0 to 4). The model achieved an average accuracy of 81.25% and 74.38% for undilated and dilated eyes, respectively. Heat map visualization using the integrated gradient method successfully identified the anatomical area of interest in certain images. This study suggests the possibility of estimating cataract density using a portable smartphone slit lamp device without dilation. Further work is under way to validate this technique in a larger and more diverse group of eyes with cataracts. Full article

Objectives: In Japan, smartphone applications are increasingly used for dietary recording in healthcare settings. This study aimed to examine the agreement between energy and nutrient intake estimates obtained using an artificial intelligence (AI)-based dietary recording application and those obtained using the weighed dietary record (WDR). Methods: The AI-based dietary recording method (FoodLog Athl method) was compared with WDR. Thirty-six university students (35 women and 1 man) simultaneously recorded their dietary intake using FoodLog Athl (FLA) and the WDR for 10 consecutive days. Energy and nutrient intakes were estimated using each method, and correlations and agreement between the two methods were evaluated. Results: Significant positive correlations were observed between the two methods for energy and most nutrients, except for iron, vitamin B1, and sodium chloride equivalent (p < 0.01). Compared with the WDR, the FLA method showed systematic overestimation of energy and major macronutrients (protein, fat, and carbohydrate) and underestimation of total dietary fiber. Bland–Altman analysis indicated fixed bias and relatively wide limits of agreement for several nutrients. Conclusions: The FLA method demonstrated moderate agreement with the WDR, with systematic bias observed for selected nutrients. These findings suggest that the application may be useful for monitoring overall dietary trends or relative intake over time, but caution is warranted when precise individual-level nutrient quantification is required. Professional review by registered dietitians may help improve estimation accuracy and reduce bias. Full article

Problematic smartphone use among adolescents can lead to various negative consequences. Parental active mediation has been identified as an effective strategy to mitigate these problems. In response, numerous parent training programs have been developed. However, it remains unclear whether these programs can effectively enhance parents’ active mediation. A 2 (group: experimental vs. control) × 3 (time: pretest, post-test, follow-up) mixed factorial design was employed to examine the effects of parent training on parents’ active mediation. A total of 60 parents of adolescents with problematic smartphone use participated in the experiment. Parents in the experimental group attended a six-session parent training program, while those in the control group received no intervention. Parental active mediation was assessed using a validated self-report scale at all three time points. Results indicated significant main effects of group and time, as well as a significant group × time interaction. Among parents with the same initial level of active mediation, those who participated in the training demonstrated higher levels of active mediation than those in the control group, and this improvement was maintained two months later. These findings provide empirical evidence that parent training can sustainably enhance parents’ active mediation in managing adolescents’ smartphone use. Full article