Search Results (1,495)

Background/Objectives: Chronic kidney disease (CKD) requires intensive dietary and lifestyle management, yet patient engagement and access to tailored education remain limited, particularly outside clinical settings. This study describes the development and implementation of NephroGo, and evaluates its usability, user engagement, and perceived acceptability among patients with CKD. Methods: The app was developed based on clinical and dietary guidelines, incorporating personalized nutrient recommendations, dialysis tracking, and educational content. Technically, it features a Django backend, Flutter mobile frontend, and secure cloud-based hosting. User feedback was collected through one-time interviews (n = 10) and a standardized Mobile App Rating Scale (MARS) survey (n = 32). Longitudinal usage data over four years were also analyzed. Results: Initially, NephroGo was downloaded by 204 users, of whom 93.6% were considered active users based on defined behavioral engagement thresholds. Over a four-year period, the app accumulated a total of 1670 downloads. This study focuses on evaluating user engagement, usability, and perceived acceptability of the NephroGo app over a four-year period. Most users were female (52.3%) and aged 30–65. Stage 5 CKD patients and those undergoing peritoneal dialysis (PD) had the highest engagement. The most-used feature was the personalized nutrition calculator, with sodium being the most frequently exceeded nutrient. The average MARS score was 4.09 ± 0.66, with functionality rated highest (4.27 ± 0.74). App ratings were significantly higher among users referred by physicians (p = 0.039). Conclusions: NephroGo offers a scalable digital tool to support dietary management and health monitoring, with potential to complement standard nephrology care in a resource-conscious manner. Full article

This paper presents a comprehensive study of an ESP32 microcontroller-based self-balancing mobile robot system designed in conjunction with an Android app for Bluetooth control. The robot employs an MPU6050 accelerometer/gyroscope to execute dynamic equilibrium control for robotic balance. This study explores the design of a system composed of an ESP32-based dual-platform architecture. The firmware for the ESP32 executes real-time motor control and sensor processing, while the Android application provides the user interface, data visualization, and command transmission. The system achieves stable operation with tilt angle variations of ±2.5° ($\sigma =0.8°$, n = 50 trials) during normal operation with a PID controller tuned to KP = 6.0, KI = 0.1, and KD = 1.5. In experimental tests, control latency was measured at 38–72 ms (mean = 55 ms, $\sigma =12$ ms) over distances of 1–10 m with a robust Bluetooth connection. Extended operational tests indicated the reliability of both autonomous obstacle avoidance mode and manual control exceeding 95%. Key contributions include gyro drift compensation using a progressive calibration scheme, intelligent battery management for operational efficiency, and a dual-mode control interface to facilitate seamless transition between manual and autonomous operation. Processing of real-time telemetry on the Android application allows visualization of important parameters like tilt angle, motor speeds, and sensor readings. This work contributes to a cost-effective mobile robotics platform (total cost: USD 127) through the provision of detailed design specifications, implementation strategies, and performance characteristics. Full article

Alzheimer’s disease is a growing global health concern, demanding innovative solutions for early detection, continuous monitoring, and patient support. This article reviews recent advances in Smart Wearable Medical Devices (SWMDs), Internet of Things (IoT) systems, and mobile applications used to monitor physiological, behavioral, and cognitive changes in Alzheimer’s patients. We highlight the role of wearable sensors in detecting vital signs, falls, and geolocation data, alongside IoT architectures that enable real-time alerts and remote caregiver access. Building on these technologies, we present LumiCare, a conceptual, context-aware mobile system that integrates multimodal sensor data, chatbot-based interaction, and emerging 6G network capabilities. LumiCare uses machine learning for behavioral analysis, delivers personalized cognitive prompts, and enables emergency response through adaptive alerts and caregiver notifications. The system includes the LumiCare Companion, an interactive mobile app designed to support daily routines, cognitive engagement, and safety monitoring. By combining local AI processing with scalable edge-cloud architectures, LumiCare balances latency, privacy, and computational load. While promising, this work remains at the design stage and has not yet undergone clinical validation. Our analysis underscores the potential of wearable, IoT, and mobile technologies to improve the quality of life for Alzheimer’s patients, support caregivers, and reduce healthcare burdens. Full article

Background/Objectives: Adolescent idiopathic scoliosis (AIS) often requires prolonged brace use to prevent curve progression. However, adherence is challenging due to discomfort, mobility restrictions, and psychosocial stressors. This study evaluated the feasibility and clinical utility of a mobile health (mHealth) system for real-time tracking of brace adherence and treatment-related experiences in adolescents with AIS. Methods: Thirty adolescents with AIS (mean age = 12.9, SD = 1.8) undergoing brace treatment at a tertiary care center used a custom app for 90 days. The app collected daily self-reports on brace wear duration, discomfort, movement limitations, emotional distress, and social challenges. A clinical alarm system alerted providers when patient input indicated potential concerns. Primary outcomes were feasibility (adherence to daily use and usability ratings) and brace adherence. Secondary outcomes included the app’s capacity to identify treatment-related challenges and its association with changes in stress, quality of life, anxiety, and depression. Results: Participants reported meeting recommended brace wear time (≥16 h/day) on 84.8% of days. The app triggered 186 clinical alarms, with the most frequent related to emotional distress (23.1%) and pain (15.6%). Alarm frequency declined over time. Improvements of ≥20% in psychological outcomes were observed in 20–26.7% of participants, while group-level changes were nonsignificant. Conclusions: mHealth-based monitoring appears feasible and acceptable for digitally engaged adolescents with AIS. The app supported early detection of treatment barriers and prompted timely clinical responses. Despite limitations, it shows promise as a tool to improve treatment engagement and address psychosocial challenges in scoliosis care. Full article

The spread of the invasive mosquitoes Aedes albopictus, Aedes aegypti, and Aedes japonicus in Spain represents an increasing public health risk due to their capacity to transmit arboviruses such as dengue, Zika, and chikungunya, among others. Traditional field entomological surveillance remains essential for tracking their spread, but it faces limitations in terms of cost, scalability, and labor intensity. Since 2014, the Mosquito Alert citizen-science project has enabled public participation in surveillance through the submission of geolocated images via a mobile app, which are identified using AI in combination with expert validation. While field surveillance provides high accuracy, citizen science offers low-cost, large-scale, real-time data collection aligned with open data management principles. It is particularly useful for detecting long-distance dispersal events and has contributed up to one-third of the municipal detections of invasive mosquito species since 2014. This study assesses the value of integrating both surveillance systems to capitalize on their complementary strengths while compensating for their weaknesses in the areas of taxonomic accuracy, scalability, spatial detection patterns, data curation and validation systems, geographic precision, interoperability, and real-time output. We present the listing of municipal detections of these species from 2004 to 2024, integrating data from both sources. Spain’s integrated approach demonstrates a pioneering model for cost-effective, scalable vector surveillance tailored to the dynamics of invasive species and emerging epidemiological threats. Full article

This paper presents a case study on the integration of embedded IoT hardware with a modern Android application, demonstrated through the development of a compact autoclave system for small-scale food sterilization. The device is controlled by an ESP8266-based module and communicates securely with a Kotlin-based Android app via MQTT using HiveMQ. The app incorporates advanced Android design patterns such as coroutines, LiveData, Navigation UI, and DataStore. Each device is uniquely addressable and fully configurable from the mobile interface. The work highlights Android’s role as a powerful interface for managing embedded IoT systems. Full article

Augmented Reality (AR) has emerged as a promising educational tool, offering new opportunities to enhance learning through immersive and interactive experiences. This study aimed to evaluate the degree of acceptance of AR by secondary school students using the Technology Acceptance Model (TAM) as the theoretical framework. A quasi-experimental post-test design was implemented with a sample of 321 students (ages 14–17) who engaged with ComputAR, a mobile AR application developed specifically for a didactic unit on computer systems. Data were collected through a validated TAM questionnaire encompassing five dimensions: “perceived usefulness”, “perceived ease of use”, “perceived enjoyment”, “attitude towards using”, and “behavioural intention to use”. The results indicate a high level of acceptance of AR-based educational tools. Significant differences were found in “perceived ease of use” depending on gender, with male students reporting higher ease, while no gender differences emerged in “perceived usefulness” or “behavioural intention”. Additionally, ICT previous experience was shown to positively affect “perceived enjoyment”, ease of use, and usefulness. In conclusion, these findings confirm the relevance of AR for fostering student motivation and engagement. Full article

Background: The World Health Organization defined specific recommendations about digital tobacco cessation modalities as a self-management tool or as an adjunct to other support for adults. Objectives: The present umbrella review primarily aimed to assess the long-term (≥6 months) effectiveness and adherence of the different standalone digital tobacco cessation modalities (mobile text messaging, smartphone apps, Internet-based websites and programs, AI-based), administered individually or in combination; secondarily, the study aimed to assess the effect on smokers’ health. Methods: The present study (PROSPERO number: CRD42024601824) followed the PRISMA guidelines. The included studies were qualitatively synthesized and evaluated through the AMSTAR-2 tool. Results: Forty-five systematic reviews were included, encompassing 164,010 adult daily smokers of combustible tobacco. At 6 months, highly interactive or human-centered digital tools showed higher effectiveness (biochemically verified continuous abstinence rates (CARs) were 11.48% for smartphone apps and 11.76% for video/telephone counseling). In contrast, at 12 months, simpler, less interactive tools demonstrated higher effectiveness (self-reported CARs was 24.38% for mobile text messaging and 18.98% for Internet-based). Adherence rates were generally high, particularly with human-centered digital tools, amounting to 94.12% at 6 months and 64.08% at 12 months. Compared with individually administered digital tobacco cessation modalities, at 12 months, combined ones registered slightly higher effectiveness (self-reported CARs were 13.12% vs. 13.94%) and adherence (62.36% vs. 63.70%), potentially attributed to the multi-component nature and longer durations. Conclusions: Clinicians should prioritize combined digital tobacco cessation interventions that incorporate human-centered engagement initially, alongside simpler, sustained digital support to enhance long-term effectiveness and adherence. Future research should explore long-term medical and oral health benefits to assess the impact on overall health and well-being. Full article

Anxiety and depression affect millions worldwide, yet stigma and long wait times often delay access to care. Mobile mental health apps can decrease these barriers by offering on-demand screening and support. Nevertheless, many machine and deep learning methods used in such tools perform poorly under severe class imbalance, yielding biased, poorly calibrated predictions. To address this challenge, this study proposes MCoG-LDPSNet, a brain-inspired model that combines dual, orthogonal encoding pathways with a novel Loss-Driven Parametric Swish (LDPS) activation. LDPS implements a neurobiologically motivated adaptive-gain mechanism via a learnable β parameter driven by calibration and confidence-aware loss signals that amplifies minority-class patterns while preserving overall reliability, enabling robust predictions under severe data imbalance. On a benchmark mental health corpus, MCoG-LDPSNet achieved AUROC = 0.9920 and G-mean = 0.9451, outperforming traditional baselines like GLMs, XGBoost, state-of-the-art deep models (CNN-BiLSTM-ATTN), and transformer-based approaches. After transfer learning to social media text, the MCoG-LDPSNet maintained a near-perfect AUROC of 0.9937. Integrated into the EmotiZen App with enhanced app features, MCoG-LDPSNet was associated with substantial symptom reductions (anxiety 28.2%; depression 42.1%). These findings indicate that MCoG-LDPSNet is an accurate, imbalance-aware solution suitable for scalable mobile screening of individuals for anxiety and depression. Full article

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, ease of use, computer literacy, and self-efficacy. Results showed that usefulness and ease of use significantly influence user attitudes and intentions. The findings suggest actionable recommendations for improving IBON system adoption, including training programs to enhance computer literacy and self-efficacy and strategies to demonstrate the system’s relevance to user needs. Future research should explore additional external factors, such as cultural influences and user experience design, and conduct longitudinal studies to assess sustained use and impact on biodiversity monitoring outcomes. This study underscores the importance of fostering user acceptance to maximize the potential of innovative technologies like IBON Monitoring in advancing biodiversity conservation efforts. Full article

This study explored a competency-based soft-skills programme supporting evolving community pharmacy professionals’ roles and sustainable practice in Serbia. Four researchers with academic and practice backgrounds developed the programme using healthcare guidelines and the International Pharmaceutical Federation’s competency framework. The process involved defining objectives, selecting methods, designing and organising activities, accreditation, and evaluating outcomes based on the Kirkpatrick model. From January 2021 to March 2025, the “Galenika Academy” was implemented through webinars, accredited tests, onsite courses, and a mobile application. Satisfaction was assessed via a validated online questionnaire among participants attending ≥80% of sessions, following evaluation of attendance and test performance. The programme reached 5107 participants, 10,427 webinar views, and 8252 test completions. The “Galiverse” mobile app, launched in February 2023, had 5558 users by March 2025. The most attended webinar was “Burnout” (787). Average test success was 82.9%, with 95.3% for “Resilience” and 61.0% for “Team Management.” Satisfaction was 95.5% for content, 94.2% for quality, 92.3% for materials, 77.1% for the application, and 96.3% would recommend it. Among those reporting improved resilience, 96.9% believed it could positively impact pharmacy operations. Pharmacists found the programme relevant and effective. Further research is needed to evaluate its impact on practice and patient outcomes. Full article

The monitoring and prediction of air quality (AQ) is key to minimizing the negative impact of air pollution, as it enables the implementation of corrective measures. An IoT-based multi-purpose monitoring system has therefore been designed. To develop a reliable remote system, this study addresses three challenges: (1) design of a low-cost compact, robust, multi-sensor system, (2) model validation over several months to ensure accurate detection, and (3) the application of machine learning (ML) techniques to classify and predict AQ. The developed system demonstrates a significant cost reduction for regular monitoring, including effective data management under harsh environmental conditions. The prototype integrates pollutant sensors, as well as the detection of liquified petroleum gas, humidity, and temperature. A dataset with more than 30,000 entries per month (data recorded approximately every minute) was saved on the platform. Results identified the three highest pollution categories, highlighting the urgency of addressing AQ in densely populated regions. The ML algorithms allowed us to predict AQ trends with 99.97% accuracy. To summarize, by reducing monitoring costs and enabling large-scale data management, this system offers an effective solution for real-time environmental monitoring. It also highlights the potential of artificial intelligence-based AQ predictions in supporting public health initiatives. This is particularly interesting for developing countries, where pollution control is limited. Future research will develop the models to include data from different environments and seasons, exploring its integration into mobile apps and cloud platforms for real-time monitoring. Full article

Neurodevelopmental disorders are one of the most prevalent conditions today, and among the limitations in activity and restrictions in the participation of children and their families, we find intervention in activities of daily living; therefore, research focused on outcome measurement is one of the most active lines, and after COVID-19, telerehabilitation has garnered special interest. Background/Objectives: The study objective was to evaluate the effectiveness of a mobile health (mHealth) application in improving the performance of activities of daily living in children with neurodevelopmental disorders. Methods: The study employed a quasi-experimental design with a control group, using a fully remote mHealth-based intervention. The instruments used were a sociodemographic ad hoc, Pediatric Evaluation of Disability Inventory Computer, Family Outcomes Survey, Family Confidence Scale, and System Usability Scale. The final sample consisted of 13 participants. Results: The mHealth intervention showed significant improvements in occupational performance in the experimental group, especially in the global score and in the Responsibility dimension of the PEDI-CAT. No relevant differences were observed in the CON-FAN and FOS scales between groups, although the latter showed improvements over time. The usability of the app was rated positively (SUS = 69.75). Conclusions: The developed application presents good usability for families of children with neurodevelopmental disorders, but to obtain better outcome measures, the intervention should combine face-to-face sessions and the use of mHealth, as well as employing the family-centered model. Full article

Public safety personnel (PSP), including correctional workers, firefighters, paramedics, police, and public safety communicators, are at increased risk for posttraumatic stress injury, yet face barriers in receiving timely support. Mobile health (mHealth) applications (apps) offer promising avenues for confidential, on-demand access to relevant information and support. The purpose of this study was to assess implementation of PeerOnCall, a new mHealth platform designed by and for PSP (the platform includes two parallel apps: one for frontline workers and one for peer support providers). A multi-site mixed methods implementation trial was conducted over 3−6 months in 42 public safety organizations across Canada. App usage trends were tracked through software analytics, and facilitators and barriers to app use were explored via interviews with organizational champions. Over 11,300 employees across 42 organizations were invited to use the PeerOnCall app over the trial period, with approximately 1759 PSP (15% of total) downloading the app. Variation within and across sectors was evident in app downloads and feature use. Approaches to communication (mode, timing, and messenger), and organizational culture related to mental health and help outreach affected uptake levels. PeerOnCall is a promising tool to facilitate access to peer support; however, culturally relevant strategies are needed to overcome barriers and integrate this tool into workplace practices. Full article

The integration of robotics and mobile networks (5G/6G) through the Internet of Robotic Things (IoRT) is revolutionizing telemedicine, enabling remote physician participation in scenarios where specialists are scarce, where there is a high risk to them, such as in conflicts or natural disasters, or where access to a medical facility is not possible. Nevertheless, touching a human safely with a robotic arm in non-engineered or even out-of-hospital environments presents substantial challenges. This article presents a novel IoRT approach for healthcare in or from remote areas, enabling interaction between a specialist’s hand and a robotic hand. We introduce the IoRT-in-hand: a smart, lightweight end-effector that extends the specialist’s hand, integrating a medical instrument, an RGB camera with servos, a force/torque sensor, and a mini-PC with Internet connectivity. Additionally, we propose an open-source Android app combining MQTT and ROS for real-time remote manipulation, alongside an Edge–Cloud architecture that links the physical robot with its Digital Twin (DT), enabling precise control and 3D visual feedback of the robot’s environment. A proof of concept is presented for the proposed tele-robotic system, using a 6-DOF manipulator with the IoRT-in-hand to perform an ultrasound scan. Teleoperation was conducted over 2300 km via a 5G NSA network on the operator side and a wired network in a laboratory on the robot side. Performance was assessed through human subject feedback, sensory data, and latency measurements, demonstrating the system’s potential for remote healthcare and emergency applications. The source code and CAD models of the IoRT-in-hand prototype are publicly available in an open-access repository to encourage reproducibility and facilitate further developments in robotic telemedicine. Full article