Search Results (106)

Background/Objectives: Coupling mobile health (mHealth) technology with community-based nutrition programs may enhance diet quality in adolescents. This pilot study evaluated the feasibility, acceptability, and preliminary efficacy of integrating PortionSize Ed (PSEd), an image-assisted dietary assessment and education app, into the six-week Hawaii Food and Lifeskills for Youth (HI-FLY) curriculum delivered via Supplemental Nutrition Assistance Program Education (SNAP-Ed). Methods: Adolescents (grades 6–8) from two classrooms were cluster-randomized into HI-FLY or HI-FLY + PSEd. Both groups received HI-FLY and completed Youth Questionnaires (YQ) and food records (written or app-based) at Weeks 0 and 7. Feasibility and acceptability were assessed via enrollment, attrition, and User Satisfaction Surveys (USS). Diet quality was measured using Healthy Eating Index-2020 (HEI-2020) scores and analyzed via mixed-effects models. Results: Of 50 students, 42 (84%) enrolled and attrition was minimal (2.4%). The sample was 49% female and 85% at least part Native Hawaiian or Pacific Islander (NHPI). PSEd was acceptable, with average USS scores above the scale midpoint. No significant HEI-2020 changes were observed, though YQ responses indicated improvements in sugary drink intake (p = 0.03) and use of nutrition labels in HI-FLY + PSEd (p = 0.0007). Conclusions: Integrating PSEd into SNAP-Ed was feasible, acceptable, and demonstrated potential healthy behavior change among predominantly NHPI youth in Hawaii. Full article

Automated food item recognition and recipe recommendation systems have gained increasing importance in dietary management and culinary applications. Recent advancements in Computer Vision, particularly in object detection, classification, and image segmentation, have facilitated progress in these areas. However, many existing systems remain inefficient and lack seamless integration, resulting in limited solutions capable of both identifying food items and providing relevant recipe recommendations. Furthermore, modern neural network architectures have yet to be extensively applied to food recognition and recipe recommendation tasks. This study aims to address these limitations by developing a system based on the MobileNetV2 architecture for accurate food item recognition, paired with a recipe recommendation module. The system was trained on a diverse dataset of fruits and vegetables, achieving high classification accuracy (97.2%) and demonstrating robustness under various conditions. Our findings indicate that the modified model, the MobileNetV2 model, can effectively recognize different food items, making it suitable for real-time applications. The significance of this research lies in its potential to improve dietary tracking, offer valuable culinary insights, and serve as a practical tool for both personal and professional use. Ultimately, this work advances food recognition technology, contributing to enhanced health management and fostering culinary creativity. Some potential applications of this work include personalized dietary management, automated meal logging for fitness apps, smart kitchen assistants, restaurant ordering systems, and enhanced food analysis for nutrition tracking. Full article

Large language models (LLMs) such as Chat Generative Pre-Trained Transformer (ChatGPT) are increasingly used across domains, yet their generic training data and propensity for hallucination limit reliability in safety-critical fields like forestry. This paper outlines the conception and prototype of ForestGPT, a domain-specialised assistant designed to support forest professionals while preserving expert oversight. It addresses two looming risks: unverified adoption of generic outputs and professional mistrust of opaque algorithms. We propose a four-level development path: (1) pre-training a transformer on curated forestry literature to create a baseline conversational tool; (2) augmenting it with Retrieval-Augmented Generation to ground answers in local and time-sensitive documents; (3) coupling growth simulators for scenario modeling; and (4) integrating continuous streams from sensors, drones and machinery for real-time decision support. A Level-1 prototype, deployed at Futa Expo 2025 via a mobile app, successfully guided multilingual visitors and demonstrated the feasibility of lightweight fine-tuning on open-weight checkpoints. We analyse technical challenges, multimodal grounding, continual learning, safety certification, and social barriers including data sovereignty, bias and change management. Results indicate that trustworthy, explainable, and accessible LLMs can accelerate the transition to Forestry 5.0, provided that human-in-the-loop guardrails remain central. Future work will extend ForestGPT with full RAG pipelines, simulator coupling and autonomous data ingestion. Whilst exemplified in forestry, a complex, safety-critical, and ecologically vital domain, the proposed architecture and development path are broadly transferable to other sectors that demand trustworthy, domain-specific language models under expert oversight. Full article

The introduction of mobile banking is essential in today’s financial sector, where technological innovation plays a critical role. To remain competitive in the current market, businesses must analyze client attitudes and perspectives, as these influence long-term demand and overall profitability. While previous studies have explored general adoption behaviors, limited research has examined how individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost affect mobile banking adoption among specific generational cohorts. This study addresses that gap by offering insights into these variables, contributing to the growing literature on mobile banking adoption, and presenting actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, data were collected from both users and non-users of mobile banking among the Gen Z population in the United States. The regression model significantly predicts mobile banking adoption, with an intercept of $-0.548$ ($p < 0.001$). Among the independent variables, perceived cost of usage has the strongest positive effect on adoption ($B=0.857$, $\beta =0.722$, $p < 0.001$), suggesting that adoption increases when mobile banking is perceived as more affordable. Social influence also has a significant positive impact ($B=0.642$, $\beta =0.643$, $p < 0.001$), indicating that peer influence is a central driver of adoption decisions. However, self-efficacy shows a significant negative relationship ($B=-0.343$, $\beta =-0.339$, $p < 0.001$), and lifestyle compatibility was found to be statistically insignificant ($p=0.615$). These findings suggest that reducing perceived costs, through lower fees, data bundling, or clearer communication about affordability, can directly enhance adoption among Gen Z consumers. Furthermore, leveraging peer influence via referral rewards, Partnerships with influencers, and in-app social features can increase user adoption. Since digital self-efficacy presents a barrier for some, banks should prioritize simplifying user interfaces and offering guided assistance, such as tutorials or chat-based support. Future research may employ longitudinal designs or analyze real-life transaction data for a more objective understanding of behavior. Additional variables like trust, perceived risk, and regulatory policies, not included in this study, should be integrated into future models to offer a more comprehensive analysis. Full article

Background/Objectives: Identifying the factors influencing compliance is essential to improve the effectiveness of interventions. However, no study has examined factors that influence the longitudinal patterns of obesity intervention compliance. We aim to identify the longitudinal trajectories of parental and child compliance using group-based trajectory modeling (GBTM) and assess the influencing factors. Methods: The Diet, ExerCIse, and CarDiovascular hEalth Children (DECIDE-Children) was a 9-month app-assisted obesity prevention intervention targeted 8–10-year-old children. Altogether, 684 child–parent pairs from the intervention group were included. Parents were required to use the mobile app to learn health knowledge, monitor children’s diet and exercise behaviors, manage children’s weight, and received the assessment results. Parental compliance was assessed as the monthly usage times and duration of the mobile app. For child compliance, we used data recorded by parents in the “behavior monitoring” module. We employed group-based trajectory modeling (GBTM) to identify distinct trajectories of parental and child compliance and examined their associations with childhood obesity outcomes. Univariate and multivariate logistic regressions were performed to identify the influencing factors associated with the identified compliance groups. Results: Distinct trajectory groups of parental and child compliance were identified. The compliance trajectories of parents and children are related to the extent of changes in the child’s obesity-related outcomes (waist circumference, waist-to-hip ratio, and body fat percentage. p < 0.05). A majority of parents were classified into the “relatively low compliance” group. Parents in this group was associated with having a daughter (OR: 1.95, 95% CI: 1.17, 3.31) and the father having a higher education level (OR: 1.65, 95% CI: 1.05, 2.60). For children, 20.2% were assigned to the “decreasing compliance” group. Children in this group were more likely to have a younger mother (OR: 1.05, 95% CI: 1.01, 1.10) and parents with poorer compliance (OR: 2.36, 95% CI: 1.16, 5.47). Conclusions: Both student and parental compliance were shown to influence the effectiveness of childhood obesity interventions, highlighting the need to prioritize the assessment and promotion of compliance in such interventions. Child sex, paternal educational level, and maternal age were identified as significant factors associated with compliance, while the level of family involvement was found to play a pivotal role in fostering healthy behaviors in children. These findings suggest that future intervention strategies should place greater emphasis on engaging families and providing targeted supervision and support for populations at risk of lower compliance in order to enhance intervention outcomes. Full article

Mobile-assisted language learning (MALL) has emerged as a powerful tool for language education, offering flexibility, multimedia integration, and personalized learning experiences. Despite its growing adoption, most studies have focused on user perceptions and learning outcomes, with limited attention given to systematically evaluating the design, content, and pedagogical efficacy of mobile language learning apps (MLLAs). To address this gap in the effective design of MALL tools, this study developed an evaluation framework by integrating and refining elements from three established models in the field. The framework is organized into four dimensions: background and characteristics, app design, app content, and app pedagogy. It incorporates objective criteria alongside a standardized scoring system (0–2) to ensure consistent and systematic evaluations. The resulting framework provides researchers and educators with a tool to analyze and compare MLLAs based on their alignment with effective teaching and learning principles. This study contributes to the advancement of MALL app evaluation, supporting their development and improving teaching practices and learner outcomes. Full article

Visually impaired people face significant obstacles when navigating complex environments. However, recent technological advances have greatly improved the functionality of navigation systems tailored to their needs. The objective of this research is to evaluate the effectiveness and functionality these navigation systems through a comparative analysis of recent technologies. For this purpose, the PRISMA 2020 methodology was used to perform a systematic literature review. After identification and screening, 58 articles published between 2019 and 2024 were selected from three academic databases: Dimensions (26 articles), Web of Science (18 articles), and Scopus (14 articles). Bibliometric analysis demonstrated a growing interest of the research community in the topic, with an average of 4.552 citations per published article. Even with the technological advances that have occurred in recent times, there is still a significant gap in the support systems for people with blindness due to the lack of digital accessibility and the scarcity of adapted support systems. This situation limits the autonomy and inclusion of people with blindness, so the need to continue developing technological and social solutions to ensure equal opportunities and full participation in society is evident. This study emphasizes the great advances with the integration of sensors such as high-precision GPS, ultrasonic sensors, Bluetooth, and various assistance apps for object recognition, obstacle detection, and trajectory generation, as well as haptic systems, which provide tactile information through wearables or actuators and improve spatial awareness. Current navigation algorithms were also identified in the review with methods including obstacle detection, path planning, and trajectory prediction, applied to technologies such as ultrasonic sensors, RGB-D cameras, and LiDAR for indoor navigation, as well as stereo cameras and GPS for outdoor navigation. It was also found that AI systems employ deep learning and neural networks to optimize both navigation accuracy and energy efficiency. Finally, analysis revealed that 79% of the 58 reviewed articles included experimental validation, 87% of which were on haptic systems and 40% on smartphones. These results underscore the importance of experimentation in the development of technologies for the mobility of people with visual impairment. Full article

Mobile technology, especially mobile learning, has long been an emerging and thriving field, and remains a main theme in mobile learning applications and systems. The extensive utilization of mobile learning has prompted the invention of many mobile applications. As a result of rapid advances in application technologies, various learning applications can combine different media or multi-modalities, such as video, audio, images, animated graphics, and text, to create multimedia learning resources that engage learners. However, the most favorable modalities in different learning applications that assist performance are worth exploring. This study employed mixed methods to investigate the current multi-modality situation in learning application utilization among 300 university students in China, where a rapid educational technology revolution is occurring. The findings revealed that the verbal modality (M = 3.99, S*D = 0.79) and the writing modality (M = 3.99, S*D = 0.75) in the learning applications were less enjoyable and less effective at enhancing learning performance. In exam-based or function-based apps, all five modalities in this research were considered important, especially the visual and aural modes. The results of this study also revealed that a majority of university learners were satisfied with the multi-modalities in different types of applications, except for game-based apps, that assist their learning performance (56.7%, M = 3.87, S*D = 0.79), which contrasts with the results of several related studies. Overall, college users perceived that multi-modalities were effective in helping them to complete tasks, and all modalities in current applications satisfied most of the users’ needs to assist their learning performance. In the end, the findings indicated a positive and strong linear relationship [r = 0.766, p < 0.05] between multi-modalities and assisted learning performance with the help of more capable (knowledgeable) others with the use of mobile applications. Full article

The aim of this paper is to create image-processing Android apps to launch on the Google Play Store. Three apps with different usages will be presented for different situations. The first app is a night-vision app on an Android phone that uses OpenCV. The second app is a tooth-brushing assistant application. The app is made for mobile phones and uses advanced image-processing techniques to detect when the tooth is brushed correctly or incorrectly. The main focus is on the direction of the toothbrush movement because this is one of the key aspects of correctly brushing teeth. The direction of movement of the brush is detected using movement vectors. The third app is a lane-detection app on the smartphone. Lane detection is carried out using OpenCV and TensorFlow libraries. The mobile app was implemented on the Android operating system. The app has a live video feed of the surroundings. When in the area of view, there will be a road with a lane. The system detects the lane and draws a green line over it. Full article

Background/Objectives: Inexperienced dentists and dental students are especially prone to misdiagnosis, and this represents a huge problem regarding antimicrobial stewardship. We aimed to develop a mobile app for rational antibiotic prescribing in dentistry based on local–systemic symptoms and patient factors, rather than solely on diagnosis, to tackle misdiagnosis. Methods: The study involved 64 participants, 50 of which were third-year dental students attending a pharmacology course focusing on antimicrobials, comprising lectures and practical sessions without (noAPP group, n = 22) or with (APP group n = 28) the assistance of a mobile application. The other 14 participants were practicing dentists who decided to register and use the application. All registered users of the application were asked to take a feedback survey, while learning outcomes were evaluated via a pharmacology quiz. Results: A decision tree was used for application development. In total, 76 impressions were collected on the application. The majority of the impressions were related to odontogenic–endodontic infections. Multiple linear regression analysis did not reveal differences in survey responses between practicing dentists and undergraduate students in the feedback survey responses. There was a significant difference in the mean pharmacology test scores between the noAPP and APP groups (5.50 ± 1.80 vs. 7.21 ± 1.03, p = 0.0001). Conclusions: The dentalantibiotic.com application was developed to support rational antibiotic prescribing, in view of tackling misdiagnosis, among inexperienced dentists, as well as to assist in undergraduates’ pharmacology learning, and the current study shows its large impact as an educational tool. The majority of participants considered it easy to use, efficient in facilitating the right antibiotic choice, and useful for everyday decision-making. Full article

Evidence is increasing for digital health programs targeting the secondary prevention of stroke. We aimed to determine the feasibility of the novel Care Assistant and support Program for people after Stroke (CAPS) or transient ischaemic attack (TIA) by combining person-centred goal setting and risk-factor monitoring through a web-based clinician portal, SMS messages, a mobile application (app), and a wearable device. We conducted a 12-week mixed-methods, open-label feasibility study. Participants (6 months–3 years after stroke or TIA, access to the internet via a smartphone/tablet) were recruited via the Australian Stroke Clinical Registry. Participants set one or two secondary prevention goals with a researcher and provided access and training in technology use. Feasibility outcomes included recruitment, retention, usability, acceptability, and satisfaction. Secondary outcomes included goal attainment, health outcomes, and program costs. Following 600 invitations, 58 responded, 34/36 (94%) eligible participants commenced the program (one withdrawal; 97% retention), and 10 were interviewed. Participants (27% female, 33% TIA) generally rated the usability of the mobile application as ‘Good’ to ‘Excellent’ (System Usability Scale). Most (94%) agreed the program helped with engagement in health self-monitoring. Overall, 52 goals were set, predominantly regarding exercise (21/52), which were the most frequently achieved (9/21). At 12 weeks, participants reported significant improvements (p < 0.05) in self-efficacy (Cohen’s d = 0.40), cardiovascular health (d = 0.71), and the mental health domain of the PROMIS GH (d = 0.63). CAPS was acceptable, with good retention and engagement of participants. Evaluation of this program in a randomised controlled trial is warranted. Full article

Advances in mobile robotics and AI have significantly expanded their application across various domains and challenging conditions. In the past, this has been limited to safe, controlled, and highly structured settings, where simplifying assumptions and conditions allowed for the effective resolution of perception-based tasks. Today, however, robotics and AI are moving into the wild, where human–robot collaboration and robust operation are essential. One of the most demanding scenarios involves deploying autonomous drones in GNSS-denied environments, such as dense forests. Despite the challenges, the potential to exploit natural resources in these settings underscores the importance of developing technologies that can operate in such conditions. In this study, we present a methodology that addresses the unique challenges of natural forest environments by integrating positioning methods, leveraging cameras, LiDARs, GNSS, and vision AI with drone technology for under-canopy wild berry mapping. To ensure practical utility for fruit harvesters, we generate intuitive heat maps of berry locations and provide users with a mobile app that supports interactive map visualization, real-time positioning, and path planning assistance. Our approach, tested in a Scandinavian forest, refines the identification of high-yield wild fruit locations using V-SLAM, demonstrating the feasibility and effectiveness of autonomous drones in these demanding applications. Full article

Musculoskeletal disorders are common among older adults, affecting mobility and quality of life. Effective rehabilitation is essential, but the implementation of programs faces challenges. Traditional methods often necessitate in-person assessments, which can be difficult for older adults with mobility limitations. Telerehabilitation offers a solution, bringing therapy closer to patients. However, the accurate remote monitoring of health and performance remains a challenge. This study addresses this gap by developing and validating the System for Tracking and Evaluating Performance (STEP). STEP is a hardware-software system that automates physical performance tests, eliminating the need for constant expert supervision. The system focuses on three standard tests: the Six-Minute Walking Test (6MWT), the Ten-Meter Walking Test (10MWT), and the 30-s Sit-to-Stand Test (30STS). Validation compared results from the STEP app with in-person assessments by physicians for patients undergoing rehabilitation after knee or hip arthroplasty. The study found strong positive correlations between the app’s results and the physicians’ assessments for all tests. These findings demonstrate the STEP system’s potential as a reliable tool for remote physical performance assessment. Further research is needed to explore its integration into clinical practice and cost-effectiveness in reducing the need for operator assistance in monitoring patients with physical limitations. Full article

The communicative approach to language learning, a teaching method commonly used in second language (L2) classrooms, places little to no emphasis on pronunciation training. As a result, mobile-assisted pronunciation training (MAPT) platforms provide an alternative to classroom-based pronunciation training. To date, there have been several meta-analyses and systematic reviews of mobile-assisted language learning (MALL) studies, but only a few of these meta-analyses have concentrated on pronunciation. To better understand MAPT’s impact on L2 learners’ perceptions and production of targeted pronunciation features, this study conducted a systematic review of the MAPT literature following PRISMA 2020 guidelines. Potential mobile-assisted articles were identified through searches of the ERIC, Educational Full Text, Linguistics and Language Behavior Abstract, MLI International, and Scopus databases and specific journal searches. Criteria for article inclusion in this study included the following: the article must be a peer-reviewed empirical or quasi-empirical research study using both experimental and control groups to assess the impact of pronunciation training. Pronunciation training must have been conducted via MALL or MAPT technologies, and the studies must have been published between 2014 and 2024. A total of 232 papers were identified; however, only ten articles with a total of 524 participants met the established criteria. Data pertaining to the participants used in the study (nationality and education level), the MPAT applications and platforms used, the pronunciation features targeted, the concentration on perception and/or production of these features, and the methods used for training and assessments were collected and discussed. Effect sizes using Cohen’s d were also calculated for each study. The findings of this review reveal that only two of the articles assessed the impact of MAPT on L2 learners’ perceptions of targeted features, with results indicating that the use of MPAT did not significantly improve L2 learners’ abilities to perceive segmental features. In terms of production, all ten articles assessed MPAT’s impact on L2 learners’ production of the targeted features. The results of these assessments varied greatly, with some studies indicating a significant and large effect of MAPT and others citing non-significant gains and negligible effect sizes. The variation in these results, in addition to differences in the types of participants, the targeted pronunciation features, and MAPT apps and platforms used, makes it difficult to conclude that MAPT has a significant impact on L2 learners’ production. Furthermore, the selected studies’ concentration on mostly segmental features (i.e., phoneme and word pronunciation) is likely to have had only a limited impact on participants’ intelligibility. This paper provides suggestions for further MAPT research, including increased emphasis on suprasegmental features and perception assessments, to further our understanding of the effectiveness of MAPT for pronunciation training. Full article

Three-dimensional printing is an emerging service delivery method for on-demand access to customized assistive technology devices. However, barriers exist in locating and designing appropriate models and having the devices printed. The purpose of this work is to outline the development of an app, 3DAdapt, which allows users to overcome these issues by searching within a curated list of 3D printable assistive devices, customizing models that support it, and ordering the device to be printed by manufacturers linked within the app or shared with local 3D printing operators. The app integrates searching and filters based on the International Classification of Functioning, Disability, and Health, with the available devices including those developed from fieldwork collaborations with multiple professionals and students within clinical, community, and educational settings. It provides users the ability to customize select models to meet their needs. The model can then be shared, downloaded, or ordered from a third-party 3D printing service. This development and expert testing phase to assess feasibility and modify the app based on identified themes then prepared the team for the next phases of beta testing to reach the overall aim of 3DAdapt to connect individuals to affordable and customizable devices to increase independence and quality of life. Full article