Search Results (1,372)

This study evaluated the validity and reliability of the Fitnessmeter mobile application for measuring change of direction (COD) performance compared to a gold-standard photocell system. Forty-six trained adolescent soccer players performed the 5 + 5 m COD test, with simultaneous data collection using the Fitnessmeter app and photocells. A strong correlation was observed between the two instruments (r = 0.971), with no significant differences in COD times (p > 0.05). Bland–Altman analysis indicated minimal systematic bias (0.02 ± 0.05 s) and acceptable limits of agreement. Reliability analysis demonstrated high consistency for both devices (Fitnessmeter ICC = 0.66–0.72; Photocells ICC = 0.57–0.76), with reproducibility confirmed by a low coefficient of variation values (<3%). The Fitnessmeter app also effectively measured inter-limb asymmetries, showing results comparable to photocells (p = 0.42). These findings validate Fitnessmeter as a reliable, portable, and cost-effective tool for COD assessment. Its accessibility makes it suitable for applications in sports, education, and rehabilitation, offering a practical alternative to expensive equipment. Further studies are recommended to confirm its reliability in other COD tests and contexts. Fitnessmeter’s innovative motion detection technology has the potential to democratize performance assessment, making it accessible to a broader audience beyond elite sports. Full article

Increasingly, growing older is something we do alongside technologies. Often, through mobile media, our everyday practices of informal caring are being digitally mediated and mediatised. From apps such as Whatsapp to iOS Health, how digital technology is used to provide informal care in later life is poorly understood. Care operates intergenerationally and bilaterally—older adults often caring for young children as well as adult children caring for older adults with declining capacity. Mobile media technology has become an integral part of these informal care practices. Understanding what older people do with technology is important to map different media literacies, possibilities, and limitations in practice. In this paper, which draws on a larger study, we explore older adults’ informal digital practices through creative practice workshops in Victoria, Australia. In what we called ‘playshops’, we deployed playful and creative methods such as postcard prompts and mapping exercises to explore informal care practices used by older adults, many of which are so mundane that they remain invisible and are potentially missed in research. We performed this to map uses, barriers, and possibilities of mobile technologies in providing and receiving care. Based on these playshops, we argue that when digital media is used in everyday ways, it can lead to greater social connection and informal care for, with, and through older people. These everyday acts of care give voice and visibility to the diverse ways older people use technology to facilitate informal care practises. Full article

Background: Research shows that media-based dietary and behavioral strategies can aid weight loss, but limited studies have been conducted among Black/African American adults. Objective: This review examines the literature on dietary and behavioral strategies for weight loss and maintenance among Black/African American adults, identifying the types of media used alongside these strategies. Methods: The PubMed, Web of Science, CINAHL, and Communication & Mass Media databases were searched for peer-reviewed articles with no restrictions on the publication date. Two reviewers conducted the screening. Studies were included if they had >75% Black/African American adult participants (18 years and older), included behavioral or dietary strategies, had a media component, weight loss or maintenance as an outcome, and published in English language. Results: Nine studies (randomized control trials (n = 5), mixed-method studies (n = 2) and qualitative studies (n = 2)) were included. Behavioral strategies used to lose and maintain weight included goal setting (n = 4), self-monitoring (n = 5), and weekly self-weighing (n = 2). Limiting sugary drinks (n = 3), limiting junk and high fat foods (n = 5), aiming for a set number of calories per day (n = 3), portion control (n = 4), and increasing fruits and vegetable intake (n = 3) were the most common dietary strategies used to lose and maintain weight. Media used in the intervention studies included mHealth text messaging (n = 2), Facebook (n = 2), a website (n = 1), television (n = 1) and a mobile app (n = 1). Conclusions: The findings highlight the limited research on the utilization of media for behavioral and dietary weight loss strategies among Black/African American adults, indicating a need for future studies to explore and optimize media-based strategies for this population. Full article

Transformers have emerged as the central backbone architecture for modern generative AI. However, most ML applications targeting low-power, low-cost SoCs (TinyML apps) do not employ Transformers as these models are thought to be challenging to quantize and deploy on small devices. This work proposes a methodology to reduce Transformer dimensions with an extensive pruning search. We exploit the intrinsic redundancy of these models to fit them on resource-constrained devices with a well-controlled accuracy tradeoff. We then propose an optimized library to deploy the reduced models using BFLoat16 with no accuracy loss on Commercial Off-The-Shelf (COTS) RISC-V multi-core micro-controllers, enabling the execution of these models at the extreme edge, without the need for complex and accuracy-critical quantization schemes. Our solution achieves up to $220\times$ speedup with respect to a naïve C port of the Multi-Head Self Attention PyTorch kernel: we reduced MobileBert and TinyViT memory footprint up to ∼94% and ∼57%, respectively, and we deployed a tinyLLAMA SLM on microcontroller, achieving a throughput of 1219 tokens/s with an average power of just 57 mW. Full article

Background/Objectives: This work brings to light the importance of forming large training datasets with diverse images generated and proposes an image dataset merging application, namely, the Data Merger App, to streamline the management and synthesis of large-scale datasets. The Data Merger can recognize common classes across various datasets and provides tools to combine and organize them in a well-structured and easily accessible way. Methods: A case study is then presented, leveraging four different Convolutional Neural Network (CNN) models, VGG16, ResNet50, MobileNetV3-small, and DenseNet-161, and a Visual Transformer (ViT), to benchmark their performance to classify skin cancer images, when trained on single datasets and on enhanced hyperdatasets generated by the Data Merger App. Results: Extended experimental results indicated that enhanced hyperdatasets are efficient and able to improve the accuracies of classification models, whether the models are trained from scratch or by using Transfer Learning. Moreover, the ViT model was reported for higher classification accuracies compared to CNNs on datasets with a limited number of classes, reporting 91.87% accuracy for 9 classes, as well as in the case of enhanced hyperdatasets with multiple numbers of classes, reporting accuracy of 58% for 32 classes. Conclusions: In essence, this work demonstrates the great significance of data combination, as well as the utility value of the developed prototype web application as a critical tool for researchers and data scientists, enabling them to easily handle complex datasets, combine datasets into larger diverse versions, to further enhance the generalization ability of models and improve the quality and impact of their work. Full article

The purpose of this paper is to present the creation, challenges and difficulties of four utility apps to increase production on Android handheld devices. The motivation of creating the apps and the problems encountered during creation are highlighted. The first app is a fast emoji app for WhatsApp. The motivation for creating such an app is that usually phone users do not like to type too much; with this app, they can communicate with each other just by sending emojis. The second app is a standalone emoji sender app, which does the same thing as the previous app but uses its own database, not the WhatsApp database. Full article

The Napo Sumaco Aspiring UNESCO Global Geopark (NSAUGG) in Ecuador represents a genuine variety of geological, cultural, and natural heritage, which aims to promote sustainable development through geotourism. This study describes the significance of NSAUGG, emphasizing its geological diversity which includes a variety of geosites, and focusing on three recently annexed geosites: the Wawa Sumaco Quarry, Puka Urku, and the Pucuno River, where geological analyses, including petrographic and mineralogical assessments, were conducted. To enhance community engagement and educational outreach, a multi-platform mobile application, “SumAppGeo”, was developed using ArcGIS and Flutterflow. This application serves as an interactive tool for visitors and local communities, providing detailed geological information, interactive maps, and educational content. The findings reveal the presence of significant geological features, such as haüyne-bearing alkaline rocks, which indicate specific volcanic activity in this region and are an element of geodiversity, validating the Wawa Sumaco Quarry, Puka Urku, and the Pucuno River as geosites. The implementation of SumAppGeo aims to foster a deeper understanding of the region’s geodiversity while promoting responsible tourism practices. This initiative not only supports the recognition of NSAUGG as part of the UNESCO Global Geoparks Network but also contributes to the socio-economic development of local communities through sustainable tourism practices. Full article

Purpose: Cancer survivors in rural and underserved areas face barriers such as limited access to oncology exercise programs and limited facilities, contributing to health inequities in cancer survivorship. This study explored cancer survivors’ thoughts on exercise and mobile technology for exercising with a mobile application (app) during and after treatment in rural and remote areas. Methods: Three online focus groups were conducted in February 2024 using semi-structured interviews with 12 open-ended questions. Eligible participants were adult cancer survivors or caregivers living in medically underserved areas, English-speaking, consented to being audiotaped, and attended one 60-min group interview. The discussions were transcribed verbatim and analyzed via a content analysis approach with consensus. Results: Fifteen participants attended from four States. None of the participants were advised to exercise; availability of exercise resources depended on geographic location and a cancer-specific exercise app was desired. They understood the benefits of exercise after diagnosis but expressed a need for more guidance during treatment. Geographic location shaped their activities, with most engaging in daily physical tasks rather than structured exercise. Most participants were receptive to using an exercise app to manage fatigue. Suggested key features to exercise with an app included live trainers, exercise checklists, visual benchmarks, and programs tailored to different fitness levels. Conclusions: These results emphasize the need for personalized resources, guidance, and on-demand accessibility to an exercise oncology app. A cancer-specific exercise mobile app will mitigate health inequities for cancer survivors residing in rural and remote areas. Full article

Telemedicine brings several benefits to patients, healthcare providers, and the wider society, including reductions in the need for hospitalizations or readmissions, as well as in overall healthcare costs and the length of inpatient stay. In addition, these services may provide psychological benefits to patients, including excellent satisfaction and medication adherence. The present study aimed to investigate an in-house-developed otorhinolaryngologic remote diagnostic system (mobile app). The basis of the comparison was the incidence between the diagnoses and therapies made by remote diagnosticians and on-site specialists based on static images and videos captured by a smartphone otoscope device. In the study, 103 patients were involved. After registering demographic data, the telemedicine software was evaluated by comparing the matching of physically established diagnoses and/or therapies with remotely established diagnoses and/or therapies. The most remarkable result was in concordance with the diagnoses, with 79 matches identified of the 103 cases examined; the rate of the matching cases was 76.7% (95% CI: 68.5–84.9%). These results support that telemedicine-based otorhinolaryngological remote diagnostics could play a significant role in future healthcare. Full article

Traditional screening methods for Mild Cognitive Impairment (MCI) face limitations in accessibility and scalability. To address this, we developed and validated a speech-based automatic screening app implementing three speech–language tasks with user-centered design and server–client architecture. The app integrates automated speech processing and SVM classifiers for MCI detection. Functionality validation included comparison with manual assessment and testing in real-world settings (n = 12), with user engagement evaluated separately (n = 22). The app showed comparable performance with manual assessment (F1 = 0.93 vs. 0.95) and maintained reliability in real-world settings (F1 = 0.86). Task engagement significantly influenced speech patterns: users rating tasks as “most interesting” produced more speech content (p < 0.05), though behavioral observations showed consistent cognitive processing across perception groups. User engagement analysis revealed high technology acceptance (86%) across educational backgrounds, with daily cognitive exercise habits significantly predicting task benefit perception (H = 9.385, p < 0.01). Notably, perceived task difficulty showed no significant correlation with cognitive performance (p = 0.119), suggesting the system’s accessibility to users of varying abilities. While preliminary, the mobile app demonstrated both robust assessment capabilities and sustained user engagement, suggesting the potential viability of widespread cognitive screening in the geriatric population. Full article

Background/Objectives: The second highest prevalence of childhood malnutrition in the world is found in Nigeria. Infant and young child feeding practices (IYCF) are crucial predictors of the nutritional status of children. This study evaluated the effects of utilization of the BabyThrive app versus control on IYCF knowledge of Nigerian teenage mothers. Methods: A parallel, randomized controlled trial was conducted with 194 low-income teenage mother–child (0–2 years) dyads in Nigeria. Outcome measures included knowledge concerning food type for an infant, exclusive breastfeeding, complementary feeding, and total IYCF knowledge scores. Results: No significant differences in demographic characteristics or IYCF knowledge were found at baseline. Post-intervention, almost all BabyThrive participants vs. only 36.1% of controls were aware that expressed breast milk is the ideal food for an infant <6 months, when a mother resumes work (p < 0.05). As compared to the BabyThrive group, knowledge of how to maintain breast milk supply (5.2% vs. 83.5%), the duration of safety of breast milk at room temperature (15.4% vs. 85.6%), responsive feeding (58.8% vs. 96.9) and dietary diversity (15.5% vs. 99%) was substantially lower in controls (p < 0.05). Mean knowledge on exclusive breastfeeding (25.17 ± 1.37 vs. 19.87 ± 1.80), complementary feeding (15.68 ± 0.60, vs. 13.51 ± 1.21) and total knowledge scores (46.8 ± 1.79 vs. 38.65 ± 2.71) was higher in the BabyThrive group (p < 0.05). Conclusions: In sum, the BabyThrive app significantly increased maternal IYCF knowledge in comparison with controls. It will be a useful tool to improve maternal IYCF knowledge in resource-limited areas. Full article

Mobile cloud computing (MCC) is a technological paradigm for providing services to mobile device (MD) users. A compromised MD may cause harm to both its user and to other MCC customers. This study explores the use of machine learning (ML) models and stochastic methods for the protection of Android MDs connected to the mobile cloud. To test the validity and feasibility of the proposed models and methods, the study adopted a proof-of-concept approach and developed a prototype system named MINDPRESS. The static component of MINDPRES assesses the risk of the apps installed on the MD. It uses a device-based ML model for static feature analysis and a cloud-based stochastic risk evaluator. The device-based hybrid component of MINDPRES monitors app behavior in real time. It deploys two ML models and functions as an intrusion detection and prevention system (IDPS). The performance evaluation results of the prototype showed that the accuracy achieved by the methods for static and hybrid risk evaluation compared well with results reported in recent work. Power consumption data indicated that MINDPRES did not create an overload. This study contributes a feasible and scalable framework for building distributed systems for the protection of the data and devices of MCC customers. Full article

App-based marketing has been widely used in the telecommunications industry to both serve and draw in new customers. Typically, telecom providers must invest an amount of company resources to develop and maintain the operations mechanism of information technology platforms (e.g., mobile apps); therefore, it is important to take the issue of marketing effectiveness into account. For example, the mismatch between what telecom providers offer in their mobile apps and customers’ marketing requirements plays a significant role in determining unmet knowledge and presentation gaps that are related to the marketing domain. This research intends to propose an integrated Fuzzy MCDM model based on 4Ps (Product, Price, Place, Promotion) and 4Cs (Customer Needs, Cost, Convenience, Communication) models for evaluating mobile telecom applications (MTAs). Therefore, the 4Ps and 4Cs models are extended to develop a hierarchy model for evaluating MTAs. Next, fuzzy theory is applied to handle the subjectiveness of qualitative evaluation criteria while the Analytic Hierarchy Process (AHP) is applied to synthesize the weight and score of the evaluation criteria. The proposed model is applied to evaluate, rank, and analyze the MTA of three telecom providers in the Kingdom of Saudi Arabia (KSA) (e.g., STC, Zain, and Mobily). The conducted case study ensures the usability and applicability of the proposed model. The evaluation results offer several managerial actions for achieving ideal app-based marketing. Full article

Background: The integration of digital health technologies has transformed mental healthcare, particularly for young adults with First-Episode Psychosis (FEP). Digital interventions, such as telepsychiatry and mobile applications, address barriers like social stigma, restricted access to services, and the urgency of timely care. Methods: A systematic literature review was conducted using PubMed and APA PsycINFO. Included studies focused on randomized controlled trials, cohort studies, and open studies of digital interventions for FEP rehabilitation. Exclusion criteria included meta-analyses, reviews, and irrelevant studies. Results: Smartphone applications like Horyzons-Canada facilitated peer support and symptom management, while tools such as My Journey 3 emphasized the need for better user engagement. Digital innovations, including Cognitive Behavioral Therapy for insomnia (CBT-I) and telepsychiatry, demonstrated improvements in recovery and treatment adherence. Discussion: Digital interventions significantly enhance the accessibility and quality of FEP care, supporting functional recovery and patient engagement. Future research should prioritize longitudinal studies, user engagement strategies, and advanced analytics to develop personalized, scalable solutions. Full article

This study considers how to quickly find the order in which to present a set of text artifacts on mobile apps or websites such that those artifacts are maximally semantically separated. Semantic separation is desirable because it ensures that users experience as much novelty as possible from one item to the next, thereby improving user attention and engagement. Since an exhaustive search of all possible sequences of text items becomes increasingly infeasible as the length of the sequence grows, a new algorithm is proposed to quickly find the semantically optimal presentation order for a set of text artifacts. The performance of the proposed algorithm is evaluated using an extensive set of experiments involving three different types of text artifacts, seven different sequence lengths, and more than 600 experimental trials. The results demonstrate that the proposed algorithm can select statistically optimal sequences of text artifacts extremely quickly, regardless of the type of text artifacts being used as input or the length of the sequence. App and website developers who are seeking to hold users’ attention and improve user engagement may therefore find the proposed algorithm very attractive in comparison to an exhaustive search. Full article