Search Results (357)

The growing use of mobile health (mHealth) technologies adds complexity and risk to the healthcare environment. This paper presents a multi-layered cybersecurity assessment of an in-house mHealth platform (PHGL-COVID), comprising a Docker-based server infrastructure, a Samsung Galaxy A55 smartphone, and a Galaxy Watch 7 wearable. The objective was to identify vulnerabilities across the server, mobile, and wearable components by emulating real-world attacks and conducting systematic penetration tests on each layer. Tools and methods specifically tailored to each technology were applied, revealing exploitable configurations, insecure Bluetooth Low Energy (BLE) communications, and exposure of Personal Health Records (PHRs). Key findings included incomplete container isolation, BLE metadata leakage, and persistent abuse of Android privacy permissions. This work delivers both a set of actionable recommendations for developers and system architects to strengthen the security of mHealth platforms, and a reproducible audit methodology that has been validated in a real-world deployment, effectively bridging the gap between theoretical threat models and practical cybersecurity practices in healthcare systems. Full article

Background: Type 2 diabetes (T2D) and overweight or obesity are strongly associated, with a high prevalence of these concomitant conditions contributing significantly to global healthcare costs. Given this burden, there is an urgent need for effective interventions. Mobile health (mHealth) technologies represent a promising strategy to address both conditions simultaneously. Objectives: This systematic review and meta-analysis aimed to evaluate the effectiveness of mHealth-based interventions for the management of adults with T2D and overweight/obesity. Specifically, it assessed the quantitative impact of these interventions on glycosylated hemoglobin (HbA1c), body weight, triglycerides, total cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) levels. Methods: A systematic search was conducted in PubMed, Web of Science, and Scopus databases from inception to 9 July 2025. The inclusion criteria focused on randomized controlled trials (RCTs) using mHealth interventions in adults with T2D and overweight/obesity, reporting HbA1c or weight as primary or secondary outcomes. The risk of bias was assessed using the Cochrane Risk of Bias tool 2. A total of 13 RCTs met the inclusion criteria. Results: Meta-analysis indicated significant improvements after 6–12 months of intervention in HbA1c (MD −0.23; 95% CI −0.36 to −0.10; p < 0.001; I² = 72%), body weight (MD −2.47 kg; 95% CI −3.69 to −1.24; p < 0.001; I² = 79%), total cholesterol (MD −0.23; 95% CI −0.39 to −0.07; p = 0.004; I² = 0%), and LDL (MD −0.27; 95% CI −0.42 to −0.12; p < 0.001; I² = 0%). Conclusions: mHealth interventions are effective and scalable for managing T2D and obesity, particularly when incorporating wearable technologies to improve adherence. Future research should focus on optimizing personalization, engagement strategies, and long-term implementation. Full article

Total Knee Arthroplasty (TKA) is a well-established surgical intervention for the management of end-stage knee osteoarthritis. While the procedure is generally successful, postoperative rehabilitation remains a key determinant of long-term functional outcomes. Traditional rehabilitation protocols, particularly those requiring in-person clinical visits, often encounter limitations in accessibility, patient adherence, and personalization. In response, emerging sensor technologies have introduced innovative solutions to support and enhance recovery following TKA. This review provides a thematically organized synthesis of the current landscape and future directions of sensor-assisted rehabilitation in TKA. It examines four main categories of technologies: wearable sensors (e.g., IMUs, accelerometers, gyroscopes), smart implants, pressure-sensing systems, and mobile health (mHealth) platforms such as ReHub^® and BPMpathway. Evidence from recent randomized controlled trials and systematic reviews demonstrates their effectiveness in tracking mobility, monitoring range of motion (ROM), detecting gait anomalies, and delivering real-time feedback to both patients and clinicians. Despite these advances, several challenges persist, including measurement accuracy in unsupervised environments, the complexity of clinical data integration, and digital literacy gaps among older adults. Nevertheless, the integration of artificial intelligence (AI), predictive analytics, and remote rehabilitation tools is driving a shift toward more adaptive and individualized care models. This paper concludes that sensor-enhanced rehabilitation is no longer a future aspiration but an active transition toward a smarter, more accessible, and patient-centered paradigm in recovery after TKA. Full article

Background/Objectives: The accurate identification of insect bites from images of skin is daunting due to the fine gradations among diverse bite types, variability in human skin response, and inconsistencies in image quality. Methods: For this work, we introduce DeepBiteNet, a new ensemble-based deep learning model designed to perform robust multiclass classification of insect bites from RGB images. Our model aggregates three semantically diverse convolutional neural networks—DenseNet121, EfficientNet-B0, and MobileNetV3-Small—using a stacked meta-classifier designed to aggregate their predicted outcomes into an integrated, discriminatively strong output. Our technique balances heterogeneous feature representation with suppression of individual model biases. Our model was trained and evaluated on a hand-collected set of 1932 labeled images representing eight classes, consisting of common bites such as mosquito, flea, and tick bites, and unaffected skin. Our domain-specific augmentation pipeline imputed practical variability in lighting, occlusion, and skin tone, thereby boosting generalizability. Results: Our model, DeepBiteNet, achieved a training accuracy of 89.7%, validation accuracy of 85.1%, and test accuracy of 84.6%, and surpassed fifteen benchmark CNN architectures on all key indicators, viz., precision (0.880), recall (0.870), and F1-score (0.875). Our model, optimized for mobile deployment with quantization and TensorFlow Lite, enables rapid on-client computation and eliminates reliance on cloud-based processing. Conclusions: Our work shows how ensemble learning, when carefully designed and combined with realistic data augmentation, can boost the reliability and usability of automatic insect bite diagnosis. Our model, DeepBiteNet, forms a promising foundation for future integration with mobile health (mHealth) solutions and may complement early diagnosis and triage in dermatologically underserved regions. Full article

Background/Objectives: A randomized controlled trial (RCT) was designed to test the emerging role of respiratory mechanics as part of physiotherapy in patients with non-specific chronic neck pain (NSCNP). Methods: Ninety patients with NSCNP and symptom duration >3 months were randomly allocated to three intervention groups of equal size, receiving either cervical spine (according to the Mulligan Concept) and diaphragm manual therapy plus breathing reeducation exercises (experimental group—EG1), cervical spine manual therapy plus sham diaphragmatic manual techniques (EG2), or conventional physiotherapy (control group—CG). The treatment period lasted one month (10 sessions) for all groups. The effect on the cervical spine range of motion (CS-ROM) and on the craniovertebral angle (CVA) was examined. Outcomes were collected before treatment (0/12), after treatment (1/12), and three months after the end of treatment (4/12). The main analysis comprised a two-way mixed ANOVA with a repeated measures factor (time) and a between-groups factor (group). Post hoc tests assessed the source of significant interactions detected. The significance level was set at p = 0.05. Results: No significant between-group baseline differences were identified. Increases in CS-ROM and in CVA were registered mainly post-treatment, with improvements maintained at follow-up for CS-ROM. EG1 significantly improved over CG in all movement directions except for flexion and over EG2 for extension only, at 1/12 and 4/12. All groups improved by the same amount for CVA. Conclusions: EG1, which included diaphragm manual therapy and breathing re-education exercises, registered the largest overall improvement over CG (except for flexion and CVA), and for extension over EG2. The interaction between respiratory mechanics and neck mobility may provide new therapeutic and assessment insights of patients with NSCNP. Full article

(1) Background: Physical activity (PA) is a key modifiable risk factor for chronic diseases, yet many adults do not meet PA guidelines. Just-in-time adaptive interventions (JITAIs), a type of mobile health (mHealth) intervention, offer tailored support based on an individual’s context to promote PA. Integrating behavior change techniques (BCTs) and theories is critical to the design of effective mHealth interventions. Understanding which BCTs and theories work best can inform future JITAI development. (2) Objective: The objective of this study is to examine how behavior change theories and BCTs are implemented in mHealth PA JITAIs and assess their relationship with PA-related outcomes. (3) Methods: This scoping review followed the PRISMA-ScR guidelines. A total of 29 studies were included. (4) Results: The most commonly used BCTs include prompts/cues (n = 29), goal-setting (behavior) (n = 15), and feedback on behavior (n = 14), while self-determination theory (n = 4) and social cognitive theory (n = 4) are the most commonly used theories. However, there is insufficient evidence as to which theories and BCTs are most effective in eliciting effective PA behavior change. (5) Conclusions: Clearer reporting and integration of BCTs and behavior change theories, along with optimized user interfaces, are needed to improve the intervention quality, replicability, and long-term effectiveness of PA JITAIs. Full article

Many breast cancer patients follow a self-managed treatment pathway, which may lead to gaps in the data available to healthcare professionals, such as information about patients’ everyday symptoms at home. Mobile apps have the potential to bridge this information gap, leading to more effective treatments and interventions, as well as helping breast cancer patients monitor and manage their symptoms. In this paper, we elicit design requirements for breast cancer symptom-management mobile apps using a systematic review following the PRISMA framework. We then evaluate existing cancer symptom-management apps found on the Apple store according to the extent to which they meet these requirements. We find that, whilst some requirements are well supported (such as functionality to record multiple symptoms and provision of information), others are currently not being met, particularly interoperability, functionality related to responses from healthcare professionals, and personalisation. Much work is needed for cancer patients and healthcare professionals to experience the benefits of digital health innovation. The article demonstrates a formal requirements model, in which requirements are categorised as functional and non-functional, and presents a proposal for conceptual design for future mobile apps. Full article

Blood pressure is a vital indicator of cardiovascular health and plays a crucial role in the early detection and management of heart-related diseases. However, current practices for recording blood pressure readings are still largely manual, leading to inefficiencies and data inconsistencies. To address this challenge, we propose a deep learning-based method for automated digit recognition and measurement-type classification (systolic, diastolic, and pulse) from images of blood pressure monitors. A total of 2147 images were collected and expanded to 3649 images using data augmentation techniques. We developed and trained three YOLOv8 variants (small, medium, and large). Post-training quantization (PTQ) was employed to optimize the models for edge deployment in a mobile health (mHealth) application. The quantized INT8 YOLOv8-small (YOLOv8s) model emerged as the optimal model based on the trade-off between accuracy, inference time, and model size. The proposed model outperformed existing approaches, including the RT-DETR (Real-Time DEtection TRansformer) model, achieving 99.28% accuracy, 96.48% F1-score, 641.40 ms inference time, and a compact model size of 11 MB. The model was successfully integrated into the mHealth application, enabling accurate, fast, and automated blood pressure tracking. This end-to-end solution provides a scalable and practical approach for enhancing blood pressure monitoring via an accessible digital platform. Full article

Digital applications for supporting health management often fail to achieve large-scale adoption. Costs related to purchasing, maintaining, and using medical or sensor devices, such as smartwatches, currently hinder uptake and sustained engagement, particularly in the prevention and monitoring of lifelong conditions. As an alternative, smartphone-based passive monitoring could provide a viable strategy for lifelong use, removing hardware-related costs and exploiting the synergies between mobile health (mHealth) and ambient assisted living (AAL). However, smartphone sensor toolkits are not designed for diagnostic purposes, and their quality varies depending on the model, maker, and generation. This narrative overview of recent reviews (narrative meta-review) on the current state of smartphone-based passive monitoring highlights the strengths, weaknesses, opportunities, and threats (SWOT analysis) of this approach, which pervasively encompasses digital health, mHealth, and AAL. The results are then consolidated into a newly defined concept of a mobile biomarker, that is, a general model of medical indices for diagnostic tasks that can be computed using smartphone sensors and capabilities. Full article

Introduction: The rapid growth of mobile health (mHealth) applications has revolutionized healthcare delivery worldwide. These digital tools encompass a broad array of functionalities, including telemedicine, appointment scheduling, medication management, and health data tracking, all of which contribute to enhanced healthcare accessibility, increased patient engagement, and improved operational efficiency. However, despite their increasing prominence, the design, deployment, and use of mHealth applications continue to face several challenges, such as usability issues and overall sustained adoption. Objectives: This study aims to evaluate mHealth applications in Saudi Arabia, focusing on their design characteristics, usability features, and current feature gaps. Method: A total of 21 mHealth applications were selected and analyzed using a thematic analysis approach. The apps were selected based on usage popularity in the Saudi market and relevance to national digital health strategies. Data were drawn from publicly available app store information, official app documentation, and expert evaluations. Results: The findings reveal that while mHealth applications excel in areas such as telemedicine, appointment booking, and health education, there are notable gaps in features such as behavior modification, patient monitoring, and health management. Conclusions: This study contributes to the growing body of research on mHealth by offering grounded insights into the functional landscape of digital health tools in Saudi Arabia. It also outlines practical recommendations to enhance usability, feature diversity, and alignment with evolving healthcare needs in Saudi Arabia and beyond. Full article

Background: Dementia is a syndrome with a high global prevalence that includes a number of progressive diseases of the brain affecting various cognitive domains such as memory and thinking and the performance of daily activities. It manifests as symptoms which often include significant mood and behaviour changes that are highly varied. Changed moods and behaviours due to dementia may reflect distress and may be stressful for both the person living with dementia and their informal and formal carers. To provide dementia care support specific to mood and behaviour changes, the Behaviours in Dementia Toolkit website (BiDT) was developed using human-centred design principles. The BiDT houses a user-friendly, digital library of over 300 free, practical, and evidence-informed resources to help all care partners better understand and compassionately respond to behaviours in dementia so they can support people with dementia to live well. Objective: (1) To characterize the users that visited the BiDT; and (2) to understand the platform’s early impact on these users. Methods: A multi-method, descriptive study was conducted in the early post-website launch period. Outcomes and measures examined included the following: (1) reach: unique visitors, region, unique visits, return visits, bounce rate; (2) engagement: engaged users, engaged sessions, session duration, pages viewed, engagement rate per webpage, search terms, resources accessed; (3) knowledge change; (4) behaviour change; and (5) website impact: relevance, feasibility, intention to use, improving access and use of dementia guidance, recommend to others. Data was collected using Google Analytics and an electronic survey of website users. Results: From 4 February to 31 March 2024, there were 76,890 unique visitors to the BiDT from 109 countries. Of 76,890 unique visitors to the BiDT during this period, 16,626 were engaged users as defined by Google Analytics (22%) from 80 countries. The highest number of unique engaged users were from Canada (n = 8124) with an engagement rate of 38%. From 5 March 2024 to 31 March 2024, 100 electronic surveys were completed by website users and included in the analysis. Website users indicated that the BiDT validated or increased their dementia care knowledge, beliefs, and activities (82%) and they reported that the website validated their current care approaches or increased their ability to provide care (78%). Further, 77% of respondents indicated that they intend to continue using the BiDT and 81.6% said that they would recommend it to others to review and adopt. Conclusions: The BiDT is a promising tool for sharing practical and evidence-informed information resources to support people experiencing dementia-related mood and behaviour changes. Early evaluation of the website has demonstrated significant reach and engagement with users in Canada and internationally. Survey data also demonstrated high ratings of website relevance, feasibility, intention to use, knowledge change, practice support, and its contribution to dementia guidance. Full article

mHealth apps can promote behavior change to prevent heart disease. This study examined the efficacy of an 8-week theory-based mHealth intervention to promote heart disease preventive behaviors. The BaseMetrics app was designed using the Health Belief Model to improve users’ understanding of heart disease and its risk factors to promote behavior change. In this proof-of-concept intervention with no control group, twenty-two participants (14F/8M; age 51 ± 8 years) received access to the BaseMetrics app for 8 weeks. Biological, behavioral, and self-assessed heart disease risk and knowledge were measured pre- and post-intervention. At post-intervention, significant improvements were seen in self-reported fruit and vegetable intake (+1.1 servings/day) and skin carotenoids (+28 a.u.). Self-tracked activity decreased (−665 steps/day). No other outcomes were significantly different. Non-significant improvements with small-to-moderate effect sizes were observed in triglycerides, energy expenditure, knowledge, perceived risk, and perceived benefits of diet and exercise. Conversely, non-significant deteriorations with small-to-moderate effect sizes were observed for total cholesterol, LDL, and AST. This study yielded preliminary findings suggesting the benefits of the BaseMetrics mobile app for heart disease prevention. The results must be validated in a larger randomized controlled trial. Full article

Interdisciplinary research catalyzes innovation in mobile health (mHealth) by converging medical, technological, and social science expertise, driving critical advancements in this multifaceted field. Our longitudinal analysis evaluates how the NIH mHealth Training Institute (mHTI) program stimulates changes in research trajectories through a computational examination of 16,580 publications from 176 scholars (2015–2022 cohorts). We develop a hybrid analytical framework combining large language model (LLM) embeddings, Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering to construct a semantic research landscape containing 329 micro-topics aggregated into 14 domains. GPT-4o-assisted labeling identified mHealth-related publications occupying central positions in the semantic space, functioning as conceptual bridges between disciplinary clusters such as clinical medicine, public health, and technological innovation. Kernel density estimation of research migration patterns revealed 63.8% of scholars visibly shifted their publication focus toward mHealth-dense regions within three years post-training. The reorientation demonstrates mHTI’s effectiveness in fostering interdisciplinary intellect with sustained engagement, evidenced by growth in mHealth-aligned publications from the mHTI scholars. Our methodology advances science of team science research by demonstrating how LLM-enhanced topic modeling coupled with spatial probability analysis can track knowledge evolution in interdisciplinary fields. The findings provide empirical validation for structured training programs’ capacity to stimulate convergent research, while offering a scalable framework for evaluating inter/transdisciplinary initiatives. The dual contribution bridges methodological innovation in natural language processing with practical insights for cultivating next-generation mHealth scholarship. Full article

There is limited evidence on key implementation outcomes for mHealth interventions that target maternal and newborn health. Hence, this study aimed to evaluate the acceptability, feasibility, and appropriateness of a mobile phone messaging-based message-framing intervention. A cross-sectional study was conducted, involving 397 mothers who participated in the mobile phone messaging-based intervention. Multivariate general linear modeling was carried out to identify factors that were associated with the acceptability, feasibility, and appropriateness of the intervention. The statistical significance level was declared at a 95% confidence interval and p-value of <0.05. The mean scores of acceptability, feasibility, and appropriateness were 27.9, 23.8, and 22.5, respectively. Acceptability was significantly affected by living in a rural area, being rich, receiving messages at night, self-efficacy, and engagement. Feasibility was affected by living in rural area, educational status, being a merchant, being rich, receiving messages at night, self-efficacy, engagement, and satisfaction. Meanwhile, appropriateness was influenced by living in a rural area, being a merchant, being a government employee, and satisfaction. The mobile phone messaging-based intervention was highly acceptable, feasible, and appropriate. Focusing on self-efficacy, engagement, satisfaction, the timing for sending messages, and sociodemographic factors would facilitate the implementation and utilization of mobile phone messaging-based interventions. Full article

Background/Aim: Child and adolescent obesity is currently one of the most pressing public health challenges. Digital technology-based interventions are becoming increasingly important in supporting weight loss motivation and promoting healthy lifestyles. This review aims to assess the effectiveness of technology tools on the BMI (body mass index) and their impact on health attitudes in children and adolescents. Materials and Methods: The study was conducted according to PRISMA guidelines, analysing studies published between 2011 and 2024 on PubMed, Scopus, Web of Science and Google Scholar databases. Of the 1475 articles identified and analysed, 59 met the inclusion criteria. Studies were assessed based on the type of technology used, the type of intervention, family involvement, the level of personalisation and their impact on BMI and motivation. Results: The systematic review showed that digital technologies—in particular mobile apps, wearables and m-health platforms—can effectively support weight reduction and improved eating habits in children and adolescents. The most beneficial results were observed in interventions that were personalised and included caregiver support. In addition, digital technology was shown to have a positive impact on participants’ psychological well-being. Conclusions: Digital technology-based interventions can be an effective tool in the prevention and treatment of obesity in children and adolescents. However, their success depends on a comprehensive approach that includes psychological, social and cognitive developmental factors. Full article