This study examines how integrating Creative Problem Solving (CPS) and generative artificial intelligence (GenAI) within animation storytelling education can foster sustainability-related competencies in higher education. A twelve-week mixed-methods action research design was implemented in a “Storytelling and Scriptwriting” course at a university of technology in northern Taiwan (N = 60). The intervention design combined a CPS-aligned instructional sequence, six scaffolded assignments (including a text-to-image resemiotization task), pre–post CPS cognition and affect scales, CPS-dimensioned assignment self-assessments, reflective journals, and expert evaluations of final story prototypes using the Consensual Assessment Technique. Quantitative results showed significant gains in students’ CPS-related narrative cognition and affective resilience (p < 0.001), as well as consistently high self-reported engagement across CPS dimensions for all assignments, particularly for the text-to-image and personal narrative tasks. Expert ratings indicated high levels of originality, narrative coherence, emotional impact, and social relevance in final prototypes, while qualitative data highlighted reduced “blank page” anxiety, greater willingness to revise, and more collaborative, systems-oriented narrative reasoning. The findings suggest that a CPS- and GenAI-supported teaching model can function as a cognitive bridge for heterogeneous cohorts, positioning GenAI as a conditional amplifier embedded within a reflective CPS framework and helping to translate abstract sustainability-related competencies—such as anticipatory, normative, strategic, and interpersonal competencies—into concrete creative media practices.

1. Abstract The ever-increasing generation of municipal solid waste and the growing demand for electrical energy pose major environmental and economic challenges, particularly in developing nations. This research presents the design, fabrication, and experimental evaluation of a small-scale prototype for electricity generation from trash and plastic waste using thermoelectric conversion. The system employs a controlled combustion chamber to convert waste materials into thermal energy, which is transferred to a thermoelectric generator (TEG). The resulting temperature gradient across the TEG produces electrical energy based on the Seebeck effect. A DC boost converter is used to enhance the low‑voltage output, and the generated power is stored in a rechargeable battery for practical utilization. Experimental testing demonstrates stable low-power electricity generation capable of powering LED loads. The results confirm the feasibility of utilizing waste materials as a supplementary energy source while simultaneously reducing landfill dependency and environmental pollution. Key Words: Waste-to-Energy, Thermoelectric Generator, Solid Waste Management, Waste Heat Recovery, Renewable Energy.

The limited digitalization of buying and selling activities remains a significant challenge for micro, small, and medium enterprises (MSMEs), particularly at the local level. Although national e-commerce platforms continue to grow, many local MSMEs still face obstacles related to accessibility, usability, and system relevance to their business context. This condition underscores the need for localized e-commerce solutions designed to support MSME digital transformation. This research aims to design and develop an Android-based e-commerce application for Baubau City, named Oli Gholi Pay. The main objective is to produce an e-commerce application design that is easy to recognize, user-friendly, and relevant for MSME actors, local government stakeholders, and the general public. The study employs a descriptive qualitative approach combined with application Development methods. Data were collected through observations, interviews, and focus group discussions involving MSME actors. The prototype Development method was applied through the following stages: requirement identification, initial prototyping, prototype evaluation, application coding, testing, final evaluation, and implementation. The research scope includes participant recruitment analysis, system requirement analysis, system architecture and interface design, Development, functional testing, and application implementation and maintenance. The study successfully produced a functional design of a local Android-based e-commerce application that supports online transaction processes and aligns with MSME operational needs. The results indicate that Oli Gholi Pay can support MSME digitalization by expanding market access and facilitating secure, sustainable digital transactions, while also serving as a reference model for future local e-commerce application Development.

This work presents an annular ring patch antenna (ARPA) on the basis of metamaterial (MTM) for 5G applications. ARPA has become a popular choice for a range of wireless applications owing to its low profile, miniature size, simplicity in integrating with printed circuit boards (PCBs), and compatibility with contemporary fabrication techniques. Nevertheless, the bandwidth, gain, and efficiency restrictions that the ARPA frequently experiences are crucial for fulfilling the stringent needs of 5G communication systems. To overcome these obstacles, researchers have tuned to materials known as MTMs, which are synthetic materials having special electromagnetic (EM) characteristics absent from natural materials. By including complementary split ring resonators (CSRR) structures into microstrip patch antenna (MPA) the important characteristics like bandwidth, gain, and efficiency are enhanced. An EM simulation software named computer simulation technology (CST) Microwave Studio is employed for the evaluation of the antenna prototype. Rogers RT5880, a commercially accessible substrate material is used to develop the prototype. Comparative analysis is conducted between conventional antennas and ARPA, in which the proposed antenna attains low electrical loss, uniform electrical properties, thermal stability, and dimensional stability with gain of 5.65 dB. The developed work proves that the addition of CSRR structure is the solution to the development of antennas with superior performance characteristics.

As engineering demands intensify, future structural materials have a greater need to be more efficient, performance-tailored, adaptive, and multifunctional, particularly for protective structures in extreme environments. This study explores applying metamaterials and bio-inspired design innovations to create a lightweight acoustic and ballistic barrier composite (LABBCOM) via experimental evaluations of prototypes and conventional baselines. LABBCOM comprises pleated layers of 10 wt.% silica nanoparticle-treated Kevlar fabric with a silicone rubber backing structure integrated with a perforated facesheet for acoustic tunability. An approximate analytical model was developed to predict pleat geometries balancing acoustic and ballistic functionalities. It is found that LABBCOM achieves over double the acoustic absorption compared to mass-equivalent baselines in the 800–1600 Hz range, while providing opportunities for optimizing pleat geometry to address lower frequency bandwidths. In ballistics tests, LABBCOM yielded the only non-penetrative impact result among cases tested, delivering a specific kinetic energy absorption in excess of 25% higher compared to mass-equivalent baselines. Inspecting the failure modes under ballistic impact reveals that the combination of the treated and pleated fabric with rubber backing amplifies energy dissipation without significant weight or volume penalties. Successful transition to applications promises lightweight multifunctional structural barriers for aerospace, defense, infrastructure, and transportation domains, among others.

Purpose Ongoing shortages of mental health professionals in the USA underscore the need for scalable, technology-mediated supports. Such supports should not only extend access but also preserve core therapeutic ingredients, including human-like conversational scaffolding via AI and immersive, therapy-like contexts via VR. This paper reports on the design and formative pilot evaluation of a low-fidelity prototype for a conceptual AI-based, VR-supported mental health application. Design/methodology/approach Because the goal was to probe usability and perceptions at an early stage, we used low-fidelity materials to simulate the VR therapy flow and conceptual AI-assisted interactions rather than implementing full AI or VR functionality. Ten young adults with mild to moderately severe depression interacted with the low-fidelity prototype, completed the Usefulness, Satisfaction, and Ease of use questionnaire, and joined semi-structured interviews. Findings Quantitative ratings indicated that the prototype was learnable and generally acceptable for this population. Thematic analysis of the interviews yielded two broad areas of concern – (1) current mental health care barriers and (2) expectations for AI- and VR-supported therapeutic experiences – which pointed to design needs such as anonymity, culturally sensitive options, lightweight onboarding, and more human-like timing and nonverbal cues. Taken together, these findings suggest that the proposed AI-supported VR concept is understandable and worth further digital prototyping, but future work must move beyond low-fidelity simulations to evaluate real interactions and emotional engagement. Originality/value This study demonstrates the feasibility of AI and VR-supported mental health interventions and provides early insights to guide the design of engaging, and accessible digital therapy.

BackgroundAmerican Indian/Alaska Native (AI/AN) people represent a culturally diverse people group within the United States. AI/AN people experience some of the most severe health disparities in the United States, including behavioral health. A quarter of AI/AN people in the United States live on tribal lands, experiencing significant barriers to mental health resources and broadband infrastructure for telehealth. We developed Amplifying Resilience Over Restricted Internet Access (ARORA)—a mobile health (mHealth) smartphone app, promoting mindfulness practices and community building through AI/AN culture and values. Originally co-designed with both Hopi/Tewa and Navajo youth and adults, this study evaluated app resonance among Hopi/Tewa youth, supporting its iterative design. While we initially planned in-person user testing, this was moved online due to the COVID-19 pandemic.ObjectiveThis study assessed the potential and acceptability of an mHealth app supporting Hopi/Tewa youth practicing mindfulness inspired by their culture, values, and beliefs. This research served as preliminary work for an ongoing, iterative participatory action research study, identifying points of improvement to align with our partner community’s goals.MethodsAfter meeting with 6 community advisory board members and focus groups prior to this study, we developed a prototype for ARORA. This study evaluated intuitiveness and usability through testing and interviews with Hopi/Tewa youth. All meetings with stakeholders were moved online due to the COVID-19 pandemic. Using screen-sharing via Zoom (Zoom Communications, Inc) and Android emulators, we received feedback for the iterative design process.ResultsThis study involved 9 participants aged 16-24 years. Of these participants, 1 was male and 8 were female; all identified as Hopi/Tewa and/or Tewa. This study included a quantitative assessment using a modified version of the User Version of the Mobile Application Rating Scale. The mean score across all questions was 3.71 (SD 0.427), suggesting generally positive reception. Qualitative results from thematically analyzing open-ended focus group data produced 5 open codes and 12 axial themes, reaching thematic saturation after engaging with 9 participants. Qualitative feedback revealed that while its use was generally enjoyable, the ARORA app could be more specific to Hopi/Tewa culture. Finally, we reflect on adaptations made to our initial protocol in response to the COVID-19 pandemic, offering guidelines for future mHealth work involving rural or hard-to-reach communities.ConclusionsIn this evaluation and usability testing of the ARORA prototype, participants expressed interest and engagement in the mindfulness activities. Participants also identified spaces in which the app could improve, both in usability and in cultural groundedness, especially with the visual dimensions of the app. Reflecting on our experience in facilitating remote user testing, we encourage future work in rural mHealth to consider practices for conducting research when in-person meetings are not feasible.

Environmental education is increasingly expected to be integrated across learning systems, yet many in-service teacher trainees in low-resource contexts have limited access to structured, locally relevant digital materials that support practical classroom implementation. Lightweight, easily maintainable platforms such as Google Sites may offer a feasible alternative to more complex learning management systems, but evidence on their usability and uptake for environmental education remains limited. This study applies the Technology Acceptance Model (TAM) to examine the usability and perceived usefulness of the EnviroEd Learning Hub, a Google Sites-based platform for teaching Environmental Education (EE) in a Ugandan teacher training institute. By using systematic sampling, a sample of 50 in-service Diploma in Education Primary (DEP) students was selected to evaluate the prototype and the refined version of the platform following the Design-Based Research design. The findings indicate that on the perceived Ease of Use, 86% of students rated the platform as easy or very easy to navigate, with a mean score of 4.2 (SD = 0.82). On the perceived usefulness, the refined platform received a rating mean score of 4.38 out of 5, from 4.23 during prototype evaluation, with 86% of respondents describing it as either very good or excellent. Overall, the study demonstrates the promise of Google Sites as a practical platform for environmental education in teacher training contexts and provides actionable recommendations for strengthening user experience, engagement, and scalability. These insights can inform the design of low-cost digital learnng hubs that support teacher capacity development for environmental sustainability education in similar settings

A review of morphing actuation systems in relation to rotary-wing aerial platforms is presented. The research highlights an inadequate maturation of rotary actuation systems, characterised by a scarcity of (1) comprehensive full-scale experimental research relative to non-rotary (fixed-wing) systems, (2) techniques used for rotary actuation systems and (3) implementation of full-chord morphing systems, with existing research only utilising partial-chord actuation techniques. Additionally, another notable shortcoming is presented to be the lack of comprehensive proportional investigation in the proposed five-step development process for rotary actuation designs. A comprehensive critical review is offered, covering the following challenges of progressing through this development process for rotary actuation systems from conceptual design to production: (1) numerical and computational studies, (2) small-scale wind-tunnel testing, (3) full-scale wind-tunnel testing, (4) demonstrator, and ultimately (5) fabrication for industrial implementation. The review examines several existing rotary actuation systems, including (but not limited to) leading-edge, trailing-edge and Gurney flaps; active twist; chord extension; variable span and camber systems. Comparisons are made between rotary morphing actuation systems and their non-morphing counterparts, highlighting the distinct difficulties encountered by rotary-wing systems due to the more complex and challenging operational conditions found in rotorcraft. The review reveals that a significant portion of existing research on rotary-wing systems has focused only on early-stage development, including computational modelling and sub-scale wind-tunnel experiments, underscoring the necessity for more comprehensive full-scale testing and prototype evaluation given that only a small number of studies have progressed to full-scale wind-tunnel testing or actual prototype evaluation, with only one example identified as having been tested on a production helicopter. In addition, a comparative Technology Readiness Level (TRL) assessment is presented for both rotary-wing and fixed-wing morphing actuation systems, enabling a structured evaluation of relative technology maturity, experimental validation depth, and proximity to operational implementation. Building upon this assessment, a morphing Actuation Concept-Transfer Feasibility (ACTF) study is also provided, examining the potential for adapting mature fixed-wing morphing actuation technologies for application in rotary-wing environments, while identifying the key structural, aerodynamic, and operational constraints that currently limit direct technology transfer. This study addresses and proposes opportunities for a novel rotary actuation system design and concludes by suggesting the potential for future research on more effectual systems to include full-chord configuration over larger spanwise blade footprints with innovative actuation mechanisms that could be utilised and progressed through all development stages from numerical studies to full-scale fabrication.

Digital technologies have been widely adopted to improve efficiency, transparency, and decision making in the construction industry. However, regulatory processes such as building license and registration applications remain complex, fragmented, and difficult for applicants to navigate, particularly for early career practitioners and small businesses. This study presents the design and development of a graph-based retrieval-augmented generation (RAG) artificial intelligence (AI) system that assists users in applying for building licenses and registrations in Australia. By integrating eight regulatory burden frameworks, this study identified ten categories of licensing-related burden. A three-layer system architecture was subsequently proposed for the Australian construction licensing context, and a prototype is implemented using the New South Wales (NSW) regulatory framework. The system provides context-aware responses, step-by-step guidance, and tailored information based on user queries, thereby reducing regulatory burden for individuals, companies, and industry bodies. Prototype evaluation against general-purpose AI tools indicates improved information accessibility and reduced application-related friction in representative licensing scenarios. This study sheds light on AI-enabled regulatory support systems and demonstrates how graph-based RAG could improve accessibility and usability of construction related licensing processes. The findings have implications for policymakers, regulators, and researchers seeking to leverage AI to support digital transformation in the construction industry.

The first phase of the Reimagining Discovery project at Harvard Library sought to address the challenge of fragmented search experiences of special collections materials using artificial intelligence (AI) technologies, such as embedding models and large language models (LLMs). The resulting platform, Collections Explorer, simplifies and enhances the search experience for more effective special collections discovery. The project team took a user-centered and trustworthy approach to implementing AI, grounding the choices of the platform in user empowerment and librarian expertise. The development process included extensive user research, including interviews, usability testing, and prototype evaluations, to understand and address user needs. Collections Explorer was developed using a multi-component architecture that integrates multiple types of AI. The team evaluated more than 12 models to select ones that were the best fit for the need, as well as being ethical and sustainable. Detailed system prompts were developed to guide LLM outputs and ensure the reliability of information. The methodical and iterative approach helped to create a flexible and scalable platform that could evolve to support other material types in the future. Initial research showed that potential users are enthused at the prospect of AI-powered features to enhance discovery, especially the item-level summaries and related search suggestions. The project demonstrated the potential of integrating AI technologies into library discovery systems while maintaining a commitment to trustworthiness and user-centered design.

Evidence suggests that older adults can improve dual-task (DT) performance through specific motor-cognitive training programs. Recent technological advancements have facilitated the development of novel rehabilitative DT methodologies. In particular, the DUAL-REHAB project exploits 360° technology to develop ecological, cost-effective DT exercises for clinical and home settings. This study aimed to comprehensively understand the DUAL-REHAB project's end users (older adults with subjective memory complaints [SMC] and mild cognitive impairment [MCI]) and obtain feedback on an initial DUAL-REHAB mobile app prototype using a user-centered design approach. The study used a 2-phase procedure. In the first phase (user requirements), we investigated end users' lifestyles, habits, perceived well-being, technology adoption, and cognitive and cognitive-motor DT training expectations. In the second phase (prototype evaluation), we developed and tested a DUAL-REHAB mobile app prototype to assess its functionality with end users. Overall, 14 participants were initially recruited for the study. The sample included 7 women with a mean age of 79 (SD 3.79) years and 7 men with a mean age of 82.43 (SD 5.65) years. One female participant was excluded for not meeting the criteria for either group; accordingly, the final sample study included 13 participants, with 8 categorized as MCI and 5 categorized as SMC. Participants reported structured daily routines with high autonomy, although some faced challenges with social connections. Perceived well-being was moderate across physical (mean 2.79, SD 0.97), psychological (mean 3.14, SD 0.86), and cognitive (mean 3.14, SD 0.53) domains. The perceived technology skills were low (mean 2.57, SD 0.94), with low digital literacy (mean 2.21, SD 0.89). Interest in cognitive training was high, with 92.31% (12/13) participants aware of its benefits and demonstrating strong interest (mean 4.15, SD 1.34) and perceived use (mean 4.15, SD 1.28). While only 46.15% (6/13) were aware of the possibility of DT training with technology, 92.31% (12/13) were willing to participate, and 84.61% (11/13) were open to home-based training. The preferred frequency was 2-3 days per week (63.64%, 7/11), with 10-20 minute sessions (8/11, 72.72%). Prototype evaluation revealed specific usability issues related to icon identification, device interaction, and navigation between training days. We obtained valuable insights into the lifestyles, habits, and technological needs of older adults with MCI and SMC, which will guide DUAL-REHAB training development to align with user needs and capabilities. Our findings emphasize the importance of simplified technological processes with intuitive interfaces. Additionally, structured interaction opportunities during clinical and at-home training could enhance motivation, facilitate timely problem resolution, and address participants' social needs.

The Circular Electron Positron Collider (CEPC) is a proposed next-generation large-scale collider for precision studies of the Higgs boson and other fundamental particles. The baseline design of the Muon Detector in the CEPC experiment employs plastic scintillator bars, wavelength-shifting fibers, and silicon photomultipliers. This detector has more than 43,000 channels, imposing stringent requirements on the front-end readout architecture. This paper presents the design and evaluation of an ASIC-based readout prototype using the 32-channel MPT2321 chip. Each channel of this chip integrates a preamplifier, a pulse shaper, a discriminator, an ADC, and a TDC. The prototype comprises a custom wire-bonding board and an FPGA evaluation board with a customized mezzanine card for configuration and data acquisition via TCP protocol. Linearity, gain uniformity, and a high signal-to-noise ratio have been demonstrated through charge-injection and SiPM coupling tests, supporting the MPT2321-based design as a promising candidate for the readout of the CEPC Muon Detector.

Negative pressure wound therapy (NPWT) is widely used to facilitate healing by improving local perfusion, reducing edema and controlling exudate. The porous foam dressing is central to NPWT effectiveness, however, its performance in viscous, particle-rich exudates remains challenging. Standard industry tests often rely on protein-free aqueous solutions, which overlook the complex rheology and particulate load of real wounds. This study reports a bench evaluation of a multilayer foam prototype compared with three commercial dressings under NPWT, using a simulated viscous exudate with suspended particles. We recorded 60-min drainage curves and quantified effluent turbidity as a simple, interpretable proxy for particulate transport, summarised as percentage of input turbidity recovered. The mass-based endpoint (percent solid matter recovered) showed the same ranking as turbidity. At -75 mmHg, the prototype recovered 31.6% of input turbidity, exceeding commercial foams (≤ 9.7%). At -125 mmHg, particulate recovery decreased across all dressings (≤ 9.1%). A matrix-only control indicated that commercial effluents, particularly at -75 mmHg, clustered near background level, whereas the prototype evacuated substantially more particulate while maintaining robust fluid drainage. These findings suggest that moderate negative pressure and multilayer architecture can help preserve channel patency and reduce clogging in complex exudates. We highlight the need for test methodologies that incorporate viscosity and particulate content, and for practical guidance that links dressing architecture and pressure settings to exudate characteristics. Prospective validation, including larger-sample confirmation, particle-size distributions and ultimately clinical endpoints, is warranted.

Proof-of-concept evaluation of a 3D laser profiling system prototype for real-time woody breast detection of broiler breast fillets

Parkinson’s disease is a representative neurodegenerative disorder that primarily affects older adults, and since it is incurable, appropriate pharmacological treatment based on continuous monitoring constitutes the core therapeutic strategy. However, frequent examinations are often difficult due to high medical costs and shortages of medical personnel, which can result in missed opportunities for timely medication adjustments. To address this limitation, we propose a multimodal monitoring application that tracks the rate of change in disease severity in a home environment at low cost and issues clinical recommendation alerts when the changes exceed predefined thresholds. Data are collected through eye-response, gait, and speech modules, and the proposed algorithm analyzes the temporal progression of disease severity and shares the results with both the patient’s mobile application and a simulated environment–based clinician-facing patient management interface. Prototype evaluation results indicate that the eye-response and gait modules demonstrated stable real-time performance, while the speech module requires further improvement in terms of severity estimation accuracy. This system aims to enable early detection of rapid deterioration based on patient-specific cumulative data trends and to bridge gaps between in-person clinical visits, with the expectation that continued data accumulation will lead to improved accuracy of the learning model over time.

Objective: To develop a chatbot for the identification and monitoring of toxicities associated with intravenous antineoplastic chemotherapy in patients with cancer. Method: This methodological study was conducted between December 2023 and August 2025 and was based on the Interdisciplinary Method for the Development of Health Technologies, which comprises two phases and six activities. Phase 1 included Activity 1 – problem understanding and scoping review; Activity 2 – literature review; and Activity 3 – development of a low-fidelity prototype, prototype evaluation by nursing staff, and development of a high-fidelity prototype. Phase 2 comprised Activity 4 – development of the interactive prototype; Activity 5 – usability analysis and content validation; and Activity 6 – final writing and technology registration. Results were analyzed using the Content Validity Index (CVI > 90%) and usability assessment conducted with the System Usability Scale (score range, 0-100). Results: Nine symptoms most frequently reported in the literature supported the development of the chatbot. In the usability evaluation, 74.4% of users rated the system at the highest possible level of usability. In the content validation process, the achieved CVI was 90%. Conclusion: The chatbot demonstrated satisfactory content validity and usability, characterizing it as an appropriate technology to support health services in the remote monitoring of toxicities associated with chemotherapy, facilitating early identification and timely interventions.

Adaptive AI-driven learning systems personalize instruction by estimating learner state and dynamically selecting content, feedback, and pacing to improve mastery and engagement. This paper synthesizes peer-reviewed evidence on adaptive learning, intelligent tutoring, knowledge tracing, educational data mining, and recommender systems, and proposes an applied engineering framework suitable for deployment in higher-education STEM contexts. We ground personalization in classic student modeling (knowledge tracing) and modern sequence modeling (deep knowledge tracing), and integrate a multidimensional view of engagement to avoid reducing “engagement” to simple clickstream metrics. We then present a modular, service-oriented system architecture encompassing data ingestion, learner modeling, pedagogical decisioning, explainability, monitoring, and governance controls. A prototype evaluation is conducted using a simulation-based testbed (explicitly illustrative, not empirical) with synthetic learners and skills. Across 600 simulated learners and 25 skills over 120 learning steps, an adaptive policy improves average mastery (fraction of skills mastered at threshold) compared to non-adaptive paging and random sequencing, with markedly higher rates of reaching “80% mastery.” The results also show that naive optimization may widen outcome gaps across learner subgroups, motivating fairness-aware objectives and human-in-the-loop controls. Ethical, privacy, and accessibility requirements are addressed through risk management practices, differential privacy–compatible training options, transparent explanations, and WCAG-aligned interface design.

Only very few educational mobile health applications (MHAs) on oral cancer exists. Based on existing literature, only one of such MHAs (application name: Prayaas) was created specifically for lay persons; unfortunately, the application has been critiqued to be non-inclusive and non-comprehensive. Hence, this study aimed to co-create a more comprehensive and inclusive educational MHA prototype on oral cancer through a participatory action research approach. A three-round modified Delphi technique was employed to engage stakeholders in a participatory action research process. Digital communication experts and individuals with oral cancer risk factors (n = 17) from five countries participated. Round one utilised focus group discussions to define essential application features. Rounds two and three involved iterative evaluations of successive prototypes using a validated questionnaire (Mobile Application Rating Scale). Quantitative data were analysed using descriptive statistics, with consensus defined a priori as a mean score > 2.5 on a 5-point Likert scale—indicating majority agreement on each evaluated item. Stakeholder input across three iterative rounds led to the co-creation of the “Beat Oral Cancer” prototype. Quantitative consensus was achieved across all evaluated domains, including usability, aesthetics, information quality, and perceived impact. The final prototype showed significant improvements: the second version was rated higher than the first in entertainment (+ 0.68), interactivity (+ 0.32), customizability (+ 0.64), and ease of use (+ 0.33). Key inclusive features, such as adjustable text size, were incorporated based on stakeholder feedback. This study successfully co-created a highly comprehensive, stakeholder-informed MHA prototype for oral cancer education via a modified Delphi process. The results demonstrate the value of iterative participatory design in creating digital health tools with high potential for acceptability and usability. This work establishes a methodological precedent for applying the Delphi technique in mobile health co-creation and provides a foundation for future efficacy testing and public deployment.

Pediatric respiratory disorders frequently necessitate clinical evaluation, often during sleep. Traditional polysomnography (PSG), while the gold standard for sleep-related respiratory assessment, is resource-intensive and can cause discomfort, particularly in children. Therefore, in a prior published study, we designed and technically validated a video-based prototype for contactless monitoring of respiratory movements. Our present study aimed to clinically validate the contactless monitoring prototype in pediatric patients, with a primary focus on detecting respiratory rate and identifying abnormal breathing patterns. Twenty-seven pediatric patients (aged 6 months to 12 years) were recruited from a pediatric sleep laboratory. To monitor thoracoabdominal movements in real time, the prototype employed a time-of-flight camera and a 3D imaging module, coupled with artificial-intelligence-based determination of the region of interest (ROI). Respiratory rates obtained from the prototype were compared to simultaneously recorded PSG data. Data were collected under various conditions, including different sleeping positions. A total of 296 h of respiratory data were acquired, of which selected 60 s segments (54 during N3 sleep and 27 during REM sleep) were analyzed using the prototype and compared with PSG-derived respiratory parameters. The contactless prototype demonstrates that reliable and non-invasive respiratory monitoring is feasible in pediatric patients. It enables accurate detection of respiratory rate as well as abnormal breathing patterns under routine clinical conditions, while reducing patient burden compared with conventional approaches. Its usability and minimal patient discomfort suggest potential for broader clinical adoption. Future work should focus on full-night recordings across all sleep stages and the development of automated data analysis pipelines to facilitate routine clinical implementation.