User Study Research Articles

Communicating the dialog between painting and music with a mobile application

Contemporary visual artists often collaborate with musicians and composers to enrich their artworks and provide multisensory viewing experiences by leveraging the power of digital technology. In this paper, we present our approach to follow in their footsteps by creating a mobile application that provides “mixed-media interpretations” for a variety of visual artworks and music pieces, spanning over different time eras, with the objective to reflect the creative dialog and multifaceted interaction between their creators through time. Targeting the broader public, we attempt to capture a variety of interconnections between works of visual arts and music that may be of interest and are comprehensible to people with varying levels of knowledge on art or music, including not only inspirational but also personal and social relationships among the artists. We provide a six-layered conceptual model to ease the description of the content produced to that end while reporting several concrete examples. Then, we focus on how to communicate and digitally mediate the intertwined and often intricate interconnections in a concise way, proposing a “three-act” story scheme to progressively display short narratives on screen, combined with music reproduction on the mobile device. Finally, we explore the potential of the proposed approach through a user study with 15 participants and report a series of promising key findings, whereas also discussing future directions and pointing out open issues.

Voice coding experiences for developers with physical impairments

PurposeThis research explores the current working practices of voice coders with physical impairments and investigates their perceptions of a research prototype utilising a fixed grammar speech coding approach.Design/methodology/approachSemi-structured interviews were initially conducted with seven voice coders with physical impairments to understand their development practices, followed by an exploratory user study with five disabled voice coders to obtain their feedback on a fixed grammar voice coding system.FindingsInterviews provided new insights around the tools voice coders utilise, the need for multimodal coding approaches, as well as experiences in working within mixed ability development teams. Findings from the user evaluation elicited views around the need for distinct monosyllable voice commands, as well as other requirements to support efficient voice coding (e.g. command chaining, intuitive navigation, and custom command definition).Research limitations/implicationsThe research was conducted with experienced voice coders which may have influenced perceptions of the voice coding prototype. The system was also evaluated over a single evaluation session, whereas longitudinal research with novice voice coders could also present additional insights.Practical implicationsFurther research is required into customisable multimodal voice coding approaches for developers with physical impairments who may have varying levels of experience in coding via alternative methods.Originality/valueThe findings present new insights around the working practices and unique requirements of voice coders with physical impairments and highlights important new avenues for future research.

AI‐assisted research collaboration with open data for fair and effective response to call for proposals

AbstractBuilding teams and promoting collaboration are two very common business activities. An example of these are seen in the TeamingForFunding problem, where research institutions and researchers are interested to identify collaborative opportunities when applying to funding agencies in response to latter's calls for proposals. We describe a novel deployed system to recommend teams using a variety of Artificial Intelligence (AI) methods, such that (1) each team achieves the highest possible skill coverage that is demanded by the opportunity, and (2) the workload of distributing the opportunities is balanced among the candidate members. We address these questions by extracting skills latent in open data of proposal calls (demand) and researcher profiles (supply), normalizing them using taxonomies, and creating efficient algorithms that match demand to supply. We create teams to maximize goodness along a novel metric balancing short‐ and long‐term objectives. We evaluate our system in two diverse settings in US and India of researchers and proposal calls, at two different time instants about 1 year apart (total 4 settings), to establish generality of our approach, and deploy it at a major US university. We validate the effectiveness of our algorithms (1) quantitatively, by evaluating the recommended teams using a goodness score and find that more informed methods lead to recommendations of smaller number of teams and higher goodness, and (2) qualitatively, by conducting a large‐scale user study at a college‐wide level, and demonstrate that users overall found the tool very useful and relevant.

Virtual Reality-Based Exposure With 360° Environments for Social Anxiety Disorder: Usability and Feasibility Study.

Social anxiety disorder (SAD) is a long-term and overwhelming fear of social situations that can affect work, school, and other daily activities. Although cognitive behavioral therapy is effective, few seek treatment, and many who do start often drop out. This may be due to the component of exposure inherent to cognitive behavioral therapy, where the patient confronts feared stimuli outside the therapist's office, which they otherwise try to avoid. As an alternative, research has explored the effectiveness of virtual reality (VR)-based exposure therapy with promising results. However, few studies have investigated the feasibility of VR tools using mixed methodologies before assessing their efficacy. This study aims to assess the usability, feasibility, and presence of four 360° virtual environments and whether these were able to evoke anxiety in patients with SAD. A total of 10 adult participants with SAD and 10 healthy controls were recruited for 1 experimental session (age range 21-32 y; 12/20, 60% male participants). Questionnaire and interview data were collected and analyzed. A mixed methods triangulation design was applied to analyze and compare the data. Participants with SAD experienced increased anxiety when exposed to VR, and environments were considered relevant and useful as an exposure tool. Participants with SAD reported significantly higher average anxiety levels (P=.01) and peak anxiety levels (P=.01) compared with controls during exposure; however, significant differences in anxiety when accounting for baseline anxiety levels were only found in 2 of 4 environments (P=.01, P=.01, P=.07, and P=.06). While presence scores were acceptable in both groups, participants with SAD scored significantly lower than controls. Qualitative analyses highlight this finding within the SAD group, where some participants experienced presence reduction due to being observed while in VR and in situations with reduced interaction in VR. VR exposure with 360° videos seems to be useful as a first step of exposure therapy for patients with SAD. Future exploration in the clinical application of VR-based exposure for SAD, as well as means of increasing presence within the virtual environments, may be useful.

Innovative mobile app solution for facial nerve rehabilitation: a usability analysis.

Facial palsy after vestibular schwannoma surgery is temporary in many cases but can significantly affect patients' quality of life. Physical training-initially guided and subsequently performed by the patient-is of paramount importance for recovery of facial nerve function. The introduction of medical application software (apps) might improve therapy by maintaining motivation for daily home-based training and surveilling patients' rehabilitation progress. We developed a mobile app, "FACEsemper", for home-based facial nerve rehabilitation. This app guides patients through a daily training program comprising six variable exercises, each performed in three repetitions. The app allows the user to customize the exercise intensity for different facial areas and includes a reminder function for daily training. Additional features include photo documentation, a calendar function, training report generation, and the possibility of direct communication with the attending physician. The app's usability was prospectively investigated with 27 subjects, including 8 physicians, 9 patients with facial palsy and 10 healthy subjects, over a two-week period. Usability was assessed using various self-rating questionnaires (i.e., mHealth App Usability Questionnaire, MAUQ; System Usability Scale, SUS; Visual Aesthetics of Apps Inventory, VisAAI) and scores were compared across the groups. The participants reported an average smartphone use of 12.19 years and completed a mean number of 290 ± 163 facial exercises during the study period. Patients used the app significantly more frequently than the other two groups (p = 0.017). The average total scores of the questionnaires were: MAUQ 5.67/7, SUS 89.6/100, VisAAI 5.88/7 and specific rating 6.13/7. In particular, the simplicity of use and craftsmanship of the app were rated very highly. Usability scores did not significantly differ between groups. A primary limitation identified was malfunction of the daily reminder feature in some Android versions. This usability study demonstrated a positive user experience and excellent usability of the FACEsemper app. However, some limitations and areas for improvement were identified. As a next step, the app should be evaluated in a large patient cohort with facial palsy to determine its potential medical benefits for facial rehabilitation compared to traditional training methods.

Interactive segmentation with curve-based template deformation for spatiotemporal computed tomography of swallowing motion.

Repeating X-ray computed tomography (CT) measurements over a short period of time allows for obtaining a spatiotemporal four-dimensional (4D) volume image. This study presents an interactive method for segmenting a 4DCT image by fitting a template model to a target organ. The template consists of a three-dimensional (3D) mesh model and free-form-deformation (FFD) cage enclosing the mesh. The user deforms the template by placing multiple curve constraints that specify the boundary shape of the template in 3D space. We also present curve constraints shared over all time frames and interpolated along the time axis to facilitate efficient curve specification. Our method formulates the template deformation using the FFD cage modification, allowing the user to switch between our curve-based method and traditional FFD at any time. To illustrate the feasibility of our method, we show segmentation results in which we could accurately segment three organs from a 4DCT image capturing a swallowing motion. To evaluate the usability of our method, we conducted a user study comparing our curve-based method with the cage-based FFD. We found that the participants finished segmentation in approximately 20% interaction time periods on average with our method.

Evaluating User Experiences and Preferred Features of a Web-Based 24-Hour Dietary Assessment Tool: Usability Study.

Intake24, a web-based 24-hour dietary recall tool developed in the United Kingdom, was adapted for use in New Zealand (Intake24-NZ) through the addition of a New Zealand food list, portion size images, and food composition database. Owing to the customizations made, a thorough evaluation of the tool's usability was required. Detailed qualitative usability studies are well suited to investigate any challenges encountered while completing a web-based 24-hour recall and provide meaningful data to inform enhancements to the tool. This study aims to evaluate the usability of Intake24-NZ and identify improvements to enhance both the user experience and the quality of dietary intake data collected. We used a mixed methods approach comprising two components: (1) completion of a single 24-hour dietary recall using Intake24-NZ with both screen observation recordings and collation of verbal participant feedback on their experience and (2) a survey. A total of 37 participants aged ≥11 years self-completed the dietary recall and usability survey (men and boys: 14/37, 38% and women and girls: 23/37, 62%; Māori: 10/37, 27% and non-Māori: 27/37, 73%). Although most (31/37, 84%) reported that Intake24-NZ was easy to use and navigate, data from the recorded observations and usability survey revealed challenges related to the correct use of search terms, search results obtained (eg, type and order of foods displayed), portion size estimation, and associated food prompts (eg, did you add milk to your tea?). This comprehensive usability study identified challenges experienced by users in completing a dietary recall in Intake24-NZ. The results informed a series of improvements to enhance user experience and the quality of dietary data collected with Intake24-NZ, including adding new foods to the food list, optimizing the search function and ordering of search results, creating new portion size images, and providing clearer instructions to the users.

A feasibility and usability study of a virtual reality tool (VESPA 2.0) for cognitive rehabilitation in patients with mild cognitive impairment: an ecological approach

A feasibility and usability study of a virtual reality tool (VESPA 2.0) for cognitive rehabilitation in patients with mild cognitive impairment: an ecological approach

An mHealth App to Support Caregivers in the Medical Management of Their Child With Cancer: Beta Stage Usability Study.

Previous research demonstrated that caregivers of children with cancer desired a mobile health (mHealth) tool to aid them in the medical management of their child. Prototyping and alpha testing of the Cope 360 app (Commissioning Agents, Inc) resulted in improvements in the ability to track symptoms, manage medications, and prepare for urgent medical needs. This study aims to engage caregivers of children with cancer in beta testing of a smartphone app for the medical management of children with cancer, assess acceptance, identify caregivers' perceptions and areas for improvement, and validate the app's design concepts and use cases. In this pilot, study caregivers of children with cancer used the Cope 360 mHealth app for 1 week, with the goal of daily logging. Demographics and a technology acceptance survey were obtained from each participant. Recorded semistructured interviews were transcribed and analyzed iteratively using NVivo (version 12, QSR International) and analyzed for information on usage, perceptions, and suggestions for improvement. A total of 10 caregivers participated in beta testing, primarily women (n=8, 80%), married, with some college education, and non-Hispanic White (n=10, 100%). The majority of participants (n=7, 70%) had children with acute lymphocytic leukemia who were being treated with chemotherapy only (n=8, 80%). Overall, participants had a favorable opinion of Cope 360. Almost all participants (n=9, 90%) believed that using the app would improve their ability to manage their child's medical needs at home. All participants reported that Cope 360 was easy to use, and most would use the app if given the opportunity (n=8, 80%). These values indicate that the app had a high perceived ease of use with well-perceived usefulness and behavioral intention to use. Key topics for improvement were identified including items that were within the scope of change and others that were added to a future wish list. Changes that were made based on caregiver feedback included tracking or editing all oral and subcutaneous medications and the ability to change the time of a symptom tracked or medication administered if unable to do so immediately. Wish list items included adding a notes section, monitoring skin changes, weight and nutrition tracking, and mental health tracking. The Cope 360 app was well received by caregivers of children with cancer. Our validation testing suggests that the Cope 360 app is ready for testing in a randomized controlled trial to assess outcome improvements.

Commercial devices for monitoring symptoms in Parkinson's disease: benefits, limitations and trends

Parkinson's disease (PD) is a neurodegenerative disorder that significantly affects patients' quality of life. Treatment of PD requires accurate assessment of motor and non-motor symptoms, which is often complicated by subjectivity in reporting symptoms, and the limited availability of neurologists. Commercial wearable devices, which monitor PD symptoms continuously and outside the clinical setting, have appeared to address these challenges. These devices include PKG™, Kinesia 360™, Kinesia U™, PDMonitor™ and STAT-ON™. These devices use advanced technologies, including accelerometers, gyroscopes and specific algorithms to provide objective data on motor symptoms, such as tremor, dyskinesia and bradykinesia. Despite their potential, the adoption of these devices has been limited, due to concerns about their accuracy, complexity of use and the lack of independent validation. The correlation between the measurements obtained from these devices and traditional clinical observations varies, and their usability and patient adherence are critical areas for improvement. Validation and usability studies with a sufficient number of patients, standardised protocols and integration with hospitals' IT systems are essential to optimise their usefulness and improve patient outcomes.

An assessment of whether educated non-researcher audiences understand how to reuse research data

PurposeThe purpose of this study is to assess whether educated non-researcher audiences understand how to reuse research data stored in a data repository.Design/methodology/approachA total of 44 participants in two user studies were asked to study a data set accessed from re3data.org. The participants were non-researcher audiences of the disciplines of the selected data sets. They were asked to figure out whether they understood how to reuse a data set after reading all the metadata or contextual information about the data set.FindingsMost participants reported that they figured out how to reuse the data, although their self-reports can be an overestimated assessment. However, the participants understand how to reuse a data set either numerically or statistically significantly worse than what the data set is, how it was collected or created and its purpose. Data set type tends to play a role in understanding how to reuse data sets and the purpose of data sets. Participants reported that unless a data set is self-explanatory, instructions on data set reuse and the purpose of data set were necessary for understanding how to reuse data set. However, because data reuse requires domain knowledge and data processing skills, some non-researcher audiences who lack domain knowledge and data processing skills may not understand how to reuse the data set in any way.Research limitations/implicationsThis study’s findings enrich the theoretical framework of data sharing and reuse by expanding the necessary information to be included in data documentation to support non-researchers’ data reuse. The findings of the study complement previous literature.Practical implicationsThis study extended previous literature by suggesting detailed data reuse instructions be included in data documentation if data producers and data curators wish to support educated non-researchers’ data reuse. This study’s findings enable policymakers of research data management (RDM) to formulate guidelines for supporting non-researchers’ data reuse. If data curators need to work with data producers to prepare the instructions on data reuse for non-researcher audiences, they probably need computing and data processing skills. This has implications for Library and Information Science schools to educate data librarians.Originality/valueThe research question is original because non-researcher audiences in the context of RDM have not been studied before. This study extended previous literature by suggesting detailed data reuse instructions be included in data documentation if data curators and data producers and data curators wish to support educated non-researchers’ data reuse. This study’s findings enable policymakers of RDM to formulate guidelines for supporting non-researchers’ data reuse.

Learning-based Artificial Intelligence Artwork: Methodology Taxonomy and Quality Evaluation

With the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering (NPR) and, latterly, neural-style transfer methods have also been investigated. As technology advances, the variety of machine-generated artworks and the methods used to create them have proliferated. However, there is no unified and comprehensive system to classify and evaluate these works. To date, no work has generalised methods of creating AI artwork including learning-based methods for painting or drawing. Moreover, the taxonomy, evaluation and development of AI artwork methods face many challenges. This paper is motivated by these considerations. We first investigate current learning-based methods for making AI artworks and classify the methods according to art styles. Furthermore, we propose a consistent evaluation system for AI artworks and conduct a user study to evaluate the proposed system on different AI artworks. This evaluation system uses six criteria: beauty, color, texture, content detail, line and style. The user study demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.

Participatory Design of an Embodied Mixed Reality Experience Aimed to Assessing Individual and Collaborative Behaviors of Children

This study is part of a broader project exploring the integration of Mixed Reality (MR) in fostering prosocial behaviors among children, particularly focusing on interpersonal synchrony (IPS) and its impact on collaboration. IPS, the coordination of rhythmic movements among individuals, has been associated with improved group cohesion and prosocial behaviors, such as collaboration. Traditional assessments of prosociality typically involve structured play activities for dyads, making them difficult to adapt to other settings. To address these limitations, this research aims to design a flexible embodied MR experience that allows children to choose between collaborative or individual engagement, reflecting the natural dynamics of children's play interactions. For this purpose, we conducted an embodied participatory design (PD) and usability study with 50 children aged 8-10 years. We provide a detailed account on how the PD process was orchestrated and an evaluation of user-system interaction, children's perceptions of play modes, and experience satisfaction. The results show how the MR experience, codesigned with children, supports a free play environment, allowing children to choose their preferred play mode. This study highlights the potential of MR in psychological research and child development, providing a new avenue for assessing individual and collaborative behaviors in larger groups of children.

Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction

Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these ‘ironies of automation’ have been observed for over three decades in Human Factors research on the introduction of automation in domains such as aviation, automated driving, and intelligence. We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems: a shift in users’ roles from production to evaluation, unhelpful restructuring of workflows, interruptions, and a tendency for automation to make easy tasks easier and hard tasks harder. We then suggest how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation. Thus, we ground developments in GenAI system usability in decades of Human Factors research, ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it.

That's the way I (dis-)like it! - Effect of Gamer Types and Competitiveness in VR Exergaming

In response to growing global concerns surrounding sedentary lifestyles among adults, the imperative to motivate physical activity has driven the exploration of Virtual Reality (VR) exergames as a promising solution. Personality traits, particularly competitiveness, can play a pivotal role in influencing individuals' motivation to engage in either competitive or non-competitive versions of the same exergame. While existing studies have primarily focused on sports-related competitive orientations, they have overlooked the potential gain in information from gaming-related personality traits. This research addresses this gap by using a modified gaming-related competitiveness questionnaire and evaluating a VR cycling exergame featuring solo, self-competition and virtual opponent cycling conditions in a controlled user study with 38 participants. Empirical findings reveal a notable difference in self-rated competitiveness between the two domains of sports and gaming, and significant correlations between some gamer-types and motivation. These findings highlight the importance of these personality types during exergaming and their potential efficacy for personalisation to increase motivation.

Comparing the Psychophysical Capabilities on Fingertip and Wrist using Method of Adjustment

Haptic technology, which refers to creating the sense of touch artificially, offers a crucial source of communication between humans and computers or machines. While conventional haptic devices are designed to render vibrotactile information on the fingertip, recent trends in the field expand the tactile communication to other body locations, like the wrist. Even though the literature has many successful applications showing the validity of such haptic applications, there is no study comparing the user perception for meaningful virtual or teleoperated task scenarios due to the lack of calibration methods between alternative body locations. In this paper, we attempt to compare the perceived intensities at the fingertip and the wrist through psychophysical experiments and to answer: (i) Is there a perpetual difference between the haptic stimuli on the wrist compared to the fingertip? (ii) Is possible to form a reasonable, linear relationship (or a pattern) between the stimuli rendered at the fingertip and the wrist? (iii) If so, do different users require different relationships that would need to be obtained through calibration? We designed a user study with 13 healthy participants, receiving three levels of haptic stimuli at their fingertips while adjusting the intensities of the stimuli rendered at their wrist using the method of adjustments. Our results indicate that there is a linear pattern between the vibrotactile stimuli rendered at the fingertip and the wrist, and each participant exhibits a different pattern. Our results can be used to equalize the perceived intensities of different forms of tactile stimuli for future research investigating the perceived performance under different haptic scenarios.

Shared storytelling with a virtual learning companion: prospects in child-AI collaboration

Advances in natural language processing techniques enhance the capabilities of conversational agents to process input text and generate fluent responses in various genre. In the educational setting, these conversational agents can function as learning companions to promote peer learning, enhance social interaction, and motivate collaborative behavior. In this paper, we investigate how children collaborate with conversational agents in the context of story construction. We conducted user studies and administered survey forms to collect children’s evaluation of their experience, captured the child-agent conversation logs, and used an observation checklist to monitor the interaction. We analyzed the logs to determine how children respond to varying roles portrayed by the agent and the dialogue moves that motivate collaborative behavior during storytelling. We correlated these with the children’s reported perception and our own observation of the interaction based on performance, collaboration and humanity attributes. Drawing on these results, we identified the roles of conversational agents that align with children’s traits and abilities as storytellers and propose dialogue strategies to facilitate collaborative storytelling. Findings from our study can inform future educational technology designers on the roles of conversational agents as collaborative learning companions who can adapt to the individual needs and preferences of the children.

Human-AI Synergy in Survey Development: Implications from Large Language Models in Business and Research

This study examines the novel integration of Large Language Models (LLMs) into the survey development process in business and research through the development and evaluation of the Behavioral Research ASSistant (BRASS) Bot. We first analyzed the traditional scale development process to identify tasks suitable for LLM integration, including both human-in-the-loop and automated LLM data collection methods. Following this analysis, we developed the details of BRASS Bot, incorporating design principles of falsifiability and reproducibility. We then conducted a comprehensive evaluation of the BRASS Bot across a diverse set of LLMs, including GPT, Claude, Gemini, and Llama, to assess its usability, validity, and reliability. We further demonstrated the practical utility of the BRASS Bot by conducting a user study and a predictive validity simulation. Our research presents both theoretical and practical implications. The augmentation approach of the BRASS Bot enriches the theoretical foundations of behavioral constructs by identifying previously overlooked patterns. Additionally, the BRASS Bot offers significant time and resource efficiency gains while enhancing scale validity. Our work lays the foundation for future research on the broader application of LLMs as both assistants and collaborators in survey analysis and behavioral research design and execution, highlighting their potential for a transformative impact on the field.

Press A or Wave: User Expectations for NPC Interactions and Nonverbal Behaviour in Virtual Reality

Non-playable characters (NPCs) are important in games, as they can provide guidance to the player, create social engagement, and advance the game's narrative. Although much research exists regarding NPC interactions for traditional gaming environments, e.g. desktop or console, fewer works have considered this from a virtual reality (VR) perspective. Our work first uncovers the salient and unique dimensions of VR NPC interactions through observations of 47 existing games. We find that VR NPC interactions have an extended set of interaction mechanisms due to two key factors - interaction triggers and player constraints within the game, driven by the unique qualities of physical motion and immersion afforded by the medium. We augment these findings through a user study performed on 18 participants in a VR environment. Participant interactions with a responsive NPC allow us to delve deeper into understanding player perception and expectations of NPC behaviour and interactions. Our findings outline player expectations for NPC realism, player agency during NPC interaction, and NPC expected behaviour and feedback. We tie our findings into discussions on player agency within VR, highlighting design suggestions to develop NPCs to better fit within social behaviour expectations.

Design and Development of a Smart Fidget Toy Using Blockchain Technology to Improve Health Data Control.

This study explores the integration of blockchain technology in wearable health devices through the design and development of a Smart Fidget Toy. We aimed to investigate design challenges and opportunities of blockchain-based health devices, examine the impact of blockchain integration user experience, and assess its potential to improve data control and user trust. Using an iterative user-centered design approach, we developed a mid-fidelity prototype of a physical fidget device with a blockchain-based web application. Our key contributions include the design of a fidget toy using blockchain for secure health data management, an iterative development process balancing user needs with blockchain integration challenges, and insights into user perceptions of blockchain wearables for health. We conducted user studies, including a survey (n = 28), focus group (n = 6), interactive wireframe testing (n = 7), and prototype testing (n = 10). Our study revealed high user interest (70%) in blockchain-based data control and sharing features and improved perceived security of data (90% of users) with blockchain integration. However, we also identified challenges in user understanding of blockchain concepts, necessitating additional support. Our smart contract, deployed on the Polygon zkEVM testnet, efficiently manages data storage and retrieval while maintaining user privacy. This research advances the understanding of blockchain applications in health wearables, offering valuable insights for the future development of this field.