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This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.

Health care technology adoption is key to improving patient care, enhancing operational efficiency, and ensuring better health outcomes. Examining the determinants that influence the acceptance and sustainable use of health care technologies is crucial for system developers, health care providers, and policymakers. The Unified Theory of Acceptance and Use of Technology (UTAUT) and task-technology fit (TTF) theoretical models offer a comprehensive framework to assess these determinants systematically, with UTAUT focusing on usage intentions (UI) and TTF emphasizing task-technology alignment for system usefulness, usability, and satisfaction. This systematic review and meta-analysis aimed to identify and analyze the key factors influencing the adoption of health care technologies based on an integrated UTAUT and TTF framework. By synthesizing existing literature, the study seeks to provide valuable insights for stakeholders to implement innovative and effective solutions in the health care domain. A search was conducted across a range of databases, including MEDLINE and Embase, IEEE Xplore, ScienceDirect, Scopus, CINAHL, Google Scholar, and Web of Science. Inclusion criteria covered studies applying either the UTAUT model, the TTF model, or both to health care technology adoption, published in English between 2012 and 2025. Exclusion criteria included nonquantitative studies, studies not focused on a health care setting, and those lacking sufficient data for meta-analysis. The reviewers collaborated to decide on the final papers for inclusion in the review through Covidence, the Cochrane Collaboration's platform for systematic reviews. Data collection involved extracting quantitative data (eg, sample sizes, reliabilities, and standardized path coefficients) analyzed using meta-analytic techniques with a random-effects model in R software (R Development Core Team) to combine findings and calculate effect sizes. A total of 50 studies (35 UTAUT with 20,723 participants and 15 TTF with 4041 participants) met the inclusion criteria, representing various health care technologies, such as electronic health records, telemedicine platforms, and mobile health apps. The meta-analysis revealed that performance expectancy emerged as the most significant predictor of UI (β=.304; P<.001), while UI was the primary predictor of usage behavior (β=.199; P<.001). Other UTAUT predictors included effort expectancy (β=.177; P<.001), social influence (β=.167; P<.001), and facilitating conditions (β=.105; P<.001). For TTF, technology characteristics had the strongest effect on TTF (β=.445; P<.001), followed by TTF on UI (β=.271; P<.001) and task characteristics on TTF (β=.263; P<.001). Variability across settings and regions suggests contextual influences, with high heterogeneity (I²=81.90%-94.87%). This study provides valuable insights for enhancing health care technology adoption by integrating UTAUT and TTF, highlighting performance expectancy, effort expectancy, social influence, facilitating conditions, task characteristics, technology characteristics, and TTF as key drivers. The findings, assessing system usefulness, usability, and satisfaction, can guide interventions to improve adoption and health care delivery.

Digital Twins (DTs) are digital replicas of physical entities. The use of DTs in healthcare is a growing area of research. With DTs, there is potential to revolutionize healthcare with the assistance of Artificial Intelligence. This can lead to achieving precision, personalization, and value addition in healthcare. Contributing to this field, we present one of the first attempts of uncovering time series models of decompensation of heart failure. This was performed using some of the first data collected from the pilot phase of the SmartHeart study, in which an at-home, wearable, wireless sensor-based digital self-monitoring system for people with heart failure was tested.

Digital health interventions, including artificial intelligence (AI)-driven solutions, offer promise for type 2 diabetes mellitus (T2DM) and prediabetes management through enhanced self-management, adherence, and personalization. However, engagement challenges and barriers, particularly among young adults and diverse populations, persist. Existing reviews emphasize clinical outcomes while neglecting engagement factors crucial to intervention success. This review highlights engagement barriers and facilitators, offering insights into improving digital health solutions for diabetes management. The objective of this systematic literature review is to explore the barriers, facilitators, and outcomes of digital health interventions, focusing on the current state of AI applications while including partial AI and non-AI interventions, for managing and preventing T2DM and prediabetes, to inform the development of user-centered, inclusive digital health interventions for diabetes care. Unlike prior reviews, this review aims to inform the development of user-centered, inclusive digital health interventions for diabetes care, with a focus on engagement across various AI interventions and diverse populations. A systematic search of PubMed, Scopus, CINAHL, and additional sources was conducted for studies published between January 2016 and October 2025. Eligibility criteria included English-language, peer-reviewed studies focused on digital health interventions for adults with T2DM or prediabetes, reporting engagement, barriers, facilitators, or outcomes. Data were synthesized narratively using thematic analysis, guided by self-determination theory and user-centered design. Quality appraisal was conducted using Critical Appraisal Skills Program, Mixed Methods Appraisal Tool, and AMSTAR-2 tools. From the 37 studies (14 quantitative, 3 qualitative, 7 mixed-methods, and 13 reviews), interventions comprised 19 AI-driven (eg, chatbots, ML models, and conversational agent or hybrid), 3 partially AI-driven, and 15 non-AI solutions (eg, apps and lifestyle programs), mostly from the USA (n=15). Key barriers to engagement included inadequate personalization (15/37, 41%), environmental constraints (11/37, 11%), cultural and language mismatches (14/37, 38%), and AI-specific concerns (eg, bias and privacy). Facilitators included personalized feedback (19/37, 51%), cultural tailoring (17/37, 46%), user-friendly design, and peer support. AI-driven interventions demonstrated moderate improvements in clinical outcomes (eg, lowering HbA1c, weight loss, and normoglycemia conversion). However, these tools often struggled with keeping users involved and building trust. Non-AI solutions performed similarly but lacked adaptive features. This review offers novel insights by synthesizing engagement barriers and facilitators across AI and non-AI intervention domains, often neglected in previous studies. It highlights the necessity for testing adaptive, culturally tailored, and user-centered AI interventions to address engagement challenges in T2DM and prediabetes management. Integrating personalization, precision, and value-based care can improve outcomes and scalability. The findings guide the creation of inclusive, AI-driven solutions aligned with self-determination theory and user-centered design principles.

Anatomy has been the cornerstone of medical education for centuries. However, given the advances in the Internet of Things, this landscape has been augmented in the past decade, shifting toward a greater focus on adopting digital technologies. Digital anatomy is emerging as a new discipline that represents an opportunity to embrace advances in digital health technologies and apply them to the domain of modern medical sciences. Notably, the use of augmented or mixed and virtual reality as well as mobile and platforms and 3D printing in modern anatomy has dramatically increased in the last 5 years. This review aims to outline the emerging area of digital anatomy and summarize opportunities and challenges for incorporating digital anatomy in medical science education and practices. Literature searches were performed using the PubMed, Embase, and MEDLINE bibliographic databases for research articles published between January 2005 and June 2021 (inclusive). Out of the 4650 articles, 651 (14%) were advanced to full-text screening and 77 (1.7%) were eligible for inclusion in the narrative review. We performed a Strength, Weakness, Opportunity, and Threat (SWOT) analysis to evaluate the role that digital anatomy plays in both the learning and teaching of medicine and health sciences as well as its practice. Digital anatomy has not only revolutionized undergraduate anatomy education via 3D reconstruction of the human body but is shifting the paradigm of pre- and vocational training for medical professionals via digital simulation, advancing health care. Importantly, it was noted that digital anatomy not only benefits in situ real time clinical practice but also has many advantages for learning and teaching clinicians at multiple levels. Using the SWOT analysis, we described strengths and opportunities that together serve to underscore the benefits of embracing digital anatomy, in particular the areas for collaboration and medical advances. The SWOT analysis also identified a few weaknesses associated with digital anatomy, which are primarily related to the fact that the current reach and range of applications for digital anatomy are very limited owing to its nascent nature. Furthermore, threats are limited to technical aspects such as hardware and software issues. This review highlights the advances in digital health and Health 4.0 in key areas of digital anatomy analytics. The continuous evolution of digital technologies will increase their ability to reinforce anatomy knowledge and advance clinical practice. However, digital anatomy education should not be viewed as a simple technical conversion and needs an explicit pedagogical framework. This review will be a valuable asset for educators and researchers to incorporate digital anatomy into the learning and teaching of medical sciences and their practice.

Young adults face rising wellness challenges, including prediabetes risk, requiring sustained engagement with preventive health interventions. Digital wellness applications offer promise for promoting healthy lifestyle behaviors, yet high dropout rates and inadequate personalization limit their effectiveness. This paper outlines the technical implementation and formative usability evaluation of MiCARE, a theory-driven progressive web application (PWA) designed to support sustained wellness engagement among young adults through user-centered design. This study aims to systematically implement theory-driven design specifications into a functional web application, the MiCARE platform, and to conduct a formative usability evaluation with a convenience sample of 20 university-affiliated young adults aged 18 to 34 years in Victoria, Australia, in both rural and urban areas using the task-technology fit and unified theory of acceptance and use of technology frameworks as organizing lenses to assess usability, usefulness, and satisfaction. This is an embedded mixed methods study conducted across 2 phases: phase 3 and phase 4. Phase 3 involves the technical implementation of 6 theory-driven features (ie, empathetic chatbot, learning hub, dynamic goal setting, gamification, personalized reminders, and progress dashboard) using HTML5, CSS3, JavaScript, Google Dialogflow ES, and Firebase services, following the Agile methodology over 6 months with biweekly self-managed sprints and clinical verification. Phase 4 is a 3-month formative usability feasibility evaluation with 20 young adults recruited from La Trobe University (Bundoora and Bendigo campuses). Participants will complete screening and initial, midpoint, and final surveys assessing usability, usefulness, and satisfaction, while real-time use analytics captures engagement patterns. Data analysis will use the task-technology fit and unified theory of acceptance and use of technology frameworks as interpretive guides, with quantitative data analyzed using descriptive statistics (R Studio) and qualitative feedback analyzed through thematic analysis (NVivo). Use analytics will provide descriptive contextual information only. The study has received ethics approval from the La Trobe University Human Research Ethics Committee (HEC24507). The study will take place between 2025 and 2026. Phase 3 (technical implementation) commenced in October 2025 and is currently ongoing, with core features under active development and verification. Phase 4 (formative usability and feasibility evaluation) is scheduled to commence following completion of phase 3. Evaluation results will be disseminated in academic forums and peer-reviewed publications in early 2027. The findings will enable us to evaluate the feasibility, acceptability, and usability of a theory-driven PWA in this university-affiliated sample, informing refinements and future larger-scale studies. This study will contribute to the technical implementation and formative usability evaluation of a multitheoretical, user-centered PWA for wellness engagement in preventive health, bridging the gap between conceptual frameworks and deployed interventions.

Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management; however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake. In this paper, we propose, develop, and implement a nudge-inspired artificial intelligence (AI)-driven health platform for self-management of diabetes. The proposed platform has been co-designed with patients and clinicians, using the adapted 4-cycle design science research methodology (A4C-DSRM) model. The platform includes (a) a cross-platform mobile application for patients that incorporates a macronutrient detection algorithm for meal recognition and nudge-inspired meal logger, and (b) a web-based application for the clinician to support the self-management regime of patients. Further, the platform incorporates behavioral intervention techniques stemming from nudge theory that aim to support and encourage a sustained change in patient lifestyle. Application of the platform has been demonstrated through an illustrative case study via two exemplars. Further, a technical evaluation is conducted to understand the performance of the MDA to meet the personalization requirements of patients with type II diabetes.

Lifestyle medicine is a rapidly growing field focused on preventing, managing, and, in some cases, reversing chronic diseases. Digital wellness applications offer opportunities to support and scale lifestyle medicine practices. However, most commercially available applications lack evidence-based design and theoretical grounding. Additionally, many compromise personalization and cultural relevance, which leads to poor sustained engagement, especially among young adults. Therefore, input from both end users and domain experts is vital to ensure responsible design and inform the design of user engagement features. This study outlines the systematic development of a design framework and feature blueprint for an AI-augmented wellness web app to support engagement and understanding among young adults. It also documents the process for digital health researchers and developers. This formative user experience design study used a three-phase user-centered design process. Phase 1 involved background analysis and conceptualization via systematic literature review, expert consultations, formative research with young adults, and rapid user interface/user experience design review. Phase 2 focused on wireframe development and evaluation through Figma prototypes with iterative expert feedback. Phase 3 covered prototype development. Conducted in Victoria, Australia, from April 2024 to October 2025, the process included an iterative feedback loop. Phase 1 revealed barriers like high dropout rates, poor personalization, and cultural issues. Feedback from experts and users (60% female (mean age 27.3 years) and 40% male (mean age 28.4 years)) yielded five objectives: empathy-driven interaction, equity-focused accessibility, culturally responsive personalization, incremental goal setting, and intuitive onboarding. Preliminary user preferences for grid layouts (55%), large buttons (65%), and minimalist designs (50%) shaped features. A rationale table maps theory to features. This study presents a theory-driven, stakeholder-informed method for designing AI-augmented wellness app features guided by inclusive principles, while providing a replicable methodology for developing the design of engagement-oriented digital health solutions for young adults. The functional prototype requires rigorous usability testing to validate these preliminary design specifications.