From lab to life: Wearables, real-world data, and the future of autonomic research.

Underserved populations and health equity in dermatology: Digital medicine and the role of artificial intelligence

Diabetes mellitus represents a major global public health challenge, with rising prevalence, substantial morbidity, and escalating health system costs. Digital health interventions (DHIs) have emerged as promising tools to support diabetes prevention, self-management, and long-term care through the use of telemedicine, mobile health applications, digital therapeutics, wearable devices, remote monitoring, and decision-support systems. This narrative review synthesizes current evidence on the scope, classification, and effectiveness of digital health interventions for diabetes management. It examines conceptual frameworks underpinning DHIs, key intervention modalities, and their impact on clinical outcomes such as glycemic control, hypoglycemia reduction, cardiometabolic risk, body weight, and patient-reported outcomes. The review further examines implementation considerations, including health system integration, reimbursement models, equity, digital literacy, data privacy, and interoperability. Overall, the evidence suggests that digital health interventions can improve glycemic outcomes, enhance patient engagement, and support lifestyle modification, particularly when interventions are multifaceted and integrated into routine care. However, heterogeneity in intervention design, methodological limitations, and inequities in access remain significant challenges. Future research should focus on long-term effectiveness, implementation in underserved populations, and the development of scalable, interoperable, and patient-centered digital solutions to strengthen diabetes care across diverse settings. Keywords: Digital health interventions, Diabetes mellitus, Mobile health (mHealth), Telemedicine and Diabetes self-management

Type 2 diabetes mellitus (T2DM) poses a significant global public health challenge, with its prevalence escalating continuously and disproportionately affecting low- and middle-income countries (LMICs), imposing a substantial burden on healthcare systems. Traditional management models have limitations in disease prediction, personalized treatment, and public health intervention. Artificial intelligence (AI) and digital health technologies provide novel insights for precise prediction and intelligent management of T2DM. This review systematically summarizes research progress in AI's role in deciphering T2DM pathogenesis, personalized treatment, and public health management. By integrating multi-omics and environmental data, AI reveals key mechanisms including gene-environment (G × E) interactions, β-cell dysfunction, and inflammatory pathways, significantly enhancing early screening and risk prediction. In clinical management, AI combined with digital health tools [e.g., continuous glucose monitoring (CGM), wearable devices, and mobile health (mHealth) apps] facilitates remote monitoring, medication optimization, and personalized interventions, improving treatment adherence and health management efficiency. At the public health level, AI optimizes resource allocation and disease burden assessment, promoting chronic disease prevention and control model transformation. Future efforts should prioritize developing low-resource-adapted tools, strengthening data privacy protection tailored to LMICs, and addressing algorithmic fairness and the digital divide to ensure safe, equitable, and sustainable AI application in global T2DM management. Overall, AI and digital health integration is driving T2DM management towards an intelligent and precision-based era, with the potential to reduce disparities in LMICs.

PCP65 - KEY CONSIDERATIONS FOR THE COLLECTION OF PATIENT REPORTED OUTCOME (PRO) DATA IN REAL WORLD (RW) STUDIES

Marginalized communities often face systemic barriers that limit their access to essential preventive health services, resulting in disproportionate rates of chronic diseases, late diagnoses, and poor health outcomes. To address these disparities, this study proposes a Digital Health Framework aimed at expanding access to preventive services through innovative, scalable, and culturally responsive digital interventions. The framework integrates telemedicine, mobile health (mHealth) applications, electronic health records (EHR), and community-based digital literacy programs to bridge the gap between healthcare providers and underserved populations. It emphasizes interoperability, real-time data sharing, and artificial intelligence-driven risk stratification to ensure timely screening, personalized health education, and proactive intervention. The framework is guided by principles of equity, accessibility, and community engagement. It leverages geospatial analytics and demographic profiling to identify high-need areas and tailor services to local needs. Additionally, it incorporates remote monitoring tools and AI-powered chatbots to facilitate early detection, promote behavior change, and support continuity of care. The model fosters partnerships between public health institutions, non-profit organizations, and local leaders to enhance trust, increase uptake, and ensure sustainability. Pilot implementation across select urban and rural communities demonstrates the framework’s potential to improve immunization coverage, cancer screening rates, and chronic disease management through digital access points. Preliminary results reveal increased utilization of preventive services, reduced travel-related costs, and enhanced patient satisfaction. Moreover, data from EHRs and mHealth platforms are used to monitor outcomes, inform policy decisions, and optimize resource allocation. This framework has significant implications for national and global health agendas, especially in meeting Sustainable Development Goal 3 on ensuring healthy lives and promoting well-being for all. By addressing digital divide issues and reinforcing health system integration, the proposed Digital Health Framework offers a transformative pathway for delivering equitable preventive care to marginalized populations.

What's New in Sports Medicine.

Background: Low-income, minority, and underserved populations are often excluded from mobile health (mHealth) research. This cross-sectional study sought to define how patients at an urban, academic safety net hospital use technology in their daily lives in an effort to incorporate mHealth into clinical care and research. Methods: Patients receiving care in the Diabetes and Weight Management subspecialty clinic at Boston Medical Center were asked to complete a 17-question survey on technology usage. It was modeled on a Pew Research Center survey and available in English, Portuguese, and Spanish. Results: Of the 394 survey respondents, 279 (70.8%) completed all questions. Majority of respondents were female (76.4%) and between 30 and 49 years old (42.9%). Respondents self-identified primarily as black/African American (35.8%), white/Caucasian (28.2%), and not Hispanic/Latino (46.4%). Over 90% owned a smartphone and more than 85% accessed the Internet on a mobile device at least once per day. Regarding mHealth usage, 33.5% and 23.1% reported current use of health- and weight loss-centric applications (apps), respectively, while only 19.6% of patients with diabetes used smartphone apps as diabetes self-management tools. Nearly three-quarters (73.3%) reported interest in using apps to manage health. Respondents preferred e-mail (48.7%), phone (39.6%), and in-person communication (36.3%) as research recruitment tools. Conclusions: The overwhelming majority of an urban, underserved minority population cared for in a subspecialty clinic have access to mHealth-compatible devices and are either using or interested in using mHealth technology.

This primer provides an overview of artificial intelligence (AI) for the oncologist and technologist, delineating its relevance, methodologies, and applications. AI, encompassing machine learning (ML) and deep learning (DL), utilizes algorithms to analyze data and make predictions, facilitating early cancer detection and personalized treatment strategies. ML, characterized by supervised and unsupervised learning, allows for pattern recognition and prediction, whereas DL, particularly through convolutional neural networks and transformers, excels in complex data analysis. AI applications span various oncology data types, including electronic health records, medical imaging (radiology and digital pathology), biomarkers (genomics, proteomics, and metabolomics), and digital health sensors. Leveraging AI in oncology enhances diagnostic accuracy, improves treatment selection, and enables early detection through innovative approaches such as wearable devices and mobile health applications. This primer underscores AI's transformative potential in advancing cancer care, exemplified by Food and Drug Administration-approved AI systems and prominent DL architectures. This overview serves as a guide for researchers, clinicians, and policymakers navigating the evolving landscape of AI-driven oncology, emphasizing its pivotal role in augmenting patient outcomes and shaping the future of cancer care.

Stability and evolution of individual-level biomarker patterns across repeated vaso-occlusive crises in sickle cell disease

Category:Other; SportsIntroduction/Purpose:Patient reported outcome measures (PROMs), especially computerized adaptive testing (CAT), are the gold standard for outcome reporting in the field of orthopaedic surgery. Wearable technology (fitbit, apple watch, etc) and other health monitoring capabilities available on smart phones applications (i.e. apple health) are rapidly evolving and being considered as alternative methods of measuring outcomes in orthopaedics. This patient wearable outcome (PWO) method has yet to be directly compared to PROMs, nor evaluated as a viable option for outcome measurement in orthopaedics. The purpose of this study is to directly compare PWOs to PROMs and determine the advantages and disadvantages of using the health monitoring capabilities of smart phones as outcome measuring tools in orthopaedics.Methods:This study is designed as a nonrandomized prospective cohort focused on a small subset of subjects within the field of orthopaedics. Subjects were recruited from an orthopaedic foot and ankle clinic with diagnoses of ankle sprain, plantar fasciitis, and achilles tendonitis. These conditions were chosen as these would generally represent nonoperative conditions at presentation. Subjects under 16 years of age and surgical candidates were ineligible. Standard PROM surveys, all of which have been previously validated by the AOFAS for outcome measurement were administered at enrollment and then electronically delivered at 3 and 7 weeks. Surveys utilized were the Foot and Ankle Ability Measure (FAAM), Visual Analog Scale (VAS), and PROMIS physical function, mobility and pain interference scores. An application (ActiveTrack) is installed on the subjects' smartphones to pull all past and future health data including step counts, distance traveled, etc. Statistical analysis was then performed to correlate PWO's with PROMs.Results:28 subjects consented and enrolled in the study. This has provided strong encouragement for the goals of the study. 21 patients were observed at 3 weeks with a Pearson correlation of step count of 0.6975 (95% CI: 0.3804-0.8679). 7 subjects were observed at 7 weeks with a Pearson correlation of step count of 0.8387 (95% CI: 0.2325-0.9756). We found that step counts and FAAM scores generally increased after treatment, and that the correlation between the changes was positive. The strongest signal was obtained from the FAAM ADL subscale.Conclusion:This study shows that there is a correlation between a patient's self-reported function (PROMs) and their activity (PWOs). PWO data may represent a more accurate measurement of a patient's activity and therefore may be a great tool in assessing functionality before and after an intervention (be it surgical or nonoperative treatments). Further analysis of the data obtained from this prospective study over time may help establish a new standard of care for measuring patients' response to orthopaedic treatment.

The public health landscape is being profoundly reshaped by the rapid convergence of information technology (IT) and healthcare. This paper explores the multi-faceted implications of emerging technology, including AI, big data analysis, telemedicine and wearable devices, for public health efforts. Author examines how these advancements facilitate enhanced disease surveillance, personalized health interventions, and improved access to care, particularly for underserved populations. Furthermore, we address the critical considerations surrounding data security, ethical implications, and digital equity. By adopting a comprehensive public health perspective, we analyze the potential of IT to revolutionize preventative care, promote population health, and mitigate health disparities. This analysis highlights the importance of strategic implementation of new technologies, in order to maximize beneficial outcomes for public health.

BackgroundThe proportion of people in the United States who are members of at least two ethnic groups is projected to increase to 10% by the year 2050. This makes addressing health disparities and health inequities in minority populations increasingly more difficult. Minority populations, including those who classify themselves as African American and Hispanic, are using mobile phones to access health information via the internet more frequently than those who classify themselves as white, providing unique opportunities for those in public health and health education to reach these traditionally underserved populations using mobile health (mHealth) interventions.ObjectiveThe objective of this review was to assess studies conducted in the United States that have used mHealth tools and strategies to develop and implement interventions in underserved populations. This review also examines the ways in which mHealth strategies are being employed in public health interventions to these priority population groups, as mobile phone capabilities include text messaging, mobile apps, internet access, emails, video streaming, social media, instant messaging, and more.MethodsA systematic literature review was conducted using key search phrases, the matrix method, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart diagram to identify key studies conducted between the years of 2009-2016 in the United States. These studies were reviewed for their use of mHealth interventions in historically underserved and minority populations.ResultsA total of 16,270 articles were initially identified using key search phrases in three databases. Titles were reviewed and articles not meeting criteria were excluded, leaving 156 articles for further review. After additional review for relevance and inclusion criteria, 16 articles were qualified and analyzed.ConclusionsmHealth is a promising area of development for public health and health education. While successful research has been done using text messaging (short message service, SMS) and other mHealth strategies, there is a need for more research using mobile phones and tablet applications. This literature review demonstrates mHealth technology has the ability to increase prevention and health education in health disparate communities and concludes that more specified research is needed.

Background The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about the patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnic minority groups, or rural or frontier communities.Objectives In this series of trial case reports, we provide lessons learned about collecting PROMs in these populations. Unbiased collection of PROM data is critical to increase the generalizability of trial outcomes and to address health inequities. Use of electronic health records (EHRs) and other digital modes of PROM administration has gained traction. However, engagement with these modes is often low among populations prone to disparity due to lower digital proficiency, device access, and uptake of EHR portals and web interfaces.Methods To maximize the completeness and representativeness of their trial outcome data, study teams tested a range of strategies to improve PROM response rates with emphasis on disparities prone and underserved patient groups. This manuscript describes the approaches, their implementation, and the targeted populations.Conclusion Optimized PROM collection required hybrid approaches with multiple outreach modes, high-touch methods, creativity in promoting digital uptake, multimodal participant engagement, and text messaging.

SummaryObjective: Consumer health informatics (CHI) has the potential to disrupt traditional but unsustainable break-fix models of healthcare and catalyse precision prevention of chronic disease – a preventable global burden. This perspective article reviewed how consumer health informatics can advance precision prevention across four research and practice areas: (1) public health policy and practice (2) individualised disease risk assessment (3) early detection and monitoring of disease (4) tailored intervention of modifiable health determinants.Methods: We review and narratively synthesise methods and published recent (2018 onwards) research evidence of interventional studies of consumer health informatics for precision prevention. An analysis of research trends, ethical considerations, and future directions is presented as a guide for consumers, researchers, and practitioners to collectively prioritise advancing two interlinked fields towards high-quality evidence generation to support practice translation. A health consumer co-author provided critical review at all stages of manuscript preparation, moderating the allied health, medical and nursing researcher perspectives represented in the authorship team.Results: Precision prevention of chronic disease is enabled by consumer health informatics methods and interventions in population health surveillance using real-world data (e.g., genomics) (public health policy and practice), disease prognosis (regression modelling, machine learning) (individualized disease risk assessment), wearable devices and mobile health (mHealth) applications that generate digital phenotypes (early detection and monitoring), and targeted behaviour change interventions based upon personalized risk algorithms (tailored intervention of modifiable health determinants). In our disease case studies, there was mixed evidence for the effectiveness of consumer health informatics to improve risk-stratified or behavioural prevention-related health outcomes. Research trends comprise both consumer-centred and healthcare-centred innovations, with emphasis on inclusive design methodologies, social licence of health data use, and federated learning to preserve data sovereignty and maximise cross-jurisdictional analytical power.Conclusions: Together, CHI and precision prevention represent a potential future vanguard in shifting from traditional and inefficient break-fix to predict-prevent models of healthcare. Meaningful researcher, practitioner, and consumer partnerships must focus on generating high-quality evidence from methodologically robust study designs to support consumer health informatics as a core enabler of precision prevention.

Introduction Mobile health (mHealth) applications gain popularity due to the increasing number of mobile phone usage and internet penetration, which might help some of Indonesia's medical issues. However, the uptake of mHealth applications is still low in Indonesia. This study is aimed at understanding the factors that drive individuals to adopt mHealth applications and their impact on the intention to recommend. Methods We applied a new model that combines three different theories with some other constructs: an extended unified theory of acceptance and use of technology, diffusion of innovation, and the internet customer trust model. The study used a cross-sectional study design with partial least squares causal modeling approach. Results There are 787 respondents in our study, with the majority of them being female, young adults. Our model could explain 53.2% of the variance of intention to adopt while explaining 48.3% of the variance of intention to recommend. Initial trust in mHealth platform (β = 0.373, p = <0.001), facilitating conditions (β = 0.131, p = <0.01), and performance expectancy (β = 0.099, p = <0.05) are the top three most important drivers of intention to adopt mHealth applications. Lastly, importance-performance map analysis (IPMA) showed that the mHealth application's initial trust is the most important construct with a high-performance score. Discussion. Mobile health developers and managers need to improve initial trust in the mHealth platform, facilitating conditions, and performance expectancy when developing the applications. With a medium Q2predict, these factors can be applied out of the research context with medium predictive power.