{ "version": "https://jsonfeed.org/version/1", "title": "Digital Health Evidence (most recently published)", "home_page_url": "http://www.digitalhealthevidence.net/", "feed_url": "http://www.digitalhealthevidence.net/feed/published/json", "description": "The most recently published Digital Health evidence", "icon": "http://www.digitalhealthevidence/logo_horizontal_small.png", "author": { "name": "James BonTempo", "url": "https://www.linkedin.com/in/jamesbontempo/" }, "items": [ { "id": "http://doi.org/10.1016/j.landig.2026.101018", "content_html": "The need to embed decolonising methodologies in health-care practice has been recognised globally. The term decolonising health, which has become a topic of increasing public and academic discourse since 2021, refers to the dismantling of colonial systems of dominance and oppression ingrained within health systems and structures. Despite the growing recognition of the need for decolonisation in broader health research and practice, the application of a decolonial lens to the rapidly evolving field of digital health remains largely underexplored. An active commitment to decolonisation should be driven by a quest for social justice. However, a key concern for those committed to decolonisation in any field of inquiry is the continuing hegemony of western Eurocentric knowledge and knowledge systems that dominate much of the global research machinery. Hence, we suggest that decolonisation of digital health should start at the ontological level of coloniality. In this Viewpoint, we present a justice-based reimagination of digital health using the established research paradigm framing of ontology, epistemology, axiology, and methodology. We also map decolonial recommendations across the learning health system model and provide a translational reflexivity checklist to support digital health project teams in decolonising research and innovation activities. ", "url": "http://doi.org/10.1016/j.landig.2026.101018", "title": "Towards a justice-based reimagination of digital health research and innovation: actionable considerations for learning health systems.", "summary": "Towards a justice-based reimagination of digital health research and innovation: actionable considerations for learning health systems. - The Lancet. Digital health", "date_modified": "2027-06-04T00:00:00.000Z", "author": { "name": "Kalla, M; Nazarian, N; Vokey, L; Van Rensburg, C; Kewalramani, D; Dlamini, S; Charani, E; Ferdous, H; Celi, LA; Milanzi, C; Schneider-Kamp, A", "url": "http://unimelb.edu.au" } }, { "id": "http://doi.org/10.1016/j.bioelechem.2026.109433", "content_html": "Heart failure (HF) is a long-term condition that often causes repeated hospital visits and requires close monitoring. Cardiac troponin I (cTnI) and cardiac troponin T (cTnT) are widely known as markers of myocardial infarction, but in HF they can also show ongoing heart-muscle injury, disease progression, and poor prognosis. This review examines the clinical value of repeated troponin measurements and the development of point-of-care testing (PoCT) biosensors for faster and more accessible monitoring. It covers electrochemical immunosensors, aptamer-based sensors, electrochemiluminescence (ECL) systems, field-effect transistor (FET) devices, and emerging wearable and microfluidic platforms. The review also discusses post-discharge follow-up, remote care, and combined interpretation with B-type natriuretic peptide (BNP), N-terminal pro-B-type natriuretic peptide (NT-proBNP), electrocardiogram (ECG), kidney function, symptoms, and wearable data. Artificial intelligence (AI) may support patient-specific trend analysis and early risk alerts. However, major challenges remain, including low-level sensitivity, whole-blood testing, assay differences, signal drift, false alarms, and limited validation in real HF patients. Future progress will require reliable devices, clear clinical pathways, and outcome-based studies. ", "url": "http://doi.org/10.1016/j.bioelechem.2026.109433", "title": "Beyond myocardial infarction: Point-of-care cardiac troponin biosensors for heart failure prognosis and remote monitoring.", "summary": "Beyond myocardial infarction: Point-of-care cardiac troponin biosensors for heart failure prognosis and remote monitoring. - Bioelectrochemistry (Amsterdam, Netherlands)", "date_modified": "2027-02-01T00:00:00.000Z", "author": { "name": "Akshay Rangnath, N; Sankar, K; Agrawal, PS; Panchabudhe, HM; Ingole, NS; Agrawal, PK; Rewatkar, P; Kulkarni, MB; Bhaiyya, M", "url": "http://manipal.edu" } }, { "id": "http://doi.org/10.5812/ijem-169084", "content_html": "Managing diabetes in children is challenging and requires continuous monitoring and structured self-care support. Artificial intelligence (AI)-driven digital tools may enhance self-management and improve health outcomes. This scoping review aimed to synthesize the existing evidence on AI-driven digital tools for pediatric diabetes self-management, focusing on their functionalities, benefits, and limitations. From an initial pool of 97 articles published between January 2010 and March 1, 2025, 19 studies met the predefined inclusion criteria. A systematic search was conducted in PubMed, Scopus, Web of Science, CINAHL, PsycINFO, and Embase using predefined keywords. Two independent reviewers performed screening and data extraction. Methodological quality was appraised using the Cochrane Risk of Bias Tool, the Newcastle-Ottawa Scale, and the Critical Appraisal Skills Programme (CASP) Checklist. Qualitative and quantitative findings were synthesized thematically. The 19 included studies addressed several overlapping application domains of AI-driven digital tools for pediatric diabetes self-management. Overall, 8 studies focused primarily on glucose prediction, glycemic monitoring, or pattern recognition; 4 studies evaluated insulin dosing optimization and clinical decision-support systems; 6 studies investigated personalized education, behavioral support, user engagement, or self-management learning interventions; 5 studies examined telemedicine, remote monitoring, or healthcare access; and 5 studies primarily addressed ethical, methodological, transparency, or implementation challenges. AI-based digital tools have considerable potential to support diabetes self-management in children. Future research should prioritize long-term evaluations, the inclusion of diverse populations, and rigorous assessments of clinical outcomes, data privacy, and algorithm performance. ", "url": "http://doi.org/10.5812/ijem-169084", "title": "Artificial Intelligence-Driven Digital Tools for Diabetes Self-management in Children: A Scoping Review.", "summary": "Artificial Intelligence-Driven Digital Tools for Diabetes Self-management in Children: A Scoping Review. - International journal of endocrinology and metabolism", "date_modified": "2027-01-31T00:00:00.000Z", "author": { "name": "Nakhaee Moghadam, M; Amini, S; Lotfi, M; Nazari Rad, M; Nourmohammadi, J", "url": "https://orcid.org/0000-0002-0080-862X" } }, { "id": "http://doi.org/10.1016/j.bios.2026.119323", "content_html": "Modern sports science is undergoing a paradigm shift from subjective coaching to objective, data-driven precision training. Consequently, wearable electronics for acquiring precise data during sports activities are attracting attention. However, conventional rigid wearables inherently suffer from mechanical mismatches between rigid wearables and the soft, deformable human skin, inducing severe motion artifacts and restricting natural biomechanics during high-intensity athletic activities. To address these limitations from a comprehensive perspective, this review systematically summarizes and analyzes recent advancements in next-generation flexible and stretchable bioelectronics tailored specifically for sports applications. We systematically examine the substrate materials, structural engineering designs, and device-level optimization strategies required to withstand repetitive strain, shear, and sweat-intensive environments. Building upon these conformable bio-interfaces, we discuss three core sensing modalities: electrochemical sensors for real-time analysis of sweat biomarkers, biophysical sensors for continuous electrophysiological and hemodynamic monitoring, and electromechanical sensors for high-fidelity kinematic and impact tracking. Furthermore, we discuss body-derived energy harvesting strategies to power sports wearables and wireless communication systems for continuous athletic monitoring. The importance of artificial intelligence and machine learning is increasing, as these techniques enable the removal of motion artifacts and facilitate objective analysis of athletic data for training and health monitoring applications. By evaluating targeted applications across aerobic and anaerobic exercise, cognitive state assessment, and post-exercise recovery, this review demonstrates how next-generation sports wearables contribute to the shift in sports science from reactive treatment toward proactive, data-driven performance enhancement and injury prevention. ", "url": "http://doi.org/10.1016/j.bios.2026.119323", "title": "Flexible and stretchable bioelectronics for sports wearable monitoring: Materials, Device Engineering, and Intelligent Wearable Platforms.", "summary": "Flexible and stretchable bioelectronics for sports wearable monitoring: Materials, Device Engineering, and Intelligent Wearable Platforms. - Biosensors & bioelectronics", "date_modified": "2027-01-15T00:00:00.000Z", "author": { "name": "Lee, J; Lee, Y; Shin, J; Lee, DJ; Yoon, H; Lee, JY; Yu, KJ; Sang, M", "url": "http://yonsei.ac.kr" } }, { "id": "http://doi.org/10.3238/arztebl.m2026.0178", "content_html": "There have not been any studies to date on trends in digital health literacy (DHL) in Germany. We studied trends in DHL and relevant factors that affect it on the basis of two population-based surveys conducted in 2019/2020 and 2024/2025. Data from the second and third Health Literacy Surveys in Germany (HLS-GER 2, 2019/2020; HLS-GER 3, 2024/2025) were evaluated. Subjective difficulty with digital health information was measured with the HLS19-DIGI survey instrument in two personal cross-sectional surveys of the adult, German-speaking population of Germany. In HLS-GER 3, 71.1% of respondents had low DHL; this was an improvement compared to 75.8% in HLS-GER 2 (p <0.05). Nonetheless, social and age-related disparities widened. Lower DHL displayed small to moderate associations with older age (β = -0.235; p <0.001) and financial deprivation (B = -5.285; p = 0.001); higher DHL was associated with a higher level of education (β = 0.087; p < 0.001), higher social status (β = 0.086; p < 0.001), and better literacy skills (β = 0.071; p < 0.001). Moreover, self-efficacy (β = 0.233; p <0.001) and more frequent use of health-related websites (B = 15.227; p <0.001) were associated with a higher DHL. Fostering DHL is essential in view of the expansion of digital health information and the increasing amount of false and misleading information in the electronic media. Important tasks include reducing social and digital inequities and improving the quality of digital information. ", "url": "http://doi.org/10.3238/arztebl.m2026.0178", "title": "Trends in Digital Health Literacy: Results of the Third Health Literacy Survey in Germany.", "summary": "Trends in Digital Health Literacy: Results of the Third Health Literacy Survey in Germany. - Deutsches Arzteblatt international", "date_modified": "2027-01-11T00:00:00.000Z", "author": { "name": "Schaeffer, D; Griese, L; Singh, H; Ewers, M; Hurrelmann, K", "url": "http://unknown.com" } }, { "id": "http://doi.org/10.1016/j.ccc.2026.07.010", "content_html": "Intensive care is defined by providing a higher level of patient monitoring, but only a fraction of the data created through continuous hemodynamic monitoring are captured in the electronic health record. The monitoring data that is captured misses clinically important deterioration events and is not adequate to represent critical illness physiology for care at the bedside or for research. This article describes the current state of patient monitoring in intensive care, advanced monitors in development, metadata and its role in creating rich intensive care data sources, and machine learning and its current and future applications in intensive care medicine. ", "url": "http://doi.org/10.1016/j.ccc.2026.07.010", "title": "Intensive Monitoring, Big Data, and Artificial Intelligence in Critical Care.", "summary": "Intensive Monitoring, Big Data, and Artificial Intelligence in Critical Care. - Critical care clinics", "date_modified": "2027-01-01T00:00:00.000Z", "author": { "name": "Rzewnicki, DI; Bhavani, SV", "url": "http://emory.edu" } }, { "id": "http://doi.org/10.1002/hpja.70280", "content_html": "Access to timely dental care remains inequitable for children in rural, remote and disadvantaged communities. Teledentistry could narrow this gap, and mobile oral health apps offer a platform, yet user acceptance remains largely unexamined. This study explored how parents, public health professionals, software and design professionals and dental professionals perceive a teledentistry feature within a children's oral health app, and the equity and governance issues it raises. Parents and caregivers, public health professionals, software and design professionals and dental professionals were recruited through purposive and snowball sampling via professional networks, social media and organisational contacts. Participants completed an online questionnaire capturing demographic characteristics and perceptions of mobile oral health technologies, followed by a semi-structured interview conducted in person, by telephone or via videoconference by one of four researchers. Interviews were audio-recorded and transcribed verbatim. Questionnaire data were summarised descriptively, and interviews were analysed using Braun and Clarke's six-phase reflexive thematic analysis. Two researchers coded the data and developed preliminary themes. A total of 59 participants (39 women, 20 men) were interviewed, and four themes were developed: (1) acceptance of a teledentistry feature within a mobile oral health application; (2) the potential for teledentistry to improve access and equity; (3) preferences for in-person care, reflecting concerns about diagnostic accuracy, communication and clinician-patient relationships; and (4) perceived logistical and governance challenges to implementation. Participants viewed teledentistry as an acceptable, complementary feature for screening, triage, follow-up and continuity of care, recognising its potential to reduce geographical, social and systemic barriers, particularly for rural and isolated paediatric populations. However, concerns persisted that diagnostic accuracy, communication, trust and cultural safety are difficult to replicate remotely, alongside logistical and governance challenges including cost, funding, workforce capacity, oversight and safeguarding. Teledentistry has the potential to improve equitable access to oral healthcare for children and families. However, its effectiveness depends on careful design and on its recognition as a trusted supplement to, rather than a replacement for, in-person care. This approach requires strong governance, sustainable funding and child-safe consent. SO WHAT?: Teledentistry is likely to be accepted by families and professionals only when it is positioned as an adjunct that extends, rather than substitutes for, in-person paediatric dental care. Developers and policymakers should therefore prioritise triage, follow-up and continuity functions, with clear pathways to a clinician and attention to cultural safety. ", "url": "http://doi.org/10.1002/hpja.70280", "title": "Acceptability of Teledentistry in Mobile Oral Health Applications for Australian Children: A Qualitative Study.", "summary": "Acceptability of Teledentistry in Mobile Oral Health Applications for Australian Children: A Qualitative Study. - Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals", "date_modified": "2027-01-01T00:00:00.000Z", "author": { "name": "Groundwater, J; Aiyar, R; Jamieson, L; Tiwari, T; Kruger, E; Tennant, M; Nath, S", "url": "https://orcid.org/0009-0008-3219-6497" } }, { "id": "http://doi.org/10.1016/j.ijmedinf.2026.106788", "content_html": "Artificial intelligence (AI) is increasingly embedded in digital smoking-cessation tools, but whether AI-enabled personalisation improves abstinence, and whether any benefit reflects the AI component rather than digital support more broadly, remain uncertain. To systematically review and meta-analyse randomised controlled trials (RCTs) of AI-enabled smoking-cessation interventions using an explicit operational framework that distinguished learning, inference and substantive language-understanding components from conventional fixed-rule automation. PubMed/MEDLINE, Scopus and the Web of Science Core Collection were searched from inception to 7 August 2026 without language, date, publication-status or study-design filters; Cochrane CENTRAL, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform were searched, and backward and forward citation searching was undertaken, on 21 October 2026. Eligible studies were RCTs in adults who smoked combustible cigarettes in which an AI-enabled component materially generated, selected, personalised, adapted, predicted, recommended or timed cessation support. Two reviewers independently screened records, extracted data and assessed risk of bias using RoB 2; disagreements were resolved by a third reviewer. Risk ratios (RRs) were pooled using random-effects restricted maximum likelihood models with Knapp-Hartung adjustment, separately for abstinence at the longest follow-up of at least six months (primary) and at approximately three months (secondary). Exploratory sensitivity analyses were restricted to biochemically verified abstinence, and further analyses examined missing-data assumptions, completeness of AI reporting and results available only in trial registries. Certainty of evidence was assessed using GRADE. Fourteen RCTs involving 101,331 randomised participants were included, eight of which contributed to meta-analysis. AI functions comprised message recommender systems (five trials), conversational agents (three trials), wearable-sensor smoking-detection models (three trials), lapse-risk prediction models applied to ecological momentary assessments (two trials) and one app described as AI-enabled without technical detail. Pooled abstinence at six months or longer was RR 1.33 (95 % CI 0.82-2.15; I-squared = 67.6 %; four trials, 2,694 participants), and at approximately three months RR 1.36 (95 % CI 0.63-2.93; I-squared = 58.4 %; four trials, 715 participants). Exploratory analyses restricted to biochemically verified abstinence yielded RR 1.53 (95 % CI 1.07-2.19; three trials, 1,207 participants) at six months or longer and RR 2.03 (95 % CI 1.03-4.03; three trials, 366 participants) at approximately three months; these analyses changed the composition of the evidence set by excluding the largest null trials and should not be interpreted as confirmatory. Adding two completed trials whose results were available only in a trial registry moved the six-month estimate towards the null (RR 1.20, 95 % CI 0.91-1.59). In an exploratory comparison by comparator architecture, point estimates more consistently favoured AI-enabled packages versus usual or minimal care than versus active non-AI digital comparators, where results were inconsistent; however, comparator type was confounded with other trial characteristics. Certainty of evidence ranged from low to very low. No included trial randomised a generative large language model intervention. Randomised evidence for AI-enabled smoking cessation remains sparse, heterogeneous and imprecise. AI-containing packages may improve abstinence when added to usual or minimal care, but current trials do not establish a consistent incremental benefit of AI over well-designed non-AI digital support. Larger trials with biochemically verified long-term endpoints, matched comparators that isolate the algorithmic contribution, and transparent AI-specific reporting are needed before superiority of AI-enabled cessation support can be inferred. ", "url": "http://doi.org/10.1016/j.ijmedinf.2026.106788", "title": "Artificial intelligence-enabled interventions for smoking cessation: a systematic review and meta-analysis of randomised controlled trials.", "summary": "Artificial intelligence-enabled interventions for smoking cessation: a systematic review and meta-analysis of randomised controlled trials. - International journal of medical informatics", "date_modified": "2027-01-01T00:00:00.000Z", "author": { "name": "Adebisi, YA; Alhur, AA; Ogunkola, IO; Olaoye, DQ; Ong, CJN; Iyagbaye, E; Lucero-Prisno Iii, DE", "url": "http://research.gla.ac.uk" } }, { "id": "http://doi.org/10.1016/j.ijmedinf.2026.106783", "content_html": "Physicians are increasingly expected to communicate health science to the public, and generative artificial intelligence (AI) makes such content cheap to produce, yet cross-national evidence remains scarce. To compare physician digital literacy and AI adoption for health communication in China, South Korea, Singapore, the USA and the UK, and to derive a transferable, testable model for Shanghai from Singapore's MedVid programme. A theory-informed comparative case study synthesised peer-reviewed studies, surveys, regulations and programme evaluations, mainly from 2023 to 2026, identified through documented searches. Evidence was graded by proximity to public-facing AI content, coded against competency domains, institutional conditions and three barriers (accuracy, intellectual property/likeness, time, with time split into barrier and motive), and calibrated for cross-case pattern matching. Two coders scored every cell independently, with complete agreement. Barrier scores rested only on physicians' reported concerns, evidentiary silence was treated as missing, and five sensitivity analyses tested robustness. Accuracy was scored 2 or above in every system where it was measured, under every specification. Physicians' likeness concerns were rarely measured, and time appeared mainly as a motive for individual adoption. Adoption was institutionally channelled in Singapore and partly in China, but individual and weakly supported in the USA and UK. Singapore's MedVid, which produced over 300 AI-generated counselling videos in four languages from real pharmacists' voices and images, shows how institutional provenance can preserve authenticity. Physician digital literacy needs to encompass content creation and stewardship of one's digital identity. A consent-based, labelled, revocable and verifiable trusted-physician digital twin, with explicit governance and pre-specified criteria for benefit, offers a testable route to dialect-appropriate communication for Shanghai's 10.48 million residents from other provinces. The associations reported are not causal, and trust remains unscored. ", "url": "http://doi.org/10.1016/j.ijmedinf.2026.106783", "title": "Physician digital health literacy and generative AI for public health communication: a five-country comparative case study with a MedVid-informed digital twin model for Shanghai.", "summary": "Physician digital health literacy and generative AI for public health communication: a five-country comparative case study with a MedVid-informed digital twin model for Shanghai. - International journal of medical informatics", "date_modified": "2027-01-01T00:00:00.000Z", "author": { "name": "Han, X; Li, J; Ampofo, J", "url": "http://outlook.com" } }, { "id": "http://doi.org/10.1016/j.ijmedinf.2026.106771", "content_html": "Artificial intelligence (AI) is increasingly used across clinical, administrative, and public health settings, but technical performance alone does not establish clinical utility, safety, equity, or sustainability. To critically analyze contemporary healthcare AI applications, characterize source types and non-ordinal evidence profiles in the supporting literature, synthesize key opportunities and risks, and identify governance requirements for clinically useful, safe, equitable, and responsible implementation. We conducted a critical narrative synthesis using PubMed, the Virtual Health Library, Web of Science, and Scopus as source-discovery tools on July 16, 2026. A curated corpus of 28 bibliographically verifiable publications was assembled judgmentally to provide complementary coverage of clinical applications, evidence characteristics, implementation, risk, and life-cycle governance. Sources were classified by primary design and non-mutually exclusive evidentiary domains, and the evidence described within them was characterized using non-ordinal profile categories. Findings were synthesized qualitatively because of substantial heterogeneity across technologies, settings, and source designs. The curated corpus addressed diagnosis and imaging, prediction and monitoring, clinical documentation, conversational systems, public health, hospital operations, and specialty applications. Within this assembled corpus, technical-performance/validation and process-utility/workflow domains were represented more often than clinical or patient-safety outcomes, economic outcomes, or post-deployment surveillance; these document-level counts are descriptive and have no inferential weight beyond the selected sources. Reported or proposed benefits included diagnostic support, personalization, documentation, surveillance, and workflow efficiency. Recurrent risks included poor generalizability, bias, miscalibration, hallucinations, opacity, privacy concerns, interoperability problems, inappropriate automation, unclear accountability, and hidden costs. Within this non-exhaustive corpus, the evidence more consistently supported assistive, context-specific uses than generalized autonomous replacement. Responsible implementation requires a clearly defined intended use, governed data, contextual validation, risk-proportionate human oversight, traceability, equity and safety assessment, continuous monitoring, and procedures to restrict, suspend, or withdraw systems when performance or implementation conditions change. ", "url": "http://doi.org/10.1016/j.ijmedinf.2026.106771", "title": "Artificial intelligence in healthcare: clinical applications, evidence maturity, risks, and life-cycle governance.", "summary": "Artificial intelligence in healthcare: clinical applications, evidence maturity, risks, and life-cycle governance. - International journal of medical informatics", "date_modified": "2027-01-01T00:00:00.000Z", "author": { "name": "León-Figueroa, DA; Acosta-Porzoliz, R; Cómina-Tamayo, MJ; Aguirre-Milachay, E; Vera-Ponce, VJ; Valladares-Garrido, MJ", "url": "http://upch.pe" } } ] }