Frontiers in digital health最新文献

This article explores the largely uncharted intersection of neurotechnology and Internet governance on the international policy agenda. Neurotechnologies encompass a broad spectrum of functions and applications, from the direct recording or alteration of brain activity to the analysis of emotions and mental states through data collected from wearable devices, applications, and AI-based tools. Innovations such as cochlear implants, sleep optimisation technologies, and immersive educational tools are already available, and significant investments are made in the next generation of devices that blur the lines between mind, machine, and action, posing unprecedented challenges. While some international organisations have begun addressing the ethical and human rights implications of neurotechnology, there remains significant fragmentation and a lack of clarity regarding its integration into Internet governance. Critical issues related to neural infrastructure, standards, access to technologies, and protections for neural data have been overlooked in the 2024 Global Digital Compact and might remain off the agenda for the upcoming 20th review of the World Summit on the Information Society. This contribution underscores the urgent need to analyse the profound implications of neurotechnology, advocating for proactive measures that align with progress made across Internet governance fora, with respect to legal safeguards, multistakeholder consultations and institutional pillars.

Background: The increasing complexity of patient care and workforce shortages in healthcare systems necessitate improved interprofessional interaction. Digital technologies offer promising solutions to facilitate such interaction across healthcare settings.

Objectives: This scoping review aimed to identify, categorize, and assess digital technologies that support interprofessional interaction among healthcare professionals, using the NASSS framework to evaluate their implementation context and impact.

Methodology: A systematic search was conducted across five databases. The eligible studies examined digital tools enabling interaction between different professional groups in healthcare. Data from 407 studies were extracted and coded using four NASSS domains (Condition, Technology, Value Proposition, and Adopter System). Thematic analysis and visualizations were employed to synthesize findings.

Results: Seven primary technology categories were identified. Most technologies were implemented at the organizational level, primarily within hospital and intersectoral care settings, with oncology being the most common clinical focus. While many tools showed positive impacts on workflow efficiency, access to specialist expertise, and team communication, challenges relating to usability, data privacy, role ambiguity, and staff workload were also reported. Value propositions and impacts on staff varied significantly across technologies.

Conclusion: Digitally supported interprofessional interaction holds promise for enhancing communication, collaboration, and efficiency in delivering healthcare. However, successful adoption depends on aligning technological design with clinical workflows, involving end-users in development, and addressing regulatory, ethical, and organizational challenges.

Introduction: This multi-center, parallel-group randomized controlled trial evaluated the app-based intervention mebix, developed by Vision2b GmbH in Germany, for people with type 2 diabetes compared to a placebo app.

Method: A total of 153 participants were randomized in a 1:1 ratio to either intervention or control group, with allocation concealment ensured by a minimization procedure.

Results: After six months, participants using mebix achieved a statistically significant reduction in HbA1c levels by 0.82 percentage points (95% confidence interval: -1.20, -0.48, p = 0.003). This reduction was greater than in the control group (mean difference: 0.24 percentage points, 95% confidence interval: -0.44, 0.09). mebix users further experienced greater weight loss, lower diabetes-related distress, and reduced depression severity. Adherence to the app was high, with more than 75% of participants using mebix throughout the study period.

Conclusion: These findings indicate that the digital approach can meaningfully improve both glycemic control and psychological well-being in people with type 2 diabetes, supporting its potential integration into routine care.

Clinical trial registration: https://www.evamebix.de, identifier DRKS00025719, DRKS00032395.

Introduction: Mental health NLP models are increasingly used to detect psychological states such as stress and depression from user-generated social media content. Although transformer based models such as MentalBERT achieve strong predictive performance, they are typically trained on sensitive data, raising concerns about memorization and unintended disclosure of personally identifiable information.

Methods: We propose DP-CARE, a simple yet effective privacy-preserving framework that attaches a lightweight classifier to a frozen, domain-specific encoder and trains it using Differentially Private AdamW (DP-AdamW). This approach mitigates privacy risks while maintaining computational efficiency.

Results: We evaluate DP-CARE on the Dreaddit dataset for stress detection. Our method achieves competitive performance, with an F1 score of 78.08% and a recall of 88.67%, under a privacy budget of ε ≈ 3.

Discussion: The results indicate that lightweight, differentially private fine-tuning offers a practical and ethical approach for deploying NLP systems in privacy-sensitive mental health contexts. DP-CARE demonstrates that strong predictive performance can be retained while significantly reducing privacy risks associated with training on sensitive user data.

Background: The emergence of trastuzumab deruxtecan has led to significant improvement in clinical outcomes for patients with HER2-low metastatic breast cancer, which accounts for approximately half (45%-55%) of breast cancer diagnoses. However, little is known about patients' awareness of diagnostic testing requirements and treatment implications associated with HER2-low status. This study aims to better understand patients' knowledge of HER2-low.

Methods: This cross-sectional survey was completed virtually on the Outcomes4Me mobile app, a direct-to-patient digital application that empowers patients to take a proactive approach to their care. Eligible patients included those with Stage IV breast cancer living in the United States. Participants were surveyed on their awareness of their tumor's HER2 biomarker status and willingness to discuss more with their oncologists if their status was unknown. Educational content about HER2 biomarker testing was accessible on the app. Responses were analyzed descriptively and reported in aggregate.

Results: Out of the 527 respondents, 362 met eligibility criteria. Among them, 42% were diagnosed over 5 years ago, 35% had Stage IV disease at diagnosis, 33% received care in a community setting, and 43% had progressed on prior metastatic therapy. The majority (78%, n = 284) knew their HER2 status, while 18% (n = 64) did not recall it and 4% (n = 14) did not respond. Among those aware of their status, 51% were at least somewhat familiar with HER2-low, compared with 23% who were unaware of their HER2 status. Among the patients with known HER2-negative disease (n = 152), 74% reported testing within the past year, yet 51% did not recall HER2-low being discussed. Following brief in-app education, 61% of patients with unknown HER2 status at diagnosis (n = 64) expressed intent to discuss HER2-low testing with their oncologist.

Conclusions: Knowledge gaps in HER2 biomarker testing persist in patients with metastatic breast cancer. Even for patients with a known HER2 status, many remain unaware of the HER2-low classification. Digital education resources such as the Outcomes4Me app can facilitate patient empowerment and provide targeted education outside of traditional clinical settings, enabling shared decision-making. After receiving a brief education within the app, the majority of patients with an unknown HER2 status expressed willingness to discuss more about HER2 testing with their oncologist.

Introduction: Chronic heart failure (CHF) is associated with frequent hospitalizations, poor quality of life, and high healthcare costs. Despite therapeutic progress, early recognition of clinical deterioration remains difficult. The SMART-CARE study investigates whether artificial intelligence (AI)-enabled remote monitoring using CE-certified wearable devices can reduce hospital admissions and improve patient outcomes in CHF.

Methods: SMART-CARE is a prospective, multicenter, observational cohort study enrolling 300 adult patients with CHF (HFrEF, HFmrEF, or HFpEF) across three Italian tertiary centers. Participants are assigned to an intervention group, using wrist-worn, chest-worn, and multiparametric CE-certified wearable devices for six months, or to a control group receiving standard CHF care. Physiological data (e.g., SpO₂, HRV, respiratory rate, skin temperature, sleep metrics) are continuously collected and analyzed in real time through AI algorithms to generate alerts for early clinical intervention. The primary endpoint is a ≥20% reduction in hospital admissions over six months. Secondary outcomes include changes in quality of life (Kansas City Cardiomyopathy Questionnaire), biomarkers (BNP, NT-proBNP, renal function, electrolytes), echocardiographic indices (LVEF, LV volumes), and safety events.

Results: We hypothesize that AI-driven remote monitoring will significantly reduce hospitalizations, improve quality of life, and favorably impact biochemical and echocardiographic parameters compared to standard care.

Conclusion: SMART-CARE is designed to evaluate the clinical utility of multimodal wearable devices integrated with AI algorithms in CHF management. If successful, this approach may transform traditional care by enabling earlier detection of decompensation, optimizing resource utilization, and supporting the scalability of remote monitoring in chronic disease management.

Clinical trial registration: ClinicalTrials.gov, identifier NCT06909682.

Optical 3D surface scanning is used increasingly to assess spinal deformity of patients with scoliosis. However, approaches based on optical 3D scanning often underestimate the spinal deformity. To improve the accuracy of such estimates, deeper understanding is required of scoliosis and its effect on the back shape. We present the PCdare research software which registers 3D surface scans with the corresponding biplanar radiographs semi-automatically and facilitates investigations into the relationship between surface and internal modalities. PCdare revealed very strong correlations between the spinous process line and internal spinal alignment, and a median Cobb angle difference of less than 1° from the clinical gold standard. These results increase confidence in the use of 3D scanning with a "back-shape-to-spine" approach and confirm the applicability of PCdare to investigate the relationship between internal alignment and back shape in research.

[This corrects the article DOI: 10.3389/fdgth.2025.1680350.].

Large language models (LLMs) are used increasingly in medicine, but their decision-making in cardiovascular risk attribution remains underexplored. This pilot study examined how an LLM apportioned relative cardiovascular risk across different demographic and clinical domains. A structured prompt set across six domains was developed, across general cardiovascular risk, body mass index (BMI), diabetes, depression, smoking, and hyperlipidaemia, and submitted in triplicate to ChatGPT 4.0 mini. For each domain, a neutral prompt assessed the LLM's risk attribution, while paired comparative prompts examined whether including the domain changed the LLM's decision of the higher-risk demographic group. The LLM attributed higher cardiovascular risk to men than women, and to Black rather than white patients, across most neutral prompts. In comparative prompts, the LLM's decision between sex changed in two of six domains: when depression was included, risk attribution was equal between men and women. It changed from females being at higher risk than males in scenarios without smoking, but changed to males being at higher risk than females when smoking was present. In contrast, race-based decisions of relative risk were stable across domains, as the LLM consistently judged Black patients to be higher-risk. Agreement across repeated runs was strong (ICC of 0.949, 95% CI: 0.819-0.992, p = <0.001). The LLM exhibited bias and variability across cardiovascular risk domains. Although decisions between males/females sometimes changed when comorbidities were included, race-based decisions remained the same. This pilot study suggests careful evaluation of LLM clinical decision-making is needed, to avoid reinforcing inequities.