Frontiers in digital health最新文献

Digital health technologies (DHTs) represent a promising strategy to improve access to HTS (HIV testing services), particularly among underserved higher-risk populations often missed by current programming, including young adults under 25 years. In 2017, Kenya's Ministry of Health introduced BeSure™, a DHT providing information on HIV, self-testing, and facility geo-location. Given increased risks for HIV among injured populations, this study assessed the acceptability of BeSure™ as a DHT for enhancing HTS in a Kenyan emergency department. Using purposive sampling, participants were provided a brief description of the tool BeSure™ and then completed in-depth interviews using a semistructured guide between August and November 2023. Deductive and inductive analyses were applied using a codebook based on a published framework for healthcare intervention acceptability, examining core themes of affect, burden, ethicality, coherence, opportunity cost, and perceived effectiveness. Among 24 participants, the median age was 25, half were female, and 58% had achieved secondary education or below. Few participants (21%) were aware of BeSure™ prior to data collection. Barriers to awareness included limited marketing of the tool and apathy toward health-related matters. However, strategic advertisement within healthcare encounters and through social media platforms including TikTok and Facebook (especially for young adult participants) could facilitate awareness. Barriers to potential use include low access to technology in rural communities, persisting stigma toward HIV, and low perceived HIV risk (especially among older participants). Despite these barriers, participants across age groups found the tool widely acceptable across the predetermined domains. These qualitative data highlight the acceptability of DHTs for HTS enhancement among injured populations in Nairobi, Kenya. Findings underscore the limited awareness of BeSure™ among this higher-risk population, suggesting that targeted advertisement, demand creation, and stigma reduction strategies are critical to successful implementation of these technologies.

Introduction: Boys and young men face an elevated risk of mental health problems and suicidality, yet they remain less likely than their female peers to seek professional help. Online counselling services such as krisenchat offer low-threshold support and may help reduce gender-specific barriers, but little is known about how young men use these services.

Objective: This study investigates male krisenchat users in comparison to other users, focusing on demographics, utilization patterns, satisfaction, chat topics, and barriers to help-seeking behavior, in order to generate insights for improving mental health support for young men.

Methods: Anonymized data were obtained from n = 29,387 krisenchat users between January and December 2023. After data cleaning, the final sample comprised of N = 9,584 participants. Demographic information, utilization behavior, suicidality, and use of professional help services were documented by counsellors, while user satisfaction, recommendation rates, and emotional distress were assessed through voluntary surveys following consultation.

Results: Young males accounted for 19.9% of krisenchat users, were on average older than female users and were less likely to have been in prior treatment. Male users sent fewer messages, accessed the service during late-night hours more often than females, and tended to find the service via search engines rather than institutional or social media channels. Compared to female users, they were less likely to disclose self-harm, family problems, or sexual violence, but more likely to bring up sexuality and LGBTQIA+ topics. Importantly, no gender difference was found for suicidality. Despite differences in some utilization patterns, acceptability outcomes - including reductions in distress, satisfaction, and likelihood of recommending the service - were comparable across genders, suggesting equivalent counselling benefits once engaged.

Conclusions: Digital crisis services like krisenchat hold potential for reducing gender disparities in mental health support. However, targeted strategies to improve visibility, adapt communication styles, and strengthen follow-up pathways are essential to increase engagement and sustained help-seeking among young men.

Study registration: DRKS00026671.

Background: Acquired brain injury (ABI), which includes traumatic brain injury (TBI) and stroke, is a leading cause of disability. Evidence shows that sex may influence functional recovery post-acquired brain injury, potentially due to biological (e.g., hormones) and social factors (e.g., caregiver availability). Meanwhile, new neurorehabilitation technologies-such as virtual reality, robotic-assistance, and brain-computer interfaces-offer promising avenues for improving functional outcomes. Understanding how these technologies interact with sex differences could advance equitable and personalized healthcare.

Research question: Does evidence support a rationale for studying, developing, or employing neurorehabilitation technologies differently in males and females to improve functional outcomes post-ABI?

Methodology: An empirical integrative narrative review was conducted. Searches were performed in PubMed, Cochrane Library, and OVID, focusing on adult populations with ABI. Key terms encompassed "acquired brain injury," "sex differences," and "neurorehabilitation technologies." Fifty-nine studies met inclusion criteria, spanning diverse methodologies, settings, and cultural contexts. Data were synthesized to compare functional outcomes impacted by sex and by neurorehabilitation technologies.

Results: Findings indicate that the effect of sex on neurorehabilitation outcomes is multifaceted. Studies using functional independence measures often reported no significant sex differences, whereas more specific measures (e.g., those measuring cognitive or social functions) identified notable sex effects. Neurorehabilitation technologies showed positive outcomes in various functional domains (e.g., upper extremity motor function, gait, cognition), but most studies focused on stroke.

Discussion: Current research does not support the use of sex-differentiated technology interventions to target upper extremity motor function or global functional independence post-stroke. Sex-differentiated treatment may be relevant for other functional domains such as cognitive recovery, psychological well-being and social outcomes, but this requires further research, particularly for non-stroke ABI.

Conclusion: These findings suggest that some neurorehabilitation technologies can be applied without sex-specific modification, whereas others may benefit from sex-specific considerations. Owing to methodological limitations and sparse data, especially for TBI, additional investigations are warranted. As novel neurorehabilitation technologies evolve, accounting for sex differences may enhance personalized care and optimize long-term outcomes.

Introduction: Adherence to weekly iron/folic acid (IFA) supplementation, a vital intervention to combat anaemia among adolescent girls, remains a global challenge, including in Maluku Province, Indonesia. This study assessed the effect of "Jaga Diri" application, in enhancing knowledge and adherence to IFA supplementation among adolescent girls from Salahutu Sub-District of Maluku Province, Indonesia.

Methods: In mid-2024, a quasi-experimental study was conducted among 82 adolescent girls from two senior high schools in Salahutu Sub-District, Maluku Province, Indonesia. The intervention group used the "Jaga Diri" Android-based application for four weeks, which delivered weekly reminders and brief educational messages on anaemia and iron-folic acid (IFA) supplementation, while the control group received routine school-based services. Knowledge was measured using a validated 15-item questionnaire. Adherence was defined as consumption of ≥75% of the provided weekly IFA tablets over the previous four weeks, assessed by self-report, and supported by haemoglobin measurement. Group differences were analyzed using non-parametric and chi-square tests, and multivariable binary logistic regression was used to assess factors associated with high knowledge and adherence.

Results: After four weeks of using the "Jaga Diri" application, adolescent girls from the intervention school showed a significantly higher level of knowledge about anaemia (p = 0.011) and adherence to weekly IFA supplementation (p < 0.001) than those from the control school. The improved adherence was shown by the reduction of anaemia prevalence in the intervention school, from 35% to 17.5%. In the control school, the prevalence increased from 19% to 28.6%.

Conclusions: The "Jaga Diri" application effectively improves knowledge about anaemia and adherence to IFA supplementation among adolescent girls. Further investigation with larger and more varied groups are required to confirm its effectiveness before it can be widely implemented in larger areas of Maluku and Indonesia.

Background: Point-of-care lung ultrasound (LUS) has been described for the evaluation of lung pathologies such as pneumothorax, pneumonia, and COVID-19 infections. It is rapidly deployed, portable, and accurate for LUS diagnoses. However, a learning curve limits its use, and teleguidance has been proposed as a solution. In this study, we primarily seek to measure the effect of tele-guided lung ultrasound (T-LUS) on chest X-ray (CXR) utilization in patients presenting with COVID-19 symptoms. Secondarily, we measure the effect of T-LUS on clinical decision-making, length of stay, and clinical outcomes.

Results: We performed a retrospective observational study using a before-after design in an adult urgent care (AUC) setting. A total of 303 patients with symptoms suggestive of COVID-19 were included. AUC providers used T-LUS on 31% of patients with COVID-19 symptoms (n = 34). Abnormal LUS findings were found in 41% of patients (n = 14), with B-lines (86%) and pleural irregularities (79%) being the most common findings. Among all patients in the study period, those who received a T-LUS did not show a statistically significant difference in CXR utilization [-12% difference; 95% confidence interval (CI) -25% to 5%] as compared to patients who did not receive a T-LUS, and a similarly non-significant difference was observed in the intervention period (-5% difference; 95% CI: -21% to 14%). Length of stay was longer for patients in whom T-LUS was used (median difference 26 min, 95% CI 11-41). However, a comparison of patients in the intervention period revealed no significant difference in length of stay between patients who received T-LUS and those that did not (median difference 16 min, 95% CI -5 to 37).

Conclusion: T-LUS is feasible and alters clinical decision-making for novice ultrasound users in the care of patients with suspected COVID-19 infection. Our results indicated that there was a no statistically significant difference trend in CXR utilization and no improvement in length of stay by the end of the 2-week trial.

Advances in artificial intelligence and multi-omic analysis are transforming fetal medicine from a diagnostic discipline into a predictive one. Yet the legal, deontological, and ethical frameworks that govern prenatal and fetal data have not evolved accordingly. Current regulations protect the mother as a patient but do not recognize the fetus-or the future child-as a legal data subject. As a result, information generated before birth remains confined within maternal medical records, creating uncertainty about who may later access or reuse it. This paper examines the emerging ethical and legal challenges of predictive fetal medicine, focusing on the transition from maternal consent to the child's future right to their own prenatal data. Through the lens of professional deontology and comparative law, we analyze the tensions between confidentiality, autonomy, and beneficence. We propose a framework of prenatal data stewardship, shifting from static notions of data ownership to shared responsibility across time. Establishing national or international repositories under transparent governance could enable ethical reuse of fetal data while safeguarding maternal privacy and ensuring future individuals' rights. Ultimately, aligning predictive fetal medicine with ethical and legal coherence requires collective action among clinicians, ethicists, jurists, policymakers, and industry. Only through such stewardship can information generated before birth become a trusted tool for care rather than control.

Background: The rapid evolution of general large language models (LLMs) provides a promising framework for integrating artificial intelligence into medical practice. While these models are capable of generating medically relevant language, their application in evidence inference in clinical scenarios may pose potential challenges. This study employs empirical experiments to analyze the capability boundaries of current general-purpose LLMs within evidence-based medicine (EBM) tasks, and provides a philosophical reflection on their limitations.

Methods: This study evaluates the performance of three general-purpose LLMs, including ChatGPT, DeepSeek, and Gemini, when directly applied to core tasks of EBM. The models were tested in a baseline, unassisted setting, without task-specific fine-tuning, external evidence retrieval, or embedded prompting frameworks. Two clinical scenarios, namely SGLT2 inhibitors for heart failure and PD-1/PD-L1 inhibitors for advanced NSCLC were used to assess performance in evidence generation, evidence synthesis, and clinical judgment. Model outputs were evaluated using a multidimensional rubric. The empirical results were analyzed from an epistemological perspective.

Results: Experiments show that the evaluated general-purpose LLMs can produce syntactically coherent and medically plausible outputs in core evidence-related tasks. However, under current architectures and baseline deployment conditions, several limitations remain, including imperfect accuracy in numerical extraction and processing, limited verifiability of cited sources, inconsistent methodological rigor in synthesis, and weak attribution of clinical responsibility in recommendations. Building on these empirical patterns, the philosophical analysis reveals three potential risks in this testing setting, including disembodiment, deinstitutionalization, and depragmatization.

Conclusions: This study suggests that directly applying general-purpose LLMs to clinical evidence tasks entails some limitations. Under current architectures, these systems lack embodied engagement with clinical phenomena, do not participate in institutional evaluative norms, and cannot assume responsibility for reasoning. These findings provide a directional compass for future medical AI, including ground outputs in real-world data, integrate deployment into clinical workflows with oversight, and design human-AI collaboration with clear responsibility.

Trauma-focused mobile health (mHealth) applications, such as PTSD-Coach, hold significant potential to address acute psychological needs following large-scale emergencies, yet adoption remains inconsistent. This study examined associations between psychosocial resources and intention to adopt the Hebrew version of PTSD-Coach in Israel after the October 7, 2023, terror attack, which triggered widespread collective trauma and ongoing war. Survey data from Israeli adults (n = 86) measured trauma literacy, self-efficacy, citizenship (willingness to share/recommend), and adoption intention. Quantitative analyses using multivariable regression identified a sequential pathway: trauma literacy enabled users to recognize symptom relevance, self-efficacy converted knowledge into capability, and citizenship extended adoption intentions into social spaces. Trauma literacy was the only significant predictor of intention in the full model, while demographic and clinical variables including trauma exposure were non-significant. Self-efficacy strongly predicted willingness to recommend the app, and once self-efficacy was included, the direct effect of knowledge diminished, supporting a sequential process: Knowledge → Self-efficacy → Citizenship → Intention. Rooted in social psychiatry and trauma-informed public mental health perspectives, this study theoretically interprets how individual psychological resources and social dynamics may shape early digital help-seeking in crisis conditions. Findings suggest that trauma literacy and perceived capability are central correlates of adoption readiness, challenging assumptions that lived trauma experience automatically increases help-seeking. This pattern may reflect how acute stress impairs information uptake and perceived self-efficacy. From a mental health systems perspective, these findings point to the potential importance of proactive psychoeducation, stigma-reduction strategies, and community-based outreach to support digital intervention uptake during collective trauma. Strengthening trauma literacy and self-efficacy may support timely self-management, help-seeking, and community resilience where formal psychiatric services are strained or inaccessible.

Introduction: This study describes the user-centred design and evaluation of AreAreaAlzheimer, a web-based digital platform developed to support family caregivers of individuals with dementia, especially Alzheimer disease. The initiative sought to ensure that technological solutions effectively address caregivers' actual needs through active user participation at every stage of development.

Methods: Following an iterative, participatory design approach, 419 individuals contributed to the project. The first phase combined a survey of 210 caregivers and focus groups with 22 participants to identify priority support dimensions. Thematic analysis highlighted four main areas: informational guidance, logistical assistance, emotional and communication strategies, and peer social connection. Based on these insights, 147 additional participants provided feedback that refined platform features and content. Finally, platform evaluation included standardized usability measures including the Single Ease Question (SEQ) for task difficulty, the System Usability Scale (SUS) for overall usability perception, the Perceived Usefulness Scale (PUS) completed by 40 caregivers, and scenario-based testing with 19 users who discussed experiences and improvement opportunities.

Results: Quantitative findings showed high ratings in accessibility (average score: 4.5/5), usability (scored 74.3/100), and perceived usefulness was rated lower (average score: 3.4/5). Qualitative feedback supported these results, emphasizing the platform's practical value in everyday caregiving. However, participants with lower digital literacy reported persistent challenges, indicating the need for simplified navigation and adaptive interface features.

Discussion: AreAlzheimer demonstrates the potential of participatory design to create inclusive, effective digital health tools for dementia care. Involving caregivers and people living with dementia enriched the design, promoting autonomy and cognitive sensitivity. Future research will integrate these insights into formal scientific protocols to expand participatory digital health innovations in dementia support.

Introduction: Colorectal cancer frequently leads to liver metastases (CRLM), posing a major challenge to long-term survival. Prognosis remains heterogeneous, and traditional clinical risk scores often lack biological depth and spatial information. Advances in radiomics and machine learning (ML) offer the potential for improved, explainable outcome prediction; however, robust and interpretable prognostic models for CRLM remain an unmet need. This study aimed to develop and validate explainable ML models based on radiomic features extracted from both metastatic lesions and background liver tissue, enhancing the prediction of recurrence and overall survival (OS) status in patients with CRLM.

Materials and methods: Patient data and contrast-enhanced CT images from two independent cohorts were analysed: a publicly available TCIA-CRLM series, employed as the discovery set, and a real-life clinical cohort, used as an external validation set. Segmentation focused on the largest liver metastasis (L-MAX) and surrounding healthy liver tissue (L-BKG), extracting radiomic features from both areas and their ratios (L-MAX/L-BKG). An end-to-end pipeline for data preprocessing and classification was designed. Multiple ML and Deep Learning (DL) classifiers were trained and validated. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) analysis to identify key predictive radiomic determinants. Performances were compared to recognized clinical models.

Results: For recurrence prediction, the best-performing classifier was a soft-voting ensemble of a multilayer perceptron (MLP) optimized via a Genetic Algorithm (GA); for OS status classification, the best performance was obtained by a hard-voting ensemble of a GA-optimized MLP. Both classifiers demonstrated robust discrimination capabilities in external validation, with AUCs of 0.78 and 0.68, respectively. The explainability analysis performed with SHAP revealed the most relevant radiomic determinants in the classification. These features retained prognostic significance in the independent cohort, supporting their use for clinical risk stratification.

Discussion: Explainable ML models leveraging both lesion-centric and contextual liver radiomics offer clinically transparent prediction of recurrence and survival in CRLM. SHAP highlighted clinically plausible, reproducible imaging determinants, enabling risk stratification. The validation of specific radiomic determinants suggests the potential practical utility of this approach, laying out the groundwork for integrating with DL and multi-omic data in future oncology strategies.