DIGITAL HEALTH最新文献

Background: This study leverages machine learning and cytokine profiles to differentiate liver and renal function abnormalities in the aging population, aiming for advancements in early detection techniques.

Methods: The analysis involved data from 760 participants, employing logistic regression, random forest, lasso regression, extreme gradient boosting, and support vector machines to create diagnostic models. Cytokine levels were measured via ELISA, alongside liver and renal clinical function tests. The data were randomly split 3:1 into training and hold-out validation sets; Synthetic Minority Over-sampling Technique (SMOTE) was applied exclusively to the training set to mitigate class imbalance. Models were assessed on precision, recall, F1 score, specificity, and the area under the curve (AUC).

Results: Lasso regression was notably effective in identifying renal function abnormalities, delivering AUCs of 0.895 for males and 0.940 for females, pointing to its robustness in feature selection and model accuracy. For liver function, logistic regression was most accurate, with AUCs of 0.918 for males and 0.794 for females, identifying VCAM-1, REG4, Thrombomodulin, Notch-3 for males, and GDF-15, LDL R, CA125, PON1 for females as key discriminative cytokines. These results illustrate the models' capability in discerning critical biomarkers for early detection, with performance improved by SMOTE through correction of class imbalance in the training data.

Conclusion: Integrating machine learning with cytokine profiling emerges as a highly promising method for early detection of liver and renal abnormalities in the aging population, suggesting significant potential for improving preventive healthcare outcomes.

Background: Digital technologies offer the potential to promote mental health by improving older adults' digital and mental health literacy. Intergenerational support is a promising medium to promote the mental health of older adults. Nonetheless, the effects of interventions employing digital and mental health literacy training through intergenerational support on the mental health of older adults remain unclear.

Objectives: This study evaluated the effects of the Digital Buddy programme on the mental health of older adults.

Methods: This study used a multi-centre, cluster-randomised, two-parallel-group (1:1 allocation ratio), controlled trial design. People aged ≥60 years without diagnosed mental illnesses were eligible. In the intervention group, participants attended 14 training sessions conducted by young volunteers over 2 months, using materials on a website and a smartphone app. The content included digital skills and mental health knowledge. Volunteers continued tele-coaching participants for 6 months. The control group received usual care. Outcomes measured at baseline (T0) and 1-week post-intervention (T1) included mental well-being, depressive symptoms, health-related quality of life, self-efficacy, and perceived social support. Generalised estimating equations tested the hypotheses.

Results: A total of 310 participants from 15 clusters entered the study, with each group containing 155 participants. The WHO-5 (mean difference = 7.0, d = 0.32, p < .001) and Patient Health Questionnaire-9 scores (mean difference = 0.9, d = 0.24, p = .02) of the intervention group improved after the intervention with statistical significance, but not in the control group. Likewise, the interaction effects of group and time on the two outcomes were not statistically significant.

Discussion: There was a main effect of time in the intervention group over the outcomes of mental well-being and depressive symptoms. However, the interaction was non-significant and therefore the change over time did not differ between groups and therefore groups had similar change trajectories. However, future studies should devise measures to enhance its effects.

Trial registration: This trial has been registered at ClinicalTrials.gov (NCT05553730) on 23 September 2022, https://clinicaltrials.gov/ct2/show/NCT05553730.

Background: The internet of medical things (IoMT) is revolutionizing digital health through continuous monitoring, real-time diagnostics, and remote care capabilities. Nonetheless, research in this domain remains disjointed, with a restricted comprehension of its growth trajectories, principal contributors, and thematic emphasis. A comprehensive evaluation is thus required to inform forthcoming research, policy, and advancements in resilient healthcare technologies.

Methods: This study performed a bibliometric and literature-based analysis of IoMT research indexed in the Scopus database from 2016 to 2023. The dataset was optimized by keyword screening, resulting in 762 pertinent papers. Bibliometric indices, including as publication and citation trends, authorship and institutional output, and funding patterns, were analyzed. Thematic evolution was examined by keyword co-occurrence and cluster mapping utilizing VOSviewer, complemented by a synthesis of literature.

Results: A total of 762 publications on IOMT were identified, comprising 63.12% journal articles, 30.97% conference papers, and 5.91% review papers. The total publications rose from 1 in 2016 to 301 in 2023, indicating a 30,000% increase. Total citations reached 19,014, with an h-index of 171. The most prolific contributors were Mohsen M. Guizani, King Saud University, and India. Collaborations and funding, particularly from international agencies, were found to significantly drive research productivity. Keyword and cluster analyses revealed two dominant thematic areas: Smart Medical Diagnostics and Privacy-Driven Health Technologies. The literature further confirmed strong integration of machine learning, blockchain, sensor technologies, and cloud computing in IOMT applications.

Conclusion: This analysis consolidates fragmented IoMT research, providing a structured overview of its development, contributors, and thematic trajectories. The findings highlight the rapid growth, global collaborations, and integration of advanced technologies driving the field. By mapping benchmarks and research hotspots, the study offers valuable evidence to guide future investigations, interdisciplinary collaborations, and policy efforts aimed at strengthening secure and patient-centered digital health systems.

Background: Uterine contraction is a meaningful indicator for labor onset and appropriate hospital admission. Inaccurate self-assessment may lead to premature admission, unnecessary interventions, and higher healthcare resource use. Traditional monitoring devices have limited portability and comfort, restricting home-based use.

Objective: This study developed and validated a wearable system integrating flexible sensors, a data acquisition platform, and machine learning models to monitor uterine contractions and identify labor onset, focusing on late pregnancy and the pre-labor period.

Methods: A flexible sensor-based device was developed and validated against hospital toco. Contraction data from 82 participants (104 recordings) were preprocessed and segmented, and features were extracted for model training. Hospital admission was classified into recommended admission (RA), deferred admission (DA), and selective admission (SA). Several ML models were trained and evaluated via 10-fold stratified cross-validation using accuracy, precision, recall, F ₁-score, and area under the curve. Shapley Additive Explanations (SHAP) analysis interpreted feature contributions.

Results: A total of 82 participants were enrolled, and 104 uterine contraction recordings were collected, ranging from 10 to 70 min (mean 20.3). Two hundred and seventy-seven processed segments were obtained for analysis. Contraction signals were generally consistent with toco measurements (r = 0.85-0.95). XGBoost achieved accuracy of 0.87 for RA classification, and SHAP identified kurtosis, signal energy area, and standard deviation as key features.

Conclusion: The system enabled accurate monitoring of uterine contractions, improved estimation of hospital admission timing, reduced premature admission risk, and demonstrated high wearability, offering a feasible solution for home obstetric monitoring.

Objective: This study aims to systematically assess the content characteristics and information quality of knee arthroplasty-related videos on TikTok and Bilibili, in order to provide evidence to support the optimization of health science communication.

Methods: On February 13, 2025, we searched for "" (knee arthroplasty in Chinese) on TikTok and Bilibili, and initially collected 100 videos from each platform according to the default sorting order, which were then subjected to further screening. Videos containing irrelevant content, lacking audio, being non-original reposts, or intended for advertising and marketing purposes were excluded. The quality and reliability of the included videos were assessed by applying four validated instruments: the modified version of DISCERN (mDISCERN), the Global Quality Score (GQS), the Video Information and Quality Index (VIQI), and the Patient Education Materials Assessment Tool (PEMAT). Interplatform variations and correlations between quality and user interactions were analyzed via Mann‒Whitney U and chi-square tests.

Results: A total of 162 knee arthroplasty related videos were analyzed, including 88 from TikTok and 74 from Bilibili. TikTok videos demonstrated higher engagement and more certified uploaders, whereas Bilibili featured more diverse professional backgrounds. Bilibili emphasizing anatomy using PPT/class based, animation/ motion and television program/documentary styles. TikTok focusing on examination/diagnosis, and treatment delivered through solo narrative and Questions and Answers (Q&A). TikTok videos achieved higher scores across all quality assessment tools. Professionally generated content consistently outperformed nonprofessional content across most quality metrics, whereas no significant difference was observed for mDISCERN. Correlation analysis showed that engagement was strongly associated with VIQI on both platforms, with additional moderate associations for GQS and PEMAT only on TikTok, while mDISCERN showed no significant correlation.

Conclusions: TikTok favors high user engagement, whereas Bilibili provides more structured educational content. Professional involvement is essential to ensure information quality and effective medical communication.

Objective: To evaluate the design, implementation, and early impact of the Tasty Spoon™ - a hybrid digital-analogue, electrostimulation device intended to restore taste perception in people with dementia - and to identify the organisational and market conditions required for its routine use.

Methods: A ProcessOriented Holistic (PrOH) Modelling Methodology was applied across four phases:1. Userneeds assessment through three focus groups (n = 28), semistructured interviews with individuals living with dementia (n = 10), caregivers (n = 5) and healthcare professionals (n = 15).2. Iterative codesign and lab prototyping, informed by thematic analysis and smallscale electrogustometry studies (n = 15; people with dementia = 10, control = 5).3. Feasibility testing the prototype in care-home dining routines to explore practicality, user acceptance, and caregiver workload, documented through field notes, post use interviews and caregiver workload diaries.4. Regulatory and commercial pathway mapping (UKCA/CE precompliance review, 3i stakeholder analysis). Quantitative data were analysed descriptively; qualitative insights were integrated into the PrOH workflow to expose implementation pinchpoints.

Results: PrOH analysis identified three design features that underpinned acceptability - familiar spoon form, automatic activation on contact, and dishwashersafe construction - while highlighting outstanding challenges in cost control, training, and individual differences in taste sensitivity. Participants consistently reported that the Tasty Spoon™ made food 'taste stronger' and restored variety to meals they had previously found bland. Our research also highlighted the importance of co-developing ethical procedures in collaboration with people with dementia.

Conclusion: Early, smallscale evidence suggests that a sensoryfocused assistive device can complement existing cognitive and mobility technologies in dementia care by enhancing mealtime enjoyment and easing caregiver burden. Larger, rigorously controlled studies are needed to quantify nutritional and clinical outcomes and to refine personalised stimulation settings before widescale deployment.

The disparity in the data from intensive care units, where stroke victims and heart attack patients make up a minority, makes this effort extremely difficult. A well-known difficulty in data mining is handling unbalanced data. The main contribution of this work is a method that accurately identifies and categorises minority-class data, even in highly imbalanced datasets with small class sizes. This work predicts stroke from the balanced and compressed data from MIMIC III dataset. The Convolutional Neural Network-Gated Recurrent Unit with Imbalanced Data Handling (CNN-GRU-IDH) is proposed. Additionally, it reduces the amount of data transferred by compressing healthcare data using the Lempel Ziv Markov Chain Algorithm (LZMA). Class imbalance problems are addressed with the Synthetic Minority Over-sampling Technique (SMOTE). Notably, this study adds a novel element by employing the Improved Multi-Objective Wolf Pack Algorithm (IMOWPA) to choose the appropriate K nearest neighbour value for SMOTE. The suggested model surpasses existing models when used on the dataset, obtaining a remarkable accuracy rate of 87.66% and 85.63% of F1 score for 70% of training and 30% of testing data. The CNN-GRU-IDH approach, which tries to forecast the incidence of strokes, is used as the major data classification technique. This study makes a substantial advancement to improving patient-specific early stroke prediction, which might save lives and lower death rates.

Aims: This study combines bibliometric and structured analyses to comprehensively examine the development, methodological characteristics, and application trends of multimodal artificial intelligence (AI) in Alzheimer's disease (AD) diagnosis.

Materials and methods: Literature from January 1, 2017 to December 31, 2024, was retrieved from the Web of Science Core Collection. Retrospective bibliometric and visual analyses were conducted using VOSviewer, CiteSpace, and the Bibliometrix R package.

Results: A total of 234 papers were identified, showing a continuous increase in publication volume, with the United States and China as dominant contributors. The analysis focused on data modalities, fusion architectures, and clinical applications. Data trends highlight the fusion of imaging data with genetics, biomarkers, and clinical data. Methodologically, five fusion approaches were categorized, with intermediate fusion being the most widely used strategy for its ability to balance heterogeneous data integration. In application, multimodal AI demonstrated clear advantages in early diagnosis, disease classification, and progression prediction.

Conclusion: Research on multimodal AI for AD has gained global attention and remains a key direction for diagnostic innovation. By synthesizing bibliometric insights with structured analyses of modalities and fusion strategies, this study offers a systematic understanding of current progress and provides valuable guidance for future methodological and translational research.

Background: Despite being a prevalent peripheral vestibular disorder in China, Meniere's disease (MD) suffers from low awareness, frequent misdiagnosis, and unsatisfactory treatment rates. As TikTok has become a prominent source of health information, no study has systematically evaluated the quality of its MD-related content. We therefore assessed the accuracy and reliability of MD videos on Chinese TikTok.

Methods: Top 100 videos for "Meniere's disease/syndrome" (TikTok, 1 May 2025) were analyzed. Quality was assessed using Video Information and Quality Index (VIQI), Global Quality Score (GQS), modified DISCERN (mDISCERN), and Patient Education Materials Assessment Tool for Audio-Visual Content (PEMAT-A/V). Descriptive statistics, correlation analyses, and predictive modeling were applied to 83 valid videos.

Results: Among 83 videos, 91.6% (n = 76) were physician-uploaded (primarily otolaryngologists/neurologists). Monologue, Q&A, and medical scenario formats showed superior quality. Symptoms dominated content (47%). Neurologists generated significantly higher normalized engagement per second than otolaryngologists (all adj. p < 0.05, r ＞ 0.35). Physicians outperformed news agencies in GQS scores (adj. p < 0.05, r = 0.291). Otolaryngologists scored higher than both neurologists and Traditional Chinese Medicine practitioners in PEMAT-A/V Understandability (all adj. p < 0.05, r > 0.37). Attending physicians exceeded chief physicians on all quality metrics (all adj. p < 0.05, r ＞ 0.35), an advantage potentially linked to their younger age, greater digital literacy, and more frequent social media use. Engagement metrics (likes, comments, favorites, shares) correlated strongly (r > 0.8). Predictive models for PEMAT-U/A were significant (p < 0.001), lacking multicollinearity/autocorrelation.

Conclusion: Physician-created MD content ensures credibility but requires quality improvement. PEMAT-U/A models guide enhancements, though broader application needs validation. Key health informatics priorities include certified creator engagement, algorithm optimization, and innovative content design.

Background: Early diagnosis of pulmonary nodules is crucial for improving the survival rate of lung cancer patients. However, significant variability in nodule size, shape, and anatomical location presents ongoing challenges for automated detection systems, often resulting in high false-positive rates.

Objective: This study aims to develop a dual-stage pulmonary nodule detection framework based on cross-layer attention fusion, with the goal of improving sensitivity while reducing false positives in chest CT scans.

Methods: We propose a two-stage detection pipeline. In the candidate detection stage, we design an Attention-guided Spatial and Channel Residual Module that integrates multi-scale residual connections with cross-dimensional attention to enhance discriminative features while preserving spatial detail. For false positive reduction, we introduce a Multi-scale Progressive Perception Network, which processes candidates across three anatomical resolutions through parallel branches and integrates top-down semantic fusion with localized attention. The model is evaluated on the LUNA16 dataset.

Results: Experimental results demonstrate that the proposed method achieves a sensitivity of 90.0% at 0.55 false positives per scan on the LUNA16 dataset. Compared to state-of-the-art approaches, our framework provides a favorable balance between sensitivity and precision.

Conclusions: The proposed dual-stage detection framework effectively enhances the performance of pulmonary nodule detection by incorporating cross-layer attention mechanisms and multi-scale feature integration. These findings suggest its potential for clinical deployment in computer-aided lung cancer screening.