DIGITAL HEALTH最新文献

Objective: Estimating the diverse symptoms of patients with advanced cancer is helpful for young physicians and medical teams in planning appropriate palliative care. We evaluated the use of medication, comorbidities, laboratory test results, and vital signs in hospitalized patients to predict death within 14 days.

Methods: We retrospectively selected hospitalized patients with advanced cancer who were admitted to the hospice ward. We are using extreme gradient boosting (XGBoost) and a combination of random forest (RF) and XGBoost (RF-XGBoost) models to analyze sixteen comorbidities, eighteen types of medications, twenty-six laboratory tests, and six vital signs. Finally, SHapley Additive exPlanations (SHAP) analysis was employed to interpret the contribution of each feature to survival prediction.

Results: Among the 2276 patients, 73% survived less than 14 days. The Area under the curve (AUC) of the XGBoost and RF-XGBoost models was 0.82 and 0.81 (P < 0.001), respectively. Among the top 10 most important feature values of both machine learning models after SHAP analysis, seven were related to medication use, whereas three were related to laboratory tests. The top three ranked feature values were stool softeners, antiemetics and sedatives. Patients who received these medications generally had a strong positive correlation with survival beyond 14 days.

Conclusions: Our results suggest that the types of medications used by patients, especially stool softeners, antiemetics, and sedatives, are valuable in predicting survival beyond 14 days for hospitalized patients with advanced cancer. This result may assist young physicians and medical teams in developing appropriate palliative care plans for patients and their families.

Background: Mental health challenges are common among older adults with mild cognitive impairment. Despite growing use of digital health interventions to improve cognitive function, their effects on mental health remain unexplored.

Objective: To assess the overall and subgroup-specific effectiveness of digital health interventions on mental health in older adults with mild cognitive impairment.

Methods: A systematic review and meta-analysis of randomized controlled trials was conducted following PRISMA guidelines, searching seven databases from inception to March 2024. Evidence quality was assessed using the GRADE framework and risk of bias with the Cochrane Collaboration's tool. Interrater agreement for screening and data extraction was assessed using the Kappa coefficient. Subgroup analyses assessed differences based on intervention characteristics such as type, setting, and duration, while meta-regression and sensitivity analysis identified other sources of heterogeneity and tested robustness.

Results: Eleven studies involving 610 participants met the criteria. Digital health interventions significantly reduced depressive symptoms (Standardized Mean Difference [SMD] -0.55, 95% CI -0.92 to -0.19) and anxiety symptoms (SMD -0.47, -0.76 to -0.18), but showed no significant effects on positive (SMD 0.74, -0.46 to 1.94) or negative affect (SMD -0.23, -0.60 to 0.14). Subgroup analyses indicated that hospital or nursing home settings with non-portable modality were optimal. Interventions over 6 weeks, with sessions exceeding 30 min up to 2 per week, were more effective for depressive symptoms. Among intervention types, only robot interventions reduced depressive symptoms. Fully digital interventions showed greater effectiveness than hybrid formats and yielded more favorable outcomes compared to controls. Overall, digital health interventions showed a significant benefit over usual care, while effects compared to waitlist controls were larger but not statistically significant.

Conclusions: This review indicates that digital health interventions hold promise for enhancing mental health in older adults with mild cognitive impairment. Future research should integrate digital therapeutic technologies to optimize interventions.

Objective: To address the critical data scarcity and privacy constraints that limit video-based motor behaviour assessment in clinical settings through a synthetic data generation framework, enabling robust human detection with high fidelity across challenging scenarios.

Methods: We employed synthetic data generation tailored to specific environments, implementing a novel synthetic retargeting approach based on procedural image synthesis. This method addresses the critical obstacles of limited training data in clinical settings due to privacy concerns, constrained views, occlusions, and uncontrolled environmental characteristics.

Results: Our synthetic retargeting approach yielded substantial and statistically significant performance improvements in human detection under real-world clinical data regimes. Evaluated across two clinical scenarios, the method improved existing models' performance (human detection score) by up to 19.4% in the more challenging scenario and up to 9.8% in the less challenging scenario (both with p < 0.001), demonstrating both high fidelity and robustness against challenging environments.

Conclusion: Synthetic retargeting provides an efficient and effective solution for adapting pre-trained human detection models to specific clinical deployment scenarios by generating scenario-tailored synthetic data, circumventing the privacy and logistical constraints that limit real data collection in healthcare settings. This approach enables robust video-based motor behaviour quantification with significant implications for both clinical management and research.

Objectives: This systematic review aims to evaluate studies that implemented and evaluated machine learning models in emergency department settings, focusing on their clinical and operational impact.

Methods: A comprehensive search was conducted across multiple databases from inception to January 2024. Studies were eligible if they assessed the implementation of machine learning models in emergency departments, with a particular focus on clinical and operational impact.

Results: A total of 84 studies met the inclusion criteria. Gradient boosting and neural networks were the most frequently used models. Mortality prediction models achieved AUC values ranging from 0.618 to 0.978, with key predictors including age, sex, race, vital signs, and comorbidities. Disposition prediction models showed AUC values of 0.675-0.96, often incorporating age, sex, vital signs, triage data, and past medical history. Length of stay prediction studies identified demographic data, triage level, chief complaints, and comorbidities as significant predictors, with gradient boosting models yielding the highest predictive accuracy. Machine learning-based treatment decision models showed promise in sepsis detection and cardiovascular triage. Wait time prediction models using gradient boosting decreased patient wait times by 18%-26%. Emergency department cost prediction studies were limited, with logistic regression models achieving AUCs of 0.71-0.76 for identifying high-cost patients.

Conclusion: Machine learning is widely used in emergency department research, but issues with generalizability and workflow integration limit its clinical use. Future work should improve data quality, representation, and ongoing model validation to enhance real-world utility.

Background: Digital health interventions are transforming hypertension management, yet the evolution and focus of this research domain remain underexplored. This study provides a comprehensive bibliometric analysis of global research trends and hotspots in digital health for hypertension management.

Methods: Relevant publications were retrieved from the Web of Science Core Collection. Bibliometric and visualization analyses were conducted using VOSviewer, CiteSpace, and R-Bibliometrix, covering the period from 1992 to 2025.

Results: A total of 1368 English-language articles, authored by 8918 researchers from 5268 institutions in over 100 countries/regions, were identified. These articles appeared in 435 journals, with publication output showing rapid growth since 2011 and peaking in 2022. The United States led in both productivity and international collaboration, with Duke University, the University of California System, and Harvard University as the top institutions. Bosworth HB emerged as the most prolific and influential author, while JMIR mHealth and uHealth and the Journal of Medical Internet Research were the leading journals. Keyword co-occurrence analysis revealed five major research clusters: (1) Digital interventions and patient management; (2) Population health and lifestyle factors; (3) Clinical practice, guidelines, and measurement; (4) Disease burden, outcomes, and epidemiology; and (5) Health equity, access, and technology utilization. Evidence suggests that digital health interventions improved patient self-management, medication adherence, and blood pressure control, highlighting their potential for better clinical outcomes. Recent burst keywords such as "burden," "telehealth," "meta-analysis," and "United States" indicate shifting research priorities toward implementation, health equity, and real-world impact.

Conclusion: This study identified rapid growth and diversification in digital health research for hypertension, with the United States, leading academic institutions, and journals such as JMIR mHealth and uHealth at the forefront. Five major research clusters were revealed, spanning digital interventions, clinical practices, lifestyle factors, disease burden, and health equity. Recent trends show increased focus on telehealth, implementation challenges, and equity of access. Future research should further integrate digital health solutions into routine hypertension care, address disparities, and systematically evaluate their real-world impact.

Background: Large language models (LLMs) are increasingly used in healthcare for patient education and clinical decision support. However, systematic benchmarking in real-world clinical contexts remains limited, particularly for high-risk conditions such as hip fractures.

Objective: To evaluate and compare the performance of three state-of-the-art LLMs-DeepSeek-V3-FW, Gemini 2.0 Flash, and ChatGPT-4.5-in answering standardized patient questions on hip fracture management.

Methods: Thirty standardized questions covering general knowledge, diagnosis, treatment, and rehabilitation were developed by three specialists in orthopedics and traumatology. Each LLM generated responses independently. Three experienced orthopedic surgeons assessed accuracy (4-point scale) and comprehensiveness (5-point scale). Statistical analyses included Kruskal-Wallis and chi-squared tests.

Results: All models demonstrated high reliability, with 96.7% of responses rated "Good" or "Excellent" and none rated "Poor." Mean accuracy scores were comparable across models, and comprehensiveness averaged 4.8/5. DeepSeek-V3-FW tended to provide longer, structured answers and performed best in general knowledge, while Gemini 2.0 Flash excelled in diagnosis and rehabilitation and produced the most concise responses. ChatGPT-4.5 offered shorter, conversational answers with similar accuracy and detail.

Conclusions: The three LLMs showed strong capabilities in delivering accurate and comprehensive information on hip fracture care, highlighting their potential as tools for patient education and clinical support. Differences in style and domain-specific strengths suggest complementary roles. Further research is needed to validate safety and integration into clinical workflows.

Objective: To systematically evaluate the effect of digital intervention on improving routine vaccination in the elderly and to conduct a comparative analysis of different intervention modalities using network meta-analysis (NMA).

Methods: PubMed, Web of Science, The Cochrane Library, Embase, Scopus, CINAHL, and WanFang Data were searched for randomized controlled trials (RCTs) using digital interventions to promote vaccination in older populations from inception to 15 June 2024. We performed a final update of the literature search in May 2025; no additional eligible studies were identified. Two researchers independently screened the literature, extracted data, and assessed the risk of bias in the included studies, and an NMA was performed using RevMan 5.4 and R Studio, PROSPERO Registration Number: CRD42024527483.

Results: Eleven RCTs were included. The traditional meta-analysis demonstrated a small but statistically significant increase in influenza vaccination rates (RR = 1.01, 95% CI [1.01, 1.01], P < 0.00001), accompanied by substantial heterogeneity (I ² = 86%). Pneumococcal vaccine uptake was significantly enhanced (RR = 1.11, 95% CI [1.03, 1.18], P < 0.01), with moderate heterogeneity (I ² = 46%). The single study on the herpes zoster vaccine reported a statistically significant effect, whereas COVID-19 vaccine reminder interventions showed no significant efficacy. In the NMA, video-based interventions ranked first based on the surface under the cumulative ranking curve, but all pairwise comparisons between different intervention modes crossed the null value.

Conclusion: Digital interventions show a significant, yet highly heterogeneous, positive impact on vaccination rates in older adults. While video-based education showed the highest ranking probability, the current evidence is insufficient to conclude that any specific digital modality is statistically superior to others. Due to the limited included studies, the findings need to be supplemented by more high-quality studies. Future research should focus on newer digital technologies to help the older population keep up with the "digital intelligence era."

Objectives: Visually-induced motion sickness (VIMS) remains an unsolved issue when using head-mounted displays (HMD) in immersive virtual reality (VR) systems. We investigate the influence of restricted visual field on sensorimotor incongruence of postural control in an immersive VR environment, as a surrogate marker of VIMS while standing steadily and viewing ambiguous optical-flow visual stimuli.

Methods: Twenty-seven healthy participants wore HMD and viewed optical flow visual stimuli (called the plaid motion pattern) using a VR system. Visual stimuli were presented on a full-screen display, either only in the center or periphery of the visual field, or nowhere (Natural). To evaluate standing posture stability, we measured spatial (length and area of the center of the pressure (COP)) and temporal dynamics of body sway using a stabilometer. Subjective feelings of VIMS were assessed for each visual stimulus condition.

Results: The full-screen condition significantly worsened the COP measurements and feelings. The COP areas of standing balance under the center of the visual field condition were significantly smaller than those under the full-screen condition; however, the periphery of the visual field condition was comparable to the full-screen condition. The condition effects for the conditions (natural, center of the visual field, periphery of the visual field and full-screen) were observed in the COP measurements and subjective feelings.

Conclusion: Optical-flow visual stimuli can induce body sway, suggesting sensorimotor incongruence in postural control. Ancillary findings demonstrated that subsequent to full-screen presentation, the periphery of the visual field potentially contributes to spatial and temporal dynamics of body sway and the sensorimotor incongruence rather than the central area.

Introduction: Frailty is defined as a clinically recognised state of increased vulnerability, reflecting a decline in an individual's psychological and physical reserves. Digitally-enabled interventions are increasingly utilised to monitor and support the health of older adults. Evidence on the effectiveness of digitally-enabled interventions in reducing frailty is limited. This systematic review aimed to investigate the types of digitally-enabled interventions tested, with what goals with respect to frailty, and the resulting outcomes.

Method: Medline, CINAHL, Scopus, PsychInfo and Embase were searched from time of origin until July 2024. Peer-reviewed RCTs assessing the impact of digitally-enabled interventions on older adults were included. Outcome measures explored were frailty, cognitive status, mental health, quality of life, adherence and usability. Data was extracted independently by two people using Covidence platform. Narrative synthesis was performed for all studies and meta-analysis was performed for outcomes reported in four or more studies.

Results: From 4476 titles and abstracts screened, 17 studies were included following full text review. Overall, 12 studies included exercises as a component or the sole form of intervention. The mean duration of intervention was 4.04(SD2.56) months. Mean adherence to the intervention was 59% which was lower in exercise-based intervention. The most and least reported frailty-specific outcome was walking speed (n = 8) and self-reported exhaustion level (n = 2). Meta-analysis showed non-exercise-based interventions showed significant improvements in SPPB. There was no statistically significant change in Timed-up and Go and handgrip strength. Narrative synthesis indicates there was insufficient evidence to evaluate the impact of digital interventions on frailty, frailty-specific outcomes, mental health, activities of daily living, health-related quality of life, sleep and cognition.

Conclusion: The findings suggest low technological readiness and adherence among digitally-enabled interventions for older adults. Narrative synthesis of overall frailty and outcome measures showed mixed results and limited evidence on the impact of digital interventions on frailty and outcomes.

Background: As part of the global digital health transformation and Saudi Vision 2030 priorities in chronic disease prevention, understanding public acceptance and preferences for telenutrition services is crucial for developing effective digital nutrition care platforms.

Objectives: This study aimed to adapt, translate, and validate a U.S.-based national telemedicine survey for the Saudi context, to assess adults' prior use, comfort, willingness, and preferences regarding direct-to-consumer (DTC) telenutrition services.

Methods: A cross-sectional survey was completed by 930 Saudi adults recruited from major urban cities. The survey instrument was adapted, translated, and validated using standard procedures, including internal consistency reliability using Cronbach's alpha coefficient. Descriptive statistics, chi-square tests, binary logistic regression, and two-step cluster analysis.

Results: Among 930 respondents, 61.5% expressed comfort and 54.9% reported willingness to use telenutrition services, increasing to 78.5% when referring to family members. Key predictors of willingness included previous experience with virtual care (OR = 1.52, p < 0.001), comfort with video consultations (OR = 1.39, p < 0.001), and access to personal health records (OR = 1.20, p < 0.041). Willingness was significantly higher among younger adults (18-29 years), employed individuals, and residents of Jeddah. Cluster analysis revealed three segments: digitally engaged and receptive, selective and independent, and supportive but personally hesitant-each characterized by differing levels of comfort and willingness to use DTC telenutrition services. Notably, only 1.3% could identify a telenutrition platform in Saudi Arabia.

Conclusions: These findings underscore the need for culturally tailored, digitally inclusive telenutrition platforms that address sociodemographic differences. Investing in digital health literacy and infrastructure is essential for empowering nutrition care delivery in Saudi Arabia.