DIGITAL HEALTH最新文献

Background: Cervical cancer remains a serious global threat to women's health, with rising incidence and younger demographic impact, challenging reproductive health. Short-video platforms have become key public sources for health information due to digital health communication advances, yet the scientific accuracy and reliability of their cervical cancer content are widely questioned. A systematic evaluation of its quality and dissemination patterns is lacking.

Objective: This cross-sectional study assessed cervical cancer-related videos on YouTube, TikTok, and Bilibili, examining content breadth, information quality, and dissemination impact.

Methods: Videos were systematically retrieved in July 2025 using "cervical cancer" keywords across the three platforms. After applying inclusion/exclusion criteria, 201 videos were analyzed. Quality, reliability, and educational value were evaluated using the Global Quality Score (GQS), modified DISCERN, Patient Education Materials Assessment Tool (PEMAT-assessing understandability and actionability), and Journal of the American Medical Association (JAMA) benchmark criteria. Platform differences were compared using the Kruskal-Wallis H test (significance p < 0.05).

Results: Platform differences emerged: YouTube videos demonstrated the highest quality (GQS mean 3.47 ± 1.06 vs. Bilibili 2.85 ± 0.89, TikTok 3.09 ± 0.75; p = 0.001) and significantly higher PEMAT understandability (76.94 ± 10.43 vs. TikTok 70.14 ± 11.07; p < 0.001). TikTok had the strongest dissemination power. Content coverage was inadequate: only 50.2% mentioned screening, 33.3% covered human papillomavirus vaccination, and a mere 8.0% recommended male vaccination. Creator expertise significantly influenced outcomes: Professionals (doctors/researchers) had higher JAMA authority scores and PEMAT actionability. Patient-created videos generated the highest interaction but scored lowest on quality metrics.

Conclusion: Cervical cancer information quality on short-video platforms is uneven. YouTube offers the highest overall quality, while TikTok achieves the widest reach but lacks content depth. Critical prevention information (e.g. male vaccination) has low coverage. Professional creators provide more reliable content but have limited reach. Platforms should enhance promotion of authoritative content and implement quality review mechanisms.

Objective: Adolescence is a critical developmental stage during which mental health vulnerabilities often emerge. Traditional self-report methods are insufficient to capture the complexity of emotional and physiological responses, underscoring the need for data-driven, personalized mental health strategies. This study aimed to develop and validate a structured multimodal data collection system for adolescents to support the future advancement of precision mental health care.

Methods: This study was conducted as the baseline phase of a longitudinal panel study designed to construct and validate a structured multimodal dataset for adolescent mental health research. A total of 74 adolescents aged 11-15 years from schools and community facilities in Korea was selected through convenience sampling. Multimodal data were collected by integrating six data types: self-reported surveys, electroencephalography (EEG), heart rate variability (HRV), genotyping, microbiome data, and video-based psychological counseling. Data collection was standardized through a three-phase protocol (pre-, on-site, and post-assessment), and participant privacy was protected via pseudonymization based on international standards. Variables were systematically labeled and structured to enable cross-modality analysis. Statistical analyses, including correlation and descriptive statistics, were performed to examine preliminary relationships across modalities.

Results: The study successfully constructed a comprehensive dataset encompassing biological and psychosocial indicators from 74 adolescents. Preliminary analysis revealed statistically significant associations between survey-based BMI and both genomic data (ρ = 0.30, p < 0.01) and microbiome-based obesity indicators (ρ = 0.27, p < 0.05), whereas other psychological constructs (e.g., stress, resilience) showed non-significant cross-modal correlations.

Conclusions: This study presents a replicable framework for collecting rich, multimodal data from adolescents in real-world settings. By enabling integrative analysis of biological and psychosocial variables, the dataset lays the groundwork for personalized mental health prediction and intervention strategies. Future research should expand longitudinally and optimize context alignment to improve predictive precision and clinical utility.

Objectives: This study aimed to systematically characterize the landscape of artificial intelligence (AI) applications in gynecologic cancers, offering a comprehensive overview of current research trends, influential publications, key contributors, and future research directions. The focus of this study was to provide a quantitative overview of the field's development and trends.

Materials and methods: A structured search was performed in the Web of Science Core Collection to identify original articles on AI use in gynecologic oncology. Two independent reviewers screened and selected studies based on predefined inclusion criteria. Extracted data-including publication trends, author and institutional collaborations, keyword co-occurrence, and citation networks-were analyzed using CiteSpace 6.2.R6 and VOSviewer software.

Results: A total of 2544 articles were included for analysis. Research activity showed a notable acceleration after 2019, reaching its highest output in 2024. China and the United States emerged as dominant contributors, with the Chinese Academy of Sciences and Fudan University leading among institutions. Influential authors such as Sala Evis, Tian Jie, and Scambia Giovanni were identified. Major research themes focused on "Radiomics," "Deep Learning," "Radiotherapy," and cancers including cervical, ovarian, and endometrial. Recent emerging topics included "Digital Pathology," "Personalized Medicine," and "Tumor Heterogeneity," signaling a shift toward precision oncology.

Conclusions: This bibliometric study delineated the evolving field of AI in gynecologic oncology, highlighting dynamic research fronts and gaps.

Background: The social compensation hypothesis posits that computer-mediated communication can offset psychosocial vulnerabilities among users who face barriers to face-to-face interaction, thereby enhancing well-being. Yet, there is no validated instrument to assess which design features of digital systems enable such compensation.

Objective: To develop and validate a Social Compensation Design Scale (SCDS) for urban older adults living alone, situated within smart-home social media as part of home-based, health-enabling environments.

Methods: We conducted a three-phase study from an information systems design perspective: item generation and expert review via a Delphi process, followed by two questionnaire surveys. Valid responses were obtained from 340 and 357 urban older adults, respectively. Psychometric analyses (reliability and validity testing) were conducted across two independent samples.

Results: The SCDS comprises four dimensions-User Interface Quality, Interaction Quality, Content Quality, and Service Quality-with 16 items overall. Across samples, the scale demonstrated strong internal consistency and construct validity.

Conclusions: The SCDS offers a concise, user-centred measure for evaluating how smart-home social media design supports psychosocial well-being in older adults aging in place. The scale provides researchers and designers with a structured toolkit for assessing user experience in health-related home environments and for informing design decisions that promote acceptance and sustained use of digital health applications among older populations.

Objectives: Electronic medical records (EMRs) are increasingly recognized as a contributing factor to patient safety incidents. Clinicians' experiences can reveal EMR-related risks that may otherwise go unnoticed. This study explores EMR-related patient safety incidents reported by physicians across diverse care settings, institutions, and EMR products.

Methods: A national sample of Swiss physicians was surveyed online and asked whether they had experienced a patient safety incident related to EMR use within the previous four weeks. Free-text descriptions of incidents were analyzed thematically using a structured, multi-step procedure.

Results: Of the 1933 inpatient and outpatient physicians who completed the survey, 23.9% (n = 398) reported experiencing an EMR-related safety incident in the previous four weeks. Half of these incidents (49.7%) had not been formally reported (e.g. through critical incident reporting or IT channels). A total of 385 incident descriptions were analyzed, revealing seven emergent themes: (1) patient identification and selection errors (16.7%), (2) system reliability and performance issues (15.8%), (3) interoperability and system integration (8.8%), (4) usability, interface, and design problems (21.8%), (5) system errors and unexpected behavior (8.8%), (6) security and access control (2.6%), and (7) problems with order entry, decision support, alerting, and verification (25.2%). There were considerable differences in the patterns of events reported in relation to the used EMR system.

Conclusions: Physicians reported a broad range of EMR-related safety problems, particularly related to ordering functionalities and usability, many of which were not formally recorded. In addition to broader socio-technical strategies, such as user training, incident reporting, and alignment with clinical workflows, systematically incorporating clinicians' experiences into EMR design is required to guide advancements in patient safety.

Objective: Depression represents a significant global health challenge, further complicated by the multifaceted and complex nature of its diagnosis and treatment. This study explores the application of multiple feature selection (FS) methodologies combined with XAI (explainable artificial intelligence) method named SHapley Additive exPlanations (SHAP) to enhance predictive accuracy in depression classification models using large-scale national survey data.

Methods: Leveraging microdata from the National Mental Health Survey of Korea (2021), encompassing 5511 Korean adults, this research systematically evaluates how different FS-machine learning classifier combinations affect model performance and identifies nondiagnostic socioeconomic, psychological, and lifestyle factors associated with clinically diagnosed depression. By employing diverse FS methods (e.g., ReliefF, Markov Blanket, and Information Gain) across multiple machine learning classifiers, we systematically compare their performance across 12 classifiers.

Results: We demonstrate that optimal FS method selection depends on machine learning classifier architecture, with ReliefF excelling in Stacking (F2-score =0.9851) and Markov Blanket performing best in ExtraTrees and LightGBM (F2-score =0.9848, 0.9838). After excluding core diagnostic criteria variables to avoid circularity, our analysis reveals that social distress (loneliness), reluctance to seek professional help, quality of life measures, and physical health comorbidities emerge as highly influential nondiagnostic predictors.

Conclusion: Our findings advance the field by: (1) systematically demonstrating that FS method effectiveness varies by machine learning classifier type, (2) providing a dual-layer XAI framework combining FS with SHAP for comprehensive interpretability, and (3) identifying culturally specific risk factors in an underrepresented Asian population using high-quality face-to-face collected data. These contributions provide methodological guidance for researchers developing interpretable depression prediction models and offer clinically actionable insights for identifying at-risk individuals in Korean populations.

Digital technologies, such as mHealth interventions and integrated data management tools, are increasingly being developed and implemented to support patients and health care providers in low-resource, high tuberculosis (TB)-burden countries in initiating and proceeding through the TB care cascade (e.g., screening, testing, diagnosis, treatment). Yet, given the proliferation of these tools, there exists a need to synthesize what technologies are being used and where, as well as build a comprehensive understanding of their respective functionality and implementation considerations. The objectives of this systematic scoping review were: (1) to systematically identify literature on digital technologies for supporting the TB cascade in high TB-burden countries; and (2) to describe the facilitators and barriers to technology implementation. Four databases were systematically searched for published literature using a search hedge of terms related to TB, technology, and implementation. Two independent reviewers conducted screening of retrieved literature, data extraction, and data analysis. Eighteen digital technologies were identified, with 10 classified as backbone technologies and eight as add-in technologies. Three key implementation domains were identified: (1) Interoperability and Integration, (2) Digital Infrastructure, and (3) User Experience. Backbone technologies showed higher integration rates with National TB Programs and were more likely to be sustainably implemented. Key barriers to technology implementation included connectivity issues, inadequate user training, and complex multistakeholder integration processes. Included sources described how implementation success was influenced by the interplay between systems-level, technology-level, and user-level factors. Future research should prioritize implementation science approaches to facilitate technology adoption and use to support the TB care cascade.

Objective: Person-centred communication in memory clinics is essential, but often not optimal. This study aimed to develop a solution that supports people with cognitive complaints in expressing their needs and preferences during memory clinic consultations.

Methods: Following a human-centred design approach, co-researchers (n = 4 people with dementia) identified a problem statement. This problem was confirmed and elaborated upon via a questionnaire (n = 25) and focus group (n = 18) for triangulation purposes, and in co-design sessions with people with cognitive complaints (n = 3), care partners (n = 2), and clinicians (n = 3). These sessions informed prototype development in collaboration with a design agency. Usability and User eXperience (UX) testing were conducted with people with cognitive complaints (n = 30), care partners (n = 4), and clinicians (n = 17) via think-aloud sessions, interviews, questionnaires, and focus groups.

Results: Co-researchers emphasized the importance of clinicians gaining a holistic understanding of someone's life and circumstances, which was confirmed in the 'triangulation' questionnaire, focus group, and co-design sessions. Co-design resulted in a digital and analogue prototype of 'Helder in Gesprek' ('Clear in Conversation'), a tool to assist people with cognitive complaints in reflecting on what they wish to share with their clinician and facilitate communication during consultations. Usability testing revealed a generally positive attitude toward the prototypes, while also identifying areas for improvement, such as navigation, system feedback, understandability, distinguishable elements, and cognitive overload.

Conclusion: Our human-centred design approach informed the design and development of two prototypes of 'Helder in Gesprek'. Usability and UX testing provide directions for re-design and feasibility testing in a real-world setting.

Background: While the link between internet use and depressive symptoms in older adults is studied, research often overlooks the interdependent nature of couples. This study examines the longitudinal actor and partner effects of internet use on depressive symptoms among older couples, testing social participation as a key mediating mechanism.

Methods: Using a multistage, stratified probability sampling method, data were drawn from 4878 heterosexual married couples participating in the 2013, 2015, and 2018 waves of the China Health and Retirement Longitudinal Study. A longitudinal dyadic analysis was conducted using structural equation modeling to test an Actor-Partner Interdependence Mediation Model.

Results: For both husbands and wives, their own internet use was associated with lower depressive symptoms, a relationship fully mediated by their own increased social participation (actor-actor effects). Crucially, significant asymmetric partner effects emerged. A husband's internet use was associated with a substantial reduction in his wife's depressive symptoms (β = -0.959, p = .039), indicating a practically meaningful protective effect. This benefit operated both directly and indirectly by increasing the wife's social participation (β = -0.072, p = .026). However, a wife's internet use had no significant effect on her husband's depression.

Conclusions: The mental health benefits of digital engagement extend beyond the individual user to their spouse, operating through enhanced social participation. These findings underscore the importance of dyadic, gender-sensitive approaches when developing interventions to promote digital literacy and social engagement to improve well-being in later life.

Objective: To develop a clinical nursing decision support system for pressure injury and explore its application in managing pressure injury in postoperative cardiac surgery patients.

Methods: A multidisciplinary research team was formed to develop a clinical nursing decision support system. Key indicators, including wound assessment accuracy, wound treatment accuracy, pressure injury healing rate, pressure injury incidence, and defect rates in nursing records, were compared before and after the clinical nursing decision support system utilization. Count data were described using frequency and composition ratio (%), and comparisons were made using the chi-square test or Fisher's exact probability method. Measurement data following a normal distribution were described by mean and standard deviation, while non-normally distributed data were described by median and interquartile range. Independent sample t-tests and rank-sum tests were used for between-group comparisons. A significance level of α = 0.05 was set, with results considered statistically significant if P < 0.05.

Results: The clinical nursing decision support system implements an intelligent decision-making engine and interactive dashboard for human-computer interaction, enabling intelligent assessment and decision-making, re-evaluation reminders, interactive modules, intelligent auditing, and a three-level quality control system for pressure injury. After applying the clinical nursing decision support system, the pressure injury incidence in postoperative cardiac surgery patients decreased from 14.8% to 12.8%, with no statistically significant difference (P > 0.05). The pressure injury healing rate increased from 89.1% to 97.2%, wound assessment accuracy improved from 90.8% to 97.2%, and wound treatment accuracy increased from 88.3% to 96.5%. The defect rate in nursing records decreased from 15.3% to 7.7%, with all differences being statistically significant (P < 0.05).

Conclusion: This study successfully developed and implemented a clinical nursing decision support system for pressure injury management in postoperative cardiac surgery patients. These results confirm the system's clinical utility in standardizing pressure injury care, optimizing nursing workflows, and elevating documentation quality. The clinical nursing decision support system provides an effective tool for enabling evidence-based, personalized interventions and strengthening closed-loop quality control in pressure injury management.