DIGITAL HEALTH最新文献

Objective: Social media influencers frequently post "What I Eat in a Day" (WIEIAD) vlogs, shaping dietary perceptions and behaviors among young audiences. There is concern that these videos contribute to negative outcomes, such as disordered eating and unhealthy social comparisons. While prior research largely focuses on the effects of short-form content (e.g., from TikTok), this study examines the impact of long-form WIEIAD videos on perceptions about the content creators and nutrition-related behaviors, and measures behavior by asking participants to select an apple or cookie after watching a WIEIAD video.

Methods: Using a 2 × 2 experimental design (N = 289), participants viewed WIEIAD YouTube videos varying in the presence and absence of sponsorship (i.e., either a service or product) and physical health benefits of the diet.

Results: Results indicate that sponsorship increased perceptions of influencer trustworthiness and attractiveness but decreased authenticity and parasocial interaction. Additionally, exposure to sponsored content increased the likelihood of selecting a cookie, supporting media priming effects in nutrition behavior. However, disclosure of physical health benefits did not influence snack choice. There was also a relationship between intentions to change diet and opting out of the snack.

Conclusions: These findings raise important considerations for digital health communication, influencer regulation, and content literacy interventions aimed at mitigating the negative health effects of social comparison and commercial nutrition messaging online.

Objective: This study explores the potential of digital technologies in reducing drug-related deaths through virtual supervised drug consumption. It assesses barriers, enablers, and strategies for adopting a remote supervision service app, Here4UScotland, fostering user engagement and ownership.

Methods: A mixed-methods evaluation was undertaken, using semi-structured interviews, focus groups, and quantitative data. Interviews and focus groups were undertaken with 26 participants. The Technology, People, Organizations, and Macro-environmental framework guided data collection and analysis, while the Transformative Technology Integration in Health conceptual model enabled analysis of the relationship between digital technology and service delivery. Qualitative data were thematically analyzed.

Results: The app was piloted in Aberdeen (Scotland) from January to December 2023. Twenty-five users received smartphones and logged 74 calls. Qualitative findings identified user concerns about privacy versus the need for real-time support, challenges in integrating new features, and the impact of police involvement on trust in digital services. The app's functionality and user engagement highlighted the need for ongoing support and improved system integration. Interviews highlighted the importance of relationships, training, and strategic outreach in successfully delivering digital harm reduction services. Technological features, such as location tracking, offer real-time support but raise privacy concerns.

Conclusion: Organizational and macroeconomic factors, including marketing, outreach, and law enforcement involvement, may impact service effectiveness and should be considered in future app implementations. Despite challenges, digital tools have enhanced accessibility and support in overdose prevention. Future research should explore cultural differences in digital adoption and improve communication strategies to maximize user engagement.

Background: General-purpose large language models (LLMs) often lack specialized knowledge for cleft lip and palate (CLP), limiting clinical utility. Accessible, accurate, and affordable AI tools could potentially supplement existing educational resources for clinicians and patients.

Objective: To develop and evaluate CLP-generative pre-trained transformer (GPT), a parameter-efficient model fine-tuned for CLP, and to assess whether a smaller specialized model can achieve performance comparable to large generalist models for clinical deployment.

Method: We built a CLP dataset of 7815 validated Q&A pairs from medical dialogs, literature, and guidelines. Using Qwen2-7B-Instruct as the base, we applied low-rank adaptation for domain adaptation. Performance was assessed via physician- and patient-oriented subjective evaluations and an objective test of 135 specialized multiple-choice questions.

Result: CLP-GPT showed significant improvements over its baseline across all dimensions (p < 0.001). In physician evaluations, its accuracy surpassed Claude-3.5-Sonnet and Gemini-1.5-Pro (p < 0.05) and showed no significant differences from models like GPT-4o (p > 0.05). In patient evaluations, CLP-GPT performed comparably to leading large models across all dimensions (p > 0.05). On the objective test, it achieved the highest accuracy (71.85%), though differences among models were not statistically significant.

Conclusion: CLP-GPT, a parameter-efficient, domain-specific model, delivers performance comparable to other LLMs in CLP. This demonstrates that cost-effective, specialized models can achieve high performance without massive computational resources.

Background: Wearables and smart devices could complement face-to-face prenatal care appointments by monitoring pregnant women's health, especially since pregnancy may be a vulnerable time when mental health issues and pregnancy-related anxiety may arise.

Aim: The aim of this study was to analyze the extent whether the willingness of women to use smart devices for pregnancy care monitoring differs between those with pregnancy-related anxiety and those without.

Methods: A survey was conducted to ascertain participant's general characteristics, attitudes toward smart devices, the willingness to use them, and the level of pregnancy-related anxiety (PRAQ-R2). Associations between the willingness and pregnancy-related anxiety parameters were analyzed.

Results: Completed questionnaires from 210 women were included in the analysis. A significant difference between women showing high and low levels of pregnancy-related anxiety was observed in terms of their willingness to use a smartwatch (median score 4.00; interquartile range (IQR) 4.00-5.00 vs. 4.00; IQR 3.00-4.00; P = .02), smart contraction counter (median score 4.00; IQR 3.00-5.00 vs. 3.00; IQR 3.00-4.00; P = .02), smart blood pressure monitor (median score 5.00; IQR 3.00-5.00 vs. 4.00; IQR 3.00-4.00; P = .003) or sleep tracker (median score 4.00; IQR 3.00-5.00 vs. 3.00; IQR 2.00-4.00; P = .007). Overall, anxious women showed significantly higher willingness to use smart devices in the context of prenatal care.

Conclusions: The data suggest that pregnant women are receptive to using smart devices to enhance their prenatal care, particularly those experiencing higher levels of anxiety. This study serves as an initial step in evaluating attitudes toward these devices. As a follow-up, it is recommended that acceptance and feasibility studies are conducted alongside the further development of existing and new pregnancy-specific wearables.

Background: At present, artificial intelligence (AI) plays a significant role in the diagnosis, treatment, and prognosis of lymphoma. This study quantitatively analyzed the research hotspots and future trends in this field by using bibliometric software. The aim is to offer researchers a full understanding of the current research along with suggestions for future directions.

Methods: All relevant articles of AI in lymphoma research were retrieved from the Web of Science Core Collection database from 2010 to 2024. Bibliometric visualization analysis of all retrieved data was conducted by using the "bibliometrix" package in R (version 4.4.1), CiteSpace (version 6.4.R1), and VOSviewer (version 1.6.20).

Results: Analysis of 662 publications shows that AI in lymphoma research is currently on rapid development. The United States and China are ahead of other countries in terms of the number of articles and citations. Jiang Huiyan from Northeastern University (China) and Michel Meignan from Assistance Publique-Hôpitaux de Paris (France) are the most prolific and highly cited authors in this field. Recent hotspots focus on molecular expression and imaging histology, while the emerging directions are molecular mechanisms and chemotherapeutic strategies in lymphoma.

Conclusions: This study comprehensively analyzes the hotspots and trends of AI research in lymphoma, which is shifting from radiomics toward molecular mechanisms and AI-optimized chemotherapy. In the future, it is necessary to meet the practical clinical demands and promote the integration of AI.

Background: Digital health interventions for self-management have reported improvement in medication adherence, but further studies have reported that this is not sustained. Clarity is needed on the effectiveness of digital health interventions (DHIs) for medication adherence and the characteristics of successful DHIs.

Objective: This review aimed to evaluate the effectiveness of DHIs versus standard care in supporting medication adherence in community-based adults.

Methods: Studies of DHIs that included community-based adults (18 + years) that required prescribed medications and either used, or have access to, technology to manage their illness were included for review. A literature search was conducted across eight key databases. Studies were pooled in statistical meta-analysis using a random effects model. Pooled results were estimated by subgroup analyses defined by study design, intervention duration and intervention type (platform).

Results: Thirteen studies were included in the final meta-analysis (N = 1320). There was no statistically significant effect of DHIs on medication adherence. Subgroup analyses showed no increase in medication adherence observed in the intervention group. However, a small significant increase in medication adherence was observed in the control group in studies of 6-month duration.

Conclusions: The impact of DHIs on medication adherence is unclear. More rigorous clinical evidence is required to understand the impact of DHIs on medication adherence over longer durations, and further studies exploring the impact of platform type on medication adherence are required, with consideration given to the potential of Artificial Intelligence (AI)-driven diagnostics and analytics in the future.

Objectives: While internet-based interventions have been used for more than 10 years in Dutch forensic psychiatric outpatient care, it is not clear if, why and for whom they work. The objective of this study was to investigate whether the addition of an internet-based intervention to forensic psychiatric outpatient treatment as usual (TAU) resulted in better treatment outcomes in regulatory emotional self-efficacy, treatment readiness and aggression.

Methods: A multicentre mixed-methods pilot randomized controlled trial was used to compare TAU to TAU with the addition of the internet-based intervention 'Dealing with Aggression'. Thirty forensic psychiatric outpatients, treated in four Dutch organizations for aggression regulation problems, were included. Three validated questionnaires were administered at baseline, mid-treatment, post-treatment and follow-up. Semi-structured interviews with patients and therapists from the experimental condition were conducted to gain in-depth insight into the quantitative results.

Results: No differences between groups on the outcome measures were found. Often-mentioned reasons for ineffectiveness were suboptimal integration in treatment and the requirement of cognitive and reflective skills. However, several patients reported having experienced benefits from the intervention, mostly because treatment was repeated, combined with new insights in their behaviour.

Conclusions: Although the trial was underpowered due to issues with recruitment, no proof for effectiveness of the internet-based intervention was found. Moreover, mostly highly motivated and relative cognitive skilled patients participated, indicating that internet-based interventions might fit only a select number of patients. New insights into why and for whom internet-based interventions might work were identified, providing points of improvement for intervention design and implementation in practice.

Objective: This study was designed to systematically evaluate the reliability and quality of content related to sleep disorders on four leading Chinese short-form video platforms (TikTok, Bilibili, Kwai, and Xiaohongshu) to inform strategies to improve the dissemination of accurate health information in China.

Methods: A cross-sectional analysis was conducted in March 2025, in which 400 short videos related to sleep disorders were identified and included. These videos were published between 2022 and 2025 on four major short-form video platforms. Video quality was assessed using the Global Quality Scale (GQS), JAMA benchmark criteria (JAMA), and a modified version of the DISCERN tool. Influencing factors were examined using Spearman correlation analysis and Poisson regression, with all multiple comparisons adjusted using the Bonferroni correction.

Results: Video popularity metrics and quality scores (GQS, JAMA, modified DISCERN) were significantly higher on TikTok than on other platforms (Adjusted P < .001). The quality of videos produced by physicians (64%) was better than that of laypersons (Adjusted P < .005), with the quality of videos produced by Traditional Chinese Medicine (TCM) practitioners significantly higher than that of Clinical medicine practitioners (Adjusted P < .05). The highest quality scores were found for videos presented as expert commentary (52%). After Bonferroni correction, quality differences among specific "disease knowledge" categories were no longer statistically significant (Adjusted P > .05). Although user interaction metrics showed a statistically significant correlation with quality scores (Adjusted P < .001), the Spearman correlation coefficients were weak to moderate (r = .18-.33), indicating the practical association was limited.

Conclusions: Information on sleep disorders on Chinese short-video platforms is of low quality. These findings are specific to the Chinese digital ecosystem and may not be generalizable to global platforms. Content posted by medical professionals (especially TCM practitioners) was associated with higher reliability scores, suggesting a potential role for professional oversight in improving the quality of sleep disorder information on short video platforms.

Background: Informal caregivers of older adults with serious health conditions, particularly stroke, dementia, and fractures, often experience substantial caregiving stress. Online platforms may offer an accessible and effective means of supporting caregivers and reducing stress.

Objective: This study aimed to evaluate the effectiveness of a newly developed online platform in reducing caregiver stress and enhancing caregiver preparedness and competence. It also examined factors associated with platform use, as well as user satisfaction and engagement.

Methods: A longitudinal open-trial design was employed, with assessments conducted at baseline, 3-month follow-up, and 15-month follow-up. Primary outcomes included caregiver stress, preparedness, and competence. User profiles were examined by comparing characteristics between users and non-users of the platform. User satisfaction and engagement were also assessed.

Results: Of the 287 caregivers, 61% used the online platform. Compared with non-users, users showed a significant reduction in caregiving stress and greater improvements in caregiver competence, while no significant difference was found in caregiver preparedness. Users were more likely to be younger, employed full-time, have upper secondary education or above, report a monthly household income of HK$25,000 or higher, and have no chronic illness. At the 15-month follow-up, users reported high levels of satisfaction with the platform.

Conclusions: The online platform effectively reduced caregiver stress and improved caregiver competence, with high user satisfaction. Further enhancements, such as increased multimedia content on practical caregiving preparation, may be needed to improve caregiver preparedness outcomes.