DIGITAL HEALTH最新文献

{"title":"Effect of AI intervention programs for older adults on the quality of life: A systematic review and meta-analysis of randomized controlled trials.","authors":"Kawoun Seo, Taejeong Jang, Jisu Seo","doi":"10.1177/20552076251324014","DOIUrl":"10.1177/20552076251324014","url":null,"abstract":"<p><strong>Background: </strong>The extension of life expectancy due to medical advancements has resulted in global aging and increased social costs for elder care. Additionally, stringent health measures related to infectious disease pandemics have adversely affected the quality of life for older adults. This study investigates AI-based interventions to address quality of life issues.</p><p><strong>Objective: </strong>This study systematically examines AI interventions for the older adults, focusing on randomized experimental studies, and aims to provide guidelines for future intervention programs through meta-analysis.</p><p><strong>Method: </strong>A comprehensive meta-analysis that examines the impact of various AI interventions on the overall quality of life experienced by older adults has been conducted, encompassing thirteen randomized controlled trials.</p><p><strong>Results: </strong>The overall effect size of AI intervention programs on the quality of life in the older adults, assessed using the random-effects model, was found to be small (Hedges' g = 0.30, 95% CI = 0.10-0.51). Additionally, the effect size of quality of life was examined based on the subfactors of the AI intervention program, revealing a range of 5-11 weeks. Robot intervention exhibited a higher effect size than smart device intervention.</p><p><strong>Conclusion: </strong>To improve the quality of life of older adults, further investigation is warranted, including a follow-up study to develop a AI-based intervention program tailored to the type of AI program and intervention duration.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251324014"},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11930482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in obstructive sleep apnea: A bibliometric analysis.","authors":"Xing An, Jie Zhou, Qiang Xu, Zhihui Zhao, Weihong Li","doi":"10.1177/20552076251324446","DOIUrl":"10.1177/20552076251324446","url":null,"abstract":"<p><strong>Objective: </strong>To conduct a bibliometric analysis using VOSviewer and Citespace to explore the current applications, trends, and future directions of artificial intelligence (AI) in obstructive sleep apnea (OSA).</p><p><strong>Methods: </strong>On 13 September 2024, a computer search was conducted on the Web of Science Core Collection dataset published between 1 January 2011, and 30 August 2024, to identify literature related to the application of AI in OSA. Visualization analysis was performed on countries, institutions, journal sources, authors, co-cited authors, citations, and keywords using Vosviewer and Citespace, and descriptive analysis tables were created by using Microsoft Excel 2021 software.</p><p><strong>Results: </strong>A total of 867 articles were included in this study. The number of publications was low and stable from 2011 to 2016, with a significant increase after 2017. China had the highest number of publications. Alvarez, Daniel, and Hornero, Roberto were the two most prolific authors. Universidad de Valladolid and the IEEE Journal of Biomedical and Health Informatics were the most productive institution and journal, respectively. The top three authors in terms of co-citation frequency are Hassan, Ar, Young, T, and Vicini, C. \"Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis\" was cited the most frequently. Keywords such as \"OSA,\" \"machine learning,\" \"Electrocardiography,\" and \"deep learning\" were dominant.</p><p><strong>Conclusion: </strong>AI's application in OSA research is expanding. This study indicates that AI, particularly deep learning, will continue to be a key research area, focusing on diagnosis, identification, personalized treatment, prognosis assessment, telemedicine, and management. Future efforts should enhance international cooperation and interdisciplinary communication to maximize the potential of AI in advancing OSA research, comprehensively empowering sleep health, bringing more precise, convenient, and personalized medical services to patients and ushering in a new era of sleep health.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251324446"},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11930495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital health interventions for non-older individuals at risk of frailty: A systematic review and meta-analysis.","authors":"Momoko Tohyama, Ryo Momosaki, Yuka Shirai, Kenta Ushida, Yuki Kato, Miho Shimizu, Issei Kameda, Yuya Sakurai, Asuka Hori, Masatsugu Okamura, Takahiro Tsuge, Hiroki Sato, Yuki Nakashima, Kaori Endo, Shota Hayashi, Norio Yamamoto, Daisuke Matsumoto, Kenichi Fudeyasu, Hidenori Arai","doi":"10.1177/20552076251328566","DOIUrl":"10.1177/20552076251328566","url":null,"abstract":"<p><strong>Objective: </strong>Frailty is a significant health problem that results in adverse outcomes, reduced quality of life, and increased medical and nursing care costs. This study aimed to review the effect of digital health interventions on improving physical activity, physical function, and social function in non-older individuals at risk of frailty.</p><p><strong>Methods: </strong>Randomized controlled trials of digital health interventions reporting frailty-related outcomes on physical activity, physical function, and social function in non-older individuals at risk of frailty were collected. The literature was reviewed, and the risk of bias was assessed. Meta-analyses were conducted for each outcome, and the certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation.</p><p><strong>Results: </strong>The review included a total of 63 records. The meta-analysis showed that digital health interventions positively affected the volume of physical activity, daily steps, walking test, walking speed, VO2peak, dynamic gait index, timed up and go test, and MOS 36-Item Short-Form Health Survey Social Functioning. However, no significant effect on the time of physical activity was found. Despite the absence of fatal bias, the overall certainty of the evidence was not high because of the small number of studies and participants included in the analysis.</p><p><strong>Conclusions: </strong>Digital health interventions can improve physical activity, physical function, and social function in non-older individuals at risk of frailty. However, the evidence had limited certainty, and the evidence supporting the effect of digital health intervention is insufficient. Thus, more studies are needed to clarify the effects of these interventions.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251328566"},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11930493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel and ultralight convolutional neural network model for real-time detection of infectious lung diseases.","authors":"Eman Alqaissi","doi":"10.1177/20552076251318155","DOIUrl":"10.1177/20552076251318155","url":null,"abstract":"<p><strong>Objective: </strong>Vectors that cause infectious lung diseases encompass viral, bacterial, fungal, and parasitic agents. Early detection of these infections is critical for timely diagnosis and effective treatment. Several studies have created solutions for early detection with varying performance, but with limitations such as image type specificity, lack of generalizability, potential overfitting, and bias problems. Our model effectively addresses these problems by using diverse image types, enhancing robustness, and generalizability across various contexts that aim for effective performance.</p><p><strong>Methods: </strong>This study creates an early detection model that works with both CT scans and X-ray images. We applied a convolutional neural network (CNN) model trained on diverse and large augmented datasets with fewer parameters. We then used a generative adversarial network (GAN) to validate our CNN model and create generalized synthetic images. The proposed model was trained primarily on COVID-19, pneumonia, and tuberculosis (TB) cases (n = 432,533 total augmented cases).</p><p><strong>Results: </strong>The proposed model is a lightweight and explainable model that assists with real-time detection, resulting in a better performance with an average accuracy of 97.93% with a standard deviation of 0.97%, average area under the curve (AUC) of 98.07%, average sensitivity of 98.46%, average specificity of 97.03%, average precision of 97.45%, and average F1 score of 97.95%.</p><p><strong>Conclusion: </strong>The proposed CNN model offers a validation and generalization capability for diverse image types in real-time. We conducted a comparative analysis of our model with the most advanced research. The integration of our approach with other clinical systems and internet of things (IoT) devices is feasible.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251318155"},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11930494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of the KARAZ platform's behavioral interventions and incentives on diabetic glycemic control in Saudi Arabia.","authors":"Sulafa T Alqutub, Faisal Aljehani","doi":"10.1177/20552076251325987","DOIUrl":"10.1177/20552076251325987","url":null,"abstract":"<p><strong>Introduction: </strong>The glucose management indicator (GMI) and time-in-range (TIR) are important glycemic parameters calculated from continuous glucose monitoring (CGM) values. KARAZ, a hybrid Internet of things-artificial intelligence platform, was developed in Saudi Arabia to help manage diabetes mellitus. The complex nature of self-care and behavior changes associated with diabetes mellitus requires breaking large behaviors into achievable ones and related incentives.</p><p><strong>Aim: </strong>This study explored how tiny habits as a behavioral intervention and incentive system affect glycemic control among KARAZ Platform users with diabetes mellitus in various age groups in Saudi Arabia.</p><p><strong>Methods: </strong>This retrospective study included KARAZ Platform users and analyzed the effects of behavioral interventions and incentives on GMI and TIR as glycemic control parameters.</p><p><strong>Results: </strong>Of 296 active users, 118 (40%) and 148 (50%) maintained a desirable TIR and GMI, respectively. Adult females aged ≥ 26 years who consistently followed tiny habits and behavior changes exhibited a significant reduction in the GMI (5%). Intrinsic motivation through behavioral modification was more effective than external incentives for maintaining glucose control.</p><p><strong>Conclusion: </strong>The findings highlight how behavioral interventions can impact GMI, suggesting their effectiveness in promoting better health behaviors and improving glycemic control in the Saudi Arabian context. Further research should investigate how these habits and behaviors can be maintained sustainably without relying on external incentives. Recommendations discussed how children with Type 1 diabetes mellitus would benefit from CGM connection to KARAZ Platform iteration and the integration of a comprehensive diabetes care program within the Saudi health system.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251325987"},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11930475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive modeling of pediatric drug-induced liver injury: Dynamic classifier selection with clustering analysis.","authors":"Zixin Shi, Linjun Huang, Haolin Wang","doi":"10.1177/20552076251330078","DOIUrl":"10.1177/20552076251330078","url":null,"abstract":"<p><strong>Background: </strong>Pediatric populations are more vulnerable to drug-induced liver injury (DILI) due to distinct pharmacokinetic profiles and ongoing physiological maturation processes. However, early identification and assessment of DILI in pediatric patients present significant clinical challenges, primarily due to the inherent complexity of pediatric cases and substantial limitations in available clinical data.</p><p><strong>Objective: </strong>This study introduces a framework that integrates clustering analysis with dynamic classifier selection (DCS) techniques to enhance pediatric DILI prediction. The proposed method addresses challenges such as patient heterogeneity and class imbalance, while optimizing predictive performance to support clinical decision-making.</p><p><strong>Methods: </strong>We investigated a retrospective cohort of 12,555 pediatric inpatients across six hospitals in Chongqing, China. The dataset encompassed a wide range of biomedical parameters, including laboratory results and liver function profiles, along with clinical documentation spanning demographic characteristics, medical histories, and medication regimens. Patients were stratified into four distinct clinical subgroups based on silhouette coefficient. A diverse pool of base classifiers was generated with varied initialization strategies and hyperparameter optimizations tailored to each patient cluster. The classification process was further refined through the implementation of Dynamic Classifier Selection with Multiple Classifier Behavior (DCS-MCB) methodology, which adaptively customizes model selection based on the distinctive clinical profiles of each subgroup.</p><p><strong>Results: </strong>The Clustering-enhanced DCS-MCB framework demonstrated superior performance compared to conventional machine learning models across evaluation metrics. The ensemble learning models consistently outperformed individual classifier models, with the presented study achieving the highest F1-score (0.926), MCC (0.917), G-mean (0.959), demonstrating the strength of this hybrid approach in addressing the complexities of pediatric DILI prediction.</p><p><strong>Conclusion: </strong>The integration of clustering analysis with dynamic classifier selection has demonstrated efficacy in complex real-world clinical settings. This methodology provides a more robust, precise, and clinically adaptable framework for patient stratification and drug safety surveillance.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251330078"},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The urge for specific standards of telepharmacy services: Lessons learned from qualitative study in Indonesian community pharmacies.","authors":"Kartika Citra Dewi Permata Sari, Adelia Nathifa Rachma Nur Setiati, Larasati Arrum Kusumawardani, Hasniza Zaman Huri, Mohamed Hassan Elnaem","doi":"10.1177/20552076251326018","DOIUrl":"10.1177/20552076251326018","url":null,"abstract":"<p><strong>Objective: </strong>Like many low- and middle-income countries, Indonesia lacks specific standards for telepharmacy practices, which requires adherence to traditional pharmacy guidelines to evaluate the services. This study aims to explore the disparities between the implementation of telepharmacy services in community pharmacies and the established Indonesian pharmaceutical service standards.</p><p><strong>Methods: </strong>This qualitative study was conducted in seven community pharmacies in Depok, an urban area near Jakarta. Data were collected in 2023 using triangulation methods: semi-structured interviews, direct observations, and simulated patient interactions. Verbatim transcriptions and thematic analysis were performed using NVivo^®12 to analyse the findings.</p><p><strong>Results: </strong>Four identified themes were the structures, daily practices, challenges, and enablers of telepharmacy services. The scope of telepharmacy services consisted of dispensing medicines, patient education, and providing drug information through mobile applications by pharmacy electronic system organisers (PSEF) or social media platforms. Challenges in daily practice revealed gaps in traditional pharmaceutical service guidelines, including systems interoperability, effective communication among pharmacists-physician-patients, and the need for standards specific to digital pharmaceutical services. Common problems in telepharmacy were uncertain medicine availability, unclear e-prescription validity, limited pharmacist-physician collaboration, and impeded patient education, particularly in services provided through PSEF applications. On the other hand, the availability of digital technologies was seen as beneficial, highlighting the necessity for predefined standardised facilities to ensure the quality of telepharmacy services.</p><p><strong>Conclusion: </strong>While traditional pharmaceutical service standards can be applied to evaluate telepharmacy practices, specific challenges must be explicitly addressed within a dedicated telepharmacy standard to ensure patient safety and service quality.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251326018"},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness, usability, and patient satisfaction of an mHealth application with an integrated ePRO system following lumbar degenerative spinal surgery: A quasi-experimental study.","authors":"Marianne Dyrby Lorenzen, Casper Friis Pedersen, Line Nielsen, Mikkel O Andersen, Jane Clemensen, Leah Y Carreon","doi":"10.1177/20552076251324687","DOIUrl":"10.1177/20552076251324687","url":null,"abstract":"<p><strong>Introduction: </strong>There is a lack of comprehensive clinical research to assess potential benefits of mHealth solutions in post discharge follow-up care after spinal surgery.</p><p><strong>Purpose: </strong>This quasi-experimental study evaluated the effectiveness, usability, and patient satisfaction of an mHealth pathway with an electronic Patient-Reported Outcome (ePRO)-based post-discharge nurse-led intervention for patients undergoing surgery for lumbar spine degenerative disorders, compared to standard care.</p><p><strong>Methods: </strong>Conducted at a Danish tertiary spine center, this study represents the final stage of a three-phase participatory design. The primary outcome was patient quality of recovery, measured by the Quality of Recovery-15 (QoR-15) questionnaire. Secondary outcomes included patient-perceived usability, assessed with the Danish System Usability Scale (SUS). To capture additional patient insights, an open-ended feedback question was included at the end of the survey.</p><p><strong>Results: </strong>Data from 150 patients (77 women and 73 men) were analyzed, with 104 in the intervention group and 46 in the comparison group. Both groups showed significant improvement over time, but no significant difference between groups. Of 154 potential SUS respondents, 110 participated. Analysis revealed 48 statements, categorized into five themes: (1) Usability and functionality of the mHealth solution, (2) Feedback on the QoR-15 questionnaire, (3) Safety and support, (4) Missing functions and suggestions for improvements, and (5) Patient satisfaction.</p><p><strong>Conclusion: </strong>No significant differences in effectiveness between the mHealth pathway and standard care were found for post-surgery recovery in lumbar spine patients. Usability and patient satisfaction were generally high, though suggestions for improvements were noted. The study highlighted the importance of post-discharge support, suggesting that the intervention provided crucial security and support, potentially demonstrating compassion through nurse-led care.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251324687"},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"How eHealth use and cancer information seeking influence older adults' acceptance of genetic testing: Mediating roles of PIGI and cancer worry\".","authors":"","doi":"10.1177/20552076251325278","DOIUrl":"https://doi.org/10.1177/20552076251325278","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1177/20552076251317658.].</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251325278"},"PeriodicalIF":2.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11924078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding vaccine hesitancy: Insights from social media on polio, human papilloma virus, and COVID-19 in Zambia.","authors":"Samuel Munalula Munjita","doi":"10.1177/20552076251326131","DOIUrl":"10.1177/20552076251326131","url":null,"abstract":"<p><strong>Objectives: </strong>Vaccine hesitancy remains a critical challenge to public health in Zambia and globally, necessitating a deeper understanding of the factors influencing this phenomenon. The study analyzed user-generated Facebook comments from January 2021 to December 2023 to understand the factors influencing vaccine hesitancy in Zambia.</p><p><strong>Methods: </strong>This study employed a qualitative case study design, focusing on the official Facebook page of the Ministry of Health in Zambia. A purposeful sampling technique was used, collecting comments that discussed vaccine hesitancy related to polio, human papilloma virus (HPV), and coronavirus disease 2019 (COVID-19) vaccines.</p><p><strong>Results: </strong>The analysis revealed that men contributed 77.5% of comments followed by women with 22.5%. The majority of comments (82.5%) pertained to COVID-19 vaccines, followed by polio (14.1%) and HPV (3.4%). Notably, women expressed greater hesitancy toward polio vaccines (60%) compared to COVID-19 (19.9%) and HPV (12.5%). Thematic analysis highlighted significant hesitancy against vaccines shaped by vaccine safety and efficacy concerns, frequent calls for vaccination particularly against polio, conspiracy theories, distrust in health authorities, and poor communication from health authorities. Other drivers of vaccine hesitancy were reliance on spiritual beliefs, herbal remedies and natural immunity, and the pervasive spread of misinformation.</p><p><strong>Conclusion: </strong>These findings underscore the barriers to vaccine acceptance, emphasizing the critical need for transparent communication and community engagement. To improve vaccine uptake, public health strategies must address community-specific concerns, foster trust, and enhance the effectiveness of health communication efforts.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251326131"},"PeriodicalIF":2.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}