Informatics for health & social care最新文献_第3页

Technology-driven diabetes management: a review of digital therapeutics, mobile apps, and clinical evidence. 技术驱动的糖尿病管理：数字治疗、移动应用和临床证据综述。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-06 DOI: 10.1080/17538157.2025.2609747

Bidisha Bhattacharya, Kunal Kar, M P Venkatesh, Inderbir Singh

{"title":"Technology-driven diabetes management: a review of digital therapeutics, mobile apps, and clinical evidence.","authors":"Bidisha Bhattacharya, Kunal Kar, M P Venkatesh, Inderbir Singh","doi":"10.1080/17538157.2025.2609747","DOIUrl":"10.1080/17538157.2025.2609747","url":null,"abstract":"Digital health technologies are revolutionizing the treatment of diabetes by providing creative ways to enhance patient outcomes. In this study, the role of digital medicines in the treatment of diabetes is examined with special attention paid to wearable technology, mobile applications, and clinical data that backs up their usage. Software-driven interventions designed to prevent, manage, or treat diabetes using specialized data-driven methods are referred to as digital therapies. The article examines many smartphone applications that support insulin management, nutritional tracking, blood glucose monitoring, and lifestyle changes. It examines important clinical trials that demonstrate how successfully these tools reduce HbA1c levels, enhance glycemic control, and promote long-term treatment regimen adherence. New advancements in AI such as personalized AI algorithms, integration of continuous glucose monitors with mobile apps, remote patient monitoring, telemedicine, behavioral nudges, machine learning, and data analytics that enhance the personalization of diabetic care are also the subject of the evaluation. Digital treatments have the potential to revolutionize diabetes care by providing more accessible, patient-centered, and effective care.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-21"},"PeriodicalIF":2.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145914527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The usability and effectiveness of the Mobithera application on musculoskeletal pain and physical function in adult caregivers of oncology patients: a single-group pilot study. Mobithera应用于肿瘤患者成人护理人员肌肉骨骼疼痛和身体功能的可用性和有效性：一项单组试点研究。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-06 DOI: 10.1080/17538157.2025.2611120

Sedef Şahin, Ege Temizkan, Ayşenur Baysal Yiğit, Başak Çağla Arslan, Alaettin Uçan, İlyas Yapar, Ali Yaşar Yiğit, Adem Ali Yılmaz, Esra Aki

{"title":"The usability and effectiveness of the Mobithera application on musculoskeletal pain and physical function in adult caregivers of oncology patients: a single-group pilot study.","authors":"Sedef Şahin, Ege Temizkan, Ayşenur Baysal Yiğit, Başak Çağla Arslan, Alaettin Uçan, İlyas Yapar, Ali Yaşar Yiğit, Adem Ali Yılmaz, Esra Aki","doi":"10.1080/17538157.2025.2611120","DOIUrl":"https://doi.org/10.1080/17538157.2025.2611120","url":null,"abstract":"This pilot study evaluated the effectiveness and usability of the Mobithera application, a mobile health intervention designed to reduce musculoskeletal pain and improve physical function in adult caregivers of oncology patients. Thirty-five caregivers with self-reported neck or lower back pain participated in this study. The intervention involved 10 sessions over two weeks, during which participants performed therapeutic exercises using the Mobithera. Pain levels, lower extremity reaction time, body awareness, and balance were assessed before and after the intervention using to the Visual Analog Scale (VAS), Rapid Foot-Tap Test, Fremantle Awareness Scales, and Tinetti Gait and Balance Test. Usability of the application was evaluated using the System Usability Scale (SUS). The Mobithera significantly reduced both neck and lower back pain among participants. Improvements were also observed in lower extremity reaction time, body awareness, and balance. The findings suggest that the Mobithera is effective in enhancing the physical health and well-being of caregivers. The Mobithera demonstrates significant potential as a supportive tool for caregivers, reducing pain and improving physical function. Further research with larger sample sizes and longer follow-up periods is recommended to confirm these findings and explore the long-term benefits of such interventions in the caregiver population.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-10"},"PeriodicalIF":2.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145914452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictors of occupational balance in informal caregivers of individuals with disabilities: the roles of age, stress, social support, and caregiver burden. 残疾人非正式照护者职业平衡的预测因素：年龄、压力、社会支持和照护者负担的作用。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-05 DOI: 10.1080/17538157.2025.2611126

Gokcen Akyurek, Ezginur Gündoğmuş, Nurten Bilgin

{"title":"Predictors of occupational balance in informal caregivers of individuals with disabilities: the roles of age, stress, social support, and caregiver burden.","authors":"Gokcen Akyurek, Ezginur Gündoğmuş, Nurten Bilgin","doi":"10.1080/17538157.2025.2611126","DOIUrl":"https://doi.org/10.1080/17538157.2025.2611126","url":null,"abstract":"Caregivers' occupational balance (OB) can be influenced by multiple factors that may vary depending on the caregiver's age. This study aimed to identify key predictors of occupational balance among informal caregivers from different age groups, with particular emphasis on stress, perceived social support, and caregiver burden. The cross-sectional survey study was conducted with 233 participants who completed the Multidimensional Scale of Perceived Social Support, the Perceived Stress Scale, the Zarit Caregiver Burden Scale, and the Occupational Balance Questionnaire. Between-group differences were analyzed using one-way ANOVA, while multiple linear regression was performed to identify significant predictors of occupational balance. Caregiver burden emerged as the strongest predictor of occupational balance across all age groups (β = 0.61, p < .001 for caregivers of children; β = 0.33, p = .007 for caregivers of adults; β = 0.54, p < .001 for caregivers of older adults). Additionally, stress (β = 0.32, p = .008) was a significant predictor among caregivers of adults, and disability percentage (β = 0.21, p = .021) predicted occupational balance among caregivers of older adults. All analyses used a significance threshold of p < .05. These findings highlight the necessity of assessing caregiver burden, stress, and social support in efforts to promote occupational balance and well-being among informal caregivers.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-13"},"PeriodicalIF":2.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145907495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and development of a competency diagnosis program for personal information protection: among Korean medical institution personnel. 韩国医疗机构人员个人信息保护能力诊断程序的设计与开发。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-01 Epub Date: 2026-01-06 DOI: 10.1080/17538157.2025.2607613

Yeonhu Lee

{"title":"Design and development of a competency diagnosis program for personal information protection: among Korean medical institution personnel.","authors":"Yeonhu Lee","doi":"10.1080/17538157.2025.2607613","DOIUrl":"10.1080/17538157.2025.2607613","url":null,"abstract":"This study aims to develop a customized Competency Diagnosis Program for Personal Information Protection that enables medical institution personnel to independently assess their competency levels in personal information protection and receive tailored educational materials. The ultimate goal is to enhance their competency and awareness regarding personal information protection. The program was developed by incorporating institutional aspects reflecting amendments to relevant laws, technical aspects considering the digitalization of medical information, and administrative aspects aligned with the practical realities of medical institutions. Additionally, to ensure precise competency diagnosis, a newly designed questionnaire was implemented, allowing diagnosis at each stage of personal information processing. With the rapid digitalization of medical information driven by advances in information and communication technology, enhancing the competency and awareness of medical institution personnel in personal information protection has become increasingly critical. By utilizing the newly designed competency diagnosis questionnaire and program developed in this study, any South Korean medical institution personnel can independently assess their competency levels and receive targeted educational materials for areas requiring improvement. This is expected to have a positive impact on enhancing competency and raising awareness of personal information protection.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"370-381"},"PeriodicalIF":2.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145914399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Informatics for health and social care volume 51, issue 1 (2026). 健康和社会保健信息学卷51，第1期（2026）。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-01 Epub Date: 2026-02-17 DOI: 10.1080/17538157.2025.2602415

Gabriela Mustata Wilson

引用次数: 0

Predicting the diabetes risk by analyzing symptoms using data mining techniques. 通过使用数据挖掘技术分析症状来预测糖尿病风险。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-01 Epub Date: 2025-12-31 DOI: 10.1080/17538157.2025.2602517

Rahaf Alhamouri, Ahmad Alaiad, Dania Rahhal

{"title":"Predicting the diabetes risk by analyzing symptoms using data mining techniques.","authors":"Rahaf Alhamouri, Ahmad Alaiad, Dania Rahhal","doi":"10.1080/17538157.2025.2602517","DOIUrl":"10.1080/17538157.2025.2602517","url":null,"abstract":"Background: Diabetes is a chronic disease with a high rate of prevalence among societies, it causes detrimental effects on individuals, societies, and governments. There is an urgent need to predict the risk of developing the disease before occurring. This study explores the machine learning models' efficiency of predicting this risk.Method: Machine learning algorithms were used including decision tree, Random Forest (RF), Naïve Bayes, and logistic regression. For performance measures, accuracy, precision, f-measure, and recall were used. Those algorithms were applied on diabetes-related symptoms dataset of 520 instances, and 16 attributes, where the attributes focused on the risk symptoms of diabetes, with the final class labeling whether the patient has diabetes or not.Results: The use of four different algorithms, with 10-fold cross-validation, and a split of 80:20 ratio, has shown that RF has outperformed other algorithms. As it achieved the highest accuracy in cross-validation of 97.5%, and 95.2% using ratio splitting. Also, the Random Forest has achieved the highest precision, recall, and F-measure of 0.975.Conclusion: RF is suggested to be the best-intended model to predict diabetes risk along with 10-fold cross-validation. It must be a part of the decision-making along with the provided implications and contributions.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"315-325"},"PeriodicalIF":2.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dynamic decision support system to tackle the spread of COVID-19: predictive and clustering approaches. 应对COVID-19传播的动态决策支持系统：预测和聚类方法。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-01 Epub Date: 2026-01-07 DOI: 10.1080/17538157.2025.2606853

Amirreza Salehi, Ardavan Babaei, Vladimir Simić, Erfan Babaee Tirkolaee

{"title":"A dynamic decision support system to tackle the spread of COVID-19: predictive and clustering approaches.","authors":"Amirreza Salehi, Ardavan Babaei, Vladimir Simić, Erfan Babaee Tirkolaee","doi":"10.1080/17538157.2025.2606853","DOIUrl":"10.1080/17538157.2025.2606853","url":null,"abstract":"This study proposes a novel Dynamic Decision Support System (DDSS) framework aimed at enhancing countries' pandemic preparedness through robust forecasting and adaptive clustering analysis of Coronavirus Disease 2019 (COVID-19) data. The forecasting module integrates multiple Machine Learning (ML) models with a recursive error-adjustment mechanism that significantly improves long-term prediction accuracy of cumulative COVID-19 cases compared to conventional time series models such as Autoregressive Integrated Moving Average (ARIMA), Error Trend Seasonality (ETS), and Long Short-Term Memory (LSTM). The clustering component performs a dynamic, year-over-year classification of countries based on health, policy, and socio-economic indicators. By employing K-means clustering and Multi-Attribute Decision-Making (MADM) techniques, the study evaluates changes in countries' pandemic performance over time and identifies the stringency index as a critical determinant influencing cluster shifts. The proposed framework not only reveals performance trajectories and degradation risks for individual countries but also provides a structured data-driven basis for strategic policymaking. Notably, the analysis demonstrates how relaxing governmental measures, despite high vaccination rates, can adversely affect country classification, highlighting the indispensable role of strict interventions. This integrated approach, combining adaptive ML forecasting with temporal clustering and interpretability via Shapley Additive explanations (SHAP) analysis, provides a dynamic and practical decision-support framework for pandemic response.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"274-297"},"PeriodicalIF":2.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI chatbots vs. web-based learning in digital health: a randomized trial on improving dry eye syndrome knowledge for nursing students. 人工智能聊天机器人与数字健康中的网络学习：提高护理专业学生干眼综合征知识的随机试验。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-01 Epub Date: 2025-12-04 DOI: 10.1080/17538157.2025.2595015

Ugur Dogan, Ibrahim Edhem Yilmaz

{"title":"AI chatbots vs. web-based learning in digital health: a randomized trial on improving dry eye syndrome knowledge for nursing students.","authors":"Ugur Dogan, Ibrahim Edhem Yilmaz","doi":"10.1080/17538157.2025.2595015","DOIUrl":"10.1080/17538157.2025.2595015","url":null,"abstract":"AI chatbots show promise for delivering interactive, personalized health education but require validation against traditional methods. This study compared effectiveness of artificial intelligence chatbot versus website-based learning for dry eye syndrome knowledge acquisition among undergraduate nursing students. In this randomized controlled study, participants completed baseline dry eye syndrome questionnaires, then were randomized to artificial intelligence chatbot (n = 32) or website groups (n = 31). Over two weeks, artificial intelligence chatbot group received personalized responses via Gemini 2.0, while website groups accessed standardized website content. Post-intervention, both groups completed dry eye syndrome knowledge questionnaire. A comparative analysis evaluated artificial intelligence-generated versus website content for knowledge acquisition, knowledge quality, readability, understandability, and actionability. Both groups showed significant knowledge acquisition, with no significant between-group difference. However, artificial intelligence chatbot responses demonstrated superior knowledge quality in preventive strategies and treatment approaches. AI chatbot showed significantly better readability, understandability, and actionability scores. These results may offer practical strategies for using chatbots not only for knowledge acquisition but also for the digital transformation of nursing education. Artificial intelligence chatbot effectively enhance dry eye syndrome education by improving health promotion through interactive, personalized content.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"71-84"},"PeriodicalIF":2.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the relationship between health professionals' artificial intelligence literacy and their attitudes toward artificial intelligence. 探讨卫生专业人员人工智能素养与其对人工智能态度的关系。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-01 Epub Date: 2026-01-07 DOI: 10.1080/17538157.2025.2602515

Hüseyin Çapuk, Muhammet Faruk Yiğit, Mehmet Uçar

{"title":"Exploring the relationship between health professionals' artificial intelligence literacy and their attitudes toward artificial intelligence.","authors":"Hüseyin Çapuk, Muhammet Faruk Yiğit, Mehmet Uçar","doi":"10.1080/17538157.2025.2602515","DOIUrl":"10.1080/17538157.2025.2602515","url":null,"abstract":"This study aims to determine the relationship between health professionals' artificial intelligence (AI) literacy and their attitudes toward AI. This descriptive and correlational study was conducted between May and July 2024 in an educational research hospital located in eastern Turkey, encompassing 1378 health professionals. The sample size was calculated as 301 participants, with a 95% confidence interval and a 0.05 margin of error, and data collection was completed with 439 participants. 58.8% of the participants were male and 49.0% of them did not have information about the use of artificial intelligence in the field of health. Men, those in the 46-55 age group, postgraduates, those who received artificial intelligence education and those who support artificial intelligence in health showed higher AI literacy and positive attitudes. A strong positive correlation was found between AI literacy and attitudes (r = 0.782**; r = 0.710**), and some sociodemographic variables explained 62.1% and 48.9% of positive and negative attitudes, respectively. Participants exhibited moderate AI literacy, high positive and moderate negative attitudes; attitudes were significantly affected by sociodemographic factors.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"139-150"},"PeriodicalIF":2.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145919687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing privacy and security in Federated learning protecting electronic health records data from adversarial attacks. 加强联邦学习中的隐私和安全性，保护电子健康记录数据免受对抗性攻击。

IF 2.4

Informatics for health & social care Pub Date : 2026-01-01 Epub Date: 2026-01-07 DOI: 10.1080/17538157.2025.2610687

Burra Lalitha Rajeswari, A S N Chakravarthy

{"title":"Enhancing privacy and security in Federated learning protecting electronic health records data from adversarial attacks.","authors":"Burra Lalitha Rajeswari, A S N Chakravarthy","doi":"10.1080/17538157.2025.2610687","DOIUrl":"10.1080/17538157.2025.2610687","url":null,"abstract":"Federated Learning (FL) is a revolutionary approach to machine learning that allows multiple institutions, such as healthcare organizations, to collaboratively train models without sharing sensitive data directly. The problem involves protecting Electronic Health Records (EHRs) in FL from adversarial attacks while maintaining privacy. This requires ensuring secure data aggregation, anonymization, and robust model training across diverse healthcare institutions without compromising sensitive patient information. The objectives are to enhance privacy and security in FL for EHRs by implementing anonymization, adversarial pattern detection, context-aware aggregation, and robust model training. This ensures protection from adversarial attacks while maintaining compliance with privacy standards like HIPAA. Adaptive Weighted Median Filtering (AWMF) improves FL robustness by reducing noise, outliers, and adversarial attacks, ensuring accurate, privacy-preserving model training. The Cuckoo-based Deep Convolutional Long-Term Memory (CDC-LSTM) combines convolutional layers and memory networks to enhance FL, improving robustness against adversarial attacks while ensuring secure EHR data processing. Federated Privacy-Preserving Mesh Networks (F-PPMN) enhance FL by creating secure, decentralized communication channels, protecting EHRs from adversarial attacks and preserving privacy. Findings show that anonymized EHR attributes (90%-290%) slightly reduce data richness compared to original attributes (100%-300%) while maintaining privacy. This demonstrates a balance between data utility and confidentiality in FL and is implemented in Python Software. Future advancements include integrating federated encryption, multi-party computation, and improved adversarial attack detection, enhancing privacy and security in FL for EHRs while ensuring robust, accurate model performance across diverse healthcare environments.","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"151-168"},"PeriodicalIF":2.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0