Health Informatics Journal最新文献_第2页

Radiotherapy department supported by an optimization algorithm for scheduling patient appointments.

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582251318252

Chavez Marcela, Gonzalez Silvia, Ruiz Alvaro, Duflot Patrick, Nicolas Jansen, Izidor Mlakar, Umut Arioz, Valentino Safran, Kolh Philippe, Van Gasteren Marteyn

{"title":"Radiotherapy department supported by an optimization algorithm for scheduling patient appointments.","authors":"Chavez Marcela, Gonzalez Silvia, Ruiz Alvaro, Duflot Patrick, Nicolas Jansen, Izidor Mlakar, Umut Arioz, Valentino Safran, Kolh Philippe, Van Gasteren Marteyn","doi":"10.1177/14604582251318252","DOIUrl":"https://doi.org/10.1177/14604582251318252","url":null,"abstract":"Prompt administration of radiotherapy (RT) is one of the most effective treatments against cancer. Each day, the radiotherapy departments of large hospitals must plan numerous irradiation sessions, considering the availability of human and material resources, such as healthcare professionals and linear accelerators. With the increasing number of patients suffering from different types of cancers, manually establishing schedules following each patient's treatment protocols has become an extremely difficult and time-consuming task. We propose an optimization algorithm that automatically schedules and generates patient appointments. The model can rearrange fixed appointments to accommodate urgent cases, enabling hospitals to schedule appointments more efficiently. It respects the different treatment protocols and should increase staff and patient satisfaction. The optimization algorithm can be connected to a mobile application allowing patients to accept or refuse appointment changes for rescheduling radiotherapy treatments.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582251318252"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Use of mobile fitness app to improve pelvic floor muscle training in puerperal women with gestational diabetes mellitus: A randomized controlled trial.

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582251316774

Xiaocheng He, Yaping Xie, Baoyuan Xie, Meijing Zhao, Honghui Zhang, Xiaoshan Zhao, Huifen Zhao

{"title":"Use of mobile fitness app to improve pelvic floor muscle training in puerperal women with gestational diabetes mellitus: A randomized controlled trial.","authors":"Xiaocheng He, Yaping Xie, Baoyuan Xie, Meijing Zhao, Honghui Zhang, Xiaoshan Zhao, Huifen Zhao","doi":"10.1177/14604582251316774","DOIUrl":"https://doi.org/10.1177/14604582251316774","url":null,"abstract":"Background: Gestational diabetes mellitus (GDM) is one of the risk factors for postpartum urinary incontinence. Pelvic floor muscle training (PFMT) improves pelvic floor dysfunction in puerperal women, but patient compliance is low. Mobile Health (mHealth) is a promising solution. Objective: To investigate PFMT compliance and effects on pelvic floor muscles in GDM puerperal women guided by the mobile fitness app Keep. Methods: This randomized controlled trial included puerperal women with GDM (n = 72) who were delivered at a tertiary general hospital, selected from November 2021 to April 2022 using convenience sampling, and randomly divided into control (n = 36) and experimental (n = 36) groups. The control group performed PFMT based on routine postpartum PFMT training instruction. The experimental group performed PFMT based on Keep. Both groups had a 4-week intervention period. The PFMT compliance, International Consultation on Incontinence Questionnaire Short Form (ICIQ-SF), Pelvic Muscle Self-efficacy Scale, and the Knowledge, Attitude, Belief, and Practice (KAP) scores of PFMT in puerperal women in the groups were compared pre- and post-intervention. Pelvic floor surface electromyographic biofeedback was used to compare the post-intervention pelvic floor muscle strength between the two groups. Results: Compared with the control group, the test group had higher post-intervention maternal PFMT compliance, pelvic floor muscle strength, pelvic floor muscle self-efficacy, and KAP scores (p < 0.05); incontinence scores were lower (p < 0.05). Pelvic floor muscles in both groups recovered better post-intervention (p < 0.05). Conclusion: The Keep app can improve PFMT adherence, urinary incontinence, KAP scores, self-efficacy, and pelvic floor muscle strength in GDM puerperal women and promote pelvic floor rehabilitation after delivery.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582251316774"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The use and readiness for eHealth and eWelfare among young adults.

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582241307208

Anna Vahteristo, Virpi Jylhä, Hanna Kuusisto

{"title":"The use and readiness for eHealth and eWelfare among young adults.","authors":"Anna Vahteristo, Virpi Jylhä, Hanna Kuusisto","doi":"10.1177/14604582241307208","DOIUrl":"https://doi.org/10.1177/14604582241307208","url":null,"abstract":"Objective: Purpose of this cross-sectional study was to investigate young adults' eHealth literacy levels, use, and readiness to use eHealth and eWelfare. Methods: An electronic survey based on Readiness and Enablement Index for Health Technology (READHY) was aimed at young adults in the geographical are of one wellbeing services county in Southern Finland. Data were analyzed using non-parametrical statistical methods. Results: Young adults (N = 110) actively used eHealth and eWelfare and assessed themselves as having good general digital skills. They were confident in their eHealth literacy and readiness for the use of eHealth and eWelfare. However, young adults not in education, employment, or training (NEETs, n = 21) were significantly less confident than non-NEETs (n = 89) in three of the five domains describing eHealth literacy, and readiness for the use of health technology. Conclusions: The differences between NEETs and non-NEETs indicate that further research on NEETs' and other subgroups' abilities to use eHealth and eWelfare is needed to ensure that these services can be fully utilized.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582241307208"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the quality of Spanish-language information for patients with type 2 diabetes on YouTube and Facebook. 评估YouTube和Facebook上针对2型糖尿病患者的西班牙语信息的质量。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582251315592

María Juliana Soto-Chávez, Candida Díaz-Brochero, Ana María Gómez-Medina, Diana Cristina Henao, Oscar Mauricio Muñoz

{"title":"Evaluating the quality of Spanish-language information for patients with type 2 diabetes on YouTube and Facebook.","authors":"María Juliana Soto-Chávez, Candida Díaz-Brochero, Ana María Gómez-Medina, Diana Cristina Henao, Oscar Mauricio Muñoz","doi":"10.1177/14604582251315592","DOIUrl":"https://doi.org/10.1177/14604582251315592","url":null,"abstract":"Introduction: Spanish speakers rely on social media for health information, with varying quality of its content. This study evaluates the reliability, completeness, and quality of type 2 diabetes (T2D) information available in Spanish-language videos on YouTube and Facebook. Methods: Analytical observational study that included Spanish-language videos on TD2 available on Facebook and YouTube. General characteristics, interaction and generating sources are described. Standardized tools were used to assess reliability, completeness and overall quality. Results: We included 172 videos, 90 from Youtube® and 82 from Facebook®. The median number of views was 1725 (IQR 213-10,000), with an average duration of 5.93 minutes (IQR 3.2-16.8) and an internet time of 834 days (IQR 407-1477). Most videos were uploaded by independent users (58.72%). Reliability (evaluated with DISCERN tool) had a median of 3 (IQR 2-3), completeness (content score) had a median of 2 (IQR 1-3), and overall quality, evaluated with the Global Quality Score (GQS) tool had a median of 3 (IQR 3-4). Using a global classification of \"subjective reliability\" 92.4% of the videos were considered reliable. Better completeness was observed in Facebook videos (p < .001). Reliability was better for videos from government or news organizations. Conclusion: Our results suggest that videos about T2D in Spanish on social media such as YouTube and Facebook have good reliability and quality, with greater exhaustiveness in content in Facebook videos and greater reliability for videos from government or news organizations.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582251315592"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting metabolic syndrome: Machine learning techniques for improved preventive medicine. 预测代谢综合征：改进预防医学的机器学习技术。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582251315602

Orit Goldman, Ofir Ben-Assuli, Shimon Ababa, Ori Rogowski, Shlomo Berliner

{"title":"Predicting metabolic syndrome: Machine learning techniques for improved preventive medicine.","authors":"Orit Goldman, Ofir Ben-Assuli, Shimon Ababa, Ori Rogowski, Shlomo Berliner","doi":"10.1177/14604582251315602","DOIUrl":"https://doi.org/10.1177/14604582251315602","url":null,"abstract":"Objectives: Metabolic syndrome (MetS) has a significant impact on health. MetS is the umbrella term for a group of interdependent metabolic threats that contribute to the emergence of diseases that can lead to death. This study was designed to better predict the risks associated with MetS to enable medical personnel to make more optimal preventive medical decisions. Study design: Data from a large hospital survey database was used to train data mining classification techniques to predict patient-level risk subsequent to extensive data engineering that included aggregating predictors from multiple visits. Methods: A prospective group of seemingly healthy volunteers from the database was studied based on data obtained during their regular annual health checkups. Results: After aggregating the variables over time, the findings indicated that the predictive power of our model outperformed methods presented in other studies (AUC = 0.947). Specific lifestyle factors were identified as contributing to MetS. Conclusion: Involvement to avoid recurring diseases can significantly decrease medical problems and treatment expenses. The findings emphasize the importance of using predictive tools in healthcare and preventive medicine. The results can be used for future prevention strategies that encourage lifestyle changes and implement directed medical treatment protocols to decrease the burden of illness.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582251315602"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying protected health information by transformers-based deep learning approach in Chinese medical text.

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582251315594

Kun Xu, Yang Song, Jingdong Ma

{"title":"Identifying protected health information by transformers-based deep learning approach in Chinese medical text.","authors":"Kun Xu, Yang Song, Jingdong Ma","doi":"10.1177/14604582251315594","DOIUrl":"https://doi.org/10.1177/14604582251315594","url":null,"abstract":"Purpose: In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. Methods: We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance. Results: Based on the annotated data, the BERT model pre-trained with the medical corpus showed a significant performance improvement to the BiLSTM-CRF model with a micro-recall of 0.979 and an F1 value of 0.976, which indicates that the model has promising performance in identifying private information in Chinese clinical texts. Conclusions: The BERT-based BiLSTM-CRF model excels in identifying privacy information in Chinese clinical texts, and the application of this model is very effective in protecting patient privacy and facilitating data sharing.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582251315594"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143042535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A blueprint for large language model-augmented telehealth for HIV mitigation in Indonesia: A scoping review of a novel therapeutic modality. 印度尼西亚用于减轻艾滋病毒的大型语言模型增强远程保健蓝图：对一种新型治疗方式的范围审查。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582251315595

Daniel Busch, Choiru Za'in, Hei Man Chan, Agnes Haryanto, Wahyudi Agustiono, Kan Yu, Kyra Hamilton, Jeroen Kroon, Wei Xiang

{"title":"A blueprint for large language model-augmented telehealth for HIV mitigation in Indonesia: A scoping review of a novel therapeutic modality.","authors":"Daniel Busch, Choiru Za'in, Hei Man Chan, Agnes Haryanto, Wahyudi Agustiono, Kan Yu, Kyra Hamilton, Jeroen Kroon, Wei Xiang","doi":"10.1177/14604582251315595","DOIUrl":"https://doi.org/10.1177/14604582251315595","url":null,"abstract":"Background: The HIV epidemic in Indonesia is one of the fastest growing in Southeast Asia and is characterised by a number of geographic and sociocultural challenges. Can large language models (LLMs) be integrated with telehealth (TH) to address cost and quality of care? Methods: A literature review was performed using the PRISMA-ScR (2018) guidelines between Jan 2017 and June 2024 using the PubMed, ArXiv and semantic scholar databases. Results: Of the 694 records identified, 12 studies met the inclusion criteria. Although the role of eHealth interventions as well as telehealth in HIV management appears well established, there is a significant literature gap on the integration of telehealth and LLM technology. To address this, we provide a blueprint for the safe and ethical integration of LLM-TH into triage, history taking, patient education highlighting opportunities for reduced consultation time and improved quality of care. Conclusions: Variable access to mobile technology and the need for empirical validation stand out as limitations for LLM-TH. However, we argue that the current evidence base suggests the benefits far outweigh the challenges in applying LLM-TH for HIV care in Indonesia. We also argue this novel therapeutic modality is broadly applicable to the subacute general practice setting.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582251315595"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Researching public health datasets in the era of deep learning: a systematic literature review.

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582241307839

Rand Obeidat, Izzat Alsmadi, Qanita Bani Baker, Aseel Al-Njadat, Sriram Srinivasan

{"title":"Researching public health datasets in the era of deep learning: a systematic literature review.","authors":"Rand Obeidat, Izzat Alsmadi, Qanita Bani Baker, Aseel Al-Njadat, Sriram Srinivasan","doi":"10.1177/14604582241307839","DOIUrl":"https://doi.org/10.1177/14604582241307839","url":null,"abstract":"Objective: Explore deep learning applications in predictive analytics for public health data, identify challenges and trends, and then understand the current landscape. Materials and Methods: A systematic literature review was conducted in June 2023 to search articles on public health data in the context of deep learning, published from the inception of medical and computer science databases through June 2023. The review focused on diverse datasets, abstracting applications, challenges, and advancements in deep learning. Results: 2004 articles were reviewed, identifying 14 disease categories. Observed trends include explainable-AI, patient embedding learning, and integrating different data sources and employing deep learning models in health informatics. Noted challenges were technical reproducibility and handling sensitive data. Discussion: There has been a notable surge in deep learning applications on public health data publications since 2015. Consistent deep learning applications and models continue to be applied across public health data. Despite the wide applications, a standard approach still does not exist for addressing the outstanding challenges and issues in this field. Conclusion: Guidelines are needed for applying deep learning and models in public health data to improve FAIRness, efficiency, transparency, comparability, and interoperability of research. Interdisciplinary collaboration among data scientists, public health experts, and policymakers is needed to harness the full potential of deep learning.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582241307839"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Features and effectiveness of electronic audit and feedback for patient safety and quality of care in hospitals: A systematic review.

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582251315414

James Soresi, Christina Bertilone, Eileen Banks, Theresa Marshall, Kevin Murray, David B Preen

{"title":"Features and effectiveness of electronic audit and feedback for patient safety and quality of care in hospitals: A systematic review.","authors":"James Soresi, Christina Bertilone, Eileen Banks, Theresa Marshall, Kevin Murray, David B Preen","doi":"10.1177/14604582251315414","DOIUrl":"https://doi.org/10.1177/14604582251315414","url":null,"abstract":"Background: Increasing digitisation in healthcare is flowing through to quality improvement strategies, like audit and feedback. Objectives: To systematically review electronic audit and feedback (e-A&F) interventions in hospital settings, examining contemporary practices and quantitatively assessing the relationship between features and effectiveness. Methods: We performed a systematic review using a structured search strategy from 2011 to July 2022. Searches yielded a total of 5095 unique publications, with 152 included in a descriptive synthesis, reporting publication characteristics and practices, and 63 in the quantitative synthesis, to evaluate the effect size of intervention features. Results: The search returned publications across characteristics, including countries of origin, feedback topics, target health professionals, and study design types. We also identified an association with effectiveness for all but one of the features examined, with a Cohen's d ranging from above +0.8 (a large positive effect), to -0.67 (a medium negative effect). Socio-technical features related to supportive organisations and the involvement of engaged health professionals were most associated with effective interventions. Conclusion: Key findings have confirmed that a common set of features of e-A&F systems can influence effectiveness. Results provide practitioners with insight into where resources should be focused during the implementation of e-A&F.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582251315414"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diabetes apps cannot "stand alone": A qualitative study of facilitators and barriers to the continued use of diabetes apps among type 2 diabetes.

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2025-01-01 DOI: 10.1177/14604582251317914

Yucong Shen, Jingyun Zheng, Lingling Lin, Liyuan Hu, Zhongqiu Lu, Chenchen Gao

{"title":"Diabetes apps cannot \"stand alone\": A qualitative study of facilitators and barriers to the continued use of diabetes apps among type 2 diabetes.","authors":"Yucong Shen, Jingyun Zheng, Lingling Lin, Liyuan Hu, Zhongqiu Lu, Chenchen Gao","doi":"10.1177/14604582251317914","DOIUrl":"10.1177/14604582251317914","url":null,"abstract":"Background: Diabetes apps have the potential to improve self-management among people with type 2 diabetes mellitus (T2DM) and thereby prevent complications. However, premature disengagement of diabetes apps hinders this potential. Objective: This study aimed to identify facilitators of and barriers to the continued use of apps among T2DM patients and to formulate recommendations to enhance patients' adherence to diabetes apps. Design: Qualitative study that followed the Consolidated Criteria for Reporting. Qualitative Research (COREQ) guidelines. Methods: Semi-structured interviews were conducted among 15 T2DM patients who continued real-world use of a diabetes app over 1 month. Data were analyzed using conventional content analysis. Results: The results showed that patients were triggered to continue app use by internally directed facilitators (health concerns, need for knowledge, self-conscious emotions) and externally directed facilitators (change in medication, reminders from health professionals). However, app use declined among all participants due to user-specific barriers (increased knowledge and experience, therapeutic inertia, diabetes stigma) and app-specific barriers. Notably, different app-specific barriers were identified in different self-managers: for novice self-managers, the app provided inconsistent information; for competent self-managers, the app provided invalid information and service; and for expert self-managers, the app was no longer being intelligent and new. Conclusions: The success of diabetes app continuance cannot be achieved by diabetes apps alone; rather, diabetes patients, health professionals, medical organizations, regulators, and integration technologies need to be gathered. Consistent, relevant, and current information, timely and continual service, psychological support should be guaranteed.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 1","pages":"14604582251317914"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0