BMC Medical Informatics and Decision Making最新文献

{"title":"Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches.","authors":"Nafiseh Hosseini, Hamid Tanzadehpanah, Amin Mansoori, Mostafa Sabzekar, Gordon A Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan","doi":"10.1186/s12911-025-02887-y","DOIUrl":"10.1186/s12911-025-02887-y","url":null,"abstract":"<p><strong>Background: </strong>The aim of this study was to evaluate the potential models to determine the most important anthropometric factors associated with type 2 diabetes mellitus (T2DM).</p><p><strong>Method: </strong>A dataset derived from the Mashhad Stroke and heart atherosclerotic disorders (MASHAD) study comprising 9354 subject aged 65 - 35. 25% (2336 people) of subjects were diabetic and 75% (7018 people) where non-diabetic was used for the analysis of 10 anthropometric factors and age that were measured in all patients. A K-nearest neighbor (KNN) model was used to assess the association between T2DM and selected factors. The model was evaluated using accuracy, sensitivity, specificity, precision and f1-measure parameters. The receiver operating characteristic (ROC) curve and factor importance analysis were also determined. The performance of the KNN model was compared with Artificial neural network (ANN) and support vector machine (SVM) models.</p><p><strong>Result: </strong>After feature selection analysis and assessing multicollinearity, six factors (Mid-arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), Body Mass Index (BMI), age) were used in the final model. BRI, BAI and MAC factors in males and BMI, BRI, and MAC factors in females were found to have the greatest association with T2DM. The accuracy of the KNN model was approximately 93% for both genders. The best K (number of neighbors) for the model was 4 which had the lowest error rate. The area under the ROC curve (AUC) was 0.985 for men and 0.986 for women. The KNN model achieved the best result of the models explored.</p><p><strong>Conclusion: </strong>The KNN model had a high accuracy (93%) for predicting the association between anthropometric factors and T2DM. Selecting the K parameter (nearest neighbor) has an essential impact on reducing the error rate. Feature selection analysis reduces the dimensions of the KNN model and increases the accuracy of final results.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"49"},"PeriodicalIF":3.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073969","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":"Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis.","authors":"Yaxin Xiong, Yuan Gao, Yucheng Qi, Yingfei Zhi, Jia Xu, Kuo Wang, Qiuyue Yang, Changsong Wang, Mingyan Zhao, Xianglin Meng","doi":"10.1186/s12911-025-02869-0","DOIUrl":"10.1186/s12911-025-02869-0","url":null,"abstract":"<p><strong>Background: </strong>Acute respiratory distress syndrome (ARDS) is a serious threat to human life. Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta-analysis evaluates the accuracy of artificial intelligence in the early diagnosis of ARDS and provides guidance for future research and applications.</p><p><strong>Methods: </strong>A search on PubMed, Embase, Cochrane, Web of Science, CNKI, Wanfang, Chinese Biomedical Literature (CBM), and VIP databases was systematically conducted, from their establishment to November 2023, to obtain eligible studies for the analysis and evaluation of the predictive effect of AI on ARDS. The retrieved literature was screened according to inclusion and exclusion criteria, the quality of the included studies was assessed using QUADAS-2, and the included studies were statistically analyzed.</p><p><strong>Results: </strong>Among the 2, 996 studies, 33 were included in this meta-analysis, which showed that the pooled sensitivity of AI in predicting ARDS was 0.81 (0.76-0.85), the pooled specificity was 0.88 (0.84-0.91), and the area under the receiver operating characteristic curve (AUC) was 0.91 (0.88-0.93). The analyzed studies included 28 models, with a pooled sensitivity of 0.79 (0.76-0.82), a pooled specificity of 0.85 (0.83-0.88), and an AUC of 0.89 (0.86-0.91). In the subgroup analysis, the pooled AUC of the AI models ANN, CNN, LR, RF, SVM, and XGB were 0.86 (0.83-0.89), 0.91 (0.88-0.93), 0.86 (0.83-0.89), and 0.89 (0.86-0.91), 0.90 (0.87-0.92), 0.93 (0.90-0.95), respectively. In an additional subgroup analysis, we evaluated the predictive performance of the AI models trained using different predictors. This meta-analysis was registered in PROSPERO (CRD42023491546).</p><p><strong>Conclusion: </strong>AI has good sensitivity and specificity for predicting ARDS, indicating a high clinical application value. Algorithmic models such as CNN, SVM, and XGB have improved prediction performance. The subgroup analysis revealed that the model trained using images combined with other predictors had the best predictive performance.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"44"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11776246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058215","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":"Correction to: A software tool for applying Bayes' theorem in medical diagnostics.","authors":"Theodora Chatzimichail, Aristides T Hatjimihail","doi":"10.1186/s12911-025-02863-6","DOIUrl":"10.1186/s12911-025-02863-6","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"43"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11773780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058216","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":"Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning.","authors":"Jingteng Li, Kimberley R Zakka, John Booth, Louise Rigny, Samiran Ray, Mario Cortina-Borja, Payam Barnaghi, Neil Sebire","doi":"10.1186/s12911-024-02812-9","DOIUrl":"10.1186/s12911-024-02812-9","url":null,"abstract":"<p><strong>Introduction: </strong>Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).</p><p><strong>Design: </strong>We applied document embedding algorithms to real-world paediatric intensive care (PICU) EHR data to extract patient-specific features from 1853 patients' PICU journeys using 647 unique lab tests and medication events. We evaluated the clinical utility of the patient features via a K-means clustering analysis.</p><p><strong>Results: </strong>We trained a document embedding model under a unique evaluation pipeline and obtained latent patient feature vectors for all 1853 patients. We performed unsupervised clustering to the patient vectors as a downstream analysis and obtained 5 distinct clusters via hyperparameter optimisation. Significant variations (p<0.0001) within both patient characteristics and surgery intervention and diagnostic profiles were detected.</p><p><strong>Conclusion: </strong>The K-means clustering results demonstrated the clinical utilities of the patient-specific features learned from the embedding algorithms. The latent patient features obtained via the embedding process enabled direct applications of other machine learning algorithms. Future work will focus on utilising the temporal information within EHR and extending EHR embedding algorithms to develop personalised patient journey predictions.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"45"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11776155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058217","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":"Insights into prescribing patterns for antidepressants: an evidence-based analysis.","authors":"Hua Min, Farrokh Alemi","doi":"10.1186/s12911-025-02886-z","DOIUrl":"10.1186/s12911-025-02886-z","url":null,"abstract":"<p><strong>Background: </strong>Antidepressants are a primary treatment for depression, yet prescribing them poses significant challenges due to the absence of clear guidelines for selecting the most suitable option for individual patients. This study aimed to analyze prescribing patterns for antidepressants across healthcare providers, including physicians, physician assistants, nurse practitioners, and pharmacists, to better understand the complex factors influencing these patterns in the management of depression.</p><p><strong>Methods: </strong>Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify variables that explained the variation in the prescribed antidepressants, utilizing a large number of claims. Models were created to identify the prescription patterns of the 14 most common antidepressants, including amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine. The accuracy of predictions was measured through the Area under the Receiver Operating Curve (AROC).</p><p><strong>Results: </strong>Our analysis revealed several key factors influencing prescribing patterns, including patients' comorbidities, previous medications, age, and gender. A history of high antidepressant use (four or more prior medications) was the most common factor across antidepressants. Age influenced prescribing patterns, with mirtazapine and trazodone more frequent among older patients, while fluoxetine and sertraline were more common in younger individuals. Condition-specific factors included trazodone for insomnia, and amitriptyline or nortriptyline for headaches. Paroxetine, venlafaxine, and sertraline more often prescribed to females, while bupropion and doxepin were commonly prescribed for patients with tobacco use disorder and opioid dependence. Predictive factors per medicine ranged from 51 (doxepin) to 168 (citalopram), with cross-validated AROC scores averaging 76.3%.</p><p><strong>Conclusions: </strong>Our findings provide valuable insights into the nuanced factors that shape evidence-based antidepressant prescribing practices, offering a foundation for more personalized, effective depression treatment. Further research is needed to validate these models in other extant databases. These findings contribute to a more comprehensive understanding of antidepressant prescribing practices and have the potential to improve patient outcomes in the management of depression.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"42"},"PeriodicalIF":3.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11773954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051794","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":"Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring.","authors":"Jiawen Wang, Chunyan Min, Feng Yu, Kai Chen, Ling Mao","doi":"10.1186/s12911-025-02879-y","DOIUrl":"10.1186/s12911-025-02879-y","url":null,"abstract":"<p><strong>Background: </strong>Many respiratory diseases such as pneumoconiosis require to close monitor the symptoms such as abnormal respiration and cough. This study introduces an automated, nonintrusive method for detecting cough events in clinical settings using a flexible chest patch with tri-axial acceleration sensors.</p><p><strong>Methods: </strong>Twenty-five young healthy persons (hereinafter referred to as healthy adults) and twenty-five clinically diagnosed pneumoconiosis patients (hereinafter referred to as patients) participated in the experiment by wearing a flexible chest patch with an embedded ACC sensor. The top 56% of the highest scoring features were then combined using several feature selection algorithms to perform the cough classification task. The multicriteria decision making (MCDM) method was used to select the classifier with the highest scores.</p><p><strong>Results: </strong>The optimized classifier proposed in this paper achieved an accuracy of 87.1%, precision of 95%, recall of 79.1%, F1 score of 86.4%, and AUC of 95.4% for recognizing coughs in healthy adults; an accuracy of 96.1%, precision of 95%, recall of 97.4%, F1 score of 96.2%, and AUC of 98.7% for recognizing coughs in patients; and an overall accuracy of 92% for distinguishing coughs in the combined group of healthy adults and patients.</p><p><strong>Conclusions: </strong>Our study demonstrated the effectiveness of an automated cough recognition system in both pneumoconiosis patients and healthy adults. This approach facilitates daily remote monitoring of cough occurrence in individuals with pneumoconiosis, potentially enhancing the ability of physicians to evaluate clinical status.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"41"},"PeriodicalIF":3.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11773742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051792","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":"Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation.","authors":"Yih-Lon Lin, Yu-Min Chiang, Tsuen-Chiuan Tsai, Sheng-Gui Su","doi":"10.1186/s12911-025-02866-3","DOIUrl":"10.1186/s12911-025-02866-3","url":null,"abstract":"<p><strong>Background: </strong>In medical education, enhancing thinking skills is vital. The Virtual Diagnosis and Treatment Platform (VP) refines medical students' diagnostic abilities through interactive patient interviews (simulated patient interactions). By analyzing the questions asked during these interviews, the VP evaluates students' aptitude in medical history inquiries, offering insights into their thinking capabilities. This study aimed to extract insights from case summaries and patient interviews to improve evaluation and feedback in medical education.</p><p><strong>Methods: </strong>This study employs a systematic approach to knowledge-point classification by utilizing both simple long short-term memory (LSTM)-based and Siamese-based networks, coupled with cross-validation techniques. The dataset under scrutiny originates from the \"Clinical Diagnosis and Treatment Skills Competitions\" spanning the first to third years in Taiwan. The methodology involves generating knowledge points from sequential questions posed during case summaries and patient interviews. These knowledge points are then subjected to classification using the designated neural network architectures.</p><p><strong>Results: </strong>The experimental findings reveal promising outcomes, particularly when the Siamese-based network is used for knowledge-point classification. Through repeated (stratified) 10-fold cross validation, the accuracies achieved consistently exceeded 93%, with a standard deviation less than 0.007. These results underscore the efficacy of the proposed methodologies in enhancing virtual clinical diagnosis systems.</p><p><strong>Conclusions: </strong>This study underscores the viability of leveraging advanced neural network architectures, particularly the Siamese-based network, for knowledge-point classification within virtual clinical diagnosis systems. By effectively discerning and classifying knowledge points derived from case summaries and patient interviews, these systems offer invaluable insights into students' thinking capabilities in medical education. The robust accuracies attained through cross-validation affirm the feasibility and efficacy of the proposed methodologies, thus paving the way for enhanced virtual clinical training platforms.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"39"},"PeriodicalIF":3.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11763137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143037017","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":"Feasibility of YOLOX computer model-based assessment of knee function compared with manual assessment for people with severe knee osteoarthritis.","authors":"Tao Yang, Jie Zhao, Ben Wang, Li Wang, Hengzhe Bao, Bing Li, Wen Luo, Huiwen Zhao, Jun Liu","doi":"10.1186/s12911-025-02877-0","DOIUrl":"10.1186/s12911-025-02877-0","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to assess the feasibility of computer model-based evaluation of knee joint functional capacity in comparison with manual assessment.</p><p><strong>Methods: </strong>This study consisted of two phases: (1) developing an automatic knee joint action recognition and classification system on the basis of improved YOLOX and (2) analyzing the feasibility of assessment by the software system and doctors, identifying the knee joint function of patients, and determining the accuracy of the software system. We collected 40-50 samples for use in clinical experiments. The datasets used in this study were collected from patients admitted to the Joint Surgery Center. In this study, the knee joint assessment items included stair climbing, walking on uneven surfaces, and knee joint function. To assess the computer model's automatic evaluation of knee joint function, MedCalc 20 statistical software was used to analyze the consistency of the Lequesne functional index between the computer model's automated determinations and manual independent assessments.</p><p><strong>Results: </strong>The weighted kappa coefficients between the doctors' assessments and the software system's assessments were 0.76 (95% confidence intervals:0.59 ~ 0.92) for climbing up and down stairs, 0.64 (95% confidence intervals:0.45 ~ 0.82) for walking on uneven floors, and 0.68 (95% confidence intervals:0.53 ~ 0.84) for the Lequesne functional index, indicating good consistency between the assessments of the software system and doctors.</p><p><strong>Conclusion: </strong>This paper introduces an automatic knee joint action recognition and classification method based on improved YOLOX. By comparing the results obtained by orthopedic doctors and the software system, the feasibility of this software system was validated in the clinic.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"40"},"PeriodicalIF":3.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11763141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143037015","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":"Enhancing doctor-patient communication using large language models for pathology report interpretation.","authors":"Xiongwen Yang, Yi Xiao, Di Liu, Yun Zhang, Huiyin Deng, Jian Huang, Huiyou Shi, Dan Liu, Maoli Liang, Xing Jin, Yongpan Sun, Jing Yao, XiaoJiang Zhou, Wankai Guo, Yang He, WeiJuan Tang, Chuan Xu","doi":"10.1186/s12911-024-02838-z","DOIUrl":"10.1186/s12911-024-02838-z","url":null,"abstract":"<p><strong>Background: </strong>Large language models (LLMs) are increasingly utilized in healthcare settings. Postoperative pathology reports, which are essential for diagnosing and determining treatment strategies for surgical patients, frequently include complex data that can be challenging for patients to comprehend. This complexity can adversely affect the quality of communication between doctors and patients about their diagnosis and treatment options, potentially impacting patient outcomes such as understanding of their condition, treatment adherence, and overall satisfaction.</p><p><strong>Materials and methods: </strong>This study analyzed text pathology reports from four hospitals between October and December 2023, focusing on malignant tumors. Using GPT-4, we developed templates for interpretive pathology reports (IPRs) to simplify medical terminology for non-professionals. We randomly selected 70 reports to generate these templates and evaluated the remaining 628 reports for consistency and readability. Patient understanding was measured using a custom-designed pathology report understanding level assessment scale, scored by volunteers with no medical background. The study also recorded doctor-patient communication time and patient comprehension levels before and after using IPRs.</p><p><strong>Results: </strong>Among 698 pathology reports analyzed, the interpretation through LLMs significantly improved readability and patient understanding. The average communication time between doctors and patients decreased by over 70%, from 35 to 10 min (P < 0.001), with the use of IPRs. The study also found that patients scored higher on understanding levels when provided with AI-generated reports, from 5.23 points to 7.98 points (P < 0.001), with the use of IPRs. indicating an effective translation of complex medical information. Consistency between original pathology reports (OPRs) and IPRs was also evaluated, with results showing high levels of consistency across all assessed dimensions, achieving an average score of 4.95 out of 5.</p><p><strong>Conclusion: </strong>This research demonstrates the efficacy of LLMs like GPT-4 in enhancing doctor-patient communication by translating pathology reports into more accessible language. While this study did not directly measure patient outcomes or satisfaction, it provides evidence that improved understanding and reduced communication time may positively influence patient engagement. These findings highlight the potential of AI to bridge gaps between medical professionals and the public in healthcare environments.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"36"},"PeriodicalIF":3.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027904","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 persian version of mhealth app usability questionnaire (MAUQ) for patients: a psychometric assessment study.","authors":"Khadijeh Moulaei, Fatemeh Dinari, Abbas Sheikhtaheri, Kambiz Bahaadinbeigy, Sadrieh Hajesmaeel-Gohari","doi":"10.1186/s12911-025-02882-3","DOIUrl":"10.1186/s12911-025-02882-3","url":null,"abstract":"<p><strong>Introduction: </strong>The growing importance of mobile apps in osteoporosis management highlights the crucial need for evaluating their utility and usability, particularly for Osteoporosis support apps. Addressing this need, the mHealth App Usability Questionnaire (MAUQ) was crafted in four different versions, categorized based on the nature of the app (interactive or standalone) and the intended user (patient or provider). Due to its usage by diverse users with varying languages, this questionnaire requires psychometric assessment in multiple languages. This study aimed to translate and validate the Persian version of MAUQ for patients.</p><p><strong>Method: </strong>After translating the standalone and interactive versions of MAUQ into the Persian language, face validity, content validity, and factor analysis were conducted. Ten patients with osteoporosis were involved for face validity, and ten experts in medical informatics and health information technology were invited to assess content validity by completing a questionnaire. A total of 99 patients with osteoporosis participated in the factor analysis. The reliability of the questionnaires was assessed by calculating Cronbach's alpha.</p><p><strong>Results: </strong>The face validity and the content of the Persian version of MAUQ were confirmed. Factor analysis of the standalone version of MAUQ showed 18 items in three dimensions: easy to use (7 items), user interface and satisfaction (6 items), and usefulness (5 items). Factor analysis of the interactive version of MAUQ showed 21 items in two dimensions: easy to use and satisfaction (11 items) and information arrangement and usefulness (10 items). The Cronbach's alpha of the questionnaire for standalone and interactive applications was 0.90.</p><p><strong>Conclusion: </strong>The psychometric assessment of the Persian MAUQ established its validity and reliability among osteoporosis patients, affirming its efficacy as a robust tool for evaluating mHealth app usability.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"38"},"PeriodicalIF":3.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11760090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027912","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}