Category: Statistics

Rev Esc Enferm USP. 2026 Mar 16;60:e20250167. doi: 10.1590/1980-220X-REEUSP-2025-0167en. eCollection 2026.

ABSTRACT

OBJECTIVE: To analyze the effectiveness of the oral administration of rosemary dry extract capsules (Rosmarinus officinalis L.) (1g/day) on the estimated cardiovascular risk, over ten years, in nursing professionals working in emergency and urgent care services.

METHOD: Quasiexperimental study conducted in Southern Brazil. Participants completed a sociodemographic and clinical questionnaire, had blood collected for glycemic and lipid profile analysis, and took oral capsules containing 500 mg of dry rosemary extract twice a day for 8 weeks; subsequently, a new blood sample was taken. The Framingham Global Risk Score was used to estimate cardiovascular risk. Data analysis used descriptive and inferential statistics; significance level of 5%.

RESULTS: The study included 36 professionals, predominantly obese and those with elevated total cholesterol. When comparing the variables before and after the intervention, differences were found in blood pressure (p = 0.048), in total cholesterol (p < 0.001), and in the Framingham Global Risk Score (p = 0.047).

CONCLUSION: Dry rosemary extract was effective in reducing the estimated cardiovascular risk of nursing professionals. Brazilian Registry of Clinical Trials No. RBR 88hrnnw.

PMID:41861393 | DOI:10.1590/1980-220X-REEUSP-2025-0167en

JMIR Med Educ. 2026 Mar 20;12:e82885. doi: 10.2196/82885.

ABSTRACT

BACKGROUND: Generative artificial intelligence tools such as ChatGPT are increasingly used by medical students for self-directed learning. Although these models demonstrate linguistic fluency, their reliability as supplementary resources for preclinical education remains uncertain. In particular, comparisons with evidence-based references such as UpToDate are lacking.

OBJECTIVE: This study evaluated the similarity between responses generated by ChatGPT (with GPT-4o mini) and those from UpToDate to preclinical medical education questions to assess ChatGPT’s potential as an adjunctive learning tool.

METHODS: We conducted a cross-sectional comparison study using 150 first-order questions derived from a preclinical question bank at a single allopathic institution under the oversight of a medical educator with more than 25 years of teaching experience. Each question was entered into ChatGPT 10 times in separate chat sessions, and responses from UpToDate were retrieved from the most relevant articles. The responses were preprocessed through lemmatization, stop-word removal, punctuation removal, and numeric normalization. Similarity between ChatGPT and UpToDate responses was quantified using term frequency-inverse document frequency (TF-IDF) cosine similarity. To determine whether the observed similarities exceeded chance, ChatGPT outputs were compared with a null distribution generated from randomized text.

RESULTS: ChatGPT responses demonstrated statistically significant similarity to UpToDate in 59.3% (89/150) of questions. Across subject areas, pharmacology showed the highest concordance (mean cosine similarity 0.338, SD 0.134), followed by pathology (mean 0.321, SD 0.142), biochemistry (mean 0.296, SD 0.120), microbiology (mean 0.297, SD 0.108), and immunology (mean 0.275, SD 0.102). All subject-level similarity scores exceeded those generated from randomized text, confirming that the observed overlap was nonrandom.

CONCLUSIONS: ChatGPT with GPT-4o mini exhibited moderate but meaningful alignment with UpToDate across preclinical topics, performing best in fact-based disciplines such as pharmacology. Although it is not a substitute for evidence-based resources, ChatGPT may serve as an accessible adjunctive tool for medical students. Integration into preclinical learning should be coupled with artificial intelligence literacy training to promote responsible use and critical appraisal.

PMID:41861392 | DOI:10.2196/82885

JMIR Mhealth Uhealth. 2026 Mar 20;14:e58835. doi: 10.2196/58835.

ABSTRACT

BACKGROUND: Internet-based cognitive behavioral therapy (iCBT) for the treatment of depression has proven to be an effective and accessible option. However, iCBTs tend to have low adherence rates, which may negatively impact their effectiveness. One such iCBT program is the browser-based, proven-effective iFightDepression (iFD) tool. An app-based version of the iFD tool is the iFD app, which was developed to improve usability with a smartphone. The iFD app provides enhanced usability and a more optimized user experience on mobile devices. Additionally, it offers more comfortable interaction with worksheets, reduced text with added videos, and quicker access to the program via the smartphone icon. These improved usability on smartphones and could have an impact on adherence.

OBJECTIVE: This study investigated (1) whether adherence parameters, that is, the number of worksheets, the number of sessions, and the number of workshops, significantly differed between users of the iFD app and the iFD tool and (2) exploratorily whether symptom reduction (delta Patient Health Questionnaire-9 [PHQ-9] scores) differed after 5 to 9 weeks between the iFD app and the iFD tool, after controlling for covariates.

METHODS: We used t tests to compare data from 56 participants using the iFD app for 8 weeks with data from 172 participants using the iFD tool in a previous 6-week study. Exploratively, symptom reduction was compared between formats. A multiple regression model was calculated with the delta PHQ-9 score as the dependent variable and format, baseline PHQ-9 score, adherence, current psychotherapy, antidepressants, age, and sex as independent variables.

RESULTS: There was no significant difference between the iFD tool and the iFD app in terms of the number of sessions per week (t67.393=0.920; P=.36; corrected P=.36), the number of workshops (t76.368=-1.217; P=.30; corrected P=.36), and the number of worksheets per week (t74=0.984; P=.33; corrected P=.36). We found no difference in delta PHQ-9 scores between the iFD app and the iFD tool, and baseline PHQ-9 scores were the only significant predictor (b=-0.61; P<.001).

CONCLUSIONS: Despite the improved availability of the app version in daily life, there were no significant differences in the use parameters we analyzed and no differences in symptom reduction. This study provides the first evidence that adherence to iCBT content is comparable for browser- and app-based interventions and that symptom reduction is similar for both formats. However, this study used a convenience sample, and therefore, the results must be interpreted with caution. Notably, in the study of the iFD tool, guidance was provided by the study assistants in a standardized manner. In the pilot study on the iFD app, the amount of guidance varied substantially, as it was provided by the participants’ health care practitioners. These differences in guidance could also have an influence on adherence.

PMID:41861387 | DOI:10.2196/58835

JMIR Public Health Surveill. 2026 Mar 20;12:e82684. doi: 10.2196/82684.

ABSTRACT

BACKGROUND: Hypertension represents an important global health challenge, closely linked to cardiovascular diseases and elevated premature mortality rates. Prehypertension, defined as elevated blood pressure not meeting the diagnostic criteria for hypertension, necessitates early intervention to prevent disease progression. Health literacy, defined as the capacity to comprehend and use health-related information, is a key determinant of health outcomes but has rarely been studied in the context of prehypertension prevention.

OBJECTIVE: This study investigated the association between health literacy and prehypertension in South Korean adults. Unlike prior research focusing on treatment adherence in diagnosed patients, this study used the most recent nationally representative data to explore how domain-specific health literacy is associated with prehypertension across various subgroups, identifying potential mechanisms for intervention.

METHODS: Data were obtained from the 2023 Korea National Health and Nutrition Examination Survey, a nationally representative cross-sectional study. A stratified, multistage clustered sampling design was used to invite participants. Adults aged 19 years and older (N=1873) who completed the Korean Health Literacy Index were included. Prehypertension was defined as a systolic blood pressure of 130 to 139 mm Hg or a diastolic blood pressure of 80 to 89 mm Hg. A multivariable survey-weighted logistic regression model was used to assess the associations between health literacy and prehypertension, adjusting for sociodemographic and health-related covariates.

RESULTS: Of the 1873 participants, 319 (17.0%) had prehypertension, and 1098 (58.6%) showed low health literacy. After adjustment, those with low health literacy had a 43% higher likelihood of prehypertension (odds ratio 1.43, 95% CI 1.07-1.91) than those with high health literacy. Subgroup analyses revealed that the protective impact of health literacy is not uniform but is modulated by demographic contexts.

CONCLUSIONS: The observed patterns may reflect three potential mechanisms: (1) motivation for and dependency on health information (eg, in women, middle-aged, lower education, and unemployed groups), (2) synergy between health literacy and resources (eg, in high-income, urban, married, and employer-insured groups), and (3) preventive efficacy in low-risk populations. Low health literacy was significantly associated with prehypertension, with variations across subgroups suggesting context-dependent mechanisms. Health literacy may serve as a modifiable determinant and compensatory resource for cardiovascular risk prevention, particularly in populations with limited access to health care. Targeted interventions that address domain-specific health literacy deficits are needed to reduce the prehypertension burden.

PMID:41861384 | DOI:10.2196/82684

JMIR Hum Factors. 2026 Mar 20;13:e73609. doi: 10.2196/73609.

ABSTRACT

BACKGROUND: Although telemedicine grew rapidly during the COVID-19 pandemic, instruments to assess general practitioners’ (GPs) attitudes and behavioral intentions to use it are scarce. In Sweden, the Physicians’ Attitudes and Intentions to use Telemedicine (PAIT) questionnaire was developed from the “theory of planned behavior” in 2019 and translated into English in 2022.

OBJECTIVE: The aim of this study was to explore similarities and differences between behavioral intentions and predictors of intentions to use telemedicine among GPs in England and Sweden.

METHODS: This study compared attitudes, behavioral intentions, and self-reported use of telemedicine after the COVID-19 pandemic among 52 GPs in England and 101 GPs in Sweden. The PAIT questionnaire has 33 items with 7-point Likert scale options ranging from “strongly disagree” to “strongly agree,” examining 3 predictors of intentions: attitudes (12 items), subjective norms (6 items), perceived behavioral control (9 items), and “intentions” (6 items) to use telemedicine; 22 items assess use of telemedicine tools, general questions about telemedicine, training experience, free-text comments, and demographic and background questions.

RESULTS: Both English and Swedish GPs reported little training and low use of telemedicine after the COVID-19 pandemic. Swedish GPs had significantly higher mean scores for intentions to use telemedicine in daily practice compared with English GPs. More positive attitudes and higher perceived behavioral control were significantly associated with higher behavioral intention scores in both English and Swedish GPs.

CONCLUSIONS: While our results are exploratory due to sample size constraints, these findings provide insights into the similarities and differences between English and Swedish GPs regarding telemedicine adoption-attitudes, behavioral intentions, and self-reported use of telemedicine assessed by the PAIT questionnaire-which proved useful for cross-country comparisons and could be used for further international studies.

PMID:41861382 | DOI:10.2196/73609

JMIR Mhealth Uhealth. 2026 Mar 20;14:e73647. doi: 10.2196/73647.

ABSTRACT

BACKGROUND: Fetal alcohol spectrum disorders (FASD) affect 1.1% to 5% of the general population. Yet, most children with FASD and their families cannot access evidence-based interventions. Mobile health (mHealth) interventions have the potential to increase access to care on a broad scale. While numerous self-directed parenting apps exist, none have been tested for FASD. The FMF (Families Moving Forward) Connect app is a self-directed intervention derived from an empirically supported intervention for caregivers raising children with FASD. FMF Connect is the first self-directed parenting app for FASD, and also one of the first parenting apps to be systematically developed and tested.

OBJECTIVE: This study aimed to test the efficacy of FMF Connect for caregivers raising children with FASD on targeted primary (child behavior, caregiver attributions, parenting efficacy and satisfaction, FASD knowledge, and family needs met) and secondary (child adaptive behavior, caregiver self-care, and app satisfaction) outcomes.

METHODS: This study involved a 3-arm randomized controlled trial with equal allocation to groups (1) FMF Connect+coaching, (2) FMF Connect, or (3) waitlist control. Participants from the United States were recruited online through an open access website. Recruitment materials were distributed by the Collaborative Initiative on FASD, FASD listserves, and social media. In total, 129 caregivers of children (aged 3-12 y) with FASD or prenatal alcohol exposure (PAE) were enrolled. Online surveys were administered at baseline, 6 weeks, and 12 weeks. Data were analyzed with linear mixed modeling, linear regressions, and structural equation modeling using SPSS (version 29.0; IBM) and Mplus 8 (Muthén & Muthén).

RESULTS: A total of 43 participants were randomized to each group. Caregivers were predominantly White adoptive mothers. Of the total, 64% (n=83) of participants were retained through the 12-week follow-up. Groups did not differ in terms of demographic characteristics, baseline levels of functioning, or attrition. Usage patterns were similar across groups, suggesting coaching did not increase engagement. Given a few differences, app intervention groups were combined for analyses. Relative to the waitlist group, caregivers in the FMF Connect group evidenced greater improvements in FASD knowledge, child behavior attributions, family needs met, and self-care after 12 weeks (P=.01-.048). After controlling for multiple comparisons, differences in FASD knowledge, self-care, and family needs met approached significance (P=.06-.07). Groups did not differ in parenting satisfaction, child behavior problems, or adaptive functioning. More app usage is related to greater changes in parenting efficacy. Caregiver behavior attributions at 6 weeks did not mediate intervention effects.

CONCLUSIONS: This study demonstrated initial efficacy of the FMF Connect app for targeted caregiver outcomes, with small to medium effect sizes. As an mHealth app, the FMF Connect intervention has potential for scalability and accessibility. This could lead to a substantial public health impact, particularly for families who face challenges accessing evidence-based resources or encounter other barriers to care.

PMID:41861374 | DOI:10.2196/73647

JMIR Hum Factors. 2026 Mar 20;13:e79866. doi: 10.2196/79866.

ABSTRACT

BACKGROUND: Living with asthma-especially in its severe forms-can significantly impact daily life, including social activities, work, travel, and household responsibilities. Collaboration between patients and health care professionals (HCPs) is frequently lacking, particularly regarding treatment goals. Self-management has been shown to mitigate the negative effects of asthma. Technical solutions might support self-management for patients with chronic diseases and their collaboration with HCPs.

OBJECTIVE: This explorative study aims to understand how patients and HCPs experience the use of a digital self-management system for asthma monitoring.

METHODS: This qualitative study was conducted at 5 primary care centers in Sweden and involved 20 participants: 14 patients who had utilized the digital self-management system Asthmatuner for at least 6 months and 6 specialist asthma nurses. Individual semistructured interviews were analyzed using qualitative content analysis to explore patterns and relationships within the data.

RESULTS: We identified 1 main theme, that is, “data-supported empowerment,” and 3 subthemes, that is, (1) empowerment by awareness, knowledge, and learning; (2) contact health care-patient; and (3) managing the monitoring. The theme of data-supported empowerment emerged as a synthesis of these findings, reflecting how the self-management system enabled patients to take a more active role in managing their medications and health. While most patients did not monitor their data continuously, they engaged with it when they felt it was necessary. Some patients expressed expectations of personalized follow-up from HCPs based on their monitoring data; however, these expectations were not always fulfilled. We also revealed a need to adapt and clarify the overlapping responsibilities of patients and HCPs.

CONCLUSIONS: The digital self-management system for asthma was well received by both patients and HCPs, as it promoted empowerment. Clear communication about changes in workflow and responsibilities is essential to ensure the successful implementation of digital systems and improved health care delivery.

PMID:41861373 | DOI:10.2196/79866

JMIR Form Res. 2026 Mar 20;10:e82685. doi: 10.2196/82685.

ABSTRACT

BACKGROUND: Extracting accurate medication information from Thai hospital records presents challenges due to the narrative style of medical notes, which often combine Thai and English terminology. Named entity recognition (NER) serves as the foundational step for advanced clinical information extraction (IE) tasks, including medical concept normalization and relation extraction. This study aimed to establish a robust NER framework to address these difficulties by leveraging ontology-based annotation and pretrained transformer models.

OBJECTIVE: The primary objective of this study was to evaluate the performance of 5 fine-tuned pretrained transformer models-BioClinicalBERT, ClinicalBERT, PubMedBERT, MultilingualBERT, and ThaiBERT-based on Bidirectional Encoder Representations from Transformers (BERT) in extracting structured medication information from unstructured Thai hospital discharge summaries.

METHODS: Ninety discharge summaries were collected from Maharaj Nakhon Chiang Mai Hospital. These documents were annotated by physicians following the annotation guidelines based on international standards, including Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) and Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR). The dataset was divided into fine-tuning (70 records, 78%, 2030 annotated spans), validation (10 records, 11%, 277 annotated spans), and testing sets (10 records, 11%, 358 annotated spans). The 5 transformer models were fine-tuned and evaluated using this annotated data to recognize and classify key medication entities (substance, route of administration, unit of measure, time patterns, and unit of presentation).

RESULTS: We found that all models had good NER performance metrics in both the validation and test datasets. Regarding test performance, ClinicalBERT achieved the highest exact F1-score at 0.973, compared with 0.968 for BioClinicalBERT, 0.925 for PubMedBERT, 0.931 for MultilingualBERT, and 0.969 for ThaiBERT. All models showed strength in accurately identifying “Substance” and “Dosage” entities, whereas “Unit of Measure” proved to be the most challenging entity type due to implicit information in the source text for all models.

CONCLUSIONS: The findings suggest that ontology-based medication IE using transformer-based models holds promise for enhancing data standardization and interoperability within the Thai health care system. Future work will need to leverage the granular annotations preserved in the dataset to develop medical concept normalization and relation extraction models to complete the medical IE system.

PMID:41861368 | DOI:10.2196/82685

J Med Internet Res. 2026 Mar 20;28:e78396. doi: 10.2196/78396.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) has shown promise for automating spinal alignment assessment in adolescent idiopathic scoliosis (AIS). However, AI models typically exhibit reduced accuracy and robustness when deployed across multiple medical centers due to variability in imaging protocols and data characteristics, potentially compromising clinical diagnosis and treatment decisions.

OBJECTIVE: This study aimed to develop a real-time, plug-and-play data transformation method to enhance the robustness of deep learning models against data heterogeneity in radiographs, thereby improving their performance in assessing AIS across multiple medical centers.

METHODS: In this retrospective multicenter study, 3899 full-spine radiographs from 7 hospitals (2 from Hong Kong and 5 from Mainland China), collected between January 2012 and August 2024, were included. Data from 2 hospitals in Hong Kong (n=3034) were used for model training and internal validation, while radiographs from the 5 mainland hospitals (n=865) formed 5 independent external validation datasets. A novel pixel intensity-based data transformation method was developed to standardize image contrast and brightness across datasets and integrated into the model training process to enhance our previously developed AI model, SpineHRNet+. The enhanced model’s accuracy and robustness for cobb angle (CA) prediction and severity classification were evaluated using both internal and external datasets. Data heterogeneity across centers was quantified by brightness and contrast differences. CA prediction accuracy was evaluated using residual analysis, linear regression (coefficient of determination [R²]), and Bland-Altman analyses. Model performance for disease severity classification was assessed using sensitivity, specificity, precision, negative predictive value, accuracy, and confusion matrix analysis. The transformation method aligns pixel intensity distributions across datasets using statistical profiling and optimization, ensuring consistent image characteristics while preserving anatomical integrity.

RESULTS: The developed data transformation method significantly reduced contrast variability between datasets, improving consistency in image characteristics and enabling more reliable AI analysis. The enhanced SpineHRNet+ achieved consistent and accurate CA predictions across external validation datasets, with mean prediction errors within 4° (SD 3.12°), and maintained an R² greater than 0.90 for all centers. The sensitivity and negative predictive value for disease severity grading improved to 90.18% and 93.16%, respectively. Bland-Altman analyses demonstrated robust agreement, with 95% limits of agreement within 7.51° across all datasets.

CONCLUSIONS: The proposed data transformation approach effectively addressed data heterogeneity, significantly improving the accuracy and robustness of SpineHRNet+ in multicenter AIS assessments. The real-time processing capability and preservation of anatomical integrity underscore the method’s clinical practicality, enabling scalable and reliable AI applications in diverse health care environments.

PMID:41861366 | DOI:10.2196/78396

Prim Care Companion CNS Disord. 2026 Mar 10;28(2):25m04074. doi: 10.4088/PCC.25m04074.

ABSTRACT

Objective: To evaluate if ultra-low-dose mirtazapine (3.75 mg) improves insomnia without next-day effects.

Methods: This retrospective study evaluated data collected from September 5, 2024, to March 7, 2025, from an outpatient setting consisting of veterans with insomnia who were treated with ultra-low-dose mirtazapine. The Insomnia Severity Index (ISI) was administered during the first appointment and at each subsequent visit with the respective psychiatrist to monitor insomnia symptoms. Summary statistics were used to compare ISI scores at baseline and 1-3 months after starting treatment.

Results: Considering all veterans evaluated (N = 53), 47% showed a meaningful decrease in ISI value (greater than 7 points). Patients who completed treatment showed a constant or decreased ISI score (mean [SD] change: 11.3 [6.46]).

Conclusion: Ultra-low-dose mirtazapine may improve symptoms and ISI values for chronic insomnia.

Prim Care Companion CNS Disord 2026;28(2):25m04074.

Author affiliations are listed at the end of this article.

PMID:41861363 | DOI:10.4088/PCC.25m04074