Nevin Manimala

JMIR Res Protoc. 2025 Sep 17;14:e67059. doi: 10.2196/67059.

ABSTRACT

BACKGROUND: Self-harm is a major public health concern, with prevalence increasing worldwide, particularly after the COVID-19 pandemic and associated lockdown restrictions. Health care workers (HCWs) face various challenges, such as pressures of social and familial responsibilities, a lack of integration within the profession, heavier workload, bullying at the workplace, and limited support in the workplace, that impact their mental health and often lead to self-harm.

OBJECTIVE: We aim to synthesize the evidence on the pooled prevalence of self-harm worldwide and identify risk factors for self-harm among HCWs.

METHODS: We will conduct a systematic review of observational and experimental studies that investigated the overall prevalence of self-harm among HCWs. We will search the PubMed, PsycINFO, Embase, and CINAHL databases for eligible articles from inception until March 2025 using specific search terms developed using the population, exposure, comparison, and outcome framework. Study selection and reporting will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the Meta-Analysis of Observational Studies in Epidemiology guidelines. We will contact the corresponding author via email if the required data are not available in the article. After completing the article search, duplicate records will be removed. Titles and abstracts will then be screened according to the inclusion and exclusion criteria, followed by retrieval of the full texts for detailed screening. All the required data for the review, such as names of authors, publication year, prevalence of self-harm, type of profession, associated risk factors to self-harm, and others, will be extracted using a standardized data extraction form. The quality of the studies will be assessed using the Joanna Briggs Institute guidelines based on the study design. Random-effects meta-analysis will be used to derive the pooled prevalence using Stata (version 17.0) software. We will conduct a subgroup meta-analysis on sex, regions, and the type of profession (physicians or nurses). We will also examine the association of risk factors of self-harm with sociodemographic factors to observe their relationship. Both analyses will be performed using RevMan software. Publication bias will be examined using the funnel plot and Egger test.

RESULTS: Data analysis is expected to be completed by August 2025, and manuscript preparation is expected to be completed by October 2025. This review is expected to be completed and published by January 2026.

CONCLUSIONS: We will provide a comprehensive synthesis of the overall prevalence of self-harm among HCWs. We will also provide important information to develop effective strategies for preventing and managing self-harm among HCWs.

TRIAL REGISTRATION: PROSPERO CRD42024581791; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024581791.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/67059.

PMID:40961495 | DOI:10.2196/67059

J Med Internet Res. 2025 Sep 17;27:e73391. doi: 10.2196/73391.

ABSTRACT

BACKGROUND: Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context.

OBJECTIVE: We examined users’ interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users’ clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions.

METHODS: We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions.

RESULTS: Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing.

CONCLUSIONS: Findings highlight the chatbot’s effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot’s scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

PMID:40961494 | DOI:10.2196/73391

JMIR Med Inform. 2025 Sep 17;13:e73960. doi: 10.2196/73960.

ABSTRACT

BACKGROUND: Emergency department (ED) overcrowding remains a critical challenge, leading to delays in patient care and increased operational strain. Current hospital management strategies often rely on reactive decision-making, addressing congestion only after it occurs. However, effective patient flow management requires early identification of overcrowding risks to allow timely interventions. Machine learning (ML)-based predictive modeling offers a solution by forecasting key patient flow measures, such as waiting count, enabling proactive resource allocation and improved hospital efficiency.

OBJECTIVE: The aim of this study is to develop ML models that predict ED waiting room occupancy (waiting count) at 2 temporal resolutions. The first approach is the hourly prediction model, which estimates the waiting count exactly 6 hours ahead at each prediction time (eg, a 1 PM prediction forecasts 7 PM). The second approach is the daily prediction model, which forecasts the average waiting count for the next 24-hour period (eg, a 5 PM prediction estimates the following day’s average). These predictive tools support resource allocation and help mitigate overcrowding by enabling proactive interventions before congestion occurs.

METHODS: Data from a partner hospital’s ED in the southeastern United States were used, integrating internal and external sources. Eleven different ML algorithms, ranging from traditional approaches to deep learning architectures, were systematically trained and evaluated on both hourly and daily predictions to determine the models that achieved the lowest prediction error. Experiments optimized feature combinations, and the best models were tested under high patient volume and across different hours to assess temporal accuracy.

RESULTS: The best hourly prediction performance was achieved by time series vision transformer plus (TSiTPlus) with a mean absolute error (MAE) of 4.19 and a mean squared error (MSE) of 29.36. The overall hourly waiting count had a mean of 18.11 and a SD (σ) of 9.77. Prediction accuracy varied by time of day, with the lowest MAE at 11 PM (2.45) and the highest at 8 PM (5.45). Extreme case analysis at (mean + 1σ), (mean + 2σ), and (mean + 3σ) resulted in MAEs of 6.16, 10.16, and 15.59, respectively. For daily predictions, an explainable convolutional neural network plus (XCMPlus) achieved the best results with an MAE of 2.00 and a MSE of 6.64. The daily waiting count had a mean of 18.11 and a SD of 4.51. Both models outperformed traditional forecasting approaches across multiple evaluation metrics.

CONCLUSIONS: The proposed prediction models effectively forecast ED waiting count at both hourly and daily intervals. The results demonstrate the value of integrating diverse data sources and applying advanced modeling techniques to support proactive resource allocation decisions. The implementation of these forecasting tools within hospital management systems has the potential to improve patient flow and reduce overcrowding in emergency care settings. The code is available in our GitHub repository.

PMID:40961493 | DOI:10.2196/73960

JMIR Res Protoc. 2025 Sep 17;14:e77532. doi: 10.2196/77532.

ABSTRACT

BACKGROUND: On a population level, digital mental health interventions effectively reduce depression and anxiety symptoms. However, middle-aged and older adults with chronic pain and coexisting depression or anxiety have not been adequately represented in digital mental health studies.

OBJECTIVE: The goal of this study is to refine an existing mobile, digital mental health intervention (Wysa for Chronic Pain) that addresses symptoms of depression, anxiety, and coexisting chronic pain for the unique challenges and technology use patterns of middle-aged and older adults.

METHODS: Using a mixed methods, human-centered design approach and a series of randomized trials, we will test and iteratively refine just-in-time adaptive interventions (JITAIs) that are designed to increase engagement with a digital mental health intervention. Participants will be aged 45 years or older, endorse at least moderately severe depression or anxiety symptoms (Patient Health Questionnaire-9 or Generalized Anxiety Disorder-7 score ≥10), and have coexisting chronic pain (ie, pain on most days or every day in the past 3 months), and live in the United States. In this open, web-based trial, participants will all receive Wysa for Chronic Pain (by Wysa), which uses a behavioral activation framework and encourages users to work toward pain acceptance. The fully automated intervention also includes cognitive behavioral therapy, mindfulness, and sleep tools, among others. In each trial, participants will be randomized during a maximum 12-week study period to receive versus not receive novel JITAIs that are intended to reduce navigation burden and improve usability (and subsequent engagement and clinical effectiveness). The JITAIs are being designed with iterative user feedback, guided by the Discover, Design/Build, and Test framework and the Behavioral Intervention Technology model. The proximal outcome for each JITAI is related to engagement with Wysa for Chronic Pain after JITAI delivery (compared to when no JITAI is delivered). The primary distal clinical outcome is the Patient Health Questionnaire Anxiety and Depression Scale. Based on statistical analysis that is triangulated with qualitative feedback from a subsample of trial participants, the JITAIs will be iteratively refined and retested in subsequent microrandomized trials until retesting of refined adaptations no longer yields meaningful improvement in immediate engagement or a maximum of 5 total trials have been completed.

RESULTS: Institutional review board approval was obtained on April 11, 2025. The first participant was enrolled on June 2, 2025, and recruitment is expected to conclude in 2026.

CONCLUSIONS: Completion of this project will result in iteratively refined JITAIs that are designed to improve usability and engagement with a digital mental health intervention by middle-aged and older adults with depression or anxiety and coexisting chronic pain.

TRIAL REGISTRATION: ClinicalTrials.gov NCT06978166; https://clinicaltrials.gov/study/NCT06978166.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/77532.

PMID:40961490 | DOI:10.2196/77532

JMIR Form Res. 2025 Sep 17;9:e41221. doi: 10.2196/41221.

ABSTRACT

BACKGROUND: Disproportionately adverse heart health outcomes in Black women, characterized by high metabolic syndrome prevalence, underscore the need for innovative, accessible interventions. Digital health strategies, particularly web-based yoga videos, show promise for engaging this high-risk group in health-promoting behaviors.

OBJECTIVE: This study aimed to evaluate the feasibility and acceptability of a web-based yoga intervention for community-dwelling Black women, providing preliminary data to inform a larger, mixed methods study on reducing cardiometabolic risks.

METHODS: In this 4-week pilot study, grounded in Pender’s Health Promotion Model, 28 participants engaged in daily online health education and yoga activities through YouTube videos. Using Fitbit trackers, electronic blood pressure monitors, and web-based logs, the study measured metabolic syndrome risk factors and sedentary behavior. Participant experiences were further explored through postintervention focus groups aiming to contextualize the intervention’s impact.

RESULTS: We enrolled 28 women, with a completion rate of 79% (22/28), demonstrating successful recruitment and retention. Participants were an average age of 43.3 years with a mean BMI of 40.9 kg/m², indicating a high-risk group for metabolic syndrome. Engagement with 2 or more intervention components were significantly correlated with study completion (χ²₁=7.14, P=.008). Specifically, viewing over one-half of the instructional videos (χ²₁=4.39, P=.04) and daily blood pressure monitoring (χ²₁=5.67, P=.02) were key to participant adherence. The intervention was well-received, with 95% (19/20) of survey respondents finding it satisfactory and suitable. Technology use was high, with all participants having access to the internet, 96% (27/28) owning smartphones, and 53% (15/28) having a YouTube account prior to the study. Recruitment was effectively conducted online, primarily via Facebook and a university newsletter, each accounting for 39.3% (11/28) of participants. The qualitative focus group data unveiled 4 major themes: (1) accountability, emphasizing the shift toward self-prioritization and collective health responsibility; (2) increased awareness, highlighting enhanced understanding of health behaviors and metabolic syndrome risks; (3) health benefits, noting observed improvements in blood pressure and stress levels; and (4) unanticipated stressors, identifying external factors that challenged engagement. These insights underscore the intervention’s multifaceted impact, from fostering health awareness to navigating external stressors.

CONCLUSIONS: This pilot study demonstrated the feasibility and acceptability of a culturally tailored, online yoga intervention among community-based, Black women at high risk for metabolic syndrome, showing promising engagement and potential health benefits. The high rates of participation and completion highlight the intervention’s acceptability and the potential for digital platforms to facilitate health behavior changes in high-risk populations. The qualitative findings reveal critical insights into the psychological and social dynamics influencing health behavior change, suggesting the importance of addressing both individual and communal barriers to improve intervention efficacy. Future research should further explore these dynamics in larger, more diverse cohorts to substantiate the intervention’s potential in reducing cardiometabolic risks.

PMID:40961483 | DOI:10.2196/41221

JMIR Res Protoc. 2025 Sep 17;14:e77864. doi: 10.2196/77864.

ABSTRACT

BACKGROUND: Neck pain with high incidence and recurrence rates significantly impairs patients’ quality of life and imposes a considerable economic burden. Traditional Chinese medicine therapies such as Yijinjing exercise and Tuina have shown promising efficacy in alleviating the local symptoms of neck pain. However, there is currently insufficient high-level evidence to robustly support these findings.

OBJECTIVE: This study aims to evaluate the efficacy and safety of combining Yijinjing exercise with Tuina for the treatment of neck pain.

METHODS: PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, Chinese Biomedical Database, VIP Chinese Science and Technology Periodicals Full-Text database, and Wanfang database will be systematically searched for all relevant randomized controlled trials (RCTs) from their inception to September 2025, without language or publication status restrictions. The Cochrane Risk of Bias 2 assessment tool will be used to evaluate the risk of bias in the included studies, and the GRADE (Grades of Recommendation, Assessment, Development, and Evaluation) system will be employed to grade the quality of evidence. Heterogeneity will be evaluated through I² statistics and Cochran’s Q test: a fixed-effect model will be used when I²<50% and P≥.01. If I²≥50% or P<.01, subgroup analysis will be conducted. When heterogeneity still exists, sensitivity analysis or exploratory subgroup analysis will be performed. If it cannot be explained ultimately, the random-effects model will be adopted and the GRADE evidence level will be reduced.

RESULTS: As of June 2025, we have completed the preliminary screening of titles and abstracts for 573 studies. The full-text screening is expected to be completed by September 2025, and data analysis is planned to be completed by December 2025. About two-thirds of the studies were published after 2015. Geographically, the samples in the studies were highly concentrated in Asia. The results were comprehensively developed around the core outcomes. The primary outcome was presented by changes in the visual analog scale. The secondary outcomes were evaluated by the neck disability index, self-rating anxiety scale score, mean vertebral artery blood flow velocity, and Cobb angle.

CONCLUSIONS: If the results of this study confirm the effectiveness of massage combined with Yijinjing, it can provide a direction for the nonpharmaceutical treatment of neck pain. However, some studies have risks of bias such as insufficient standardization of massage operations and difficulty in implementing blinding methods. The expected heterogeneity is significant due to differences in intervention plans and patients’ cultural backgrounds, and the original RCTs are few and regionally concentrated, with limited extrapolation of conclusions. In the future, it is necessary to optimize the plan and supplement data through high-quality multicenter research to enhance reliability.

TRIAL REGISTRATION: PROSPERO CRD420251026508; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251026508.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77864.

PMID:40961482 | DOI:10.2196/77864

JMIR Res Protoc. 2025 Sep 17;14:e72451. doi: 10.2196/72451.

ABSTRACT

BACKGROUND: Coronary artery disease (CAD) is a leading cause of global morbidity and mortality. Although CAD prevalence in the general population is well-documented, its occurrence among executive patients remains largely unexplored. An executive is an individual in a major leadership role, such as a C-suite officer, senior manager, board member, trustee, founder, or business owner, responsible for high-level decision-making and strategic direction. These roles often involve demanding schedules and significant stress. Despite their influence and better access to health care, this demographic faces unique challenges such as demanding work schedules, chronic stress, frequent travel, and reduced control over lifestyle. To address executives’ unique health needs, many health care organizations offer specialized programs emphasizing preventive cardiovascular care, using advanced tools such as lipid panels, stress tests, and coronary calcium scans not typically included in primary care, to detect risks early and to promote long-term wellness.

OBJECTIVE: This protocol aims to design a study to determine the prevalence of CAD in executive patients and compare it to the established prevalence in the US general population with the overarching goal of improving screening and care of CAD among executive patients.

METHODS: This protocol proposes a retrospective review of medical records for patients with CAD seen at the Mayo Clinic’s Executive Health Program from January 1, 2020, to December 31, 2023, with the aim of determining the prevalence of CAD in executive patients. The primary outcome is CAD prevalence, which will be identified through clinical diagnoses in the electronic medical records. Secondary outcomes include demographics, cardiovascular medications, social determinants of health, laboratory and diagnostic results, coronary calcium scores, and treatment interventions. The prevalence of CAD will be calculated as the proportion of patients with a documented CAD diagnosis relative to the total number of patients in the study cohort.

RESULTS: A total of 24,272 patients were seen in the executive health clinic between January 1, 2020, and December 31, 2023. After applying the inclusion criteria, 6466 executive patients were eligible, with 3290 identified as having a potential CAD diagnosis pending confirmation through a detailed chart review.

CONCLUSIONS: In this protocol, we outline a research design and methodology to address a critical gap in understanding the prevalence of CAD among executive patients. This demographic is often overlooked despite their unique risk factors such as high stress and lifestyle choices.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72451.

PMID:40961480 | DOI:10.2196/72451

Health Informatics J. 2025 Jul-Sep;31(3):14604582251381233. doi: 10.1177/14604582251381233. Epub 2025 Sep 17.

ABSTRACT

Background: Large language models (LLM) still face challenges in accurately extracting and summarizing medical information from EHR and EMR. The variability in EHR and EMR formats across institutions further complicates information integration. Moreover, doctors need to spend a lot of time reviewing patient information, which affects the efficiency and effectiveness of clinical decision-making. Objective: This study aims to develop a medical record summarization system that uses the innovative X-RAG technique with GPT-4o to extract medical information from EHR and EMR and convert them into structured FHIR format. The system ultimately generates a doctor-friendly report to improve the efficiency and effectiveness of clinical decision-making. Methods: We propose an innovative X-RAG, which adds page-based chunking, chunk filtering, and guided extraction prompting to the basic framework of RAG and combines it with GPT-4o to extract medical measurement data, diagnostic reports, and medication history records from EHR and EMR with high accuracy. Results: The system achieved 96.5% accuracy in medical data extraction and reduced approximately 40% of the time doctors spend reviewing patient information in clinical applications. Conclusion: The proposed system improves the efficiency and effectiveness of clinical decision-making and provides a valuable tool to optimize medical information management and clinical workflows.

PMID:40961463 | DOI:10.1177/14604582251381233

Natl Vital Stat Rep. 2025 Aug 27;(11):1. doi: 10.15620/cdc/174614.

ABSTRACT

OBJECTIVES: This report presents and compares trends in U.S. births and deaths from 2010 through 2023. Births and deaths are shown by race and Hispanic origin and urbanicity of county of residence.

METHODS: Descriptive tabulations of trends in the numbers, rates, and ratios of births and deaths for the United States from 2010 through 2023 are presented and interpreted.

RESULTS: From 2010 through 2023, the number of births for the United States declined by a total of 10%. Births were essentially stable from 2010 through 2016, declined from 2016 through 2019, and then fluctuated from 2019 through 2023. In contrast, the number of deaths generally increased from 2010 through 2023, by a total of 25%. Deaths increased from 2010 through 2019 and fluctuated from 2019 through 2023. The crude birth rate decreased 18% from 2010 through 2023, declining 0.8% per year from 2010 through 2015 and 2.0% per year from 2015 through 2019; the rate then fluctuated from 2019 through 2023. In contrast, the crude death rate increased 15% from 2010 through 2023, rising 1.0% on average from 2010 through 2019, and then fluctuating from 2019 through 2023. The birth-to-death ratio declined from 2010 through 2023, by a total of 28%, with the ratio decreasing 1.6% per year from 2010 through 2014 and 2.8% per year from 2014 through 2019; the ratio then fluctuated from 2019 through 2023. The ratio generally declined for the three largest race and Hispanic-origin groups from 2010 through 2023, fluctuating but increasing from 2019 through 2023. The differences in the ratios among the groups narrowed from 2010 through 2023. The birth-to-death ratio declined for both urban and rural counties from 2010 through 2023, with differences between ratios narrowing.

PMID:40961445 | DOI:10.15620/cdc/174614

Lupus. 2025 Sep 17:9612033251375856. doi: 10.1177/09612033251375856. Online ahead of print.

ABSTRACT

ObjectiveTo investigate whether obesity is a risk factor for chronic kidney disease G3 (CKD G3; eGFR <60 mL/min/1.73 m²) in lupus nephritis (LN).MethodsWe retrospectively reviewed 132 cases of biopsy-proven class III, IV or V incident LN for which quarterly data were available during a long follow-up period (median 140 months). Rates of complete renal remission, renal flare and CKD G3 were compared between obese (body mass index ≥30 kg/m²) and non-obese patients. Complete renal remission was defined as a urine protein to creatinine ratio (uPCR) < 0.5 g/g and a serum creatinine value <120 % of baseline. Renal flare was defined as the reappearance of an uPCR >1 g/g, leading to a repeat kidney biopsy and/or treatment change.ResultsBaseline characteristics of obese patients did not differ from non-obese patients. By contrast, time to CKD G3 and time to renal flare were statistically shorter in obese patients. Obesity significantly increased long-term risk for the progression of CKD [HR = 2.72 (CI95% 1.11-6.64), p = .028]. In a multivariate analysis, obesity was an independent risk factor for CKD G3 (p = .029).ConclusionA BMI ≥30 kg/m² is an independent poor prognostic factor for the progression of CKD in LN. More attention should therefore be paid to weight control in LN patients.

PMID:40961424 | DOI:10.1177/09612033251375856