Tag: pubmed

JMIR Res Protoc. 2025 Mar 25;14:e67128. doi: 10.2196/67128.

ABSTRACT

BACKGROUND: Climate change is affecting public health and well-being. In 2016, Swiss emergency departments (EDs) treated 1,722,000 cases, with 4718 daily admissions. In 2023, the ED of Sion Regional Hospital recorded 75,000 consultations. The links between climate change and health are complex, necessitating urgent research on its impact on cardiopulmonary health in Valais, Switzerland. Raising awareness among frontline professionals is crucial for developing health promotion and disease prevention strategies.

OBJECTIVE: This study explores the preliminary effects of climate change on cardiopulmonary health in Valais and assesses adult patients’ knowledge of its health consequences. Findings will inform adaptations in patient care, health promotion, and disease prevention at Sion Hospital’s ED. The feasibility of patient selection and data collection will also be evaluated.

METHODS: Using a convergent, parallel, mixed methods design, data will be collected from September 21, 2024, to September 20, 2025, with a target sample of 60 patients. The quantitative phase will examine patient recruitment feasibility, consultation reasons, and triage levels, correlating them with climate variables (temperature, nitrogen dioxide, particulate matter, sulfur dioxide, and ozone). It will also analyze sociodemographic profiles. The qualitative phase will explore patients’ knowledge of climate change and its potential links to their ED visits. The feasibility and acceptability of the study process will be assessed. The protocol follows the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) Extension for Pilot and Feasibility Trials.

RESULTS: Data collection started on September 21, 2024, following the approval by the ethical commission. Data collection will take place over 1 year, until September 20, 2025.

CONCLUSIONS: This study will test the feasibility of a larger investigation and examine potential associations between Valais’ changing microclimate and population health. Findings will establish patient profiles and explore their perceptions and knowledge of climate change, informing future health interventions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67128.

PMID:40132196 | DOI:10.2196/67128

J Med Internet Res. 2025 Mar 25;27:e66393. doi: 10.2196/66393.

ABSTRACT

BACKGROUND: Digital cognitive training (DCT) has been found to be more effective than traditional paper-and-pencil training in enhancing overall cognitive function. However, a significant barrier to its long-term implementation is that older adults with mild cognitive impairment (MCI) do not continue to use it or even show a dropoff in usage after the initial engagement. Such short-term engagement may limit the potential benefits of DCT, as sustained use is required to achieve more pronounced cognitive improvements. Exploring the reasons for the shift in discontinuous usage behavior is crucial for promoting successful DCT implementation and maximizing its positive effects.

OBJECTIVE: This study aimed to explore the intrinsic reasons for the transition from initial acceptance to discontinuous usage behavior among older adults with MCI throughout the DCT process, by employing the extended model of IT continuance (ECM-ITC).

METHODS: We employed a qualitative research methodology and conducted 38 semistructured interviews before and after the use of DCT (3 times per week over 1 month, with each session lasting 30 minutes) with 19 older adults with MCI (aged 60 years or older) and 4 family members between January and March 2024. Thematic analysis and deductive framework analysis were used to identify the reasons for the discontinuous usage of DCT, with mapping to the ECM-ITC.

RESULTS: Most participants failed to complete the standard dosage of DCT. Data analysis revealed the reasons for the shift to discontinuous usage. Despite their need to improve cognitive function, participants found the cognitive training confusing and discovered that DCT did not align with their preferred method of training upon actual use. The disparity between their vague expectations and reality, combined with the contradiction between the “delayed gratification” of DCT and their desire for “immediate gratification,” made it difficult for them to discern the usefulness of DCT. Participants also viewed DCT as an additional financial burden and tended to avoid training under family pressure. They relied on motivational measures, which further weakened their intention to continue DCT, ultimately leading to the inability to develop continuous usage behavior.

CONCLUSIONS: Continuous usage behavior differs from initial acceptance as it evolves dynamically with user experience over time. To encourage older adults with MCI to persistently engage with DCT, it is essential to not only thoroughly consider their genuine preferences and the potential disruptions DCT may bring to their lives but also bridge the gap between expectations and actual experiences. While ensuring that older adults receive appropriate external incentives and encouragement, it is equally important to foster their intrinsic motivation, thereby gradually cultivating the habit of sustained DCT usage.

PMID:40132189 | DOI:10.2196/66393

JMIR AI. 2025 Mar 25;4:e59094. doi: 10.2196/59094.

ABSTRACT

BACKGROUND: The application of machine learning methods to data generated by ubiquitous devices like smartphones presents an opportunity to enhance the quality of health care and diagnostics. Smartphones are ideal for gathering data easily, providing quick feedback on diagnoses, and proposing interventions for health improvement.

OBJECTIVE: We reviewed the existing literature to gather studies that have used machine learning models with smartphone-derived data for the prediction and diagnosis of health anomalies. We divided the studies into those that used machine learning models by conducting experiments to retrieve data and predict diseases, and those that used machine learning models on publicly available databases. The details of databases, experiments, and machine learning models are intended to help researchers working in the fields of machine learning and artificial intelligence in the health care domain. Researchers can use the information to design their experiments or determine the databases they could analyze.

METHODS: A comprehensive search of the PubMed and IEEE Xplore databases was conducted, and an in-house keyword screening method was used to filter the articles based on the content of their titles and abstracts. Subsequently, studies related to the 3 areas of voice, skin, and eye were selected and analyzed based on how data for machine learning models were extracted (ie, the use of publicly available databases or through experiments). The machine learning methods used in each study were also noted.

RESULTS: A total of 49 studies were identified as being relevant to the topic of interest, and among these studies, there were 31 different databases and 24 different machine learning methods.

CONCLUSIONS: The results provide a better understanding of how smartphone data are collected for predicting different diseases and what kinds of machine learning methods are used on these data. Similarly, publicly available databases having smartphone-based data that can be used for the diagnosis of various diseases have been presented. Our screening method could be used or improved in future studies, and our findings could be used as a reference to conduct similar studies, experiments, or statistical analyses.

PMID:40132187 | DOI:10.2196/59094

Interact J Med Res. 2025 Mar 25;14:e54651. doi: 10.2196/54651.

ABSTRACT

BACKGROUND: Taiwan’s categorization of hospital emergency capability (CHEC) policy is designed to regionalize and dispatch critical patients. The policy was designed in 2009 to improve the quality of emergency care for critical time-sensitive diseases (CTSDs). The CHEC policy primarily uses time-based quality surveillance indicators.

OBJECTIVE: We aimed to investigate the impact of Taiwan’s CHEC policy on CTSDs.

METHODS: Using Taiwan’s 2005 Longitudinal Health Insurance Database, this nationwide retrospective cohort study examined the CHEC policy’s impact from 2005 to 2011. Propensity score matching and difference-in-differences analysis within a generalized estimating equation framework were used to compare pre- and postimplementation periods. The study focused on acute ischemic stroke (AIS), ST-segment elevation myocardial infarction (STEMI), septic shock, and major trauma. AIS and STEMI cases, monitored with time-based indicators, were evaluated for adherence to diagnostic and treatment guidelines as process quality measures. Mortality and medical use served as outcome indicators. Major trauma, with evolving guidelines and no time-based monitoring, acted as a control to test for policy spotlight effects.

RESULTS: In our cohort of 9923 patients, refined through 1:1 propensity score matching, 5566 (56.09%) were male and were mostly older adults. Our analysis revealed that the CHEC policy effectively improved system efficiency and patient outcomes, resulting in significant reductions in medical orders (-7.29 items, 95% CI -10.09 to -4.48; P<.001), short-term mortality rates (-0.09%, 95% CI -0.17% to -0.02%; P=.01) and long-term mortality rates (-0.09%, 95% CI -0.15% to -0.04%; P=.001), and total medical expenses (-5328.35 points per case, 95% CI -10,387.10 to -269.60; P=.04), despite a modest increase in diagnostic fees (376.37 points, 95% CI 92.42-660.33; P=.01). The CHEC policy led to notable increases in diagnostic fees, major treatments, and medical orders for AIS and STEMI cases. For AIS cases, significant increases were observed in major treatments (β=0.77; 95% CI 0.21-1.33; P=.007) and medical orders (β=15.20; 95% CI 5.28-25.11; P=.003) compared to major trauma. In STEMI cases, diagnostic fees significantly increased (β=1983.75; 95% CI 84.28-3883.21; P=.04), while upward transfer rates significantly decreased (β=-0.59; 95% CI -1.18 to -0.001; P=.049). There were also trends toward increased major treatments (β=0.30; 95% CI -0.03 to 0.62, P=.07), medical orders (β=11.92; 95% CI -0.90 to 24.73; P=.07), and medical expenses (β=24,275.54; 95% CI -640.71 to 4,991,991.78; P=.06), although these were not statistically significant. In contrast, no significant changes were identified in process or outcome quality indicators for septic shock. These findings suggest policy spotlight effects, reflecting a greater emphasis on diseases directly prioritized under the CHEC policy.

CONCLUSIONS: The CHEC policy demonstrated the dual benefits of reducing costs and improving patient outcomes. We observed unintended consequences of policy spotlight effects, which led to a disproportionate improvement in guideline adherence and process quality for CTSDs with time-based surveillance indicators.

PMID:40132185 | DOI:10.2196/54651

J Med Internet Res. 2025 Mar 25;27:e64591. doi: 10.2196/64591.

ABSTRACT

BACKGROUND: In China, there is limited research on digital health literacy (DHL) among patients with stroke. This is mainly due to the lack of validated tools, which hinders the precision and sustainability of our country’s digital transformation.

OBJECTIVE: This study aimed to develop and validate a DHL scale specifically for stroke survivors in China.

METHODS: We used a sequential, exploratory, mixed methods approach to develop a DHL questionnaire for stroke survivors. This study comprised 418 patients with stroke aged 18 years and older. To evaluate the questionnaire’s psychometric qualities, we randomly assigned individuals to 2 groups (subsample 1: n=118, subsample 2: n=300). Construct validity was evaluated through internal consistency analysis, exploratory and confirmatory factor analyses, hypothesis testing for structural validity, measurement invariance assessments using the eHealth Literacy Scale, and Rasch analyses to determine the questionnaire’s validity and reliability.

RESULTS: This study underwent 4 stages of systematic development. The initial pool of items contained 25 items, 5 of which were eliminated after content validity testing; 19 items were subsequently retained through cognitive interviews. After an interitem correlation analysis, 2 more items were excluded, leaving 17 items for exploratory factor analysis. Finally, 2 items were excluded by Rasch analysis, resulting in a final version of the questionnaire containing 15 items. The total score range of the scale was 15-75, with higher scores indicating greater DHL competence. Results showed that principal component analysis confirmed the theoretical structure of the questionnaire (69.212% explained variance). The factor model fit was good with χ²₄=1.669; root mean square error of approximation=0.047; Tucker-Lewis Index=0.973; and Comparative Fit Index=0.977. In addition, hypothesis-testing construct validity with the eHealth Literacy Scale revealed a strong correlation (r=0.853). The internal consistency (Cronbach α) coefficient was 0.937. The retest reliability coefficient was 0.941. Rasch analysis demonstrated the item separation index was 3.81 (reliability 0.94) and the individual separation index was 2.91 (reliability 0.89).

CONCLUSIONS: The DHL Questionnaire for Stroke Survivors is a reliable and valid measure to assess DHL among stroke survivors in China.

PMID:40132183 | DOI:10.2196/64591

J Med Internet Res. 2025 Mar 25;27:e65872. doi: 10.2196/65872.

ABSTRACT

BACKGROUND: Torsades de pointes (TdP) is a rare yet potentially fatal cardiac arrhythmia that is often drug-induced. Drug-drug interactions (DDIs) are a major risk factor for TdP development, but the specific drug combinations that increase this risk have not been extensively studied.

OBJECTIVE: This study aims to identify clinically significant, high-priority DDIs to provide a foundation to minimize the risk of TdP and effectively manage DDI risks in the future.

METHODS: We used the following 4 frequency statistical models to detect DDI signals using the Food and Drug Administration Adverse Event Reporting System (FAERS) database: Ω shrinkage measure, combination risk ratio, chi-square statistic, and additive model. The adverse event of interest was TdP, and the drugs targeted were all registered and classified as “suspect,” “interacting,” or “concomitant drugs” in FAERS. The DDI signals were identified and evaluated using the Lexicomp and Drugs.com databases, supplemented with real-world data from the literature.

RESULTS: As of September 2023, this study included 4313 TdP cases, with 721 drugs and 4230 drug combinations that were reported for at least 3 cases. The Ω shrinkage measure model demonstrated the most conservative signal detection, whereas the chi-square statistic model exhibited the closest similarity in signal detection tendency to the Ω shrinkage measure model. The κ value was 0.972 (95% CI 0.942-1.002), and the P_positive and P_negative values were 0.987 and 0.985, respectively. We detected 2158 combinations using the 4 frequency statistical models, of which 241 combinations were indexed by Drugs.com or Lexicomp and 105 were indexed by both. The most commonly interacting drugs were amiodarone, citalopram, quetiapine, ondansetron, ciprofloxacin, methadone, escitalopram, sotalol, and voriconazole. The most common combinations were citalopram and quetiapine, amiodarone and ciprofloxacin, amiodarone and escitalopram, amiodarone and fluoxetine, ciprofloxacin and sotalol, and amiodarone and citalopram. Although 38 DDIs were indexed by Drugs.com and Lexicomp, they were not detected by any of the 4 models.

CONCLUSIONS: Clinical evidence on DDIs is limited, and not all combinations of heart rate-corrected QT interval (QTc)-prolonging drugs result in TdP, even when involving high-risk drugs or those with known risk of TdP. This study provides a comprehensive real-world overview of drug-induced TdP, delimiting both clinically significant DDIs and negative DDIs, providing valuable insights into the safety profiles of various drugs, and informing the optimization of clinical practice.

PMID:40132181 | DOI:10.2196/65872

Int J Nurs Educ Scholarsh. 2025 Mar 26;22(1). doi: 10.1515/ijnes-2024-0076. eCollection 2025 Jan 1.

ABSTRACT

OBJECTIVES: To explore post-pandemic mental wellbeing status and identify positive factors influencing mental wellbeing among nursing students.

METHODS: A cross-sectional survey of undergraduate nursing students from three public colleges in Thailand was conducted. A convenience sample of 983 participants completed a paper questionnaire.

RESULTS: The mental wellbeing mean score was 43.67 (SD=6.75, possible range of 10-60). Mental wellbeing was negatively associated with participant’s age and class level while positively associated with income, BMI, exercise hours/week, sleep hours/day, academic support, perceived social support, community involvement, and grit. Using hierarchical multiple regression, six significant predictors were identified: income, sleep hours/day, academic support, perceived social support, community involvement, and grit. These predictors combined explained 44 % of the variance, F(11, 722)=55.97, p<0.001, adjusted R²=0.44.

CONCLUSIONS: To promote mental wellbeing of nursing students, colleges should explore how to increase academic support, encourage healthy habits in students, and enhance their community involvement.

PMID:40132175 | DOI:10.1515/ijnes-2024-0076

Womens Health (Lond). 2025 Jan-Dec;21:17455057251326419. doi: 10.1177/17455057251326419. Epub 2025 Mar 25.

ABSTRACT

BACKGROUND: Gender differences in the associated health outcomes of different forms of intimate partner violence (IPV) are understudied. The long-term effects of IPV on specific physical health conditions are also under-researched in comparison to the effects on general health and mental health.

OBJECTIVES: To examine gender differences in the association between IPV and specific physical health conditions, accounting for differences in the types and number of types of IPV experienced.

DESIGN: We used data from the 2014 Adult Psychiatric Morbidity Survey, a cross-sectional survey using a stratified, multistage random sampling design to cover the household population of England aged 16 years and older.

METHODS: Descriptive and multivariable regression analyses of 4120 women and 2764 men who had ever had a partner. Lifetime IPV by types (physical, sexual, psychological, and economic), any lifetime and recent IPV, the number of IPV types experienced, and multiple chronic health conditions experienced over the past 12 months were included in the analyses.

RESULTS: Gender differences were observed in both the prevalence of IPV and associated health conditions. Women were more likely to experience any type and a higher number of IPV types than men. Women’s exposure to any lifetime and 12-month IPV were significantly associated with an increased likelihood of reporting 12 and 11 conditions, respectively, while men’s exposure to any lifetime and 12-month IPV were significantly associated with 4 and 1 conditions, respectively. Specific IPV types had varied health impacts, particularly among women. A cumulative association was evident for women but not for men.

CONCLUSION: Healthcare systems need to be mobilised to address IPV as a priority health issue for the female population. Our findings highlight the need for gender-informed approaches in IPV intervention strategies and healthcare provision, emphasising the development of IPV-responsive healthcare systems and comprehensive IPV curricula in medical and health training.

PMID:40132162 | DOI:10.1177/17455057251326419

Anesth Analg. 2025 Mar 25. doi: 10.1213/ANE.0000000000007504. Online ahead of print.

ABSTRACT

BACKGROUND: After fracture-related surgery, chronic posttraumatic and/or postsurgical pain (CPSP) has a high incidence rate of up to 43% a year after surgery. Yet the underlying mechanisms are poorly understood. Murine and clinical evidence suggest immunological modulation of postsurgical pain. However, the specific cytokine profiles of patients who develop CPSP after fracture-related surgery remain to be determined. Therefore, we analyzed in an exploratory manner cytokines, chemokines and adipocytokines in patients with and without CPSP up to 1 year after fracture-related surgery.

METHODS: A prospective longitudinal serum profiling of 30 patients with traumatic fractures that required osteosynthesis was conducted on the first day (D1), at 6 weeks (W6) and 1 year after surgery (Y1). Patients with CPSP at Y1 were compared to those who did not develop CPSP. A total of 22 pro- and anti-inflammatory serum cytokines, including adipocytokines, were quantified using Luminex technology. Statistical analyses included χ² test, t test, and Mann-Whitney U test, Spearman’s rank correlations, and repeated-measures mixed models with Bonferroni correction for cytokine differences between patients with and without CPSP. Receiver-operating characteristic (ROC) curves evaluated the discriminatory ability of specific cytokines regarding the development of CPSP.

RESULTS: Patients with CPSP 1 year after surgery (n = 12/30, 40%) exhibited elevated resistin levels at Y1 (CPSP: 1.04 ± 1.04 vs no-CPSP: 0.41 ± 0.31 pg/mL; P < .001) as well as higher adiponectin levels at Y1 (CPSP: 9.37 ± 8.23 vs no-CPSP: 5.57 ± 2.75 μg/mL; P = .008). Patients with CPSP had higher Rantes/CCL5 (CC-chemokine ligand 5) levels immediately after surgery on D1 than patients without CPSP (mean difference [MD] = 5.5, confidence interval [CI], 1.7-9.3 ng/mL; P = .014). At W6 and Y1, adiponectin and CCL5 levels correlated with pain intensity in patients with CPSP (adiponectin: r = 0.50, P = .03; CCL5: r = -0.50, P = .03). Across the entire patient population, resistin levels were correlated with pain intensity (r = 0.34, P < .001; D1-Y1).

CONCLUSIONS: Our explorative cytokine analysis uncovered an imbalance in serum cytokines and chemokines during the chronification process in patients who developed CPSP 1 year after surgically treated fractures. In particular, adiponectin and resistin were noted to be novel biomarkers for CPSP development. These data provide preliminary insight into a potential unexplored crosstalk between chronic postoperative pain and adipocytokines in the chronification of CPSP, which remains to be further analyzed.

PMID:40132159 | DOI:10.1213/ANE.0000000000007504

Neurology. 2025 Apr 22;104(8):e213490. doi: 10.1212/WNL.0000000000213490. Epub 2025 Mar 25.

ABSTRACT

BACKGROUND AND OBJECTIVES: Among the participants of Alzheimer disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals can increase power to detect treatment effects. We aimed to develop machine learning-based predictive models to identify persons unlikely to show decline on placebo treatment over 80 weeks.

METHODS: We used the data from the placebo arm of EXPEDITION3 AD clinical trial and a subpopulation from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Participants in the EXPEDITION3 trial were patients with mild dementia and biomarker evidence of amyloid burden. For this study, participants were identified as those who demonstrated clinically meaningful cognitive decline (CMCD) or cognitively stable (CS) at final visit of the trial (week 80). Machine learning-based classifiers were trained to classify participants into CMCD vs CS groups using combinations of demographics, APOE genotype, neuropsychological tests, and biomarkers (volumetric MRI). The results were developed in 70% of the EXPEDITION3 placebo sample using 5-fold cross-validation. Trained models were then used to classify the participants in an internal validation sample and an external matched sample ADNI_AD.

RESULTS: Eight hundred ninety-four of the 1,072 participants in the placebo arm of the EXPEDITION3 trial had necessary follow-up data, who were on average aged 72.7 (±7.7) years and 59% female. 55.8% of those participants showed CMCD (∼2 years younger than those without) at the final visit. In the independent validation sample within the EXPEDITION3 data, all the models showed high sensitivity and modest specificity. Positive predictive values (PPVs) of models were at least 11% higher than base prevalence of CMCD observed at the end of the trial. The subset of matched ADNI participants (ADNI_AD, N = 105) were aged 74.5 (±6.4) years and 46% female. The models that were validated in ADNI_AD also showed high sensitivity, modest specificity, and PPVs of at least 15% higher than the base prevalence in ADNI_AD.

DISCUSSION: Our results indicate that predictive models have the potential to improve the design of AD trials through selective inclusion and exclusion criteria based on expected cognitive decline. Such predictive models need further validation across data from different AD clinical trials.

PMID:40132145 | DOI:10.1212/WNL.0000000000213490