Tag: statistics

J Spec Oper Med. 2025 Mar 10:FEE1-GMH5. doi: 10.55460/FEE1-GMH5. Online ahead of print.

ABSTRACT

PURPOSE: This study evaluated the feasibility of performing a surgical cricothyrotomy (CTT) in lieu of a tube tracheostomy (TT) as the first-line emergent surgical airway access technique in military working dogs (MWDs).

METHODS: In a crossover, randomized trial, five emergency medicine physician residents (MD group), trained in performing CTT in people but not canines, and five early career veterinarians (DVM group), trained in performing TT in canines but not trained in performing CTT in canines, performed a CTT and TT on 10 canine cadavers.

RESULTS: The time to complete CTT within the MD group was statistically shorter than the time to complete TT (P<.05). In the DVM group, the time to complete TT was shorter than that of CTT, but the time difference was not statistically significant (CTT: 239.6 [SD 251.7] s vs. TT: 133.4 [SD 88.0] s). In the MD group, the TT damage score was statistically higher than the CTT damage score (CTT: 0 vs. TT: 1.6 [SD 0.9], P<.01). There was no statistically significant difference between the damage scores of CTT and TT in the DVM group (CTT: 1.4 [SD 1.1] vs. TT: 1.6 [SD 0.9]). Overall, the participants reported a positive response with CTT compared to TT.

CONCLUSION: CTT is a viable first-line emergent surgical airway access technique when used by veterinarians and human healthcare clinicians with limited surgical experience or no proficiency in performing TT.

PMID:40063951 | DOI:10.55460/FEE1-GMH5

JMIR Form Res. 2025 Mar 10;9:e64239. doi: 10.2196/64239.

ABSTRACT

BACKGROUND: Many couples undergoing fertility treatment face multiple lifestyle risk factors that lower their chances of achieving pregnancy. The MyFertiCoach (MFC) app was designed as an integrated lifestyle program featuring modules on healthy weight management, nutrition, exercise, quitting smoking, reducing alcohol and drug use, and managing stress. We hypothesized that supplementing standard care with the MFC app would improve lifestyle outcomes.

OBJECTIVE: This study aims to assess the impact of the MFC app on changing multiple lifestyle habits in women seeking fertility treatment. The primary outcome is the change in the total risk score (TRS) at 3- and six-month follow-ups. The TRS is calculated for each individual as the sum of all risk scores per behavior (eg, vegetable/fruit/folic acid intake, smoking, and alcohol use) at 3 and 6 months. A higher TRS indicates unhealthier nutrition and lifestyle habits and a lower likelihood of achieving pregnancy. The secondary endpoints include changes in BMI, activity score, preconception dietary risk score, distress score (eg, perceived burden), smoking habits, alcohol intake, and program adherence.

METHODS: This retrospective, observational, single-center evaluation included patients between January 1, 2022, and December 31, 2023. Subfertile female patients aged 18-43 years and their partners, who were referred to a gynecologist, were invited to participate in online lifestyle coaching via the MFC app. The gynecologist selected relevant lifestyle modules based on the results of integrated screening questionnaires. We used (hierarchical) linear mixed models (LMMs) to estimate changes in outcomes. For missing data patterns deemed missing not at random, joint modeling was applied. Statistical significance was set at P≤.05, with methods in place to maintain the same false-positive rate.

RESULTS: A total of 1805 patients were invited to participate in the evaluation, with an average of 737 (40.83%) completing the screening questionnaire at baseline. For the TRS, 798 (44.21%) patients were included at baseline, of whom 517 (64.8%) involved their partner. On average, 282 of 744 (37.9%) patients submitted at least one follow-up questionnaire. Patients rated the app above average (n=137, median score of 7 on a 1-10 scale) on days 7 and 14. The TRS decreased by an average of 1.5 points (P<.001) at T3 and T6 compared with baseline, a clinically meaningful improvement. All secondary outcomes showed statistically significant positive changes for patients who used a relevant lifestyle module (P<.001). Most improvements were achieved by 3 months and remained significant at 6 months (P<.001), except for alcohol intake (P<.53). These findings were consistent across both LMMs and joint models.

CONCLUSIONS: Our evaluation of a mobile health app integrated into standard care demonstrates immediate and clinically meaningful improvements in key lifestyle parameters among women seeking to become pregnant. Additional scientific research is needed to identify the causal pathways leading to sustained effectiveness. To maintain and enhance these outcomes, further tailoring of patient-specific programs is essential.

PMID:40063944 | DOI:10.2196/64239

J Med Internet Res. 2025 Mar 10;27:e55844. doi: 10.2196/55844.

ABSTRACT

BACKGROUND: The key to gestational weight management intervention involves health-related behaviors, including dietary and exercise management. Behavioral theory-based interventions are effective in improving health-related behaviors. However, evidence for mobile health interventions based on specific behavioral theories is insufficient and their effects have not been fully elucidated.

OBJECTIVE: This study aimed to examine the effects of a gestational mobile health intervention on psychological cognition and behavior for gestational weight management, using an integrated behavioral model as the theoretical framework.

METHODS: This study was conducted in a tertiary maternity hospital and conducted as a single-blind randomized controlled trial (RCT) in Changzhou, Jiangsu Province, China. Using the behavioral model, integrated with the protection motivation theory and information-motivation-behavioral skills model (PMT-IMB model), the intervention group received a mobile health intervention using a self-developed app from 14 to 37 gestational weeks, whereas the control group received routine guidance through the application. Psychological cognition and behaviors related to weight management during pregnancy were the main outcomes, which were measured at baseline, and at the second and third trimesters of pregnancy using a self-designed questionnaire. Generalized estimation and regression equations were used to compare the outcome differences between the intervention and control groups.

RESULTS: In total, 302 (302/360, 83.9%) participants underwent all measurements at 3 time points (intervention group: n=150; control group: n=152). Compared with the control group, the intervention group had significantly higher scores for information, perceived vulnerability, response cost, and exercise management in the second trimester, while their scores for perceived vulnerability, response cost, and diet management were significantly higher in the third trimester. The results of repeated measures analysis revealed that, in psychological cognition, the information dimension exhibited both the time effects (T3 β=3.235, 95% CI 2.859-3.611; P<.001) and the group effects (β=0.597, 95% CI 0.035-1.158; P=.04). Similarly, response costs demonstrated both the time effects (T3 β=0.745, 95% CI 0.199-1.291; P=.008) and the group effects (β=1.034, 95% CI 0.367-1.700; P=.002). In contrast, perceived vulnerability solely exhibited the group effects (β=0.669, 95% CI 0.050-1.288; P=.03). Regarding weight management behaviors, both time (T3 β=6, 95% CI 4.527-7.473; P<.001) and group (β=2.685, 95% CI 0.323-5.047; P=.03) had statistically significant impacts on the total points. Furthermore, the exercise management dimension also demonstrated both the time effects (T3 β=3.791, 95% CI 2.999-4.584; P<.001) and the group effects (β=1.501, 95% CI 0.232-2.771; P=.02).

CONCLUSIONS: The intervention program was effective in increasing psychological cognitions in terms of information, perceived vulnerability, and response costs, as well as promoting healthy behaviors among Chinese pregnant women. This study provides new evidence supporting the effectiveness of mobile intervention based on behavioral science theory in gestational weight management.

TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2100043231; https://www.chictr.org.cn/showproj.html?proj=121736.

PMID:40063942 | DOI:10.2196/55844

J Med Internet Res. 2025 Mar 10;27:e66321. doi: 10.2196/66321.

ABSTRACT

BACKGROUND: Young people are disproportionately impacted by self-harm and suicide, and concerns exist regarding the role of social media and exposure to unsafe content. Governments and social media companies have taken various approaches to address online safety for young people when it comes to self-harm and suicide; however, little is known about whether key stakeholders believe current approaches are fit-for-purpose.

OBJECTIVE: From the perspective of young people, policy makers and professionals who work within the social media industry, this study aimed to explore (1) the perceived challenges and views regarding young people communicating on social media about self-harm and suicide, and (2) what more social media companies and governments could be doing to address these issues and keep young people safe online.

METHODS: This qualitative study involved 6 focus groups with Australian young people aged 12-25 years (n=7), Australian policy makers (n=14), and professionals from the global social media industry (n=7). Framework analysis was used to summarize and chart the data for each stakeholder group.

RESULTS: In total, 3 primary themes and six subthemes are presented: (1) challenges and concerns, including the reasons for, and challenges related to, online communication about self-harm and suicide as well as reasoning with a deterministic narrative of harm; (2) roles and responsibilities regarding online safety and suicide prevention, including who is responsible and where responsibility starts and stops, as well as the need for better collaborations; and (3) future approaches and potential solutions, acknowledging the limitations of current safety tools and policies, and calling for innovation and new ideas.

CONCLUSIONS: Our findings highlight tensions surrounding roles and responsibilities in ensuring youth online safety and offer perspectives on how social media companies can support young people discussing self-harm and suicide online. They also support the importance of cross-industry collaborations and consideration of social media in future suicide prevention solutions intended to support young people.

PMID:40063940 | DOI:10.2196/66321

J Med Internet Res. 2025 Mar 10;27:e50313. doi: 10.2196/50313.

ABSTRACT

BACKGROUND: eHealth literacy has increasingly emerged as a critical determinant of health, highlighting the importance of identifying its influencing factors; however, these factors remain unclear. Numerous studies have explored this concept across various populations, presenting an opportunity for a systematic review and synthesis of the existing evidence to better understand eHealth literacy and its key determinants.

OBJECTIVE: This study aimed to provide a systematic review of factors influencing eHealth literacy and to examine their impact across different populations.

METHODS: We conducted a comprehensive search of papers from PubMed, CNKI, Embase, Web of Science, Cochrane Library, CINAHL, and MEDLINE databases from inception to April 11, 2023. We included all those studies that reported the eHealth literacy status measured with the eHealth Literacy Scale (eHEALS). Methodological validity was assessed with the standardized Joanna Briggs Institute (JBI) critical appraisal tool prepared for cross-sectional studies. Meta-analytic techniques were used to calculate the pooled standardized β coefficient with 95% CIs, while heterogeneity was assessed using I2, the Q test, and τ2. Meta-regressions were used to explore the effect of potential moderators, including participants’ characteristics, internet use measured by time or frequency, and country development status. Predictors of eHealth literacy were integrated according to the Literacy and Health Conceptual Framework and the Technology Acceptance Model (TAM).

RESULTS: In total, 17 studies met the inclusion criteria for the meta-analysis. Key factors influencing higher eHealth literacy were identified and classified into 3 themes: (1) actions (internet usage: β=0.14, 95% CI 0.102-0.182, I2=80.4%), (2) determinants (age: β=-0.042, 95% CI -0.071 to -0.020, I2=80.3%; ethnicity: β=-2.613, 95% CI -4.114 to -1.112, I2=80.2%; income: β=0.206, 95% CI 0.059-0.354, I2=64.6%; employment status: β=-1.629, 95% CI -2.323 to -0.953, I2=99.7%; education: β=0.154, 95% CI 0.101-0.208, I2=58.2%; perceived usefulness: β=0.832, 95% CI 0.131-1.522, I2=68.3%; and self-efficacy: β=0.239, 95% CI 0.129-0.349, I2=0.0%), and (3) health status factor (disease: β=-0.177, 95% CI -0.298 to -0.055, I2=26.9%).

CONCLUSIONS: This systematic review, guided by the Literacy and Health Conceptual Framework model, identified key factors influencing eHealth literacy across 3 dimensions: actions (internet usage), determinants (age, ethnicity, income, employment status, education, perceived usefulness, and self-efficacy), and health status (disease). These findings provide valuable guidance for designing interventions to enhance eHealth literacy.

TRIAL REGISTRATION: PROSPERO CRD42022383384; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022383384.

PMID:40063939 | DOI:10.2196/50313

J Med Internet Res. 2025 Mar 10;27:e67188. doi: 10.2196/67188.

ABSTRACT

BACKGROUND: The pursuit of sleep quality has become an important aspect of people’s global quest for overall health. However, the objective neurobiological features corresponding to subjective perceptions of sleep quality remain poorly understood. Although previous studies have investigated the relationship between electroencephalogram (EEG) and sleep, the lack of longitudinal follow-up studies raises doubts about the reproducibility of their findings.

OBJECTIVE: Currently, there is a gap in research regarding the stable associations between EEG data and sleep quality assessed through multiple data collection sessions, which could help identify potential neurobiological targets related to sleep quality.

METHODS: In this study, we used a portable EEG device to collect resting-state prefrontal cortex EEG data over a 3-month follow-up period from 42 participants (27 in the first month, 25 in the second month, and 40 in the third month). Each month, participants’ sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) to estimate their recent sleep quality.

RESULTS: We found that there is a significant and consistent positive correlation between low α band activity in the prefrontal cortex and PSQI scores (r=0.45, P<.001). More importantly, this correlation remained consistent across all 3-month follow-up recordings (P<.05), regardless of whether we considered the same cohort or expanded the sample size. Furthermore, we discovered that the periodic component of the low α band primarily contributed to this significant association with PSQI.

CONCLUSIONS: These findings represent the first identification of a stable and reliable neurobiological target related to sleep quality through multiple follow-up sessions. Our results provide a solid foundation for future applications of portable EEG devices in monitoring sleep quality and screening for sleep disorders in a broad population.

PMID:40063935 | DOI:10.2196/67188

J Med Internet Res. 2025 Mar 10;27:e64307. doi: 10.2196/64307.

ABSTRACT

BACKGROUND: Effective communication is essential for promoting preventive behaviors during infectious disease outbreaks like COVID-19. While consistent news can better inform the public about these health behaviors, the public may not adopt them.

OBJECTIVE: This study aims to explore the role of different media platforms in shaping public discourse on preventive measures to infectious diseases such as quarantine and vaccination, and how media exposure influences individuals’ intentions to adopt these behaviors in the United States.

METHODS: This study uses data from 3 selected top national newspapers in the United States, Twitter discussions, and a US nationwide longitudinal panel survey from February 2020 to April 2021. We used the Intermedia Agenda-Setting Theory and the Protective Action Decision Model to develop the theoretical framework.

RESULTS: We found a 2-way agenda flow between selected national newspapers and the social media platform Twitter, particularly in controversial topics like vaccination (F_1,426=16.39; P<.001 for newspapers; F_1,426=44.46; P<.001 for Twitter). Exposure to media coverage increased individuals’ perceived benefits of certain behaviors like vaccination but did not necessarily translate into behavioral adoption. For example, while individuals’ media exposure increased perceived benefits of mask-wearing (β=.057; P<.001 for household benefits; β=.049; P<.001 for community benefits), it was not consistently linked to higher intentions to wear masks (β=-.026; P=.04).

CONCLUSIONS: This study integrates media flow across platforms with US national panel survey data, offering a comprehensive view of communication dynamics during the early stage of an infectious disease outbreak. The findings caution against a one-size-fits-all approach in communicating different preventive behaviors, especially where individual and community benefits may not always align.

PMID:40063934 | DOI:10.2196/64307

J Med Internet Res. 2025 Mar 10;27:e51804. doi: 10.2196/51804.

ABSTRACT

BACKGROUND: Google Trends (GT) data have shown promising results as a complementary tool to classical surveillance approaches. However, GT data are not necessarily provided by a representative sample of patients and may be skewed toward demographic and clinical groups that are more likely to use the internet to search for their health.

OBJECTIVE: In this study, we aimed to assess whether GT-based models perform differently in distinct population subgroups. To assess that, we analyzed a case study on asthma hospitalizations.

METHODS: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 3 different countries (Portugal, Spain, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold for the same countries and time period were retrieved from GT. We estimated the correlation between GT data and the weekly occurrence of asthma hospitalizations (considering separate asthma admissions data according to patients’ age, sex, ethnicity, and presence of comorbidities). In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations (for the different aforementioned subgroups) for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years.

RESULTS: Overall, correlation coefficients between GT on the pseudo-influenza syndrome topic and asthma hospitalizations ranged between 0.33 (in Portugal for admissions with at least one Charlson comorbidity group) and 0.86 (for admissions in women and in White people in Brazil). In the 3 assessed countries, forecasted hospitalizations for 2015-2016 correlated more strongly with observed admissions of older versus younger individuals (Portugal: Spearman ρ=0.70 vs ρ=0.56; Spain: ρ=0.88 vs ρ=0.76; Brazil: ρ=0.83 vs ρ=0.82). In Portugal and Spain, forecasted hospitalizations had a stronger correlation with admissions occurring for women than men (Portugal: ρ=0.75 vs ρ=0.52; Spain: ρ=0.83 vs ρ=0.51). In Brazil, stronger correlations were observed for admissions of White than of Black or Brown individuals (ρ=0.92 vs ρ=0.87). In Portugal, stronger correlations were observed for admissions of individuals without any comorbidity compared with admissions of individuals with comorbidities (ρ=0.68 vs ρ=0.66).

CONCLUSIONS: We observed that the models based on GT data may perform differently in demographic and clinical subgroups of participants, possibly reflecting differences in the composition of internet users’ health-seeking behaviors.

PMID:40063932 | DOI:10.2196/51804

J Med Internet Res. 2025 Mar 10;27:e59792. doi: 10.2196/59792.

ABSTRACT

BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care.

OBJECTIVE: While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science.

METHODS: We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality.

RESULTS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand.

CONCLUSIONS: We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

PMID:40063929 | DOI:10.2196/59792

Pol Merkur Lekarski. 2025;53(1):100-107. doi: 10.36740/Merkur202501114.

ABSTRACT

OBJECTIVE: Aim: The research aims to confirm or reject the hypothesis on the possible relationship between the influence of changes in pancreatic vascular variations on the mechanisms of occurrence of pancreatic diseases based on the collected scientific literature..

PATIENTS AND METHODS: Materials and Methods: The bibliosemantic, bibliographic, statistical research methods were used in the research. Scientometric databases Web of Science, Scopus, PubMed, and archives of scientific papers such as Google Scholar and Research Gate for the last 6 years (2018-2023) were used to form a study basis.

CONCLUSION: Conclusions: Based on the analyzed sources, blood supply variants to the pancreas do not influence the development of pathological processes. However, vein and artery features impact treatment choices and surgical outcomes. Main and additional pancreatic ducts, and the common bile duct, may be linked to diseases. Atypical pancreatic artery branching affects surgical tactics and increases bleeding risk.

PMID:40063918 | DOI:10.36740/Merkur202501114