Tag: pubmed

JMIR Diabetes. 2026 Aug 7;11:e99345. doi: 10.2196/99345.

ABSTRACT

BACKGROUND: Serious digital games have been proposed as a novel approach to support diabetes education and self-management, but evidence regarding their effectiveness remains limited.

OBJECTIVE: This study aimed to evaluate the effects of SugarVita, a serious game for people with type 2 diabetes, on diabetes-related knowledge, self-confidence, and self-management. Secondary outcomes included hemoglobin A1c (HbA1c), engagement, and user evaluation.

METHODS: In this pilot randomized controlled trial, 30 adults with type 2 diabetes were randomized to SugarVita plus standard care or standard care alone for 8 weeks. Outcomes were assessed before and after the intervention using validated questionnaires and laboratory HbA1c values. Within-group changes were analyzed using Wilcoxon signed-rank tests and between-group differences using Mann-Whitney U tests. Bonferroni correction was applied for multiple primary outcomes.

RESULTS: No statistically significant between-group differences were observed for diabetes-related knowledge, self-confidence, or self-management after correction for multiple testing. Both groups showed numerical improvements over time. HbA1c decreased significantly within the intervention group (median 73.0, IQR 70.8-81.5 to median 64.5, IQR 60.8-72.0 mmol/mol; P=.007), whereas no significant change was observed in the control group. Greater total playtime was moderately associated with HbA1c reduction. User evaluations indicated high perceived educational value.

CONCLUSIONS: Participants reported positive experiences with SugarVita and perceived the game as educational and user-friendly. No statistically significant between-group differences were observed for the primary outcomes. These findings support the feasibility and acceptability of SugarVita as a digital educational intervention and warrant further evaluation in larger studies.

PMID:42566777 | DOI:10.2196/99345

J Med Internet Res. 2026 Aug 7;28:e96800. doi: 10.2196/96800.

ABSTRACT

BACKGROUND: Pain remains a critical issue among hospitalized children and may negatively affect postoperative recovery. In addition to pharmacological pain management, nonpharmacological approaches have been used to support pediatric care. Among these emerging approaches, socially assistive robots (SARs) may offer an opportunity to support children during hospitalization. However, limited evidence exists regarding the use of SARs in pediatric postoperative recovery and their influence on children’s emotional responses during child-robot interaction (CRI).

OBJECTIVE: This study aimed to examine changes in postoperative pain levels following a SAR intervention among hospitalized children. In addition, it aimed to explore emotional responses during CRI using automated facial expression analysis.

METHODS: A single-arm pre-post study was conducted in a pediatric surgical ward. Children recovering from surgery participated in a structured SAR intervention consisting of 3 phases: warm-up, educational video, and interactive engagement. Pain outcomes were assessed using the self-reported Wong-Baker FACES pain rating scale and the observer-rated FLACC (face, legs, activity, cry, and consolability) scale. Emotional responses were evaluated using automated facial expression analysis, which generated continuous emotional valence scores ranging from -1 (negative) to +1 (positive). Wilcoxon signed-rank tests were used to analyze pain outcomes, and Friedman tests were used to examine differences in emotional valence across intervention phases.

RESULTS: A total of 37 children were included in the pain outcome analysis, and 35 (95%) children were included in the emotional valence analysis after excluding participants with insufficient facial expression data. Significant reductions were observed in both self-reported and observed behavioral pain following the intervention. Self-reported pain scores decreased from a median of 6 (IQR 4-6) to 4 (IQR 2-4; P<.001), and FLACC scores decreased from a median of 3 (IQR 2-4) to 1 (IQR 1-2; P<.001). Emotional valence remained negative across all intervention phases. The Friedman test did not reach statistical significance across the 3 phases and showed a small effect size (P=.05).

CONCLUSIONS: The SAR interventions may be associated with lower postoperative pain scores among hospitalized children. Although emotional valence did not significantly change during CRI, automated facial expression analysis was implemented and demonstrated the feasibility of continuous affective assessment in a real-world pediatric clinical setting. These findings support the potential use of the SAR interventions as a complementary strategy in pediatric postoperative care and provide preliminary evidence supporting the integration of real-time affective assessment into pediatric health care.

PMID:42566770 | DOI:10.2196/96800

JMIR Perioper Med. 2026 Aug 7;9:e84457. doi: 10.2196/84457.

ABSTRACT

BACKGROUND: Patient satisfaction is a key indicator of health care quality, and it guides improvement efforts. Although many local studies have examined perioperative patient satisfaction in Ethiopia, there is no comprehensive national synthesis. This gap limits the development of targeted strategies to enhance patient care.

OBJECTIVE: The aim of this systematic review and meta-analysis is to determine the pooled prevalence of patient satisfaction with perioperative services in Ethiopia and identify associated factors.

METHODS: This study included all observational research articles on patient satisfaction with perioperative services in Ethiopia. A multidatabase search strategy, incorporating PubMed/MEDLINE, HINARI, Web of Science, Cochrane Library, African Journals Online, and Scopus, was used alongside a gray literature search to identify all Ethiopian studies on perioperative satisfaction available before January 1, 2024. The Newcastle-Ottawa Scale was used to assess the quality of the studies. To assess heterogeneity, subgroup analyses were conducted, and I² statistics were calculated. This study used funnel plots, the Egger test, and a nonparametric trim-and-fill analysis to assess publication bias. A sensitivity analysis was also used to identify any influential studies. Univariate meta-regression examined the association between study-level covariates and perioperative satisfaction.

RESULTS: This review included 21 studies comprising 6858 participants. Overall satisfaction with perioperative services was expressed by 5072 participants (73.96%, 95% CI 68.84%-79.08%; I²=96.56%). Factors significantly associated with higher satisfaction included effective postoperative pain management (adjusted odds ratio [AOR] 2.23, 95% CI 1.56-2.90), illiteracy (AOR 3.18, 95% CI 1.23-5.13), primary school education (AOR 6.55, 95% CI 3.61-9.49), local anesthesia use (AOR 2.80, 95% CI 2.03-3.57), and history of prior surgery or anesthesia (AOR 2.76, 95% CI 1.51-4.01).

CONCLUSIONS: This study found that the pooled prevalence of patient satisfaction with perioperative services in Ethiopia was 73.96% (5072/6858 participants). Postoperative pain management, illiteracy, primary school, local anesthesia, and a history of surgery or anesthesia were significantly associated with patient satisfaction with perioperative services. Health care facilities should focus on providing effective postoperative pain management, clear information about perioperative services, and training for surgical and anesthesia teams to boost patient satisfaction with perioperative services in Ethiopia.

PMID:42566769 | DOI:10.2196/84457

Online J Public Health Inform. 2026 Aug 7;18:e97291. doi: 10.2196/97291.

ABSTRACT

BACKGROUND: Financial toxicity can contribute to adverse health and care-access outcomes among US veterans, yet scalable methods to identify individuals at elevated risk remain limited. Public health informatics frameworks may enable the translation of patient-reported financial risk signals into streamlined screening, risk stratification, and care-navigation workflows.

OBJECTIVE: This study aimed to examine concept-level indicators of financial literacy and financial toxicity among US veterans and explore how these findings could inform future informatics-enabled screening strategies for identifying subgroups at increased risk of health-related financial strain.

METHODS: We conducted an exploratory cross-sectional survey of 88 US veterans from 2024 to 2025. Financial literacy was assessed using 3 benchmark items from the National Financial Capability Study. Financial toxicity was assessed using items aligned with domains reflected in the Comprehensive Score for Financial Toxicity framework, including difficulty affording care, reduced or quit work, borrowing money or using savings for care, and treatment-adherence impact. Analyses included descriptive statistics, Fisher exact tests, unadjusted logistic regression, and a minimally adjusted sensitivity model for work disruption, controlling for age and education.

RESULTS: Female veterans had lower rates of high financial literacy than male veterans (15/29, 52% vs 48/59, 81%; P=.006) and lower correct-response rates on compound interest (10/29, 35% vs 36/59, 61%; P=.02) and inflation (14/29, 48% vs 43/59, 73%; P=.03). Black veterans had lower correct-response rates than non-Black veterans on inflation (11/24, 46% vs 46/64, 72%; P=.03) and retirement strategy (15/24, 63% vs 56/64, 88%; P=.014), although composite high-literacy rates did not differ significantly by race. In unadjusted models among participants with complete outcome data (n=75), lower financial literacy was directionally associated with higher odds of all 4 financial toxicity outcomes, with the clearest association observed for work disruption (odds ratio 0.56 per 1-point increase in financial literacy score, 95% CI 0.33-0.95; P=.03). Black female veterans reported elevated financial toxicity across multiple domains. Financial support program use was low overall (29%).

CONCLUSIONS: These findings suggest that financial literacy may be a marker of vulnerability to financial toxicity among veterans, but observed associations should be regarded as preliminary and hypothesis-generating. The results identify concept-level financial literacy domains and work disruption as candidate signals for future screening evaluation. Future research should evaluate whether brief screening, financial literacy assessment, and benefit-navigation strategies improve identification, referral, adherence, and downstream financial and health-related outcomes in larger and more representative veteran populations.

PMID:42566768 | DOI:10.2196/97291

Traffic Inj Prev. 2026 Aug 7:1-7. doi: 10.1080/15389588.2026.2694621. Online ahead of print.

ABSTRACT

OBJECTIVE: Driver drowsiness is a critical factor in road accidents. This study aimed to model behavioral indicators of drowsiness using a driving simulator to support noninvasive detection systems.

METHODS: Twenty-four participants completed simulated driving tasks under varying alertness levels. Behavioral metrics including eye-blinking frequency, head movement acceleration, and eye movement variability were recorded. Drowsiness classification was performed using supervised Partial Least Squares Discriminant Analysis (PLS-DA), with the Karolinska Sleepiness Scale as the reference standard. Preprocessing steps included general mean-centering, pairwise mean-centering, and unit variance scaling.

RESULTS: Blink frequency significantly increased with drowsiness, while fluctuations in head movement acceleration and eye movements also rose, indicating reduced alertness. Derived variables such as Corrected Turning Ratio (CTR) showed predictive relevance, whereas angular velocity of head movement was not statistically significant. Model evaluation demonstrated strong performance (ROC AUC = 0.935), with low misclassification rates and acceptable residual normality.

CONCLUSIONS: Behavioral metrics provide practical predictive value for noninvasive drowsiness detection. Although limited by sample size, the model achieved meaningful separation between alertness and drowsiness. Future studies should expand sample size, incorporate additional behavioral indicators, and validate findings externally to strengthen predictive performance.

PMID:42566753 | DOI:10.1080/15389588.2026.2694621

Dev Med Child Neurol. 2026 Aug 7. doi: 10.1111/dmcn.70426. Online ahead of print.

ABSTRACT

AIM: To assess neurodevelopmental and neurological features, including neuroimaging abnormalities, in children with molecularly confirmed hypochondroplasia.

METHOD: A retrospective cohort study of children with molecularly confirmed hypochondroplasia seen at Evelina London Children’s Hospital skeletal dysplasia service was performed. Data collected included referral characteristics, neuroimaging findings, special educational needs, and diagnosed neurodevelopmental disorders. Statistical comparisons with UK population prevalence were performed using χ² testing for educational outcomes and exact binomial testing for neurodevelopmental disorders, with Bonferroni-adjusted p-values reported for multiple comparisons.

RESULTS: Forty-four children (24 females and 20 males; median age 9 years 10 months [interquartile range 6 years 7 months-15 years 7 months]) with molecularly confirmed hypochondroplasia were included. Twenty-five had received brain imaging with hippocampal malrotation (HIMAL) identified in 23 (92%). Among school-aged children, 71% required special educational support and 29% had formal education, health, and care plans (EHCPs), statistically higher than both UK population prevalence and previously reported hypochondroplasia prevalence. Formally diagnosed neurodevelopment disorders affected 20.5% of the cohort. Specific learning disorders remained significantly more common than UK population prevalence estimates after Bonferroni correction, with higher observed autism and attention-deficit/hyperactivity disorder rates also identified.

INTERPRETATION: Hypochondroplasia is associated with a substantial burden of neurodevelopmental and neurological abnormalities, including a high prevalence of HIMAL, which exceeds previous estimates. These findings support the need for clinical guidelines, developmental surveillance, and further research into fibroblast growth factor receptor 3 (FGFR3)-related brain development.

PMID:42566751 | DOI:10.1111/dmcn.70426

J Med Internet Res. 2026 Aug 7;28:e92183. doi: 10.2196/92183.

ABSTRACT

BACKGROUND: Multimodal large language models are increasingly used in radiological diagnosis, but their performance has not been systematically evaluated across volumetric (3D) imaging, real-world clinical versus public teaching cases, and bilingual contexts.

OBJECTIVE: The aim of the study is to develop a bilingual radiology benchmark and characterize the diagnostic performance of state-of-the-art multimodal large language models across input modality, clinical setting (public teaching vs routine clinical), disease rarity, and clinical-history language and to disentangle linguistic from clinical-content effects through a cross-linguistic control experiment.

METHODS: We constructed RadM-Bench, comprising 720 cases evenly distributed across 9 radiological subspecialties: 360 English public teaching cases enriched in rare diseases (RadEdu) and 360 Chinese routine clinical cases (RealClin). In total, 4 proprietary models (GPT-4o, O3, Gemini-2-Flash, and Gemini-2.5-Flash-Thinking) and 6 open-source models (Qwen2.5-VL-72B/7B, InternVL3-78B/8B, Llama-4-Scout-17B-16E, and MedGemma-4B) were evaluated under 4 input conditions: clinical history alone, history with radiologist-selected 2D key images, and history with volumetric data sampled at 2 and 10 frames per second (fps). Each response was scored on a 4-tier 0-3 diagnostic-quality rubric by 2 board-certified radiologists blinded to model identity. Mean scores with bias-corrected and accelerated bootstrap 95% CIs are reported. To disentangle language from clinical content, all 360 RealClin histories were translated into English and re-evaluated, with paired comparisons by Wilcoxon signed-rank tests and Benjamini-Hochberg false-discovery-rate correction.

RESULTS: Mean performance remained below 1.5 on the 0-3 scale for all 10 models on both datasets. Adding radiologist-selected 2D key images to clinical history improved performance in all 10 models (+19.8% to +139.2%). In RealClin at fps=10, all 8 evaluable models scored lower with volumetric input than with the 2D-image baseline (-5.3% to -31.4%); MedGemma-4B and Llama-4-Scout-17B-16E could only be evaluated at fps=2 due to context-window and graphics processing unit-memory constraints. At fps=2, a total of 8 out of 10 models declined (-6.8% to -28.6%), while Qwen2.5-VL-7B and InternVL3-8B showed marginal improvements (+2.4% and +2.1%). Cross-dataset transfer diverged by model category: proprietary models declined from RadEdu to RealClin (eg, O3 with images: 1.14 to 0.79), whereas Chinese-centric open-source models improved (eg, InternVL3-78B: 0.48 to 0.75). The rare-disease premium observed in 9 out of 10 models in RadEdu reversed in RealClin, where common-disease scores exceeded rare-disease scores in 7 out of 10 models under history-only input. Translating RealClin histories into English produced a numerical decrease in mean score for all 10 models, which were statistically significant in 9 out of 10 models after false discovery rate correction, excluding a Chinese-language penalty.

CONCLUSIONS: Within the scope of this benchmark, multimodal inputs improved performance over clinical history alone, but performance gaps remain in volumetric data processing and cross-context generalization, with mean diagnostic performance across the 10 evaluated models remaining below clinically actionable levels on both datasets.

PMID:42566748 | DOI:10.2196/92183

JMIR Form Res. 2026 Aug 7;10:e89837. doi: 10.2196/89837.

ABSTRACT

BACKGROUND: Systematic collection of social determinants of health (SDoH) data remains inconsistent across health care settings, despite its critical impact on patient outcomes. Large language model-powered chatbots offer promise for scalable SDoH data collection, but rigorous, feasible evaluation methods for patient-facing applications are lacking.

OBJECTIVE: This study aimed to describe an efficient, iterative, multidisciplinary approach for developing and evaluating a patient-facing SDoH chatbot using synthetic data and case simulation, with the goal of optimizing both chatbot performance and the evaluation rubric prior to clinical deployment.

METHODS: A 10-criterion evaluation rubric was adapted from established health care AI frameworks and applied to 27 synthetic clinical scenarios representing diverse SDoH profiles. Scenarios were role-played by a licensed clinical social worker, and chatbot-patient interactions were rated by 3 members of the research team that were multidisciplinary experts: a social worker, a nurse practitioner, and a physician. Quantitative analysis used percent agreement and Fleiss κ to characterize chatbot performance and rater consensus, with percent agreement selected due to the high prevalence of ceiling effects in several domains. Qualitative analysis synthesized rater feedback to guide iterative refinement of both chatbot prompts and rubric domains.

RESULTS: Across 27 simulated cases, the chatbot received high proportions of positive ratings for accurate interpretation (agreement=0.98%, 95% CI 0.91-0.99), communication quality, and cultural sensitivity (agreement=0.99%, 95% CI 0.93-1.00), and appropriately adaptive questioning (agreement=0.99%, 95% CI 0.93-1.00). Lower performance was observed in domain focus and completeness (agreement=0.51%, 95% CI 0.40-0.61), completeness of data capture (agreement=0.59%, 95% CI 0.48-0.69; Fleiss κ=0.18), and safety (agreement=0.69%, 95% CI 0.58-0.78; Fleiss κ=-0.04), prompting targeted adaptations. Qualitative feedback highlighted the importance of distinguishing screening from clinical interviewing capabilities and informed the refinement of the rubric, including clarifying the definition of safety to focus on recognition of physical and mental health emergencies.

CONCLUSIONS: This study describes a formative feasibility approach for iterative refinement of a patient-facing SDoH chatbot and its evaluation rubric using synthetic case simulation. Future work will include independent external raters, patient stakeholders, repeated scenario testing, and prospective clinical evaluation.

PMID:42566745 | DOI:10.2196/89837

JMIR Infodemiology. 2026 Aug 7;6:e88849. doi: 10.2196/88849.

ABSTRACT

BACKGROUND: Influenza seasons may be associated with increased anxiety-related expressions on social media. Social media can reflect population-level emotional expression patterns in real time.

OBJECTIVE: The aim of the study is to characterize diurnal and full-season dynamics of anxiety-related language during the 2023-2024 influenza season in China and its association with influenza activity.

METHODS: We retrieved Sina Weibo posts in February 2025 covering September 4, 2023, to April 28, 2024. Posts containing Diagnostic and Statistical Manual of Mental Disorders (DSM)-based anxiety terms were cleaned and deduplicated (N=169,728 → 106,440). We first linked weekly influenza incidence with anxiety-related postings. Then, we plotted diurnal patterns by epidemiologic phase, conducted supplementary within-sample hourly normalization analyses, and modeled longitudinal symptom trajectories using ARIMA (autoregressive integrated moving average) and supplementary ARIMAX (autoregressive integrated moving average with exogenous regressors) time-series models.

RESULTS: Anxiety-related posts closely followed influenza activity, surging during the outbreak and peak phases and remaining elevated even after influenza declined. Initial Spearman correlation analyses showed significant negative associations for irritability (r=-0.413; P=.02) and restlessness or feeling keyed up or on edge (r=-0.396; P=.02). However, supplementary ARIMAX analyses further revealed that being easily fatigued and difficulty concentrating or mind going blank exhibited more stable positive temporal associations with influenza activity after controlling for autocorrelation and lagged effects. Diurnal patterns shifted across stages, showing mild early-evening variation during the outbreak, clear morning peaks with secondary afternoon and evening rises during prevalence and decline, and morning-afternoon concentration in the end stage. Supplementary within-sample hourly normalization analyses showed that the major diurnal structures remained generally stable after normalization. ARIMA time-series analysis revealed that irritability and being easily fatigued consistently dominated the discussions, whereas others remained at relatively low levels. Out-of-sample forecasting based on a chronological 80% training and 20% testing split suggested generally stable short-term trajectories, with being easily fatigued showing a slight increase.

CONCLUSIONS: This study demonstrates how social media can capture diurnal and seasonal fluctuations of anxiety symptoms associated with influenza activity, advancing understanding of affective dynamics in population health.

PMID:42566744 | DOI:10.2196/88849

JCO Glob Oncol. 2026 Aug;12(8):e2500661. doi: 10.1200/GO-25-00661. Epub 2026 Aug 7.

ABSTRACT

PURPOSE: Clinical trial availability often varies between countries, independent of the local burden of disease. We aimed to evaluate the current global availability of prostate cancer clinical trials and the factors affecting it.

METHODS: We searched for clinical trials involving prostate cancer between June 2019 and June 2024 through Clinicaltrials.gov. Noninterventional trials were excluded. Countries were classified according to the World Bank Ranking (WBR) as high-, upper-middle-, lower-middle-, and low-income countries (HICs, UMICs, LMICs, and LICs). Multivariable negative binomial regression was used to evaluate the association between the number of trials and country’s characteristics. Multivariable logistic regression was used to evaluate the association between trial availability and country’s characteristics. In addition, we collected data on sponsorship, funding, intervention, intervention intent, end point selection, trial phase, and cancer stage.

RESULTS: Of the 1,531 trials identified, 1,413 met the eligibility criteria. Most trials were conducted exclusively in HICs (80.4%), included patients with metastatic disease (55.4%), and were sponsored by academia (68.8%). Pharma-funded trials had a higher number of participating countries (mean 2.7 v 1.0, P < .001) and were more likely to include metastatic disease (72.0% v 32.3%, P < .001) and to investigate systemic therapy (76.3% v 29.4%, P < .001). In the multivariable analysis, WBR, gross national income (GNI), and annual health expenditure were all associated with the presence of at least one trial (P < .001). Only GNI was independently associated with the number of trials within a country (P < .001).

CONCLUSION: Prostate cancer trials are disproportionately concentrated in HICs despite a lower burden of disease. Intentional efforts are needed to ensure global representation and long-term benefits for populations historically excluded from research.

PMID:42566737 | DOI:10.1200/GO-25-00661