JMIR mHealth and uHealth

Aims: To understand how users believe continuous glucose monitoring (CGM) changes their behaviour, which app features they credit, and whether endorsing more mechanisms of action (MoAs) is linked to reported diet and physical activity change. Methods: We surveyed 112 U.S. adults with type 1 or type 2 diabetes who had used a Dexcom or Abbott Libre CGM within 12 months. Participants rated 26 MoA statements mapped to the Capability, Opportunity, Motivation-Behaviour (COM-B) model, identified contributing app features, and reported CGM-attributed diet and physical activity change. Logistic regression examined MoA count and COM-B domains as predictors of behaviour change. Results: Participants (mean age 54.5 years; 60.7% female) reported dietary (82.1%) and physical activity (46.4%) change and endorsed a median of 18 MoAs, most often relating to knowledge, skills and behavioural consequences. Real-time glucose and graphs were attributed to many MoAs. Each additional MoA was associated with higher odds of dietary (OR 1.27, 95% CI 1.15-1.44) and physical activity change (OR 1.27, 95% CI 1.16-1.43). Motivation made the strongest distinct contribution among COM-B domains. Conclusions: Users perceive CGM as engaging many behavioural mechanisms; endorsing more was linked to behaviour change. Motivation may be a key target for CGM-based behavioural support.

Background: The first thousand days are a sensitive window for child health and social health inequalities. When routine care falls short, parents of young children turn to online sources and, recently, to generative artificial intelligence (AI). Evidence on their concerns, child-health literacy, and AI use is scarce. Objective: To describe health informational needs and concerns, child-health literacy, and use of AI for child-health questions, including differences by parental role and parity, among parents of young children. Methods: In this cross-sectional study, two anonymous self-administered online questionnaires were disseminated through a digital health application between May 12 and June 11, 2026, to French-speaking parents of at least one child aged 0 to 47 months in France. Questionnaire 1 covered unmet information needs, concerns across 16 topics, perceived intra-couple concordance, and confidence in routine care. Questionnaire 2 covered a four-item child-health literacy quiz and AI use (frequency, topics, 12-month trend, barriers, trust conditions, perceived effect on consultations, and disclosure). Results: In total, 12,634 parents responded (Questionnaire 1, n=5,410; Questionnaire 2, n=7,224), mostly mothers (94.6% and 92.9%). Most (61.8%) had searched for information without finding a clear, reliable answer, selecting a median of 4 (Interquartile range [IQR] 3-7) over 16 topics. Leading concerns were sleep (51.3%), nutrition (48.1%), and education (i.e., upbringing and behavior) (47.8%). Mothers were nearly twice as likely as fathers to select accident prevention (26.5% vs 16.3%) and parental mental health (38.7% vs 28.6%); first-time parents selected most topics more often than experienced parents, most markedly dental hygiene (43.8% vs 27.6%). The median child-health literacy score was 3 (IQR 3-4) on a 0-to-4 quiz, with 19.9% of parents scoring 2 or below; tooth-brushing age was the weakest item (68.4% correct). Mothers outscored fathers on every item. More than half of parents (54.2%) had used AI for a child-health question, 11.2% weekly; among those reporting a trend, 68.3% had increased AI use for child-health questions over the year and 6.2% decreased it. The main barriers to AI use were a preference for professionals (55.9%) and reliability concerns (48.7%); trust depended most on medical oversight of the tool (76.6%). AI use appeared additive, with 66.8% reporting no change in healthcare visit frequency. One third (35.5%) had told an AI something shared with neither a professional nor their network, rising to 64.1% among regular users. Conclusions: Parental informational needs were widespread and frequently unmet. AI has become a common and fast-rising source for child-health questions, at times a confidential one, used alongside rather than instead of professionals. Child-health literacy was generally high but uneven, with oral health the clearest gap. Strengths include the sample size and early-generative-AI timing; limitations include the cross-sectional design, self-report, digital-recruitment selection bias, and the small proportion of fathers.

Abstract Many women experience menopausal symptoms after stopping hormone replacement therapy (HRT). An evidence gap remains regarding how women and healthcare professionals (HCPs) make decisions about discontinuing and restarting HRT. Aim To explore experiences of HCPs that support women when they discontinue and restart HRT. Design and setting Qualitative study with HCPs working in NHS primary care. Method Semi-structured interviews with HCPs (n = 15) conducted between October 2025 and March 2026. A preliminary coding template was created using the discussion guide as a framework. Each interview was then coded inductively against the template which developed iteratively. Team based discussion helped refine codes into themes and sub-themes, and a subsequent conceptual model was created. Results The model describes HCPs experiences of supporting women to discontinue HRT as an iterative process characterised by uncertainty. Discussions about discontinuation were commonly prompted by changes in clinical risk or annual HRT review, but decisions about whether and when to stop also depended on womens symptoms, side effects, life stage and readiness. HCPs described agreeing an individualised discontinuation strategy, providing support during the process, and reviewing women after discontinuation to consider alternative treatments or, where appropriate, restarting HRT. Conclusion HCPs recognise the importance of readiness to discontinue and that decision making should be woman led, with HCP support, clear practice guidance and opportunities to revisit treatment. Further research is needed to support women weigh up their personalised risk and benefit of continuing/discontinuing and identify discontinuation strategies that limit resurgent symptoms.

Introduction: Primary care electronic health records (EHRs) are an important resource for health research, but death recording in primary care EHRs may be incomplete or differ from Office for National Statistics (ONS) death registrations. We used OpenSAFELY-TPP to assess the completeness and concordance of death recording and provide recommendations for using date-of-death information. Methods: Using linked primary care EHRs and ONS death registrations, we assessed whether ONS-registered deaths from 2020-2025 had a primary care date of death and, where present, calculated the difference between dates. Where a primary care date was absent, we assessed death-related SNOMED CT codes. Analyses were conducted overall and by year, population subgroup, and general practice. We also examined GP registration end dates and primary-care-recorded deaths without a corresponding ONS record, including monthly patterns near the data extraction date. Results: Among 1,409,275 ONS-registered deaths, 88% had the same date recorded in primary care and 8% had no corresponding primary care date. Among 1,324,995 deaths recorded in primary care, <2% had no corresponding ONS record. Deaths recorded in only one source were more frequent among people aged <45 years and those from Mixed, Asian or Asian British, Black or Black British, and Chinese or Other ethnic groups. ONS-only dates were also more frequent in London and the West Midlands. Primary-care-only deaths increased close to date of data extraction, reaching 7% in February 2026. Same-day GP deregistration increased from 9% in 2020 to 85% in 2025. Conclusions: Death recording in primary care EHRs showed high completeness and concordance with ONS registrations, but discrepancies remained for recent deaths and some population groups. Data recency and population-specific completeness should therefore be considered when using linked mortality data for research.

Background HPV-vaccination prevents HPV-related cancers, but uptake remains suboptimal. In the Netherlands, a catch-up campaign invited 18-27-year-olds for an additional cost-free opportunity to initiate or complete their HPV-vaccination in 2023-2024, which led to 24% of this invited population getting vaccinated. As young adults increasingly seek health information online, understanding online questions, concerns and misinformation in response to the campaign may help improve future vaccination efforts. Supplemental to overall evaluation of the vaccination campaign, we piloted online social listening processes to generate an insight report for Public Health Service (PHS) Amsterdam. Objective This study piloted online social listening processes at the PHS Amsterdam to identify actionable insights into questions, concerns and misinformation surrounding the HPV-vaccination catch-up campaign among young adults. Methods Upon inventory of potential platforms and data sources, a retrospective online social listening analysis was conducted across the platforms Instagram, Facebook, and X, collecting data from PHS, the National Public Health Institute for Public Health and the Environment (RIVM), the Dutch public broadcasting foundation (NOS), and LINDA.meiden sources from January 1, 2023, to June 1, 2024. Publicly available posts, comments, and engagement concerning HPV-vaccination were manually collected, and inductively coded. Themes were assessed for risk, based on expected potential harm and observed reach, and used to develop recommendations. Results A total of 821 posts and 5157 comments were collected and analysed. Ten recurring themes were identified across varying levels of risk. Two themes were classified as high risk: an information gap regarding the effectiveness of HPV-vaccination and concerns about the transparency on HPV-vaccine side effects. Three themes were classified as medium risk: questioning HPV-vaccine effectiveness under certain circumstances, reduced general vaccine trust following COVID-19, and accessibility issues related to vaccination locations and service times. Two themes were classified as neutral risk: questions regarding eligibility criteria for the campaign and calls for broader HIPV-prevention measures. Finally, three low risk themes concerned questions regarding HPV-vaccine brand specifics, campaign choices, and questions about scheduling or rescheduling HPV-appointments. Comments also referred to subsequent information sources, including reliable sources (public health institutes), false narratives and misinformation. Recommendations focused on addressing questions and information gaps (i.e. improving HPV risk perception and providing more detailed HPV-vaccine information), improving communication about HPV-vaccination, and reducing practical barriers to vaccination. Conclusions Manual social listening across selected online sources provided actionable insights into the public discourse during the HPV-vaccination catch-up campaign. Results mostly reflected questions, concerns and information gaps. Although some themes were critical of HPV-vaccination, false narratives and misinformation did not dominate the discourse. Recommendations aim to reduce practical barriers and address information needs. This pilot showed that online social listening can be effectively implemented even with limited resources.

Awareness of comorbidities in people with dementia is vital context for dementia research and clinical management. Natural Language Processing (NLP) applied to free text in the electronic health record (EHR) may help ascertain comorbidity presence. Our aim was to assess the validity of the MedCAT Comorbidity NLP app in a cohort of people with dementia. We used text from the EHR of South London and Maudsley NHS Mental Health Trust via Clinical Records Interactive Search (CRIS), linked to structured data from local primary care (Lambeth DataNet) and hospital admission data in a cohort of 3,543 people with dementia. We evaluated MedCAT Comorbidity, examining internal validity via manual review of text and external validity by cross-validation against linked primary care and hospital data; and comparison of the predictive value of comorbidity measures for mortality. Internal precision was highest for atrial fibrillation (100%) and lowest for diabetes (78%). Cross-validation with linked data varied substantially across conditions, with precision from 96% to 59% and recall 77% to 4%. The median number of comorbidities identified was two using any single source, or three when combined. MedCAT Comorbidity demonstrated predictive ability for mortality (HR 1.11 per condition), although the model fit was better using comorbidity from hospital admission data. In people with dementia, the MedCAT Comorbidity app showed high precision for most conditions during internal validation. The variability of precision and recall on external validity assessment partly reflects incomplete documentation. Results from the MedCAT comorbidity app can assist researchers in understanding comorbidity of cohorts, particularly when primary care or hospital data is not available. Our findings highlight the need to develop quality assurance protocols that include internal and external validity checks before deploying general data extraction apps for specific clinical subpopulations.

Although physical activity is associated with better physical functioning, the effects of initiating regular physical activity among older adults with persistent inactivity remain unclear. We conducted a sequential target trial emulation using Health and Retirement Study (HRS) data to estimate the effect of physical activity initiation versus noninitiation on incident mobility difficulty. Participants were included in the analysis if they were aged [&ge;]65 years at the previous wave, reported no regular physical activity at the previous two waves, and had no mobility, activities of daily living (ADL), or instrumental activities of daily living (IADL) difficulties at baseline. Stabilized inverse probability of treatment weighting was applied to adjust for measured confounding. Weighted cumulative incidence was then estimated with death treated as a competing event, and risk differences were calculated at approximately 2, 4, and 6 years. At 2 years, the cumulative incidence of mobility difficulty was 32.75% among initiators and 39.73% among noninitiators, with a risk difference of -6.98 percentage points (95% CI, -13.11 to -0.88). At 4 and 6 years, risk differences were -2.90 percentage points (95% CI, -9.77 to 4.29) and -5.44 percentage points (95% CI, -12.27 to 1.99), respectively. Initiating regular moderate or vigorous physical activity was associated with a lower risk of incident mobility difficulty at approximately 2 years among older adults with persistent inactivity. This difference was less evident at later follow-up, and no clear differences were observed for incident ADL or IADL difficulty.

Background: Although sleep disorders are generally associated with cognitive decline, definitions of sleep conditions and cognitive function vary greatly across studies. Therefore, we evaluated associations between consensus definitions of sleep and cognitive outcomes across multiple studies. Methods: Consensus sleep measures were ascertained at baseline visits in three cohort studies (HCHS/SOL, MESA and ARIC, N=11,563): short and long sleep ([&le;]6hours and [&ge;]9hours), insomnia (Women's Health Initiative Insomnia Rating Scale [&ge;]10 or [&ge;]6 for 3 questions), daytime sleepiness (Epworth Sleepiness [&ge;]11) and obstructive sleep apnea (OSA, Apnea-Hypopnea Index [&ge;]15). Cognitive measures were ascertained at baseline and follow-up visits in each study and used to calculate harmonized outcomes: global cognitive function (GCF) at baseline, change in GCF between baseline and follow-up, poor GCF at baseline ([&ge;]1SD below the median), and incident poor GCF at follow-up. Study-specific analyses were adjusted for age, sex, center, education, race/ethnicity, APOE4 and language of cognitive assessments as needed. Results were meta-analyzed across studies. Results: Long sleep was consistently associated with lower baseline GCF ({beta}=-0.19, SE=0.03, p=3.78x10-14, FDR p=7.56x10-13, p-heterogeneity=0.78) and increased odds of poor baseline GCF (OR=1.77, 95% CI=1.49-2.09, p=2.96x10-11, FDR p=2.96x10-10, p-heterogeneity=0.22) across cohorts. Insomnia was also suggestively associated with lower baseline GCF ({beta}=-0.051, SE=0.02, p=0.01, FDR p=0.07, p-heterogeneity=0.005) and increased odds of poor baseline GCF (OR=1.22, 95% CI=1.06-1.41, p=6.76x10-3, FDR p=0.05, p-heterogeneity=0.03). Sleep measures were not associated with change in cognitive function. Discussion: Associations of long sleep and insomnia with harmonized cross-sectional GCF across three large, multiethnic cohorts suggest that cognitive function may be associated with distinct sleep phenotypes.

Background: Although point-of-care testing is the standard method for inpatient glucose monitoring; it is associated with patient discomfort and late detection of dysglycaemic events. Continuous glucose monitoring (CGM) may improve glycaemic management; however, limited staff education and experience among may hinder its implementation. Aim: To investigate experience, perceptions and training needs on CGM use among inpatient nursing staff across diverse hospital wards. Methods: This is a single-centre, 38-item online survey recruited inpatient nurses in a hospital in the UK. Participants were invited via the e-newsletter and posters; the survey remained open for five months. Results: Nineteen nurses reported positive perceptions of inpatient CGM. Most considered CGM reliable (77.8%) and preferred CGM combined with POCT (66.7%). Views on its impact on workload and patient-room entries were mixed (30% agreed, 30% disagreed). Training needs included sensor placement (92.9%) and alarm management (78.6%). Conclusion: Nurses viewed CGM favourably but highlighted important training needs for inpatient use.

OBJECTIVE: Research on neighborhood context and cognitive health often relies on composite indices that collapse neighborhood socioeconomic status (SES) and aspects of the built environment into a single score. This study examined the extent to which neighborhood SES and built environment were associated with late-life cognitive impairment. METHOD: Participants were 281 predominantly Black older adults who completed the Self-Administered Gerocognitive Exam (SAGE), a brief cognitive screening measure. Participants also provided their home address to obtain four neighborhood metrics: Two composite measures of neighborhood disadvantage, the Environmental Justice Index (EJI) and the EJI Social-Environmental Rank, as well as the EJI Social Vulnerability Rank as a measure of neighborhood SES, and the EJI Environmental Burden Rank to measure exposure to hazardous aspects of the built environment. RESULTS: Lower neighborhood SES was associated with higher odds of cognitive impairment. In contrast, greater environmental burden was associated with lower odds of impairment, with years of education as a moderator; the association was strongest among individuals with high levels of education. The two composite scores were not associated with cognitive impairment. CONCLUSION: Composite measures of neighborhood features may obscure distinct pathways of their effects on cognition.

Background: Ambient artificial-intelligence (AI) scribes are being deployed beyond medicine into allied-health practice, but comparative evidence mainly concerns physician documentation. Objective: To compare quality and transcript-grounded clinical-risk error burden of AI-generated and allied health professional notes from identical simulated consultations. Methods: Paired simulation across dietetics, physiotherapy, occupational therapy and speech and language therapy (five consultations each; 20 pairs; 40 notes). Heidi generated an unedited first-draft note and a professional documented each encounter. Professionals rated PDQI-9 quality. Professional and automated review identified unsupported statements and omissions; two language models graded clinical risk. Co-primary outcomes were PDQI-9 score and Critical+High error density. Results: Mean PDQI-9 scores were 38.45 and 35.05 for AI and professional notes (paired difference +3.40, 95% CI +0.01 to +6.79; Wilcoxon p=0.042; dz=0.47). The Bayesian estimate was +3.47 (94% equal-tailed credible interval +0.66 to +6.30; P[AI>professional]=0.989). Automated-reviewer all-error density was 3.35 vs 8.55 per note (professional-minus-AI difference 5.20, 95% CI 3.85 to 6.55); omission density was 1.45 vs 5.20 (difference 3.75, 2.80 to 4.65). Critical+High density was 0.35 vs 0.75 (difference 0.40, -0.10 to 1.00). The pooled quality result was driven principally by dietetics. Conclusions: AI-generated notes achieved modestly higher pooled quality and fewer transcript-grounded errors, chiefly omissions; serious-error evidence was inconclusive. Small discipline samples and incomplete ground truth preclude safety or discipline-wide superiority claims.

Background: Thinness-oriented body ideals are well described among young Japanese women, but variation in visual and numerical ideals by sex, age, and current BMI remains unclear. Methods: We analysed an unweighted web-panel sample of 27,359 Japanese adolescents and adults aged 15-69 years from the Survey of Body Shape and Health Awareness in Japanese People, a cross-sectional web-based survey. Current and ideal BMI were derived from self-reported height and weight, and visual orientation from ideal versus perceived current silhouette. Adult BMI-stratified analyses included participants aged 20-69 years. Findings: Among 27,359 participants (10,094 men and 17,265 women), relative thinness-oriented indicators increased with current BMI in both sexes. Among normal-weight women, 74.7-83.7% selected a smaller ideal silhouette and 79.2-91.2% reported an ideal BMI lower than current BMI. These indicators were also present among underweight women aged 20-29 years (44.9% and 43.4%, respectively), but declined with age. Most underweight men reported weight-gain-oriented ideals. Fine stratification showed that thinness-oriented ideals among women were common in low-normal and mid-normal BMI strata. Interpretation: Body-size ideals differed by sex, age, and current BMI. Thinness-oriented ideals among women extended beyond obesity and young adulthood, whereas underweight men generally preferred a larger body. Longitudinal and behavioural studies are needed. Funding: Cabinet Office of Japan Strategic Innovation Promotion Program (SIP), "Inclusive Community Platform" (grant no. SIPCP23C02).

The explosive growth of ambient AI scribes in healthcare has focused almost exclusively on relieving clinician documentation burdens and enhancing clinician efficiency and reducing burnout. While most research has focused on the clinician experience, these same types of tools, such as a patient held AI Personal Health Assistant, can be put in the hands of patients to facilitate communication with healthcare providers, to provide greater autonomy and activation for patients in managing their own healthcare, and to facilitate the task of receiving and retaining complex new information. However, to ensure these tools serve as more than just administrative aids, we partnered with a large technology company to conceptually codesign how such a tool might be studied in real world settings in a way that reflected outcomes of greatest importance to patients and families. We used the Nominal Group Technique and engaged thirty three patients and caregivers and ten multidisciplinary clinicians in structured virtual sessions to identify the outcomes on which subsequent studies should hinge. Our findings reveal a significant misalignment between current industry trends and patient priorities. While the market emphasizes the scribe function for billing, stakeholders overwhelmingly prioritized the AIPHAs role in longitudinal care management and post visit coordination. The most critical outcome identified was the impact on communication and respect, suggesting that patients view AI as a mechanism to improve the human to human dynamic and ensure a shared understanding of the care plan. Our findings further highlighted that the primary hurdle for the use of patient held AI is not technical utility but clinician trust and the need for formal consent processes. Importantly, as this technology is new and integration into the clinical experience is still being explored, patients cannot assume they will have access to the transcript of their own encounter when that record is created and held by the health system. This gap highlights the potential value of a patient held tool, preserving discussions from their visit and follow up steps in clearer detail than they may receive in their clinical note or traditional visit summary. From a system perspective, this transition requires a strategic shift from measuring workflow focused efficiency to measuring patient side advocacy and engagement. By centering the evaluation of AI on these patient defined metrics and fundamentally designing subsequent AI investigations using a partnership model, health systems can move beyond simple automation toward a model of care delivery that empowers patients to navigate complex clinical journeys with greater autonomy and clarity.

Summary Background We used sequential target trial emulation (TTE) to examine associations between opioid analgesic initiation and adverse maternal and neonatal outcomes while accounting for gestational timing, time at risk, and potential confounding. Methods Using the Early Life Course platform, comprising linked population-based data for births in New South Wales, Australia (2013-2019), we emulated a new trial at each gestational week, comparing women who initiated a prescribed opioid analgesic with eligible non-initiators. Baseline covariates were balanced using inverse probability weighting. Weighted logistic regression estimated odds ratios (ORs) for neonatal outcomes, postpartum haemorrhage, and severe maternal morbidity. Pooled logistic regression estimated hazard ratios (HRs) for other maternal outcomes, combined across gestational weeks using fixed-effects meta-analysis. Sensitivity analyses assessed unmeasured confounding, exposure misclassification, and potential reverse causation. Findings Among 511,357 pregnancies, over 80% of opioid initiators received only one dispensing. Opioid initiation was not associated with increased risks of placental abruption (HR 0.94, 95% CI 0.75-1.16), severe maternal morbidity (OR 1.05, 0.97-1.11), postpartum haemorrhage (OR 0.98, 0.95-1.02), low Apgar score (OR 0.99, 0.87-1.08), small for gestational age (OR 0.90, 0.85-0.95), or neonatal abstinence syndrome (OR 0.98, 0.72-1.30). Risks were elevated for PPROM (HR 1.10, 0.98-1.23), preterm birth (HR 1.26, 1.19-1.33), severe neonatal morbidity (OR 1.08, 1.02-1.17), and perinatal death (OR 1.29, 1.10-1.52). Perinatal death estimates attenuated in analyses addressing potential reverse causation. Interpretation Using a TTE approach that accounted for gestational timing and time at risk, we found no appreciable increase in most maternal or neonatal outcomes following predominantly short-term opioid initiation. Elevated risks of preterm birth, perinatal death, and third-trimester PPROM may reflect causal effects or confounding by pregnancy-related indications. Studies using time-appropriate designs and detailed information on prescribing indications and their timing are needed to clarify these associations.

Financial well-being (FWB) encompasses objective and subjective financial status and may influence health. We developed and validated a parsimonious FWB index using data from 23,006 Health and Retirement Study (HRS) participants aged 50 years and older from 2006 to 2022. The FWB-5 Index was derived using correlated two-factor confirmatory factor analysis accounting for household clustering. Indicators included household wealth, household income-to-poverty ratio, monthly financial strain, satisfaction with one's financial situation, and perceived financial mastery. We assessed convergent and discriminant validity using the Consumer Financial Protection Bureau Financial Well-Being (CFPB-FWB) Scale and Satisfaction with Life Scale, respectively. Associations with pre-baseline socioeconomic factors and subsequent depressive symptoms and health conditions were examined to assess construct and predictive validity. Model fit indices supported the two-factor structure (robust comparative fit index = 0.99; robust root mean square error of approximation = 0.05). Correlations were stronger with the CFPB-FWB Scale (Spearman P = 0.80) than with life satisfaction (Spearman P = 0.43). Associations with socioeconomic factors and subsequent health outcomes were in the hypothesized directions. These findings support the FWB-5 Index as a parsimonious measure for examining financial well-being and its associations with health across HRS waves, including those predating the CFPB-FWB Scale.

Objective. Temperature readings are routinely stratified by race and ethnicity, though significant group differences may reflect physiology, instrument noise, or categorization rather than biology. This study tests whether these categories partition facial infrared thermography measurements beyond individual variation and baseline device noise. Approach. A secondary analysis of thermography data (n = 951 adults) compares inner-canthus maximum (TCEmax) and full-face maximum (Tmax) temperatures across five administrative categories via one-way analysis of variance (ANOVA), mixed-effects modeling, Bland-Altman analysis against oral reference, and Monte Carlo sensitivity analysis. Main results. ANOVA returns significant group differences (p < 0.001; eta-squared = 0.04 for TCEmax, 0.03 for Tmax), but the total spread (0.2 to 0.3 degrees Celsius) is comparable to device accuracy and within-subject variability. Individual variation accounts for 95% of TCEmax variance, and adding simulated Gaussian noise to participant-level temperatures (sigma = 0.10 to 0.30 degrees Celsius) preserves the complete group-mean rank order in as few as 37.5% of iterations. Significance. Testing whether a group difference survives perturbation at the scale of instrument accuracy offers a check against treating significance alone as evidence of physiology. Here, observed group differences are small relative to measurement variability, and their physiological interpretation remains uncertain, with implications for clinical tools that treat administrative categories as biologically meaningful.

OBJECTIVE: To describe (1) US emergency department-treated pickleball injury burden and the injury-to-participation measure among total players, with burden among adults aged 60 to 79 years as a descriptive subgroup, and (2) determine whether general internet search interest for 'pickleball' is also associated with total pickleball-related injuries. DESIGN: Ecological time series study, United States, 2014 to 2023. DATA SOURCES: National Electronic Injury Surveillance System (NEISS) emergency department microdata yielded 1,738 adjudicated pickleball cases, 921 of them orthopedic, weighted to an estimated 101,450 injuries nationally. Google Trends, English Wikipedia pageviews, and GDELT news volume served as general pickleball attention signals, with the Trends index for 'sourdough' as a negative control. Sports and Fitness Industry Association (SFIA) participation estimates supplied a total player denominator. MAIN OUTCOME MEASURES: Annual national injury estimates with design-based 95 percent confidence intervals; injury-to-participation measures; and correlations between total pickleball-related injuries and general pickleball attention signals before and after removing trend and seasonality. RESULTS: Estimated emergency department-treated pickleball injuries rose 17.5-fold, from 1,391 in 2014 (highly unstable) to 24,294 in 2023. 70 percent of adjudicated cases involved adults aged 60 to 79. During 2017-2023, injury estimates rose 4.0-fold and participation rose about 4.4-fold. No temporal trend was detected in the injury-to-participation measure (P=0.59). Search interest looked like an excellent proxy: in annual levels it correlated with total pickleball-related injuries at R=0.96, and Pickleball Wikipedia pageviews at R=0.98. However, so did searches for "sourdough", at R=0.76. Removing trend and seasonality left little (monthly residual R=0.084; year-on-year R=0.28 over 96 months, 2015-2023), and the seasons ran opposite, with injuries peaking in February and searches in July, such that, on 12-month mean profiles, the sourdough negative control fitted the injury season (R=0.96) while pickleball ran against it (R=-0.40). General pickleball search interest and pickleball news coverage remained correlated in year-on-year changes in 2017-2023 (R=0.64), whereas their corresponding correlations with total pickleball-related injuries in 2017-2023 were 0.25 and 0.20. CONCLUSIONS: The absolute burden of emergency department-treated pickleball injury has grown substantially, but the available surveillance data cannot resolve whether the sport has become more hazardous per participant. The observed rise is consistent with growth in exposure alone. The strong correlation between search interest and injury counts was not specific, the association was substantially attenuated after detrending, and the seasonal patterns were opposed. These descriptive results do not establish that search interest can substitute for injury surveillance, and series that rise together should not be assumed to be related.

Polyendocrine metabolic ovarian syndrome (PMOS) and early-life irregular cycles (ELIC) carry higher metabolic risks, but real-world glycemic patterns remain inadequately characterized. We analyzed 247,316 continuous glucose monitoring (CGM) days from 716 consented and enrolled participants (11/2019-08/2026) in the Apple Women's Health Study, using cluster-weighted generalized estimating equations to evaluate associations between PMOS, ELIC, and glycemic metrics. Same-day wearable-assessed physical activity, digital dietary logs, and medication health records were assessed as effect modifiers. Compared to participants without PMOS and with regular cycles, those with PMOS had higher mean glucose (6.81 mg/dL; 95% CI: -0.70, 14.72) and %observations above target range (3.77%; 95% CI: 0.04, 8.14). Estimates were smaller-in-magnitude for participants without PMOS but with ELIC, and larger for PMOS with androgen excess. Meeting activity goals, consuming protein-/fiber-rich diets, or using metformin/GLP-1 receptor agonists attenuated these associations. PMOS/ELIC were associated with unfavorable glycemic profiles, suggesting opportunities for risk identification and modification.

Large language models (LLMs) are increasingly consulted for health advice, but they can produce fluent statements that are false or unsupported, known as hallucinations, and this risk is poorly characterised in low-resource languages such as Bangla. We measured hallucinations of locally deployable open-weight LLMs in Bangla diabetes question answering and tested whether retrieval-augmented generation (RAG) grounded in Bangladeshs National Guideline on Diabetes Mellitus reduces them. Forty patient questions spanning six clinical sub-domains were each answered twice, under zero-shot prompting, by Qwen3.5-9B, TigerLLM-9B, Qwen3.6-35B-A3B and Gemma-4-26B-A4B, and by BanglaDiaRAG, which adds hybrid dense (BGE-M3) and sparse (BM25) retrieval from a physician-reviewed Bangla guideline knowledge base to Qwen3.5-9B. Two physicians rated the 400 de-identified responses by consensus for medical accuracy, completeness, safety, readability and reliability on a 0-1 scale, and responses with accuracy below 0.70 were classified as hallucinated. Hallucination rates were 2.5% for Gemma-4-26B-A4B, 3.8% for TigerLLM-9B, 22.5% for Qwen3.6-35B-A3B and 77.5% for Qwen3.5-9B. Guideline grounding lowered the rate of Qwen3.5-9B to 11.3% (difference -66.3 percentage points; 95% CI -76.3 to -55.0) and raised every criterion by 0.24 to 0.38 points (Holm-adjusted p < 0.001). BanglaDiaRAG outscored Qwen3.6-35B-A3B on all five criteria and matched or exceeded TigerLLM-9B, but it remained below Gemma-4-26B-A4B in safety, readability and reliability, and its residual errors were specific, confident and potentially harmful. Guideline grounding can turn a weak general-purpose model into a much safer Bangla diabetes assistant, but the choice of base model mattered as much as retrieval and no configuration was free of hallucination, so clinical oversight remains essential before patient-facing use.

Purpose: To compare the performance of three large language models (LLMs), ChatGPT-o1, Grok 3, and Gemini 2.0 Pro, on the American College of Prosthodontists' National Prosthodontic Resident Examination (NPRE). Materials and Methods: Official NPRE examinations from years 2015-2024 were retrieved from the American College of Prosthodontists website and used to prompt each LLM. Paid access tiers and advanced reasoning were enabled. Responses from LLMs were recorded and compared against the official answer keys for accuracy, variability across years, and consistency of responses. Duplicate questions were retained for the primary analysis and removed in a sensitivity analysis. Accuracy (percent correct) was compared using generalized estimating equations binary logistic regression (primary); Cochran's Q with Bonferroni-adjusted McNemar tests (sensitivity). Year-to-year variability (SD of annual accuracy) was compared using weighted Levene tests. Consistency across duplicate items was quantified as the percentage of perfect matches and compared using Cochran's Q with Bonferroni-adjusted McNemar tests. Results: Across 1,487 questions, overall accuracy differed among models (global P < 0.001). ChatGPT-o1 achieved 78.4% correct, Grok 3 76.4%, and Gemini 2.0 Pro 73.2%; ChatGPT-o1 and Grok 3's accuracy significantly exceeded Gemini 2.0 Pro (both P [&le;] 0.016), with no significant difference between ChatGPT-o1 and Grok 3 (P = 0.099). After removing duplicates (n = 1,200), results were similar: ChatGPT-o1, Grok 3, and Gemini 2.0 Pro were 77.1%, 76.6%, and 71.5% correct, respectively; ChatGPT-o1 and Grok 3's accuracy was significantly higher than Gemini 2.0 Pro (both P < 0.001). Variability across years did not differ significantly (P [&ge;] 0.097). Grok 3 showed significantly higher perfect-match consistency (79.2%) than ChatGPT-o1 (69.4%) and Gemini 2.0 Pro (64.5%) (P [&le;] 0.010). Conclusions: Contemporary LLMs showed 70-80% accuracy on postgraduate prosthodontic multiple-choice content. While promising for educational support, residual error and inconsistency warrant human oversight and transparent methods reporting before curricular adoption.