JMIR mHealth and uHealth

Background: Passively collected smartphone and wearable data, combined with genomic information, may enable scalable early identification of youth who are at risk for substance use disorder. However, heterogeneous data collected in naturalistic settings limit researcher control over data quality, requiring substantial preprocessing. Data cleaning and feature engineering decisions can meaningfully influence analytic outcomes yet are often minimally described or omitted in published work. Objectives: We provide rationales and recommendations for common preprocessing challenges in passive sensing research, including establishing inclusion criteria when "outliers" may reflect true behavioral variability, analyzing differences across smartphone operating systems, handling observations collected outside protocol windows, and engineering features for machine learning applications. Methods: We illustrate principled approaches to these challenges using smartphone and Fitbit data from the Adolescent Brain Cognitive Development Study (ABCD). The sample was drawn from the available half of the year-4 follow-up (data release 5.1; N = 4,754; ages 13-14). Results: Extreme outlying observations were concentrated among low-engagement participants with few observed days. Smartphone usage among Apple users appeared under-recorded relative to Android users when compared with self-report measures. Fitbit-derived behavior differed systematically between observations collected within versus outside protocol windows. We also identified potential errors in the study's default preprocessing pipeline. Conclusion: We recommend prioritizing person-level inclusion criteria that preserve meaningful behavioral variability, examining temporal distributions rather than assuming observations are exchangeable, and thoroughly investigating and reporting data quality issues. Although illustrated using the ABCD Study, these principles broadly apply to research using smartphones, wearables, and other digital data sources.

Patient narratives can reveal aspects of health that standardized measures miss, yet collecting open-ended responses at population scale remains difficult. We evaluated whether speech-to-text (STT) could enable richer responses in online health surveys without the resource demands of interviews. Across four surveys involving people with post-COVID-19 condition, healthy adults, people who engage in sex work, and older adults, participants could answer open-ended questions by typing or using STT. We compared response length, lexical diversity, stopword proportion, and total unique content words, alongside STT uptake and participant feedback. Quantitative comparisons were made using a mixed-effects model including modality (STT vs typed) as fixed effect and a random intercept per participant. STT uptake varied markedly across populations (0.7-80.0%). Spoken responses were estimated to contain more characters than typed responses in two studies, by 245.0 and 891.8 characters on average (both p<0.001), and contained more unique content words, despite lower lexical diversity and higher stopword proportions. Participants generally valued STT for its convenience and spontaneity, but uptake was constrained by context, privacy, interface design and individual preferences. STT may enable population-scale collection of richer health narratives, provided implementation is tailored to the study population and research context.

Background: Approximately 2.5 billion people require assistive products (APs), yet access remains limited partly because of shortages of trained personnel. eHealth tools may help strengthen workforce capacity and translate evidence into practice. The World Health Organization's Training in Assistive Products (TAP) program is an open-access eHealth tool designed to strengthen AP provision. However, its impact on learner outcomes remains insufficiently evaluated. This study assessed the usability and acceptability of the TAP module and changes in self-efficacy in mobility aid provision. Methods: We conducted a quasi-experimental pre-post study with health science students who completed the WHO TAP "Introduction to Assistive Products" module. We assessed usability using the System Usability Scale, acceptability using the Technology Acceptance Model and Theoretical Framework of Acceptability, and self-efficacy using an adapted version of Bandura's Multidimensional Scales of Perceived Self-Efficacy. We analyzed data using descriptive statistics and paired-samples t-tests. Results: Of 53 participants, 33 completed both assessments. Usability exceeded established benchmarks (76.97 +/- 9.66), indicating a positive user experience. Acceptability findings demonstrated favourable responses across domains, with high ratings for ease of use and intervention coherence, and comparatively lower burden scores. Mean self-efficacy increased across all competencies from 2.32 +/- 0.58 before the module to 4.23 +/- 0.36 afterward (p < 0.001), reflecting substantial gains in confidence in AP provision. Conclusion: The eHealth TAP module demonstrated above-average usability and favourable acceptability, while significantly improving self-efficacy in AP provision. These findings highlight the potential for eHealth training implementation to strengthen workforce capacity and bridge training gaps in global health.

Background Wearable heart rate (HR) monitors collect minute-level HR continuously across days, yet these data are commonly reduced to scalars such as resting HR (RHR) and heart rate reserve (HRR), defined as the difference between maximum HR and RHR. This discards two dimensions of within-day HR dynamics: how HR range varies across the day and the magnitude and duration of individual HR rises and declines. We introduce diurnal heart rate range (dHRR), a time-of-day-specific measure of HR range across repeated days, and heart rate excursions (HREs), individual HR rises and declines characterized by amplitude and duration. Methods We analyzed minute-level HR from 750 participants in the Baltimore Longitudinal Study of Aging (mean [SD] age, 66 [12] years). We estimated the 5th, 50th, and 95th HR percentiles at each minute of the 24-hour cycle across repeated days, defining diurnal HR (dHR) as the median curve and dHRR as the 95th minus 5th percentile curve. A moving-average algorithm segmented HR into HREs. Function-on-scalar regression modeled dHR and dHRR by age, sex,and BMI across the day; local polynomial regression examined age and sex differences in HRR and HREs. Results Conventional HRR decreased with age, indicating a smaller overall observed HR range. Age-related differences in dHR varied substantially across the day: between ages 40 and 80, fitted dHR was approximately 2 bpm lower at 3:00 AM but 8 bpm lower at 6:00 PM. dHRR showed that this contraction was not uniform across the day: the evening peak near 6:00 PM at younger ages was largely absent by age 70, while the morning peak shifted from approximately 9:00 AM toward noon. At the event level, HREs showed two parallel age-related changes: between ages 40 and 80, mean excursion amplitude decreased from approximately 40 to 32 bpm, while mean excursion duration increased from approximately 90 to 104 minutes. Conclusions Aging was characterized by a smaller global HR range, compression and reorganization of HR range across the day, and smaller, longer-lasting HREs. dHRR and HREs provide complementary views of the diurnal organization and event-level dynamics of wearable HR and are implemented in the open-source ihr R package.

Introduction: Chronic pelvic pain disorders (CPPDs) affect up to one in four reproductive-age women and are characterized by fluctuating, heterogeneous symptoms that complicate engagement in regular physical exercise. Standard programs rarely adjust to daily symptom variability. WorkoutCPP is a pilot series of N-of-1 trials evaluating the feasibility of an adaptive mobile-health (mHealth) intervention using reinforcement learning (RL) to personalize exercise for individuals with CPPDs. Methods and Analysis: This pilot study consists of a series of N-of-1 trials employing a randomized ABAB or BABA design, in which 45 participants (aged 18-55 years) with CPPDs each serve as their own control. Each participant completes one baseline week followed by four alternating 2-week intervention phases (A = generic exercise guidance; B = adaptive RL-generated recommendations), totaling 9 weeks. The RL agent integrated into the ehive app suggests daily exercise recommendations based on participant-reported pain and prior exercise behavior. Primary outcomes are exercise recommendation adherence rate and participant retention rate; safety and additional feasibility indicators are also monitored. Secondary outcomes assess algorithm interpretability. Exploratory outcomes assess contextual sensitivity of the RL algorithm and changes in pain and behavioral engagement. Ethics and Dissemination: Ethical approval was granted by the Icahn School of Medicine IRB (Protocol # STUDY-23-00721). Electronic informed consent will be obtained. Findings will be shared via peer-reviewed publications, conferences, and de-identified data summaries. Registration: This study protocol was publicly registered on the Open Science Framework (OSF) Registries (URL: https://osf.io/d9f36) prior to participant enrollment, to support international open-science data transparency and reproducibility standards for mobile health adaptive intervention trials. The study was subsequently registered on ClinicalTrials.gov (NCT07810218), consistent with ICMJE recommendations for trials informing future confirmatory research. All documented study procedures match the baseline human subjects tracking mechanisms approved by the Icahn School of Medicine at Mount Sinai Institutional Review Board (Protocol # STUDY-23-00721).

Background: Estimating nutrient intake from meal photographs with vision-language models (VLMs) is now practical, but patient photographs often cannot leave the institution. We developed PLANS (Personal-device Local Assessment of Nutrients System), in which a small open VLM runs entirely on a personal device. Methods: Open VLMs were compared on two image sets with reference values (NutriImage, 887 Japanese meals; SNAPMe, 1,134 US meals), and the selected model (Qwen3-VL-4B, 4-bit) was run on an iPad Air without network access. Energy (primary outcome), protein, fat, carbohydrate and salt were evaluated by median absolute percentage error (MedAPE) and Spearman rank correlation (rho). The effect of supplying the food weight, including errors in it, was examined. Results: Energy estimates on the iPad were identical to those of a desktop-computer run for 84-86% of images (other values, 75-87%). MedAPE on the iPad was 37.0% and 36.5% for energy (26-28% of images within +/-20%), 43-65% for the macronutrients and 68-101% for salt (rho 0.51-0.85). On the desktop computer, supplying the true weight reduced MedAPE of the 4B model on NutriImage from 37.0% to 32.5% for energy and from 100.0% to 66.8% for salt; energy MedAPE stayed below the base value for declared weights of 0.5-1.25 times the true weight or rounded to 50 g, but rose markedly when the container weight was included. Conclusions: An offline VLM on a tablet reproduced server-side energy accuracy and is a candidate tool for dietary counselling where photographs cannot leave the institution; given the wide limits of agreement, its usefulness in practice remains to be examined.

Stillbirth remains a major public health concern, particularly in resource-constrained settings. Barriers to timely, high-quality antenatal care may delay the recognition and management of pregnancy complications. We explored the perspectives of women, Village Health Team (VHT) members and healthcare workers on community-delivered approaches to improve pregnancy care, in particular prevention of stillbirth in Uganda. An exploratory qualitative study was conducted at four referral hospitals in Uganda. Data were collected through focus group discussions, in-depth interviews, and key informants interviews with women, Village Health Team members and healthcare workers. Audio-recordings were transcribed verbatim and translated into English where required. Data were analysed thematically using an iteratively developed codebook and NVivo software. In total, 56 participants took part, comprising women (n=27), Village Health Team members (n=11) and frontline healthcare workers (n=18). Participants highlighted the need for the adoption of simple and user-friendly wearable pregnancy monitoring tools for self-monitoring of early warning signs. Health education sessions focused on identifying early warning signs of pregnancy complications highlighted, emphasizing the importance of empowering women with information to seek care early through digital platforms. Limited follow-up support was also a critical concern, with the majority of the women advocating for better emotional and psychological care after a stillbirth, both at a facility and at home. Participants viewed community-delivered antenatal care as an acceptable approach to reducing access barriers. Implementation would require trained personnel, quality-assured tools, clear referral pathways and integration with facility-based care. Structured communication and psychosocial support after stillbirth were also identified as priorities. These findings should inform co-design and prospective evaluation of community-delivered models.

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: Patients increasingly consult free artificial intelligence (AI) chatbots about their symptoms, but these models may favour agreement with the patient over accuracy. In obstructive sleep apnoea (OSA), where most cases in the general population remain undiagnosed and diagnosis depends on referral, this could validate symptom denial. We tested whether consumer chatbots maintain guideline-concordant referral when a simulated patient minimises symptoms. Methods: Seven guideline-concordant and standardised OSA vignettes (apnoea-hypopnoea-index, AHI 8-52 events/h; body mass index 28-42 kg/m2) were presented to five free chatbots (GPT-5, Gemini 2.5 Flash, Claude Sonnet 4.6, DeepSeek V4 Flash and Grok-4.3) under neutral and symptom-minimising conditions (10 simulated conversations per model-vignette-condition cell; N=700). Referral maintenance was scored by a structured rubric and every capitulation was human-confirmed. Results: All chatbots maintained referral recommendations in neutral conversations (350/350; 100%). When simulated patients minimised symptoms and resisted referral, maintenance fell to 64.3% (225/350), a reduction of 35.7 percentage points (95% CI 5.5-66.0; p=0.028). Rates ranged from 55.7% to 85.7% across chatbots, but evidence of between-chatbot differences was inconsistent across analyses. Rates differed across clinical vignettes (p=0.001). A variance analysis estimated greater variation between vignettes than between chatbots. In the three driving-risk vignettes, referral recommendations were abandoned in 46-68% of symptom-minimising conversations, pooling all five chatbots within each vignette. Conclusions: Consumer AI chatbots abandoned correct OSA referral recommendations in about one-third of symptom-minimising conversations, with higher observed abandonment in simulated cases involving driving risk. This may delay appropriate assessment and raises patient- and public-safety concerns.

Background: Artificial intelligence (AI) is increasingly used in healthcare, but data on its real-world use and perceptions among obstetricians and gynecologists remain limited. Objectives: To assess familiarity, use, perceptions, and expectations regarding AI among physicians practicing obstetrics and gynecology in France. Study Design: We conducted a cross-sectional survey between June 2 and July 30, 2025, mainly distributed through professional organizations or associations and social media. Participation was voluntary and self-selected. AI use was defined as any use of an AI tool in routine clinical practice, regardless of type or frequency. Comparisons were made between AI users and non-users and between junior and senior physicians. Results: Among 469 respondents, representing approximately 5% of the target population, 199 of 466 (42.7%) reported AI use in practice; three respondents were excluded from this analysis because of discordant questionnaire responses. AI use was associated with male gender and professional status, with residents more represented among users. Junior physicians reported greater AI use than senior physicians and expressed greater concern regarding medical confidentiality. Current applications primarily involved administrative tasks, medical writing, literature retrieval, and translation; anticipated future uses centered on clinical decision support and medical data interpretation. Perceptions of several AI applications differed between groups. Conclusions: AI was reported to be used by a substantial proportion of respondents, primarily for supportive and informational tasks. These findings support further evaluation of training, validation, governance, and safe implementation of AI in obstetrics and gynecology.

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.

Background Heart rate fragmentation (HRF) has emerged as a novel marker of sinoatrial instability and neuroautonomic dysregulation and is a predictor of incident cardiovascular disease (CVD) events and cardiovascular mortality. Leveraging heart rate fragmentation as a novel measure may help us better understand the prognostic significance of physical activity. Physical activity (PA) patterns are multidimensional and difficult to study holistically, however. We sought to examine for novel PA clusters in a cohort of veteran men and evaluate their prognostic significance based on their associations with neuroautonomic dysregulation. Methods In this cross-sectional study, we examined 602 male veterans from the Vietnam Era Twins Study of Electrophysiology in Posttraumatic Stress Disorder (VETS-EP) who were enrolled between 2022 and 2026. Accelerometry data were used to derive physical activity parameters across 6 domains: volume, sedentary behavior, intensity, diurnal pattern, fragmentation, and variability. Heart rate fragmentation was assessed using percentage of inflection points (PIP), inverse average length of segments (IALS), percentage of short segments (PSS), and percentage of alternating segments (PAS). Gaussian mixture models were used to derive digital phenotypes of physical activity, and associations with heart rate fragmentation were assessed using linear mixed models. Results Participants had a mean age of 73.8 {+/-} 2.9 years and 93.2% reported White race. Two overlapping phenotypes of physical activity patterns were identified: a compact-early phenotype, characterized by earlier activity timing and slightly greater overall activity, and a diffuse-late phenotype, characterized by later activity timing and greater heterogeneity in activity volume. In fully adjusted models, the diffuse-late phenotype was associated with higher PIP, the primary outcome ({beta}: 1.199, 95% CI: 0.034 to 2.363, p-value: 0.044), and higher IALS ({beta}: 0.01210, 95% CI: 0.00048 to 0.02373, p-value: 0.041), but not PSS or PAS, compared with the compact-early phenotype. Conclusions In older male veterans, Gaussian mixture model phenotyping identified two overlapping physical activity phenotypes, differing mainly in diurnal timing and in the dispersion of overall activity, that were associated with modest differences in heart rate fragmentation. These phenotypes provide a framework for capturing complex physical activity patterns. Future prospective studies representing diverse populations are needed to validate such phenotypes.

Background: Pregnancy is a uniquely complex ethical and clinical context in which precision medicine approaches, including genomics-driven risk prediction tools, biomarker-based stratification, and data-intensive analytics, are rapidly evolving. There is a need for community consultation on the ethical, social, and practical implications of precision medicine in pregnancy research and healthcare. Aim: The primary aim of this study was to engage healthcare consumers to build consensus on the acceptability of precision medicine approaches in pregnancy research and healthcare. We also sought to identify consumer priorities for future research. Methods: A two-round modified Delphi study was conducted to determine consumer opinions on the use of precision medicine approaches in pregnancy research and healthcare. Seven in-person workshops (round one) were held, with purposeful recruitment to ensure a representative group of healthcare consumers who engage with the healthcare system during pregnancy. These included first-time parents, culturally and linguistically diverse women, Aboriginal and Torres Strait Islander women, and women and their partners who had experienced previous pregnancy complications. Recruitment was conducted through established advocacy networks, and each workshop was facilitated by representatives from each organization. A second-round online survey, based on workshop content, was completed to assess the acceptability of precision medicine and to determine consumer priorities for future precision medicine research on pregnancy health. Results: Precision medicine research and the implementation of new technologies in clinical care were acceptable to participants; however, the need for transparency and trust between participants and researchers regarding the use and storage of genetic material was highlighted, particularly among Aboriginal and culturally and linguistically diverse participants. Outcomes related to survival and perinatal complications (stillbirth, preterm birth, and labor complications) were the highest-rated consumer research priorities, along with maternal mental health and breastfeeding. When asked whether participants would like to see PM available as part of routine pregnancy care for conditions with available treatments, complete consensus (100%) was reached. Conclusion: Healthcare consumers strongly support the use of precision medicine in pregnancy research and its translation into routine clinical care, particularly when effective interventions are available. However, implementation must be underpinned by transparency, trust, culturally appropriate governance, and clear communication about the collection, storage, and use of genetic material. These findings provide a consumer-informed framework for prioritizing future precision medicine research and guiding its responsible translation into pregnancy care.

. Abstract Objectives. Prior research has identified associations between sleep health and cognitive function. Our aim was to investigate multiple domains of sleep health and their associations with cognitive function in a diverse sample of older adults. Methods. These data are from an observational study in Chicago, USA, that enrolled adults aged 55 years and older without cognitive impairment at baseline and followed a subset for up to 2 years. Sleep was measured with 7 days of wrist actigraphy and we examined sleep duration (amount), sleep fragmentation (an index of restlessness and sleep quality), sleep start time and sleep regularity (based on the within-subject standard deviation of sleep start time). . Participants also completed tasks from the NIH Toolbox to estimate fluid cognition and the Trail Making Test (Trails B) to assess attention and processing speed. A subset of participants was examined at a follow-up visit that included only the NIH Toolbox assessment. Results. Our sample included 324 participants (63.6% women, 37.3% African American/Black, 13.6% Hispanic/Latino). Greater sleep fragmentation and less sleep regularity were associated with worse fluid cognition and Trails B scores (all p<.01). Longer sleep duration and earlier sleep start times were associated with better performance on Trails B. No sleep measures were associated with change in the fluid cognition score over an average of 1.9 years (n=133). Discussion. These findings support a cross-sectional association between cognitive function and multiple domains of sleep health, including duration, quality, timing and regularity, in a diverse sample of older adults.

Digital-medicine systems can objectively confirm medication ingestion, but interpretation of a no-detection day depends on whether the wearable receiver was in contact with the body. We analyzed 997 person-days from 20 outpatients prescribed aripiprazole through Abilify MyCite by integrating ingestion records with patch-contact data; 17 participants also contributed smartphone sensing. Of 322 days without a detected ingestion, 53.7% had insufficient patch wear. Counting all no-detection days as nonadherent yielded an apparent nonadherence rate of 32.3%, whereas conditioning on adequate wear yielded 18.1%. Because adherence during patch-off periods is unobserved, the rate is bounded between 14.9% and 32.3%. These findings support a three-state outcome that distinguishes detected ingestion, no ingestion despite adequate wear, and patch-off or indeterminate days. Among 744 patch-worn days, detected ingestion was more likely on days that more closely matched each participant's usual activity pattern (odds ratio 1.52, 95% credible interval 1.14-2.03) and when participants spent more time at home around the habitual dose period (1.31, 1.04-1.64). These behavioral associations were exploratory and characterize the context of medication-taking rather than causal effects. Overall, device wear was integral to interpretation of the adherence outcome, and passive behavioral measures were most interpretable after observation status was established.

Objectives: To investigate whether the PneumoRator wearable sensor could accurately assess Respiratory Rate (RR) and signs of air hunger (SAH) compared with clinician-observed assessment in adults with and without breathing pattern disorder (BrPD). Design: A pilot cross-sectional observational study. Setting: A research laboratory on a university campus. Participants: Healthy adults aged 18-40, who were not pregnant, no smokers or history of cardiovascular, respiratory or neurological illness. Thirty consented to the study, Twenty-nine had full data for analysis. Interventions: Participants wore the PneumoRator chest-worn sensor continuously for 10 minutes while seated at rest. Main outcome measures: Respiratory Rate was compared between PneumoRator-derived measurements and randomised 1-minute observer manual counts. Clinician-observed SAH and BrPD were assessed using the Breathing Pattern Assessment Tool (BPAT). Participants also completed the Nijmegen Questionnaire. PneumoRator signals were analysed to identify large-volume breaths as potential SAH events. Results: Thirty participants were recruited. Twelve participants met BPAT criteria for BrPD and four met Nijmegen Questionnaire criteria. There was no significant difference between manual and PneumoRator RR measurements (p=0.89). No significant difference was observed between methods for SAH detection (p=0.17). Participants with clinician-observed SAH demonstrated significantly higher PneumoRator signal amplitudes than those without SAH (p=0.035). Conclusion: The PneumoRator produced RR measurements comparable to clinician assessment and demonstrated potential for identifying signs of air hunger. Findings from this pilot study indicate that the PneumoRator device shows promise as a tool for continuous RR monitoring and as an adjunct to BrPD assessment. Larger clinical validation studies are now warranted.

Background Staying physically active is essential for healthy ageing. Exposure to high levels of air pollutants such as particulate matter (PM) is associated with less active, more sedentary lifestyles in older adults. Whether results extend to typical fluctuations in daily PM exposure is unclear. We examined associations of higher-than-usual personal exposure to PM (PM2.5 and PM10) with next-day movement behaviour in older adults, assessed using concurrently worn personal air quality monitors and accelerometers. Methods The present study used 304 days of data from 67 adults aged 50-85 years living in the UK. For five days, participants wore accelerometers 24-hours per day and personal air quality monitors during waking hours. Average daily time spent in movement behaviours (moderate-to-vigorous physical activity [MVPA], light physical activity [LPA], sedentary behaviour [SB]) and step count was extracted from accelerometers, and average daily exposure to PM2.5 from personal air quality monitors. PM10 was examined in secondary exploratory analyses. We used linear mixed models to examine within-person associations of deviation from usual PM exposure with next-day movement outcomes, with estimates presented for 'usual' (individual mean) and 'higher-than-usual' (+1 interquartile range, equivalent to 2.6 g/m3 beyond individual mean PM2.5 exposure and 2.8 g/m3 beyond individual mean PM10 exposure) exposure. Analyses were repeated using area-based PM estimates. Results Compared with usual exposure, higher-than-usual PM2.5 was associated with 8 minutes less LPA (95% CI=-14 to -2) and 483 fewer steps (-855 to -82) on the following day. Exposure to higher than usual PM10 was associated with 8 minutes less LPA (-13 to -3), 8 minutes more SB (0 to 15), and 412 fewer steps (-739 to -85) on the following day. There was no association with MVPA, and no associations when area-level PM estimates were used as opposed to estimates from personal air quality monitors. Conclusions Typical daily variability in PM exposure in a setting with relatively low overall PM levels was associated with modest next-day differences in movement behaviour in older adults. Higher-than-usual PM exposure was associated with less next-day activity and more sedentary time. Personal monitoring may be important for capturing short-term exposure-behaviour associations. Trial registration N/A

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.

The Muse 2 EEG headset also records forehead photoplethysmography (PPG). We tested whether its pulse intervals can replace Polar H10 RR intervals for ultra-short heart-rate variability in 28 seated recordings (60 s eyes open, then 60 s eyes closed), using fixed processing, no exclusions and eleven sensitivity analyses. Mean heart rate agreed almost perfectly (ICC [&ge;] 0.999; bias -0.22 and 0.00 beats min-1). SDNN agreement was good to excellent (ICC 0.86 and 0.91; bias +8.6 and +8.2 ms). RMSSD agreement was moderate (ICC 0.60): Muse exceeded Polar in at least 27 of 28 sessions (bias +19.2 and +18.5 ms; about 1.9-fold), and the ratio bias fell as RMSSD rose. Interval differences (SD 23 ms) implied about 16 ms of independent timing error per pulse, which reproduced the RMSSD bias; without ECG, sensor jitter could not be separated from variation in pulse transit time. Tracking of the eyes-closed change in ln RMSSD was not demonstrated ({rho} = 0.35, 95% CI -0.07 to 0.67; detectable {rho} [&ge;] 0.51). Muse 2 PPG can supply mean heart rate and, with a stated bias, SDNN; its ultra-short RMSSD is not interchangeable with Polar RMSSD and should be reported as pulse-rate variability.