International Journal of Behavioral Nutrition and Physical Activity

Introduction: Current guidelines on physical activity (PA) and sedentary behaviour (SB) are based on self-reported questionnaire data, which are prone to recall bias and measurement error. As such, they provide limited guidance on the intensity, frequency and duration of activities. To improve and accelerate research on the health benefits of a physically active lifestyle and inform future guidelines, large-scale accelerometry in population-based studies is needed. The ActiveLIFE cohort study aims to create a comprehensive, deeply-phenotyped dataset of thigh- and wrist-worn accelerometry in relation to health characteristics and outcome data that can be used to accelerate lifestyle and prevention research. Methods and analysis: ActiveLIFE is an add-on study to the 4th assessment wave of the Lifelines Biobank study, aiming to collect ~60,000 accelerometry measurements. Lifelines participants aged [≥]8 years old will be invited to wear a triaxial thigh-worn accelerometer (Axivity AX3, settings: 100 Hz, 8g) for 8 consecutive days. Morover, to evaluate how wear location impacts effect estimates, a subsample of at least 10% will be asked to wear both a thigh- and wrist-worn accelerometer. Raw accelerometry data will be processed into PA metrics, including steps per day, time spent in moderate-to-vigoruous PA, light-intensity PA, SB, and sleep and enriched with data on demographics, body composition, health status, medication use, lifestyle characteristics, biomarkers, cognitive function, cardiopulmonary performance, genetics, healthcare consumption and health outcome data. ActiveLIFE will allow to further refine, extend and personalize future guidelines on PA and SB for optimal PA prescription. Ethics & dissemination: Ethics approval for the Lifelines Biobank, including the ActiveLIFE add-on study, was obtained from the Medical Ethics Committee of the University Medical Centre Groningen, the Netherlands (#2007/152). Informed consent was obtained from all participants. Study findings will be disseminated through peer-reviewed publications and conference presentations, and societal outreach approaches.

Background: Physical inactivity remains a major public health challenge, and policy action across sectors is needed to create environments that enable active lifestyles. Several tools have been developed to evaluate national physical activity policies, but it remains unclear whether they assess similar policy aspects or produce comparable results. This study compared evaluation results across the 27 EU member states and examined the indicators used by these tools. Methods: Eight physical activity policy evaluation tools were included that (a) assess national physical activity policies and (b) had been applied in multiple countries. For five tools covering at least half of the EU member states, aggregate scores were extracted or calculated and converted into country rankings. A total of 202 indicators were coded and mapped by category (policy content, process, structure), and data type (audit, content, implementation, impact assessment). Results: Country rankings varied considerably, with a mean difference of 13.0 positions between highest and lowest ranks. Some countries displayed consistent results (e.g., Austria, France, Germany), while others showed large discrepancies (e.g., Hungary, Spain). Indicator mapping revealed a focus on policy content (47.6%), followed by structure (21.6%) and process (13.7%) (6.2% other). Across tools, 20.3% of indicators audited policies, 26.0% assessed policy content, and 26.4% assessed policy implementation (16.3% other). Conclusion: The systematic comparison of indicators across physical activity policy evaluation tools provided insights into differences in country rankings. Future evaluations should clearly communicate their purpose, identify synergies between tools, address gaps in policy evaluation, and strengthen the reproducibility of evaluation processes.

Background Sports apps are increasingly used to promote physical activity and support physical education among students. However, their effects on different dimensions of physical fitness and the factors influencing intervention effectiveness remain unclear. Objective This study aims to evaluate the effects of sports app-based interventions on multiple dimensions of physical fitness among adolescents and students and to examine whether intervention duration, exercise frequency, intensity, and session duration influence intervention effects. Methods Following PRISMA guidelines, six databases were systematically searched through June 20, 2026. Randomized controlled trials and quasi-experimental studies were included. Risk of bias was assessed, and meta-analyses and subgroup analyses were performed. Results Twenty moderate-quality studies involving 3,268 adolescents and University students were included. Interventions lasted 6-24 weeks, with 2-7 sessions per week and 20-80 minutes per session. Sports app interventions significantly reduced BMI (MD = -0.41, 95% CI -0.76 to -0.07) and improved cardiorespiratory endurance (SMD = 0.85, 95% CI: 0.35 to 1.35), male pull-ups (MD = 0.74, 95% CI 0.19 to 1.28), female sit-ups (MD = 1.99, 95% CI 1.13 to 2.86), male 1000-m run time (MD = -13.44 s, 95% CI -25.14 to -1.74), female 800-m run time (MD = -9.02 s, 95% CI -13.40 to -4.64), and sit-and-reach performance (MD = 2.01 cm, 95% CI 0.91 to 3.10). Significant subgroup differences by intervention duration were found for cardiorespiratory endurance (P = 0.03) and flexibility (P = 0.01), but no subgroup differences in body mass index were found across the intervention characteristics examined. Conclusions Sports app interventions can improve multiple dimensions of students physical fitness, although current evidence is insufficient to establish an optimal intervention dose. Further high-quality studies need to clarify dose-response relationships and optimize intervention procedures.

Importance: While increased physical activity (PA) and decreased sedentary behavior (SB) are associated with favorable health outcomes, evidence regarding their causal effects on healthcare costs remains limited. Objective: To assess the causal effects of PA and SB on healthcare costs. Design: A two-sample Mendelian randomization (MR) study. Setting: Separate, non-overlapping cohorts with genetic instruments for self-reported and device-based PA and SB, and healthcare costs. Participants: The instruments used to assess self-reported PA were derived from a genome-wide meta-analysis of 606,820 individuals across 51 cohorts. Two large genome-wide association studies (GWASs) were used for self-reported SB (leisure screen time N=526,725; television watching N=408,815), while accelerometer-based GWASs (N=89,683-91,105) were used for device-based PA and SB. The instruments used to assess the outcome data were obtained from the FinnGen cohort (N=373,160). Exposures: Genetically predicted PA and SB. Main Outcomes and Measures: Validated genetic instruments for log-transformed annual healthcare costs derived from registers, including primary care, secondary care, and medication costs. Inverse variance weighting was used as the primary MR measure, while the sensitivity analyses included MR-Egger, weighted median, simple mode, weighted mode, F-score, Cochran's Q, and leave-one-out analysis. Results: Higher genetically predicted self-reported PA was associated with lower healthcare costs (causal estimate, {beta} = -0.166; 95% CI, -0.270 to -0.062). In contrast, higher genetically predicted SB (leisure screen time or television watching) was associated with higher healthcare costs across self-reported datasets ({beta} = 0.097; 95% CI, 0.064 to 0.130; {beta} = 0.114; 95% CI, 0.063 to 0.165, respectively). No associations were observed for device-based PA ({beta} = -0.014; 95% CI, -0.040 to 0.014) or SB ({beta} = -0.009; 95% CI, -0.197 to 0.179). Conclusions and Relevance: Findings based on genetically predicted PA and SB support a causal association between these behaviors and healthcare costs, suggesting that increasing population's leisure-time PA and reducing SB may decrease healthcare expenditure. This highlights the importance of promoting PA for both population health and long-term sustainability of healthcare systems. However, causal evidence remains partly limited, particularly for device-based measures of these behaviors.

Objectives: To describe device-measured movement behaviours in a large sample of middle-aged and older Australian adults using complementary posture- and intensity-based accelerometers, examine variation across demographic groups, and identify behavioural phenotypes using clustering approaches Design: Cross-sectional analysis of the Australian Breakthrough Cancer (ABC) Study Accelerometer Sub-study (ACM). Methods: Participants from the ABC cohort completed seven days of simultaneous monitoring using a thigh-mounted activPAL and waist-mounted ActiGraph GT3X+. activPAL characterised posture-based behaviours (sitting, lying, standing, stepping, postural transitions, and sedentary accumulation), while ActiGraph characterised intensity-based activity (sedentary, light, and moderate-to-vigorous physical activity [MVPA]). Movement behaviours were summarised overall and by gender, age group, body mass index (BMI), and education. Behavioural phenotypes were identified using k-means clustering. Results: Among 4,238 ACM participants, 3,426 met inclusion criteria with valid data from both devices (1,711 females; 1,715 males). Participants accumulated substantially more time in sedentary and low-intensity behaviours than in MVPA. activPAL estimates indicated mean daily time of 379.3 min sitting, 282.2 min lying, 160.8 min standing, and 64.5 min stepping, with a mean of 5,185 steps/day. ActiGraph estimates indicated 584.0 min/day sedentary time, 290.9 min/day light-intensity activity, and 33.2 min/day MVPA. Considerable heterogeneity in movement behaviours was observed between individuals, whereas demographic differences were comparatively modest. Three behavioural phenotypes were identified: active/fragmented (24%), low activity (41%), and prolonged sedentary (35%). Notably, the low-activity and prolonged sedentary phenotypes were distinct, indicating that low overall movement and prolonged uninterrupted sitting represented different behavioural patterns. The prolonged sedentary phenotype was characterised by greater uninterrupted sitting time, lower stepping time, fewer steps, lower MVPA, and fewer sit-to-stand transitions. Conclusions: Movement behaviours in middle-aged to older Australian adults (40-74 yrs) were characterised by high sedentary time, low accumulation of MVPA, and substantial between-person heterogeneity. Distinct behavioural phenotypes highlighted differences in both movement volume and sedentary accumulation patterns, suggesting that movement behaviour is multidimensional and not adequately described by single summary measures alone. These findings may help inform our understanding of population movement patterns relevant to cancer and cardiometabolic disease prevention. Key words: Accelerometry, Motor Activity, Sedentary Behaviors, Cluster Analysis, Postural Allocation, Behavioural Phenotypes

Background: Valid and feasible measures of preschoolers' 24-hour movement behaviours are needed for population-level research. We developed a Japanese version of the Movement Behaviour Questionnaire-Child (MBQ-C) and evaluated its measurement properties and practicality. Methods: After translation and cross-cultural adaptation following the COSMIN procedure, including cognitive interviews, we evaluated the questionnaire in a cross-sectional validation study of Japanese children aged 3-6 years. Concurrent validity was assessed against hip-worn accelerometry and a 7-day diary; we also examined test-retest reliability, measurement error, agreement between response formats and modes, and practicality (completion time, burden, and clarity). Analyses used Spearman correlations, intraclass correlation coefficients, the SEM, smallest detectable change (SDC), and Wilcoxon signed-rank tests. Results: Of 208 children, 170 provided valid accelerometer and diary data for the concurrent-validity analysis, and 187 completed the MBQ-C for the questionnaire-based analyses. Concurrent validity was weak for active and energetic play (Spearman {rho} = 0.12-0.26), strong for total screen time (0.78-0.85), and moderate for total sleep (0.48-0.58). Test-retest reliability was moderate to good (ICC, 0.66-0.81), and within-child variability was lowest for sleep (CV 3%) and highest for the physical activity items (21-33%). The open- and closed-ended versions showed no significant differences; paper-electronic agreement was good for screen time and sleep but poor for active play. Completion took approximately 6-9 minutes, with low reported burden. Findings were robust in sensitivity analyses. At the group level, SDC for a mean was SDCind/{surd}N: 119.8/{surd}N, 74.0/{surd}N, 112.6/{surd}N, and 92.5/{surd}N min/day for active play, energetic play, total screen time, and total sleep, respectively (N = number of children surveyed). The systematic differences between the two test-retest occasions were +15.7, +6.5, +4.0, and -5.4 min/day, respectively. Conclusions: The Japanese MBQ-C is a practical option for assessing preschoolers' active play, screen time, and sleep at the group level. By quantifying its group-level measurement error, this study provides benchmarks for interpreting group-level differences and change, supporting the use of the MBQ-C in surveillance and evaluation of programmes delivered at scale.

Aim: Neighbourhood food outlet availability influences dietary behaviours and nutrition related health outcomes. However, food environment research across Europe remains limited by inconsistent spatial data, heterogenous food outlet classifications, and the lack of publicly available high-resolution retail food data. We developed and evaluated a scalable framework to acquire, classify, validate, and analyse food-related points of interest (POIs) from Google Maps across 39 countries in Europe and Turkey. We also developed a method for characterizing the joint co-occurrence of multiple outlet types as neighbourhood-level food environment exposures. Methods: We harmonized ~3.26 million food-related POIs. Outlets were classified into five main categories (cafes and bakeries, restaurants, fast-food and snack outlets, groceries and food retail, and bars and pubs) and 25 subcategories. We assessed data quality against official registers in five regions (the Netherlands, United Kingdom, Denmark, Madrid (Spain), and Brno (Czech Republic)), testing agreement in positional accuracy, completeness, spatial clustering (Nearest Neighbour Index), and spatial density (kernel density estimation (KDE)). To enable cross-country comparisons, POIs were aggregated to national and city scales, with food outlet density calculated as outlets per 1,000 residents and standardised using z-scores. To characterize neighbourhood-level co-exposures, we applied Principal Component Analysis (PCA) followed by Latent Class Analysis (LCA) to z-standardized outlet densities across 500 m hexagonal grid cells in eight major European cities. Results: Google Maps data showed high positional accuracy, with 91% of outlets located within 30m of registry records. Completeness varied by region, while KDE comparisons showed moderate-to-strong spatial agreement with city-specific variation. PCA identified two components explaining 68.4% of the variance (PC1: 51.3%, PC2: 17.1%), with PC2 differing between cafe/bar and fast-food/supermarket densities. LCA identified four neighbourhood classes: low-access, moderate-mixed, dining-out, and high-density-mixed. Conclusion: Our harmonized high-resolution framework supports cross-national monitoring of retail food environments and spatial epidemiological research across European settings.

Background: The retail food environment is a widely used exposure in behavioural-nutrition and obesity research, on the premise that nearby food retailers shape diet and obesity risk. Over the past quarter-century, grocery stores have declined across rural and small-town America while limited-assortment discount ("dollar") stores have proliferated. Standard food-environment indices classify retailers as healthy or less-healthy but typically exclude dollar stores, now the fastest-growing food-retail format. As a result, a single classification decision may alter how the food environment is measured and the conclusions drawn from it. We develop a dollar-aware index, quantify how counting dollar stores changes the measured exposure, and derive a longitudinal trajectory typology. Methods: Using establishment-level data from Data Axle for all 878 Mississippi census tracts (1997-2024), we classified food retailers into five mutually exclusive categories using a previously validated approach and calculated the modified Retail Food Environment Index (mRFEI) in both its standard and dollar-aware forms, with the latter counting dollar stores as less-healthy outlets. We fitted Nagin-style group-based trajectory models to the tract-level dollar-aware index, related class membership to the Social Vulnerability Index (SVI) and urbanicity with multinomial regression, and characterised spatial clustering (Getis-Ord Gi*, join-counts) and grocery access. Results: Grocery stores fell from 1,616 to 716 while dollar stores rose from 315 to 1,005, intersecting in 2018. Counting dollar stores lowered the index by a margin that widened over time, and a growing number of tracts had only dollar-store retail, undefined under the standard index. Six trajectory classes emerged: stable adequate (5.6% of tracts), steady decline (13.1%), early collapse (11.1%), late collapse (6.7%), persistently constrained (34.1%) and chronic desert (29.3%); only the stable-adequate class (5.2% of children) stayed adequate throughout. Constrained and steady-decline membership rose steeply with vulnerability (RRR 11.7 and 9.9); chronic desert was urban (RRR 5.2, a food-swamp pattern); collapse classes had no cross-sectional social signature. Conclusions: In the US state with the highest adult obesity prevalence, a single retailer-classification decision substantially changes the measured food environment. The dollar-aware index and trajectory typology offer a transferable, time-varying exposure for behavioural-nutrition and obesity research and establish a foundation for future childhood-obesity studies.

Introduction Higher physical activity (PA) is associated with lower risk of several cancers, but the biological mechanisms underlying these associations remain unclear. We integrated observational and Mendelian randomization (MR) approaches to investigate circulating proteins as potential intermediates linking PA to cancer risk. Methods We analysed accelerometer-derived PA measures (~100,000 participants), circulating protein levels (~53,000 participants) derived using the Olink Explore 3072 panel and cancer incidence (breast, colorectal, endometrial, and prostate cancers) via linked registry data among UK Biobank (UKB) participants. Observational associations were assessed using regression models and Cox proportional hazard models adjusting for demographic, lifestyle, and clinical covariates, including body mass index (BMI). Proteins associated with both overall acceleration average and cancer incidence were taken forward in a two-sample and two-step MR analyses using genome-wide association data. Results Higher overall acceleration average was observationally associated with lower risk of breast, colorectal, and endometrial cancer, with attenuation after BMI adjustment for endometrial and colorectal cancer, and a positive association with prostate cancer. MR analyses supported a protective causal effect of higher overall PA on breast, colorectal, and prostate cancer risk, and suggested a much larger effect than observational analyses. Overall, PA was observationally associated with 436 circulating proteins of which 44 were also associated with incident cancer risk. MR analyses provided some support that TNFSRF13B was influenced by physical activity in the same direction as the observational analyses but found conflicting evidence for an effect of TNFSRF13B on breast cancer, although the MR analysis is consistent with a protective effect of physical activity on breast cancer risk. Conclusions While higher overall physical activity appears to causally reduce risk of several cancers, circulating proteins associated with PA largely do not show strong evidence of being intermediates of these effects, suggesting they may act primarily as biomarkers of physical activity rather than causal intermediates.

Background: Childhood cancer survivors (CCS) are at elevated risk of chronic health conditions. Sufficient physical activity and sleep, and limited sedentary behavior mitigate those risks, but device-based assessments of these behaviors among CCS are scarce. We described physical activity, sedentary behavior, and sleep among CCS using thigh-worn accelerometers and examined associated factors. Methods: We included participants of the 2024-2025 Swiss Childhood Cancer Survivor Study questionnaire wave who consented to wear an activPAL4 accelerometer continuously for eight days. Using ActiPASS, we derived physical activity types and intensities, sedentary behavior, and sleep. We stratified analyses by age (children: 5-17 years; adults: [≥]18 years). Results: 108 CCS (68 children, 40 adults) pariticipated. Children accumulated a median of 85 minutes/day of moderate-to-vigorous physical activity [IQR 57-107] and 578 minutes/day sedentary behavior [516-625]; adults 78 [61-100] and 603 minutes/day [522-650]. Both groups spent 4-5 hours/day in sedentary bouts [≥]30 minutes. Children slept 466 minutes/night [418-499], and adults 435 [407-488]. Only 9% of children and 20% of adults met recommendations for physical activity, sedentary behavior, and sleep simultaneously. Radiotherapy was associated with lower physical activity among children and higher sedentary time among children and adults. Older children achieved less physical activity and sleep, and had more sedentary time. Conclusion: CCS engage in extensive sedentary behavior, and especially in long uninterrupted sedentary bouts. Many sleep less than recommended. Healthcare practitioners should prioritize promoting physical activity among adolescent survivors and particularly those treated with radiotherapy, and support all survivors in improving sleep duration and reducing sedentary time.

Background: Over half of UK adults eat food prepared out-of-home (OOH) weekly and the poor nutritional profile of OOH food contributes to ill health. Nutri-Score is a form of interpretative labelling which assigns food products a value of A (healthiest) to E (least healthy) based on nutritional quality, and inclusion on menus could be a public health policy option to reduce obesity. This is the first real-world Randomised Control Trial to test the effectiveness of Nutri-Score alongside calorie labelling on food menus in UK OOH settings. Methods: Adult participants (N=672), majority White (80%) female (63%), with a mean age of 47(SD18) years, were recruited from the local community to visit OOH food businesses to order, consume, and pay for meals. Data collection days were randomised to be control (calorie labelling) or Nutri-Score (calorie labelling with Nutri-Score). Linear mixed models assessed impacts of labelling condition on perceived effectiveness of menu labelling and nutritional quality of food orders. Findings: Scores for perceived effectiveness of labelling were significantly greater in the Nutri-Score condition compared to control (B = 0.21, p=0.010; 95% CI 0.05, 0.37) and food orders were significantly better nutritional quality, indicated by lower Nutrient Profiling Model scores in the Nutri-Score vs. control condition (B = -0.89, p=0.003; 95% CI -1.48, -0.31). Interpretation: Compared to providing calorie labelling alone, inclusion of Nutri-Score could be an effective policy approach to improve nutritional quality of diet and reduce diet-related disease.

Background Sleep duration, physical activity, and nutrition (SPAN) are each associated with mortality risk, yet they are usually examined individually or in pairs, with limited attention to their combined associations and differences across physical activity domains. We examined combined SPAN behaviours in relation to all-cause mortality across different physical activity domains (occupational physical activity (OPA), leisure-time physical activity (LTPA) and transportational physical activity (TPA)). Methods We included adults aged 18 years or older from the National Health and Nutrition Examination Survey 2007-2018. Sleep duration was self-reported in hours/day. Physical activity, including moderate-to-vigorous physical activity (MVPA) across domains, was assessed using the Global Physical Activity Questionnaire. Diet quality was estimated from 24-hour dietary recalls using the Healthy Eating Index-2020 (HEI-2020). We created 27 mutually exclusive joint categories using tertiles of physical activity and HEI-2020, and guideline-based sleep-duration groups (<7, 7-8, and >8 h/day), with the lowest combination as the reference. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% CIs for all-cause mortality. To estimate the minimum combined variations associated with lower mortality risk, we used the 5th percentile of each behaviour as the reference. Results Among 31,875 participants (median age 48.0 years; 51.4% female), 2,623 deaths occurred over a median follow-up of 6.75 years. In joint categorical analyses, a combination of optimal sleep (7-8 h/day), high diet quality (HEI-2020: >56.0), and high LTPA (>35.4 min/day) or high TPA (>25.7 min/day) was associated with the lowest all-cause mortality risk (HR = 0.44, 95%CI: 0.29-0.65; HR = 0.54, 95%CI: 0.37-0.77). The lowest HR for OPA was observed for sleep duration (3.-6.5 h/day), moderate OPA (1.4-114.0 min/day), and high diet quality (HR = 0.53, 95%CI: 0.37-0.75). Relative to the reference values (sleep: 5 h/day, MVPA/PA domains: 0 min/day and HEI-2020: 32.29), a combined minimum increment of 15 min/day of sleep, 5 HEI-2020 points, in combination with either 10.8 min/day of total MVPA, 24.0 min/day of OPA, or 5.2 min/day of LTPA, were associated with 10% lower all-cause mortality. For TPA, the 10% lower risk corresponded to 30 additional min/day of sleep, 5 HEI-2020 points, and 5.5 min/day of transport PA. Conclusions Modest combined increments in SPAN were associated with lower all-cause mortality among US adults across physical activity domains, with combinations including higher amounts of LTPA showing the most favourable profile.

The analysis uses a global 2010-2023 panel comprising 3,038 economy-years across 217 economies. It explicitly separates between-economy and within-economy estimands and tests the longitudinal interpretation using an identical-sample temporal analysis with cluster-aware coefficient contrasts, a formal isometric log-ratio sensitivity analysis, independent fixed-effects replication, and wild-cluster-bootstrap inference. The central finding is deliberately calibrated: cross-economy agreement cannot validate national sugar availability for longitudinal obesity surveillance. The study identifies temporal and construct instability without claiming that sugar is protective or that the mechanisms producing the instability have been identified. The manuscript aligns well with PLOS ONEs emphasis on technically sound, transparent and reproducible research of broad relevance. All data required to reproduce the findings, complete metadata, executable code, full-precision results, diagnostic outputs and a completed STROBE checklist are provided as S1-S5. Figures are provided separately as compliant 350-dpi TIFF files. The study used only publicly available, aggregated economy-year statistics and involved no individual participants, identifiable information or biological specimens; institutional ethics review and consent were therefore not required. This is original work; it is not under consideration elsewhere, and the sole author has approved the submission and accepts responsibility for its content. Funding and competing-interest declarations will be entered accurately in the submission portal. An Academic Editor with expertise in nutritional epidemiology, global health metrics, longitudinal panel methods, or food-system surveillance would be well placed to assess the work.

Background and Aims: Steps are increasingly used to prescribe physical activity, but their impact on quality of life (QoL) remains unclear. We examined the dose-response association between step metrics and QoL, and whether cardiovascular disease (CVD) status moderates this association. Methods: Individual-level data of five studies were pooled. Physical activity was measured with thigh-worn accelerometry. We assessed steps/day, daily minutes of fast stepping ([&ge;]100 steps/min), peak 1- and 30-min cadence. We investigated the association of step metrics and QoL (questionnaire-based; standardized) with multivariable (non-)linear regression, and the interaction with CVD status. Results: We included 9,371 participants (62 [54-68] years; 47% female), comprising 1,977 individuals with and 7,394 without CVD. Significant, curvilinear dose-response associations between step metrics and QoL were found. The optimal step volume was 6,561 steps/day which associated with a 0.35 SD (95%CI: 0.28-0.42) higher QoL compared to the referent 4,000 steps/day. The optimal doses for peak 1-min and peak 30-min cadence were 107 steps/minute (+0.34 SD; 95%CI: 0.28-0.41) and 74 steps/minute (+0.29 SD; 95%CI: 0.24-0.34) respectively, compared to references of 90 and 60 steps/minute. Only fast stepping interacted with CVD status, with a lower optimum in those with versus without CVD (4 minutes/day, +0.20 SD, 95%CI: 0.12-0.28 versus 9 minutes/day, +0.19 SD, 95%CI: 0.12-0.25), compared to the referent 2 minutes/day. Conclusions: Step metrics were curvilinearly associated with QoL with optimal benefits at ~6,500 steps/day. Optimal QoL benefits can be reached at feasible stepping targets, and at slightly fewer daily minutes of fast stepping in CVD versus non-CVD.

Importance: Food and non-alcoholic beverage marketing drives children's dietary intake, yet updated evidence quantifying effects by marketing medium and sociodemographic factors is needed to inform policy. Objective: To quantify the effect of food marketing on dietary intake among children and adolescents (0-19 years) and examine variations by age, sex, socioeconomic position (SEP), weight status, marketing medium, and exposure duration. Data Sources: Nineteen electronic databases were searched for articles published from April 2020 to February 2026, complemented by World Health Organization-commissioned reviews covering 1970 to March 2020. Study Selection: Two reviewers independently selected peer-reviewed primary studies that assessed the association between food marketing and dietary intake, following PRISMA guidelines, with no language restrictions. Data Extraction and Synthesis: Two reviewers independently extracted data and assessed the risk of bias. Random-effects meta-analyses were conducted. The certainty of evidence was assessed using GRADE. Main Outcomes and Measures: Dietary intake (energy, quantity, or number of items consumed). Results: A total of 55 studies (N = 6,877; range 2-18 years) were included. Food marketing was associated with higher dietary intake (mean difference [MD], 34.8 kcal; 95% CI, 20.2-49.4) compared with no or less marketing. Unhealthy marketing via television (20 studies; MD, 44.5 kcal; 95% CI, 11.2-77.8), digital media (11 studies; MD, 37.5 kcal; 95% CI, 20.1-54.9), and packaging (11 studies; MD, 20.5 kcal; 95% CI, 0.7-40.3) all increased intake; the difference across media was significant (p < .001). Higher intake was observed in males (3 studies; MD, 51.9 kcal; 95% CI, 45.4-58.3) but not in females (MD, -6.8 kcal; 95% CI, -60.3-46.6); difference was not significant (p = .082). Differences by weight status (p = .012) were seen (5 studies; normal weight: MD, 55.6 kcal; 95% CI, -51.3-162.5; overweight/obese: 146.9 kcal; 95% CI, 34.1-259.7). Effects varied by age (p = .003) and by digital media exposure duration (p = .044). One study examined ethnicity; none studied SEP. Conclusions and Relevance: Food marketing is associated with increased dietary intake, with low certainty of evidence. Variations were observed across age, sex, weight status, and marketing medium. Further research is needed for adolescents and the role of SEP.

Wearable (accelerometer) data and machine-learning allow objective assessment of the amount of daily physical activity. However, wearable-derived human activity is subject to measurement error. No studies have corrected the dose-response association between physical activity and survival time to chronic diseases, including cardiovascular disease (CVD). The objective is to estimate the measurement error-corrected association between CVD events and multiple measures of daily duration of light and total physical activity, derived from machine-learning and conventional accelerometer-processing methods. Our method combined an accelerated failure time model, spline, and simulation-extrapolation (SIMEX). The method recovered the true dose-response non-linear association in simulated data, while the naive model failed to capture it due to substantial attenuation. Application to the UK Biobank accelerometer cohort also showed an increased protective association of total physical activity after SIMEX correction (Time Ratio [TR] = 1.56, 95% CI: 1.28-1.82 vs. TR = 1.38, 95% CI: 1.24-1.54 for SIMEX-corrected vs. uncorrected dose-response association between the 95th and 5th percentiles of total activity), with a similar increase for light physical activity. Sensitivity analysis indicates that the female population experiences a substantially larger protective association after SIMEX correction than males. Dose-response survival analysis is a widely used analytical method in physical activity epidemiology and benefits from measurement error correction.

This study aimed to examine factors associated with behavioural difficulties among school-aged children (aged 7 -11) using self-reported survey data linked with education data from a national cohort study of over 50,000 children (the HAPPEN national cohort) in Wales, UK between 2014 and 2024. Data include self-reported health and wellbeing linked to additional learning needs (ALN) status collected from education data in Wales. Measures included self-reported time spent on screens, frequency of sugary snack, fizzy drink and takeaway food consumption, physical activity levels, sleep duration and behavioural difficulties (as measured by the Me and My Feelings Survey). Associations were analysed using descriptive trends, K-means cluster analysis and multivariate regression modelling. Findings showed a rise in behavioural difficulties during and post Covid, which are now improving in Wales. Cluster analysis identified that children with poor sleep and high takeaway and fizzy drinks dietary habits had the highest levels of behavioural difficulties, a trend that remained significant after adjusting for all potential confounders. Physical activity associated with reduced behavioural difficulty, with 5-6 days of activity associated with the lowest difficulty scores. These findings suggest that behavioural difficulty is driven by health behaviours especially sleep and diet rather than single factors. The results highlight the need for integrated public health approaches that address dietary quality and sleep hygiene rather than focusing on single factors in isolation. Promoting more balanced daily activity patterns, promoting good sleep routines, improving food environments and building movement into the school day may have important benefits for childrens behaviour.

Background: Self reported height and weight are prone to misreporting, which can bias BMI estimates. This study identifies misreporting determinants, develops calibration equations and examines how measured, self-reported, and calibrated BMI affect estimates of obesity prevalence and socioeconomic inequalities. Methods: We analysed survey-weighted, sex stratified data from 3,404 adults (18-64 years) in the Portuguese National Food, Nutrition and Physical Activity Survey (IAN-AF 2015-2016), including self reported and measured anthropometry. Misreporting determinants were assessed using multinomial logistic regression. Calibration equations for height and weight were estimated using measured values, self-reports, age, region of residence and education level. Calibrated BMI was derived from predicted values. Obesity prevalence was estimated for each BMI assessment method (30 kg/m^2). Education, income and employment inequalities in obesity were compared across BMI methods using prevalence difference and ratio, slope index and relative indexes of inequality. Results: Height is systematically overreported and weight underreported, with misreporting increasing with age and BMI. Calibration eliminates underestimation of obesity prevalence from self-reported BMI, bringing calibrated estimates close to measured values. Regarding education-related inequalities in obesity, calibration widen disparities among women, whereas among men corrects the overestimation observed from self-reported BMI. Income and employment-inequality patterns are similar across BMI methods. Conclusions: Among Portuguese adults, the systematic and socially patterned misreport of self-reported anthropometry affects obesity prevalence and inequality estimates. Calibration based on simple sociodemographic models improves validity and equity of obesity surveillance and could be routinely integrated into national surveys to strengthen monitoring of obesity and its socioeconomic distribution.

Objective Reducing household air pollution exposure during gestation and infancy may have longer-term benefits for child linear growth. We examined whether a liquefied petroleum gas (LPG) cookstove and fuel intervention delivered during gestation and infancy could improve height-for-age Z scores (HAZ) and reduce the risk of stunting at ages 2-5 years in India, Rwanda, and Guatemala. Materials and method The Household Air Pollution Intervention Network (HAPIN) was a randomized controlled trial that enrolled ~3200 pregnant women across Guatemala, India, Peru, and Rwanda. Women in the intervention group received an LPG cookstove and free fuel during pregnancy and until the infant reached 12 months of age. Following the HAPIN trial, we continued following children in Guatemala, India, and Rwanda (N=2169) and measured child height at ages 2, 3, 4, and 5 years (HAPIN 2.0). As prespecified in the study protocol, we used mixed-effects models, as well as linear and log-binomial regression, within the intention to treat (ITT) framework, to evaluate the interventions effect on HAZ and stunting. Subgroup analyses were performed to assess effect modification by child sex, maternal height, and intervention country. Results The intervention reduced gestational exposures to particulate matter (PM2.5: 80.7 vs 117.5 {micro}g/m3), carbon monoxide (CO: 1.3 vs 2.0 ppm), and black carbon (BC: 8.6 vs 12.1 {micro}g/m3) and infancy exposures (PM2.5: 36.9 vs 113.2 {micro}g/m3; CO: 0.60 vs 2.2 ppm). Post-trial PM2.5 exposure were similar between arms: 99.9 vs 98.7 {micro}g/m3. We found no statistically significant effect of the intervention on repeated measures of HAZ ({beta} = -0.06, 95% CI: -0.16, 0.03 p=0.204), annual HAZ, stunting, nor severe stunting at any age during the follow-up. There was no statistically significant effect modification by subgroups. Conclusion Despite substantial reductions in air pollution exposures during the trial, the LPG intervention did not improve growth or reduce stunting risk from ages 2 through 5 years in this multi-country cohort. These findings, consistent with prior results of the lack of effects on growth through 12 months, provide additional evidence that a clean cooking intervention alone may not be sufficient to address childhood stunting in low-resource settings. Future exposure-response analyses will supplement these ITT analyses.

Returning individual results to research participants is increasingly expected in genomic studies. Yet, genomic dietary data have unique characteristics. New DNA sequencing techniques reconstruct diet from the residual plant and animal DNA recoverable in stool, reporting not nutrients or calories but a list of the species a person ate. Turning that list into something a participant can understand does not yet have an established framework. The Everyone EATS (Edible Atlas Through Sequencing) study returned personalized genomic dietary profiles to a non-clinical pilot cohort. Participants collected a stool sample at home, samples were processed with FoodSeq, and each participant received an interactive Diet Data Return Report that translated detected taxa into familiar food groups and benchmarked them against prior FoodSeq cohorts. No monetary compensation was offered, and the Diet Data Return Report was the sole incentive. Of 111 kits mailed, 80 were returned (72.1%) and sequenced (100% sequencing success for plant DNA; 98.7% for animal). Among report recipients with engagement data (n = 73), all accessed their report and 74% engaged interactively with its content. Among 16 feedback survey completers, 13/16 (81.2%) found the report easy to understand, 13/16 (81.2%) reported improved understanding of their dietary variety, and 5/16 (31.2%) reported a dietary behavior change. Curiosity about personal dietary data and supporting broader research goals were the most common reasons for enrolling (79.8%, respectively). Notably, the foods participants flagged as missing or unexpected clustered among herbs, spices, seafood, and processed items, the foods most easily forgotten in self-report and least readily assigned from sequence. Overall, our results suggest that genomic dietary data can be returned in a form participants find understandable and engaging, and that curiosity alone can motivate participation without payment.