International Journal of Behavioral Nutrition and Physical Activity

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

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.

Background: High levels of sedentary behaviour are an emerging global public health concern, but its impact on cancer risk remains unclear. Methods: Within the Global Cancer Update Programme (CUP Global), we systematically searched the literature in PubMed and Embase until September 2024 for observational cohort studies on sedentary behaviour and adult cancer risk. Using dose-response meta-analyses, we investigated sedentary behaviour domains (total, occupational, recreational, transportation, and/or other) and dimensions (duration, frequency), and additionally pooled across domains. The quality of evidence was graded by the CUP Global Expert Panel (protocol registration: https://osf.io/7utbm/). Findings: We identified 62 publications from 27 cohorts comprising 162,902 incident cancer cases across 19 anatomical sites. There was evidence for a probable causal positive association between sedentary time (mixed definitions) and breast (RRper 2 hours/day=1.03; 95%CI=1.02-1.05; I2=10%; n=11 studies) and colon (RR=1.05; 95%CI=1.03-1.07; I2=0%; n=9) cancer risk, and between television watching time and colon cancer risk (RRper 2 hours/day=1.08; 95%CI=1.05-1.11; I2=0%; n=6). Limited suggestive evidence supported positive associations between sedentary time (mixed definitions) and lung (RR=1.04; 95%CI=1.00-1.09; I2=69%; n=7), ovarian (RR=1.06; 95%CI=1.01-1.10; I2=0%; n=7), premenopausal (RR=1.03; 95%CI=0.99-1.08; I2=20%; n=7) and postmenopausal breast (RR=1.02; 95%CI=1.00-1.04; I2=7%; n=11), and colorectal (RR=1.02; 95%CI=1.00-1.04; I2=47%; n=11) cancers, and between occupational sitting time and breast (RRper 2 hours/day=1.05; 95%CI=1.01-1.09; I2=0%; n=4) and colon (RR=1.08; 95%CI=1.02-1.15; I2=28%; n=2) cancers. An interactive evidence platform is available at: https://teacup.cc.ic.ac.uk/sedentary-behaviour-cancer.html. Interpretation: Evidence supports that prolonged sedentary behaviour is probably a cause of breast and colon cancers, while limited suggestive evidence supports positive associations for several other exposure-cancer pairs, including lung and ovarian cancers. This evidence should lead to revised cancer prevention recommendations. Future research should focus on device-based exposure assessments, repeated measurements, exposure substitution models, inclusion of diverse populations and investigation of biological mechanisms.

Background While weekend warrior (WW) physical activity (PA) pattern has been associated with cardiovascular benefits, most existing evidence is based on self-reported PA. Evidence using harmonized, thigh-worn accelerometry to examine associations between the weekend warrior phenotype and comprehensive cardiometabolic profiles remains limited. Methods We pooled harmonized individual-participant data from six cohorts in the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. In this cross-sectional analysis, participants were classified as inactive (<150 min/week MVPA), weekend warriors (150 min/week with 50% accumulated on 1-2 days), or regularly active 150 min/week but not meeting the WW criterion). A composite cardiometabolic score averaged eight standardized indicators: triglycerides, HDL cholesterol, total cholesterol, HbA1c, body mass index, waist circumference, systolic blood pressure, and diastolic blood pressure. Generalized linear models were used to estimate associations between PA patterns and both composite and individual cardiometabolic outcomes, adjusted for age, sex, smoking, alcohol intake, self-rated health, CVD history, medication use, blood biomarker fasting status, and cohort. Results Among 13,904 adults (mean age 54.3{+/-}9.5 years; 54.7% women), 61.8% were inactive, 24.5% weekend warriors, and 13.7% regularly active. Compared with inactivity, both active patterns were associated with more favorable composite cardiometabolic scores (WW: = -0.118 (95% CI: -0.142, -0.095); regularly active: = -0.111 (95% CI: -0.141, -0.081)), with negative values indicating better cardiometabolic health. Weekend warriors and regularly active adults showed broadly similar associations across individual markers, including lower adiposity (BMI and waist circumference) and more favorable metabolic biomarkers (higher HDL cholesterol; lower triglycerides and HbA1c), with no meaningful differences between the two active patterns for the composite score or any individual outcome. Associations with total cholesterol and blood pressure were small. Conclusions Both active patterns were associated with more favorable cardiometabolic profiles than inactivity, and profiles were broadly comparable whether MVPA was concentrated on 1-2 most active days (weekend warrior) or accumulated more regularly across the week. Keywords: weekend warrior; physical activity; accelerometry; cardiometabolic health

Climate change draws attention to the planetary health perspective in sport and exercise sciences, that is, to physical activity that supports both human wellbeing and environmental sustainability. Active commuting is a sustainable form of physical activity with well-established somatic health benefits. However, more knowledge is needed on its relationship with mental health. We examined dose-response associations between active commuting, anxiety symptoms, and mental wellbeing among Finnish adults, and whether green commuting environment moderates these relationships. We used data from the cross-sectional Environment and Health Survey collected in June-September 2023 in the ten largest cities in Finland. Employed participants with data on anxiety symptoms (Generalized Anxiety Disorder-7, GAD-7), mental wellbeing (World Health Organization-Five Well-Being Index, WHO-5), commuting profile over a year (mode, frequency, distance, and perceived greenness along the commute route), and sociodemographic and lifestyle factors were included (n=1,672; mean age 45.3 years; 53.8% women). Active commuting was defined as travelling the entire commute by walking or cycling (including e-biking) that was converted into approximated annual km/week and MET-h/week. We used linear and logistic regression with restricted cubic splines to evaluate dose-response associations, adjusted for key covariates. The role of perceived greenness was tested using an active commuting x commute greenness interaction term. We found no dose-response relationships between active commuting and anxiety symptoms or mental wellbeing in any of the models. No effect modification by commute greenness was observed. More research on how active commuting may support planetary health from a mental health perspective is needed.

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.

Abstract Objective Wrist-worn accelerometers are common in large-scale epidemiological studies, but their ability to measure sedentary behaviour in free-living environments is unknown. We therefore aimed to evaluate the accuracy of openly-available methods to infer sedentary time from wrist-worn accelerometers. Methods We analysed data from 662 working-age adults in the SMART Work & Life study (20-70 years; mean age 45 years; 72% female) who concurrently wore wrist- and thigh-worn accelerometers for up to eight free-living days. Reference measurements of sedentary time were derived from the thigh accelerometer data using proprietary algorithms. Wrist accelerometer data were processed using widely used, publicly available activity recognition models. Performance was evaluated at 30-second epochs to generate per-participant metrics, alongside comparisons of mean daily sedentary time, mean daily number of prolonged sedentary bouts ([&ge;] 30 minutes) and proportion of sedentary time in prolonged bouts. Model performance was examined across subgroups defined by age, sex, body mass index, season, recruitment centre, and in sensitivity analyses restricted to daytime hours (08:00-22:00). Results The best performing machine learning model (Actinet) accurately classified sedentary time from wrist-worn accelerometer data with a mean per-participant accuracy of 0.87 and F1 score of 0.85. Cut point-based approaches demonstrated lower accuracy of 0.80 (F1 score of 0.79). The ActiNet machine learning model showed strong agreement in daily sedentary time, daily number of prolonged sedentary bouts and proportion of sedentary time in prolonged bouts, all within 10% of the free-living thigh reference. Findings were consistent across subgroups and in analyses restricted to daytime hours. Conclusion Wrist-worn accelerometers can provide accurate measurements of sedentary behaviour in free-living settings, when assessed using current machine learning models, particularly ActiNet. This work provides confidence in future epidemiological research to examine sedentary behaviour patterns from wrist-worn accelerometers and their associations with health outcomes.

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.

Objectives: To examine how self-reported physical activity and walking pace in UK healthcare workers differ ethnicity, migration status, and job role. Design: Prospective cohort study with 6 waves of data collection between 2020 and 2025, analysed using multi-level logistic regression, informed by the UK REACH patient and professional advisory group. Setting: Healthcare settings across the UK. The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers (UK-REACH) cohort. Participants: 18,721 healthcare workers employed in the UK: 28.2% Allied Health Professionals; 75% female; 34% from ethnic minority backgrounds; and 32% born overseas. Main outcome measures: Physical activity and walking pace, as measured by the General Practice Physical Activity Questionnaire (GPPAQ). A Total Physical Activity score, and a separate Exercise and Cycling score excluding occupational physical activity were derived. Results: Walking pace and physical activity varied significantly by ethnicity, migration status and job role. Compared to White UK-born staff, slower walking pace was reported in ethnic minority staff, with differences greater in staff born overseas (e.g. Asian UK (aOR 0.63, 95%CI 0.56 to 0.71, p<0.001), Asian overseas (aOR 0.38, 95%CI 0.35 to 0.42, p<0.001)). Compared to White UK-born staff, lower odds of reporting being physically active were reported in multiple ethnic groups. Differences were larger for Exercise and Cycling, indicating that occupational physical activity accounted for a large proportion of total physical activity in several groups (e.g. Total Physical Activity: Asian overseas-born (aOR 0.79, 95%CI 0.72 to 0.87, p<0.001); Exercise and Cycling: Asian overseas-born (aOR 0.61, 95%CI 0.56 to 0.67, p=<0.001). Compared toMedicalstaff, Nursing and Midwifery staff, Pharmacy staff, Dental staff, Other staff, and Healthcare Scientists hadsignificantly lower odds of reporting being physically active. Ambulance staff had higher odds of reporting being physically active when using the Total Physical Activity score (aOR 1.64, 95%CI 1.37 to 1.97 p<0.001), but lower odds for Exercise and Cycling (aOR 0.61 95%CI 0.53 to 0.72, p<0.001). Conclusions: Self-reported physical activity and walking pace vary significantly in UK healthcare workers, by ethnicity, migration status, and job role. Our findings have important implications for understanding workforce health and ethnic health inequalities.

The Healthy Eating Index (HEI-2020) is conventionally computed by aggregating intake across days before scoring. Digital food logging enables an alternative: scoring each day and averaging daily scores. These methods are not equivalent. The HEI's density-based structure and component caps cause aggregation to inflate adequacy scores when intake is irregular. Using Food & You data, we show daily HEI correlates more strongly with microbiome diversity, and recommend co-reporting both metrics.

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.

To estimate the life expectancy gains that could be realised from increases in Queenslanders physical activity (PA) levels. Design Lifetable analysis Setting, Participants We modelled the 2025 Queensland population aged [&ge;]40 years. Modelled scenarios We applied two approaches. In the first, we estimated life expectancy differences between device-measured PA quartiles, with quartile1 representing the least active and quartile 4 the most active. In the second, we compared observed device-measured PA levels in Queensland with scenarios in which all individuals moved to either [&ge;]12,000 steps/day or [&le;]2,000 steps/day. We converted the steps per day by age group and PA quartile into equivalent daily minutes of moderate-intensity walking at 4.8 km/h. Additional scenarios were explored in sensitivity analyses. Main outcomes Changes in life expectancy, and total life-years gained over the lifetime of the modelled population. Benefits were also translated into minutes of life gained per additional hour walked. Results If all Queenslanders aged [&ge;]40 years were as active as the most active quartile, life expectancy at birth could be 88.3 years, an increase of 4.8 years above the life expectancy at observed activity levels. The life expectancy differences between individuals in the least active quartile and the most active quartile was 9.7 years. Achieving the activity level of the most active quartile would require individuals in the lowest activity quartile to undertake an additional 85.9 minutes/day of moderate-intensity walking, with each extra hour of PA associated with an average gain of approximately 3 hours (177 minutes) of life. In step-based modelling, life expectancy in the most active scenario (all achieving [&ge;]12,000 steps/day) was higher by {approx}7.1 years compared with the least active scenario (all at [&le;]2,000 steps/day). Conclusions Increasing PA could yield meaningful gains in life expectancy for Queenslanders, with the largest gains seen in least active individuals. Our findings strengthen the case for prioritising investment in PA -promoting programs and environments.

Background and Aims: MASLD has become the most prevalent chronic liver disease globally. Although MVPA and plasma fatty acids have been individually studied in relation to metabolic health, their independent and combined associations with MASLD incidence remain unclear. We aimed to investigate these associations. Methods: This study included 51,717 UK Biobank participants free of liver disease at baseline, with MVPA measured using wrist-worn accelerometers and plasma fatty acids quantified via NMR. Multivariable-adjusted Cox models and restricted cubic splines were used. Results: Over a median follow-up of 7.8 years, 472 incident cases were identified. In fully adjusted models, meeting recommended MVPA levels together with higher n-6 PUFA concentrations was associated with a 71% lower risk (HR 0.29, 95% CI 0.18-0.45). The MVPA-MASLD association was nonlinear, with risk reduction plateauing at approximately 189 minutes per week. Higher n-6 PUFA was associated with reduced risk, whereas n-3 PUFA showed no significant association. Conclusions: These findings suggest that behavioral and metabolic factors may jointly influence MASLD risk. Further studies in diverse populations are needed to confirm these associations.

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.