Medicine & Science in Sports & Exercise

Altered walking mechanics in individuals with obesity can contribute to knee osteoarthritis. The gait deviations may become more pronounced during obstacle crossing. In women, body fat distribution may further influence knee load, especially when excess fat accumulates in the thighs and hips. However, relatively little is known about how regional fat distribution affects gait in women with obesity. This study investigated how obesity and, among women, different fat distributions (Apple: more abdominal fat; Pear: more lower-limb fat) influence knee biomechanics during walking with and without obstacle crossing. Participants were 15 controls without obesity (NB) and 27 with obesity (OB). Within female participants, 10 without obesity (fNB) were compared with 20 with obesity, stratified by waist-hip ratio (Apple:10, Pear:10). Speed-adjusted statistical parametric mapping applied a general linear model (NB vs. OB) and an analysis of covariance (fNB vs. Apple vs. Pear). OB exhibited a significantly greater late-stance knee extension moment than NB across all tasks, and this difference persisted among fNB, Apple, and Pear in obstacle tasks (p<0.05). OB walked with reduced knee flexion during the early-stance leading limb after crossing a medium-height obstacle (p=0.048) and a high-height obstacle (p=0.008) compared to NB. There were significant body-shape effects (p<0.05), and post-hoc comparisons confirmed that Pear had lower knee angles than fNB in both leading-limb conditions after crossing medium- and high-height obstacles (p=0.008 and p=0.001, respectively). These findings suggest that obstacle crossing helps illuminate how excess weight influences knee biomechanics, and how regional fat distribution modulates the degree of this alteration.

The metabolic cost of walking is widely used to evaluate human performance and effectiveness of clinical interventions. Decades of laboratory research, largely based on treadmill experiments, have established a canonical relationship between walking speed and metabolic cost, and suggested that ageing shifts this relationship upward, implying reduced efficiency in older adults. However, this relationship has not been well tested during overground walking across matched speeds. We compared healthy younger (n=16; 26{+/-}2yr) and older (n=11; 74{+/-}3yr) adults across eight outdoor overground walking trials at different speeds: preferred walking speed (PWS), three fixed speeds (0.8, 1.2, 1.6 m{middle dot}s-{superscript 1}), and four speeds at {+/-}5% and {+/-}10% of PWS. Contrary to our hypothesis, older adults did not show higher gross or net metabolic cost of walking (GCOW and NCOW) than younger adults at any speed; rather, both trended consistently lower in older adults, reaching significance for GCOW at 0.8 m{middle dot}s-{superscript 1} only. Comparisons of resting metabolic rate and respiratory exchange ratio to prior reference groups did not indicate that our older cohort was unusually fit. Independent of age, GCOW was significantly higher at 0.8 m{middle dot}s-{superscript 1} than at the remaining speeds (1.2-1.6 m{middle dot}s-{superscript 1}), confirming that walking at slower speeds increases GCOW. These findings challenge the view that ageing intrinsically increases the energetic cost of walking, suggesting instead that previously reported upward shifts in cost may reflect treadmill-specific constraints or speed effects. Future work is needed to explore direct comparisons of outdoor, overground walking with treadmill walking at fixed speeds in both younger and older adults.

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.

Objective: To investigate the association between usual moderate-to-vigorous leisure-time physical activity (MVLTPA) and longitudinal trajectories of BMI and waist-to-height ratio (WHtR), as well as the incidence of obesity (BMI >=30 kg/m2) and central adiposity (WHtR >=0.6), in older adults at high cardiovascular risk. Methods: In PREDIMED participants aged 60-80 years (61% women; n=5,273 for BMI, n=4,940 for WHtR), MVLTPA was assessed using the Minnesota questionnaire (MET-min/day, cumulative mean). Mixed-effects models with cubic splines were used to estimate BMI and WHtR trajectories according to MVLTPA levels (>=100 [adequate] vs <100 [insufficient]). Incident obesity outcomes were assessed with Cox models. Results: Participants reporting >=100 MET-min/day showed lower BMI and WHtR values compared with less active individuals (differences ranging from -1.27 to -1.48 kg/m2 and -0.021 to -0.026, respectively), and slower declines in BMI. Adequate MVLTPA was associated with a 34% lower risk of central adiposity (HR 0.66 [0.56; 0.77]) and a 24% lower risk of obesity only in women (HR 0.76 [0.57; 1.00], p-interaction = 0.084). Nonlinear analyses suggested that central adiposity risk decreased up to 400 MET-min/day and plateaued thereafter. Conclusions: Higher MVLTPA is associated with favourable adiposity trajectories and lower central adiposity risk in older adults at high cardiovascular risk.

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 Lean mass is positively associated with bone mineral density (BMD), and this relationship may vary according to age and sex. Sedentary behavior has also been associated with BMD, although findings differ across populations and skeletal sites. However, it remains unclear whether sedentary time modifies the relationship between regional lean mass and BMD and whether such modification varies across sex and age. These relationships were explored using nationally representative U.S. data. Methods Adults aged 18-59 years from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) were analyzed. Mean bilateral leg lean mass and mean bilateral leg BMD were derived from dual-energy X-ray absorptiometry measurements, and sedentary time was obtained from self-report. Survey-weighted regression models incorporated NHANES examination weights, strata, and primary sampling units. The primary model examined the four-way interaction among leg lean mass, sedentary time, sex, and continuous age, with adjustment for race/ethnicity and height. Quadratic lean-mass terms were examined in sensitivity analyses. Results The primary complete-case analysis included 2,543 adults (1,191 men and 1,352 women). Women showed a more apparent decline in both leg lean mass and leg BMD at ages 50-59 years, whereas sedentary time did not increase in parallel with these age-related changes. Leg lean mass was positively associated with leg BMD in both sexes, with modest nonlinearity, particularly among women. In the primary survey-weighted model, the four-way interaction among leg lean mass, sedentary time, sex, and age was positive (beta = 0.000111 per year, 95% CI 0.000039-0.000183; P = 0.0051), indicating increasing sex divergence in sedentary-time modification of the lean mass-BMD association with age. At younger ages, conditional interaction estimates were similar between sexes; at older ages, estimates became increasingly positive in women while remaining negative but imprecise in men. Quadratic sensitivity analyses attenuated the four-way interaction estimate by approximately 9-12% and reduced its statistical precision, while broadly preserving its direction and magnitude. Conclusions In this exploratory cross-sectional analysis, sedentary-time modification of the association between DXA-derived leg lean mass and leg BMD varied according to sex and age. The sex difference was small at younger ages and became progressively more apparent with increasing age. These findings suggest that behavioral and demographic context may contribute to variation in the muscle-bone relationship itself, although confirmation in independent and longitudinal datasets is needed.

Purpose: Physical behaviors, including physical activity, sedentary time, and sleep, are associated with health and together comprise the 24-hour day. Among working-aged adults, the composition of these 24-hour physical behaviors may vary across occupations, but the extent of these differences remains unknown. This population-based study examined how the composition of 24-hour physical behaviors differs in working-aged adults across occupational groups during work and leisure time. Methods: The study population consisted of 2,809 adults from the Northern Finland Birth Cohort 1966 46-year follow-up study, who wore a hip-worn accelerometer for 14 days. The accelerometry data was combined with self-reported diaries to determine time in bed and work hours. Four occupational groups were aggregated based on occupation type and occupational skill level: high-skill white-collar, low-skill white-collar, high-skill blue-collar and low-skill blue-collar. Time spent on physical behaviors during workdays (including working hours and leisure time separately) and non-workdays were calculated. Differences in physical behavior compositions were assessed using a compositional data analysis approach based on log-ratios of geometric means, with the low-skill blue-collar group as the reference group. Results: Significant differences in 24-hour physical behaviors across occupational groups were mainly observed during workdays. During workday working hours, all other occupational groups had less physical activity (both moderate-to-vigorous and light-intensity physical activity) compared to the reference group. During workday leisure time, significant differences were observed in all physical behaviors, except for sedentary time. On non-working days, the high-skill white collar group had higher moderate-to-vigorous physical activity compared to the reference group. Conclusions: The composition of domain-specific 24-hour physical behaviors differs across occupational groups, particularly during workdays.

Objectives The association between occupational physical activity (OPA) and cardiovascular disease (CVD) may depend on how OPA is classified. This study examined the association between OPA and incident CVD using individual level and occupational group level classifications, and assessed whether factors contributing to attenuation differed. Methods A total of 137,592 employed UK Biobank participants aged 40 to 69 years who were free of CVD at baseline were included. OPA was classified using (i) individual level self reported physical activity at work and (ii) occupational group level two digit Standard Occupational Classification. Associations with incident CVD were examined using Cox proportional hazards models. Attenuation of associations by sociodemographic, lifestyle, health, occupational and psychosocial factors was assessed using univariate adjustments and Heller's coefficient. Results During 1,656,378 person-years of follow up (median 12.6 years), 10,465 incident CVD events occurred. The association between OPA and CVD differed markedly according to exposure classification. High individual level self reported OPA was associated with increased CVD incidence after full adjustment [hazard ratio (HR) 1.19, 95% confidence interval (CI) 1.10 to 1.27]. In contrast, no association was observed when OPA was classified at the occupational-group level using Standard Occupational Classification groupings (HR 1.00, 95% CI 0.94 to 1.06). Sociodemographic and health factors accounted for the greatest attenuation across both classifications, whereas psychosocial factors contributed primarily to associations based on individual-level self-reported OPA. Conclusions The association between occupational physical activity and incident cardiovascular disease differed substantially according to how OPA was classified. These findings highlight exposure classification as a critical consideration when interpreting evidence on the physical activity paradox.

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.

Objective: To examine associations between volume and intensity of leisure time physical activity (LTPA) and fracture risk during midlife and determine whether adherence to World Health Organisation (WHO) physical activity recommendations was associated with fracture risk. Methods: Cross-sectional analysis of UK Biobank participants aged 40 to 65 years using self-reported physical activity and fracture data. LTPA volume (LTPAV) was derived from walking, moderate and vigorous activity. Participants were categorised into LTPAV quintiles and by adherence to WHO physical activity recommendations. Multivariable logistic regression examined associations with self-reported fracture within the previous five years, with sex stratified analyses. Results: Among 322,749 participants, 53% were female with a mean age of 47 years. Fracture risk increased with higher LTPAV, although the association was non-linear and varied by age and sex. The strongest associations were in the highest quintile among men and women aged 40 to 49 years (OR 1.81, 95% CI 1.64 to 1.99 and OR 1.42, 95% CI 1.28 to 1.58 respectively). In mutually adjusted models, vigorous activity demonstrated the strongest independent association with fracture risk. Meeting WHO physical activity recommendations, without exceeding them, was not associated with increased fracture risk. Conclusions: Higher fracture risk was observed in individuals undertaking higher volumes of leisure-time physical activity, with the association largely driven by vigorous-intensity activity. The association was strongest in adults aged 40 to 49 years and in men. Physical activity consistent with current WHO recommendations was not associated with increased fracture risk, supporting continued promotion of physical activity during midlife.

Background: Handgrip strength is associated with cardiometabolic abnormalities, but whether it adds information once detailed adiposity and muscle mass are known is unclear. Methods: We studied adults aged 40-59 years from NHANES 2011-2012 (development, n=508) and 2013-2014 (temporal validation, n=616). The outcome was at least two of four non-adiposity abnormalities (elevated blood pressure, fasting glucose or triglycerides, or low HDL cholesterol); waist circumference was not part of the outcome. A prespecified feature ladder added DXA body fat percentage and visceral adipose tissue to age, sex, BMI and waist circumference (M1), then appendicular skeletal muscle index (M2), then absolute (M3A, primary) or weight-normalised (M3B) grip strength. Logistic regression was primary; elastic-net and XGBoost were supportive. Development performance used nested cross-validation; frozen models were scored once in the validation cohort. Paired increments used 2000 stratified bootstrap resamples. Results: The outcome occurred in 249 (49.0%) and 277 (45.0%) participants. Adding adiposity increased the logistic regression AUC by 0.0209 (95% CI 0.0024-0.0402) in development and 0.0209 (0.0032-0.0375) in temporal validation. Adding absolute grip strength to M2 changed the AUC by -0.0046 (-0.0107 to 0.0013) and 0.0045 (-0.0009 to 0.0098), respectively. All 12 grip-strength AUC increments across algorithms, definitions and cohorts had intervals that included zero. Calibration was unchanged and the incremental net benefit was not consistently positive. Model attributions to grip strength varied by algorithm. Conclusions: In middle-aged adults, detailed adiposity, but not grip strength, added reproducible information for concurrent cardiometabolic risk stratification beyond conventional anthropometry and muscle mass.

Healthy lifestyle habits are a key pillar of physical, psychological, and social well-being, yet university years are often marked by declines in sleep quality, physical activity, and other health behaviors that may affect academic performance. This cross-sectional study examined these associations in 390 Spanish university students, assessed via an online and in-person questionnaire covering academic trajectory (grade point average, credits passed, curricular progression), sleep quality (PSQI), physical activity (IPAQ), tobacco use, Body Mass Index (BMI), and sociodemographic data. Bivariate associations were tested with chi-square, Pearson correlation, t-tests, and ANOVA, followed by generalized linear models for significant variables. Sports participation, but not IPAQ-measured physical activity, was significantly associated with academic progression, defined as advancing one grade level per year. Sleep indicators showed mixed, partly counterintuitive associations: poorer nocturnal sleep was linked to better progression, whereas better sleep quality predicted a higher percentage of credits completed. BMI was a modest positive predictor of credit completion, and underweight students performed significantly worse than overweight or obese peers. Tobacco use showed no significant associations. These findings suggest that sleep-related behaviors, sports participation, and, to a lesser extent, BMI, are relevant correlates of academic progress, supporting university health policies addressing sleep, physical activity, and nutrition.

Abstract Objective HYROX is a rapidly growing hybrid fitness competition combining running with functional exercise stations. Our objective was to describe the 12-month prevalence, characteristics and severity of self-reported HYROX-related injuries. Methods We conducted an international cross-sectional online survey of 418 HYROX athletes. The primary outcome was the self-reported 12-month period prevalence of at least one HYROX-related injury; secondary outcomes included an exposure-adjusted lower-bound rate per 1000 hours of total training exposure and the profile and severity of the most significant injury. Associated factors were examined by multivariable logistic regression. Results Overall, 208 of 418 participants (49.8%, 95% CI 45.0 to 54.5) reported at least one HYROX-related injury. The exposure-adjusted lower-bound rate was 1.65 reported injuries per 1000 hours of total training exposure. Injuries mainly affected the lower extremity, most commonly the knee (20.8%); tendon-related complaints were the leading type (41.6%) and most were of gradual onset. Among participants with severity data, 20.3% reported more than 28 days of training interruption or no return to their previous performance level. Higher HYROX-specific training frequency was the only factor independently associated with injury reporting (adjusted OR 1.61, 95% CI 1.20 to 2.16; p = 0.001). Conclusion Approximately half of respondents reported at least one HYROX-related injury during the preceding 12 months, predominantly involving gradual-onset lower-extremity complaints. Higher HYROX-specific training frequency was associated with injury reporting, although the cross-sectional design precludes causal interpretation. Prospective, exposure-based surveillance is needed to quantify HYROX-specific injury incidence and burden and examine whether training frequency, load distribution and recovery contribute to injury risk.

Cross-sectional studies suggest associations between physical exercise and white matter in older adults, but evidence from randomized controlled trials is scarce. Neurite orientation dispersion and density imaging indices are biophysically informed metrics of white matter microstructure. Here, we tested whether a remotely delivered, 8-week multicomponent physical exercise intervention impacts neurite density (NDI) and orientation dispersion (ODI) across major white matter tracts in older adults. This secondary analysis of a randomized controlled trial included participants with available diffusion MRI data (n = 66; age: 66.4 {+/-} 3.6 y; 43 females). Participants were randomized to a multicomponent exercise (PAG, n = 34) or an active control (CON, n = 32) intervention. Intervention effects on NDI/ODI were tested using linear mixed-effects and Bayesian multilevel models adjusted for age and sex. A significant Timepoint x Group interaction was observed for NDI (p = 0.003) but not for ODI (p = 0.785), further confirmed in Bayesian analyses for 22 white matter tracts, indicating a greater increase in NDI in the PAG. The standardized composite VO2max score increased from pre- to post-intervention within the PAG, although the Timepoint x Group interaction was not significant (p = 0.079). Across all participants, pre-to-post changes in mean NDI were positively correlated with changes in VO2max, but this association did not differ between groups. Our results indicate that white matter microstructure remains responsive to short-term, multicomponent physical exercise in older adults.

Paraspinal muscles support and move the spine, and reduced size and density of these muscles is associated with increased risk of vertebral fracture and back pain. We test the hypothesis that subsistence-oriented forager-horticulturalists (Bolivian Tsimane) have larger and denser paraspinal muscles, and smaller age-related differences in paraspinal muscle size and density, than more sedentary post-industrialized suburbanites (Framingham Heart Study [FHS]). We measured cross-sectional area (CSA) and density of trunk extensors and psoas major at L3 from computed tomography scans of 411 FHS and 330 Tsimane (50% women, aged 50-90 years). Sex-stratified linear regressions determined associations of muscle CSA and density with age and cohort (Tsimane or FHS [ref.]), with and without an age-cohort interaction term. Covariates were height and weight. After adjusting for covariates, Tsimane women had 11-14% larger and denser extensors, and 17-26% smaller and less-dense psoas major, than FHS women. Tsimane men had 8-21% smaller and less-dense psoas major than FHS men. The age-related difference in extensors density was 41% smaller for Tsimane men than for FHS men. There were no other significant age-related cohort differences in muscle CSA or density. We observed a different balance in the size and density of trunk extensors versus psoas major between Tsimane and FHS. A possible explanation for this observation is cohort differences in the amount and types of physical activity, although future work is required to determine the influence of lifestyle and environmental factors on paraspinal muscle size and density.

Purpose: Identifying head impacts linked to brain injury in sport remains challenging. Instrumented mouthguards quantify head-impact kinematics, and finite element (FE) modelling can transform these data into brain strain estimates, which may better reflect injury risk than kinematics alone. Here, we examined associations between mouthguard-measured kinematics, FE-derived strain, and plasma brain injury biomarker GFAP following head impacts. Methods: We analysed 41 video-verified impacts from male Australian football players, including 22 assessed for concussion (17 diagnosed) and 19 unassessed. Instrumented mouthguards recorded peak linear acceleration (PLA), peak rotational acceleration, and peak rotational velocity (PRV). Brain strain was estimated using the Imperial College FE brain model, and plasma GFAP was quantified using Simoa. Biomechanical-GFAP associations were examined using Spearman correlations and segmented regression. Results: For impacts overall, plasma GFAP was moderately correlated with PLA ({rho}=0.46, 95% CI: 0.20-0.66), PRV ({rho}=0.53, 95% CI: 0.20-0.78), and strain ({rho}=0.60, 95% CI: 0.32-0.80). Associations were stronger within concussion cases for strain ({rho}=0.86, 95% CI: 0.58-0.97) and PRV ({rho}=0.64, 95% CI: 0.15-0.93). Piecewise regression identified strain levels above which strain-GFAP relationships steepened across the whole-brain and brainstem. In concussion cases, supra-threshold brainstem strain was associated with greater symptoms. Conclusion: Finite element brain strain may better predict brain injury risk following a sport-related head impact than peak acceleration metrics. Stronger associations with plasma GFAP, particularly among concussion cases, and evidence of a biomechanical threshold, support the use of biomarker-informed strain measures in future risk modelling and the development of brain injury screening thresholds.

Background: Large-scale prevention and population health monitoring require measurement approaches that are both feasible and informative. Although several self-measurable anthropometric and fitness indicators have been associated with cardiometabolic risk, it remains unclear whether combining multiple measurements provides meaningful improvements over simpler approaches. We evaluated whether a parsimonious set of self-measurable indicators can achieve classification performance comparable to a full candidate set and quantified the incremental value of additional measurements. Methods: Using data from 8,275 adults in the NHANES 1999-2004 cohorts, we evaluated a predefined minimal set of four self-measurable anthropometric and fitness indicators (body mass index (BMI), waist-to-height ratio (WHtR), mid-upper arm circumference (MUAC), and VO2max (as a proxy for the 6-minute walk test) as candidate indicators of cardiometabolic risk. Their ability to reflect underlying clinical risk factors related to adiposity, glucose and lipid metabolism, and physical fitness was assessed using nested logistic regression models, likelihood ratio tests, discrimination metrics, and decision tree analyses. Results: WHtR consistently showed the strongest discriminative performance, with {Delta}PR-AUC values for BMI versus WHtR ranging from -0.002 to -0.037, and emerged as the primary splitting variable. Adding BMI to WHtR resulted in small gains in PR-AUC for most outcomes, ranging from 0.000 to 0.008, except for triglycerides where the gain was larger ({Delta}PR-AUC=0.039). Further inclusion of MUAC and VO2max provided limited additional value overall, with evidence of variation across outcomes and sex stratified analyses. Conclusion: Most classification performance was achieved using a limited number of simple self-measurable indicators, with little additional benefit from incorporating further measurements. These findings suggest that parsimonious measurement strategies may provide a feasible approach for cardiometabolic risk classification in population health and prevention settings while reducing measurement burden.

Weekly training volume is the primary driver of resistance-training hypertrophy, but the independent role of frequency can be isolated only when weekly volume is equated. We quantified the effect of training frequency on hypertrophy across one to three volume-equated sessions per muscle per week. Following PRISMA 2020, five databases were searched to October 2026 for trials of at least six weeks that equated weekly volume between per-muscle frequencies and measured muscle size. One change-score standardized mean difference per trial (Hedges g; negative favoring higher frequency) was pooled with a random-effects model, and certainty was rated with GRADE. Sixteen trials (380 participants) were pooled. The frequency effect was trivial and non-significant (g = -0.02; 95% CI -0.21 to 0.16; I-squared = 0%; prediction interval -0.23 to 0.18); the interval admits at most a small effect favoring the higher frequency. Contrast-specific estimates were similarly trivial, and no moderator was significant, although the moderator analyses could detect only large effects. Certainty was low to very low. Within this range, the data give no basis for preferring one, two or three sessions at a fixed weekly volume. Most trials measured muscle thickness, which does not track whole-muscle growth reliably.

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.

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.