Physical activity (PA) is recommended in the management of non-alcoholic fatty liver disease (NAFLD) given its beneficial effects on liver fat and cardiometabolic risk. Using data from the UK Biobank population-cohort, this study examined associations between habitual PA and hepatic fibro-inflammation. A total of 840 men and women aged 55-70 years were included in this cross-sectional study. Hepatic fibro-inflammation (iron-corrected T1 [cT1]) and liver fat were measured using MRI, whilst body fat was measured using dual-energy X-ray absorptiometry. PA was measured using accelerometry. Generalised linear models examined associations between PA (light [LPA], moderate [MPA], vigorous [VPA], moderate-to-vigorous [MVPA] and mean acceleration) and hepatic cT1. Models were fitted for the whole sample and separately for upper and lower median groups for body and liver fat. Models were adjusted for sociodemographic and lifestyle variables. In the full sample, LPA (-0.08 ms [-0.12 to -0.03]), MPA, (-0.13 ms [-0.21 to -0.05]), VPA (-1.16 ms [-1.81 to -0.51]), MVPA (-0.14 ms [-0.21 to -0.06]) and mean acceleration (-0.67 ms [-1.05 to-0.28]) were inversely associated with hepatic cT1. With the sample split by median liver or body fat, only VPA was inversely associated with hepatic cT1 in the upper median groups for body (-2.68 ms [-4.24 to -1.13]) and liver fat (-2.33 [-3.73 to -0.93]). PA was unrelated to hepatic cT1 in the lower median groups. Within a population-based cohort, device-measured PA is inversely associated with hepatic fibro-inflammation. This relationship is strongest with VPA and is greater in people with higher levels of body and liver fat. This study has shown that people who regularly perform greater amounts of physical activity have a reduced level of inflammation and fibrosis in their liver. This beneficial relationship is particularly strong when more intense physical activity is undertaken (i.e., vigorous-intensity), and is most visible in individuals with higher levels of liver fat and body fat.

Background: Physical activity, sedentary behaviour, and sleep are interdependent components of the 24 h movement profile that may influence appetite control. While acute exercise can alter appetite perceptions and food reward, less is known about how reallocating time between daily behaviours affects appetite outcomes under free-living conditions. Methods: We applied isotemporal-substitution modelling in a cross-sectional study of 130 young, healthy, active adults. Accelerometer-derived estimates of sedentary time, light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and sleep were analysed in relation to energy intake (food diaries, laboratory meals), subjective appetite perceptions, appetite-related hormones (acylated ghrelin, PYY, leptin), and psychological traits, including food reward (Leeds Food Preference Questionnaire, LFPQ), food cravings (Control of Eating Questionnaire, CoEQ), and eating behaviour traits (Three-Factor Eating Questionnaire, TFEQ). Results: Reallocating 30 min/day of sedentary time to MVPA was associated with higher energy intake in free-living (+113 kcal/day, 95% CI: 34–192) and laboratory settings (+120 kcal/day, 95% CI: 55–185), along with greater postprandial hunger and prospective food consumption, reduced fullness, elevated fasting acylated ghrelin, and lower postprandial PYY. No associations were observed for reallocations to LPA or sleep. Furthermore, sedentary time reallocations were unrelated to leptin or psychological eating traits assessed by the LFPQ, CoEQ, or TFEQ. Conclusions: In this population, reallocating sedentary time to MVPA was linked to physiological and behavioural compensation consistent with elevated energy demands, whereas reallocating to LPA or sleep showed no associations. Trait-level eating behaviours were unaffected, suggesting MVPA influences appetite primarily through acute physiological rather than enduring cognitive or hedonic pathways.

Background: Physical activity, sedentary behaviour, and sleep are interdependent components of the 24-hour movement profile that may influence appetite control. While acute exercise can alter appetite perceptions and food reward, less is known about how reallocating time between daily behaviours affects appetite outcomes under free-living conditions. Methods: We applied isotemporal-substitution modelling in a cross-sectional study of 130 young, healthy, active adults. Accelerometer-derived estimates of sedentary time, light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and sleep were analysed in relation to energy intake (food diaries, laboratory meals), subjective appetite perceptions, appetite-related hormones (acylated ghrelin, PYY, leptin), and psychological traits, including food reward (Leeds Food Preference Questionnaire, LFPQ), food cravings (Control of Eating Question-naire, CoEQ), and eating behaviour traits (Three-Factor Eating Questionnaire, TFEQ). Results: Reallocating 30 minutes/day of sedentary time to MVPA was associated with higher energy intake in free-living (+113 kcal/day, 95% CI:34–192) and laboratory settings (+120 kcal/day, 95% CI:55–185), along with greater postprandial hunger and prospective food consumption, reduced fullness, elevated fasting acylated ghrelin, and lower postprandial PYY. No associations were observed for reallocations to LPA or sleep. Furthermore, sedentary time reallocations were unrelated to leptin or psychological eating traits assessed by the LFPQ, CoEQ, or TFEQ. Conclusions: In this population, reallocating sedentary time to MVPA was linked to physiological and behavioural compensation consistent with elevated energy demands, whereas reallocating to LPA or sleep showed no associa-tions. Trait-level eating behaviours were unaffected, suggesting MVPA influences appetite primarily through acute physiological rather than enduring cognitive or hedonic pathways.

Background: Consumer wearables are now widely used across health promotion, wellness, and recreational fitness. Although wearable-supported interventions can improve physical activity and related outcomes, effects are typically modest, heterogeneous, and more consistent when devices are embedded within broader behavior change support rather than used alone. In applied settings, however, wearable use often remains focused on metric display and passive monitoring, leaving practitioners without a clear framework for translating data into behaviorally meaningful support. Objective: This paper proposes FRAME, a theory-informed conceptual framework intended to help practitioners interpret consumer wearable outputs as behaviorally relevant signals rather than as self-sufficient indicators of progress or failure. Methods: The framework was developed through a targeted narrative synthesis of literature on wearable-supported interventions, self-monitoring, feedback design, engagement, adaptive support, and applied behavior change theory. The purpose of the synthesis was not to estimate pooled effects, but to identify recurring interpretive and implementation problems that arise when wearable data are used in real-world behavior support encounters. Framework overview: FRAME comprises five linked steps: Filter the signal, Read the behavior pattern, Align a behavior change target, Message and micro-plan, and Evaluate and iterate. Across these steps, wearable data are treated as inputs into interpretation, hypothesis generation, collaborative planning, and iterative review. The framework is grounded primarily in self-regulation, the Capability-Opportunity-Motivation-Behavior (COM-B) model, self-determination theory, social cognitive theory, habit formation, and relapse prevention. Conclusion: For practitioners, the framework offers a structured way to judge whether a wearable signal is usable, what behavior pattern it may reflect, which target to prioritize, and how to discuss the data in an autonomy-supportive way. For wearable users, this is intended to yield more collaborative and sustainable behavior change support.

The hepatokine leukocyte cell-derived chemotaxin-2 (LECT2) promotes insulin resistance and hepatic fibrogenesis. In rodents, acute exercise suppresses circulating LECT2; however, human data are lacking. This study compared circulating LECT2 across populations and explored whether acute exercise impacts circulating LECT2. In Part A (n = 43), data were pooled from three experimental studies, regarding the following groups: healthy individuals, individuals with impaired glycaemic regulation (IGR), and individuals with type 2 diabetes and metabolic dysfunction-associated steatotic liver disease (T2DM-MASLD). Generalised linear models assessed differences in circulating LECT2 among groups. Part B (n = 20) involved exercise (30 min, 65% peak oxygen uptake) and control (resting) trials in the healthy and IGR groups. Circulating LECT2 was measured before and at 0, 1, 2 and 3 h post-exercise. Generalised estimating equations assessed differences in LECT2 responses to the trials among groups. In Part A, circulating LECT2 levels were 28.7% and 37.3% higher in the IGR and T2DM-MASLD groups, vs. healthy individuals (p ≤ 0.038), with BMI identified as the main predictor (p = 0.008). In Part B, average circulating LECT2 levels were 6.3% higher after exercise vs. in the control (p < 0.001), with similar responses between groups (p = 0.829). In the combined cohort, circulating LECT2 levels were elevated 1–3 h after exercise vs. control (p ≤ 0.009). LECT2 is elevated in people with dysglycaemia, with BMI as a leading predictor. Contrary to previous rodent work, acute exercise augments, rather than suppresses, circulating LECT2 in humans.