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Background: Eating self-regulatory capacity can help individuals to cope with the obesogenic environment and achieve, as well as maintain, a healthy weight and diet. At present, there is no comprehensive, reliable and valid questionnaire for assessing this capacity and measuring change in response to self-regulation interventions in adults. This paper reports the development of the Self-regulation of Eating Behaviour Questionnaire (SREBQ) for use in UK adults, and presents evidence for its reliability and construct validity. The development of the SREBQ involved generation of an item pool, followed by two pilot studies (Samples 1 and 2) and a test of the questionnaire's underlying factor structure (Sample 3). The final version of the SREBQ was then assessed for reliability and construct validity (Sample 4). Results: Development of the SREBQ resulted in a 5-item questionnaire. The face validity was satisfactory, as assessed by the pilot studies. The factor structure analysis (Sample 3) suggested that it has a single underlying factor, which was confirmed in a second sample (Sample 4). The SREBQ had strong construct validity, showing a positive correlation with general measures of self-regulation. It was also positively correlated with motivation and behavioural automaticity, and negatively correlated with food responsiveness and emotional over-eating (p < 0.001). It showed good discriminant validity, as it was only weakly associated with satiety responsiveness, food fussiness and slowness in eating. Conclusions: The SREBQ is a reliable and valid measure for assessment of eating self-regulatory capacity in the general UK adult population. [ABSTRACT FROM AUTHOR]

Background: Several previous studies have shown that dietary patterns are associated with the incidence of depressive symptoms. However, the results have been inconsistent. This study aimed to prospectively investigate the association between dietary patterns and the risk of depressive symptoms in two large cohort studies. Methods: The Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) cohort study included a total of 7,094 participants living in Tianjin, China from 2013 to 2019, and the UK Biobank cohort study includes 96,810 participants who were recruited from 22 assessment centers across the UK taken between 2006 and 2010. All participants were free of a history of cardiovascular disease (CVD), cancer, and depressive symptoms at baseline. Dietary patterns at baseline were identified with factor analysis based on responses to a validated food frequency questionnaire in TCLSIH or Oxford WebQ in UK Biobank. Depressive symptoms were evaluated using the Chinese version of the Zung Self-Rating Depression Scale (SDS) in TCLSIH or hospital inpatient records in UK Biobank. Cox proportional hazards regression models were used to estimate the association between dietary patterns and depressive symptoms. Results: A total of 989, and 1,303 participants developed depressive symptoms during 17,410 and 709,931 person-years of follow-up. After adjusting for several potential confounders, the multivariable HRs (95% CIs) of the depressive symptoms were 0.71 (0.57, 0.88) for traditional Chinese dietary pattern, 1.29 (1.07, 1.55) for processed animal offal included animal food dietary pattern, and 1.22 (1.02, 1.46) for sugar rich dietary pattern in TCLSIH (all Q4 vs Q1). In the UK Biobank, the HRs (95% CIs) of depressive symptoms were 1.39 (1.16, 1.68) for processed food dietary pattern (Q4 vs Q1), 0.90 (0.77, 1.00) for healthy dietary pattern (Q3 vs Q1), and 0.89 (0.75, 1.05) for meat dietary pattern (Q4 vs Q1) in the final adjusted model. Conclusion: Dietary patterns rich in processed foods were associated with a higher risk of depressive symptoms, and following a traditional Chinese dietary pattern or healthy dietary pattern was associated with a lower risk of depressive symptoms, whereas meat dietary pattern was not associated. [ABSTRACT FROM AUTHOR]

Background: There is increasing policy interest in ensuring that the school environment supports healthy behaviours. We examined the cross-sectional and longitudinal associations between schools' policies, programmes and facilities for physical activity (PA) and adolescents' objectively-measured activity intensity during the school day and lunchtime. Methods: Accelerometer-derived PA (proportion of time spent in sedentary (SED), light PA (LPA) and moderate-tovigorous PA (MVPA)) during school hours and lunchtime from 325 participants in the SPEEDY study were obtained from baseline measurements (primary school, age 9/10 years) and +4y follow-up (secondary school). School environment characteristics were assessed by teacher questionnaire. Multivariable multi-level linear regression analyses accounting for school and adjusted for sex, age, BMI and family socio-economic status assessed cross-sectional associations with lunchtime and school-day SED, LPA and MVPA; effect modification by sex was investigated. The association of changes in school environment with changes in outcomes was examined using multivariable cross-classified linear regression models. Results: There were significant differences between primary and secondary schools for 6/10 school environment characteristics investigated (including secondary schools reporting shorter breaks, more lunchtime PA opportunities, and higher number of sports facilities). Cross-sectional analyses showed that boys attending secondary schools with longer breaks spent significantly less time in SED and more time in MVPA during the school day. Longitudinally, an increase in break-time duration between primary and secondary school was associated with smaller reductions in MVPA during the school day. Moreover, participants who moved from a primary school that did not provide opportunities for PA at lunchtime to a secondary school that did provide such opportunities exhibited smaller increases in SED and smaller reductions in MVPA at lunchtime. Conclusions: Schools should consider the potential negative impact of reducing break time duration on students' MVPA and SED during the school day. School-based interventions that combine longer breaks and more PA opportunities during lunchtime may be a fruitful direction for future research. Further research should also explore other factors in the school environment to explain the school-level clustering observed, and study sex differences in the way that the school environment influences activity intensity for adolescent populations. [ABSTRACT FROM AUTHOR]

Background: Young children are thought to be inactive in childcare, but little is known about location-specific activity levels. This observational study sought to describe the in-care and out-of-care activity patterns of preschool-aged children and explore differences in physical activity level by childcare attendance. Methods: Three to four-year-old children were recruited from 30 preschool and nursery 'settings' in Cambridgeshire, UK. Average minutes per hour (min/h) spent sedentary (SED), in light physical activity (LPA) and in moderate-to-vigorous PA (MVPA) were measured by accelerometry for up to 7 days (mean: 6.7 ±1.1). Weekly childcare attendance patterns were reported by parents. The within-child association between childcare attendance and outcomes was assessed using two- and three-level hierarchical regression; sex by care (in/out) interactions were considered. Results: Two hundred and two children (51 % female) had valid activity data for ≥2 days. Children, and particularly boys, were less sedentary and more active when in care compared to at home (SED: Boys: β(SE): -6.4 (0.5) min/h, Girls: -4.8 (0.5); LPA: Boys: 0.6 (0.4), Girls: 1.8 (0.4); MVPA: Boys: 5.7 (0.5); Girls: 3.0 (0.4)). Differences between in-care and at-home activity were largest in the (early) mornings and early evenings for boys; no compensation in at-home activity occurred later in the day. On days when children were in care part-time (1-5 h) or full-time (>5 h), they were significantly less sedentary and more active compared with non-care days. Conclusions: Young children, and particularly boys, accumulate more MVPA in care compared to at home. Future research should identify factors accounting for this difference and consider targeting non-care time in intervention efforts to increase higher-intensity activity and decrease sedentary time in preschoolers. [ABSTRACT FROM AUTHOR]