School gate to dinner plate : sedentary and physically active behaviours in adolescents after school

Against a backdrop of rising overweight and obesity, there is a need to further our understanding of physical activity and sedentary behaviour patterns in young people to aid the development of behaviour change strategies that may bring about an increase in energy expenditure. Sedentary and active behaviours exhibit temporal patterning, thus it may be beneficial to examine behaviour and associations between behaviours during specific periods of the day This thesis presents five studies that examine sedentary and physically active behaviours in adolescents during the three hours immediately after school. Chapter 2, using data from a large study of adolescents in the UK, describes physical activity and sedentary behaviour patterns between 15.30h - 18.30h, and examines the contribution of this period relative to broader leisure-time behaviour patterns. In chapter 3.1, the' associations between a broad range of sedentary behaviours and objectively assessed physical activity are examined during the after school hours, providing unique insight into the interactions between behaviours at this time. Chapter 3.2 explores the social and environmental context of selected sedentary and active behaviours after school, enabling a more complete understanding of where and with whom young people spend their time during these hours. Chapter 4 is a systematic literature review of interventions to increase physical activity in young people conducted in the hours immediately after school. Together, the four studies described above established a rationale and informed the content of a pilot family-based intervention to reduce screen-time and increase physical activity after school, presented in Chapter 5. Approximately 40% of adolescents' leisure-time physical activity occurs in the three hours immediately after school, suggesting that this is a critical period in which young people obtain a significant proportion of their daily leisure-time activity. However, sedentary behaviours, particularly screen-based media, account for the majority of time-use during these hours, and may displace participation in physical activity. The development of time-targeted intervention strategies, with a focus upon the after school period, holds considerable promise for the promotion of physical activity in young people

Effects of human recombinant growth hormone on exercise capacity, cardiac structure, and cardiac function in patients with adult-onset growth hormone deficiency

Objective Epidemiological studies suggest that adult-onset growth hormone deficiency (AGHD) might increase the risk of death from cardiovascular causes. Methods This was a 6-month double-blind, placebo-controlled, randomised, cross-over trial followed by a 6-month open-label phase. Seventeen patients with AGHD received either recombinant human growth hormone (rGH) (0.4 mg injection daily) or placebo for 12 weeks, underwent washout for 2 weeks, and were then crossed over to the alternative treatment for a further 12 weeks. Cardiac magnetic resonance imaging, echocardiography, and cardiopulmonary exercise testing were performed at baseline, 12 weeks, 26 weeks, and the end of the open phase (12 months). The results were compared with those of 16 age- and sex-matched control subjects. Results At baseline, patients with AGHD had a significantly higher systolic blood pressure, ejection fraction, and left ventricular mass than the control group, even when corrected for body surface area. Treatment with rGH normalised the insulin-like growth factor 1 concentration without an effect on exercise capacity, cardiac structure, or cardiac function. Conclusion Administration of rGH therapy for 6 to 9 months failed to normalise the functional and structural cardiac differences observed in patients with AGHD when compared with a control group

Family circumstance, sedentary behaviour and physical activity in adolescents living in England: Project STIL

<p>Abstract</p> <p>Background</p> <p>Identification of non-modifiable correlates of physical activity and sedentary behaviour in youth contributes to the development of effective targeted intervention strategies. The purpose of this research was to examine the relationships between family circumstances (e.g. socio-economic status, single vs. dual parent household, presence/absence of siblings) and leisure-time physical activity and sedentary behaviours in adolescents.</p> <p>Methods</p> <p>A total of 1171 adolescents (40% male; mean age 14.8 years) completed ecological momentary assessment diaries every 15 minutes for 3 weekdays outside of school hours and 1 weekend day. Analysed behaviours were sports/exercise, active travel, TV viewing, computer use, sedentary socialising (hanging-out, using the telephone, sitting and talking) and total sedentary behaviour. Linear regression was employed to estimate levels of association between individual family circumstance variables and each behaviour.</p> <p>Results</p> <p>Compared to girls from higher socioeconomic status (SES) groups, girls from low SES groups reported higher weekend TV viewing and higher weekday total sedentary behaviour. For boys, single parent status was associated with greater total sedentary behaviour compared to those from dual parent households. Boys and girls from low socio-economic neighbourhoods reported lower participation in sports/exercise compared to those living in higher socio-economic neighbourhoods.</p> <p>Conclusion</p> <p>Associations were not consistent across behaviours or between genders. Overall, findings indicate that boys from single parent households and girls from low socio-economic families may be at increased risk of high sedentary behaviour. Those living in low socioeconomic neighbourhoods may be at increased risk of reduced participation in sports and exercise.</p

Bedroom media, sedentary time and screen-time in children:A longitudinal analysis

Background: Having electronic media in the bedroom is cross-sectionally associated with greater screen-time in children, but few longitudinal studies exist. The aim of this study was to describe longitudinal patterns of ownership and examine cross-sectional and longitudinal associations of bedroom media with children's sedentary behaviour. Methods: Data are from the Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people (SPEEDY) study, collected at 3 time-points: baseline (2007, T0; age 10.3 ± 0.3 years), 1-year (T1y) and 4-year (T4y) follow-up. For each assessment, 1512 (44.9% male), 715 (41.0% male), and 319 (48.3% male) participants provided valid accelerometer data. Outcome variables were accelerometer-assessed sedentary time and self-reported screen-time. The presence of a television or computer in the bedroom was self-reported by participants and a combined bedroom media score calculated as the sum of such items. Cross-sectional and longitudinal associations between bedroom media and each outcome were examined using multi-level linear regression. Results: Bedroom TV ownership fell from 70.9% at T0 to 42.5% at T4y. Having a TV in the bedroom (beta; 95% CI*100, T0: -1.17; -1.88, -0.46. T1y: -1.68; -2.67, -0.70) and combined bedroom media (T0: -0.76; -1.26, -0.27. T1y: -0.79; -1.51, -0.07) were negatively associated with objectively measured weekly sedentary time at T0 and T1y. Having a computer in the bedroom (beta; 95% CI, T0: 0.15; 0.02, 0.29. T4y: 0.35; 0.10, 0.60) and combined bedroom media (T0: 0.09: 0.01, 0.18. T4y: 0.20; 0.05, 0.34) were positively associated with screen-time at T0 and T4y. Relative to participants without a computer throughout the study, children that had a computer in their bedroom at T0 but not at T4y (beta; 95% CI for change in screen-time: -8.02; -12.75, -3.29) reported smaller increases in screen-time. Conclusions: The bedroom media environment changes with age and exhibits a complex relationship with children's sedentary behaviour. Modifying children's bedroom media environment may impact upon screen-time but appears unlikely to influence overall sedentary time

Clustering and correlates of multiple health behaviours in 9-10 year old children.

BACKGROUND: Sleep, physical activity, screen time and dietary behaviours influence health during childhood, but few studies have looked at all of these behaviours simultaneously and previous research has relied predominantly on self- or proxy-reports of physical activity and food frequency questionnaires for the assessment of diet. PURPOSE: To assess the prevalence and clustering of health behaviours and examine the socio-demographic characteristics of children that fail to meet multiple health behaviour guidelines. METHODS: Data are from the Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people (SPEEDY) study. Participants (n = 1472, 42.9% male) were dichotomized based on whether or not they met public health guidelines for accelerometer-assessed physical activity, diet-diary assessed fruit/vegetable intake and fat/non-milk extrinsic sugar (NMES) intake, and self-reported screen time and sleep duration. Behavioural clustering was assessed using an observed over expected ratio (O/E). Socio-demographic characteristics of participants that failed to meet multiple health behaviour guidelines were examined using ordinal logistic regression. Data were analysed in 2013. RESULTS: 83.3% of children failed to meet guidelines for two or more health behaviours. The O/E ratio for two behavioural combinations significantly exceeded 1, both of which featured high screen time, insufficient fruit/vegetable consumption and excessive fat/NMES intake. Children who were older (Proportional odds ratio (95% confidence interval): 1.69 (1.21,2.37)) and those that attended a school with a physical activity or diet-related policy (1.28 (1.01,1.62)) were more likely to have a poor health behaviour profile. Girls (0.80 (0.64,0.99)), participants with siblings (0.76 (0.61,0.94)) and those with more highly educated parents (0.73 (0.56,0.94)) were less likely to have a poor health behaviour profile. CONCLUSIONS: A substantial proportion of children failed to meet guidelines for multiple health behaviours and there was evidence of clustering of screen viewing and unhealthy dietary behaviours. Sub-groups at greatest risk may be targeted for intervention.This is the final published version, which can also be found online at: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0099498#ac

Seasonal Variation in Children's Physical Activity and Sedentary Time.

PURPOSE: Understanding seasonal variation in physical activity is important for informing public health surveillance and intervention design. The aim of the current study was to describe seasonal variation in children's objectively measured physical activity and sedentary time. METHODS: Data are from the UK Millennium Cohort Study. Participants were invited to wear an accelerometer for 7 d on five occasions between November 2008 and January 2010. Outcome variables were sedentary time (2241 counts per minute, min·d(-1)). The season was characterized using a categorical variable (spring, summer, autumn, or winter) and a continuous function of day of the year. Cross-classified linear regression models were used to estimate the association of each of these constructs with the outcome variables. Modification of the seasonal variation by sex, weight status, urban/rural location, parental income, and day of the week (weekday/weekend) was examined using interaction terms in regression models. RESULTS: At least one wave of valid accelerometer data was obtained from 704 participants (47% male; baseline age, 7.6 (0.3) yr). MVPA was lower in autumn and winter relative to spring, with the magnitude of this difference varying by weekday/weekend, sex, weight status, urban/rural location, and family income (P for interaction <0.05 in all cases). Total sedentary time was greater in autumn and winter compared with spring; the seasonal effect was stronger during the weekend than during the weekday (P for interaction <0.01). CONCLUSIONS: Lower levels of MVPA and elevated sedentary time support the implementation of intervention programs during autumn and winter. Evidence of greater seasonal variation in weekend behavior and among certain sociodemographic subgroups highlights targets for tailored intervention programs.The co-operation of the participating families is gratefully acknowledged. The fourth sweep of the Millennium Cohort Study was funded by grants to Professor Health Joshi, former director of the study, from the Economic and Social Research Council and a consortium of government funders. The current director is Professor Lucinda Platt. The authors acknowledge: the Centre for Longitudinal Studies, Institute of Education for the use of these data; the UK Data Service for making them available; the MRC Centre of Epidemiology for Child Health (Grant reference G0400546), Institute of Child Health, University College London for creating the accelerometer data resource which was funded by the Wellcome Trust (grant reference 084686/Z/08/A). The institutions and funders acknowledged bear no responsibility for the analysis or interpretation of these data. The work of Andrew J Atkin, Flo Harrison, and Esther M F van Sluijs was supported, wholly or in part, by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence (RES-590-28-0002). Funding from the British Heart Foundation, Department of Health, Economic and Social Research Council, Medical Research Council, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The work of Soren Brage, Stephen Sharp and Esther MF van Sluijs was supported by the Medical Research Council (MC_UU_12015/7, MC_UU_12015/3, MC_UU_12015/1).This is the final version of the article. It first appeared from Wolters Kluwer via http://dx.doi.org/10.1249/MSS.000000000000078

Patterns of adolescent physical activity and dietary behaviours

<p>Abstract</p> <p>Background</p> <p>The potential synergistic effects of multiple dietary and physical activity behaviours on the risk of chronic conditions and health outcomes is a key issue for public health. This study examined the prevalence and clustering patterns of multiple health behaviours among a sample of adolescents in the UK.</p> <p>Methods</p> <p>Cross-sectional survey of 176 adolescents aged 12–16 years (49% boys). Adolescents wore accelerometers for seven days and completed a questionnaire assessing fruit, vegetable, and breakfast consumption. The prevalence of adolescents meeting the physical activity (≥ 60 minutes moderate-to-vigorous physical activity/day), fruit and vegetable (≥ 5 portions of FV per day) and breakfast recommendations (eating breakfast on ≥ 5 days per week), and clustering patterns of these health behaviours are described.</p> <p>Results</p> <p>Boys were more active than girls (p < 0.001) and younger adolescents were more active than older adolescents (p < 0.01). Boys ate breakfast on more days per week than girls (p < 0.01) and older adolescents ate more fruit and vegetables than younger adolescents (p < 0.01). Almost 54% of adolescents had multiple risk behaviours and only 6% achieved all three of the recommendations. Girls had significantly more risk factors than boys (p < 0.01). For adolescents with two risk behaviours, the most prevalent cluster was formed by not meeting the physical activity and fruit and vegetable recommendations.</p> <p>Conclusion</p> <p>Many adolescents fail to meet multiple diet and physical activity recommendations, highlighting that physical activity and dietary behaviours do not occur in isolation. Future research should investigate how best to achieve multiple health behaviour change in adolescent boys and girls.</p

Cost-effectiveness of physical activity interventions in adolescents: model development and illustration using two exemplar interventions.

OBJECTIVE: To develop a model to assess the long-term costs and health outcomes of physical activity interventions targeting adolescents. DESIGN: A Markov cohort simulation model was constructed with the intention of being capable of estimating long-term costs and health impacts of changes in activity levels during adolescence. The model parameters were informed by published literature and the analysis took a National Health Service perspective over a lifetime horizon. Univariate and probabilistic sensitivity analyses were undertaken. SETTING: School and community. PARTICIPANTS: A hypothetical cohort of adolescents aged 16 years at baseline. INTERVENTIONS: Two exemplar school-based: a comparatively simple, after-school intervention and a more complex multicomponent intervention compared with usual care. PRIMARY AND SECONDARY OUTCOME MEASURES: Incremental cost-effectiveness ratio as measured by cost per quality-adjusted life year gained. RESULTS: The model gave plausible estimates of the long-term effect of changes in physical activity. The use of two exemplar interventions suggests that the model could potentially be used to evaluate a number of different physical activity interventions in adolescents. The key model driver was the degree to which intervention effects were maintained over time. CONCLUSIONS: The model developed here has the potential to assess long-term value for money of physical activity interventions in adolescents. The two applications of the model indicate that complex interventions may not necessarily be the ones considered the most cost-effective when longer-term costs and consequences are taken into account.This report is an independent research commissioned and funded by the Department of Health Policy Research Programme (opportunities within the school environment to shift the distribution of activity intensity in adolescents, PR-R5-0213-25001). The views expressed in this publication are those of the author(s) and not necessarily those of the Department of Health. This work was also supported by the Medical Research Council (unit programme number: MC_UU_12015/7). The work was undertaken under the auspices of the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence which is funded by the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, and the Wellcome Trust (MR/K023187/1)

Cross-sectional and longitudinal associations between active commuting and patterns of movement behaviour during discretionary time: A compositional data analysis.

BACKGROUND: Active living approaches seek to promote physical activity and reduce sedentary time across different domains, including through active travel. However, there is little information on how movement behaviours in different domains relate to each other. We used compositional data analysis to explore associations between active commuting and patterns of movement behaviour during discretionary time. METHODS AND FINDINGS: We analysed cross-sectional and longitudinal data from the UK Biobank study. At baseline (2006-2010) and follow up (2009-2013) participants reported their mode of travel to work, dichotomised as active (walking, cycling or public transport) or inactive (car). Participants also reported activities performed during discretionary time, categorised as (i) screen time; (ii) walking for pleasure; and (iii) sport and do-it-yourself (DIY) activities, summed to produce a total. We applied compositional data analysis to test for associations between active commuting and the composition and total amount of discretionary time, using linear regression models adjusted for covariates. Adverse events were not investigated in this observational analysis. The survey response rate was 5.5%. In the cross-sectional analysis (n = 182,406; mean age = 52 years; 51% female), active commuters engaged in relatively less screen time than those who used inactive modes (coefficient -0.12, 95% confidence interval [CI] -0.13 to -0.11), equating to approximately 60 minutes less screen time per week. Similarly, in the longitudinal analysis (n = 4,323; mean age = 51 years; 49% female) there were relative reductions in screen time in those who used active modes at both time points compared with those who used inactive modes at both time points (coefficient -0.15, 95% confidence interval [CI] -0.24 to -0.06), equating to a difference between these commute groups of approximately 30 minutes per week at follow up. However, as exposures and outcomes were measured concurrently, reverse causation is possible. CONCLUSIONS: Active commuting was associated with a more favourable pattern of movement behaviour during discretionary time. Active commuters accumulated 30-60 minutes less screen time per week than those using inactive modes. Though modest, this could have a cumulative effect on health over time.LF was funded by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged (087636/Z/08/Z, ES/G007462/1, MR/K023187/1). DO (MC_UU_12015/6) and KW (MC_UU_12015/3) were supported by the Medical Research Council

Lessons from building an automated pre-departure sequencer for airports

Commercial airports are under increasing pressure to comply with the Eurocontrol collaborative decision making (CDM) initiative, to ensure that information is passed between stakeholders, integrate automated decision support or make predictions. These systems can also aid effective operations beyond the airport by communicating scheduling decisions to other relevant parties, such as Eurocontrol, for passing on to downstream airports and enabling overall airspace improvements. One of the major CDM components is aimed at producing the target take-off times and target startup-approval times, i.e. scheduling when the aircraft should push back from the gates and start their engines and when they will take off. For medium-sized airports, a common choice for this is a “pre-departure sequencer” (PDS). In this paper, we describe the design and requirements challenges which arose during our development of a PDS system for medium sized international airports. Firstly, the scheduling problem is highly dynamic and event driven. Secondly, it is important to end-users that the system be predictable and, as far as possible, transparent in its operation, with decisions that can be explained. Thirdly, users can override decisions, and this information has to be taken into account. Finally, it is important that the system is as fair as possible for all users of the airport, and the interpretation of this is considered here. Together, these factors have influenced the design of the PDS system which has been built to work within an existing large system which is being used at many airport