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Background Non-communicable diseases (NCDs) place severe financial strain on global health resources. Diabetes mellitus, the second most prevalent NCD, has been attributed to 8.4% of deaths worldwide for adults aged 20-79 years (International Diabetes Federation, 2013) with physical inactivity attributable to 7% of cases (Lee et al., 2012). The recent surge in commercially available wearable technology has begun to allow individuals to self-monitor their physical activity and sedentary behaviour as well as the physiological response to these behaviours (e.g., health markers such as glucose levels). Equipped with feedback obtained from such wearables, individuals are better able to understand the relationship between the lifestyle behaviours they take (e.g. going for a walk after dinner) and health consequences (e.g. less glucose excursions (area under the curve)). However, in order to achieve true behaviour change, the feedback must be optimised. Innovative communications research suggest that health messages (and in our case feedback) that activates brain regions such as the medial prefrontal cortex (Falk, Berkman, Mann, Harrison & Lieberman, 2010) can predict and are associated with successful behaviour change. Fortunately, functional magnetic resonance imaging (fMRI) can map this neural activity whilst individuals receive various forms of personalised feedback. Such insight into the optimisation of feedback can improve the design and delivery of future behaviour change interventions. Aim Examine neural activity in response to personalised feedback in order to identify health messages most potent for behaviour change. Methods A mixed gender sample of 30 adults (aged 30-65 years) will be recruited through campus advertisements at Loughborough University, UK. Physical activity and sedentary behaviour will be assessed using waist-worn ActiGraph GT3x-BT accelerometer (100Hz) and LUMO posture sensor (30Hz), respectively. Both devices will be removed for sleep and water-based activities and are valid measures of activity (Santos-Lozano et al., 2013) and sedentary behaviour (Rosenberger, Buman, Haskell, McConnell & Carstensen, 2015). Continuous (15 minute epochs) interstitial glucose levels will be measured through a minimally-invasive 5mm flexible fibre (Freestyle Libre) inserted into the posterior brachium. This device will be worn throughout the whole day, including sleep and water-based activities, and has been validated against venous sampling (Bailey, Bode, Christiansen, Klaff & Alva, 2015). All devices will be returned using internal mail or freepost services after 14 days of wear. Personalised feedback messages will be extracted from the devices. Accelerometry will generate feedback in relation to time spent in activity intensities (e.g. moderate-to-vigorous) in the context of current UK physical activity guidelines and frequently used goal setting targets (e.g. 10,000 steps per day). The LUMO will generate feedback on time spent sitting and the number of sit-to-stand transitions in the context of international sedentary behaviour guidelines. The Freestyle Libre will generate feedback in relation to time spent in the normal glucose range (4.0-6.0mmol/L); average glucose concentration; number of hyperglycaemic and hypoglycaemic events; and level of daily glycaemic variation. Participants will then attend a one-off appointment to undergo task-based fMRI. Imaging data of the whole-brain will be acquired via a 32-channel phased-array head coil on a Discovery MR750w 3.0T MR system. The fMRI investigation will monitor brain activation whilst messages are presented to each participant in a counterbalanced blocked design. Exposure to each stimulus (5 seconds) will be compared to the reference stimulus (5 seconds); shown between each block of stimuli. Following the fMRI, participants will provide self-reported effectiveness scores via a four-point Likert scale (1-4; higher the number, higher the perceived level of effectiveness) for each feedback message (Falk, Berkman & Lieberman, 2012) with scores summed to rank each message within and between feedback topics. Results The primary outcome of the study will be the level of neural activity in the medial prefrontal cortex in response to physical activity, sedentary behaviour and glucose feedback messages. Secondary outcomes will be to (i) analyse fMRI scans to identify other areas of the brain that are activated by the stimuli; (ii) compare neural activity between participants grouped by levels of physical activity, sedentary behaviour and glycaemic control; and (iii) evaluate self-reported scales of feedback message effectiveness. Conclusion The study will identify the most neurologically stimulating messaging types for behaviour change relating to physical activity, sedentary behaviour and glucose levels. Findings will inform an intervention which will use the identified personalised feedback platforms to attempt to change activity, sedentary behaviour and/or tighten glycaemic control.

Introduction Chronic Obstructive Pulmonary Disease (COPD) is characterised by persistent airflow limitation. After an acute exacerbation of COPD, pulmonary rehabilitation is offered to patients typically 4 weeks after hospital discharge (McCarthy et al., 2015). As only just over 50% of UK patients accept a referral to pulmonary rehabilitation with only 9% of these completing the program (Harrison et al., 2014) we may need to precondition patients for effective rehabilitation. Targeting sedentary behaviour, ‘any waking behaviour characterised by an energy expenditure ≤1.5 metabolic equivalents while in a sitting or reclining posture’ (Sedentary Behaviour Research Network, 2012), may provide a more palatable conduit for at-home disease management. Wearable technology, such as the LUMO posture sensor, may facilitate successful interventions at home (Chiauzzi, Rodarte, & DasMahapatra, 2015). The LUMO provides validated (Rosenberger, Buman, Haskell, McConnell, & Carstensen, 2015) real-time feedback on sitting, standing and walking via a mobile application; prompting the wearer to stop sitting by gently vibrating on the lower back after a pre-defined period of consecutive sitting (e.g. 30 minutes). These self-monitoring and feedback features are well-supported behaviour change techniques (Michie, Abraham, Whittington, McAteer, & Gupta, 2009; Gardiner, Smith, Lorencatto, Hamer, & Biddle, 2015). Aims COPD-SEAT will assess (i) the feasibility of implementing a home-based sedentary behaviour program using self-monitoring wearable technology; (ii) the acceptability of the intervention among patients and health professionals; and (iii) the short-term effectiveness of the intervention in reducing prolonged periods of sedentary behaviour. Methods Up to 60 COPD patients will be randomly stratified into three groups (1:1:1): Usual Care, Education, and Feedback. Additional to their usual care, the Education and Feedback groups will receive verbal and written information (adapted from Gardiner and colleagues (2014)) during hospital admission which will provide suggestions for reducing sedentary time and breaking up prolonged periods of sitting with standing and/or light activity. The Feedback group will also receive real-time feedback and vibration prompts from the LUMO at home. Physical activity and sedentary behaviour at home will be measured following discharge using waist-worn accelerometry and the LUMO, respectively, for 28 days. Within existing sedentary behaviour interventions, there is a lack of strong evidence of ‘what works’ and ‘why’ (Gardiner, Smith, Lorencatto, Hamer, & Biddle, 2015). A process evaluation will thus be conducted using a combination of ethnographic methods including observations of usual care, interviews and audio recordings of intervention delivery. Results Primary outcomes of the trial will focus on the feasibility of the trial implementation (e.g. uptake, refusal and acceptability); delivery (e.g. patient burden, time commitments and consistency) and completeness (e.g. monitor compliance, missing data and follow-up attendance). For the Feedback group, engagement with the LUMO application will be assessed using Flurry analytics; providing time-stamped user interactions with the LUMO application such as screen-time for feedback panels (e.g. step count or sit-stand transitions). These analytics will supplement the feedback group patient interviews which will examine experiences of the intervention for both the educational and LUMO components (e.g. the vibration prompts and clarity of the visual feedback). Education group patient interviews will explore experiences of the intervention regarding the educational information provided (e.g. barriers to performing the tips to reduce sitting and patient understanding of the information). All interviews will explore patient perceptions on the timing and duration of the intervention in the context of their recovery from an exacerbation of COPD. Secondary outcomes will include changes in sedentary behaviour and physical activity across groups as well as any alterations in physical function (e.g. 20m gait analysis, short physical performance battery and fear of falling), symptoms (e.g. breathlessness, anxiety and depression) and body composition (e.g. body mass index and waist circumference). Conclusion COPD-SEAT will be one of the first trials aimed at reducing sedentary behaviour at home in patients hospitalised for an acute exacerbation of COPD. This trial will provide valuable insight into the feasibility of implementing an at-home technology-based feedback intervention for reducing sedentary behaviour into patients existing care. Findings will inform a future large-scale trial acting as an adjuvant to pulmonary rehabilitation.

BACKGROUND: Birth weight is an early correlate of disease later in life, and animal studies suggest that low birth weight is associated with reduced activity and increased sedentary time. Whether birth weight predicts later sedentary time in humans is uncertain.OBJECTIVES: We examined the relation between birth weight and sedentary time in youth and examined whether this association was mediated by central adiposity.DESIGN: We used pooled cross-sectional data from 8 observational studies conducted between 1997 and 2007 that consisted of 10,793 youth (boys: 47%) aged 6-18 y from the International Children's Accelerometry Database. Birth weight was measured in hospitals or maternally reported, sedentary time was assessed by using accelerometry (RESULTS: The mean (±SD) time spent sedentary was 370 ± 91 min/d. Birth weight was positively associated with sedentary time (B = 4.04, P = 0.006) and waist circumference (B = 1.59, P < 0.001), whereas waist circumference was positively associated with sedentary time (B = 0.82, P < 0.001). Results of the mediation analysis showed a significant indirect effect of birth weight on sedentary time through waist circumference (B: 1.30; 95% bias-corrected CI: 0.94, 1.72), and when waist circumference was controlled for, the effect of birth weight on sedentary time was attenuated by 32% (B = 2.74, P = 0.06).CONCLUSION: The association between birth weight and sedentary time appears partially mediated by central adiposity, suggesting that both birth weight and abdominal adiposity may be correlates of sedentary time in youth.

ICAD studies in alphabetic order: country of origin, design and characteristics of study participants included in the present analyses (DOC 49 kb)

Average percentage time in MVPA across selected countries. CIâ =â confidence interval. The Australian sample pools the two ICAD studies collected in Melbourne; both produced similar findings when analysed separately. (PPT 210 kb)

Avon Longitudinal Study of Parents and Children (ALSPAC). (DOC 27 kb)

Dose–response relationship between weight status, MVPA (left panel) and sedentary time (right panel) by sex. CI = confidence interval, owt = overweight, cpm = counts per minute. (PPT 138 kb)

ObjectivesTo examine the contribution of body size, biological maturation, and nonelite sports participation to longitudinal changes of left ventricular mass (LVM) in healthy boys. MethodsOne hundred and ten boys (11.0-14.5 years at baseline) were assessed biannually for 2 years. Stature, body mass, and four skinfolds were measured. Lean body mass (LBM) was estimated. Biological maturation was assessed as years from age at peak height velocity (APHV). Sports participation was assessed by questionnaire. LVM was obtained from M-mode echocardiograms using two-dimensional images. To account for the repeated measures within individual nature of longitudinal data, multilevel random effects regression analyses were used in the analysis. ResultsLVM increased on average 4218 g from 11 to 15 years (P ConclusionsAmong healthy, male adolescents aged 11-15 years individual differences in growth and biological maturation influence growth of LVM. Subcutaneous adiposity and sports participation were not associated with greater LVM. Am. J. Hum. Biol. 27:530-537, 2015. (c) 2015 Wiley Periodicals, Inc.

Objectives To identify the prevalence of knowledge of the current UK physical activity guidelines which were introduced in 2011 and prior physical activity guidelines (30 min on 5 days each week) within two large samples of UK adult's. To investigate whether knowledge of physical activity guidelines differs according to demographics such as ethnicity, age, education and employment status. Design Descriptive cross-sectional study comparing two distinctive adult samples. Setting National survey and online-administered survey conducted in England. Participants The 2007 Health Survey for England provides data on knowledge of physical activity guidelines from 2860 UK adults (56% women, 89% white, 63% under 45 years old). In 2013, an online survey was disseminated and data were collected from 1797 UK adults on knowledge of the most recent physical activity guidelines. The 2013 sample was 70% women, 92% white and 57% under 45 years old. All adults in both samples were >18 years old and without illnesses/disorders likely to restrict physical activity. Main outcomes Knowledge of physical activity guidelines in 2007 and 2013. Demographic correlates of knowledge of moderate-to-vigorous physical activity guidelines. Results 18% of the 2013 sample accurately recalled the current physical activity guidelines compared with 11% of the 2007 sample who accurately recalled the previous guidelines. The differences in knowledge of physical activity guidelines existed for marital status, gender, age, education and employment status within both 2007 and 2013 samples (p

OBJECTIVES: To date, only a few studies have attempted to study the processes by which community design and the built and social environments affect individual physical activity, especially in children. Qualitative enquiry is useful for exploring perceptions and decision-making, and to understand the processes involved in how people interact with their environments. This study used qualitative methods to gain insight into the pathways linking the neighbourhood environment with children's activity patterns. METHODS: Data were collected in semi-structured interviews with 24 child-parent dyads (children aged 10-14 years). Families lived in neighbourhoods ranging from lowest to highest median income and representing the three main design types found in Saskatoon - urban, semi-suburban and suburban. RESULTS: Parents and children underscored the importance of safe environments for children's physical activity: streets or paths they can cycle on without feeling threatened, parks and green spaces free of criminal activity, and neighbourhoods where people know each other and children have friends to play with. Although grid-pattern urban neighbourhoods with a high density of destinations may in principle promote active transportation, the higher levels of crime and traffic danger that tend to exist in these areas may hinder physical activity in children.CONOCLUSION: Understanding what facilitates activity in children is a complex endeavour. It requires understanding the barriers to physical activity present at the neighbourhood level as well as social and perceptual factors that act in interdependent ways to either promote or hinder children's physical activity. Language: en

Background Understanding of physical activity (PA) tracking during childhood is important to predict PA behaviors and design appropriate interventions. We compared tracking of PA according to PA level and type of day (weekday/weekend) in a pool of five children’s cohort studies. Methods Data from ALSPAC, CLAN, Iowa Bone Development Study, HEAPS, PEACH were extracted from the International Children’s Accelerometry Database (ICAD), resulting in 5,016 participants with age, gender, and accelerometry data at both baseline and follow-up (mean age: 10.3 years at baseline, 12.5 years at follow-up). Daily minutes spent in moderate- and vigorous-intensity PA (MVPA) and vigorous-intensity PA (VPA) was categorized into quintiles. Multinomial logistic regression models were fit to predict follow-up (M)VPA from baseline (M)VPA (reference: 20

Background. The importance of aerobic performance in youth soccer is well established. The aim of the present study was to evaluate the contributions of chronological age (CA), skeletal age (SA), body size, and training to the longitudinal development of aerobic performance in youth male soccer players aged 10 to 18 years. Material and Methods. Players (n=83) were annually followed up during 5 years, resulting in an average of 4.4 observations per player. Decimal CA was calculated, and SA, stature, body weight, and aerobic performance were measured once per year. Fat-free mass (FFM) was estimated from age- and gender-specific anthropometric formulas, and annual volume training was recorded. After testing for multicollinearity, multilevel regression modeling was used to analyze the longitudinal data aligned by CA and SA (Model 1 and 2, respectively) and to develop aerobic performance scores. Results. The following equations provide estimations of the aerobic performance for young soccer players: (y) over cap (Model 1 [deviance from the null model =388.50; P(Model 2 [deviance from the null model=327.98; P Conclusions. The development of aerobic performance in young soccer players was found to be significantly related to CA, biological development, and volume of training.