been subject to criticism: self-reported tools measure perceptions of physicalactivity rather than physicalactivity per se and can therefore overestimate levels and prevalence of physicalactivity (Basterfield et al., 2008); they rely heavily on a respondent’s ability to recall activity which may be age-dependent (Baranowski et al., 1984); and the quality of self-reported data is reliant upon the questions asked being matched to the cognitive capabilities of the respondent (Biddle, Gorely, Pearson, & Bull, 2011). The main group of alternative methods to measure physicalactivity fall under what is considered ‘objective measurement’ i.e. activity monitors, such as accelerometers, which are small, unobtrusive, and robust battery operated devices that can measure and record movement of the body (or limb). Common places for these to be worn are on the wrist or around the waist. Quite simply, these devices measure and record ‘movement’ (more precisely, acceleration) and it is this movement that can be translated into useful information such as frequency, intensity, and duration of physicalactivity. As such, these devices record ‘actual’ activity, and can provide valid and reliable estimates of physicalactivitylevels in a number of different population groups, including children (Trost, 2007). Although viewed as a promising tool for quantifying physicalactivitylevels in children, this type of method is accompanied by a number of challenges: i) these devices are rarely waterproof and so normally require to be removed before water based activities; and ii) the increased energy expenditure associated with stair ascent, cycling, lifting or carrying objects are often underestimated (a single waist mounted device will not measure upper body activity for instance). However, the contribution of these activities to overall physicalactivity is assumed to be relatively small (i.e. hip mounted activity monitors will capture the majority of total body movement) and these devices often produce strong positive correlations with energy expenditure as measured in concurrent validity studies (Freedson, Pober, & Janz, 2005; Trost, 2007).
Overall, 70.0% of our sample achieved ≥ 180 min of TPA, which differs to findings from the UK [7,8], Belgian , Australian , and Canadian  studies which found that 100%, 11.0%, 5.1%, and 83.8% of preschool-aged children achieved recommended guidelines, respectively. Compared to the TPA threshold used in our study ( ≥ 800 cpm) , the two UK studies and Canadian study used thresholds of ≥ 152 cpm [7,41], ≥ 20 cpm , and ≥ 100 cpm [11,45], respectively. These thresholds are lower than the ones used in this study, and therefore, a greater percentage of their participants could have achieved the PA guidelines. Similarly, the Belgian  and Australian  studies used thresholds described by Reilly et al. ( ≥ 1100 cpm)  and Sirard et al. (3-years: ≥ 1208 cpm; 4-years: ≥ 1456 cpm; 5-years: ≥ 1596 cpm)  which are higher than our study and may explain why such a small percentage of their samples achieved daily TPA guidelines compared to our sample. The Canadian study  found that 13.7% of five-year-olds spent ≥ 60 min in MVPA per day, whereas 78.8% of our sample achieved these recommendations. In comparing the different thresholds used in the studies, one might expect the percentage of our participants who achieved the recommended MVPA guidelines to be lower than the Canadian study, as they used a lower MVPA threshold, but this is not the case (78.8% vs. 13.7%). This highlights the difficulties with making comparisons between studies due to study differences in not only the accelerometry thresholds for different intensities but also the exclusion of participants based on insufficient accelerometry wear time [12,13]. As we used a pooled dataset in which data has been processed in the same way across studies , the differences we have found between countries cannot be attributed to differences in data processing. We found that the greatest proportion of children reaching recommended TPA and MVPA guidelines were in the USA, followed by the UK, Switzerland, and Belgium. An exploratory subgroup analysis (data not shown), found the percentage of four-year-olds was highest in Switzerland followed by the UK, Belgium, and the USA, and the ratio of girls to boys was similar across the four countries. Most of the data were collected in autumn for UK, USA, and Switzerland-based children and in spring for Belgium-based children. Minutes of wear time were highest in the USA followed by Switzerland, Belgium, and the UK (see Supplementary Table S1). It is, therefore, unlikely that the between-country differences are a result of age, gender, season or minutes of wear time differences; which had been adjusted for in the regression analyses.
The findings of our study have implications for public health practice. Specifically, our findings suggest that in- terventions should consider active transportation to des- tinations other than school, the focal point of almost all active travel interventions in children , and be cogni- sant of sex disparities and the influence of season on ac- tive transportation. The average of 11 min/day of active transportation in this study represents only 18% of the target needed to meet the public health recommendation of 60 min/day of moderate-to-vigorous physicalactivity . Other data from the studysample that was not presented in this paper indicates that they accumulated an average of 54 min/day of moderate-to-vigorous activity, which is within a few minutes of the moderate- to-vigorous activitylevels observed in a nationally repre- sentative of Canadian children . Collectively, these observations suggest that even modest increases in active transportation would result in a substantial increase in the proportion of children meeting public health recommendations for physicalactivity.
Studies of children (ages 2–18 years) without chronic conditions were included. Cross-sectional, longitu- dinal and intervention studies addressing obesogenic behaviours (screen time, physicalactivity and dietary intake) or weight outcomes (waist circumference, body mass index and anthropometrics) were included in the search. Studies were excluded if they were twin studies, adult sibling (18 years or older) studies, studies with chil- dren who have a sibling with a chronic disease (such as paediatric cancer) or acute conditions (such as urinary tract infections) and studies with a discordant sibling anal- ysis or using the sibling as a matched control. Twin sibling relationships have different characteristics, and twins are more alike genetically than non-twin siblings; these vari- ances may confound any relationship between only-child status and the outcomes of obesogenic behaviours and anthropometry. 34 Studies comparing between siblings
To take these possible confounds into account, multivariate regression analysis was used. This analysis allows the examination of the relationships between an outcome variable and multiple explanatory variables whilst controlling for the inter-relationships between each of the explanatory variables. This means it is possible to identify an independent relationship between any single explanatory variable and the outcome variable; to show, for example, that there is a relationship between maternal age and screen time that does not simply occur because both education and maternal age are related.
In 2000, Sallis et al. concluded that parental influence over child physicalactivity was indeterminate . A more recent review by van der Horst and colleagues  found that several factors were positively associated with child physicalactivity including gender (male), self- efficacy, parental physicalactivity (for boys), and parent support. In a meta-analytic review, Pugliese and Tinsley  also found that a moderate positive relationship exists between parental support and modeling behavior and child physicalactivitylevels. In fact, children had a relative risk of being inactive that was 1.41 times greater if parents did not engage in certain socialization beha- viors (encouragement, instrumental, and modeling beha- viors) than when they did engage in those behaviors. Other parental behaviors may also influence child physi- cal activity. In a study of 800 Latino parents and their children, Arredondo et al.  found that parental rein- forcement and monitoring were both positively asso- ciated with child physicalactivity. However, the authors noted that, in general, these broader aspects of parent- ing behaviors toward child physicalactivity remain understudied, perhaps due to a paucity of measurement tools to assess these constructs .
environmental influences. A second complication relates to a difficulty in establishing the nature and timing of any environmental influences, especially when using data gathered over a restricted time period. It is important to bear in mind that associations found between the child’s environment and obesity do not necessarily show a causal relationship, but might reflect other influences operating at an earlier date. Several studies have pointed to early risk factors for children’s obesity that predate the birth of the child. These include mother’s pre- pregnancy BMI (Hawkins and Law 2006), mother’s smoking during pregnancy (Oken et al. 2008) and intra-uterine effects on appetite, metabolism, and activitylevels (Smith et al. 2007; Oken 2009). These complications mean that while ecological “levels of influence” form a useful conceptual model, they leave many unanswered questions about mechanisms for any risk factors identified.
etc.) and fixed play items (trampoline, swing, etc.) in the home environment, number of days children are in the childcare center, and indoor living area per person (m 2 ) were included. In order to assess parental perceptions of neighborhood safety, parents were asked to indicate how much they agreed or disagreed with a series of 11 state- ments. These statements were related to perceptions about traffic density, road safety, crime, strangers and ac- cess to outdoor play facilities in their local area. The items were adapted from the Neighborhood Environment Walkability Scale  and other previously tested and validated instruments [40, 41]. A sum score with a poten- tial range of zero to 44 was used in analysis. High scores indicate more concerns regarding neighborhood safety. Scale reliability analysis revealed a Cronbach’s α of 0.79 and construct validation by means of principal component analysis revealed a one factor solution indicating that all 11 items were meaningfully affected by one underlying di- mension (neighborhood safety). Furthermore, we assessed whether a dog was kept as a pet. Season was established using the start date of accelerometer recording and cat- egorized according to seasonal weather patterns into summer vs. autumn and spring. Based on the definition of the Organization for Economic Co-operation and Development (OECD) on urban areas , geographic region was dichotomized into rural (<50′000 inhabi- tants) vs. urban (≥50′000 inhabitants) areas.
Participants were sent an accelerometer with a prepaid re- turn envelope, a log diary and questionnaire (see below). Participants were asked to wear the GT3x accelerometer (Actigraph, Pensacola, Florida) over the right hip on an elasticated belt for 7 days, during waking hours, removing it for swimming or bathing. Data were processed using standard methods; raw data collected from movements reg- istering on the vertical axis were integrated into 60 second increments periods (epochs). Non-wear time was identified and excluded using a commonly used and freely available R package “PhysicalActivity” . Periods of continuous zeros lasting more than 90 minutes were assigned as non- wear time; short spells of non-zero counts lasting up to 2 minutes during the 90 minute period were allowed as non-wear time if no activity counts were detected during both the 30 minutes before and after that interval, to reflect the possibility of artefactual monitor movements (e.g. due to accidental movement of the monitor being disturbed while left on a table). This means that any non-zero counts except the allowed short interval of up to 2 minutes are considered as wear time. Valid wear days were defined as ≥600 minutes wear time, and participants with 3 or more valid days were included in analyses, a conventional re- quirement to estimate usual PA level [15,17,21]. High activ- ity levels (>10,000 cpm) or high step counts (>20,000 steps/ day) were verified against men’s daily log diaries. The num- ber of minutes per day spent in PA of different intensity levels was categorised using count-based intensity thresh- old values of counts per minute developed for older adults : <100 cpm for sedentary behavior (<1.5 MET),100- 1040 for light activity (1.5-3 MET) and >1040 for MVPA, (≥3 MET). 1040 cpm is the favoured cut-point to define MVPA in this study as it was calibrated to identify mod- erate intensity activities (≥3 MET) in a sample of older adults , we also investigate the more widely used cut-point of 1952 cpm which was calibrated to identify moderate intensity activities (≥3 MET) in middle-aged adults .
Methods: A longitudinal observational study of PA intensity, type and duration using objective and subjective measurement methods. Fifty five pregnant women with booking body mass index (BMI) ≥ 25 kg/m 2 were recruited from a hospital ultrasound clinic in North East England. 26 (47%) were nulliparous and 22 (40%) were obese (BMI ≥ 30 kg/m 2 ). PA was measured by accelerometry and self report questionnaire at 13 weeks, 26 weeks and/or 36 weeks gestation. Outcome measures were daily duration of light, moderate or vigorous activity assessed by accelerometry; calculated overall PA energy expenditure, (PAEE), and PAEE within four domains of activity based on self report. Results: At median 13 weeks gestation, women recorded a median 125 mins/day light activity and 35 mins/day moderate or vigorous activity (MVPA). 65% achieved the minimum recommended 30 mins/day MVPA. This proportion was maintained at 26 weeks (62%) and 36 weeks (71%). Women achieving more than 30 mins/day MVPA in the first trimester showed a significant reduction in duration of MVPA by the third trimester (11 mins/day, p = 0.003). Walking, swimming and floor exercises were the most commonly reported recreational activities but their contribution to estimated energy expenditure was small.
actigraphy found substantially less sleep in the sum- mer than during the school year. Sleep duration varies considerably among individuals. The duration is affected by day of the week, season, and having youn- ger siblings. The present study might be missing shifts in wake-sleep times that may be seasonal and the differences in wake and sleep time between differ- ent children. Future studies should consider sleep times where possible, though this is presents practical problems for researchers. For example, Dayyat et al.  reported that the description of a child’s sleep by the parent does not result in a correct estimate of sleep onset or duration. Therefore, the present study stipulated a set sleep time for all participants. The vast majority of children in the present study were not obese. As a consequence, it may not be appropri- ate to extend our results to more obese populations. The potential moderators of seasonal changes were limited to those readily available, and other potential moderators (e.g. socio-economic status) might have been important but were not measured and not read- ily available. Finally, while the present study identified seasonal changes, the precise reasons why these changes occurred (weather, differences in behavior be- tween school days and school holidays) could not be confirmed. Nonetheless, the present study had a num- ber of strengths. The longitudinal design and object- ive measures of PA and SB were strengths. The study had a larger sample size than most previous longitu- dinal studies of seasonality in children. Finally, the relatively short period between baseline and follow up measures in the present study would have minimized potential age/maturation-related differences in the be- haviors measured, so that any changes could be inter- preted as seasonal rather than maturational. Future studies should prospectively examine the change in patterns of SB to obtain more evidence on this im- portant issue.
highlights the importance of providing parents with appropriate and integrated support which will, amongst other things, allow them “to develop the skills needed to provide a nurturing and stimulating home environment free from conflict” and “meet a range of needs they [parents] may have” (Scottish Government, 2008a: 11). Supporting parents is not just about providing greater access to the right sort of formal services, informal social support networks also play a significant part in helping parents in their role. The importance of informal support for families with multiple disadvantages, but particularly with low incomes, has already been recognised in previous GUS research (Mabelis and Marryat, 2011; Bradshaw et al, 2009; Bradshaw et al., 2008). Low social support has been associated with poor maternal mental health, a factor linked to poorer child outcomes (Marryat and Martin, 2010). In addition, strong maternal social networks have been shown to protect children living in persistently low incomes from poorer wellbeing (Treanor, 2015).
One of the reasons GUS asks only limited information about income is to allow the interviewer sufﬁcient time to ask mothers about a range of issues regarding their children. We looked at ﬁve indicators of child disadvantage, including being overweight, concerns over language development, and social, emotional and behavioural problems – and explored whether persistently poor children were at greater risk. We also counted how many of these disadvantages children have and focused on children that face multiple problems. Children in persistently poor families were more likely to face disadvantages than children in temporary poor families. For example, children in both cohorts were more likely to have accidents or injuries, and suffer from social emotional and behavioural difﬁculties, the longer they had been poor. However, when controlling for other family and area factors in our statistical models, the direct relationship between the duration of low income and child outcomes disappeared. Furthermore, there was no relationship between any experience of poverty over the period and child outcomes. Instead we saw a range of other factors being associated with child outcomes, including gender, family size and mothers’ ethnicity and health.
protective factors is a related primary objective of GUS, as noted above. We have already detailed how the National Performance Framework, which has underpinned and provided focus to all policy development since 2007, has as one of its national outcomes that “children should have the best start in life and are ready to succeed”. The particular economic benefits of early intervention to Scotland’s public spending have also recently been explored (Finance Committee of the Scottish Parliament, 2011; Burnside, 2010). The preventative spending enquiry led by the Scottish Parliament’s Finance Committee examined how public spending could be focused more on preventing negative outcomes than dealing with them when they occur. In written evidence to the enquiry, the Scottish Government noted that preventative action was “integral to the approach to government in Scotland and delivering the outcomes set out in the National Performance Framework”. The establishment of the Early years Taskforce in November 2011, alongside the Early Years Change Fund and, more recently, the Early years Collaborative, demonstrates the firm commitment fromScottish Government to shifting the balance of public services towards early intervention and prevention. GUS presents an opportunity for consideration of progress towards this goal, an assessment of the change in outcomes such a commitment may generate, and examination of the particular experiences which contributed to any change.
Our findings contrasted with those of Leatherdale and colleagues  who found that students were more likely to be active if they attended a school that used physicalactivity as a reward and had established community partner- ships. Differences in methodology (accelerometry in the current study versus self-reported physicalactivity data in the former) and sample size (18 versus 30 schools, respectively) might play a role. One commonality among these studies was the finding that the majority of schools were in the initiation phase for the overall score for Instruction and Programs (72.2% in the current study compared to 73.3% in the former). That is, a majority of schools in both studies required increasing their capacity to provide opportunities for physicalactivity through trad- itional programming such as physical education, intramural and interschool sport programming. The introduction of written policies may be one way in which schools can begin to address this gap. Developing written policies regarding physicalactivity demonstrates a commitment to encour- aging this health behaviour, while outlining expectations of the roles and responsibilities of staff, students and parents. Such policies might also reflect on strategies that could address consistent disparities in physicalactivity patterns of children, particularly between boys and girls.
The main strengths of the present study were large sample size, socioeconomically representative nature of the sample, objective measurement of PA and longitudinal design. This combination of strengths is almost unique in the ﬁ eld of physicalactivity – aca- demic outcome relationships. 24 However, the present study had a number of limitations. The restricted range of habitual PA within the cohort may limit conclusions about the impact of effects of higher levels of PA, although the low levels of PA observed are typical of adolescents in the western world. 1 While the sample size in the present study is large, it represents less than half of those invited to attend the research clinic at 11 years. Only small differences were found in the characteris- tics of those who attended the clinic compared with those who did not attend though. Further, while the loss of data in the fully adjusted models compared with the unadjusted models could be considered a limitation, it is worth noting that when models were re-analysed including only participants with com- plete confounding information, no substantial differences were detected. In addition, as similar patterns of results were found in associations across three time points and for all subjects, the loss of data should not be considered a major limitation and still well above recommendations for minimum sample required for such analyses. 56
Participants were 113, 10–11year old children recruited from11 primary schools in Bristol, UK. The schools were recruited to approximate the economic diversity of the local area based on the Index of Multiple Deprivation (IMD), a UK government produced area level measure of deprivation that includes assessments of income, employ- ment, health and education . We obtained the IMDs for the postcodes of all local schools and then recruited 4 schools from the lowest third (Low SES schools), 4 from the middle third (Middle SES schools) and three from the highest third (High SES schools). Although this approach only provides information on the school and not where the children live it was intended to provide a reasonable range of participants from different economic neighbor- hoods within the city. Once schools were recruited we held a briefing session for all Year 6 students (10–11 years of age) in the school in which we invited volunteers to participate in a focus group to tell us about physical activ- ity and their friends. One or two focus groups were then held at each school between March and June 2007 with 2– 12 participants per group. The study was approved by the School of Applied Community and Health Studies Ethics committee at the University of Bristol (ref 017/06) and informed parental consent and childhood assent was obtained for all participants .
to focus on more symptomatic patients since it was a cross- sectional study that explored factors that were associated with morning symptoms. One other limitation of this study is that there might be selection bias, since nonparticipants were most likely patients who were not able to come to the study center in the morning. This might have resulted in an underestima- tion of morning symptoms in the overall COPD population. A limitation for the use of a MoveMonitor was the non-water resistance. For some patients, taking a shower is the most intensive physicalactivity of the day, and this has not been measured. This resulted in an underestimation of active time. Furthermore, patients were not blinded for the accelerometer. This could have resulted in increased activity since patients felt they were being watched and would not be categorized as “inactive.” However, patients took a comparable amount or fewer steps than reported in previous studies, 41,42 suggesting
In this study, neighborhood was defined as a polygon-based street-network buffer around each respondent’s home address, which was geocoded at the level of small city block (Banchi or Go). Neighborhood walkability score was calculated by combining three built environment measures; i.e., residential density, street connectivity and land use mix within each neighborhood polygon. These three features of neighborhood are commonly studied for their associations with physical activities [6,10,29]. Given that the relevant size of a neighborhood could vary according to the age group, we considered a street network distance of 500 m as representing the easily accessible space for an ambulatory older adult. A street network distance of 1000 m was also considered because de Sa and Ardern indicated that the association between neighborhood walkability and transport-related physicalactivity differed by 500-m and 1000-m buffer zones . We used ArcGIS 9.3 software with Network Analyst Extension (ESRI Inc. (Redlands, CA, USA)) for all spatial calculations.