Kelly A. Mackintosh

Promoting healthy weight in primary school children through physical activity and nutrition education: a pragmatic evaluation of the CHANGE! randomised intervention study

BackgroundThis pragmatic evaluation investigated the effectiveness of the Children’s Health, Activity and Nutrition: Get Educated! (CHANGE!) Project, a cluster randomised intervention to promote healthy weight using an educational focus on physical activity and healthy eating.MethodsParticipants (n = 318, aged 10–11 years) from 6 Intervention and 6 Comparison schools took part in the 20 weeks intervention between November 2010 and March/April 2011. This consisted of a teacher-led curriculum, learning resources, and homework tasks. Primary outcome measures were waist circumference, body mass index (BMI), and BMI z-scores. Secondary outcomes were objectively-assessed physical activity and sedentary time, and food intake. Outcomes were assessed at baseline, at post-intervention (20 weeks), and at follow-up (30 weeks). Data were analysed using 2-level multi-level modelling (levels: school, student) and adjusted for baseline values of the outcomes and potential confounders. Differences in intervention effect by subgroup (sex, weight status, socio-economic status) were explored using statistical interaction.ResultsSignificant between-group effects were observed for waist circumference at post-intervention (β for intervention effect =−1.63 (95% CI = −2.20, -1.07) cm, p<0.001) and for BMI z-score at follow-up (β=−0.24 (95% CI = −0.48, -0.003), p=0.04). At follow-up there was also a significant intervention effect for light intensity physical activity (β=25.97 (95% CI = 8.04, 43.89) min, p=0.01). Interaction analyses revealed that the intervention was most effective for overweight/obese participants (waist circumference: β=−2.82 (95% CI = −4.06, -1.58) cm, p<0.001), girls (BMI: β=−0.39 (95% CI = −0.81, 0.03) kg/m2, p=0.07), and participants with higher family socioeconomic status (breakfast consumption: β=8.82 (95% CI = 6.47, 11.16), p=0.07).ConclusionsThe CHANGE! intervention positively influenced body size outcomes and light physical activity, and most effectively influenced body size outcomes among overweight and obese children and girls. The findings add support for the effectiveness of combined school-based physical activity and nutrition interventions. Additional work is required to test intervention fidelity and the sustained effectiveness of this intervention in the medium and long term.Trial registrationCurrent Controlled Trials ISRCTN03863885.

Using formative research to develop CHANGE!: a curriculum-based physical activity promoting intervention

BackgroundLow childhood physical activity levels are currently one of the most pressing public health concerns. Numerous school-based physical activity interventions have been conducted with varied success. Identifying effective child-based physical activity interventions are warranted. The purpose of this formative study was to elicit subjective views of children, their parents, and teachers about physical activity to inform the design of the CHANGE! (Childrens Health, Activity, and Nutrition: Get Educated!) intervention programme.MethodsSemi-structured mixed-gender interviews (group and individual) were conducted in 11 primary schools, stratified by socioeconomic status, with 60 children aged 9-10 years (24 boys, 36 girls), 33 parents (4 male, 29 female) and 10 teachers (4 male, 6 female). Questions for interviews were structured around the PRECEDE stage of the PRECEDE-PROCEDE model and addressed knowledge, attitudes and beliefs towards physical activity, as well as views on barriers to participation. All data were transcribed verbatim. Pen profiles were constructed from the transcripts in a deductive manner using the Youth Physical Activity Promotion Model framework. The profiles represented analysis outcomes via a diagram of key emergent themes.ResultsAnalyses revealed an understanding of the relationship between physical activity and health, although some children had limited understanding of what constitutes physical activity. Views elicited by children and parents were generally consistent. Fun, enjoyment and social support were important predictors of physical activity participation, though several barriers such as lack of parental support were identified across all group interviews. The perception of family invested time was positively linked to physical activity engagement.ConclusionsFamilies have a powerful and important role in promoting health-enhancing behaviours. Involvement of parents and the whole family is a strategy that could be significant to increase childrens physical activity levels. Addressing various perceived barriers to such behaviours therefore, remains imperative.Trial RegistrationISRCTN: ISRCTN03863885

A calibration protocol for population-specific accelerometer cut-points in children

Purpose To test a field-based protocol using intermittent activities representative of childrens physical activity behaviours, to generate behaviourally valid, population-specific accelerometer cut-points for sedentary behaviour, moderate, and vigorous physical activity. Methods Twenty-eight children (46% boys) aged 10–11 years wore a hip-mounted uniaxial GT1M ActiGraph and engaged in 6 activities representative of childrens play. A validated direct observation protocol was used as the criterion measure of physical activity. Receiver Operating Characteristics (ROC) curve analyses were conducted with four semi-structured activities to determine the accelerometer cut-points. To examine classification differences, cut-points were cross-validated with free-play and DVD viewing activities. Results Cut-points of ≤372, >2160 and >4806 counts•min−1 representing sedentary, moderate and vigorous intensity thresholds, respectively, provided the optimal balance between the related needs for sensitivity (accurately detecting activity) and specificity (limiting misclassification of the activity). Cross-validation data demonstrated that these values yielded the best overall kappa scores (0.97; 0.71; 0.62), and a high classification agreement (98.6%; 89.0%; 87.2%), respectively. Specificity values of 96–97% showed that the developed cut-points accurately detected physical activity, and sensitivity values (89–99%) indicated that minutes of activity were seldom incorrectly classified as inactivity. Conclusion The development of an inexpensive and replicable field-based protocol to generate behaviourally valid and population-specific accelerometer cut-points may improve the classification of physical activity levels in children, which could enhance subsequent intervention and observational studies.

Using formative research to develop the healthy eating component of the CHANGE! school-based curriculum intervention

BackgroundChildhood obesity is a significant public health concern. Many intervention studies have attempted to combat childhood obesity, often in the absence of formative or preparatory work. This study describes the healthy eating component of the formative phase of the Children’s Health Activity and Nutrition: Get Educated! (CHANGE!) project. The aim of the present study was to gather qualitative focus group and interview data regarding healthy eating particularly in relation to enabling and influencing factors, barriers and knowledge in children and adults (parents and teachers) from schools within the CHANGE! programme to provide population-specific evidence to inform the subsequent intervention design.MethodsSemi-structured focus group interviews were conducted with children, parents and teachers across 11 primary schools in the Wigan borough of North West England. Sixty children (N = 24 boys), 33 parents (N = 4 male) and 10 teachers (N = 4 male) participated in the study. Interview questions were structured around the PRECEDE phases of the PRECEDE-PROCEED model. Interviews were transcribed verbatim and analysed using the pen-profiling technique.ResultsThe pen-profiles revealed that children’s knowledge of healthy eating was generally good, specifically many children were aware that fruit and vegetable consumption was ‘healthy’ (N = 46). Adults’ knowledge was also good, including restricting fatty foods, promoting fruit and vegetable intake, and maintaining a balanced diet. The important role parents play in children’s eating behaviours and food intake was evident. The emerging themes relating to barriers to healthy eating showed that external drivers such as advertising, the preferred sensory experience of “unhealthy” foods, and food being used as a reward may play a role in preventing healthy eating.ConclusionsData suggest that; knowledge related to diet composition was not a barrier per se to healthy eating, and education showing how to translate knowledge into behavior or action is required. The key themes that emerged through the focus groups and pen-profiling data analysis technique will be used to inform and tailor the healthy eating component of the CHANGE! intervention study.Trial registrationCurrent Controlled Trials ISRCTN03863885

A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans

Physical inactivity is one of the most prevalent risk factors for non-communicable diseases in the world. A fundamental barrier to enhancing physical activity levels and decreasing sedentary behavior is limited by our understanding of associated measurement and analytical techniques. The number of analytical techniques for physical activity measurement has grown significantly, and although emerging techniques may advance analyses, little consensus is presently available and further synthesis is therefore required. The objective of this review was to identify the accuracy of emerging analytical techniques used for physical activity measurement in humans. We conducted a search of electronic databases using Web of Science, PubMed, and Google Scholar. This review included studies written in English and published between January 2010 and December 2014 that assessed physical activity using emerging analytical techniques and reported technique accuracy. A total of 2064 papers were initially retrieved from three databases. After duplicates were removed and remaining articles screened, 50 full-text articles were reviewed, resulting in the inclusion of 11 articles that met the eligibility criteria. Despite the diverse nature and the range in accuracy associated with some of the analytic techniques, the rapid development of analytics has demonstrated that more sensitive information about physical activity may be attained. However, further refinement of these techniques is needed.

Parental influences on children's physical self-perceptions, body composition, and physical activity levels

Abstract Background In the UK, 28% of children are overweight or obese, the deleterious effects of which are well documented. Promotion of physical activity is one solution to preventing obesity. Previous studies have identified parental influence as a factor that can shape a childs physical self-perceptions, and act as a stimulus for physical activity. Therefore, we aimed to assess parental influence and physical self-perceptions on childrens physical activity, and to examine whether these factors affect body composition. Methods We recruited a convenience sample of 13 children from a local primary school in Wales. Testing was done at two timepoints with a 1 week interval. At baseline, anthropometric data of the children were collected, and ActiSleep+ accelerometers (ActiGraph, Pensacola, FL, USA) distributed. Participants were directed to go about their normal activities for 7 consecutive days while wearing the monitor. At the second timepoint, parental influence and the childrens physical self-perception were measured with questionnaires based on the Youth Physical Activity Model and the Childrens Physical Self-Perception Profile, respectively. Spearmans correlation coefficient was used to measure associations between variables of parental influence, physical self-perception, and physical activity. Additionally, multiple regressions were used to measure pathway coefficients. Findings Mean age of the children was 10·46 years (SD 0·52), with mean weight 45·18 kg (11·51) and mean height 1·44 m (0·07). Most of the variables were poorly correlated (p>0·05), with certain exceptions. The strongest correlation was between moderate-to-vigorous physical activity (MVPA) levels and physical condition, a subcategory of physical self-perception ( r =0·752, p=0·002). The weakest correlation was between MVPA and parental involvement ( r =0·644, p=0·009). Analysis of correlations between subcategories of parental influence and childs physical self-perception showed that physical condition was strongly correlated with parental involvement ( r =0·729, p=0·002). Physical condition was also indirectly associated with physical activity levels (path coefficient association with parental involvement r =0·213, p=0·05). Interpretation The present study supports the notion that parental influence, in the form of parental involvement, has a direct, statistically significant, and positive effect on a childs levels of physical activity; it also has an indirect positive effect through a childs perception of their own physical condition, which can subsequently increase physical activity levels. Practitioners should encourage parents to become more involved in their childs choice of physical activity; and parents could provide positive appraisal of their childs perceived physical condition. However, further research with a larger sample size is needed. Funding Applied Sports Science Technology and Medicine Research, Swansea University.

Energy Cost of Free-Play Activities in 10- to 11-Year-Old Children

OBJECTIVE This study sought to ascertain the energy expenditure (EE) associated with different sedentary and physically active free-play activities in primary school-aged children. METHODS Twenty-eight children (13 boys; 11.4 ± 0.3 years; 1.45 ± 0.09 m; 20.0 ± 4.7 kg∙m-2) from 1 primary school in Northwest England engaged in 6 activities representative of childrens play for 10 minutes (drawing, watching a DVD, playground games and free-choice) and 5 minutes (self-paced walking and jogging), with 5 minutes rest between each activity. Gas exchange variables were measured throughout. Resting energy expenditure was measured during 15 minutes of supine rest. RESULTS Child (Schofield-predicted) MET values for watching a DVD, self-paced jogging and playing reaction ball were significantly higher for girls (P < .05). CONCLUSION Utilizing a field-based protocol to examine childrens free-living behaviors, these data contribute to the scarcity of information concerning childrens EE during play to update the Compendium of Energy Expenditures for Youth.

Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers

To enable inter- and intrastudy comparisons it is important to ascertain comparability among accelerometer models. Purpose The purpose of this study was to compare raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers. Methods Adults (n = 26 (n = 15 women); age, 49.1 ± 20.0 yr) wore GT3X+ and Link accelerometers over the right hip for an 80-min protocol involving 12–21 sedentary, household, and ambulatory/exercise activities lasting 2–15 min each. For each accelerometer, mean and variance of the raw (60 Hz) data for each axis and vector magnitude (VM) were extracted in 30-s epochs. A machine learning model (Montoye 2015) was used to predict energy expenditure in METs from the raw data. Raw data were also processed into activity counts in 30-s epochs for each axis and VM, with Freedson 1998 and 2011 count-based regression models used to predict METs. Time spent in sedentary, light, moderate, and vigorous intensities was derived from predicted METs from each model. Correlations were calculated to compare raw and count data between accelerometers, and percent agreement was used to compare epoch-by-epoch activity intensity. Results For raw data, correlations for mean acceleration were 0.96 ± 0.05, 0.89 ± 0.16, 0.71 ± 0.33, and 0.80 ± 0.28, and those for variance were 0.98 ± 0.02, 0.98 ± 0.03, 0.91 ± 0.06, and 1.00 ± 0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00 ± 0.01, 0.98 ± 0.02, 0.96 ± 0.04, and 1.00 ± 0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher percent agreement for activity intensity (95.1% ± 5.6% and 95.5% ± 4.0%) compared with the Montoye 2015 raw data model (61.5% ± 27.6%; P < 0.001). Conclusions Count data were more highly comparable than raw data between accelerometers. Data filtering and/or more robust raw data models are needed to improve raw data comparability between ActiGraph GT3X+ and Link accelerometers.

Investigating optimal accelerometer placement for energy expenditure prediction in children using a machine learning approach

Accurate measurement of energy expenditure (EE) is imperative for identifying and targeting health-associated implications. Whilst numerous accelerometer-based regression equations to predict EE have been developed, there remains little consensus regarding optimal accelerometer placement. Therefore, the purpose of the present study was to validate and compare artificial neural networks (ANNs) developed from accelerometers worn on various anatomical positions, and combinations thereof, to predict EE. Twenty-seven children (15 boys; 10.8  ±  1.1 years) participated in an incremental treadmill test and 30 min exergaming session wearing a portable gas analyser and nine ActiGraph GT3X+  accelerometers (chest and left and right wrists, hips, knees, and ankles). Age and sex-specific resting EE equations (Schofield) were used to estimate METs from the oxygen uptake measures. Using all the data from both exergames, incremental treadmill test and the transition period in between, ANNs were created and tested separately for each accelerometer and for combinations of two or more using a leave-one-out approach to predict EE compared to measured EE. Six features (mean and variance of the three accelerometer axes) were extracted within each 15 s window as inputs in the ANN. Correlations and root mean square error (RMSE) were calculated to evaluate prediction accuracy of each ANN, and repeated measures ANOVA was used to statistically compare accuracy of the ANNs. All single-accelerometer ANNs and combinations of two-, three-, and four-accelerometers performed equally (r  =  0.77-0.82), demonstrating higher correlations than the 9-accelerometer ANN (r  =  0.69) or the Freedson linear regression equation (r  =  0.75). RMSE did not differ between single-accelerometer ANNs or combinations of two, three, or four accelerometers (1.21-1.31 METs), demonstrating lower RMSEs than the 9-accelerometer ANN (1.46 METs) or Freedson equation (1.74 METs). These findings provide preliminary evidence that ANNs developed from single accelerometers mounted on various anatomical positions demonstrate equivalency in the accuracy to predict EE in a semi-structured setting, supporting the use of ANNs in improving EE prediction accuracy compared with linear regression.

A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle

BackgroundWe are increasingly using recording devices with multiple sensors operating at high frequencies to produce large volumes of data which are problematic to interpret. A particularly challenging example comes from studies on animals and humans where researchers use animal-attached accelerometers on moving subjects to attempt to quantify behaviour, energy expenditure and condition.ResultsThe approach taken effectively concatinated three complex lines of acceleration into one visualization that highlighted patterns that were otherwise not obvious. The summation of data points within sphere facets and presentation into histograms on the sphere surface effectively dealt with data occlusion. Further frequency binning of data within facets and representation of these bins as discs on spines radiating from the sphere allowed patterns in dynamic body accelerations (DBA) associated with different postures to become obvious.MethodWe examine the extent to which novel, gravity-based spherical plots can produce revealing visualizations to incorporate the complexity of such multidimensional acceleration data using a suite of different acceleration-derived metrics with a view to highlighting patterns that are not obvious using current approaches. The basis for the visualisation involved three-dimensional plots of the smoothed acceleration values, which then occupied points on the surface of a sphere. This sphere was divided into facets and point density within each facet expressed as a histogram. Within each facet-dependent histogram, data were also grouped into frequency bins of any desirable parameters, most particularly dynamic body acceleration (DBA), which were then presented as discs on a central spine radiating from the facet. Greater radial distances from the sphere surface indicated greater DBA values while greater disc diameter indicated larger numbers of data points with that particular value.ConclusionsWe indicate how this approach links behaviour and proxies for energetics and can inform our identification and understanding of movement-related processes, highlighting subtle differences in movement and its associated energetics. This approach has ramifications that should expand to areas as disparate as disease identification, lifestyle, sports practice and wild animal ecology.UCT Science Faculty Animal Ethics 2014/V10/PR (valid until 2017).