Rebecca M. Stanley

Physical activity during school recess: a systematic review

CONTEXT Interest has increased in examining the physical activity levels of young people during school recess. Identifying correlates of their recess physical activity behaviors is timely, and would inform school-based physical activity programming and intervention development. The review examined the correlates of childrens and adolescents physical activity during school recess periods. EVIDENCE ACQUISITION A systematic search of six electronic databases, reference lists, and personal archives identified 53 studies (47 focused on children) published between January 1990 and April 2011 that met the inclusion criteria. Data were analyzed in 2011. Correlates were categorized using the social-ecological framework. EVIDENCE SYNTHESIS Forty-four variables were identified across the four levels of the social-ecological framework, although few correlates were studied repeatedly at each level. Positive associations were found of overall facility provision, unfixed equipment, and perceived encouragement with recess physical activity. Results revealed that boys were more active than girls. CONCLUSIONS Providing access to school facilities, providing unfixed equipment, and identifying ways to promote encouragement for physical activity have the potential to inform strategies to increase physical activity levels during recess periods.

Correlates of children's time-specific physical activity: A review of the literature

Assessment of correlates of physical activity occurring at different times of the day, locations and contexts, is imperative to understanding children’s physical activity behaviour. The purpose of this review was to identify the correlates of children’s physical activity (aged 8–14 years) occurring during the school break time and after-school periods. A review was conducted of the peer-reviewed literature, published between 1990 and January 2011. A total of 22 studies (12 school break time studies, 10 after-school studies) were included in the review. Across the 22 studies, 17 studies were cross-sectional and five studies were interventions. In the school break time studies, 39 potential correlates were identified, of which gender and age were consistently associated with school break time physical activity in two or more studies, and family affluence, access to a gym, access to four or more physical activity programs and the condition of a playing field were all associated with school break time physical activity in only one study. Access to loose and fixed equipment, playground markings, size of and access to play space and the length of school break time were all positively associated with changes in school break time physical activity in intervention studies. Thirty-six potential correlates of after-school physical activity were identified. Gender (with boys more active), younger age, lower body mass index (for females), lower TV viewing/playing video games, and greater access to facilities were associated with higher levels of after-school physical activity in two or more studies. Parent supervision was negatively associated with females’ after-school physical activity in one study. This review has revealed a relatively small number of studies investigating the school break time and after-school periods in the specified age range and only a few correlates have demonstrated a consistent association with physical activity. This highlights the infancy of this area and a need for further investigation into time-specific physical activity behaviour so that interventions designed for these specific periods can target the important correlates.

Children's, physical activity assessed with wrist- and hip-worn accelerometers

BACKGROUND Recently, triaxial raw acceleration accelerometers have become available from GENEActiv and ActiGraph; both are designed for wrist and hip wear. It is important to determine whether the output from these monitors is comparable with the wealth of data already collected from the hip-worn, epoch-based, uniaxial ActiGraph. PURPOSE This study aimed to assess the concurrent validity of measures of total activity and time spent at different activity intensities from the GENEActiv relative to the ActiGraph GT3X+. METHODS Fifty-eight children age 10-12 yr wore two accelerometers at the hip (ActiGraph GT3X+ and GENEActiv) and one at the wrist (GENEActiv) for 7 d. Wear time was matched for all monitors before analysis. RESULTS Mean daily accelerometer output, time spent sedentary, and time in moderate-to-vigorous physical activity (MVPA) from the hip- or wrist-worn GENEActiv were strongly correlated with the corresponding output from the hip-worn ActiGraph (r > 0.83, P < 0.001). However, less time was estimated to be sedentary and more time was estimated to be MVPA using the hip- or wrist-worn GENEActiv (Phillips cut points) than that when using the Evenson vertical axis cut points with the hip-worn ActiGraph. Output from the vertical axis ActiGraph cut points could be predicted with 95% limits of agreement, equating to 23%-28% and 33%-35% of the mean value, by the hip- and wrist-worn GENEActiv, respectively. CONCLUSIONS The assessment of childrens activity level, time spent sedentary, and time in MVPA estimated from the hip- or wrist-worn GENEActiv seems to be comparable with that of the uniaxial ActiGraph. On the basis of the strong linear correlations, ActiGraph output can be predicted from the hip- or wrist-worn GENEActiv for comparative purposes at the group level. However, because of relatively wide limits of agreement, individual-level comparisons are not recommended.

The type and prevalence of activities performed by Australian children during the lunchtime and after school periods

OBJECTIVES The aim of this study was to identify the most prevalent reported activities performed by Australian children during the lunchtime and after school periods; and estimate the mean duration of a typical bout of the most prevalent activities performed during the lunchtime and after school periods. DESIGN This study was a secondary data analysis of the 2007 Australian National Childrens Nutrition and Physical Activity Survey. METHOD Use of time data were collected from Australian children aged 10.0-13.9 years (n=794) using the Multimedia Activity Recall for Children and Adults (MARCA). The most prevalent self-reported activities for the lunchtime and after school period on school days were determined by mean duration across the sample. The estimated energy cost for each of the activities was reported based on the Compendium of Energy Expenditures for Youth. RESULTS A list of the 20 most prevalent lunchtime activities and 30 most prevalent after school activities is presented. Of the most prevalent lunchtime activities, 35% were classified as sedentary and 65% as moderate to vigorous physical activities. During the after school period, 57% of the most prevalent activities were classified as sedentary and only 43% as moderate to vigorous physical activities. CONCLUSIONS These data may assist in the development or refinement of activity checklists with greater content validity, which may be used in combination with objective measures to provide important contextual information about the types of activities being performed and inform the development of appropriately targeted interventions.

Identifying correlates and determinants of physical activity in youth:How can we advance the field?

This commentary provides a critical discussion of current research investigating the correlates and determinants of physical activity in young people, with specific focus on conceptual, theoretical and methodological issues. We draw on current child and adolescent literature and our own collective expertise to illustrate our discussion. We conclude with recommendations that will strengthen future research and help to advance the field.

Comparability of Measured Acceleration from Accelerometry-Based Activity Monitors.

BACKGROUND Accelerometers that provide triaxial measured acceleration data are now available. However, equivalence of output between brands cannot be assumed and testing is necessary to determine whether features of the acceleration signal are interchangeable. PURPOSE This study aimed to establish the equivalence of output between two brands of monitor in a laboratory and in a free-living environment. METHODS For part 1, 38 adults performed nine laboratory-based activities while wearing an ActiGraph GT3X+ and GENEActiv (Gravity Estimator of Normal Everyday Activity) at the hip. For part 2, 58 children age 10-12 yr wore a GT3X+ and GENEActiv at the hip for 7 d in a free-living setting. RESULTS For part 1, the magnitude of time domain features from the GENEActiv was greater than that from the GT3X+. However, frequency domain features compared well, with perfect agreement of the dominant frequency for 97%-100% of participants for most activities. For part 2, mean daily acceleration measured by the two brands was correlated (r = 0.93, P < 0.001, respectively) but the magnitude was approximately 15% lower for the GT3X+ than that for the GENEActiv at the hip. CONCLUSIONS Frequency domain-based classification algorithms should be transferable between monitors, and it should be possible to apply time domain-based classification algorithms developed for one device to the other by applying an affine conversion on the measured acceleration values. The strong relation between accelerations measured by the two brands suggests that habitual activity level and activity patterns assessed by the GENE and GT3X+ may compare well if analyzed appropriately.

Increasing specificity of correlate research: exploring correlates of children's lunchtime and after-school physical activity

Background The lunchtime and after-school contexts are critical windows in a school day for children to be physically active. While numerous studies have investigated correlates of children’s habitual physical activity, few have explored correlates of physical activity occurring at lunchtime and after-school from a social-ecological perspective. Exploring correlates that influence physical activity occurring in specific contexts can potentially improve the prediction and understanding of physical activity. Using a context-specific approach, this study investigated correlates of children’s lunchtime and after-school physical activity. Methods Cross-sectional data were collected from 423 South Australian children aged 10.0–13.9 years (200 boys; 223 girls) attending 10 different schools. Lunchtime and after-school physical activity was assessed using accelerometers. Correlates were assessed using purposely developed context-specific questionnaires. Correlated Component Regression analysis was conducted to derive correlates of context-specific physical activity and determine the variance explained by prediction equations. Results The model of boys’ lunchtime physical activity contained 6 correlates and explained 25% of the variance. For girls, the model explained 17% variance from 9 correlates. Enjoyment of walking during lunchtime was the strongest correlate for both boys and girls. Boys’ and girls’ after-school physical activity models explained 20% variance from 14 correlates and 7% variance from the single item correlate, “I do an organised sport or activity after-school because it gets you fit”, respectively. Conclusions Increasing specificity of correlate research has enabled the identification of unique features of, and a more in-depth interpretation of, lunchtime and after-school physical activity behaviour and is a potential strategy for advancing the physical activity correlate research field. The findings of this study could be used to inform and tailor gender-specific public health messages and interventions for promoting lunchtime and after-school physical activity in children.

Increasing physical activity among young children from disadvantaged communities: study protocol of a group randomised controlled effectiveness trial.

BackgroundParticipation in regular physical activity (PA) during the early years helps children achieve healthy body weight and can substantially improve motor development, bone health, psychosocial health and cognitive development. Despite common assumptions that young children are naturally active, evidence shows that they are insufficiently active for health and developmental benefits. Exploring strategies to increase physical activity in young children is a public health and research priority.MethodsJump Start is a multi-component, multi-setting PA and gross motor skill intervention for young children aged 3–5 years in disadvantaged areas of New South Wales, Australia. The intervention will be evaluated using a two-arm, parallel group, randomised cluster trial. The Jump Start protocol was based on Social Cognitive Theory and includes five components: a structured gross motor skill lesson (Jump In); unstructured outdoor PA and gross motor skill time (Jump Out); energy breaks (Jump Up); activities connecting movement to learning experiences (Jump Through); and a home-based family component to promote PA and gross motor skill (Jump Home). Early childhood education and care centres will be demographically matched and randomised to Jump Start (intervention) or usual practice (comparison) group. The intervention group receive Jump Start professional development, program resources, monthly newsletters and ongoing intervention support. Outcomes include change in total PA (accelerometers) within centre hours, gross motor skill development (Test of Gross Motor Development-2), weight status (body mass index), bone strength (Sunlight MiniOmni Ultrasound Bone Sonometer), self-regulation (Heads-Toes-Knees-Shoulders, executive function tasks, and proxy-report Temperament and Approaches to learning scales), and educator and parent self-efficacy. Extensive quantitative and qualitative process evaluation and a cost-effectiveness evaluation will be conducted.DiscussionThe Jump Start intervention is a unique program to address low levels of PA and gross motor skill proficiency, and support healthy lifestyle behaviours among young children in disadvantaged communities. If shown to be efficacious, the Jump Start approach can be expected to have implications for early childhood education and care policies and practices, and ultimately a positive effect on the health and development across the life course.Trial registrationAustralian and New Zealand Clinical Trials Registry No: ACTRN12614000597695, first received: June 5, 2014.

Steps toward improving diet and exercise for cancer survivors (STRIDE): a quasi-randomised controlled trial protocol.

BackgroundCancer survivorship rates have increased in developed countries largely due to population ageing and improvements in cancer care. Survivorship is a neglected phase of cancer treatment and is often associated with adverse physical and psychological effects. There is a need for broadly accessible, non-pharmacological measures that may prolong disease-free survival, reduce or alleviate co-morbidities and enhance quality of life. The aim of the Steps TowaRd Improving Diet and Exercise (STRIDE) study is to evaluate the effectiveness of an online-delivered physical activity intervention for increasing walking in cancer survivors living in metropolitan and rural areas of South Australia.Methods/DesignThis is a quasi-randomised controlled trial. The intervention period is 12-weeks with 3-month follow-up. The trial will be conducted at a university setting and community health services in South Australia. Participants will be insufficiently active and aged 18 years or older. Participants will be randomly assigned to either the intervention or control group. All participants will receive a pedometer but only the intervention group will have access to the STRIDE website where they will report steps, affect and ratings of perceived exertion (RPE) during exercise daily. Researchers will use these variables to individualise weekly step goals to increase walking.The primary outcome measure is steps per day. The secondary outcomes are a) health measures (anthropometric and physiological), b) dietary habits (consumption of core foods and non-core foods) and c) quality of life (QOL) including physical, psychological and social wellbeing. Measures will be collected at baseline, post-intervention and 3-month follow-up.DiscussionThis protocol describes the implementation of a trial using an online resource to assist cancer survivors to become more physically active. It is an innovative tool that uses ratings of perceived exertion and daily affect to create individualised step goals for cancer survivors. The research findings may be of relevance to public health policy makers as an efficacious and inexpensive online-delivered intervention can have widespread application and may improve physical and psychological outcomes among this vulnerable population. Findings may indicate directions for the implementation of future physical activity interventions with this population.Trial registrationAustralian New Zealand Clinical Trials Registry: ACTRN12613000473763.

Accelerometer wear-site detection: When one site does not suit all, all of the time

OBJECTIVES Choice of accelerometer wear-site may facilitate greater compliance in research studies. We aimed to test whether a simple method could automatically discriminate whether an accelerometer was worn on the hip or wrist from free-living data. DESIGN Cross-sectional. METHODS Twenty-two 10-12y old children wore a GENEActiv at the wrist and at the hip for 7-days. The angle between the forearm and the total acceleration vector for the wrist-worn monitor and between the pelvis and the total acceleration vector for the hip-worn monitor (i.e. the angle between the Y-axis component of the acceleration and the total acceleration vector) was calculated for each 5s epoch. The standard deviation of this angle (SDangle) was calculated over time for the wrist-worn and hip-worn monitor for windows of varying lengths. We hypothesised that the wrist angle would be more variable than the hip angle. RESULTS Wear site could be discriminated based on SDangle; the shorter the time window the lower the optimal threshold and Area under the Receiver-Operating-Characteristic curve (AUROC) for discrimination of wear-site (AUROC=0.833 (1min) - 0.952 (12h)). Classification accuracy was good for windows of 8min (sensitivity=90%, specificity=87%, AUROC=0.92) and plateaued for windows of ≥60min (sensitivity and specificity >90%, AUROC=0.95-0.96). CONCLUSIONS We have presented a robust, computationally simple method that detects whether an accelerometer is being worn on the hip or wrist from 8 to 60min of data. This facilitates the use of wear-site specific algorithms to analyse accelerometer data.