Background: Accelerometers measure complex movements of children’s free play moderate-vigorous physical activity (MVPA), including step and non-step movements. Current accelerometer technology has introduced algorithms to measure steps, along with counts. Precise interpretation of accelerometer-based cadence (steps/min) cut-points is necessary for accurately measuring and tracking children’s MVPA. The purpose of this study was to assess the relationships and agreement between accelerometer-based cut-points (cadence and counts/min) to estimate children’s MVPA compared to measured values. Methods: Forty children (8–12 years; 25 boys) played 6–10 games while wearing a portable metabolic analyzer and GT3X+ to measure and estimate MVPA, respectively. Correlation, kappa, sensitivity, and specificity assessed the relationships and agreement between measured and estimated MVPA. Results: Games elicited, on average, 6.3 ± 1.6 METs, 64.5 ± 24.7 steps/min, and 3318 ± 1262 vertical (V) and 5350 ± 1547 vector-magnitude (VM) counts/min. The relationship between measured and estimated MVPA intensity was higher for cadence (r = 0.50) than V and VM counts/min (r = 0.38 for both). Agreement using V and VM counts/min for measuring PA intensity varied by cut-points (range: 6.8% (κ = −0.02) to 97.6% (κ = 0.49)), while agreement was low using cadence cut-points (range: 4.0% (κ = 0.0009) to 11.3% (κ = 0.001)). Conclusion: While measured and estimated values were well correlated, using cadence tended to misclassify children’s free-play MVPA.

Objective: This study assessed home environment (HE) factors associated with diet quality and meeting physical activity (PA) and sedentary behaviour (SB) National Guidelines among a sample of pre-school children.Design: Analysis of cross sectional baseline data of a communitybased study collected 2011-13.Setting: Twenty geographically bounded metropolitan/regional South Australian communities.Participants: Parents (n=1790) of children aged 4-5 years from participating pre-schools in selected communities.Variables measured: Parent-reported child food intake (fruit, vegetables, milk, non-core foods), eating behaviours, home environment factors (parental knowledge/ modelling, family practices, and availability of equipment /electronic media).Analysis: Linear regression model examined child diet quality and home environment. Logistic regression examined associations between HE and meeting PA and SB guidelines.Results: HE was a significant predictor of child dietary score, with a higher (more health promoting) score positively associated with an increased healthy diet score (ß=0.44; p<0.001). Children whose parents had a university degree, knew recommendations and were active were more likely to meet PA guidelines. Children whose parents knew the recommendations, were active and in homes where the TV was not left on frequently/all the time were more likely to meet SB guidelines. Conclusions and Implications:Targeting HE factors through family-based interventions may help improve lifestyle behaviours for pre-schoolers.