We examine the relative importance of both objective and perceived environmental features for physical activity in older English adults. Self-reported physical activity levels of 8,281 older adults were used to compute volumes of outdoor recreational and commuting activity. Perceptions of neighborhood environment supportiveness were drawn from a questionnaire survey and a geographical information system was used to derive objective measures. Negative binominal regression models were fitted to examine associations. Perceptions of neighborhood environment were more associated with outdoor recreational activity (over 10% change per standard deviation) than objective measures (5–8% change). Commuting activity was associated with several objective measures (up to 16% change). We identified different environmental determinants of recreational and commuting activity in older adults. Perceptions of environmental supportiveness for recreational activity appear more important than actual neighborhood characteristics. Understanding how older people perceive neighborhoods might be key to encouraging outdoor recreational activity.
Yu-Tzu Wu, Natalia R. Jones, Esther M.F. van Sluijs, Simon J. Griffin, Nicholas J. Wareham and Andrew P. Jones
Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham and Søren Brage
Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation study data are often unavailable. This study examines an alternative method of network harmonization using indirect models. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-based physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct models (linear regression of DLW-based PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10%–63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonized models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-based PAEE were identical after harmonization using continuous linear but not categorical models. Wrist acceleration harmonized to DLW-based PAEE via combined accelerometry and heart rate sensing had the lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: −1.6; 3.4) kJ·day−1·kg−1. Associations between PAEE and BMI were similar for directly and indirectly harmonized values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonization. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.