García‐Aymerich, Judith

Abstract Background Neighborhood social and built environment characteristics may promote physical activity, a modifiable factor for dementia risk, which may in turn promote cognitive and brain health. We explored the associations of the U.S. National Walkability Index ‐ a nationwide geographic data resource that ranks block groups according to their relative walkability – with cognitive function, gray matter (GM) and white matter hyperintensities (WMH), neighborhood disadvantage, sedentary behavior and frequency of physical activity in cognitively unimpaired older adults at increased Alzheimer's disease (AD) risk. Method We included baseline data from 1398 older adults (68.3 ± 5.17 years, 68.3% females, 30.5% APOE4 carriers) from the U.S. POINTER trial. Walkability was classified as: least, below average, above average and most walkable. Neighborhood disadvantage (low, moderate and high) was assessed using Area Deprivation Index (ADI) ( N = 1384). Physical inactivity was assessed using the Sedentary Behavior Questionnaire (SBQ, hours sitting/week, N = 1106), and physical activity was measured with a composite score from the Community Health Activities Model Program for Seniors (CHAMPS) questionnaire. Linear and logistic regressions examined associations between walkability and (1) physical activity and sedentary behavior; and (2) cognition assessed with the Preclinical Alzheimer's Cognitive Composite (PACC‐5), GM volume and WMHs, Additionally, associations between neighborhood disadvantage and physical activity and sedentary behavior were explored. Models were adjusted for age, sex, race, APOE4 and total intracranial volume. Result More deprived areas were generally less walkable (β = ‐0.008, p < 0.001) (Figure 1). Higher walkability (Above average: β = 0.192, p = 0.011 and most walkable: β = 0.183, p = 0.032) was associated with higher PACC‐5 (Figure 2A) scores but not with more preserved GM or lower WMH. Walkability scores were not associated with physical activity and sedentary measurements, while greater neighborhood disadvantage was linked to sedentary behavior (β = 0.126, p < 0.001) (Figure 2B). Conclusion These results suggest that physical inactivity in more deprived areas is associated with neighborhood socioeconomic disadvantages rather than built environment. Additionally, walkability scores were not associated with any measure of physical activity or brain health, suggesting the need to further investigate other health effects of neighborhood disadvantage and walkability.

We described physical activity measures and hourly patterns in patients with chronic obstructive pulmonary disease (COPD) after stratification for generic and COPD-specific characteristics and, based on multiple physical activity measures, we identified clusters of patients. In total, 1001 patients with COPD (65% men; age, 67 years; forced expiratory volume in the first second [FEV 1 ], 49% predicted) were studied cross-sectionally. Demographics, anthropometrics, lung function and clinical data were assessed. Daily physical activity measures and hourly patterns were analysed based on data from a multisensor armband. Principal component analysis (PCA) and cluster analysis were applied to physical activity measures to identify clusters. Age, body mass index (BMI), dyspnoea grade and ADO index (including age, dyspnoea and airflow obstruction) were associated with physical activity measures and hourly patterns. Five clusters were identified based on three PCA components, which accounted for 60% of variance of the data. Importantly, couch potatoes (i.e. the most inactive cluster) were characterised by higher BMI, lower FEV 1 , worse dyspnoea and higher ADO index compared to other clusters ( p < 0.05 for all). Daily physical activity measures and hourly patterns are heterogeneous in COPD. Clusters of patients were identified solely based on physical activity data. These findings may be useful to develop interventions aiming to promote physical activity in COPD.