Physical Activity Change in an RCT: Comparison of Measurement Methods.

Objectives: We aimed to quantify the agreement between self-report, standard cut-point accelerometer, and machine learning accelerometer estimates of physical activity (PA), and exam- ine how agreement changes over time among older adults in an intervention setting. Methods: Data were from a randomized weight loss trial that encouraged increased PA among 333 postmenopausal breast cancer survivors. PA was estimated using accelerometry and a validated questionnaire at baseline and 6-months. Accelerometer data were processed using standard cut-points and a validated machine learning algorithm. Agreement of PA at each time-point and change was assessed using mixed effects regression models and concordance correlation. Results: At baseline, self-report and machine learning provided similar PA estimates (mean dif- ference = 11.5 min/day) unlike self-report and standard cut-points (mean difference = 36.3 min/ day). Cut-point and machine learning methods assessed PA change over time more similarly than other comparisons. Specifically, the mean difference of PA change for the cut-point versus machine learning methods was 5.1 min/day for intervention group and 2.9 in controls, whereas it was ≥ 24.7 min/day for other comparisons. Conclusions: Intervention researchers are facing the issue of self-report measures introducing bias and accelerometer cut-points being insensi- tive. Machine learning approaches may bridge this gap.

Neighborhood preference, walkability and walking in overweight/obese men.

OBJECTIVES: To investigate whether self-selection moderated the effects of walkability on walking in overweight and obese men. METHODS: 240 overweight and obese men completed measures on importance of walkability when choosing a neighborhood (selection) and preference for walkable features in general (preference). IPAQ measured walking. A walkbility index was derived from geographic information systems (GIS). RESULTS: Walkability was associated with walking for transportation (p = .027) and neighborhood selection was associated with walking for transportation (p = .002) and total walking (p = .001). Preference was associated with leisure walking (p = .045) and preference moderated the relationship between walkability and total walking (p = .059). CONCLUSION: Walkability and self-selection are both important to walking behavior.