@article {bai2016activity, title = {An activity index for raw accelerometry data and its comparison with other activity metrics}, journal = {PloS ONE}, volume = {11}, number = {8}, year = {2016}, pages = {e0160644}, publisher = {Public Library of Science}, author = {Bai, Jiawei and Di, Chongzhi and Xiao, Luo and Evenson, Kelly R and LaCroix, Andrea Z and Crainiceanu, Ciprian M and Buchner, David M} } @article {798, title = {Assessing the "physical cliff": detailed quantification of age-related differences in daily patterns of physical activity.}, journal = {J Gerontol A Biol Sci Med Sci}, volume = {69}, year = {2014}, month = {2014 Aug}, pages = {973-9}, abstract = {

BACKGROUND: In spite of evidence that physical activity has beneficial effects on health and age-related functional decline, there is a scarcity of detailed and accurate information on objectively measured daily activity and patterns of such activity in older adults.

METHODS: Participants in the Baltimore Longitudinal Study of Aging (n = 611, 50\% male, mean age 67, range 32-93) wore the Actiheart portable activity monitor for 7 days in the free-living environment. The association between activity and age was modeled using a continuous log-linear regression of activity counts on age with sex, body mass index, employment status, functional performance, and comorbid conditions as covariates.

RESULTS: In the fully adjusted model, continuous analyses demonstrated that overall physical activity counts were 1.3\% lower for each year increase in age. Although there were no differences among morning levels of activity, there was significantly lower afternoon and evening activity in older individuals (p < .01). After adjusting for age, poor functional performance, nonworking status, and higher body mass index were independently associated with less physical activity (p < .001).

CONCLUSIONS: The use of accelerometers to characterize minute-by-minute intensity, cumulative physical activity counts, and daily activity patterns provides detailed data not gathered by traditional subjective methods, particularly at low levels of activity. The findings of a 1.3\% decrease per year in activity from mid-to-late life, and the corresponding drop in afternoon and evening activity, provide new information that may be useful when targeting future interventions. Further, this methodology addresses essential gaps in understanding activity patterns and trends in more sedentary sectors of the population.

}, keywords = {Accelerometry, Adult, Age Factors, Aged, Aged, 80 and over, Employment, Female, Health Behavior, Humans, Linear Models, Longitudinal Studies, Male, Middle Aged, Motor Activity, Physical Fitness, Sedentary Lifestyle}, issn = {1758-535X}, doi = {10.1093/gerona/glt199}, author = {Schrack, Jennifer A and Zipunnikov, Vadim and Goldsmith, Jeff and Bai, Jiawei and Simonsick, Eleanor M and Crainiceanu, Ciprian and Ferrucci, Luigi} }