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      Understanding physical activity in cancer patients and survivors: new methodology, new challenges, and new opportunities

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          Abstract

          Since the early 1990s, accumulating evidence has suggested that regular, sustained participation in physical activity may help prevent the onset and development of certain types of cancer. Given the worldwide incidence and prevalence of cancer, there is increasing interest in physical activity as a nonpharmacological intervention and prevention method. Moreover, the effectiveness of new and improved cancer therapies has also increased interest in the potential health benefits of physical activity during and after treatment. The development of wearable device technology (e.g., accelerometers) to monitor physical activity has created unprecedented opportunities to better understand the potential health benefits of physical activity in cancer patients and survivors by allowing researchers to observe, quantify, and define physical activity in real-world settings. This granular, detailed level of measurement provides the opportunity for researchers and clinicians to obtain a greater understanding of the health benefits of daily physical activity beyond the well-established benefits of “moderate-to-vigorous” physical activity and to tailor recommendations to a feasible level of activity for older and/or sicker patients and survivors. This article provides an overview of accelerometers, the potential benefits—and challenges—of using these devices in the research and clinical settings, and recommendations for future applications.

          Most cited references33

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          Calibration of the Computer Science and Applications, Inc. accelerometer.

          We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories. Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8, 6.4, and 9.7 km x h(-1)). Activity counts and steady-state oxygen consumption were highly correlated (r = 0.88), and count ranges corresponding to light, moderate, hard, and very hard intensity levels were or = 9499 cnts x min(-1), respectively. A model to predict energy expenditure from activity counts and body mass was developed using data from a random sample of 35 subjects (r2 = 0.82, SEE = 1.40 kcal x min(-1)). Cross validation with data from the remaining 15 subjects revealed no significant differences between actual and predicted energy expenditure at any treadmill speed (SEE = 0.50-1.40 kcal x min(-1)). These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer.
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            Compendium of physical activities: classification of energy costs of human physical activities.

            A coding scheme is presented for classifying physical activity by rate of energy expenditure, i.e., by intensity. Energy cost was established by a review of published and unpublished data. This coding scheme employs five digits that classify activity by purpose (i.e., sports, occupation, self-care), the specific type of activity, and its intensity as the ratio of work metabolic rate to resting metabolic rate (METs). Energy expenditure in kilocalories or kilocalories per kilogram body weight can be estimated for all activities, specific activities, or activity types. General use of this coding system would enhance the comparability of results across studies using self reports of physical activity.
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              Assessment of physical activity by self-report: status, limitations, and future directions.

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                Author and article information

                Journal
                Cold Spring Harb Mol Case Stud
                Cold Spring Harb Mol Case Stud
                cshmcs
                cshmcs
                cshmcs
                Cold Spring Harbor Molecular Case Studies
                Cold Spring Harbor Laboratory Press
                2373-2873
                July 2017
                : 3
                : 4
                : a001933
                Affiliations
                [1 ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA;
                [2 ]Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland 21205, USA
                Author notes
                Corresponding author: jschrac1@ 123456jhu.edu
                Article
                SchrackMCS001933
                10.1101/mcs.a001933
                5495035
                28679694
                61d1a729-0988-42a4-b9a1-59b9354a3c08
                © 2017 Schrack et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits reuse and redistribution, except for commercial purposes, provided that the original author and source are credited.

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                Pages: 7
                Categories
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