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      Measuring moderate-intensity walking in older adults using the ActiGraph accelerometer

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          Abstract

          Background

          Accelerometry is the method of choice for objectively assessing physical activity in older adults. Many studies have used an accelerometer count cut point corresponding to 3 metabolic equivalents (METs) derived in young adults during treadmill walking and running with a resting metabolic rate (RMR) assumed at 3.5 mL · kg −1 · min −1 (corresponding to 1 MET). RMR is lower in older adults; therefore, their 3 MET level occurs at a lower absolute energy expenditure making the cut point derived from young adults inappropriate for this population. The few studies determining older adult specific moderate-to-vigorous intensity physical activity (MVPA) cut points had methodological limitations, such as not measuring RMR and using treadmill walking.

          Methods

          This study determined a MVPA hip-worn accelerometer cut point for older adults using measured RMR and overground walking. Following determination of RMR, 45 older adults (mean age 70.2 ± 7 years, range 60–87.6 years) undertook an outdoor, overground walking protocol with accelerometer count and energy expenditure determined at five walking speeds.

          Results

          Mean RMR was 2.8 ± 0.6 mL · kg −1 · min −1. The MVPA cut points (95% CI) determined using linear mixed models were: vertical axis 1013 (734, 1292) counts · min −1; vector magnitude 1924 (1657, 2192) counts · min −1; and walking speed 2.5 (2.2, 2.8) km · hr −1. High levels of inter-individual variability in cut points were found.

          Conclusions

          These MVPA accelerometer and speed cut points for walking, the most popular physical activity in older adults, were lower than those for younger adults. Using cut points determined in younger adults for older adult population studies is likely to underestimate time spent engaged in MVPA. In addition, prescription of walking speed based on the adult cut point is likely to result in older adults working at a higher intensity than intended.

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          Most cited references35

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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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            The pleasure and displeasure people feel when they exercise at different intensities: decennial update and progress towards a tripartite rationale for exercise intensity prescription.

            The public health problem of physical inactivity has proven resistant to research efforts aimed at elucidating its causes and interventions designed to alter its course. Thus, in most industrialized countries, the majority of the population is physically inactive or inadequately active. Most theoretical models of exercise behaviour assume that the decision to engage in exercise is based on cognitive factors (e.g. weighing pros and cons, appraising personal capabilities, evaluating sources of support). Another, still-under-appreciated, possibility is that these decisions are influenced by affective variables, such as whether previous exercise experiences were associated with pleasure or displeasure. This review examines 33 articles published from 1999 to 2009 on the relationship between exercise intensity and affective responses. Unlike 31 studies that were published until 1998 and were examined in a 1999 review, these more recent studies have provided evidence of a relation between the intensity of exercise and affective responses. Pleasure is reduced mainly above the ventilatory or lactate threshold or the onset of blood lactate accumulation. There are pleasant changes at sub-threshold intensities for most individuals, large inter-individual variability close to the ventilatory or lactate threshold and homogeneously negative changes at supra-threshold intensities. When the intensity is self-selected, rather than imposed, it appears to foster greater tolerance to higher intensity levels. The evidence of a dose-response relation between exercise intensity and affect sets the stage for a reconsideration of the rationale behind current guidelines for exercise intensity prescription. Besides effectiveness and safety, it is becoming increasingly clear that the guidelines should take into account whether a certain level of exercise intensity would be likely to cause increases or decreases in pleasure.
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              Actigraph GT3X: validation and determination of physical activity intensity cut points.

              The aims of this study were: to compare energy expenditure (EE) estimated from the existing GT3X accelerometer equations and EE measured with indirect calorimetry; to define new equations for EE estimation with the GT3X in youth, adults and older people; and to define GT3X vector magnitude (VM) cut points allowing to classify PA intensity in the aforementioned age-groups. The study comprised 31 youth, 31 adults and 35 older people. Participants wore the GT3X (setup: 1-s epoch) over their right hip during 6 conditions of 10-min duration each: resting, treadmill walking/running at 3, 5, 7, and 9 km · h⁻¹, and repeated sit-stands (30 times · min⁻¹). The GT3X proved to be a good tool to predict EE in youth and adults (able to discriminate between the aforementioned conditions), but not in the elderly. We defined the following equations: for all age-groups combined, EE (METs)=2.7406+0.00056 · VM activity counts (counts · min⁻¹)-0.008542 · age (years)-0.01380 ·  body mass (kg); for youth, METs=1.546618+0.000658 · VM activity counts (counts · min⁻¹); for adults, METs=2.8323+0.00054 · VM activity counts (counts · min⁻¹)-0.059123 · body mass (kg)+1.4410 · gender (women=1, men=2); and for the elderly, METs=2.5878+0.00047 · VM activity counts (counts · min⁻¹)-0.6453 · gender (women=1, men=2). Activity counts derived from the VM yielded a more accurate EE estimation than those derived from the Y-axis. The GT3X represents a step forward in triaxial technology estimating EE. However, age-specific equations must be used to ensure the correct use of this device. © Georg Thieme Verlag KG Stuttgart · New York.
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                Author and article information

                Contributors
                Anthony.Barnett@acu.edu.au
                dan.vandenhoek@deakin.edu.au
                david.barnett@myacu.edu.au
                Ester.Cerin@acu.edu.au
                Journal
                BMC Geriatr
                BMC Geriatr
                BMC Geriatrics
                BioMed Central (London )
                1471-2318
                8 December 2016
                8 December 2016
                2016
                : 16
                : 211
                Affiliations
                [1 ]Institute for Health & Ageing, Australian Catholic University, Melbourne, Victoria Australia
                [2 ]School of Exercise and Nutrition Sciences, Deakin University, Burwood, Victoria Australia
                [3 ]School of Public Health, The University of Hong Kong, Hong Kong, Hong Kong SAR People’s Republic of China
                Author information
                http://orcid.org/0000-0002-7599-165X
                Article
                380
                10.1186/s12877-016-0380-5
                5146877
                27931188
                e6ee9663-7d95-4e84-8ed1-6b79b25dc7ad
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 16 July 2016
                : 25 November 2016
                Funding
                Funded by: Australian Research Council Future Fellowship
                Award ID: FT#140100085
                Award Recipient :
                Funded by: Faculty Reseach Development Grant - Deakin University, Faculty of Health
                Award ID: 2013/14
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Geriatric medicine
                physical activity,met,energy expenditure,resting metabolic rate,measurement,vector magnitude

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