Energy cost of walking in older adults: accuracy of the ActiGraph accelerometer predictive equations
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
BACKGROUND/OBJECTIVES: Various accelerometer equations are used to predict energy expenditure (EE). On the other hand, the development of these equations and their validation studies have been conducted primarily without including older adults. This study assessed the accuracy of 8 ActiGraph accelerometer equations to predict the energy cost of walking in older adults. SUBJECTS/METHODS: Thirty-one participants with a mean age of 74.3 ± 3.3 yrs were enrolled in this study (20 men and 11 women). The participants completed 8 walking activities, including 5 treadmill and 3 self-paced walking activities. The EE was measured using a portable indirect calorimeter, with each participant simultaneously wearing the ActiGraph accelerometer. Eight ActiGraph equations were assessed for accuracy by comparing the predicted EE with indirect calorimetry results. RESULTS: All equations resulted in an overall underestimation of the EE across the activities (bias −1 to −1.8 kcal·min−1 and −0.7 to −1.8 metabolic equivalents [METs]), as well as during treadmill-based (bias −1.5 to −2.9 kcal·min−1 and −0.9 to −2.1 METs) and self-paced (bias −1.2 to −1.7 kcal·min−1 and −0.2 to −1.3 METs) walking. In addition, there were higher rates of activity intensity misclassifications, particularly among vigorous physical activities. CONCLUSIONS: The ActiGraph equations underestimated the EE for walking activities in older adults. In addition, these equations inaccurately classified the activities based on their intensities. The present study suggests a need to develop ActiGraph equations specific to older adults.
Abstract
BACKGROUND/OBJECTIVES: Various accelerometer equations are used to predict energy expenditure (EE). On the other hand, the development of these equations and their validation studies have been conducted primarily without including older adults. This study assessed the accuracy of 8 ActiGraph accelerometer equations to predict the energy cost of walking in older adults. SUBJECTS/METHODS: Thirty-one participants with a mean age of 74.3 ± 3.3 yrs were enrolled in this study (20 men and 11 women). The participants completed 8 walking activities, including 5 treadmill and 3 self-paced walking activities. The EE was measured using a portable indirect calorimeter, with each participant simultaneously wearing the ActiGraph accelerometer. Eight ActiGraph equations were assessed for accuracy by comparing the predicted EE with indirect calorimetry results. RESULTS: All equations resulted in an overall underestimation of the EE across the activities (bias −1 to −1.8 kcal·min−1 and −0.7 to −1.8 metabolic equivalents [METs]), as well as during treadmill-based (bias −1.5 to −2.9 kcal·min−1 and −0.9 to −2.1 METs) and self-paced (bias −1.2 to −1.7 kcal·min−1 and −0.2 to −1.3 METs) walking. In addition, there were higher rates of activity intensity misclassifications, particularly among vigorous physical activities. CONCLUSIONS: The ActiGraph equations underestimated the EE for walking activities in older adults. In addition, these equations inaccurately classified the activities based on their intensities. The present study suggests a need to develop ActiGraph equations specific to older adults.
참고문헌 (45)
- World Population Ageing 2017 - Highlights (ST, "ESA", SER.A, United Nations, pp.UN, 2017[DOI]
- Arredondo A, "Costs and epidemiological changes of chronic diseases : implications and challenges for health systems", PLoS One, 10, pp.e0118611~ (2015)
- Kennedy BK, "Aging : a common driver of chronic diseases and a target for novel interventions", Cell, 159, pp.709~713 (2014)
- "Noncommunicable diseases among older adults in low- and middle-income countries" (2012)[URL]
- Sowa A, "Predictors of healthy ageing : public health policy targets", BMC Health Serv Res, 16(Suppl 5), pp.289~ (2016)
- Hanson MA, "Developmental aspects of a life course approach to healthy ageing", J Physiol, 594, pp.2147~2160 (2016)
- Crichton GE, "Physical activity, sedentary behavior time and lipid levels in the Observation of Cardiovascular Risk Factors in Luxembourg study", Lipids Health Dis, 14, pp.87~ (2015)
- Qiu S, "Impact of walking on glycemic control and other cardiovascular risk factors in type 2 diabetes : a meta-analysis", PLoS One, 9, pp.e109767~ (2014)
- Friedenreich CM, "Physical activity and cancer outcomes : a precision medicine approach", Clin Cancer Res, 22, pp.4766~4775 (2016)
- Bherer L, "A review of the effects of physical activity and exercise on cognitive and brain functions in older adults", J Aging Res, 2013, pp.657508~ (2013)
- StefflM, "Relationship between sarcopenia and physical activity in older people : a systematic review and meta-analysis", Clin Interv Aging, 12, pp.835~845 (2017)
- Iolascon G, "Physical exercise and sarcopenia in older people : position paper of the Italian Society of Orthopaedics and Medicine(OrtoMed)", Clin Cases Miner Bone Metab, 11, pp.215~221 (2014)
- Welk GJ, "Physical Activity Assessments for Health-Related Research" (2002), Human Kinetics
- WHO, "Global Recommendations on Physical Activity for Health" (2010), WHO Press
- Pinheiro Volp AC, "Energy expenditure : components and evaluation methods", Nutr Hosp, 26, pp.430~440 (2011)
- Didace Ndahimana, "Measurement Methods for Physical Activity and Energy Expenditure: a Review", Clinical Nutrition Research, 6(2), pp.68~80 (2017), 한국임상영양학회
- Crouter SE, "A novel method for using accelerometer data to predict energy expenditure", J Appl Physiol(1985), 100, pp.1324~1331 (2006)
- "Kcal estimates from activity counts using the potential energy method" (1998)[URL]
- Lyden K, "A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations", Eur J Appl Physiol, 111, pp.187~201 (2011)
- Santos-Lozano A, "ActiGraph GT3X : validation and determination of physical activity intensity cut points", Int J Sports Med, 34, pp.975~982 (2013)
- Aguilar-Farias N, "Comparing ActiGraph equations for estimating energy expenditure in older adults", J Sports Sci, 37, pp.188~195 (2019)
- Hall KS, "METs and accelerometry of walking in older adults : standard versus measured energy cost", Med Sci Sports Exerc, 45, pp.574~582 (2013)
- Jones LM, "Walking speed at self-selected exercise pace is lower but energy cost higher in older versus younger women", J Phys Act Health, 6, pp.327~332 (2009)
- Whitcher L, "Accelerometer derived activity counts and oxygen consumption between young and older individuals", J Aging Res, 2014, pp.184693~ (2014)
- Pinnington HC, "The level of accuracy and agreement in measures of FEO2, FECO2 and VE between the Cosmed K4b2 portable, respiratory gas analysis system and a metabolic cart", J Sci Med Sport, 4, pp.324~335 (2001)
- McLaughlin JE, "Validation of the COSMED K4 b2 portable metabolic system", Int J Sports Med, 22, pp.280~284 (2001)
- Schrack JA, "Comparison of the Cosmed K4b2 portable metabolic system in measuring steady-state walking energy expenditure", PLoS One, 5, pp.e9292~ (2010)
- Mcminn D, "Measuring activity energy expenditure : accuracy of the GT3X+ and Actiheart monitors", Int J Exerc Sci, 6, pp.217~229 (2013)
- Aadland E, "Reliability of the ActiGraph GT3X+ accelerometer in adults under free-living conditions", PLoS One, 10, pp.e0134606~ (2015)
- Schneller MB, "Validation of five minimally obstructive methods to estimate physical activity energy expenditure in young adults in semi-standardized settings", Sensors(Basel), 15, pp.6133~6151 (2015)
- Sasaki JE, "Validation and comparison of ActiGraph activity monitors", J Sci Med Sport, 14, pp.411~416 (2011)
- Robusto KM, "Comparison of three generations of ActiGraph™ activity monitors in children and adolescents", J Sports Sci, 30, pp.1429~1435 (2012)
- Freedson PS, "Calibration of the Computer Science and Applications, Inc. accelerometer", Med Sci Sports Exerc, 30, pp.777~781 (1998)
- Swartz AM, "Estimation of energy expenditure using CSA accelerometers at hip and wrist sites", Med Sci Sports Exerc, 32, pp.S450~S456 (2000)
- Yngve A, "Effect of monitor placement and of activity setting on the MTI accelerometer output", Med Sci Sports Exerc, 35, pp.320~326 (2003)
- Brooks AG, "Predicting walking METs and energy expenditure from speed or accelerometry", Med Sci Sports Exerc, 37, pp.1216~1223 (2005)
- Ainsworth BE, "Compendium of physical activities : an update of activity codes and MET intensities", Med Sci Sports Exerc, 32, pp.S498~S516 (2000)
- Institute of Medicine of the National Academies (US), "Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids" (2002), National Academies Press;
- Byrne NM, "Metabolic equivalent: one size does not fit all", J Appl Physiol(1985), 99, pp.1112~1119 (2005)
- Kwan M, "The standard oxygen consumption value equivalent to one metabolic equivalent (3.5 ml", min, Int J Food Sci Nutr, pp.2004, 55[DOI]
- Bird SR, "Update on the effects of physical activity on insulin sensitivity in humans", BMJ Open Sport Exerc Med, 2, pp.e000143~ (2017)
- Mansikkamäki K, "Long-term effect of physical activity on health-related quality of life among menopausal women : a 4-year follow-up study to a randomised controlled trial", BMJ Open, 5, pp.e008232~ (2015)
- Crouter SE, "Estimating energy expenditure using accelerometers", Eur J Appl Physiol, 98, pp.601~612 (2006)
- Ortega JD, "Individual limb work does not explain the greater metabolic cost of walking in elderly adults", J Appl Physiol(1985), 102, pp.2266~2273 (2007)
- Mian OS, "Metabolic cost, mechanical work, and efficiency during walking in young and older men", Acta Physiol(Oxf), 186, pp.127~139 (2006)