Assessing sedentary behavior using wearable devices: An overview and future directions

Accurate assessments of sedentary behavior are critically important to monitor the proportion of prolonged sedentary behavior, to investigate causal relationships with health outcomes, and to test the effectiveness of interventions aimed at reducing sedentary behaviors. The purpose of this review was to provide an overview of assessments of sedentary behavior using wearable devices, and to briefly discuss the future directions of this field. Sedentary behavior is defined as any waking activity characterized by energy expenditure ≤ 1.5 metabolic equivalents and a sitting or reclining posture. Globally accepted wearable devices to assess sedentary behavior are ActiGraph (developed in the United States of America) and activPAL (developed in Scotland). The ActiGraph, worn at the waist, classifies sedentary behavior based on its unique metric of ‘counts’ less than 100 per minute. The activPAL is attached to a participant’s thigh, and classifies sedentary behavior using an inclinometer with a proprietary algorithm. In Japan, Omron’s Active style Pro is the most widely used device to measure sedentary behavior. A systematic review on sedentary behavior measurements in Japan found that almost all studies adopted this device. A data reduction approach is a key process to obtain consistent summary statistics of sedentary behavior. To accomplish this, seven R packages have been developed, mainly for ActiGraph and activPAL. Unfortunately, none of these can integrate data from Japanese devices; although a macro program for processing data from Active style Pro has been developed. Use of activity recognition techniques and multi-sensor devices may reduce measurement errors, and provide contextual information on sedentary behavior. Other challenges include standardization and harmonization of measurement protocols. Tackling these challenges may facilitate international comparisons in sedentary behavior and eventually study integrations.

[1]  L. Mâsse,et al.  Physical activity in the United States measured by accelerometer. , 2008, Medicine and science in sports and exercise.

[2]  D. Alter,et al.  Sedentary Time and Its Association With Risk for Disease Incidence, Mortality, and Hospitalization in Adults , 2015, Annals of Internal Medicine.

[3]  Dinesh John,et al.  Detection of lying down, sitting, standing, and stepping using two activPAL monitors. , 2014, Medicine and science in sports and exercise.

[4]  Y. Oshima,et al.  Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm. , 2011, The British journal of nutrition.

[5]  David R Bassett,et al.  2011 Compendium of Physical Activities: a second update of codes and MET values. , 2011, Medicine and science in sports and exercise.

[6]  Tomoaki 知明 Matsuo 松尾,et al.  Percentage-Method Improves Properties of Workers’ Sitting- and Walking-Time Questionnaire , 2016, Journal of epidemiology.

[7]  M. Granat,et al.  The validation of a novel activity monitor in the measurement of posture and motion during everyday activities , 2006, British Journal of Sports Medicine.

[8]  Jung-Min Lee,et al.  Accuracy of inclinometer functions of the activPAL and ActiGraph GT3X+: A focus on physical activity. , 2017, Gait & posture.

[9]  K. Yonemoto,et al.  Sedentary bout durations and metabolic syndrome among working adults: a prospective cohort study , 2016, BMC Public Health.

[10]  W. Brown,et al.  Validity of a Self-Report Recall Tool for Estimating Sedentary Behavior in Adults. , 2015, Journal of physical activity & health.

[11]  Maria Hagströmer,et al.  The descriptive epidemiology of sitting. A 20-country comparison using the International Physical Activity Questionnaire (IPAQ). , 2011, American journal of preventive medicine.

[12]  Catrine Tudor-Locke,et al.  A Catalog of Rules, Variables, and Definitions Applied to Accelerometer Data in the National Health and Nutrition Examination Survey, 2003–2006 , 2012, Preventing chronic disease.

[13]  S. Kuno,et al.  Association between physical activity and metabolic syndrome in middle-aged Japanese: a cross-sectional study , 2011, BMC public health.

[14]  Russell R. Pate,et al.  The Evolving Definition of "Sedentary" , 2008, Exercise and sport sciences reviews.

[15]  Mark E. Benden,et al.  Letter to the Editor: Standardized use of the terms “sedentary” and “sedentary behaviours” Applied Physiology, Nutrition, and Metabolism, 2012, 37(3): 540-542, 10.1139/h2012-024 , 2013 .

[16]  Noriko Yokoyama,et al.  Objectively measured light-intensity lifestyle activity and sedentary time are independently associated with metabolic syndrome: a cross-sectional study of Japanese adults , 2013, International Journal of Behavioral Nutrition and Physical Activity.

[17]  Klaus-Hendrik Wolf,et al.  Challenges and Opportunities for Harmonizing Research Methodology: Raw Accelerometry , 2016, Methods of Information in Medicine.

[18]  C. Vandelanotte,et al.  Validation of a pouch-mounted activPAL3 accelerometer. , 2014, Gait & posture.

[19]  Leena Choi,et al.  Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. , 2012, Medicine and science in sports and exercise.

[20]  Wendy J Brown,et al.  ActiGraph GT3X+ cut-points for identifying sedentary behaviour in older adults in free-living environments. , 2014, Journal of science and medicine in sport.

[21]  Kazunobu Okazaki,et al.  A new device to estimate VO2 during incline walking by accelerometry and barometry. , 2009, Medicine and science in sports and exercise.

[22]  C. Matthews,et al.  Measurement of adults' sedentary time in population-based studies. , 2011, American journal of preventive medicine.

[23]  I-Min Lee,et al.  Definition, measurement, and health risks associated with sedentary behavior. , 2015, Medicine and science in sports and exercise.

[24]  K. Narazaki,et al.  Identifying associations between sedentary time and cardio-metabolic risk factors in working adults using objective and subjective measures: a cross-sectional analysis , 2014, BMC Public Health.

[25]  Eri Matsuo,et al.  Tri-Axial Accelerometer-Determined Daily Physical Activity and Sedentary Behavior of Suburban Community-Dwelling Older Japanese Adults. , 2015, Journal of sports science & medicine.

[26]  Catriona Dolan,et al.  Novel technology to help understand the context of physical activity and sedentary behaviour , 2016 .

[27]  Lucas J Carr,et al.  Letter to the editor: standardized use of the terms "sedentary" and "sedentary behaviours". , 2012, Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme.

[28]  PATTY S. FREEDSON,et al.  Utilization and Harmonization of Adult Accelerometry Data: Review and Expert Consensus , 2015, Medicine and science in sports and exercise.

[29]  Lauren B Sherar,et al.  Technologies That Assess the Location of Physical Activity and Sedentary Behavior: A Systematic Review , 2015, Journal of medical Internet research.

[30]  P. Freedson,et al.  Amount of time spent in sedentary behaviors in the United States, 2003-2004. , 2008, American journal of epidemiology.

[31]  K. Ohkawara,et al.  Percentage-Method Improves Properties of Workers’ Sitting- and Walking-Time Questionnaire , 2016 .

[32]  Tao Chen,et al.  Associations of Sedentary Time and Breaks in Sedentary Time With Disability in Instrumental Activities of Daily Living in Community-Dwelling Older Adults. , 2016, Journal of physical activity & health.

[33]  U. Ekelund,et al.  Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. , 2004, Journal of applied physiology.

[34]  I-Min Lee,et al.  Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study , 2014, BMC Public Health.

[35]  Y. Oshima,et al.  Classifying household and locomotive activities using a triaxial accelerometer. , 2010, Gait & posture.

[36]  ALEX V. ROWLANDS,et al.  Raw Accelerometer Data Analysis with GGIR R-package: Does Accelerometer Brand Matter? , 2016, Medicine and science in sports and exercise.

[37]  Joel D. Reece,et al.  Validation of the SenseWear Armband as a Measure of Sedentary Behavior and Light Activity. , 2015, Journal of physical activity & health.

[38]  Kenneth Meijer,et al.  Activity identification using body-mounted sensors—a review of classification techniques , 2009, Physiological measurement.

[39]  SHAOPENG LIU,et al.  Computational methods for estimating energy expenditure in human physical activities. , 2012, Medicine and science in sports and exercise.

[40]  H. Murakami,et al.  "Add 10 min for your health": the new Japanese recommendation for physical activity based on dose-response analysis. , 2015, Journal of the American College of Cardiology.

[41]  M. Brady,et al.  Optimising the validity and completion of adherence diaries: a multiple case study and randomised crossover trial , 2016, Trials.