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Modeling Energy Expenditure Estimation in Occupational Context by Actigraphy: A Multi Regression Mixed-Effects Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph181910419. [PMID: 34639718 PMCID: PMC8508338 DOI: 10.3390/ijerph181910419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/14/2021] [Accepted: 09/30/2021] [Indexed: 11/17/2022]
Abstract
The accurate prediction of energy requirements for healthy individuals has many useful applications. The occupational perspective has also been proven to be of great utility for improving workers' ergonomics, safety, and health. This work proposes a statistical regression model based on actigraphy and personal characteristics to estimate energy expenditure and cross-validate the results with reference standardized methods. The model was developed by hierarchical mixed-effects regression modeling based on the multitask protocol data. Measurements combined actigraphy, indirect calorimetry, and other personal and lifestyle information from healthy individuals (n = 50) within the age of 29.8 ± 5 years old. Results showed a significant influence of the variables related to movements, heart rate and anthropometric variables of body composition for energy expenditure estimation. Overall, the proposed model showed good agreement with energy expenditure measured by indirect calorimetry and evidenced a better performance than the methods presented in the international guidelines for metabolic rate assessment proving to be a reliable alternative to normative guidelines. Furthermore, a statistically significant relationship was found between daily activity and energy expenditure, which raised the possibility of further studies including other variables, namely those related to the subject's lifestyle.
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Clark CCT, Barnes CM, Stratton G, McNarry MA, Mackintosh KA, Summers HD. A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans. Sports Med 2018; 47:439-447. [PMID: 27402456 DOI: 10.1007/s40279-016-0585-y] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Physical inactivity is one of the most prevalent risk factors for non-communicable diseases in the world. A fundamental barrier to enhancing physical activity levels and decreasing sedentary behavior is limited by our understanding of associated measurement and analytical techniques. The number of analytical techniques for physical activity measurement has grown significantly, and although emerging techniques may advance analyses, little consensus is presently available and further synthesis is therefore required. The objective of this review was to identify the accuracy of emerging analytical techniques used for physical activity measurement in humans. We conducted a search of electronic databases using Web of Science, PubMed, and Google Scholar. This review included studies written in English and published between January 2010 and December 2014 that assessed physical activity using emerging analytical techniques and reported technique accuracy. A total of 2064 papers were initially retrieved from three databases. After duplicates were removed and remaining articles screened, 50 full-text articles were reviewed, resulting in the inclusion of 11 articles that met the eligibility criteria. Despite the diverse nature and the range in accuracy associated with some of the analytic techniques, the rapid development of analytics has demonstrated that more sensitive information about physical activity may be attained. However, further refinement of these techniques is needed.
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Affiliation(s)
- Cain C T Clark
- Applied Sports Technology, Exercise and Medicine (A-STEM) Research centre, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales. .,Engineering Behaviour Analytics in Sport and Exercise (E-BASE) Research group, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales.
| | - Claire M Barnes
- Centre for Nanohealth, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales.,Engineering Behaviour Analytics in Sport and Exercise (E-BASE) Research group, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales
| | - Gareth Stratton
- Applied Sports Technology, Exercise and Medicine (A-STEM) Research centre, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales.,Engineering Behaviour Analytics in Sport and Exercise (E-BASE) Research group, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales
| | - Melitta A McNarry
- Applied Sports Technology, Exercise and Medicine (A-STEM) Research centre, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales.,Engineering Behaviour Analytics in Sport and Exercise (E-BASE) Research group, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales
| | - Kelly A Mackintosh
- Applied Sports Technology, Exercise and Medicine (A-STEM) Research centre, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales.,Engineering Behaviour Analytics in Sport and Exercise (E-BASE) Research group, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales
| | - Huw D Summers
- Centre for Nanohealth, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales.,Engineering Behaviour Analytics in Sport and Exercise (E-BASE) Research group, College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales
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Dowd KP, Szeklicki R, Minetto MA, Murphy MH, Polito A, Ghigo E, van der Ploeg H, Ekelund U, Maciaszek J, Stemplewski R, Tomczak M, Donnelly AE. A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. Int J Behav Nutr Phys Act 2018; 15:15. [PMID: 29422051 PMCID: PMC5806271 DOI: 10.1186/s12966-017-0636-2] [Citation(s) in RCA: 187] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 12/18/2017] [Indexed: 01/08/2023] Open
Abstract
The links between increased participation in Physical Activity (PA) and improvements in health are well established. As this body of evidence has grown, so too has the search for measures of PA with high levels of methodological effectiveness (i.e. validity, reliability and responsiveness to change). The aim of this “review of reviews” was to provide a comprehensive overview of the methodological effectiveness of currently employed measures of PA, to aid researchers in their selection of an appropriate tool. A total of 63 review articles were included in this review, and the original articles cited by these reviews were included in order to extract detailed information on methodological effectiveness. Self-report measures of PA have been most frequently examined for methodological effectiveness, with highly variable findings identified across a broad range of behaviours. The evidence-base for the methodological effectiveness of objective monitors, particularly accelerometers/activity monitors, is increasing, with lower levels of variability observed for validity and reliability when compared to subjective measures. Unfortunately, responsiveness to change across all measures and behaviours remains under-researched, with limited information available. Other criteria beyond methodological effectiveness often influence tool selection, including cost and feasibility. However, researchers must be aware of the methodological effectiveness of any measure selected for use when examining PA. Although no “perfect” tool for the examination of PA in adults exists, it is suggested that researchers aim to incorporate appropriate objective measures, specific to the behaviours of interests, when examining PA in free-living environments.
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Affiliation(s)
- Kieran P Dowd
- Department of Sport and Health Science, Athlone Institute of Technology, Athlone, Ireland
| | - Robert Szeklicki
- University School of Physical Education in Poznan, Poznan, Poland
| | - Marco Alessandro Minetto
- Division of Endocrinology, Diabetology and Metabolism, Department of Internal Medicine, University of Turin, Corso Dogliotti 14, 10126, Torino, Italy
| | - Marie H Murphy
- School of Health Science, University of Ulster, Newtownabbey, UK
| | - Angela Polito
- National Institute for Food and Nutrition Research, Rome, Italy
| | - Ezio Ghigo
- Division of Endocrinology, Diabetology and Metabolism, Department of Internal Medicine, University of Turin, Corso Dogliotti 14, 10126, Torino, Italy
| | - Hidde van der Ploeg
- Department of Public and Occupational Health, VU University Medical Center, EMGO Institute for Health and Care Research, Amsterdam, The Netherlands.,Sydney School of Public Health, University of Sydney, Sydney, Australia
| | - Ulf Ekelund
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, UK.,The Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo, Norway
| | - Janusz Maciaszek
- University School of Physical Education in Poznan, Poznan, Poland
| | | | - Maciej Tomczak
- University School of Physical Education in Poznan, Poznan, Poland
| | - Alan E Donnelly
- Department of Physical Education and Sport Sciences, Health Research Institute, University of Limerick, Limerick, Ireland.
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Lyden K, Keadle SK, Staudenmayer J, Freedson PS. A method to estimate free-living active and sedentary behavior from an accelerometer. Med Sci Sports Exerc 2017; 46:386-97. [PMID: 23860415 DOI: 10.1249/mss.0b013e3182a42a2d] [Citation(s) in RCA: 105] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
INTRODUCTION Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings. PURPOSE The purpose of this study was to develop and validate two novel machine-learning methods (Sojourn-1 Axis [soj-1x] and Sojourn-3 Axis [soj-3x]) in a free-living setting. METHODS Participants were directly observed in their natural environment for 10 consecutive hours on three separate occasions. Physical activity and SB estimated from soj-1x, soj-3x, and a neural network previously calibrated in the laboratory (lab-nnet) were compared with direct observation. RESULTS Compared with lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias [95% confidence interval] = 33.1 [25.9 to 40.4], root-mean-square error [RMSE] = 5.4 [4.6-6.2]; soj-1x: % bias = 1.9 [-2.0 to 5.9], RMSE = 1.0 [0.6 to 1.3]; soj-3x: % bias = 3.4 [0.0 to 6.7], RMSE = 1.0 [0.6 to 1.5]) and minutes in different intensity categories {lab-nnet: % bias = -8.2 (sedentary), -8.2 (light), and 72.8 (moderate-to-vigorous PA [MVPA]); soj-1x: % bias = 8.8 (sedentary), -18.5 (light), and -1.0 (MVPA); soj-3x: % bias = 0.5 (sedentary), -0.8 (light), and -1.0 (MVPA)}. Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time. CONCLUSIONS Compared with the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light-intensity activity and MVPA. In addition, soj-3x is superior to soj-1x in differentiating SB from light-intensity activity.
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Affiliation(s)
- Kate Lyden
- 1Department of Kinesiology, University of Massachusetts, Amherst, MA; and 2Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA
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Bouarfa L, Atallah L, Kwasnicki RM, Pettitt C, Frost G, Guang-Zhong Yang. Predicting Free-Living Energy Expenditure Using a Miniaturized Ear-Worn Sensor: An Evaluation Against Doubly Labeled Water. IEEE Trans Biomed Eng 2014; 61:566-75. [DOI: 10.1109/tbme.2013.2284069] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Hurvitz PM, Moudon AV, Kang B, Saelens BE, Duncan GE. Emerging technologies for assessing physical activity behaviors in space and time. Front Public Health 2014; 2:2. [PMID: 24479113 PMCID: PMC3904281 DOI: 10.3389/fpubh.2014.00002] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2013] [Accepted: 01/10/2014] [Indexed: 11/13/2022] Open
Abstract
Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the "LifeLog." A graphic interface tool, "LifeLog View," enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors.
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Affiliation(s)
- Philip M. Hurvitz
- Urban Form Laboratory, Department of Urban Design and Planning, University of Washington, Seattle, WA, USA
| | - Anne Vernez Moudon
- Urban Form Laboratory, Department of Urban Design and Planning, University of Washington, Seattle, WA, USA
| | - Bumjoon Kang
- Department of Urban and Regional Planning, State University of New York, Buffalo, NY, USA
| | - Brian E. Saelens
- Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Glen E. Duncan
- Nutritional Sciences Program, Department of Epidemiology, University of Washington, Seattle, WA, USA
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Strath SJ, Kaminsky LA, Ainsworth BE, Ekelund U, Freedson PS, Gary RA, Richardson CR, Smith DT, Swartz AM. Guide to the Assessment of Physical Activity: Clinical and Research Applications. Circulation 2013; 128:2259-79. [DOI: 10.1161/01.cir.0000435708.67487.da] [Citation(s) in RCA: 584] [Impact Index Per Article: 53.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Methodological issues when analysing the role of physical activity in gastric cancer prevention: a critical review. Eur Rev Aging Phys Act 2012. [DOI: 10.1007/s11556-012-0113-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Abstract
Abstract
The beneficial effect of physical activity (PA) has been confirmed in several types of cancer (especially colon and breast tumours). However, the role of PA as a risk factor directly related to the incidence of gastric cancer is still open to doubt. This is in part due to the fact that most studies have not considered gastric sub-site or histology of oesophageal cancer, as well as the different approaches used in order to measure PA. Indeed, some studies have tried to link gastric cancer to PA intensity and timing, whereas others have focused on a specific PA type such as recreational, occupational or sporting activity. Furthermore, most of them do not use validated questionnaires, and others create a PA index and employ different unit measures (metabolic equivalents, hours/week, times per week, etc.), which makes it difficult to compare its findings. Under these circumstances, this brief critical review aims to explore and show all the methodological issues that need to be taken into account in order to objectify the link between PA and gastric cancer, as well as provide alternative solutions to these matters.
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Duncan GE, Lester J, Migotsky S, Higgins L, Borriello G. Measuring slope to improve energy expenditure estimates during field-based activities. Appl Physiol Nutr Metab 2012; 38:352-6. [PMID: 23537030 DOI: 10.1139/apnm-2012-0223] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
This technical note describes methods to improve activity energy expenditure estimates by using a multi-sensor board (MSB) to measure slope. Ten adults walked over a 4-km (2.5-mile) course wearing an MSB and mobile calorimeter. Energy expenditure was estimated using accelerometry alone (base) and 4 methods to measure slope. The barometer and global positioning system methods improved accuracy by 11% from the base (p < 0.05) to 86% overall. Measuring slope using the MSB improves energy expenditure estimates during field-based activities.
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Affiliation(s)
- Glen E Duncan
- a Department of Epidemiology, Nutritional Sciences Program, University of Washington, Seattle, WA 98195, USA
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Van Remoortel H, Giavedoni S, Raste Y, Burtin C, Louvaris Z, Gimeno-Santos E, Langer D, Glendenning A, Hopkinson NS, Vogiatzis I, Peterson BT, Wilson F, Mann B, Rabinovich R, Puhan MA, Troosters T. Validity of activity monitors in health and chronic disease: a systematic review. Int J Behav Nutr Phys Act 2012; 9:84. [PMID: 22776399 PMCID: PMC3464146 DOI: 10.1186/1479-5868-9-84] [Citation(s) in RCA: 183] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2011] [Accepted: 06/13/2012] [Indexed: 01/19/2023] Open
Abstract
The assessment of physical activity in healthy populations and in those with chronic diseases is challenging. The aim of this systematic review was to identify whether available activity monitors (AM) have been appropriately validated for use in assessing physical activity in these groups. Following a systematic literature search we found 134 papers meeting the inclusion criteria; 40 conducted in a field setting (validation against doubly labelled water), 86 in a laboratory setting (validation against a metabolic cart, metabolic chamber) and 8 in a field and laboratory setting. Correlation coefficients between AM outcomes and energy expenditure (EE) by the criterion method (doubly labelled water and metabolic cart/chamber) and percentage mean differences between EE estimation from the monitor and EE measurement by the criterion method were extracted. Random-effects meta-analyses were performed to pool the results across studies where possible. Types of devices were compared using meta-regression analyses. Most validation studies had been performed in healthy adults (n = 118), with few carried out in patients with chronic diseases (n = 16). For total EE, correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices. For active EE, correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices. Uniaxial devices tended to underestimate TEE (−12.07 (95%CI; -18.28 to −5.85) %) compared to triaxial (−6.85 (95%CI; -18.20 to 4.49) %, p = 0.37) and were statistically significantly less accurate than multisensor devices (−3.64 (95%CI; -8.97 to 1.70) %, p<0.001). TEE was underestimated during slow walking speeds in 69% of the lab validation studies compared to 37%, 30% and 37% of the studies during intermediate, fast walking speed and running, respectively. The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome. Triaxial and multisensor devices tend to be more valid monitors. Since activity monitors are less accurate at slow walking speeds and information about validated activity monitors in chronic disease populations is lacking, proper validation studies in these populations are needed prior to their inclusion in clinical trials.
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Affiliation(s)
- Hans Van Remoortel
- Faculty of Kinesiology and Rehabilitation Sciences, Department of Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
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Shephard RJ, Aoyagi Y. Measurement of human energy expenditure, with particular reference to field studies: an historical perspective. Eur J Appl Physiol 2011; 112:2785-815. [PMID: 22160180 DOI: 10.1007/s00421-011-2268-6] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2011] [Accepted: 11/23/2011] [Indexed: 01/23/2023]
Abstract
Over the years, techniques for the study of human movement have ranged in complexity and precision from direct observation of the subject through activity diaries, questionnaires, and recordings of body movement, to the measurement of physiological responses, studies of metabolism and indirect and direct calorimetry. This article reviews developments in each of these domains. Particular reference is made to their impact upon the continuing search for valid field estimates of activity patterns and energy expenditures, as required by the applied physiologist, ergonomist, sports scientist, nutritionist and epidemiologist. Early observers sought to improve productivity in demanding employment. Direct observation and filming of workers were supplemented by monitoring of heart rates, ventilation and oxygen consumption. Such methods still find application in ergonomics and sport, but many investigators are now interested in relationships between habitual physical activity and chronic disease. Even sophisticated questionnaires still do not provide valid information on the absolute energy expenditures associated with good health. Emphasis has thus shifted to use of sophisticated pedometer/accelerometers, sometimes combining their output with GPS and other data. Some modern pedometer/accelerometers perform well in the laboratory, but show substantial systematic errors relative to laboratory reference criteria such as the metabolism of doubly labeled water when assessing the varied activities of daily life. The challenge remains to develop activity monitors that are sufficiently inexpensive for field use, yet meet required accuracy standards. Possibly, measurements of oxygen consumption by portable respirometers may soon satisfy part of this need, although a need for valid longer term monitoring will remain.
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Affiliation(s)
- Roy J Shephard
- Faculty of Physical Education and Health, University of Toronto, Toronto, ON, Canada.
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