- Email: [email protected]
Wrist-worn triaxial accelerometry predicts the energy expenditure of non-vigorous daily physical activities
Accepted Manuscript Title: Wrist-worn triaxial accelerometry predicts the energy expenditure of non-vigorous daily physical activities Authors: Worawan Sirichana, Brett A. Dolezal, Eric V. Neufeld, Xiaoyan Wang, Christopher B. Cooper PII: DOI: Reference:
S1440-2440(17)30259-1 http://dx.doi.org/doi:10.1016/j.jsams.2017.01.233 JSAMS 1453
To appear in:
Journal of Science and Medicine in Sport
Received date: Revised date: Accepted date:
20-6-2016 28-11-2016 2-1-2017
Please cite this article as: Sirichana Worawan, Dolezal Brett A, Neufeld Eric V, Wang Xiaoyan, Cooper Christopher B.Wrist-worn triaxial accelerometry predicts the energy expenditure of non-vigorous daily physical activities.Journal of Science and Medicine in Sport http://dx.doi.org/10.1016/j.jsams.2017.01.233 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Wrist-worn triaxial accelerometry predicts the energy expenditure of non-vigorous daily physical activities
Worawan Sirichana a,b, Brett A. Dolezala, Eric V. Neufelda, Xiaoyan Wangc, Christopher B. Coopera,*
a
Exercise Physiology Research Laboratory, Departments of Medicine and Physiology, David Geffen
School of Medicine, University of California at Los Angeles, Los Angeles, California, USA b
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Faculty of Medicine,
Chulalongkorn University, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand c
Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine,
University of California at Los Angeles, Los Angeles, California, USA.
*Corresponding author: Christopher B Cooper, MD Professor Emeritus of Medicine and Physiology, David Geffen School of Medicine, University of California, Los Angeles 10833 Le Conte Avenue, 37-131 CHS, Los Angeles, CA 90095-1690, United States Tel: 1-310-470-3983 Fax: 1-310-206-8211 e-mail: [email protected] Word count: (1989/3000) Abstract word count: 246/250 Number of Tables: 1 Number of Figures: 2 1
Abstract Objectives: Triaxial accelerometry is commonly used to estimate oxygen uptake (VO2) and energy expenditure in health and fitness studies. We tested the correlation of a triaxial accelerometer in terms of a summation of vector magnitudes with gravity subtracted (SVMgs) and VO2 for different daily physical activities. Design: Original research, cross-sectional. Methods: Twenty volunteers wore a triaxial accelerometer on both wrists while performing 12 assigned daily physical activities for 6 minutes for each activity. The VO2 was determined by indirect calorimetry using a portable metabolic measurement system. The last 3 minutes of each activity was assumed to represent steady-state. The VO2 measured during these periods was averaged and converted into metabolic equivalents (METs). Results: The range of VO2 for all activities was 0.18-3.2 L/min (0.8-12.2 METs). The range of SVMgs was 13.6-3117.3 g·min for the dominant hand and 19.5-3448.4 g·min for the non-dominant hand. Significant differences in SVMgs existed between accelerometer placements on the dominant (120.9 8.7 g∙min) versus non-dominant hand (99.9 6.8 g∙min; P = 0.016) for the lowest levels of physical activity defined as <1.5 METs. Piecewise linear regression model using 6 METs as the transition point showed similar significant correlations for the non-dominant wrist (r2 = 0.85; P < 0.001) and the dominant wrist (r2 = 0.86; P < 0.001). Conclusions: Wrist-worn triaxial accelerometry reliably predicted energy expenditure during common physical activities <6 METs. Stronger correlations were found when the accelerometer was worn on the non-dominant wrist rather than the dominant wrist.
Keywords: triaxial accelerometer, physical activity, energy expenditure, oxygen uptake 2
1. Introduction Maintaining a physically active lifestyle is a core tenant of leading a life of health and vitality. Higher levels of physical activity strongly correlate with reduced risk of chronic diseases and general mortality.1, 2 The American College of Sports Medicine (ACSM) has classified physical activity intensity according to the level of metabolic equivalents (METs) into mild (<3 METs), moderate (3-6 METs), and vigorous (>6METs).3,
4
They also recommend that adults should perform at least 150 minutes of
moderate-intensity aerobic exercise per week.3 Daily activities can provide an adequate physical challenge to be considered moderate-intensity exercise depending on the oxygen uptake (VO2 or METs) of the task. However, daily physical activity is difficult to quantify because of varying intensity and duration of activities throughout the day. Furthermore, self-reported physical activity logs or physical activity questionnaires demonstrate inconsistent validity and reliability, especially in elderly.5, 6 Activity trackers, such as accelerometers and pedometers, have been used to monitor and quantify physical activity in daily life, sports, and research. Accelerometry quantifies movements in terms of velocity over time in one or more perpendicular axes, accelerometry allowing continuous monitoring of physical activity over a period of time. The measured output varies among devices, e.g. steps, activity count, intensity count and vector magnitude units. A wide range of correlations has been reported between these outputs and activity related energy expenditure, depending on the devices, device placement, and populations studied.7-9 We compared energy expenditure calculated from oxygen uptake, which we measured by exhaled breath analysis, with vector magnitude units recorded form a triaxial accelerometer during various physical activities, representative of daily living. We aimed to build upon current evidence that accelerometry may be beneficial in measuring certain movements and certain levels of exercise intensity.
3
2. Methods Twenty healthy volunteers were recruited from the UCLA campus and the surrounding community using flyers which had been approved by the UCLA Institutional Review Board. There were 10 men and 10 women. Mean (SD) height was 1.69 (0.09) m and weight was 66.3 (9.3) kg. Participants were asked to fast for at least 3 hours before testing except they were allowed to drink water to maintain normal hydration. They were also asked to avoid alcohol, caffeine or tobacco at least 8 hours before testing and to avoid excessive physical exertion for at least 12 hours. The study was approved by the UCLA Institutional Review Board and all participants gave written informed consent.
Participants
completed standard health, medical, and exercise history forms following enrolment and prior to data acquisition. The GENEActiv wrist-worn triaxial accelerometer (GENEActiv, Cambridge, UK) is intended for measuring acceleration of movement in three perpendicular axes in gravitation units with a range 8 g. The device is small (36L x 20W X 12H mm), lightweight (16 grams without strap), waterproof and can be worn on the wrist, hip, or ankle. This device has been validated as a determinant the intensity of various physical activities10-12, and has been shown to match the performance of the waist-worn ActiGraph and RT3.13 The GENEActiv accelerometer also has user-defined sampling frequencies ranging from 10 to 100 Hz and provides both raw data and software for end-user analysis. We obtained triaxial accelerometry data using a sampling rate of 40 Hz which is associated with high classification accuracy but lower data load, longer battery life and higher efficiency of processing.14 These data were downloaded using USB 2.0 charging cable immediately after testing each individual subject. Oxygen uptake (VO2) during rest and physical activity was measured simultaneously by a previously validated portable metabolic analyser (Oxycon Mobile 5.0, CareFusion, Yorba Linda, CA). This apparatus, which incorporates a turbine flow transducer and discrete oxygen and carbon dioxide analyzers, was calibrated before every test. Heart rate was recorded via a chest strap (Polar Electro, OY, Kempele, Finland) that was integrated with the
4
metabolic measurement system. All tests were conducted by trained and experienced personnel in accordance with established guidelines for cardiopulmonary exercise testing.15 Each participant was fitted with a triaxial accelerometer on both wrists while VO2 was being measured by exhaled gas analysis. They then performed 12 different physical activities (Table 1), each for 6-minute intervals, with VO2 ranging from 1-11 METs. The metabolic equivalent of each activity was estimated by data from Compendium of Physical Activities.4 Initially, participants were asked to lie quietly on a bed for 10 minutes in order to measure resting VO2 and heart rate at baseline. Five activities were structured to represent movements performed in day-to-day life. The other 7 activities were geared towards higher levels of physical activity such as walking and running on a treadmill at different speeds and grades. The activities were performed in same sequence for all subjects. The resting period between each activity was at least 2 minutes or until the participant’s heart rate had returned to baseline. The last 3 minutes of measured VO2 for each activity was averaged unless the difference between the last 3 minutes was greater than 100 mL/min. If the difference of last 3 minutes VO2 was greater than 100 mL/min, then VO2 from the last minute was averaged. Then, the average VO2 in mL/kg/min was converted into METs to represent the oxygen consumption for each activity and each individual subject. We did this using the standard assumption that 1 MET = 3.5 ml/kg/min of oxygen uptake. The sum of vector magnitudes with gravity subtracted (SVMgs) was derived in 60-second epochs from the triaxial accelerometer using GENEActiv software (version 2.1). An equation for derived SVMgs is shown below (Figure 1).
𝑆𝑉𝑀𝑔𝑠 = ∑ |√(𝑥 2 + 𝑦 2 + 𝑧 2 ) − 1𝑔|
Then SVMgs was averaged over last 3 minutes of each activity. Relationships between activity level (SVMgs) and directly measured oxygen uptake (METs) were first plotted as dependent and independent variables for dominant and non-dominant wrists over the full range of METs recorded. Recognizing that these relationships appeared to have inflection points at 6 METS, we then used piecewise linear regression to explore the component slopes. Also recognizing a linear relationship below 6 METs we 5
defined this using simple linear regression. Both types of regression analysis used standard least-squares methods. We compared the results obtained when the accelerometer was placed on the dominant and nondominant wrists. All analyses were performed using SAS 9.3.16 3. Results All twenty participants completed the twelve 6-minute physical activities of varying intensity. Their mean±SD age was 21±1 years, height 1.69±0.09 m and weight 66.3±9.3 kg. The range of VO2 and METs from all activities was 0.18 L/min to 3.20 L/min and 0.8 to 12.2 METs, respectively. SVMgs ranged from 13.6 to 3117.3 g·min for the dominant wrist and from 19.5 to 3448.4 g·min for the non-dominant wrist (90% of participants were right hand dominant). Significant differences in SVMgs were found between accelerometer placements on the dominant (120.9 8.7 g∙min) versus non-dominant wrist (99.9 6.8 g∙min; P = 0.016) for sedentary activities defined as <1.5 METs. We observed more variability of SVMgs in relation to METs for activity intensities >6 METs. Piecewise linear regression models using METs = 6 as a transition point revealed significant correlations (r2 = 0.85 for non-dominant, P < 0.001; r2 = 0.86 for dominant, P < 0.001). For the non-dominant wrist-worn accelerometer below 6 METs, SVMgs = 32.53 + 83.30*METs and above 6 METs, SVMgs = 1708.13 + 373.41*METs (Figure 2A). For all activities, a significant correlation was found between SVMgs and METs for both the dominant (r2 = 0.686; P < 0.001) and non-dominant wrists (r2 = 0.77; P < 0.001). Using the non-dominant wrist below 6 METs, the slope of the relationship between SVMgs and METs was 105.3 ± 4.3 (95% CI 96.9 to 113.7) indicating an increase in SVMgs of approximately 100 units for every MET increase in oxygen uptake (Figure 2B). 4. Discussion We demonstrated a reliable correlation between the output of a triaxial accelerometer and VO2 in terms of METs for a wide range of daily physical activities in healthy adults. Correlations were especially strong for activities below 6 METs. By contrast, we observed more variability in the output of the triaxial accelerometer for more vigorous activities over 6 METs. This finding may be explained by increased
6
muscle group activation and more complex movement patterns in the upper and lower extremities during vigorous activities such as running on the treadmill. Further studies involving other vigorous physical activities are needed to further explore this finding. Our study demonstrated that measured output in SVMgs could predict activity related energy expenditure for non-vigorous activities below 6 METs. Despite this apparent limitation, the use of triaxial accelerometry to track energy expenditure in debilitated individuals or those with chronic disease could be useful since these types of individuals are unlikely to exercise above 6 METs. Furthermore, these accelerometry measures could be sufficiently reliable to distinguish between light and moderate exercise intensity. We also demonstrated that accelerometry data is more reliable using the non-dominant wrist when estimating energy expenditure categorized as sedentary (<1.5 METs).17,18 There is a variety of possible explanations for this observation. One explanation is that the dominant hand tends to be involved in extraneous movements such as fidgeting. Another explanation may relate to the difference in inherent movement patterns for the activities tested. For example, while sitting and reading a book or sitting and doing computer work (Activities 2 and 3 in Table 1, respectively) only one arm is turning the pages or operating the computer mouse. These types of activity tend to involve small muscle movements which have a lower contribution to total energy expenditure. For activities of higher intensity, such as brisk walking or running, both arms perform roughly the same task, i.e., swinging up and down, in an alternating but otherwise symmetrical fashion. One limitation of this study was that we recruited a small and homogeneous group of participants— all being healthy, young adults. Patterns of movement and oxygen consumption may differ among members of more diverse populations.8,19,20 For example, elderly individuals or those with motor disorders, such as incoordination, may not complete the activities in the same manner as healthy, young participants. Likewise, patients with metabolic disorders may not exhibit the same relationship between physical activity and VO2.21
5. Conclusions 7
Our study adds to the current body of evidence relating triaxial accelerometry to oxygen uptake or energy expenditure which is important because many aspects of fitness research are based on accelerometry measurements.12,22 Although previous studies23,24 suggested that the optimal location of an accelerometer is on the hip or trunk, our study has shown that wrist-worn accelerometry can reliably predict energy expenditure in young, healthy participants performing physical activities in the mild to moderate intensity range. Further studies will be needed to verify this finding in specific populations and conditions. We found that an accelerometer worn on the non-dominant wrist was effective for predicting oxygen uptake with sedentary activities below 1.5 METs. When studying physical activities of mild to moderate intensities (<6 METs), our data support using a triaxial accelerometer worn on the nondominant wrist and, in these circumstances for young, healthy participants, one can expect an increase in summated acceleration vectors of 100 units for every MET increase in energy expenditure.
Practical Implications
Investigators and individuals interested in physical activity can rely upon a wrist-worn triaxial accelerometer as a simple, convenient method to determine energy expenditure for most daily, non-vigorous physical activities.
The recommended site for wearing an accelerometer is the non-dominant wrist in order to minimize the influence of extraneous movements typically seen with the dominant hand. This is especially important when predicting energy expenditure associated with sedentary activities.
Summated vector units from a triaxial accelerometer worn on the non-dominant wrist can reliably predict energy expenditure up to 6 METs in healthy, young participants. The output of these devices is less predictable during more vigorous activities probably due to the influence of body mass and mechanical efficiency.
A sampling frequency of 40 Hz offers the best combination of high classification accuracy but lower data load, longer battery life and higher efficiency of processing.
8
Acknowledgements The authors wish to thank Krishnal Sail, Ivanna L. Kenwood, and Ymi N. Ton for their contribution to participant enrollment. This study was part of a larger project funded by eResearch Technology, Inc.
References 1.
Bauman AE. Updating the evidence that physical activity is good for health: an epidemiological review 2000-2003. J Sci Med Sport 2004; 7(1 Suppl):6-19.
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Kokkinos P, Myers J. Exercise and physical activity: clinical outcomes and applications. Circulation. 2010; 122(16):1637-1648.
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Haskell WL, Lee IM, Pate RR et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. 2007; 116(9):1081-1093.
4.
Ainsworth BE, Haskell WL, Herrmann SD et al. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exer 2011; 43(8):1575-1581.
5.
Bonnefoy M, Normand S, Pachiaudi C et al. Simultaneous validation of ten physical activity questionnaires in older men: a doubly labeled water study. J Am Geriatr Soc 2001; 49(1):28-35.
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Eckert KG, Lange MA. Comparison of physical activity questionnaires for the elderly with the International Classification of Functioning, Disability and Health (ICF)--an analysis of content. BMC public health 2015; 15:249.
7.
Corder K, Brage S, Ekelund U. Accelerometers and pedometers: methodology and clinical application. Curr Opin Clin Nutr Metab Care 2007; 10(5):597-603.
8.
Cheung VH, Gray L, Karunanithi M. Review of accelerometry for determining daily activity among elderly patients. Arch Phys Med Rehabil 2011; 92(6):998-1014. 9
9.
Lyden K, Kozey SL, Staudenmeyer JW et al. A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations. Eur J Appl Physiol 2011; 111(2):187-201.
10.
Zhang S, Rowlands AV, Murray P et al. Physical activity classification using the GENEA wristworn accelerometer. Med Sci Sports Exer 2012; 44(4):742-748.
11.
Phillips LR, Parfitt G, Rowlands AV. Calibration of the GENEA accelerometer for assessment of physical activity intensity in children. J Sci Med Sport 2013; 16(2):124-128.
12.
Schaefer CA, Nigg CR, Hill JO et al. Establishing and evaluating wrist cutpoints for the GENEActiv accelerometer in youth. Med Sci Sports Exer 2014; 46(4):826-833.
13.
Esliger DW, Rowlands AV, Hurst TL et al. Validation of the GENEA Accelerometer. Med Sci Sports Exer 2011; 43(6):1085-1093.
14.
Zhang S, Murray P, Zillmer R et al. Activity classification using the GENEA: optimum sampling frequency and number of axes. Med Sci Sports Exer 2012; 44(11):2228-2234.
15.
Cooper CB, Storer TW. Exercise Testing and Interpretation: A Practical Approach, Cambridge, UK, Cambridge University Press, 2001.
16.
What’s new in SAS® 9.3. Cary, NC, USA: SAS Institute Inc; 2012.
17.
Pate RR, O'Neill JR, Lobelo F. The evolving definition of "sedentary". Exer Sport Sci Rev 2008; 36(4):173-178.
18.
Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms "sedentary" and "sedentary behaviours". Appl Physiol Nutr Metab 2012; 37(3):540-542.
19.
Falck RS, McDonald SM, Beets MW et al. Measurement of physical activity in older adult interventions: a systematic review. Br J Sports Med 2016; 50(8):464-470.
20.
Alhassan S, Lyden K, Howe C. Accuracy of accelerometer regression models in predicting energy expenditure and METs in children and youth. Pediatr Exer Sci 2012; 24(4):519-536.
21.
Shephard RJ, Aoyagi Y. Measurement of human energy expenditure, with particular reference to field studies: an historical perspective. Eur J Appl Physiol 2012; 112(8):2785-2815. 10
22.
Huisingh-Scheetz M, Kocherginsky M, Dugas L et al. Wrist Accelerometry in the Health, Functional, and Social Assessment of Older Adults. J Am Geriatr Soc 2016; 64(4):889-891.
23.
Trost SG, McIver KL, Pate RR. Conducting Accelerometer-Based Activity Assessments in FieldBased Research. Med Sci Sports Exer. 2005; 37(Supplement):S531-S543.
24.
Ward DS, Evenson KR, Vaughn A et al. Accelerometer Use in Physical Activity: Best Practices and Research Recommendations. Med Sci Sports Exer 2005; 37(Supplement):S582-S588.
11
Figure 1
Figure 1. Diagrammatic representation of the resolution of triaxial acceleration vectors to give a summated vector magnitude with -1 subtracted for gravity (SVMgs). The unit “g” represents the gravitation unit.
12
Figure 2 (A)
(B)
Figure 2. Relationship between summated vector magnitudes with gravity subtracted (SVMgs) versus oxygen uptake (METs). (A) Piecewise linear regression analysis demonstrated two slope relationships between METs and SVMgs using 6 METs as a transition point. (B) Linear regression analysis examining the correlation between SVMgs measured by the triaxial accelerometer worn on the non-dominant wrist and levels of physical activity up to 6 METs. Below 6 METs, SVMgs is a reliable predictor of oxygen uptake in these participants.
13
Table 1. The twelve activities performed by all participants. Activity
Estimated METs*
Duration
1. Lying quietly (lying quietly on back)
1-1.3
10 min
2. Sitting & reading a book
1.3
6 min
3. Sitting & doing computer work
1.5-1.8
6 min
4. Washing dishes
2.5
6 min
5. Sweeping floor (general effort, walking)
3.3-3.8
6 min
6. Organizing room (stacking & organizing chairs)
4.8
6 min
7. Walking 1.5 mph, 0% grade
2.0
6 min
8. Walking 3 mph, 0% grade
3.5-4.3
6 min
9. Walking 3 mph, 3% grade
5.3
6 min
10. Walking 4 mph, 0% grade
4.3-5
6 min
11. Walking 3 mph, 8% grade
8
6 min
12. Running 6 mph, 0% grade
8.3-11
6 min
*METs were estimated from Compendium of Physical Activities2
All walking and running activities occurred on a treadmill.
14
S1440-2440(17)30259-1 http://dx.doi.org/doi:10.1016/j.jsams.2017.01.233 JSAMS 1453
To appear in:
Journal of Science and Medicine in Sport
Received date: Revised date: Accepted date:
20-6-2016 28-11-2016 2-1-2017
Please cite this article as: Sirichana Worawan, Dolezal Brett A, Neufeld Eric V, Wang Xiaoyan, Cooper Christopher B.Wrist-worn triaxial accelerometry predicts the energy expenditure of non-vigorous daily physical activities.Journal of Science and Medicine in Sport http://dx.doi.org/10.1016/j.jsams.2017.01.233 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Wrist-worn triaxial accelerometry predicts the energy expenditure of non-vigorous daily physical activities
Worawan Sirichana a,b, Brett A. Dolezala, Eric V. Neufelda, Xiaoyan Wangc, Christopher B. Coopera,*
a
Exercise Physiology Research Laboratory, Departments of Medicine and Physiology, David Geffen
School of Medicine, University of California at Los Angeles, Los Angeles, California, USA b
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Faculty of Medicine,
Chulalongkorn University, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand c
Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine,
University of California at Los Angeles, Los Angeles, California, USA.
*Corresponding author: Christopher B Cooper, MD Professor Emeritus of Medicine and Physiology, David Geffen School of Medicine, University of California, Los Angeles 10833 Le Conte Avenue, 37-131 CHS, Los Angeles, CA 90095-1690, United States Tel: 1-310-470-3983 Fax: 1-310-206-8211 e-mail: [email protected] Word count: (1989/3000) Abstract word count: 246/250 Number of Tables: 1 Number of Figures: 2 1
Abstract Objectives: Triaxial accelerometry is commonly used to estimate oxygen uptake (VO2) and energy expenditure in health and fitness studies. We tested the correlation of a triaxial accelerometer in terms of a summation of vector magnitudes with gravity subtracted (SVMgs) and VO2 for different daily physical activities. Design: Original research, cross-sectional. Methods: Twenty volunteers wore a triaxial accelerometer on both wrists while performing 12 assigned daily physical activities for 6 minutes for each activity. The VO2 was determined by indirect calorimetry using a portable metabolic measurement system. The last 3 minutes of each activity was assumed to represent steady-state. The VO2 measured during these periods was averaged and converted into metabolic equivalents (METs). Results: The range of VO2 for all activities was 0.18-3.2 L/min (0.8-12.2 METs). The range of SVMgs was 13.6-3117.3 g·min for the dominant hand and 19.5-3448.4 g·min for the non-dominant hand. Significant differences in SVMgs existed between accelerometer placements on the dominant (120.9 8.7 g∙min) versus non-dominant hand (99.9 6.8 g∙min; P = 0.016) for the lowest levels of physical activity defined as <1.5 METs. Piecewise linear regression model using 6 METs as the transition point showed similar significant correlations for the non-dominant wrist (r2 = 0.85; P < 0.001) and the dominant wrist (r2 = 0.86; P < 0.001). Conclusions: Wrist-worn triaxial accelerometry reliably predicted energy expenditure during common physical activities <6 METs. Stronger correlations were found when the accelerometer was worn on the non-dominant wrist rather than the dominant wrist.
Keywords: triaxial accelerometer, physical activity, energy expenditure, oxygen uptake 2
1. Introduction Maintaining a physically active lifestyle is a core tenant of leading a life of health and vitality. Higher levels of physical activity strongly correlate with reduced risk of chronic diseases and general mortality.1, 2 The American College of Sports Medicine (ACSM) has classified physical activity intensity according to the level of metabolic equivalents (METs) into mild (<3 METs), moderate (3-6 METs), and vigorous (>6METs).3,
4
They also recommend that adults should perform at least 150 minutes of
moderate-intensity aerobic exercise per week.3 Daily activities can provide an adequate physical challenge to be considered moderate-intensity exercise depending on the oxygen uptake (VO2 or METs) of the task. However, daily physical activity is difficult to quantify because of varying intensity and duration of activities throughout the day. Furthermore, self-reported physical activity logs or physical activity questionnaires demonstrate inconsistent validity and reliability, especially in elderly.5, 6 Activity trackers, such as accelerometers and pedometers, have been used to monitor and quantify physical activity in daily life, sports, and research. Accelerometry quantifies movements in terms of velocity over time in one or more perpendicular axes, accelerometry allowing continuous monitoring of physical activity over a period of time. The measured output varies among devices, e.g. steps, activity count, intensity count and vector magnitude units. A wide range of correlations has been reported between these outputs and activity related energy expenditure, depending on the devices, device placement, and populations studied.7-9 We compared energy expenditure calculated from oxygen uptake, which we measured by exhaled breath analysis, with vector magnitude units recorded form a triaxial accelerometer during various physical activities, representative of daily living. We aimed to build upon current evidence that accelerometry may be beneficial in measuring certain movements and certain levels of exercise intensity.
3
2. Methods Twenty healthy volunteers were recruited from the UCLA campus and the surrounding community using flyers which had been approved by the UCLA Institutional Review Board. There were 10 men and 10 women. Mean (SD) height was 1.69 (0.09) m and weight was 66.3 (9.3) kg. Participants were asked to fast for at least 3 hours before testing except they were allowed to drink water to maintain normal hydration. They were also asked to avoid alcohol, caffeine or tobacco at least 8 hours before testing and to avoid excessive physical exertion for at least 12 hours. The study was approved by the UCLA Institutional Review Board and all participants gave written informed consent.
Participants
completed standard health, medical, and exercise history forms following enrolment and prior to data acquisition. The GENEActiv wrist-worn triaxial accelerometer (GENEActiv, Cambridge, UK) is intended for measuring acceleration of movement in three perpendicular axes in gravitation units with a range 8 g. The device is small (36L x 20W X 12H mm), lightweight (16 grams without strap), waterproof and can be worn on the wrist, hip, or ankle. This device has been validated as a determinant the intensity of various physical activities10-12, and has been shown to match the performance of the waist-worn ActiGraph and RT3.13 The GENEActiv accelerometer also has user-defined sampling frequencies ranging from 10 to 100 Hz and provides both raw data and software for end-user analysis. We obtained triaxial accelerometry data using a sampling rate of 40 Hz which is associated with high classification accuracy but lower data load, longer battery life and higher efficiency of processing.14 These data were downloaded using USB 2.0 charging cable immediately after testing each individual subject. Oxygen uptake (VO2) during rest and physical activity was measured simultaneously by a previously validated portable metabolic analyser (Oxycon Mobile 5.0, CareFusion, Yorba Linda, CA). This apparatus, which incorporates a turbine flow transducer and discrete oxygen and carbon dioxide analyzers, was calibrated before every test. Heart rate was recorded via a chest strap (Polar Electro, OY, Kempele, Finland) that was integrated with the
4
metabolic measurement system. All tests were conducted by trained and experienced personnel in accordance with established guidelines for cardiopulmonary exercise testing.15 Each participant was fitted with a triaxial accelerometer on both wrists while VO2 was being measured by exhaled gas analysis. They then performed 12 different physical activities (Table 1), each for 6-minute intervals, with VO2 ranging from 1-11 METs. The metabolic equivalent of each activity was estimated by data from Compendium of Physical Activities.4 Initially, participants were asked to lie quietly on a bed for 10 minutes in order to measure resting VO2 and heart rate at baseline. Five activities were structured to represent movements performed in day-to-day life. The other 7 activities were geared towards higher levels of physical activity such as walking and running on a treadmill at different speeds and grades. The activities were performed in same sequence for all subjects. The resting period between each activity was at least 2 minutes or until the participant’s heart rate had returned to baseline. The last 3 minutes of measured VO2 for each activity was averaged unless the difference between the last 3 minutes was greater than 100 mL/min. If the difference of last 3 minutes VO2 was greater than 100 mL/min, then VO2 from the last minute was averaged. Then, the average VO2 in mL/kg/min was converted into METs to represent the oxygen consumption for each activity and each individual subject. We did this using the standard assumption that 1 MET = 3.5 ml/kg/min of oxygen uptake. The sum of vector magnitudes with gravity subtracted (SVMgs) was derived in 60-second epochs from the triaxial accelerometer using GENEActiv software (version 2.1). An equation for derived SVMgs is shown below (Figure 1).
𝑆𝑉𝑀𝑔𝑠 = ∑ |√(𝑥 2 + 𝑦 2 + 𝑧 2 ) − 1𝑔|
Then SVMgs was averaged over last 3 minutes of each activity. Relationships between activity level (SVMgs) and directly measured oxygen uptake (METs) were first plotted as dependent and independent variables for dominant and non-dominant wrists over the full range of METs recorded. Recognizing that these relationships appeared to have inflection points at 6 METS, we then used piecewise linear regression to explore the component slopes. Also recognizing a linear relationship below 6 METs we 5
defined this using simple linear regression. Both types of regression analysis used standard least-squares methods. We compared the results obtained when the accelerometer was placed on the dominant and nondominant wrists. All analyses were performed using SAS 9.3.16 3. Results All twenty participants completed the twelve 6-minute physical activities of varying intensity. Their mean±SD age was 21±1 years, height 1.69±0.09 m and weight 66.3±9.3 kg. The range of VO2 and METs from all activities was 0.18 L/min to 3.20 L/min and 0.8 to 12.2 METs, respectively. SVMgs ranged from 13.6 to 3117.3 g·min for the dominant wrist and from 19.5 to 3448.4 g·min for the non-dominant wrist (90% of participants were right hand dominant). Significant differences in SVMgs were found between accelerometer placements on the dominant (120.9 8.7 g∙min) versus non-dominant wrist (99.9 6.8 g∙min; P = 0.016) for sedentary activities defined as <1.5 METs. We observed more variability of SVMgs in relation to METs for activity intensities >6 METs. Piecewise linear regression models using METs = 6 as a transition point revealed significant correlations (r2 = 0.85 for non-dominant, P < 0.001; r2 = 0.86 for dominant, P < 0.001). For the non-dominant wrist-worn accelerometer below 6 METs, SVMgs = 32.53 + 83.30*METs and above 6 METs, SVMgs = 1708.13 + 373.41*METs (Figure 2A). For all activities, a significant correlation was found between SVMgs and METs for both the dominant (r2 = 0.686; P < 0.001) and non-dominant wrists (r2 = 0.77; P < 0.001). Using the non-dominant wrist below 6 METs, the slope of the relationship between SVMgs and METs was 105.3 ± 4.3 (95% CI 96.9 to 113.7) indicating an increase in SVMgs of approximately 100 units for every MET increase in oxygen uptake (Figure 2B). 4. Discussion We demonstrated a reliable correlation between the output of a triaxial accelerometer and VO2 in terms of METs for a wide range of daily physical activities in healthy adults. Correlations were especially strong for activities below 6 METs. By contrast, we observed more variability in the output of the triaxial accelerometer for more vigorous activities over 6 METs. This finding may be explained by increased
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muscle group activation and more complex movement patterns in the upper and lower extremities during vigorous activities such as running on the treadmill. Further studies involving other vigorous physical activities are needed to further explore this finding. Our study demonstrated that measured output in SVMgs could predict activity related energy expenditure for non-vigorous activities below 6 METs. Despite this apparent limitation, the use of triaxial accelerometry to track energy expenditure in debilitated individuals or those with chronic disease could be useful since these types of individuals are unlikely to exercise above 6 METs. Furthermore, these accelerometry measures could be sufficiently reliable to distinguish between light and moderate exercise intensity. We also demonstrated that accelerometry data is more reliable using the non-dominant wrist when estimating energy expenditure categorized as sedentary (<1.5 METs).17,18 There is a variety of possible explanations for this observation. One explanation is that the dominant hand tends to be involved in extraneous movements such as fidgeting. Another explanation may relate to the difference in inherent movement patterns for the activities tested. For example, while sitting and reading a book or sitting and doing computer work (Activities 2 and 3 in Table 1, respectively) only one arm is turning the pages or operating the computer mouse. These types of activity tend to involve small muscle movements which have a lower contribution to total energy expenditure. For activities of higher intensity, such as brisk walking or running, both arms perform roughly the same task, i.e., swinging up and down, in an alternating but otherwise symmetrical fashion. One limitation of this study was that we recruited a small and homogeneous group of participants— all being healthy, young adults. Patterns of movement and oxygen consumption may differ among members of more diverse populations.8,19,20 For example, elderly individuals or those with motor disorders, such as incoordination, may not complete the activities in the same manner as healthy, young participants. Likewise, patients with metabolic disorders may not exhibit the same relationship between physical activity and VO2.21
5. Conclusions 7
Our study adds to the current body of evidence relating triaxial accelerometry to oxygen uptake or energy expenditure which is important because many aspects of fitness research are based on accelerometry measurements.12,22 Although previous studies23,24 suggested that the optimal location of an accelerometer is on the hip or trunk, our study has shown that wrist-worn accelerometry can reliably predict energy expenditure in young, healthy participants performing physical activities in the mild to moderate intensity range. Further studies will be needed to verify this finding in specific populations and conditions. We found that an accelerometer worn on the non-dominant wrist was effective for predicting oxygen uptake with sedentary activities below 1.5 METs. When studying physical activities of mild to moderate intensities (<6 METs), our data support using a triaxial accelerometer worn on the nondominant wrist and, in these circumstances for young, healthy participants, one can expect an increase in summated acceleration vectors of 100 units for every MET increase in energy expenditure.
Practical Implications
Investigators and individuals interested in physical activity can rely upon a wrist-worn triaxial accelerometer as a simple, convenient method to determine energy expenditure for most daily, non-vigorous physical activities.
The recommended site for wearing an accelerometer is the non-dominant wrist in order to minimize the influence of extraneous movements typically seen with the dominant hand. This is especially important when predicting energy expenditure associated with sedentary activities.
Summated vector units from a triaxial accelerometer worn on the non-dominant wrist can reliably predict energy expenditure up to 6 METs in healthy, young participants. The output of these devices is less predictable during more vigorous activities probably due to the influence of body mass and mechanical efficiency.
A sampling frequency of 40 Hz offers the best combination of high classification accuracy but lower data load, longer battery life and higher efficiency of processing.
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Acknowledgements The authors wish to thank Krishnal Sail, Ivanna L. Kenwood, and Ymi N. Ton for their contribution to participant enrollment. This study was part of a larger project funded by eResearch Technology, Inc.
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Figure 1
Figure 1. Diagrammatic representation of the resolution of triaxial acceleration vectors to give a summated vector magnitude with -1 subtracted for gravity (SVMgs). The unit “g” represents the gravitation unit.
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Figure 2 (A)
(B)
Figure 2. Relationship between summated vector magnitudes with gravity subtracted (SVMgs) versus oxygen uptake (METs). (A) Piecewise linear regression analysis demonstrated two slope relationships between METs and SVMgs using 6 METs as a transition point. (B) Linear regression analysis examining the correlation between SVMgs measured by the triaxial accelerometer worn on the non-dominant wrist and levels of physical activity up to 6 METs. Below 6 METs, SVMgs is a reliable predictor of oxygen uptake in these participants.
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Table 1. The twelve activities performed by all participants. Activity
Estimated METs*
Duration
1. Lying quietly (lying quietly on back)
1-1.3
10 min
2. Sitting & reading a book
1.3
6 min
3. Sitting & doing computer work
1.5-1.8
6 min
4. Washing dishes
2.5
6 min
5. Sweeping floor (general effort, walking)
3.3-3.8
6 min
6. Organizing room (stacking & organizing chairs)
4.8
6 min
7. Walking 1.5 mph, 0% grade
2.0
6 min
8. Walking 3 mph, 0% grade
3.5-4.3
6 min
9. Walking 3 mph, 3% grade
5.3
6 min
10. Walking 4 mph, 0% grade
4.3-5
6 min
11. Walking 3 mph, 8% grade
8
6 min
12. Running 6 mph, 0% grade
8.3-11
6 min
*METs were estimated from Compendium of Physical Activities2
All walking and running activities occurred on a treadmill.
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