Daily physical activity is important

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1 ICAN: Infant, Child, & Adolescent Nutrition August 2012 Clinical Research Report A Stand-Alone Accelerometer System for Free-Living Individuals to Measure and Promote Physical Activity Chinmay U. Manohar, MS, Gabriel A. Koepp, MHA, Shelly K. McCrady-Spitzer, MS, and James A. Levine, MD, PhD Abstract: Background. Physical activity is important for multiple aspects of health, for example, cancer prevention, metabolic disease treatment, cardiovascular health, and obesity management. Despite the improved capability of measuring physical activity in the research arena, the options are scarce and less reliable for measurements in free-living people. In this article, the authors tested the validity of a robust stand-alone patient-operated Accelerometer System that can measure physical activity and sedentariness in active people. Methods. Thirty subjects wore the Accelerometer System along with the validated physical activity monitoring system (PAMS) with different body postures and during graded walking at 7 velocities. Energy expenditure was measured using indirect calorimetry. Results. In all the 30 subjects, the Accelerometer System distinguished sedentary and walking activity reliably even with ½ mph increments in walking and was accurate and precise compared with PAMS, with an intraclass correlation coefficient (r 2 >.98). The Accelerometer System showed excellent sequential increases with increase in walking velocity and energy expenditure (r 2 >.9). Conclusion. A stand-alone Accelerometer System was accurate and reliable in measuring and quantifying physical activity in the laboratory setting. Keywords: energy expenditure; obesity; weight loss; physical activity Daily physical activity is important for multiple aspects of health, including cancer prevention, metabolic disease treatment (diabetes, Daily physical activity is important for multiple aspects of health, including cancer prevention, metabolic disease treatment (diabetes, hyperlipidemia, hypertension), cardiovascular health, and obesity management. hyperlipidemia, hypertension), cardiovascular health, and obesity management. 1,2 There is growing need for technologies that promote physical activity. Moreover, a positive dose response relationship exists between physical health and mortality in which more is better. In the research arena, the capability of measuring physical activity has improved, largely through the dissemination of electronic accelerometer systems combined with the availability of doubly labeled water to validate such systems. 3 However, there are few options for active people to measure and modulate their own physical activity. Our goal was to devise an accurate, precise, and robust stand-alone patientoperated Accelerometer System that can measure physical activity and sedentariness in active people. Our prediction was that the Accelerometer System is an accurate, precise, and robust device for measuring physical activity in active patients. The hypotheses we tested were the following: 222 DOI: / From the Endocrine Research Unit, Mayo Clinic, Rochester, Minnesota. Address correspondence to James A. Levine, Endocrine Research Unit, Mayo Clinic, Rochester, MN 55905; Levine.james@mayo.edu. For reprints and permissions queries, please visit SAGE s Web site at Copyright 2012 The Author(s)

2 vol. 4 no The Accelerometer System could be used to detect changes in walking velocity. 2. That the Accelerometer System output showed a significant positive correlation with the output from a gold standard system known to be valid (physical activity monitoring system [PAMS]). 3. The energy expenditure measured by the Accelerometer System showed a significant positive correlation with energy expenditure measured using indirect calorimetry. ICAN: Infant, Child, & Adolescent Nutrition Figure 1. The Accelerometer System. Laboratory validation is the first step in building a stand-alone biosensor-based system that will enable people to measure and modulate their physical activity to improve their health; in these studies we tested it. Materials and Methods Description of Accelerometer System We devised a stand-alone consumer grade Accelerometer System that freeliving people could use (Gruve; Muve Incorporated, Minneapolis, MN).4 The Accelerometer System is a body-worn device that monitors and records a patient s physical activity (Figure 1). The Accelerometer System is configured via a Web-based application. Once initially synchronized with the Web application, the device monitors a patient s daily physical activity at 20 Hz and stores the minute-data on the device. When the patient synchronizes the device with his or her computer via a universal synchronous bus (USB) interface, the patient s data are uploaded to the computer. The patient can then view his or her activity data using straightforward graphical user interfaces. The Accelerometer System has a builtin vibrating motor that gives a short vibrating pulse when it senses extended period of inactivity. Additionally, with a touch of a button the light bar on the Accelerometer System lights up with a color corresponding to the user s daily activity level. For example, at the beginning of the day the light bar is red, but as the day progresses, if the user has been sufficiently active, then there is color progression from red to yellow to orange to blue to green. The green light indicates that the daily activity goal has been achieved. At the core of the Accelerometer System is a triaxial accelerometer (MMA7361L, Freescale Semiconductor, Austin, TX) with a dynamic range set at ±2g, which gathers the data in the 3 orthogonal axes of movement (x, y, 20 Hz. The raw data stream is converted for Offset and Gain. After a lowpass filter (cutoff frequency, 5 Hz), the filtered data stream from the 3 axes are integrated by calculating the signcorrected sum of the displacements and summating them, corrected for gravity, as follows: Acceleration = 2 ( x g2 + y g2 + z g2 ) 1 AU Acceleration System = (1) (2) 1 60 abs ( Accelerationt Accelerationt 1 ) 60 t =1 The Accelerometer System includes a real time clock chip (32 khz crystal), used to time stamp the accelerometer data. The device architecture is shown in Figure 2. The vibrating feedback mentioned before is produced by the vibrating motor shown in the device architecture. The bank of LEDs and the push button seen in the device architecture is what constitute the color bar that gives the visual feedback as discussed earlier. The data are acquired and stored on board in a flash memory module using an 8 bit microcontroller PIC 18LF4550 (Microchip, Chandler, AZ) using a program written in C language. The microcontroller itself contains a 48 kbit internal memory, while the onboard memory has 4 MBit of external flash memory. The Accelerometer System is powered by a 3.7-V rechargeable Li-ion battery. The approximate battery life is 7 days. The Accelerometer System is recharged via the USB port in less than 3 hours. Experimental Design Subjects. We tested the Accelerometer System in 30 people (17 women, 13 men), 35 ± 9 years, 82 ± 17 kg, and body mass index (BMI) 28 ± 6 kg/m2. Subjects were excluded if they were pregnant, had any acute or chronic illness, had unsteady body weight (>2 kg fluctuation over the 6 months prior to study), had a medical history of thyroid dysfunction, 223

3 ICAN: Infant, Child, & Adolescent Nutrition August 2012 Figure 2. The Accelerometer System Architecture. or were taking medications capable of altering metabolic rate. Subjects provided informed written consent, and the Mayo Clinic Institutional Review Board approved the study. Protocol. The study was conducted in a temperature controlled and quiet laboratory. Subjects were orientated to the procedures and then weighed on a calibrated standing scale (Model 644, Seca Corporation, Hanover, MD), and height was measured using a stadiometer. Subjects were asked to abstain from alcohol for at least 12 hours prior to the start of the study. Subjects also fasted for 6 hours and had not undertaken exertional activity or consumed caffeine for >6 hours. Throughout the study, subjects were in thermal comfort (68-74 F; C). Subjects wore a PAMS suit (Figure 3A). This validated system allows body posture and physical activity to be measured every half second continuously and has been validated against both room calorimetry and doubly labeled water. 5-9 The PAMS involves wearing Lycra-Spandex undergarments into which 4 inclinometers (Crossbow Technology, Inc, San Francisco, CA) are integrated that measure body angle on the right and left lateral aspects of the torso and the right and left lateral aspects of the mid-thigh. In addition, there are 2 accelerometers (Crossbow Technology, Inc) placed at the base of the spine, and 2 data loggers (Crossbow Technology, Inc) are worn around the waist. The PAMS weighs 1 kg. Subjects also wore 2 Gruve devices (to assess reproducibility) in the prescribed fashion (Figure 3B). Relaxed subjects lay supine and awake with their head at a 10 tilt. First, subjects rested for 30 minutes and then resting energy expenditure was measured for the following 30 minutes using an indirect calorimeter (described below). The order of activities was fixed and standardized throughout the experiment, as follows. Energy expenditure was measured for 15 minutes each under the following conditions: 1. Sitting. Subjects were seated in a backed, armed office chair with their back, arms, and legs supported. Subjects were asked to remain relaxed during the measurement. 2. Standing motionless. Subjects were instructed to stand motionless with arms hanging by their sides and feet spaced 6 inches apart. Subjects were asked to remain relaxed and still during the measurement. 3. Graded walking. Subjects walked on a calibrated treadmill (True 600, O Fallon, MO) at 0.5, 1, 1.5, 2, 2.5, 3, and 3.5 mph. Indirect calorimetry. Measurements of energy expenditure were performed using a high-precision indirect calorimeter (Columbus Instruments, Columbus, OH) as described previously. 9 Expired air was collected using a fullface transparent dilution mask (Scott Aviation, Lancaster, NY) connected to the calorimeter by leak-proof tubing (Vacumed, Ventura, CA). We have found 10 that while wearing this equipment volunteers can complete tasks inside and outside the laboratory such as walking on level ground, climbing stairs in stairwells, or working in an office environment, and even in these circumstances highly precise measures of energy expenditure can be made. Repeated alcohol burn experiments yielded CO 2 and O 2 recoveries of >98%. The standard deviation of the respiratory quotient for the last 15 minutes of the resting measurements was <1% of the mean. Statistical analysis Mean activity (accelerometer units) for the Accelerometer System and the PAMS as well as the energy expenditure for each phase of the protocol was calculated. All values are provided as mean ± standard deviation. ANOVA (energy expenditure, age, sex, and BMI) and post hoc paired t-tests were used to compare paired changes in energy expenditure for the 30 subjects. To examine our hypotheses that the Accelerometer System was accurate and reliable for measuring physical activity compared with PAMS in the laboratory setting and also in predicting energy expenditure, regression analyses were used comparing the physical activity as measured by the Accelerometer System against that measured by PAMS. Statistical significance was defined as P <

4 vol. 4 no. 4 ICAN: Infant, Child, & Adolescent Nutrition Results Figure 3. (A) The Physical Activity Monitoring System (PAMS) Comprising Inclinometers (I) on Both Sides of Torso and Lateral Thigh and 2 Triaxial Accelerometers (Ax) on the Back. (B) The Accelerometer System (G) Worn as Along With the PAMS. Figure 4. Progressive Increase in Accelerometer System While Walking With 0.5 mph Increase in Walking Velocities for the 30 Study Participants (11 Lean, 10 Overweight, 9 Obese). Data Are Shown as Mean ± Standard Deviation. All subjects tolerated the data collection procedures without problem or complaint. Our first hypothesis was that the Accelerometer System could be used to detect changes in walking velocity. For all the 30 subjects there were progressive increases in accelerometer system s output with increases in walking velocity even at 0.5 mph increments (r2 >.9 for each subject; Figure 4). Our second hypothesis was that the Accelerometer System output showed a significant positive correlation with the output from a valid gold standard system known to be valid (PAMS). The Accelerometer System reliably sensed sedentary and walking activity at different speeds and was accurate and precise when compared with the PAMS, with intraclass correlation coefficient (r2 >.97; Figure 5A). We estimated energy expenditure from both measurement platforms using accelerometer calorimetry regressions plots derived for each person.5 We compared the agreement between the 2 techniques using Bland Altman plots (Figure 5B). The data suggest a close agreement between the 2 accelerometer systems in predicting energy expenditure. Our third hypothesis was that the energy expenditure predicted by the Accelerometer System showed a significant positive correlation with energy expenditure measured by indirect calorimetry. This is important because if this hypothesis is supported, it would justify generating regression equations that in future could be (a) tested and (b) used to convert the output of the Accelerometer System into a person s free-living energy expenditure. As expected,10,11 the resting as well as walking energy expenditure, expressed in absolute terms, was significantly less in the lean compared with the obese participants. When corrected for the body weight, resting as well as walking energy expenditure was significantly greater in the lean compared with the obese (Table 1). Energy expenditure showed a significant linear response to progressive increases in walking velocity for 225

5 ICAN: Infant, Child, & Adolescent Nutrition August 2012 Figure 5. Progressive Increase in Accelerometer System While Walking With 0.5 mph Increase in Walking Velocities for the 30 Study Participants (11 Lean, 10 Overweight, 9 Obese). Data Are Shown as Mean ± Standard Deviation. (A) Accelerometer System Output (AU Gruve ) Versus PAMS Accelerometer Output (AU PAMS ) for the 30 Study Participants (11 Lean, 10 Overweight, 9 Obese). (B) Bland Altman Plot Comparing the Energy Expenditure Estimated by the Accelerometer System and PAMS Accelerometer System. all the participants. The hypothesis was affirmed, and the data demonstrated that energy expenditure increased significantly with every increase in walking velocity irrespective of whether it was represented in absolute terms or corrected for body weight. The accelerometer system s output also showed a significant linear response while detecting walking energy expenditure for all subjects (Figure 6). The relationships for accelerometer output and energy expenditure were the following: Lean: Energy Expenditure = AU Gruve Overweight: Energy Expenditure = AU Gruve Obese: Energy Expenditure = AU Gruve The single unifying equation was the following: Energy Expenditure = AU Gruve There were no significant differences for the 3 groups for the sign-correct residuals from this unified equation. However, the y-axis intercept was significantly lower for people with obesity. Discussion and Conclusion Physical activity benefits patients with cardiovascular disease, hypertension, hyperlipidemia, and diabetes and is also important in cancer prevention and weight control. 12,13 Pedometers, despite their inaccuracy, are widely used to monitor physical activity. 4,14,15 Pedometers are however limited in accuracy and precision particularly at the velocities that free-living people walk at. 16 The Accelerometer System we describe is a self-contained commercial-grade, lightweight electronic device that enables patients to self-monitor their physical activity and self-assess their response to intervention. The device provides users an immediate visual feedback with respect to their goals. Laboratory validation of such system is very important to test for accuracy and precision. We conducted laboratory validation of the Accelerometer System as discussed earlier, and the data demonstrate that, at least in the laboratory setting, the Accelerometer System is accurate and precise and can be used to estimate energy expenditure. Currently there is a plethora of physical activity measurement devices available. Pedometers have long been recommended as a simple way to measure daily steps and thus estimate energy expenditure. 17,18 They are cheap but their accuracy falls when detecting normal walking activity that occurs at less than 3 mph. 16,19-21 Also, they were found to be less accurate when it came to predicting energy expenditure. 22 Devices such as Actigraph (Actigraph, Pensacola, FL) and Actical (Mini-Mitter Company, Inc, Bend, OR) are more sophisticated than 226

6 vol. 4 no. 4 ICAN: Infant, Child, & Adolescent Nutrition Table 1. Characteristics and Energy Expenditure Data for the 31 Patients a Total Lean Overweight Obese N (women; men) (6:5) 10 (6:4) 9 (5:4) Weight (kg) 82 ± ± ± ± 12 BMI (kg/m 2 ) 28 ± 6 23 ± 2 27 ± 1 35 ± 3 Age (years) 35 ± 9 33 ± ± 9 38 ± 8 Energy expenditure (kcal/h) Resting 75 ± ± ± ± 15 Sitting 81 ± ± ± ± 14 Standing 88 ± ± ± ± 19 Walking: 0.5 mph 159 ± ± ± ± 29 Walking: 1 mph 198 ± ± ± ± 30 Walking: 1.5 mph 221 ± ± ± ± 38 Walking: 2 mph 251 ± ± ± ± 51 Walking: 2.5 mph 287 ± ± ± ± 56 Walking: 3 mph 337 ± ± ± ± 68 Walking: 3.5mph 414 ± ± ± ± 83 Energy expenditure/weight (kcal/kg/h) Resting 0.94 ± ± ± ± 0.09 Sitting 1.01 ± ± ± ± 0.10 Standing 1.09 ± ± ± ± 0.13 Walking: 0.5 mph 1.95 ± ± ± ± 0.22 Walking: 1 mph 2.44 ± ± ± ± 0.22 Walking: 1.5 mph 2.72 ± ± ± ± 0.18 Walking: 2 mph 3.09 ± ± ± ± 0.25 Walking: 2.5 mph 3.53 ± ± ± ± 0.28 Walking: 3 mph 4.13 ± ± ± ± 0.37 Walking: 3.5 mph 5.06 ± ± ± ± 0.47 Abbreviation: BMI, body mass index. a Data are expressed as mean ± standard deviation. the pedometers and use accelerometers to detect motion and have long been validated and regularly used in research studies These systems, although accurate, are complex and lack a simple user interface and thus require a trained person to analyze and interpret the data they gather. The Accelerometer System we describe is the first stand-alone, patientoperated, behavior reinforcement device. The Accelerometer System solution takes a fresh approach toward measurement and interpretation of physical activity data. By using a simple Web-based interface, the Accelerometer System device uploads patient s activity data to the Web site. The patients can track their performance by logging into their personal page on the Web site. The novelty of the solution is in the way the Accelerometer System interfaces and is 227

7 ICAN: Infant, Child, & Adolescent Nutrition August 2012 Figure 6. Accelerometer System Output (AU Gruve ) Versus Energy Expenditure Measured by Indirect Calorimetry in kcal/h/kg for the 30 Study Participants (11 Lean, 10 Overweight, 9 Obese). Muve, Inc, Minneapolis, Minnesota. The investigators have not accrued any royalties from licensing of the aforementioned technology. Mayo Clinic has received royalties of less than the federal threshold for significant financial interest. Mayo Clinic holds an equity position in Muve, Inc. used by the patient without the need of a trained personnel or technical support to interpret physical activity data it collects. The Accelerometer System monitors patients physical activity patterns and makes necessary adjustments to alter their activity goals every week, thereby modulating their physical activity. The system also sends periodic communication via to the user with recommendations to keep their activity level high and educate the patient regarding habits to maintain a healthy lifestyle. We acknowledge the limitation that the data come from a research study that was conducted in a controlled setting. Also, the study lacks data from elderly and adolescent or children population to truly validate the device across different age groups. These are some limitations and more studies need to be conducted taking the aforementioned points into consideration. Also, one important aspect that we propose to study next is to compare the Accelerometer System with Doubly Labeled Water. It remains to be seen whether the Accelerometer System is effective in the long term. Such a device could help people promote their physical activity. Acknowledgment We thank Dr Warren Thompson for independent oversight of the data analysis. Author Note The project described was supported by Grants DK and DK50456 (Minnesota Obesity Center) and Grant Number 1 UL1 RR from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Information on NCRR is available at Information on Reengineering the Clinical Research Enterprise can be obtained from The primary author and the corresponding author along with the Mayo Clinic have a financial interest associated with the technology demonstrated in this manuscript. The technology has been licensed to References 1. Blair SN, Kohl HW 3rd, Barlow CE, Paffenbarger RS Jr, Gibbons LW, Macera CA. Changes in physical fitness and all-cause mortality. A prospective study of healthy and unhealthy men. JAMA. 1995;273: Blair SN, Brodney S. Effects of physical inactivity and obesity on morbidity and mortality: current evidence and research issues. Med Sci Sports Exerc. 1999;31(11 suppl):s646-s Westerterp KR. Assessment of physical activity: a critical appraisal. Eur J Appl Physiol. 2009;105: Bliley KE, Schwab DJ, Zahn SK, et al. Design of posture and activity detector (PAD). Conf Proc IEEE Eng Med Biol Soc. 2007;2007: Levine J, Melanson EL, Westerterp KR, Hill JO. Tracmor system for measuring walking energy expenditure. Eur J Clin Nutr. 2003;57: Levine JA, Baukol PA, Westerterp KR. Validation of the Tracmor triaxial accelerometer system for walking. Med Sci Sports Exerc. 2001;33: Levine JA, Baukol PA, Westerterp K. Comparison of the Tracmor accelerometer output between walking on a treadmill and on level ground. In: North American Association for the Study on Obesity: 2000; Long Beach, CA; Levine J, Melanson EL, Westerterp KR, Hill JO. Measurement of the components of nonexercise activity thermogenesis. Am J Physiol Endocrinol Metab. 2001;281:E670-E Levine JA, Lanningham-Foster LM, McCrady SK, et al. Interindividual variation in posture allocation: possible role in human obesity. Science. 2005;307: Levine JA, Schleusner SJ, Jensen MD. Energy expenditure of nonexercise activity. Am J Clin Nutr. 2000;72: Jakicic JM, Winters C, Lang W, Wing RR. Effects of intermittent exercise and use of home exercise equipment on adherence, weight loss, and fitness in overweight women: a randomized trial. JAMA. 1999;282: Saris WH, Blair SN, van Baak MA, et al. How much physical activity is enough to prevent unhealthy weight gain? Outcome 228

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