PHYSICAL ACTIVITY IN daily life can be considered as

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1 ORIGINAL ARTICLE Step Counting and Energy Expenditure Estimation in Patients With Chronic Obstructive Pulmonary Disease and Healthy Elderly: Accuracy of 2 Motion Sensors Karina C. Furlanetto, PT, Gianna W. Bisca, PT, Nicoli Oldemberg, PT, Thaís J. Sant Anna, PT, Fernanda K. Morakami, PT, Carlos A. Camillo, PT, Vinicius Cavalheri, PT, Nidia A. Hernandes, PT, Vanessa S. Probst, PT, Ercy M. Ramos, PhD, Antonio F. Brunetto, PhD, Fábio Pitta, PhD 261 ABSTRACT. Furlanetto KC, Bisca GW, Oldemberg N, Sant Anna TJ, Morakami FK, Camillo CA, Cavalheri V, Hernandes NA, Probst VS, Ramos EM, Brunetto AF, Pitta F. Step counting and energy expenditure estimation in patients with chronic obstructive pulmonary disease and healthy elderly: accuracy of 2 motion sensors. Arch Phys Med Rehabil 2010;91: Objective: To compare the accuracy of 2 motion sensors (a and a ) in terms of step counting and estimation of energy expenditure (EE) in patients with chronic obstructive pulmonary disease (COPD) and in healthy elderly. Design: In this descriptive study, all participants wore both motion sensors while performing a treadmill walking protocol at 3 different speeds corresponding to 30%, 60%, and 100% of the average speed achieved during a six-minute walk test. As criterion methods, EE was estimated by indirect calorimetry, and steps were registered by videotape. Setting: Research laboratory at a university hospital. Participants: Patients with COPD (n 30; 17 men; mean age SD, 67 8y; mean forced expiratory volume in the first second [FEV 1 ] predicted SD, 46% 17%; mean body mass index [BMI] SD, 24 4kg m 2 ) and matched healthy elderly (n 30; 15 men; mean age SD, 68 7y; mean FEV 1 predicted SD, 104% 21%; mean BMI SD, 25 3kg m 2 ). Interventions: Not applicable. Main Outcome Measure: Step counting and EE estimation during a treadmill walking protocol. Results: The was accurate for step counting and EE estimation in both patients with COPD and healthy elderly at the higher speed. However, it showed significant underestimation at the 2 slower speeds in both groups. The did not detect steps accurately at any speed, although it accurately estimated EE at all speeds in healthy elderly and at the intermediate and higher speeds in patients with COPD. Conclusions: In both patients with COPD and healthy elderly, the showed better EE estimates during most From the Laboratory of Research in Respiratory Physiotherapy, Department of Physiotherapy, Universidade Estadual de Londrina, Londrina, Paraná (Furlanetto, Bisca, Oldemberg, Sant Anna, Morakami, Camillo, Cavalheri, Hernandes, Probst, Brunetto, Pitta); Postgraduate Program in Physiotherapy, Department of Physiotherapy, Faculdade de Ciências e Tecnologia, Universidade Estadual Paulista Júlio de Mesquita Filho, Presidente Prudente, São Paulo (Cavalheri, Hernandes, Ramos, Pitta); Universidade Norte do Paraná, Londrina, Paraná (Probst), Brazil. Supported by the National Council for Scientific and Technological Development, Brazil. No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit on the authors or on any organization with which the authors are associated. Reprint requests to Fábio Pitta, PhD, Departamento de Fisioterapia, CCS, Hospital Universitário da UEL, Av Robert Koch, 60, Vila Operária, , Londrina, PR, Brazil, fabiopitta@uol.com.br /10/ $36.00/0 doi: /j.apmr walking speeds than the. Conversely, for step counting, accuracy is observed only with the during the higher walking speed in both groups. Key Words: Pulmonary disease, chronic obstructive; Rehabilitation by the American Congress of Rehabilitation Medicine PHYSICAL ACTIVITY IN daily life can be considered as the totality of voluntary movement produced by skeletal muscles during everyday functioning. 1 Its correct quantification has become a challenge in order to obtain an adequate assessment of the relationship between free-living physical activity and health. 2 COPD is characterized by air flow limitation; dyspnea; and reduced exercise capacity, muscle strength, and quality of life. 3 Patients with COPD spend less time walking in daily life than age-matched subjects and walk at a lower intensity. 4 Moreover, previous studies 5-8 have shown that physical inactivity is an important predictor of hospital readmission and morbidity/ mortality risk in this population. Because of the close relationships among physical inactivity, disability, and mortality, the interest in objective measurement of daily physical activity in patients with COPD has gained growing interest. 9 Energy expenditure and step counting are common outcomes when assessing physical activity in daily life. To obtain an accurate assessment of these outcomes, the application of reference methods is recommended. For energy expenditure assessment, the literature usually recommends the doubly-labeled water method or indirect calorimetry assessment For step counting, direct observation and videotaping have been considered as reference methods. 14 However, these techniques are not easily used in everyday life because of their methodologic complexity, limited practicality, and/or high cost. Recently, the use of motion sensors has gained widespread recognition. These are instruments for detection of body movement. They are used to quantify physical activity in daily life objectively during a period 14 of time. Pedometers (eg, Digiwalker SW701) a and s (eg, SenseWear Armband) b are among the most used motion sensors. Both of them quantify steps and estimate total energy expenditure, providing information from free living conditions and not just information derived from laboratory tests. However, the instruments BMI COPD FEV 1 MET 6MWT List of Abbreviations body mass index chronic obstructive pulmonary disease forced expiratory volume in the first second metabolic equivalent six-minute walk test

2 262 MOTION SENSORS IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE, Furlanetto present marked differences concerning technologic complexity and cost. The SenseWear is more costly because it is composed of a biaxial accelerometer and diverse physiologic sensors, in contrast with the lower cost and technologic simplicity of the, which involves a simple system for detection of the hip vertical movement as a step Schneider et al 15 showed that the Digiwalker SW701 is more accurate for step counting in healthy adults than a diversity of other devices. The SenseWear, on the other hand, was compared with uniaxial, biaxial, and triaxial accelerometers and showed the most accurate total energy expenditure estimation at most treadmill speeds. 17 However, there are no previous studies comparing the accuracy of a simple instrument such as the Digiwalker SW701 and a technologically advanced such as the SenseWear concerning step counting and energy expenditure estimation during physical activity, especially in subjects characterized by inactivity and slow walking such as patients with COPD. Such information could help inform decision-making when choosing an activity monitoring device for this patient group. Because of the increasing need for objective techniques which accurately detect physical activity, the objective of this study was to investigate the accuracy of the Digiwalker SW701 and the SenseWear in estimating energy expenditure and counting steps compared with reference methods and with each other in patients with COPD and healthy elderly subjects. METHODS Participants The study involved 43 patients with COPD from the Outpatient Respiratory Physiotherapy Clinic at the Hospital Universitário de Londrina (Brazil) and 39 healthy elderly subjects who were relatives or acquaintances of students from the aforementioned university hospital. Groups were paired for age, BMI, and sex. Inclusion criteria for healthy subjects were (1) absence of spirometry abnormalities; (2) absence of bone, nervous, and/or muscle dysfunction that could interfere on the assessment of physical activity; and (3) BMI less than 30kg m 2. Besides criteria 2 and 3, inclusion criteria in the COPD group were (1) diagnosis of COPD based on spirometry, clinical, and radiologic internationally accepted criteria, 3 and (2) clinical stability (absence of exacerbations) for at least 3 months before inclusion in the study. All subjects were informed about study procedures and provided written formal consent to their participation. The study was approved by the Ethics and Research Committee of Universidade Estadual de Londrina/HU-UEL. Study Design and Protocol In this descriptive study, all subjects were submitted to an initial assessment of lung function and functional exercise capacity (6MWT) as screening measures. For step size determination, subjects were instructed to walk (as in their daily walking) 10 steps in a straight line. Total distance was measured and divided by 10. Afterward, each subject performed the following protocol: walk on a treadmill c with no inclination at 3 different speeds corresponding to 30%, 60%, and 100% of the average speed achieved during the 6MWT (speeds 1, 2, and 3, respectively). Each speed was sustained for 1 minute, with 1 minute of rest in between. During the treadmill walking protocol, subjects wore both motion sensors as study measures: the Digiwalker SW701 at the right side of the waist (hemiclavicle line) and the SenseWear on the right arm. As a criterion method for energy expenditure estimation, simultaneous indirect calorimetry was performed by a portable gas analyzer. The device was calibrated before each test in accordance with manufacturer instructions. Energy expenditure (in kilocalories for standardization of units) was derived from oxygen uptake assessment (ml kg 1 min 1 ). The exact beginning and ending of walking at each speed were synchronized in all devices because of the presence of at least 3 investigators during each test (ie, 1 investigator initiating the portable gas analyzer, 1 initiating the camera, 1 initiating the 2 motion sensors simultaneously). At the same time, the treadmill walking protocol was videotaped by a digital camera (Sony Cyber Shot, DSC-W50 d ) as criterion method for step counting. The results from both motion sensors (energy expenditure and number of steps) were compared with the criterion methods and with each other. METHODS Lung Function Assessment Simple spirometry was performed by the Pony spirometer. e The technique was in accordance with American Thoracic Society. 18 FEV 1 and forced vital capacity were obtained postbronchodilator. Reference values were those by Knudson et al. 19 Six-minute walk test. The 6MWT was performed in accordance with international standards. 20 Patients were encouraged to walk 6 minutes as fast as they could in a straight leveled 30-m corridor. Two tests were performed with each subject, and the longest distance was used to calculate speed average and, consequently, the 3 protocol walking speeds. Normative values were those by Troosters et al. 21 Multisensor SenseWear armband. The Multisensor SenseWear armband is a small ( cm) and light (82g) monitor that is worn on the upper-posterior region of the right arm. Information regarding various parameters including accelerometry, multiple physiological sensors, and demographic characteristics such as sex, age, weight, height, and dominant arm are used to estimate energy expenditure through algorithms developed by the manufacturer. 11,14 Among the device s main outcomes, the most commonly used are total energy expenditure, average of MET, energy expenditure in activities requiring above 3 MET, time spent in sedentary ( 3 MET), moderate (3 6 MET) and vigorous activities (6 9 MET), as well as the number of detected steps. A final report is obtained through analysis of the data by a specific software (Inner View). b Pedometer Digiwalker SW701. The Digiwalker SW701 is a simple and relatively inexpensive device, worn attached to the waist, providing the number of steps performed, distance, and energy expenditure estimation in a determined period. For this, the device requires a few characteristics of the wearer such as weight and step length. Its mechanism consists of an internal spring-levered system that is sensitive to vertical hip movements. This spring lever is connected to an electric circuit that computes each deflection as a step. Furthermore, based on the device movement counting, it also provides an active energy expenditure estimation. Portable gas analyzer. The portable metabolic system VO 2000 AeroGraph f is a transducer for metabolic analysis of pulmonary gas exchanges, projected to operate connected to a computer, previously tested and validated. 11 The system provides energy expenditure estimation by indirect calorimetry executing continuous analysis of oxygen uptake, carbon dioxide production, and expired volume. Statistical Analysis The analysis was performed with Prism software. g The Kolmogorov-Smirnov test was used to analyze normality of the data.

3 MOTION SENSORS IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE, Furlanetto 263 Because data presented normal distribution, parametric tests were used, and results were expressed as mean and SD. Comparison between the COPD and healthy elderly groups was performed by the unpaired t test. One-way analysis of variance was used in the comparison of devices (number of steps and energy expenditure estimative at each speed and on the sum of the 3 speeds), with Tukey as post hoc test. In addition, agreement between the measures was studied by the Bland and Altman graphic method. For all analysis, statistical significance was set at P less than.05. RESULTS Thirteen patients with COPD and 9 healthy elderly subjects were excluded because of intolerance to any of the tests or protocol interruption as a result of noncompliance to the metabolic system mouthpiece. The group of patients with COPD who remained (n 30; 17 men) was characterized by reduced pulmonary function and functional capacity and normative BMI. Most subjects (73%) had moderate airflow obstruction according to the Global Initiative for Chronic Obstructive Lung Disease classification 3 (table 1). The healthy elderly group (n 30; 15 men) showed normal values of lung function, functional exercise capacity, and BMI (see table 1). Walking speed was significantly different between the groups at all speeds (see table 1). The groups did not show significant differences concerning age, weight, height, and BMI. Patients With Chronic Obstructive Pulmonary Disease Table 2 shows that, in the COPD group, the number of steps registered by the was significantly lower than the videotape at the first and second speeds (P.001), with no statistical difference at the third speed. With regard to the step counting, there was significant underestimation compared with the videotape at the 3 speeds (P.001). The comparison of step detection provided by the 2 motion sensors showed significant underestimation by the compared with the at the third speed and in the sum of all speeds (see table 2). Table 3 shows that the energy expenditure estimation was statistically similar to indirect calorimetry only at the third speed, with significant underestimation at the first and second speeds (P.001). On the other hand, the Table 1: Sample Characteristics of Patients With COPD and Healthy Elderly Characteristics Patients With COPD (n 30) Healthy Elderly (n 30) Men/Women 17/13 15/15 Age (y) Weight (kg) Height (m) BMI (kg/m 2 ) FEV 1, % predicted 46 17* FVC, % predicted 67 19* FEV 1 /FVC 52 12* 80 5 GOLD (I/II/III/IV) 1/12/10/7 NA (km/h) * (km/h) * (km/h) * NOTE. Values are mean SD or as otherwise indicated. Abbreviations: FVC, forced vital capacity; GOLD, Global Initiative for Chronic Obstructive Lung Disease; NA, not applicable;, 2, and 3, based respectively on 30%, 60%, and 100% of the average speed during a 6MWT. *P.05 vs healthy elderly. Table 2: Number of Steps Registered by Each Method in Patients With COPD at 1, 2, and 3 (30%, 60%, and 100% of the 6MWT Average Speed, Respectively) and During the Entire Protocol (Summing All ) Steps km/h km/h km/h Digiwalker 26 26* 73 35* * SenseWear 19 20* 59 31* 91 38* * Video *P.05 vs video. P.05 vs Digiwalker. showed similar energy expenditure estimation to indirect calorimetry at the second and third speeds, with significant underestimation only at the first speed (P.05). When comparing energy expenditure estimation between the 2 motion sensors, the provided significantly lower values at the first speed (see table 3). There was significant underestimation of the total number of steps taken during the whole protocol (ie, summing the 3 speeds) by both the and the (see table 2). Bland and Altman plots for the number of steps (fig 1A) depict an average underestimation of 100 steps for the and 158 steps for the out of an average of 324 steps performed during the 3 speeds. For the (see fig 1A, left), the plot showed negative significant correlation (r.50), indicating that the lower the total number of performed steps, the higher the underestimation. On the other hand, total energy expenditure estimation provided by the during the whole protocol did not show statistical difference to indirect calorimetry, while the showed significant underestimation (see table 3). Bland and Altman plots for energy expenditure (fig 1B) depict an average underestimation of 7.3kcal for the and 3.9kcal for the out of an average of 16kcal spent during the 3 speeds. Both for the and for the, the plots showed positive significant correlations (r.60 and r.52, respectively), indicating that the higher the cumulative energy expenditure, the higher the underestimation. Healthy Elderly Table 4 shows that the number of steps registered by in the healthy elderly group was statistically similar to the Table 3: Energy Expenditure (kcal) Registered by Each Method in Patients With COPD at 1, 2, and 3 (30%, 60%, and 100% of the 6MWT Average Speed, Respectively) and During the Entire Protocol (Summing All ) Kcal Digiwalker SenseWear Indirect calorimetry km/h *P.05 vs indirect calorimetry. P.05 vs Digiwalker km/h km/h * 2.6 2* * *

4 264 MOTION SENSORS IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE, Furlanetto Fig 1. Bland & Altman plots comparing the results of (A) number of steps and (B) energy expenditure (kcal) registered by the and the versus the criterion methods (video and indirect calorimetry) in patients with COPD during the entire protocol (summing all speeds). Graphics on the left show the comparison between and the criterion methods, whereas graphics on the right show the comparison between and the criterion methods. In each graphic, the central dotted line corresponds to the average difference between the respective methods, whereas the upper and lower dotted lines correspond to the upper and lower limits of agreement, respectively. videotape just at the third speed, whereas the showed significant underestimation compared with the videotape at all speeds (P.001). The comparison of step detection between the 2 Table 4: Number of Steps Registered by Each Method in Healthy Elderly at 1, 2, and 3 (30%, 60%, and 100% of the 6MWT Average Speed, Respectively) and During the Entire Protocol (Summing All ) Steps km/h km/h km/h Digiwalker 36 27* 85 27* * SenseWear 35 22* 82 29* * * Video *P.05 vs video. P.05 vs Digiwalker. motion sensors showed significant underestimation by the compared with the at the third speed. Concerning energy expenditure, table 5 shows significant underestimation by the compared with indirect calorimetry at the first and second speeds (P.001). On the other hand, the estimation did not show differences to indirect calorimetry at any speed. When comparing energy expenditure provided by the 2 motion sensors, there was significant underestimation by the at the first and second speeds (see table 5). On the healthy elderly group, as observed in the COPD group, there was significant underestimation on the total number of steps both by the and the (see table 4). Bland and Altman plots for the number of steps (fig 2A) depict an average underestimation of 88 steps for the and 110 steps for the out of an average of 348 steps performed during the 3 speeds. For the (see fig 2A, left), the plot showed negative significant correlation (r.57), indicating that the lower the total number of

5 MOTION SENSORS IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE, Furlanetto 265 Table 5: Energy Expenditure (kcal) Registered by Each Method in Healthy Elderly at 1, 2, and 3 (30%, 60%, and 100% of the 6MWT Average Speed, Respectively) and During the Entire Protocol (Summing All ) Kcal Digiwalker SenseWear Indirect calorimetry km/h *P.05 vs indirect calorimetry. P.05 vs Digiwalker km/h performed steps, the higher the underestimation. Once again similarly to the COPD group, total energy expenditure estimation provided by the did not show a difference from indirect calorimetry, whereas the showed significant underestimation (see table 5). There was also significant underestimation of energy expenditure by the compared with the. Bland and Altman plots for energy expenditure (fig 2B) showed positive significant correlations both for the and for the (r.67 and r.61, respectively), indicating that the higher the cumulative energy expenditure, the higher the underestimation. DISCUSSION km/h 1 1* 3 1.5* * Pedometer The spring-levered used in this study was previously suggested as superior to other devices in different treadmill speeds 22,23 and predetermined distances. 22,24 However, step counting by the in the present protocol was inaccurate both in patients with COPD and healthy elderly during low and moderate speeds compared with the criterion method, whereas it was accurate for step counting at higher speeds. Figures 1A and 2A (left) depict that s underestimation is higher at slower walking speeds. These results corroborate some previous literature data showing that s adequately detected steps compared with a uniaxial accelerometer (Computer Science and Applications Inc h ) at higher speeds, but underestimated steps at slow walking, characteristic usually observed in elderly people. 16 Furthermore, Pitta et al 4 showed that patients with COPD walk 25% less briskly compared with healthy elderly. These facts raise important questions about the use of s to count steps during daily life in patients with COPD, and likewise in healthy elderly who walk slowly. To corroborate this concern, the sum of steps detected by the during the whole protocol was significantly lower than the reference method, confirming the device s limitation for both populations included in this study. A probable explanation for the inaccuracy of this kind of device is the fact that vertical movements of the hip are less marked at lower speeds (ie, with insufficient magnitude to generate the contact of the spring to the electric circuit), and the sensor usually fails to register some of these movements. 1,16 Additionally, Tudor-Locke et al 25 showed that the Yamax SW-200, a device with a similar mechanism as the present study s, needs a force of.35g to register a step, underestimating steps compared with an accelerometer whose sensitivity is.30g. This indicates that the might not have adequately detected steps as a result of being a device with relatively lower sensitivity. The sensitivity of each motion sensor is determined not only by its type (, accelerometer) but also by each device s technical specifications, which implies that not necessarily all types of s are less sensitive than all types of accelerometers. In the present study, the estimation of energy expenditure was similar to the estimation provided by indirect calorimetry only at the third speed for both groups. This strengthens the concept that s show better accuracy at higher speeds. However, at the first and second speeds (corresponding to 30% and 60% of the 6MWT speed, respectively) and in the sum of all speeds, the did not repeat this performance. According to Crouter et al, 23 s usually have limited accuracy to estimate distance walked and even lower accuracy to estimate energy expenditure. This might occur because of s mechanism for calculating energy expenditure: it is based on the subjects step count. If step counting is not accurate at lower speeds, energy expenditure will not be correctly estimated either. However, this might not be the only factor, because the Bland and Altman plots (see figs 1B and 2B, left) show that the underestimation is higher at higher metabolic rates, regardless of the number of steps and speed. Reasons for this might include increased energy expenditure at high metabolic rates linked to a high work of breathing (in the case of patients with COPD) and the use of generic prediction equations (in the case of both patients with COPD and the elderly), as discussed in more detail below for the. Multisensor The did not count steps accurately in any group at any speed in the present study. A probable explanation for these findings is that this device is worn at the arm, although the mechanism of steps detection is not clearly described by the device s manufacturer. In contrast to the, the SenseWear mechanism for calculating energy expenditure does not depend on step count. The device combines signals of 4 sensor types: skin temperature, body heat flux, galvanic skin resistance, and a biaxial accelerometer. Hence, regardless of the number of steps detected or the isolated contribution of the biaxial accelerometer, energy expenditure estimation is improved because of an important contribution of the additional physiologic sensors. 9 This is a peculiar and useful characteristic of the SenseWear compared with typical devices based on accelerometry alone, because it was suggested that typical accelerometers are more accurate for quantification and differentiation of body movements than for estimation of energy expenditure 26 (in contrast with the SenseWear). The was able to estimate energy expenditure adequately at the 3 walking speeds in the healthy elderly group, as well as at speeds corresponding to 60% and 100% of the 6MWT speed in patients with COPD, in addition to the total protocol duration in both populations. On the other hand, results from the Bland and Altman plots (see figs 1B and 2B) showed correlation between higher metabolic rates and overall underestimation of energy expenditure not only with the but also with the, corroborating previous findings by Patel et al. 9 Underestimation of energy expenditure at high metabolic rates in the COPD group might be at least partly attributed to the fact that increased energy expenditure in these patients is linked to a high work of breathing, which is not reflected by accelerometry or step counting. In addition, both for patients with COPD and for the elderly, energy expenditure underestimation may be related to the use of generic equations for energy expenditure prediction, because previous

6 266 MOTION SENSORS IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE, Furlanetto Fig 2. Bland and Altman plots comparing the results of (A) number of steps and (B) energy expenditure (kcal) registered by the and the versus the criterion methods (video and indirect calorimetry) in healthy elderly during the entire protocol (summing all speeds). Graphics on the left show the comparison between and the criterion methods, whereas graphics on the right show the comparison between and the criterion methods. In each graphic, the central dotted line corresponds to the average difference between the respective methods, whereas the upper and lower dotted lines correspond to the upper and lower limits of agreement, respectively. literature confirms that population-specific prediction equations increase the estimation accuracy. 27,28 Comparison Between Pedometer and Multisensor For step counting at slow speeds, the motion sensors were similar in the sense that neither of them provided adequate estimations, and therefore they are equally inaccurate in subjects who walk slowly. At the highest speed, however, the showed to be more sensitive than the. This indicates that the usefulness of the step counting feature of the SenseWear is very limited even at higher waking speeds, possibly because the device is worn at the arm and not at the waist or ankle, as previously discussed. On the other hand, for the estimation of energy expenditure, the was clearly superior to the at slow walking speeds, possibly because of the contribution of the physiologic sensors in addition to the biaxial accelerometer. Limitations and Future Perspectives In the present protocol, each treadmill speed was sustained for 1 minute, and one can correctly argue that it takes more than 1 minute when changing exercise intensity to achieve steady-state energy expenditure. However, the present protocol did not aim at achieving the steady state for each speed. It aimed simply to allow comparison of energy expenditure estimations among the methods at the same points in time in subjects walking at different speeds without necessarily achieving steady-state oxygen consumption at these speeds. Conclusions of the present study can only be generalized to subjects with BMI lower than 30 kg m 2. The SenseWear Armband has shown limited accuracy for the measurement of energy expenditure in obese subjects. 12 Furthermore, the might not have ideal placement in subjects with a large abdominal volume because it might stay in a different position

7 MOTION SENSORS IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE, Furlanetto 267 than the vertical alignment suggested for its use. 24 For these reasons, subjects with high BMI were not included in this study. Future studies should investigate to what extent the conclusions of this study also apply to obese subjects, an important target population for physical activity interventions. In the present study, the accuracy of movement sensors was analyzed in a laboratory protocol, and therefore no assessment in free-living conditions was involved. This suggests some caution when generalizing the present results because previous literature has shown that floor walking may induce higher energy expenditure than treadmill walking. 29,30 For the advance of this research field, future investigations should consider nonlaboratory conditions, or at least simulate daily activities in a laboratory setting. These activities can include daily tasks involving arms and legs, as well as walking with frequent interruption and direction changes. CONCLUSIONS It can be inferred that, both in patients with COPD and in healthy elderly, the SenseWear Armband had better energy expenditure estimates during most walking speeds than the Digiwalker SW701. Conversely, for step counting, accuracy was observed only with the during high walking speeds in both groups. Technological efforts in this research field should focus on making the devices more sensitive during slow walking speeds. References 1. Steele BG, Belza B, Cain K, Warms C, Coppersmith J, Howard J. Bodies in motion: monitoring daily activity and exercise with motion sensors in people with chronic pulmonary disease. J Rehabil Res Dev 2003;40: Le Masurier GC, Lee SM, Tudor-Locke C. Motion sensor accuracy under controlled and free-living conditions. Med Sci Sports Exerc 2004;36: Rabe KF, Hurd S, Anzueto A, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 2007;176: Pitta F, Troosters T, Spruit MA, Probst VS, Decramer M, Gosselink R. Characteristics of physical activities in daily life in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2005;171: Pitta F, Troosters T, Probst VS, Spruit MA, Decramer M, Gosselink R. Physical activity and hospitalization for exacerbation of COPD. Chest 2006;129: Yohannes AM, Baldwin RC, Connolly M. Mortality predictors in disabling chronic obstructive pulmonary disease in old age. Age Ageing 2002;31: Garcia-Aymerich J, Lange P, Benet M, Schnohr P, Antó JM. Regular physical activity reduces hospital admission and mortality in chronic obstructive pulmonary disease: a population based cohort study. Thorax 2006;6: Pitta F, Troosters T, Probst VS, Lucas S, Decramer M, Gosselink R. Potential consequences for stable chronic obstructive pulmonary disease patients who do not get the recommended minimum daily amount of physical activity. J Bras Pneumol 2006;32: Patel SA, Benzo RP, Slivka WA, Sciurba FC. Activity monitoring and energy expenditure in COPD patients: a validation study. COPD 2007;4: Fruin ML, Rankin JW. Validity of a multi-sensor armband in estimating rest and exercise energy expenditure. Med Sci Sports Exerc 2004;36: Wahrlich V, Anjos LA, Going SB, Lohman TG. Validation of the VO2000 calorimeter for measuring resting metabolic rate. Clin Nutr 2006;25: Papazoglou D, Augello G, Tagliaferri M, et al. Evaluation of a armband in estimating energy expenditure in obese individuals. Obesity 2006;14: Jakicic JM, Marcus M, Gallagher KI, et al. Evaluation of the SenseWear Pro Armband to assess energy expenditure during exercise. Med Sci Sports Exerc 2004;36: Pitta F, Troosters T, Probst VS, Spruit MA, Decramer M, Gosselink R. Quantifying physical activity in daily life with questionnaires and motion sensors in COPD. Eur Respir J 2006;27: Schneider PL, Crouter SE, Basset DR. Pedometers measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exerc 2004;36: Melanson EL, Knoll JR, Bell ML, et al. Commercially available s: considerations for accurate step counting. Prev Med 2004;39: King GA, Torres N, Potter TJ, Coleman KJ. Comparison of activity monitors to estimate energy cost of treadmill exercise. Med Sci Sports Exerc 2004;36: Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J 2005;26: Knudson RJ, Slatin RC, Lebowitz MD, Burrows B. The maximal expiratory flow-volume curve: normal standards, variability, and effects of age. Am Rev Respir Dis 1976;113: ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med 2002;166: Troosters T, Gosselink R, Decramer M. Six minute walking distance in healthy elderly subjects. Eur Respir J 1999;14: Basset DR, Ainsworth BE, Leggett SR, et al. Accuracy of five electronic s for measuring distance walked. Med Sci Sports Exerc 1996;28: Crouter SE, Schneider PL, Karabulut M, Basset DR. Validity of ten electronic s for measuring steps, distance and energy cost. Med Sci Sports Exerc 2003;35: Schneider PL, Crouter SE, Lukajic O, Basset DR. Accuracy and reliability of ten s for measuring steps over a 400-m walk. Med Sci Sports Exerc 2003;35: Tudor-Locke C, Ainsworth BE, Thompson RW, Matthews CE. Comparison of s and accelerometers measures of freeliving physical activity. Med Sci Sports Exerc 2002;34: Leenders NY, Nelson TE, Sherman WM. Ability of different physical activity monitors to detect movement during treadmill walking. Int J Sports Med 2003;24: Cole PJ, LeMura LM, Klinger TA, Strohecker K, McConnell TR. Measuring energy expenditure in cardiac patients using the Body Media Armband versus indirect calorimetry: a validation study. J Sports Med Phys Fitness 2004;44: Levine J, Melanson EL, Westerterp KR, Hill JO. Tracmor system for measuring walking energy expenditure. Eur J Clin Nutr 2003; 57: Pearce ME, Cunningham DA, Donner AP, Rechnitzer PA, Fullerton GM, Howard JH. Energy cost of treadmill and floor walking at selfselected paces. Eur J Appl Physiol Occup Physiol 1983;52: Heus R, Wertheim AH, Havenith G. Human energy expenditure when walking on a moving platform. Eur J Appl Physiol Occup Physiol 1998;77: Suppliers a. Yamax, 1-5-7, Chuo-cho, Meguro-ku, Tokyo Japan. b. BodyMedia, 4 Smithfield St, 11th Fl, Pittsburgh, PA c. Imbramed Millennium, Rua 7 de abril 266, Porto Alegre, RS , Brazil. d. Sony, United States of America ( e. Cosmed, Via dei Piani di Mt Savello, 37, Pavona di Albano, Rome, I Italy. f. Medical Graphics Corp, 350 Oak Grove Pkwy, St Paul, MN g. GraphPad Software Inc, 2236 Avenida de la Playa, La Jolla, CA h. MTI Health Services, 709 Anchors St, Fort Walton Beach, FL