Accelerometry‐based method for assessing energy expenditure in patients with diabetes during walking (original) (raw)
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Experimental Gerontology, 2020
The aim of this study was to develop specific prediction equations based on acceleration data measured at three body sites for estimating energy expenditure (EE) during static and active conditions in middleaged and older adults with and without type 2 diabetes (T2D). Research methods: Forty patients with T2D (age: 40-74 yr, body mass index (BMI): 21-29.4 kg•m −2) and healthy participants (age: 47-79 yr, BMI: 20.2-29.8 kg•m −2) completed trials in both static conditions and treadmill walking. For all trials, gas exchange was monitored using indirect calorimetry and vector magnitude was calculated from acceleration data measured using inertial measurement units placed to the participant's center of mass (CM), hip and ankle. Stepwise multiple regression analyses were conducted to select relevant variables to include in the three EE prediction equations, and three Monte Carlo cross-validation procedures were used to evaluate each separate equation. Results: Vector magnitude (p < 0.0001) and personal data (gender, diabetes status and BMI; p < 0.0001) were used to develop three linear prediction equations to estimate EE during static conditions and walking. Crossvalidation revealed similar robust coefficients of determination (R 2 : 0.81 to 0.85) and small bias (mean bias: 0.008 to −0.005 kcal•min −1) for all three equations. However, the equation based on CM acceleration exhibited the lowest root mean square error (0.60 kcal•min −1 vs. 0.65 and 0.69 kcal•min −1 for the hip and ankle equations, respectively; p < 0.001). Conclusion: The three equations based on acceleration data and participant characteristics accurately estimated EE during sedentary conditions and walking in middle-aged and older adults, with or without diabetes.
International Journal of Behavioral Nutrition and Physical Activity, 2011
Background: Objective measurement of physical activity remains an important challenge. For wearable monitors such as accelerometer-based physical activity monitors, more accurate methods are needed to convert activity counts into energy expenditure (EE). Purpose: The purpose of this study was to examine the accuracy of the refined Crouter 2-Regression Model (C2RM) for estimating EE during the transition from rest to walking and walking to rest. A secondary purpose was to determine the extent of overestimation in minute-by-minute EE between the refined C2RM and the 2006 C2RM. Methods: Thirty volunteers (age, 28 ± 7.7 yrs) performed 15 minutes of seated rest, 8 minutes of over-ground walking, and 8 minutes of seated rest. An ActiGraph GT1M accelerometer and Cosmed K4b 2 portable metabolic system were worn during all activities. Participants were randomly assigned to start the walking bout at 0, 20, or 40 s into the minute (according to the ActiGraph clock). Acceleration data were analyzed by two methods: 2006 Crouter model and a new refined model. Results: The 2006 Crouter 2-Regression model over-predicted measured kcal kg-1 hr-1 during the first and last transitional minutes of the 20-s and 40-s walking conditions (P < 0.001). It also over-predicted the average EE for a walking bout (4.0 ± 0.5 kcal kg-1 hr-1), compared to both the measured kcal kg-1 hr-1 (3.6 ± 0.7 kcal kg-1 hr-1) and the refined Crouter model (3.5 ± 0.5 kcal kg-1 hr-1) (P < 0.05). Conclusion: The 2006 Crouter 2-regression model over-predicts EE at the beginning and end of walking bouts, due to high variability in accelerometer counts during the transitional minutes. The new refined model eliminates this problem and results in a more accurate prediction of EE during walking.
Calorimetric validation of the Caltrac accelerometer during level walking
Physical Therapy
The primary purpose of this study was to compare the Caltrac ® accelerometer output with measured energy expenditure (Ee). Twenty-five volunteers (10 men, 15 women) walked on a level motor-driven treadmill at four different speeds (54, 81, 104, and 130 m . min -1 ) with the Caltrac ® device affixed to the waistline. Each of the four experimental trials lasted eight minutes, and the testing was completed within an hour. During the test, oxygen consumption (Vo 2 ) (in L . min -1 and in mL . kg -1 . min -1 ) and nonprotein respiratory exchange ratio were monitored by the Beckman Horizon metabolic cart. The accelerometer output at the end of each exercise bout was also monitored and subsequently divided by 8 to convert the readings to counts . min -1 . The mean Vo 2 (L . min -1 ) at steady state (ie, 6th-8th minutes of exercise) was converted to a caloric value. We obtained a moderate correlation coefficient (r) of. 76 between the accelerometer output and the Vo 2 (mL . kg -1 . min -1 ) and a high correlation coefficient of .92 between the Ee and the accelerometer readings. The Caltrac ® accelerometer output (counts . min -1 ) was significantly higher (p < .01) than the Ee (kcal . min -1 ) at the four walking speeds. The difference between the accelerometer output and the Ee ranged from 133% to 52.9%. The data were further analyzed with linear, polynomial, multiple, and stepwise regression models. The results of the analyses revealed that the Caltrac ® accelerometer output is a valid predictor of Ee during level walking when the appropriate regression equation is used to adjust the values. Because the accelerometer device tends to overestimate Ee, the raw accelerometer readings should be applied with caution. [Balogun J A, Martin DA, Clendenin MA Calorimetric validation of the Caltrac ® accelerometer during level walking. Phys Ther 69: [501][502][503][504][505][506][507][508][509] 1989] Over the last 20 years, there has been a considerable interest in the assess ment of physical activity levels and how activity levels relate to cardiovas cular fitness and health. 1-3 A variety of methods have been used in measur ing physical activity levels including self-reports by questionnaires and interviews, direct observation of phys ical activities, monitoring of heart rate by telemetry, calorimetric measure ment of oxygen uptake (Vo 2 ), isotope ratio mass spectrometry, and the use of motion activity sensors. Of all of these methods, the motion sensors are reasonably nonobstructive, have the advantage of being objective, and are the most cost-effective for clinical use.
The British journal of nutrition, 2004
Assessing the total energy expenditure (TEE) and the levels of physical activity in free-living conditions with non-invasive techniques remains a challenge. The purpose of the present study was to investigate the accuracy of a new uniaxial accelerometer for assessing TEE and physical-activity-related energy expenditure (PAEE) over a 24 h period in a respiratory chamber, and to establish activity levels based on the accelerometry ranges corresponding to the operationally defined metabolic equivalent (MET) categories. In study 1, measurement of the 24 h energy expenditure of seventy-nine Japanese subjects (40 (SD 12) years old) was performed in a large respiratory chamber. During the measurements, the subjects wore a uniaxial accelerometer (Lifecorder; Suzuken Co. Ltd, Nagoya, Japan) on their belt. Two moderate walking exercises of 30 min each were performed on a horizontal treadmill. In study 2, ten male subjects walked at six different speeds and ran at three different speeds on a t...
Energy Expenditure in People with Diabetes Mellitus: A Review
Frontiers in Nutrition, 2016
Physical activity (PA) is an important non-therapeutic tool in primary prevention and treatment of diabetes mellitus (DM). To improve activity-based health management, patients need to quantify activity-related energy expenditure and the other components of total daily energy expenditure. This review explores differences between the components of total energy expenditure in patients with DM and healthy people and presents various tools for assessing the energy expenditure in subjects with DM. From this review, it appears that patients with uncontrolled DM have a higher basal energy expenditure (BEE) than healthy people which must be considered in the establishment of new BEE estimate equations. Moreover, studies showed a lower activity energy expenditure in patients with DM than in healthy ones. This difference may be partially explained by patient with DMs poor compliance with exercise recommendations and their greater participation in lower intensity activities. These specificities of PA need to be taken into account in the development of adapted tools to assess activity energy expenditure and daily energy expenditure in people with DM. Few estimation tools are tested in subjects with DM and this results in a lack of accuracy especially for their particular patterns of activity. Thus, future studies should examine sensors coupling different technologies or method that is specifically designed to accurately assess energy expenditure in patients with diabetes in daily life.
Validation of a method for estimating energy expenditure during walking in middle-aged adults
European Journal of Applied Physiology, 2017
The aim of this study was to test the validity of a method using an inertial measurement unit for estimating activityrelated energy expenditure (AEE) during walking in middle-aged adults. Methods Twenty healthy middle-aged participants completed different treadmill walking trials with an inertial measurement unit adhered to their lower back. Gas exchange was monitored with indirect calorimetry. Mechanical data were used to estimate AEE from an algorithm developed by Bouten et al. (Med Sci Sport Exer 26(12):1516-1523, 1994). Three methods for removing the gravitational component were proposed and tested: mean subtraction method (MSM), high-pass filter method (HPM) and free acceleration method (FAM). Results The three methods did not differ significantly from the indirect calorimetry [bias = − 0.08 kcal min −1 ; p = 0.47 (MSM), bias = − 0.08 kcal min −1 ; p = 0.48 (HPM) and bias = − 0.15 kcal min −1 ; p = 0.23 (FAM)]. Mean root mean square errors were 0.43, 0.42 and 0.51 kcal min −1 for MSM, HPM and FAM, respectively. Conclusion This study proposed an accurate method for estimating AEE in middle-aged adults for a large range of walking intensities, from slow to brisk walking, based on Bouten's algorithm.
Comparison of 2 accelerometers for assessing daily energy expenditure in adults
Background: Daily energy expenditure (EE) assessment plays an important role in clinical strategies for lifestyle-related diseases. The purpose of this study was to compare the performance of 2 activity monitors from different manufacturers to estimate total energy expenditure (TEE) and physical activity related-energy expenditure (PAEE) in daily living conditions. Methods: Sixteen adults stayed in a respiratory chamber for 24 h. The subjects wore 2 accelerometers based on uniaxial (Lifecorder; UNI) and triaxial accelerometry (Tritrac-R3D; TRI). Results: A highly significant correlation was observed between measured TEE and estimated values (r=0.868 in UNI and r=0.819 in TRI; P<0.001). However, TEE and PAEE were significantly underestimated: TEE UNI by -9% and TEE TRI by -12%; PAEE UNI by -10% and PAEE TRI by -55%. Conclusions: The EE of structured activity was adequately estimated by both accelerometers, whereas the EE of the non-structured activities involved much more errors. The results also suggest that the algorithm for EE calculation may be more important than the number of planes used for detecting acceleration.
Estimating energy expenditure using accelerometers
European Journal of Applied Physiology, 2006
The purpose of this study was to examine the validity of published regression equations designed to predict energy expenditure (EE) from accelerometers (Actigraph, Actical, and AMP-331) compared to indirect calorimetry, over a wide range of activities. Forty-eight participants (age: 35 § 11.4 years) performed various activities that ranged from sedentary behaviors (lying, sitting) to vigorous exercise. The activities were split into three routines of six activities, and each participant performed at least one routine. The participants wore three devices (Actigraph, Actical, and AMP-331) and simultaneously, EE was measured with a portable metabolic system. For the Actigraph, 15 previously published equations were used to estimate EE from the accelerometer counts. For the Actical, two published equations were used to estimate EE from the accelerometer counts. For the AMP-331 we used the manufacturer's equation to estimate EE. The Actigraph and Actical regressions tended to overestimate walking and sedentary activities and underestimate most other activities. The AMP-331 gave a close estimate of EE during walking, but overestimated sedentary/light activities and underestimated all other activities. The only equation not signiWcantly diVerent from actual time spent in both light and moderate physical activity was the Actigraph Freedson kcal equation. All equations signiWcantly underestimated time spent in vigorous physical activity (P < 0.05). In conclusion, no single regression equation works well across a wide range of activities for the prediction of EE or time spent in light, moderate, and vigorous physical activity.
Objective Measurement of Energy Expenditure During Normal Ambulation
Objective measurement of energy expenditure (EE) is a complex task with many possible methodologies, each with inherent strengths and weaknesses. While the measurement of EE in relation to normal ambulation does limit the complexity somewhat, the overarching difficulties in determining the appropriate method remain the same. The most accurate methods are too impractical to measure EE during every-day walking. By contrast, the most practical method, the Pedometer, is far too inaccurate to be of clinical use. Methods which strike a happy medium, such as accelerometers show promise. However, there is a lack of strong basis for comparison owing to differing methods of data processing and other factors.
Effect of type 2 diabetes on energy cost and preferred speed of walking
European Journal of Applied Physiology, 2018
Purpose Although walking is the most commonly recommended activity for patients with type 2 diabetes (T2D), these patients walk daily less than their healthy peers and adopt a lower self-selected speed. It has been suggested that gait alterations observed in this population could be responsible for a higher metabolic rate (MR) during walking. Thus, the aim of this study was to compare relationship between MR, the energy cost of walking per unit of distance (Cw) and self-selected walking speed in T2D patients and healthy individuals. Methods We measured metabolic and spatiotemporal parameters for 20 T2D patients and 20 healthy control subjects, while they walked on a treadmill at different speeds (0.50-1.75 m s −1) using a breath-by-breath gas analyzer and an inertial measurement unit, respectively. Results Net MR was 14.3% higher for T2D patients on average across all speeds, and they preferred to walk 6.8% slower at their self-selected compared with their non-diabetics counterparts (1.33 vs. 1.42 m s −1 , respectively; p = 0.045). Both groups naturally walked at a self-selected speed close to their minimum gross Cw per distance, with similar values of minimum gross Cw (3.53 and 3.32 J kg −1 m −1 in T2D patients and control subjects, respectively). Conclusion When compared with healthy subjects, T2D patients walk with a higher MR at any given speed. Thus, the slower self-selected speed observed in T2D patients seems to correspond to the speed at which their gross energy cost per distance was minimized and allows T2D patients to walk at the same intensity than healthy subjects. Keywords Diabetes mellitus • Metabolic rate • Energy expenditure • Comfortable speed Abbreviations ANCOVA Analysis of covariance ANOVA Analysis of variance CI Confident interval Cw Cost of walking T2D Type 2 diabetes MR Metabolic ratė VCO 2 Rate of dioxygen productioṅ VO 2 Rate of oxygen consumption * Georges Dalleau