Data for "Validating the anatomical positioning of the Actigraph Accelerometer in UK Military Amputees in the prediction of physical activity energy expenditure"

Data collection method:

Thirty participants, consisting of unilateral (n=10), and bilateral (n=10) amputees, and non-injured controls (n=10) volunteered to complete eight activities; resting in a supine position, walking on a flat (0.48, 0.67, 0.89, 1.12, 1.34 m.s-1) and an inclined (3 and 5% gradient at 0.89 m.s-1) treadmill. Participants were then asked to complete an arm crank ergometer (ACE) exercise at three difference cadences (50, 70 and 90 rpm) and a fixed resistance (50W). Patient information relating to age, body mass, body height, hip and waist circumference, level of amputation and length of rehabilitation was measured prior to commencing the activities. During each task, expired gases were collected using indirect calorimetry and an Actigraph GT3X+ accelerometer was worn on the right hip, left hip and lumbar spine.

Data processing and preparation activities:

The Metamax 3B and the three GT3X+ activity monitors were synchronised before use. Breath-by-breath data was exported into Microsoft Excel from the Metamax 3b. PAEE was then calculated using the V̇O2 and CO2 values (l·min-1) from the Metamax in an Excel spreadsheet using the Weir equation. Resting metabolic rate (RMR; kcal·min-1) was subtracted from total energy expenditure to determine PAEE. Metabolic equivalent (METs) were then calculated using measured exercise V̇O2 divided by resting V̇O2 to derive individual METs in the last 2 minutes of each treadmill intensity. Comparisons between accelerometer outputs (PAC) and criterion PAEE were made between the final two-minutes of each activity (representative of steady-state). The GT3X+ accelerometer units were downloaded using ActiLife software (ActiGraph, Pensocola, FL, USA). Data was exported to Microsoft Excel in a time and date stamped comma-separated value (CSV) file format. Activity counts (counts·min-1) from the GT3X+ were then averaged over the corresponding final two minutes of each activity. This data is presented in the attached file. Statistics: PAEE prediction models were developed using corresponding data from each task for devices at each location, using linear regression analysis. The dependent variable was PAEE (kcal·min-1) during the final 2 minutes of each task (that is 80 values in each group). The independent variable was accelerometer outputs (counts·min-1) for the GT3X+. Pearson product moment correlation coefficients (r) and coefficients of determination (R2) statistics were reported to assess the association between criterion PAEE and outputs from devices at each location. Standard Error of the Estimate (SEE) was also calculated for each correlation (Model 1). The GT3X+ worn at the anatomical position with the strongest relationship to the criterion PAEE was then selected for further analysis, to develop a predictive model for PAEE. Covariates, which included age, body mass, waist circumference, time since amputation, and level of amputation, were analysed to determine their association with the criterion PAEE depending on if data was discrete or continuous. These covariates were selected due to their influence upon mobility in US military amputees. Significant covariates were included in the stepwise regression analysis to strengthen the predictive PAEE equations in each group (Model 2). These predictive models were cross-validated using an independent sample. However, this is not always possible in hard to reach populations due to recruitment issues. Therefore, we adopted a leave-one-out analysis as performed previously by Nightingale et al. Error statistics involved calculating the mean absolute error, mean absolute percentage error and mean signed error for each activity; the later displayed graphically using Bland and Altman plots and limits of agreement analysis. A two way mixed model ANOVA was performed to determine differences between criterion PAEE and predicted PAEE at each treadmill task. Where a significant interaction effect was observed, a Bonferroni correction was applied to Post Hoc tests where multiple comparisons were considered. This was to identify the specific treadmill tasks in which there was a significant difference between the criterion and predicted PAEE. Statistical significance was set a priori of P < 0.05. All analysis was performed using IBM SPSS Statistics 21 for Windows (IBM, Armonk, NY, USA).

Technical details and requirements:

Technology used: Accelerometer: Actigraph GT3X+ Treadmill: Woodway Desmo Portable metabolic system: Metamax 3B IBM SPSS Statistics 21 Acronyms: RMR = resting metabolic rate, VO2 = volume of oxygen, VCO2 = volume of carbon dioxide, METS = metabolic equivalent, TEE = Total Energy Expenditure, PAEE = physical activity energy expenditure, RPE = rate of perceived exertion, ACE = Arm Crank Ergometer.

Additional information:

The tabs along the bottom of the spread sheet demonstrate the indirect calorimetry and accelerometer outputs at during each activity. On each sheet the data is presented in relation to their group (Unilateral, Bilateral and Control). Resting metabolic rate is highlighted in green and 3 alternate methods(kcal.min-1, MJ/day, kcal/ day) are used to demonstrate its value. Indirect calorimetry outputs are highlighted in yellow. Outputs taken from the Metamax 3B software include VO2, METs, CO2, TEE and PAEE. This data is expressed as the mean and standard deviation. Actigraph acceleromter outputs are highlighted in red and categorised according to the anatomical location that they were worn during the activity (longest Limb, spine, shortest limb). raw signal (physical activity counts) are displayed as the mean and standard deviation (counts·min-1). Highlighted in bold along the bottom of the unilateral, bilateral and control data is the mean and standard deviation of each group.