Data for "Validating the use of multi-sensor devices to estimate physical activity energy expenditure in UK military amputees"

Data collection was performed at the Defence Medical Rehabilitation Centre (DMRC) at Headley Court. This research was a collaboration between the Academic Department of Military Rehabilitation (ADMR) and the Department for Health at the University of Bath as part of a PhD programme. The PhD aimed to investigate the impact of exercise rehabilitation in a UK military rehabilitation centre of unilateral and bilateral amputees. Part of this process is to assess the energy expenditure of these amputee groups at home and during rehabilitation. As physical activity monitors have not been validation in amputees before, the aim of this study was to validate an accelerometer that could accurately predict physical activity energy expenditure during free living.

These data were collected to support a study that assessed the influence of a tri-axial accelerometer (Actigraph GT3X+) worn at the side of the hip on the shortest residual limb in combination with a physiological variable (heart rate) versus a research grade multi-sensor device with pre-determined algorithms (Actiheart) on the prediction of physical activity energy expenditure (PAEE) in traumatic lower-limb amputees during walking in order to develop valid population-specific prediction algorithms.

The dataset consists of indirect calorimetric data, accelerometer and heart rate data collected from participants whilst walking on a treadmill at range of velocities and performing an upper body exercise protocol on the arm crank ergometer at incremental speeds with a fixed resistance.

Amputee, Military, physical activity energy expenditure
Food science and nutrition
Medical and health interface

Cite this dataset as:
Ladlow, P., 2019. Data for "Validating the use of multi-sensor devices to estimate physical activity energy expenditure in UK military amputees". Version 2. Bath: University of Bath Research Data Archive. Available from:


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application/vnd.openxmlformats-officedocument.spreadsheetml.sheet (83kB)
Creative Commons: Attribution 4.0

Mixed access regime: The experimental data in this set are openly available for download. Further information about the participants is available on request but, due to privacy concerns, access will only be granted to bona fide researchers working on a related project, and subject to the completion of a non-disclosure agreement.


Peter Ladlow
UK Ministry of Defence


James Bilzon
University of Bath

M Polly McGuigan
University of Bath

University of Bath
Rights Holder


Collection date(s):

From 1 March 2014 to 1 August 2017

Time period:

Post Iraq and Afghanistan military conflict

Geographical coverage:

Defence Medical Rehabilitation Centre, Headley Court


Data collection method:

Twenty-eight participants [unilateral (n=9), bilateral (n=10) with lower-limb amputations, and non-injured controls (n=9)] completed eight activities; rest, ambulating at 5 progressive treadmill velocities (0.48, 0.67, 0.89, 1.12, 1.34m.s-1) and 2 gradients (3 and 5%) at 0.89m.s-1. 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, an Actigraph GT3X+ accelerometer was worn on the hip of the shortest residual limb, an Actiheart monitor was attached to the chest using electrodes and a Polar heart rate belt attached around the chest.

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) from the Actigraph, PAEE from the Actiheart, heart rate (bpm) from the Polar 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. AHR data was ascertained via entering the measured RMR (via indirect calorimetry), age, weight, height and sleeping HR (measured the night before testing) into the ActiheartTM software (Version 4.0.23), according to the manufacturer’s instructions. Activity counts (counts·min-1) from the GT3X+, heart rate (bpm) and PAEE (kcal.min-1) from the Actiheart were then averaged over the corresponding final two minutes of each activity. This data is presented in the attached file. Statistics: PAEE estimation models for the GT3X+HR were developed using corresponding data from each task, using multiple linear regression analyses. The dependent variable was indirect calorimetry PAEE (kcal·min-1). The independent variables were PAC (counts·min-1) from the GT3X+ with HR (bpm). Pearson product moment correlation coefficients (r) and coefficients of determination (R2) statistics were conducted to assess the association between the criterion PAEE and predicted PAEE for GT3X+HR, HR and AHR (AHR data; using proprietary group calibration). Standard Error of the Estimate (SEE) statistics was also calculated for each relationship. Ideally the population specific equation (GT3X+HR) Ideally the population specific equation (GT3X+HR) would have been 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 (displayed graphically using modified box and whisker plots), Bland-Altman plots with 95% limits of agreement analysis and root mean squared error (RMSE). One-way ANOVA tests by group were performed with post-hoc Bonferroni corrections applied when comparing across 8 activities (rest, five progressive treadmill speeds and 2 gradients). Statistical significance was set a priori of P<0.05. All analyses were performed using IBM SPSS Statistics 21 for Windows (IBM, Armonk, NY, USA). 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+ Actiheart TM Polar Heart Rate Belt 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, GT3X+ accelerometer, Polar heart rate and Actiheart outputs during each activity. On each sheet the data is presented in relation to their group (Unilateral, Bilateral and Control). 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. Actiheart data are highlighted in green and expressed as kcal.min-1. Polar heart rate (bpm) is highlighted in brown. Rate of perceived exertion (RPE) is highlighted in pink. Actigraph accelerometer outputs worn at the hip of the shortest residual limb are highlighted in red. 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.


University Research Studentship Allowance, Graduate School, Faculty of Humanities and Social Sciences

Publication details

Publication date: 16 January 2019
by: University of Bath

Version: 2

This is the latest version of this item.

  • Version 1. (5 October 2017)
  • Version 2. (16 January 2019) [Currently Displayed]


URL for this record:

Related papers and books

Ladlow, P., Nightingale, T. E., McGuigan, M. P., Bennett, A. N., Phillip, R., and Bilzon, J. L. J., 2017. Impact of anatomical placement of an accelerometer on prediction of physical activity energy expenditure in lower-limb amputees. PLOS ONE, 12(10), e0185731. Available from:

Ladlow, P., Nightingale, T. E., McGuigan, M. P., Bennett, A. N., Phillip, R. D., and Bilzon, J. L. J., 2019. Predicting ambulatory energy expenditure in lower limb amputees using multi-sensor methods. PLOS ONE, 14(1), e0209249. Available from:

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: Peter Ladlow


Faculty of Humanities & Social Sciences