Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction in Manual Wheelchair Users

PLOS ONE, Dec 2019

Purpose To assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs). Methods Seventeen MWUs (36 ± 10 yrs, 72 ± 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr-1) and propulsion at 4km·hr-1 (with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis. Results Accelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W. Conclusion The results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.

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Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction in Manual Wheelchair Users

May Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction in Manual Wheelchair Users Tom Edward Nightingale 0 1 2 Jean-Philippe Walhin 0 1 2 Dylan Thompson 0 1 2 James Lee John Bilzon 0 1 2 0 Received: December 8 , 2014 1 Academic Editor: Maciej Buchowski, Vanderbilt University , UNITED STATES 2 Centre for DisAbility Sport and Health (DASH), Department for Health, University of Bath , Bath, Somerset , United Kingdom To assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs). Seventeen MWUs (36 10 yrs, 72 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km hr-1) and propulsion at 4km hr-1(with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis. - Accelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean SD PAEE estimation errors for all activities combined were 15 45%, 14 50%, 3 25% and 4 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W. The results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device. The positive contribution of regular physical activity (PA) to weight balance, metabolic regulation and cardiovascular fitness is well documented and broadly accepted in the able-bodied population [1]. Epidemiological studies concerning the impact of PA on health in wheelchair users with a spinal cord injury (SCI) have been limited to assessing associations between subjective reports of activities of daily living (ADL) [2] or leisure time physical activity (LTPA) [3] and chronic disease risk factors. The assessment of these constructs in previous studies were quantified via the Physical Activity Recall Assessment for people with a SCI [PARA-SCI; [4]], which is administered via a telephone interview. This subjective PA assessment tool asks participants to recall activities undertaken during the previous 3 days, which is a relatively short monitoring period. The results, like other self-report measures, depend on the accuracy of the participants memory and recall [5]. Ullrich et al, [6] also suggested that the use of the PARA-SCI might have limited application for other investigators besides the developers due to the exclusion of subjective appraisals and the technical complexity of interview administration. To date, despite the aforementioned limitations, quantifying free living PA among wheelchair users has mostly been restricted to self-report measurements. As such there is a requirement to develop unobtrusive objective measurement tools that can be easily used to characterise the association between physical activity and metabolic health in this population. The PA monitoring literature has evolved rapidly, particularly in able-bodied populations, yet there is a paucity of research focussing on the use of activity monitors to predict physical activity energy expenditure (PAEE) in certain populations, including manual wheelchair users (MWUs). Broadly speaking, various devices used previously to determine PA in MWUs have distinct limitations. For example despite being unobtrusive, a tri-axial accelerometer attached to a wheelchair [7] is unable to distinguish between self or assisted propulsion and is also unable to quantify any activity out of the wheelchair, or during stationary arm crank ergometry exercise. Monitors attached on the waist near the participants centre of mass, as advised by manufacturers for use in able-bodied cohorts, have been shown to under-estimate energy expenditure by 24% in MWUs [8]. Previous work by our research group [9] and others [10] has indicated that the anatomical placement location is critical to accurately estimate PAEE. It is perhaps not surprising due to restricted patterns of movement that a tri-axial accelerometer worn on the wrist explains more of the variance in PAEE, resulting in the lowest random error compared to the waist. The use of multi-senso (...truncated)


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Tom Edward Nightingale, Jean-Philippe Walhin, Dylan Thompson, James Lee John Bilzon. Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction in Manual Wheelchair Users, PLOS ONE, 2015, 5, DOI: 10.1371/journal.pone.0126086