Accelerometer output and its association with energy expenditure during manual wheelchair propulsion

Abstract

Study design: This is an experimental design. Objectives: This study examined the association between rates of energy expenditure (that is, oxygen consumption (VO 2 )) and accelerometer counts (that is, vector magnitude (VM)) across a range of speeds during manual wheelchair propulsion on a motor-driven treadmill. Such an association allows for the generation of cutoff points for quantifying the time spent in moderate-to-vigorous physical activity (MVPA) during manual wheelchair propulsion. Setting: The study was conducted in the University Laboratory. Methods: Twenty-four manual wheelchair users completed a 6-min period of seated rest and three 6-min periods of manual wheelchair propulsion on a motor-driven wheelchair treadmill. The 6-min periods of wheelchair propulsion corresponded with three treadmill speeds (1.5, 3.0 and 4.5 mph) that elicited a range of physical activity intensities. Participants wore a portable metabolic unit and accelerometers on both wrists. Primary outcome measures included steady-state VO 2 and VM, and the strength of association between VO 2 and VM was based on the multiple correlation and squared multiple correlation coefficients from linear regression analyses. Results: Strong linear associations were established between VO 2 and VM for the left (R = 0.93 ± 0.44; R 2 = 0.87 ± 0.19), right (R = 0.95 ± 0.37; R 2 = 0.90 ± 0.14) and combined (R = 0.94 ± 0.38; R 2 = 0.88 ± 0.15) accelerometers. The linear relationship between VO 2 and VM for the left, right and combined wrists yielded cutoff points for MVPA of 3659 ± 1302, 3630 ± 1403 and 3644 ± 1339 counts min − 1 , respectively. Conclusion: We provide cutoff points based on the linear association between energy expenditure and accelerometer counts for estimating time spent in MVPA during manual wheelchair propulsion using wrist-worn accelerometry. The similarity across wrist location permits flexibility in selecting a location for wrist accelerometry placement.

Motion sensors in multiple sclerosis: Narrative review and update of applications

“…One study recently addressed this concern, wherein 24 manual wheelchair users (including persons with MS) completed three treadmill conditions (wheeling on a motorized wheelchair treadmill at 1.5, 3.0 and 4.5 mph) while wearing an ActiGraph GT3X accelerometer on each wrist and a Cosmed K4b2 portable metabolic system. The regression of accelerometer output on energy expenditure resulted in a cut-point for MVPA of 3644 ± 1339 counts/min that can be applied to either left or right wrist placements [48]. …”

Validity of activity monitors in wheelchair users: A systematic review

“…The specifications of each device are shown in Appendix 2. Of the 29 articles selected, 6 studies evaluated commercially available monitors with default algorithms for quantifying PA in MWUs [24][25][26][27][28][29] (Appendix 3), 15 evaluated commercially available monitors with custom algorithms [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] (Appendix 4), and 10 evaluated custom-made devices and algorithms [43][44][45][46][47][48][49][50][51][52] (Appendix 5). These activity monitors could be categorized into three types: accelerometer-based, multisensor-based, and others (gyroscope-or HR-based).…”

“…Among the 29 articles, 14 investigated accelerometer-based monitors (1 uniaxial [38] and 13 triaxial [26,[32][33][39][40][41][42][46][47][48][49][50][51]), 9 evaluated multisensor-based devices [24][25][28][29]31,[34][35][36][37], 1 evaluated a gyroscope-based monitor [44], and 5 evaluated combinations of any 2 of the 3 types [27,30,43,45]. Among all articles, 19 included only MWUs with spinal cord injuries (SCIs) [24,[26][27][28][29]31,[33][34][35][36][37][40][41][43][44]46,[50][51][52]; 7 included MWUs with a mix of diagnoses, including SCI, amputation, congenital bone disorder, complex regional pain syndrome, CharcotMarie-Tooth disease, demyelinating disease, dystonia, fib...…”

“…Two studies used doubly labeled water [24,35], and 14 studies used metabolic analysis systems such as Cosmed K4b2 or Quark b2 (COSMED; Rome, Italy), TrueOne 2400 computerized metabolic system (TrueOne; Salt Lake City, Utah), Oxycon mobile (Viasys Healthcare; Hochberg, Germany), gas analyzer (AR-1 type-4, Arco System; Chiba, Japan), and Metabolic Analysis System (Aerosport Inc; Ann Arbor, Michigan) as the criterion for measuring energy cost [27][28][29][30][31][32][33][34][37][38][39][40]42,50]. Other methods, including video analysis, observation, direct measurements, and motion capture systems, were used as reference comparisons for validating wheelchair and user movements [34,36,41,[43][44][45][46]49,[51][52].…”

“…For MSE, the negative number indicates overestimation while the positive number indicates underestimation. In addition, 13 out of 18 studies reported the correlation coefficient (Pearson or Spearman) between the estimation and the criterion [24][25][27][28][30][31][32][33]35,[37][38][39]42]. The correlation coefficient between the estimated energy cost by the commercial monitors with the default algorithms and the criterion ranged from 0.35 [25] to 0.84 [27] (Table 1), while the correlation coefficient between the estimated energy cost by the commercial monitors with the custom algorithms and the criterion ranged from 0.40 [35] to 0.95 [39] ( Table 2).…”