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Original article
Predicting energy expenditure through hand rim propulsion power output in individuals who use wheelchairs
  1. Scott A Conger,
  2. Stacy N Scott,
  3. David R Bassett Jr
  1. Department of Kinesiology, Recreation, and Sport Studies, University of Tennessee, Knoxville, Tennessee, USA
  1. Correspondence to Dr Scott A Conger, Department of Kinesiology, Boise State University, 1910 University Drive, Boise, ID 83725-1710, USA; scottconger{at}boisestate.edu

Abstract

Aim To examine the relationship between hand rim propulsion power and energy expenditure (EE) during wheelchair wheeling and to investigate whether adding other variables to the model could improve on the prediction of EE.

Methods Individuals who use manual wheelchairs (n=14) performed five different wheeling activities in a wheelchair with a PowerTap power meter hub built into the right rear wheel. Activities included wheeling on a smooth, level surface at three different speeds (4.5, 5.5 and 6.5 km/h), wheeling on a rubberised track at one speed (5.5 km/h) and wheeling on a sidewalk course that included uphill and downhill segments at a self-selected speed. EE was measured using a portable indirect calorimetry system. Stepwise linear regression was performed to predict EE from power output variables. A repeated-measures analysis of variance was used to compare the measured EE to the estimates from the power models. Bland-Altman plots were used to assess the agreement between the criterion values and the predicted values.

Results EE and power were significantly correlated (r=0.694, p<0.001). Regression analysis yielded three significant prediction models utilising measured power; measured power and speed; and measured power, speed and heart rate. No significant differences were found between measured EE and any of the prediction models.

Conclusion EE can be accurately and precisely estimated based on hand rim propulsion power. These results indicate that power could be used as a method to assess EE in individuals who use wheelchairs.

  • Physical activity measurement

https://doi.org/10.1136/bjsports-2014-093540

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    Background

    There are an estimated 3.3 million Americans who regularly use some type of wheelchair.1 The type of movement that is associated with locomotion for individuals who use wheelchairs is very different from that of able-bodied populations. Until recently, there were a limited number of instruments available to assess the physical activity of individuals who use wheelchairs. Most of the methods used to assess the physical activity in individuals who use wheelchairs have consisted of subjective methods such as questionnaires.2–5 However, these methods are limited in that they rely on the individual to accurately recall their physical activity, which can be problematic.6 Thus, researchers have become interested in developing valid, objective methods for assessing physical activity in populations that use wheelchairs.

    Previous research has utilised a number of objective methods to assess physical activity levels of individuals who use wheelchairs. These include wheel revolution counters that provide information on the total volume of activity7 ,8 and activity monitors attached to the wheel of the wheelchair.9 ,10 Similar to pedometers, these devices are limited in that they are only able to account for the total volume of activity, with limited information on the intensity. Other researchers have investigated activity monitors worn on the arm11–13 or global positioning system units attached to the wheelchair14 during wheelchair activities. These methods demonstrated the potential to provide useful information about the physical activity habits of people who use wheelchairs; however, it is unclear if they can detect the increased energy costs associated with locomotion on different surfaces and different grades.15

    Power meters, typically used with bicycles,16 ,17 are a potential new technique that could be used to measure the power and intensity of wheelchair physical activity. These devices measure mechanical power by assessing the torque and angular velocity at the crank axle or in the hub of the rear bicycle wheel.16 The application of this technology to a wheelchair could improve on the validity of physical activity assessment in individuals who use wheelchairs. Previously, power output during manual wheelchair wheeling has been used to assess mechanical efficiency and biomechanical properties associated with wheelchair wheeling in the laboratory setting.18–21 However, the application of this technology for assessing physical activity during wheelchair movement has not been examined. Similar to accelerometer-based activity monitors used in able-bodied populations, wheelchair power measurement may improve on existing methodologies by quantifying the intensity associated with wheelchair wheeling on different surfaces and grades.

    The data that are collected using the various methods of physical activity assessment often do not have any direct physiological relevance. To establish physiological relevance, activity monitor data are collected simultaneously with a variable such as oxygen consumption to determine the energy expenditure (EE) associated with the activity. Thus, the purpose of this study was to examine the relationship between power and measured EE during wheelchair wheeling. If there is a significant relationship between power and EE, we will investigate if power can be used to provide a valid estimate of EE during wheelchair wheeling. A secondary purpose was to investigate if the addition of other variables to the prediction model could improve on the prediction of EE.

    Methods

    Participants

    Participants in this study were healthy men and women between the ages of 18 and 65 who used a manual wheelchair at least 20 h/week. All reasons for wheelchair use were included, with the exception of individuals with a spinal cord injury (SCI) at the level of C8 or above. Fourteen individuals who use wheelchairs (11 men and 3 women) volunteered for this study. Demographic information of the participants is presented in table 1. Each participant was informed of the potential risks and benefits and signed an informed consent form approved by the university's Institutional Review Board prior to taking part in the study. Participants were excluded if they had any history of cardiovascular disease or uncontrolled metabolic disorder. Prior to testing, body weight was measured on a calibrated wheelchair scale (LWC-800LB, Tree Scale, Fujian, China) with the chair weight subtracted out and self-reported height was recorded.

    View this table:
    Table 1

    Participant characteristics

    Power meter

    A PowerTap SL+ Track Hub (Saris Cycling Group, Madison, Wisconsin, USA) was modified for use on a wheelchair (figure 1). The PowerTap is a power meter that is contained within the rear wheel hub of a bicycle. Inside the PowerTap hub is a series of eight strain gauges. In a traditional bicycle set-up as force is applied by the rider to the pedals, this force is translated through the cranks arm to the bicycle chain and to the cog that is attached to the hub. The force generated by the rider causes the strain gauges to deform. This deformation is proportional to the torque that is generated by each pedal rotation. Torque and wheel velocity are used to calculate instantaneous power.16 ,22 The PowerTap bearings were modified to accommodate the existing axle of a Quickie GP wheelchair (Quickie Wheelchairs, Phoenix, Arizona, USA). The PowerTap hub was laced to a 650C road cycling rim. To ensure that the power generated by the user during wheelchair activities was measured by the PowerTap hub, the push rim of an existing wheelchair wheel was attached to six aluminium spindles that radiated out from a splined cycling cog. The cog was locked into place on the PowerTap hub with a locking ring. The PowerTap wheel was attached to the right side of the wheelchair (figure 2A).

    Figure 1
    Figure 1

    The PowerTap hub laced to a wheel with aluminium spindles.

    Figure 2
    Figure 2

    (A) PowerTap hub adapted for use on a wheelchair and (B) Oxycon Mobile during wheeling activities.

    The PowerTap hub is capable of measuring a number of variables including torque (N m), speed (km/h), power (W), distance (km) and heart rate (bpm)—sent via telemetry from a heart rate chest strap. The PowerTap hub samples at a rate of 60 Hz, averages power data each second and then records data at intervals of 1.26 s.16 After excluding the first 30 s of each bout to allow for the participant to reach the predetermined speed, the average values for each variable were calculated for the remainder of the bout. Variables, such as relative power, were scaled based on body mass after the data were downloaded.

    Procedures

    Prior to beginning testing, each participant transferred to a rigid frame wheelchair outfitted with the power meter (figure 2A). The wheelchair was adjusted to accommodate the participant by adjusting the wheel position and/or adding additional seat back cushions if needed, and each participant was given an opportunity to become familiar with the wheelchair prior to beginning data collection. Tyre pressure was maintained at 100 psi for each trial.23 ,24 The participants were asked to rest quietly for 15 min before beginning any activities. Each participant then performed five different wheeling activities. These activities included wheeling on a level surface that elicited a low rolling resistance at three different speeds (4.5, 5.5 and 6.5 km/h), wheeling on a rubberised 400 m track that elicited a higher rolling resistance at one speed (5.5 km/h) and wheeling on a sidewalk course that included uphill and downhill segments at a self-selected speed. During the four activities at standardised speeds, participants were able to observe their wheeling speed on a bicycle computer and were monitored by a study investigator to ensure that they were wheeling at the predetermined velocity. To ensure that participants were working at a submaximal intensity, subjective rating of perceived exertion was monitored periodically to ensure that they were working at a level equivalent to 8 or below on a 10-point scale. Each activity was performed for 8 min. Between each activity, the participants were asked to rest quietly for at least 3 min.

    Indirect calorimetry

    Each participant wore the Oxycon Mobile (Viasys Healthcare, Hochberg, Germany) portable, indirect calorimeter during the rest period and while performing each activity. The Oxycon Mobile has been previously validated over a range of work rates on a cycle ergometer25 and served as the criterion measure for this study. The Oxycon Mobile was mounted on the back of the participant via a chest harness. The positioning of the Oxycon Mobile on the participant's back was high enough to not interfere with the participant's positioning in the wheelchair (figure 2B). A flexible mask (Hans Rudolph, Kansas City, Missouri, USA) that covered the participant's mouth and nose was secured to the participant via a head strap. Attached to the facemask was a transducer holder with a turbine inside. The turbine rotations are detected by an optoelectrical sensor allowing for the determination of minute ventilation.25 Expired air was analysed for oxygen (VO2) and carbon dioxide concentrations via a sampling line connected to the transducer holder. The Oxycon Mobile was calibrated immediately before each test with a 3 L syringe and with a certified calibration gas mixture. After the calibration procedures were completed, the participant characteristics were entered into the Oxycon computer. The metabolic data were collected in breath-by-breath measurements. To ensure that steady state metabolic activity was achieved, the first 3 min of each activity were excluded from the analysis26 and the mean VO2 of the activity was averaged over the final 5 min.

    Data analysis

    Linear regression analysis was performed to predict EE (kcal/kg/h; where 1 kcal/kg/h≈1 MET15) from power output for all activities using variables collected by the power meter and measured by the portable metabolic system. Stepwise linear regression was performed for other variables (such as speed, distance and heart rate) to improve on the prediction of EE from power output. EE estimates were calculated using separate prediction models based on the PowerTap hub. A repeated-measures analysis of variance (ANOVA) was used to compare the measured EE (kcal/kg/h) to the estimates from the prediction methods. In the case of significant interactions, post hoc pairwise comparisons with Bonferroni adjustments were performed to detect differences between the criterion measurement and the estimates. The regression models were evaluated using a ‘leave-one-out’ cross-validation technique to determine the error and bias associated with each equation.27 Bland-Altman plots were used to assess the agreement between the criterion values and the predicted values for each model.28 The strength of agreement between the criterion values and the estimates was determined by the mean values and bandwidth of the plots (mean±95% CI). The strength of the relationship between the two variables is based on the spread of the 95% CIs around zero: values that are tightly spaced around zero signify higher accuracy, values above zero are overestimates and values that are under zero are underestimates. All statistical analyses were performed using SPSS software (V.18, SPSS Inc, Chicago, Illinois, USA), with statistical significance set at an α level of 0.05.

    Results

    Eight of the 14 participants completed all five activities. For six participants, the rubberised track surface was not available during the time of testing; thus, these participants completed four of the five activities. One participant was unable to maintain the predetermined speed during the 6.5 km/hr activity or complete the sidewalk course for at least five consecutive minutes. These activities were excluded from the analysis. Of the 62 activities that were completed, 60 were completed for the requested 8 min. The remaining two trials were between 5 and 6 min. For these activities, the mean VO2 for the final 2 or 3 min were calculated and used in the analysis. Measured metabolic cost and power information for each of the activities are shown in table 2.

    View this table:
    Table 2

    Metabolic costs, power variables and heart rate for each activity

    The relationship between EE (kcal/kg//h) and power output (W·kg/body weight) yielded statistically significant correlations (r=0.694, p<0.001). Other variables that yielded statistically significant correlations included speed (r=0.829, p<0.001), distance (r=0.787, p<0.001) and heart rate (r=0.547, p<0.001). Stepwise linear regression analysis yielded three significant prediction models utilising (1) measured power, (2) measured power and speed and (3) measured power, speed and heart rate. Using the ‘leave-one-out’ cross-validation technique, the root mean squared error (rMSE) and the bias associated with each equation were low. The prediction model that demonstrated the highest R2 and the lowest rMSE was model 3, which utilised measured power, speed and heart rate. The prediction models are displayed in table 3.

    View this table:
    Table 3

    Equations to predict gross energy expenditure (kcal/kg/h) of wheelchair activities

    Measured EE values obtained from the criterion measurement of the Oxycon Mobile were compared with the different prediction methods (figure 3). A repeated-measures ANOVA demonstrated a significant main effect between measured EE and estimated EE (p<0.01). Overall, there were no significant differences between the criterion method and the power models (p>0.05).

    Figure 3
    Figure 3

    Influence of different wheelchair activities on estimated energy expenditure by different prediction models. HR, heart rate.

    The overall accuracy of each prediction method is represented in figure 4 using Bland-Altman plots to show the differences between measured EE and estimated EE for each method. The precision and accuracy of the three power models were high in each of the three models.

    Figure 4
    Figure 4

    Bland-Altman plots depicting error scores for (A) Power model 1 (W/kg), (B) Power model 2 (W/kg, km/h) and (C) Power model 3 (W/kg, km/h, bpm). Bold line represents the mean difference, dashed line represents the 95% CI and solid line represents the line of perfect agreement. EE, energy expenditure.

    Discussion

    The results of this study suggest that wheelchair power output measurement can differentiate between changes that occur in EE during wheelchair locomotion. The magnitude of the correlation between the wheelchair power output and EE across a range of intensities (r=0.69) in the present study was similar to those reported previously. During an incremental test on a wheelchair ergometer, Theisen et al29 reported correlations between power output and VO2 of 0.72. Other studies that used motion sensors during wheelchair activities on a firm, level surface found similar relationships between activity counts and EE. Washburn and Copay30 reported correlations between activity counts by ActiGraph monitors worn on the wrist and oxygen consumption measured using a portable metabolic system of 0.52 for the right wrist and 0.67 for the left wrist (p<0.01) during wheelchair propulsion at three different speeds on a firm, level surface.

    During activities ranging from resting and deskwork to wheelchair propulsion and arm crank ergometry, Hiremath and Ding11 reported correlations between EE measured by a metabolic system and those estimated by the SenseWear armband and the RT3 triaxial accelerometer of 0.79 and 0.71 (p<0.01), respectively. During wheelchair propulsion activities, Hiremath and Ding12 also reported correlations of 0.47 by the SenseWear armband and 0.52 by the RT3 triaxial accelerometer (p<0.05). The relationship between upper body movement and EE during wheelchair activities was correlated in this study. However, this study did not include surfaces that elicited higher rolling resistances and different grades. Thus, the present study demonstrated similar relationships between wheelchair propulsion power and EE with the inclusion of wheelchair activities on a surface that elicited higher rolling resistance and different grades from those that have been reported between wrist accelerometry and EE.

    Using stepwise linear regression, the results of the present study were used to develop three models for predicting EE from power output. Each of these models used wheelchair power and added other variables that can easily be obtained simultaneously via the PowerTap hub (ie, speed and heart rate). A repeated-measures ANOVA indicated that none of the mean values obtained from the power models were significantly different from the criterion measure for EE. On the surface that elicited a higher rolling resistance at 5.5 km/h, all three models underestimated the EE. However, owing to logistical constraints, the number of participants who completed this activity (n=8) was the fewest of any activities. Since this activity accounted for only 12% of the total trials, it had a relatively minor influence on the prediction equations. However, model 3 (incorporating power, speed and HR) did improve on the prediction. Nevertheless, none of the power prediction models overpredicted or underpredicted EE when all trials were considered together.

    The PowerTap hub was originally designed to provide an estimate of EE during bicycling. We chose not to use the estimates of EE by the PowerTap because the mechanical efficiency of bicycling and wheelchair locomotion differs. Mechanical efficiency has been defined as the ratio of work accomplished to energy expended in performing the work.31 The mechanical efficiency of cycling is generally considered to be between 20% and 25%.31–33 During manual wheelchair locomotion, mechanical efficiency is considerably lower, ranging between 4% and 15%.29 ,34–36 Thus, using the EE estimates reported by the PowerTap in the analysis of this study would have considerably underestimated the actual EE of wheelchair locomotion.

    In this study, we adapted a PowerTap hub to measure power output during wheelchair activities. This device yields similar power output values to a laboratory ergometer and another power meter, the Schoberer Rad Messtechnik (SRM) crankset.22 ,37 Although the PowerTap and the SRM operate by measuring power output in different locations on a bicycle,16 adapting these devices (and others that utilise a similar design) to a wheelchair should produce similar power output values. Therefore, the results of this study can be applied to other methods that can be used for measuring hand rim propulsion power during manual wheelchair locomotion.

    It is worth noting that speed and distance had higher individual correlations than power (0.829, 0.787 and 0.694, respectively). Others have also found that speed is a good predictor of EE during wheelchair wheeling.38 As speed and time in four of the five activities that were used in this study were controlled, it would be expected that distance would also yield a high correlation. The higher correlation for speed in predicting EE is most likely due to the activities that were selected. Additionally, only 12% of the total trials in this study utilised a surface with a higher rolling resistance. Future research on the EE and power relationship with more activities of wheeling uphill, downhill and over surfaces with a greater variety of rolling resistances and at different velocities is needed to confirm this.

    The objective assessment of physical activity in ambulatory populations has focused on the use of accelerometer-based physical activity monitors for predicting EE.39 Owing to the unique nature of body movement for locomotion when using a wheelchair, it is unclear if an accelerometer-based activity monitor worn by the individual would be able to detect the increase in force and associated EE that is required with wheeling uphill or over surfaces that have a higher rolling resistance.15 The addition of wheeling power adds an important variable to be considered when assessing physical activity for individuals who use wheelchairs.

    This study has several strengths and limitations. One of the strengths was that EE was directly measured during wheelchair locomotion. Another strength was that the wheelchair speed was closely monitored and surfaces that elicited different rolling resistances were examined. Limitations of the study include a relatively small sample size and a relatively limited number of different activities. Also, this study was not limited to only one population of people who use wheelchairs. This could be viewed as a limitation to the study design. Many studies of EE during wheelchair activities have been limited to only individuals with SCI. While these studies have provided an important insight into the different energy requirements that occur in populations with SCI,40 the results are limited in their generalisability to other individuals who use wheelchairs. In this study, 50% of the participants used a wheelchair for reasons other than SCI. In addition, the power meter used in this study was designed for use on bicycles. With this commercially available device, any speeds below 3 km/h are registered as zero in the software. The device also features a power-saving mode in which the device powers off after 5 min of inactivity to conserve battery life. This would be problematic during intermittent activities. This proof of concept study demonstrates the use of power measurements as a method to estimate EE; however, the current device configuration limits its usefulness during free-living studies.

    In conclusion, this study demonstrated that EE can be accurately and precisely estimated based on power output measurements during wheelchair propulsion. EE estimates from the three prediction models based on wheelchair power output were not significantly different from the criterion measurement. The addition of speed and heart rate to power measurement improved the model's ability to predict EE. Future studies using the wheelchair power method for estimating EE should examine higher intensity activities and free-living activities. In addition, a cross-validation of the equations developed in this study is needed.

    What are the new findings?

    • Energy expenditure during wheelchair locomotion can be accurately estimated based on propulsion power.

    • The addition of other variables such as heart rate and speed improves on the energy expenditure estimation.

    • This method may be an alternative for free-living physical activity assessment of individuals who use wheelchairs.

    How might the study impact clinical practice?

    • This method could be used by wheelchair athletes and their coaches to monitor the intensity of their training sessions.

    • Clinicians can use it to encourage their patients to perform health-enhancing physical activity. Eventually, this methodology may give clinicians a better idea of the total volume of physical activity required for optimal health in individuals who use wheelchairs.

    Acknowledgments

    The authors would like to thank all the participants for their time and dedication to this study. They thank Richard Sawiris of Wheelbuilder.com for providing the PowerTap and for making the necessary modifications to the device, to Jennifer Flynn, Dana Wolff and Brian Tyo for assistance during data collection and to Dixie Thompson, Eugene Fitzhugh, Paul Erwin and Gene Hayes for helpful feedback during the preparation of this manuscript.

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    Footnotes

    • Contributors SAC and DRB conceptualised this paper and developed the study protocol. SAC and SNS collected and analysed the data. All authors were involved in the interpretation and review of the results. SAC drafted the manuscript and revised it according to feedback from the other authors. All authors have reviewed and approved the final version of the manuscript.

    • Competing interests None.

    • Patient consent Obtained.

    • Ethics approval Institutional Review Board.

    • Provenance and peer review Not commissioned; externally peer reviewed.

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