1. Introduction
Manual wheelchair users (MWUs) with spinal cord injury (SCI) are dependent on their upper extremity for almost all activities of daily living (ADL). This includes shoulder loading activities, like wheelchair ambulation, pressure relief to avoid pressure injuries, transferring in or out of the wheelchair into a bed or onto a toilet, and transfer of the wheelchair into or out of a car, for instance [1,2,3]. Due to the high dependency on the upper extremity, there is a risk of shoulder overuse. MWUs with SCI frequently report shoulder problems like pain or pathology. In this population, the prevalence of musculoskeletal shoulder pain ranges from 39% to 71% [4,5]. MWUs with SCI have a six-times-higher likelihood to suffer from shoulder pain and a three-times-higher likelihood of developing rotator cuff tendon pathology than able-bodied controls [6,7]. As most MWUs are dependent on their upper extremities for many aspects of daily life, shoulder health is also highly correlated with independence, social participation, and quality of life [8,9,10,11]. Considering shoulder load from ADL, from these activities, only the magnitude of load on the shoulders is known, but neither the frequency nor duration of these activities is known in real life. If the magnitude, frequency, duration, or a combination thereof is too high, there is no room for rest or recovery, which could lead to fatigue and/or overload [12].
Wheelchair propulsion is a dominating activity in the life of a MWU, but numbers on actual daily wheelchair use are hardly available. Despite the increased availability of, and growing interest in, inertial measurement units (IMUs) to quantify wheelchair use in the past decades, methods for quantifying the daily use of a wheelchair are scarce, and comprehensive data on the MWUs’ exposure to risk factors for shoulder problems are hardly available. Methods that quantify wheelchair use with IMUs can be grossly classified into the following domains:
Activity: activity levels, activity classification, arm use [13,14,15,16,17,18,19];
Energy expenditure [20,21,22,23,24,25];
Temporal wheelchair propulsion characteristics [26];
Performance in sports settings [27,28,29,30].
Single variables of wheelchair use include distance covered [31,32,33,34,35,36], speed [31,32,35], number of pushes [13,14,26,37,38], turns [39], or duration of movement [35,40,41].
Furthermore, as turning a wheelchair can double the shoulder load [42], the number of turns and magnitude thereof are also an essential variable to consider when trying to estimate daily wheelchair ambulation, with respect to shoulder load. The same accounts for uphill wheelchair propulsion, which increases the load on the upper extremity [43,44,45].
This study focusses on wheelchair mobility metrics (WCMMs) from the perspective of shoulder problems. Measuring WCMMs can serve as a proxy for shoulder load for the most dominant activity in the daily life of MWUs. While measuring activity levels or energy expenditure can provide information about general activity, it cannot provide the same detailed and long-term information as WCMMs. The method, as described by de Vries et al. [46], is comprehensive in quantifying wheelchair mobility in the above-mentioned aspects, on metrics that relate to shoulder load. It is based on two IMUs, one on the wheelchair frame and one on the wheelchair wheel, from which a range of relevant mobility metrics can be derived from one measurement. Furthermore, combinations of variables appeared to be of particular clinical interest, as emerged during focus group discussions [47], like distance covered over a certain incline or average distance per bout of mobility (BoM) [41], to obtain more detailed insight into the daily mobility behavior of an individual. In the following examples, detailed WCMMs could be especially helpful.
For some MWUs living in an inclined environment, the quantification of daily distance covered over inclines could help in the decision to acquire assistive technology, as additional training might not be the key solution here to prevent or reduce shoulder problems. Other MWUs who mainly work from home and WCMMs only show that short-distance BoM could benefit from going outdoors over lunch time, to perform some physical activity at a higher intensity and longer duration. For MWUs with a higher lesion level and, therefore, reduced shoulder muscle function, WCMMs could support the prescription of assistive technology when the demands of daily ambulation rise above capacity. Therefore, the quantification of the actual wheelchair use could help to adapt daily behavior or tailor interventions towards the individual, based on the individual’s lesion characteristics.
The current study describes the results of the WCMM method [46] applied to data collected from 19 manual wheelchair users with SCI, over a full day in their living environment.
The aim of this study is to implement the non-invasive WCMM method in real-life settings and to examine different interrelated variables of wheelchair use. The findings can act as a basis to shape future individualized assessments of mobility behavior. Measuring and summarizing such mobility behavior with methods like the WCMM will support clinicians in their guidance or education of individual patients in relation to their shoulder problems.
2. Materials and Methods
2.1. Participants
We recruited 19 manual wheelchair users with SCI (14 males, 5 females, age 46 ± 15 years, weight 71 ± 15 kg, height 174 ± 13 cm, time since injury 21 ± 12 years). Inclusion criteria were age between 18 and 75 years, paraplegia below T2, and having an active lifestyle (e.g., being employed, non-sedentary leisure time). Exclusion criteria were shoulder problems limiting the execution of daily activities, a current pressure injury, or cardiac problems. The sample size was calculated for the larger encompassing study, based on performance measures of deep learning classification algorithms. This study was approved by the Ethikkomission Nordwest- und Zentralschweiz (EKNZ, project ID: 2022-00825); informed consent was obtained from all participants before the start of the experiment.
2.2. Data Collection
Participant’s wheelchairs were instrumented with two Shimmer IMUs (IMU, Shimmer3 IMU unit, Shimmer, Dublin, Ireland). One IMU was placed at the wheelchair frame, the other on the right wheel, and they were configured as follows: wide-range accelerometer at 8 g in 16-bit resolution, having a root mean square (RMS) noise value of 27.5 × 10−3 m/s2. The gyroscope was configured at 2000 degrees/s, in 16-bit resolution, having an RMS noise value of 0.0481 degrees/s. Sample frequency for both accelerometer and gyroscope was set at 100 Hz. Additionally, a video camera (GoPro© Hero 8, GoPro, San Mateo, CA, USA) was mounted on the back of the wheelchair for screening wheelchair use during the day. A technical calibration from mV to acceleration and angular velocity was conducted before and after the series of experiments and revealed no differences larger than noise level.
Both sensors and video recordings were synchronized before and after the actual measurements over the day. For that purpose, the IMUs were attached to a rigid beam, which was rotated around the longitudinal axis 10 times, in view of the video camera; the clearly recognizable pattern in the gyroscope signals can then be synchronized by cross-correlation. This procedure also determines the start of the sensor measurements in the video recordings.
2.3. Data Analysis
The video recordings of the real-life measurements were screened and annotated for wheelchair-related activities. Any portion of data that contained wheelchair ambulation with some sort of support (being pushed, power assist devices, etc.) was excluded. The next seven basic WCMMs were derived from the setup with two IMUs: linear velocity of the wheelchair, distance covered, number and magnitude of turns, number and duration of pushes, and incline of the wheelchair.
In preparation of the calculation of these WCMMs from IMU data, two specific periods of at least 10 s were selected from the IMU recordings: complete standstill at level surface, and straightforward wheelchair propulsion. Orientation of the IMU with respect to wheelchair frame was derived from the complete standstill and averaged over 10 s. The portion of gyroscope data from straightforward wheelchair propulsion was averaged over the 10 s to represent the wheel axis in the local coordinate system of the IMU at the wheel. Afterward, for the analysis of data collected over the day, any rotation around this wheel axis was attributed to distance covered, linear velocity, BoM, and used for push detection to count the number of pushes. Any deviation from this wheel axis, in terms of the angular velocity vector, as measured by the wheel IMU, was attributed to a turn. The assumption used here is that a wheelchair will only rotate around the wheel axis and the vertical axis, not around the frontal axis. The latter is only the case in sidewards rocking of the wheelchair or when entering or leaving a walkway with a sidewards inclination for rain-water drainage, which is usually around 3° and, as such, neglectable in comparison to the number and magnitude of turns made daily. Inclination was calculated in the sagittal plane as angle between the vertical from the wheelchair as measured during standstill and 10 s average of the current acceleration signal. For further details on the calculation of all variables, and the validation of the WCMM method, see de Vries et al. [46]. To enable comparison over participants, the resulting outcome variables were normalized to a time span of 8 h (to reflect a regular working day).
Furthermore, an extended analysis was applied, in which several of the basic WCMM variables were combined:
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Bouts of mobility (BoM, according to the definition of Sonenblum et al. [41]): bout duration, mean distance and mean velocity per bout.
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Distance covered over a range of positive inclines (2 to 10°, which represents uphill wheelchair propulsion).
Additionally, several groupings of variables were visualized in scatter and radar plots, to further explore the inter-individual diversity of mobility behavior observed, as an example of how such summaries of real-life data on wheelchair mobility behavior could support and enhance clinicians and their patients in clinical decision making.
3. Results
3.1. Real-Life Measurements
The average measurement duration was 5.8 h in the living environment of the participants. Due to technical problems with some IMUs, the measurement duration varied from as little as 2 h for three participants up to 9.2 h for most of the participants. Video recordings were screened for the type of wheelchair ambulation. Any period with a form of support (power assist like Swiss Trac or E-Motion or being pushed) was excluded from the analysis.
3.2. Basic WCMMs
The main finding is the wide variation in wheelchair mobility behavior among participants, as can be observed from the overview in Table 1. For a more detailed comparison of the specific WCMMs over participants, see Figure 1, Figure 2 and Figure 3, as described hereafter.
For instance, total distance covered varies over participants from around 570 m to 10.7 km (Figure 1a). “Distance over inclines” also varies among participants, although inclines higher than 6° are hardly observed (Figure 1b). The number of pushes varied from 438 to 4820, with an average over participants of 2055 pushes (Figure 1c). The average push duration ranged from 0.47 to 0.97 s (Figure 1d). The number of turns (larger than 30°) was high (Figure 1e). On average, 635 turns were detected over participants, with a minimum of 269 and a maximum of 1396 turns. Most of the turns (97%) are less than 210°, but inter-individual differences can be clearly observed in Figure 1f.
Participant 6 showed higher values than other participants for almost all variables, as he took his dog out for a long walk twice that day (background information based on personal communication). This is in contrast with participants 13 and 15, who both worked from home on the day of measurement (background information based on personal communication), and both made a relatively high number of turns given the lower average distance covered. Participants 6 and 13 both made a higher-than-average number of large turns in the range of 180° to 210°.
3.3. Extended Analysis
The extended analysis on the basic WCMMs revealed further details from the wheelchair mobility behavior. As can be observed from Figure 2, there was high variation in the distance covered per BoM. Participants 11 and 12 had around the same number of bouts over the day (79 and 74 BoM, respectively); however, for participant 11, the average distance per BoM was 8 m, while for participant 12, this was 47 m.
Figure 2This graph depicts, per participant, the number of, and the distances per bout of mobility (numbers in brackets are total number of BoM for that participant).
[Figure omitted. See PDF]
A further combination of variables could be useful when norm-values become available, or for group comparison, as can be observed from Figure 3. Extreme values for the several variables can easily be detected in comparison to the results of other participants. For instance, participant 13 covered around 2000 m, making almost 1200 turns, during 273 BoM, with an average distance per mobility bout of around 5 m. In contrast, participant 19 covered around 6000 m, made 270 turns, and had 99 BoM, with a mean distance of 60 m.
Figure 3This figure displays a bubble chart combining four variables per participant; total distance covered, number of turns, number of BoM and the average BoM distance, which is useful to do group comparisons. Position of the circle indicates total daily distance (horizontal axis) and number of turns (vertical axis), the number of BoM are indicated within the circle, while the diameter of the circle is proportional with the mean BoM distance for that participant.
[Figure omitted. See PDF]
In Figure 4, data from two participants are depicted in comparison to the mean results of the study sample. Participant 5 (blue line) has average values for the number of turns and BoM over the 8 h but covers considerably larger distances, with more pushes and above-average mean BoM distance. Participant 13 (yellow line) shows an almost opposite profile; far-below-average distance is covered, with an average number of pushes, an above-average number of turns and number of BoM, but with a below-average mean BoM distance. Average values for the study sample are indicated by the black line.
4. Discussion
4.1. General Findings
Large variation in wheelchair use was seen in the study population, regarding distance covered, number of pushes, and number of turns per day. This large variation in itself is an argument for individual monitoring and speaks against the generalization of average values towards a population. In the clinical management of individuals who rely on wheelchairs for mobility, particularly those with shoulder pain, the integration of such detailed real-life data on wheelchair use on an individual level can support clinical decision making. By quantifying the actual wheelchair use, interventions can be adapted to the individual, not only with respect to the individual’s capacity but also with respect to the mobility requirements of the person’s daily environment. Furthermore, longitudinal monitoring of real-life wheelchair use on a detailed level, as described in the WCMM method, will increase insight in daily behavior of wheelchair use and its impact on shoulder health. As such, it can be a promising additional source of information for developing effective treatment plans and improving patient outcomes on physical activity and shoulder health.
4.2. Distance and Number of Pushes
Considering the distance covered, some participants covered only 500 to 700 m over the 8 h, whereas others propelled over 10 km. The general variation observed, the average distance covered of around 3500 m, and the average number of pushes of 2064 grossly resemble findings from the scarce literature available [19,35,39,41,48].
4.3. Turns
The average number of 635 turns in this study is lower than the average of 900 turns per day that Togni et al. found [39]. Most important is the difference in the definition of a turn. Whereas the current study used a threshold of 30°, based on an internal consensus discussion with several disciplines, Togni et al. used a threshold of 15° as a minimum for a turn, likely resulting in more turns detected. The differences described above illustrate the fact that research on the topic of wheelchair mobility characteristics in real-life settings is in its infancy, and standardization of the definition of a turn in wheelchair propulsion has yet to come.
Furthermore, due to the measurement capacity of sensors and technical problems with some IMUs, the measurement duration varied from as little as 2 h for three participants up to 9.2 h for most of the participants. On average, the measurement duration was 5.8 h. WCMMs were normalized to 8 h in this study to reflect a working day, whereas the study of Togni et al. measured 7 days continuously. However, normalizing our results to 12 or 14 h (estimated active part of a day) seemed inappropriate.
Since a simple 90° wheelchair turn can double the joint compression forces at the shoulder during a push turn and can increase peak negative force or force impulse by a factor of 15 or 4.5, respectively, when braking [42], it is evident this is an important variable to measure when monitoring wheelchair mobility behavior.
4.4. Distance over Incline
As incline wheelchair propulsion significantly increases the load on the shoulder [2,43,44,49,50,51], detailed information on this variable from real-life settings can help the MWU and clinician to find the optimal balance between daily WCM behavior and eventual related shoulder under- or overuse. From Figure 1b, it can be observed that hardly any of the participants propelled over an incline higher than 6°. There might be several reasons for this. First, many of the study participants worked at a rehabilitation center for SCI, for which the infrastructure complies very well with standards like DIN18040 [52] considering accessibility, with ramps of a maximal incline 6% (which equals 3.4°). Furthermore, inclines higher than 4–6% are hardly doable for most MWUs, as they require a mean force application per push of more than 80 N on average [53], which is relatively high. Furthermore, due to the incline, the center of gravity of the wheelchair and user will move closer to the real wheel axis, increasing the risk of tipping backwards.
4.5. BoM
As stated, participants showed a large variation in wheelchair use, which is also reflected in the variable BoM, in terms of the numbers of BoM and average distance covered per BoM. For example, one participant with around 80 BoM over a day had an average distance per BoM as little as 5 m, while for another participant, performing the same number of BoMs, the average distance per BoM was 59 m. Duration or distance per BoM could be used as an indicator for general physical activity. SCI-specific guidelines for physical activity recommend a minimum of 20 min of moderate to vigorous activity twice a week [54]. It is obvious that with an average distance per BoM of 5 m, such guidelines are difficult to meet. Furthermore, a single BoM includes start-up propulsion, which leads to higher shoulder loads than straightforward level ground wheelchair propulsion and, as such, the number of BoMs be used as a gross indicator for shoulder (over-) load.
4.6. Clinical Perspective
Real-life data provide insights into the actual activities of patients, and analyzing data from diverse populations can reveal patterns in wheelchair ambulation, in terms of speeds, distances, turns, and inclines, that may exacerbate or mitigate shoulder pain. This knowledge will help clinicians identify specific strategies that minimize shoulder strain and inform patients about optimal wheelchair use.
Additionally, tailored programs can be designed that consider individual out-patient characteristics and daily activities. For instance, a patient who regularly engages in frequent long-distance propulsion may benefit from different interventions than someone using a wheelchair primarily for short distances. As an example from this study, participant 6 showed the highest WCMM values among all participants. He took his dog for a longer walk, covered almost 11 km on the day of measurement, made around 200 turns in the range of 180° to 210° when looking for his dog (background information from observation of video recordings), and covered more than 1100 m over inclines of 3° or more. When this is a regular ambulation pattern for this person, and this person presents to a clinician with shoulder pain, the WCMM could be very helpful in formulating some advice to reduce shoulder load that could ignite the shoulder pain. Possible advice could be to keep moving, but for shorter distances or with longer breaks in between, to reduce the number of turns, and to try to find another route with less elevation gain. Or, when unavoidable, consider the use of power assist technology. By aligning rehabilitation strategies with actual usage patterns, clinicians can enhance the effectiveness of their interventions. In fact, monitoring wheelchair use before and after any intervention can also help in evaluating the effectiveness of such an intervention.
Data-driven insights might also help identify specific risk factors associated with shoulder pain in wheelchair users. The analysis of longitudinal real-life wheelchair mobility behavior can reveal an eventual relation between the several aspects of wheelchair use and development of shoulder problems over time. For example, excessive use of the upper extremities without proper ergonomic adjustments may lead to chronic pain [5,55,56]. But, for a person with a low ambulation distance, other risks and priorities might exist. Based on the real-life data, clinicians can pinpoint these risks and develop preventative strategies, such as recommending ergonomic modifications, discussing alternative routes, etc. Furthermore, real-life data serve as a powerful tool for patient education and shared decision making. When clinicians present data on how specific wheelchair mobility behavior impacts shoulder health, patients can better understand the importance of adopting recommended practices.
4.7. Limitations
The current WCMM algorithm assumes manual wheelchair ambulation. For data collected in this study, additional video recordings were available and used to exclude any portion of data in which any form of power assist was used. However, the current WCMM algorithms cannot automatically distinguish between manual ambulation and any form of power support. This will lead to an overestimation of the actual number of pushes and distance covered. To distinguish between manual wheelchair propulsion and being pushed [33], or using any form of power assist, more complex algorithms are required, which are still rare.
Only one day of WCMM was collected (varying from 2 to 9 h of measurements, normalized to 8 h) due to limitations in sensor recording capacity. The results were normalized to an 8 h period to enable comparison. However, this approach might skew the results of that participant towards his or her activity level of that specific measurement period, when activity had not been constant throughout the day. For a proper representation of movement behavior, a minimum of 2 to 5 consecutive days of continuous monitoring is recommended, with longer durations providing more accurate and reliable patterns [57,58]. Data collection should include weekdays and weekends to gain a full overview. Such is possible with newer types of IMUs, with lower power consumption and larger battery capacity.
The data presented in this manuscript are based on 19 participants, selected and recruited for having an active lifestyle. Furthermore, participants with shoulder problems limiting the execution of daily activities were excluded. Therefore, the results are most likely not representative for the general MWU-SCI population, in which the prevalence of shoulder problems is high and for which a much larger sample would be needed, further stratified over age categories, sex, lesion severity, and time since injury. However, with the WCMM methodology described, and the resulting data presented here, the authors aim to encourage researchers and collaborative clinicians to adapt their procedures and not rely on population statistics but monitor individual wheelchair mobility behavior.
WCMMs only address manual wheelchair ambulation, neither pressure relief nor transfers or other shoulder loading activities of daily life. For such, additional methodologies should be, and are being, developed, to increase the available information, e.g., the number and duration of weight relief lifts or transfers [59], by adding a pressure mat to the measurement equipment. Also, propulsion patterns, as described by Boninger 2002 (semi-circular, single loop, double loop, arching) [60], are clinically relevant variables that should be measured in real-life settings. These require, however, an additional sensor on the wrist or forearm and dedicated algorithms for detection. Some attempts have been made to develop algorithms to identify those propulsion patterns from IMU data; however, these are neither published nor available to the scientific community, yet.
4.8. Future Directions
The results from the real-life monitoring of wheelchair use with a method like the WCMM can be used on three distinct levels: (1) individual level, to quantify wheelchair use and define personalized goals like increasing daily distance (to increase daily physical activity) or diminishing the number of turns (e.g., to reduce observed shoulder pain when making turns); (2) clinical level, to observe wheelchair use in the daily environment of patients with shoulder problems, and to design, prescribe, or evaluate interventions tailored to the individual’s capacity and requirements of daily ambulation; (3) research level, to examine relations between typical wheelchair use in terms of WCMMs and development of shoulder problems over time, which, in turn, may inform the first two levels. Future studies, including larger numbers of MWUs observed over longer periods of time, are needed. We recently started such a project, monitoring 60 MWU four times during a full week, distributed over a year. Such a dataset will deliver first insights in the longitudinal process of wheelchair use and the potential relationships in the development or progression of shoulder pain. It provides a good starting point for establishing normative data and, even further down the road, informs clinical guidelines. However, in the individual case, individual monitoring should be the preference.
IMUs have broad potential for monitoring daily behavior. However, further advancements in algorithms for data analysis are needed, for instance, for the identification of propulsion patterns (both motion and force application) in the case of manual wheelchair use. Furthermore, even though manual wheelchair use is a dominating activity for MWUs, the quantification of other wheelchair-related activities is highly desired to obtain a full picture of shoulder loading in daily wheelchair life and its consequences for the development of shoulder problems.
5. Conclusions
The WCMM method implemented in this study provided valuable insights into real-life wheelchair use, offering a foundation for future research and individualized clinical assessments. We found wide variation in WCMMs, which emphasizes the importance of data-driven clinical decision making and patient education. Further studies with larger, more diverse study samples and extended monitoring periods are currently being conducted to fully understand MWUs’ mobility patterns and their implications for shoulder health.
Conceptualization, W.d.V., I.E.-H., A.H.-G., S.K.-B., C.P. and U.A.; methodology, W.d.V. and U.A.; software, W.d.V.; validation, W.d.V.; formal analysis, W.d.V., I.E.-H. and U.A.; investigation, W.d.V., S.K.-B. and U.A.; resources, W.d.V.; data curation, W.d.V.; writing—original draft preparation, W.d.V.; writing—review and editing, W.d.V., I.E.-H., A.H.-G., S.K.-B., C.P. and U.A.; visualization, W.d.V., I.E.-H., A.H.-G., S.K.-B., C.P. and U.A.; supervision, W.d.V. and U.A.; project administration, W.d.V. and U.A. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki and approved by the local ethics committee (Ethikkomission Nordwest- und Zentralschweiz, EKNZ, project ID: 2022-00825, approved on 7 June 2022).
Informed consent was obtained from all subjects involved in the study.
Intermediate data are available upon reasonable request.
We would like to thank all our participants for their cooperation in this study. Furthermore, we would like to thank Laura Mayrhuber and Manual Burger for their support during the various measurements in the larger project. The authors thank the SwiSCI Steering Committee with its members Xavier Jordan, Fabienne Reynard (Clinique Romande de Réadaptation, Sion); Michael Baumberger, Luca Jelmoni (Swiss Paraplegic Center, Nottwil); Armin Curt, Martin Schubert (Balgrist University Hospital, Zürich); Margret Hund-Georgiadis (REHAB Basel, Basel); Laurent Prince (Swiss Paraplegic Association, Nottwil); Daniel Joggi (Representative of persons with SCI); NN (Parahelp, Nottwil); Mirjam Brach, Gerold Stucki (Swiss Paraplegic Research, Nottwil); and Carla Sabariego (SwiSCI Coordination Group at Swiss Paraplegic Research, Nottwil).
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Six of the WCMMs, derived from a full day of measurements with IMUs on a wheelchair, from 19 participants in their living environment. (a) Total distance covered; (b) distance over inclines; (c) number of pushes; (d) duration of pushes; (e) number of turns; (f) magnitude of turns.
Enlarge this image.Figure 4. Spider plots with 5 relevant WCMM variables to compare two participants (participant 5 in blue, participant 13 in yellow). The figure shows that both participants have quite different profiles in comparison with each other, and the study sample averages (in black). Variables shown are total distance in meters number of pushes, number of turns (>30°), number of BoM and mean BoM distance.
WCMMs for 19 participants, normalized to 8 h, depicting participant number, distance covered, number of bouts, average (and standard deviation) of distance per BoM, number of pushes, average (and standard deviation) push duration, and number of turns.
| Participant | Distance [m] | Bouts | Distance per Bout [m] | Pushes | Push Duration [s] | Turns |
|---|---|---|---|---|---|---|
| 1 | 3035 | 78 | 38 (84) | 1485 | 0.67 (0.30) | 348 |
| 2 | 3122 | 226 | 14 (28) | 1994 | 0.78 (0.36) | 762 |
| 3 | 4665 | 166 | 28 (67) | 2446 | 0.67 (0.30) | 764 |
| 4 | 3815 | 186 | 20 (29) | 2034 | 0.66 (0.27) | 683 |
| 5 | 8877 | 152 | 58 (168) | 4312 | 0.61 (0.29) | 542 |
| 6 | 10,769 | 356 | 30 (96) | 4820 | 0.76 (0.37) | 1396 |
| 7 | 578 | 98 | 7 (8) | 438 | 0.97 (0.41) | 344 |
| 8 | 2799 | 136 | 20 (30) | 1879 | 0.66 (0.31) | 568 |
| 9 | 3676 | 166 | 22 (41) | 2081 | 0.65 (0.33) | 633 |
| 10 | 574 | 107 | 5 (3) | 631 | 0.84 (0.39) | 467 |
| 11 | 687 | 79 | 8 (5) | 662 | 0.65 (0.33) | 357 |
| 12 | 3481 | 74 | 47 (90) | 1912 | 0.47 (0.26) | 371 |
| 13 | 1933 | 273 | 6 (11) | 2107 | 0.72 (0.31) | 1169 |
| 14 | 1001 | 136 | 7 (6) | 914 | 0.77 (0.37) | 667 |
| 15 | 1942 | 266 | 7 (7) | 1802 | 0.80 (0.35) | 982 |
| 16 | 2130 | 61 | 34 (59) | 939 | 0.69 (0.31) | 359 |
| 17 | 5522 | 211 | 26 (38) | 2974 | 0.68 (0.34) | 855 |
| 18 | 2141 | 134 | 16 (25) | 1565 | 0.66 (0.31) | 536 |
| 19 | 5874 | 99 | 59 (133) | 4042 | 0.57 (0.26) | 269 |
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; Eriks-Hoogland, Inge 2 ; Hertig-Godeschalk, Anneke 1
; Koch-Borner, Sabrina 3
; Perret, Claudio 4
; Arnet, Ursina 1
1 Swiss Paraplegic Research, Guido A. Zächstrasse 4, 6207 Nottwil, Switzerland;
2 Swiss Paraplegic Centre, Guido A. Zächstrasse 2, 6207 Nottwil, Switzerland;
3 Swiss Paraplegic Research, Guido A. Zächstrasse 4, 6207 Nottwil, Switzerland;
4 Swiss Paraplegic Research, Guido A. Zächstrasse 4, 6207 Nottwil, Switzerland;