Skip to main content

Effects of multidisciplinary inpatient rehabilitation on everyday life physical activity and gait in patients with multiple sclerosis



Multiple sclerosis is a progressive neurological disease that affects the central nervous system, resulting in various symptoms. Among these, impaired mobility and fatigue stand out as the most prevalent. The progressive worsening of symptoms adversely alters quality of life, social interactions and participation in activities of daily living. The main objective of this study is to bring new insights into the impact of a multidisciplinary inpatient rehabilitation on supervised walking tests, physical activity (PA) behavior and everyday gait patterns.


A total of 52 patients, diagnosed with multiple sclerosis, were evaluated before and after 3 weeks of inpatient rehabilitation. Each measurement period consisted of clinical assessments and 7 days home monitoring using foot-mounted sensors. In addition, we considered two subgroups based on the Expanded Disability Status Scale (EDSS) scores: ‘mild’ (EDSS < 5) and ‘severe’ (EDSS ≥ 5) disability levels.


Significant improvements in fatigue, quality of life and perceived mobility were reported. In addition, walking capacity, as assessed by the 10-m walking test, two-minute walk test and timed-up-and-go test, improved significantly after rehabilitation. Regarding the home assessment, mildly disabled patients significantly increased their locomotion per day and complexity of daily PA pattern after rehabilitation, while severely disabled patients did not significantly change. There were distinct and significant differences in gait metrics (i.e., gait speed, stride length, cadence) between mildly and severely disabled patients, but the statistical models did not show a significant overall rehabilitation effect on these gait metrics.


Inpatient rehabilitation showed beneficial effects on self-reported mobility, self-rated health questionnaires, and walking capacity in both mildly and severely disabled patients. However, these improvements do not necessarily translate to home performance in severely disabled patients, or only marginally in mildly disabled patients. Motivational and behavioral factors should also be considered and incorporated into treatment strategies.


Multiple sclerosis (MS) is a progressive neurological disease that affects the central nervous system, resulting to various symptoms. Among these, impaired mobility and fatigue stand out as the most prevalent in people with MS (pwMS) [8, 27, 50]. The progressive worsening of symptoms adversely alters quality of life, social interactions and participation in activities of daily living (ADLs) [12, 25, 46, 62]. Nowadays, there is no curative treatment for MS. However, inpatient rehabilitation can improve the ability to walk by addressing the problem with a variety of approaches such as strengthening leg muscles, improving balance, increasing cardio-pulmonary fitness, adapting walking aids, reducing fatigue and cognitive deficits, or optimizing medical treatment. In particular, exercise training demonstrated positive effects on muscle strength, mobility functions and aerobic capacity, improving balance, gait and quality of life [34, 38, 51].

The scientific evidences for the effectiveness of inpatient rehabilitation is usually based on either questionnaires or clinical functional assessments. Several walking tests have been developed to evaluate mobility (e.g., 10-m walk test (10mWT), timed 25-foot walk (T25FW), timed-up-and-go (TUG), 2-min walk test (2MWT)) in pwMS. Although supervised assessments performed in the clinic provide important information about improvement in functional capacity, they do not provide objective information about the impact of therapy in daily life (i.e., actual performance of the pwMS). The ecological validity of supervised clinical tests has been questioned, and discrepancies between mobility parameters measured in supervised and unsupervised settings have been demonstrated [14, 26, 57]. For example, the T25FW or 2MWT correlate only weakly with walking in free-living as reported in cross-sectional studies [17]. Unsupervised environments often involve challenging situations such as obstacles, busy corridors, or multitasking (e.g., walking and talking) that are not captured by standardized walking tests conducted in the laboratory.

Thus, in recent years, the field of home monitoring has gained interest, and several studies have examined physical activity (PA) in the daily lives of pwMS. The most commonly used outcome is steps count using actigraphy during several consecutive days [1, 14, 16, 17, 39, 58]. Two of these studies also assessed PA intensity using walking cadence [39] or metabolic equivalent of task [16]. These former studies yielded consistent results: pwMS tend to have a sedentary lifestyle with PA being significantly lower than the PA recommendations given by the World Health Organization. In addition, a significant negative correlation was found between the number of steps per day and the Expanded Disability Status Scale (EDSS) [1, 16]. Free-living actigraphy provides an opportunity to objectively assess the mobility of pwMS. However, the step/activity count approach only partially addresses the multidimensional aspect of PA. Recently, robust algorithms for ambulatory locomotion detection have been developed and validated that take into account different PA dimensions such as activity type, duration, and intensity [43, 48, 59]. These different PA dimensions can be combined to obtain a symbolic sequence of PA states, also called “barcoding” [44]. Remarkably, the symbolic time series of PA contain information about both the PA performed during the day (i.e., classical metrics) and the temporal fluctuation/organization of activities (i.e., complexity metrics). Previous studies have shown that the complexity of PA barcode significantly captures pain-related functional limitations in patients with chronic pain [44], concern about falling in older adults [42], or mobility limitations in young older adults [61].

In conjunction with daily PA, gait parameters are considered as important mobility-related measure [18, 54]. Among these metrics, gait speed is a dependable marker of functional decline [10, 47]. The main challenge in measuring free-living gait speed is the need for validated algorithms for reliable and accurate feature extraction. In recent years, researchers have developed robust algorithms for locomotion detection in a free-living environment using trunk-mounted (chest/lower back) [30, 43, 59], or foot-mounted inertial measurement unit (IMU) [48]. The advantage of the foot-mounted IMU is the clinically acceptable accuracy of the gait spatio-temporal parameters which can be further extracted [31]. The distribution of walking speed in daily life has already been studied in patients with Parkinson's disease and showed promising results [4]. However, to the best of our knowledge, no previous work has investigated gait parameters in free-living environment in pwMS, and the effects of a rehabilitation period on PA and gait.

Therefore, our project’s primary objective is to bring new insights into the impact of a multidisciplinary inpatient rehabilitation (MIR) on self-reported questionnaires, supervised walking tests, PA behavior and everyday gait patterns using shoe-attached IMUs. We also consider the disability level, distinguishing between mildly and severely disabled pwMS. Then, our secondary objective is to compare and discuss gait speed measured in free-living conditions (i.e. unsupervised) with gait speed measured in the clinic (i.e. supervised), as their difference is an important outcome for the evaluation of an intervention [4, 57]. Supervised assessments performed in the clinic reflect functional capacity, whereas unsupervised assessments during daily activities are indicative of the actual performance of the patients [20].


Participants and study design

A total of 52 patients diagnosed with MS (EDSS: 4—6.5; age ≥ 18 years) were included in the study. PwMS with severe cognitive or arm/hand impairments interfering with study participation, or with comorbidities, such as musculoskeletal or cardiovascular diseases, that reduced walking ability were excluded from the study. In addition, pwMS with EDSS scores greater than 6.5 were excluded. The study was approved by the Ethical Committee of eastern Switzerland (EKOS, 2017-00728), and performed in agreement with the Declaration of Helsinki’s Ethical Principles for Medical Research Involving Human Subjects. For provisional inclusion, pwMS who were registered for planned rehabilitation were contacted by telephone by a researcher who verified the inclusion criteria and provided verbal information about the study. After provisional inclusion, a letter was sent to patients with written information about the study, an informed consent form, a box containing two IMUs (Physilog® 5, Gait Up, CH) with instructions, and a set of questionnaires. Definite inclusion was at the start of rehabilitation, when inclusion criteria and patients’ ability to use the sensors were checked.

PwMS were evaluated pre- and post- MIR as explained in Fig. 1. Each measurement period consisted of clinical assessments (i.e. supervised), and 7 days home monitoring (i.e. unsupervised). The personalized MIR program, of 2 to 3 weeks, generally included the following components: balance and walking training (physiotherapy, 5 times/week), strength training (3 times/week), aerobic exercise training (3 times/week), occupational therapy focusing on energy management and activities of daily living (2–3 times/week) [19], and neuropsychological training (2 times/week). The program was adapted to every patient to ensure an appropriate training in terms of difficulty (e.g. free overground walking vs walking with weight support, flat vs uneven ground, difficult (steep) vs easy (wide, not steep, with handrails) stairs). There were no specific interventions to target daily levels of PA.

Fig. 1
figure 1

Study design overview. Measurement pre-rehabilitation: 7 consecutive days within 1–4 weeks before the multidisciplinary inpatient rehabilitation (MIR), depending on patients’ availabilities; measurement post-rehabilitation: 7 consecutive days immediately after the MIR

Clinical assessments and supervised tests

Each clinical assessment included a set of questionnaires: Fatigue Scale for Motor and Cognitive Functions (FSMC), patient-reported walking ability (Twelve Item Multiple Sclerosis Walking Scale (MSWS-12)), and the patient's self-rated health on a vertical visual analogue scale (EQ-VAS, a sub-score of the EQ-5D-5L questionnaire). Three scores were reported for the FSMC questionnaire: the total score (FSMCt), the motor sub-score (FSMCm) and the cognitive sub-score (FSMCk). Then, pwMS were asked to perform three walking tests to objectively measure their functional capacity: the timed-up and go (TUG), the 10 m walk test (10mWT) at fast speed, and the 2 min walk test (2MWT). The time required to perform the TUG and 10mWT was measured with a stopwatch. The Expanded Disability Status Scale (EDSS) was evaluated by a neurologist.

Home assessments

PwMS were asked to wear two sensors (Physilog® 5, Gait Up, CH) for seven days, one fixed on each foot (shoe), after getting dressed in the morning and to take it off before going to bed. Participants were allowed to go outside the home and perform their usual activities. Therefore, home assessments can also include daily activities that had been done outside their living space. The sensors were programmed to start recording automatically at 9:00 for 12 h, i.e., until 21:00. To promote adherence to the protocol, physiotherapists contacted each pwMS during the home measurement period. This was done to ensure they understood the protocol clearly and to address any potential issues related to sensor usage. In addition, participants were offered the choice to receive morning reminders for wearing the sensors and evening reminders for charging them. A day of measurement was considered valid if the sensors were worn for, at least, 8 h. Participants with three or more valid days per period (i.e., pre- and post- rehabilitation) were included in the analysis. A minimum of three days is necessary for a reliable estimation of usual behaviour [1, 38]. In order to have the same data length for all participants and all days, the data were segmented to obtain exactly 8 h of wearing time per day, starting from the first movement detected.

Feature extraction from home assessments

Locomotion detection

The locomotion periods or walking bouts (WBs) were automatically extracted using a previously validated algorithm [48]. The algorithm is based on a peak enhancement filtering method using continuous wavelet transforms of the triaxial angular velocity norm recorded at the foot. Only the walking episodes that contained at least two consecutive strides (i.e. 5 steps) are considered as true locomotion [24]. The accuracy of locomotion detection is crucial for the correct calculation of gait and PA metrics.

Gait metrics

With a previously validated gait analysis algorithm [31], the gait parameters were extracted for each stride within each WB. In order to have more steady-state gait, very short WBs with less than 6 detected strides per foot were removed. Then, for each gait cycle, the speed, cadence, stride length and gait cycle time were computed. Finally, for each WB, we calculated the mean value of the gait parameters extracted per gait cycle. There is no clear consensus on the granularity by which the gait parameters should be assessed (i.e., stride-wise, averaged over WBs or averaged over time intervals based on multiple WBs) [24]. However, it seems well-accepted to compute the walking speed over a minimal number of consecutive strides, which agrees with the definition of a WB. We thus decided to adopt the WB granularity to extract the digital mobility outcomes as done in previous studies [57].

Physical activity (PA) metrics

Based on the type of activity (locomotion, non-locomotion), duration (very short, short, medium, long) and intensity (acceleration magnitude, cadence), PA was divided into 25 states [42], starting from lowest state (state 1) to highest states (state 25), also called "barcode" (details are provided in appendix 1 in supplementary material). Three classical metrics were evaluated; percentage of daily locomotion (Loc), light PA (LPA) and moderate-to-vigorous PA (MVPA) per day. A range from 83 to 104 steps/min, depending on the subject’s height, was found to correspond to 3 metabolic equivalent of task (METs), which is commonly used as the threshold between light to moderate exercise intensities [3]. Consequently, we choose 90 steps/min as an appropriate cut-off between LPA and MVPA. The number of steps per day, and the WB duration were also computed. Then, two complexity metrics were calculated to characterize the temporal fluctuations of the daily PA patterns; the information entropy (Hn) and the permutation Lempel–Ziv complexity (PLZC). The Hn is defined as a structural-static complexity metric which characterizes the amount of different states in the barcode. If there are many (few) different types of PA states in the barcode, Hn takes a large (small) value. Hn is sensitive to the variety of PA states, however is insensitive to the temporal ordering of the sequence. PLZC is a structural-dynamic metric that quantify the temporal behaviour (i.e. ordering of different states). This metric captures the number of distinct sub-strings and their rate of occurrence as the sequence evolves from left to right [7].

Data aggregation

PA metrics in daily life

The classical and complexity metrics were extracted per day from the barcode. Consequently, we expect between 3 and 7 values per subject and per measurement period depending on the number of valid days analysed. In such study design, in which multiple observations from the same participant are collected, the linear mixed-effect (LME) model is particularly well-adapted [55]. This statistical model considers the repeated measures data nested within-subject as the level-1 of the linear regression. As a consequence, the multiple values obtained from the same patient did not need to be further aggregated by using the mean or median for example.

Gait pattern in daily life

The gait metrics were computed per WB (i.e., WB granularity). In terms of gait assessment, contrarily to the PA metrics, we were not interested in a daily-based behavior, rather the general behavior at home. Consequently, the gait parameters from all the measurement days were aggregated to obtain a unique weekly distribution (as illustrated in Fig. 2). We then computed the kernel density smoothening function, a non-parametric method to estimate the probability distribution function (pdf), to extract the mode and the 95th-percentile, giving a statistically robust representation of the patient’s gait performances in daily living. The mode (i.e., the value that occurs most frequently in a set of data values) represents the patient’s usual gait pattern, and the 95th-percentile, on the other hand, refers to the maximum performance that patients perform in daily life. We reported the results of a subset of the extracted metrics based on their meaningful clinical interpretations. We thought it relevant to report the mode of cadence, gait speed, and stride length as the patient's usual gait pattern. In addition, we included the 95th-percentile of the WB time, gait speed, and the stride length, because we expect these parameters to capture endurance and gait capacity improvements.

Fig. 2
figure 2

Data aggregation procedure for the gait metrics assessed in daily life. WB: walking bout

Statistical analysis

A linear mixed-effects model (LME) was applied to investigate the influence of the rehabilitation (i.e., pre vs. post-intervention) on the clinical assessments (i.e., questionnaires and functional tests) and home measurements (i.e., PA and gait metrics). This statistical analysis method allows to test relations among both within- and between-levels data without violating standard assumptions of independence. In addition, LME accommodates missing data. We considered two subgroups, ‘mild’ (EDSS < 5) and ‘severe’ (EDSS ≥ 5) disability levels based on the EDSS scores. A 2-levels LME model was designed with the “rehab” (i.e., ‘pre’ vs. ‘post’), the “group” (i.e., ‘mild’ vs. ‘severe’), and the interaction between “rehab” and “group” as the fixed effects (see Eq. 1). Then, a random effect (intercept and slope) at the subject level was defined to consider the repeated measures data nested within-subject (i.e., “(rehab|subject)” in Eq. 1). The following equation was used as input to the “fitlme” MATLAB function (with the “responder” corresponding to PA or gait metric):

$${\text{responder}}\hspace{0.17em}\sim \hspace{0.17em}\mathrm{rehab }*\mathrm{ group}\hspace{0.17em}+\hspace{0.17em}({\text{rehab}}|{\text{subject}})$$

This model corresponds to.

$$\mathrm{Level }1:{y}_{ij }= {\beta }_{0j}+ {\beta }_{1j}({rehab)}_{ij} + {\varepsilon }_{ij}$$
$$\mathrm{Level }2:{\beta }_{0j}= {\gamma }_{00} + {\gamma }_{01}{(group)}_{j}+ {\mu }_{0j}; {\beta }_{1j}= {\gamma }_{10} + {\gamma }_{11}{(group)}_{j} + {\mu }_{1j}$$

The overall model:

$${y}_{ij }= {\gamma }_{00} + {\gamma }_{01} ({group)}_{j}+ {\gamma }_{10} ({rehab)}_{ij}+ {\gamma }_{11}{ \left(rehab\right)}_{ij}*{\left(group\right)}_{j}+ {\mu }_{0j}+ {\mu }_{1j} ({rehab)}_{ij}+ {\varepsilon }_{ij}$$

where \(i=1, 2,\dots , n,\) (n is the number of observations), \(j=1, 2,\dots , 43,\) corresponds to the pwMS. The model estimates (\({\gamma }_{00}\), \({\gamma }_{01}\), \({\gamma }_{10}\), and \({\gamma }_{11}\)), p-value, and 95% confidence interval (CI) values of the fixed effects were used to understand apparent significant effects. Statistical significance was accepted if p ≤ 0.05 and if the lower and upper limits of the 95% CI did not include 0. In addition, the conditional \({R}_{c}^{2}\) was computed to assess the total variance explained by both fixed and random effects.



Of the 52 pwMS involved in the study, 4 were not assessed after the rehabilitation due to unavailability for personal reasons. The therapy was provided as part of an inpatient program at the clinic, where the adherence was closely monitored. Missed sessions were usually rescheduled later in the same week. Across all pwMS, irrespective of their level of disability, the average total weekly rehabilitation duration was about 790 min. From this group, 43 pwMS met the inclusion criteria of, at least, three valid days per measurement period. The average number of valid days recorded at pre- and post- intervention were 5.5 ± 1.9 and 6.4 ± 1.2 days respectively. In total 631 valid days were analyzed. The average start and stop time were 09:06 ± 00:20 a.m. and 05:06 ± 00:20 p.m. respectively. The Table 1 provides the characteristics of the pwMS involved in the rehabilitation program.

Table 1 Clinical characteristics of the MS study group

Effects of MIR on self-reported questionnaires

The scores of the five self-reported questionnaires (i.e., FSMCt, FSMCm, FSMCk, EQ-VAS, MSWS-12) assessed before and after rehabilitation are shown in Fig. 3. The statistical analyses based on the LME models (appendix 2 in supplementary material) indicate significant effects of the rehabilitation period on the FSMCt, FSMCm, EQ-VAS, and MSWS-12 questionnaires (p ≤ 0.05 for \({\gamma }_{10}\)-estimate, appendix 2 in supplementary material). Interestingly, we observe significant differences between groups for the FCMSm and MSWS-12 questionnaires (p ≤ 0.05 for \({\gamma }_{01}\)-estimate, appendix 2 in supplementary material), both of which evaluate motor capacity functions.

Fig. 3
figure 3

Patient-reported questionnaires and functional tests pre- and post- rehabilitation. The boxplots on the left (dark and light blue) correspond to the scores obtained for mildly disabled pwMS, whereas the boxplots on the right (dark and light orange) summarize the values obtained for severely disabled pwMS. FSMC Fatigue Scale for Motor and Cognitive Functions, EQ-VAS patient’s self-rated health on a vertical visual analogue scale, MSWS-12 Twelve Item Multiple Sclerosis Walking Scale, TUG timed-up and go, 10mWT 10 m walking test, 2MWT 2 min walking test, EDSS Expanded Disability Status Scale

Effects of MIR on supervised walking tests

The results obtained for the supervised walking tests (i.e., TUG, 10mWT, and 2MWT) indicate significant effects for both the “group” and “rehab” estimates (Fig. 3 and table in appendix 2 in supplementary material), meaning a significant difference between mildly and severely disabled pwMS and an improvement after the intervention. Furthermore, the significant interaction effect “group*rehab” (p ≤ 0.05 for \({\gamma }_{11}\)-estimate, appendix 2 in supplementary material) for the 2MWT highlights that one group improved more than the other. Figure 3 illustrates this trend clearly, the mildly disabled pwMS experienced a higher increase in the walking distance than those with severe disabilities.

Effects of MIR on physical activity and gait in daily life

Effects of MIR on PA behavior

As can be seen in Table 2 and Fig. 4, there are significant interaction effects (i.e., “group*rehab”, p ≤ 0.05 for \({\gamma }_{11}\)-estimate) for Loc and the two complexity metrics; Hn and PLZC. Mildly disabled pwMS significantly increased their percentage of locomotion per day, and complexity of daily PA pattern. Increasing, but not significant, trends of the remaining PA metrics (i.e. LPA, MVPA, and #steps/day) are observed for the mildly disabled pwMS (“group*rehab”, \({\gamma }_{11}\)-estimate in Table 2). In addition, the Loc, MVPA, #steps/day and Hn metrics are significantly higher for the mildly disabled than the severely disabled pwMS (p ≤ 0.05 for \({\gamma }_{01}\)-estimate, Table 2) regardless of rehabilitation (i.e., pre- vs. post-rehabilitation).

Table 2 LME models obtained for PA and gait metrics in daily life assessments
Fig. 4
figure 4

Physical activity (PA) and gait metrics pre- and post- rehabilitation. Loc percentage locomotion per day, LPA percentage of light PA per day, MVPA percentage of moderate-to-vigorous PA per day, Hn information entropy, PLZC permutation Lempel–Ziv complexity;

Effects of MIR on gait pattern in daily life

What stands out in Fig. 4 and Table 2 are the clear and significant differences between mildly and severely disabled pwMS (i.e., “group” effects, \({\gamma }_{01}\)-estimates, Table 2) for all reported gait metrics. However, our LME models do not show significant “rehab” effect for all reported gait metrics. The gait speed distributions at home and the average values of the gait speed measured in supervised conditions (10mWT) for each patient before and after the rehabilitation period are provided in the Fig. 5. First of all, we notice that the average walking speed obtained during the 10mWT lay at the extreme end of the gait speed distribution measured at home, and increase post-rehabilitation. Another striking observation to emerge from Fig. 5 is the decrease in gait speed as the EDSS scores increase.

Fig. 5
figure 5

Distribution of gait speed at home (small color dot) for each patient before and after the rehabilitation period. Each dot corresponds to the average walking speed during one walking bout. The large black squares represent the average values of the 10 m walking test assessed in supervised condition


Effect of MIR on self-reported questionnaires and supervised walking tests

Self-reported questionnaires

In the present MS cohort, mildly and severely disabled pwMS reported significant improvements in their walking ability as assessed by the MSWS-12 questionnaire (-6.6 and -9.4 points, respectively), which is in the range of clinically meaningful changes between -6 and -11 points [5, 6]. The improvement in fatigue (FSMC) by -4 and -3 for mildly and severely disabled pwMS, respectively, is consistent with previous studies [26, 33], but below the reported minimum for a clinical meaningful change (-9 points) [53]. In addition, pwMS also reported significant improvements in their self-rated health (EQ-VAS) of about + 10 and + 9 points for mildly and severely disabled pwMS (see appendix 3 in supplementary material).

Supervised walking tests

The significant improvements in walking capacity during the 2MWT, measured in both mildly (mean ± std: + 30.2 ± 36.0 m) and severely (mean ± std: + 11.7 ± 16.8 m) disabled pwMS, exceed the reported minimum for clinically relevant changes (i.e., 9.6 m) [5]. Similarly, the 10mWT improvements reach approximately -13.9% and -20.0% for mildly and severely disabled pwMS, nearing the clinically relevant change of -23% [40]. Then, TUG test was assessed as a complementary measure of functional mobility for activities such as sitting, standing or turning around [49]. The improvements measured in TUG test of 15.4% and 12.0% in the mildly and severely disabled pwMS, respectively, are statistically significant but lower than the established threshold of 23% for genuine changes [40]. Our results confirm previous findings demonstrating statistically significant improvements in all clinical walking outcome measurements (i.e., 10mWT, 2MWT and TUG) after a physical rehabilitation program [13, 14, 22, 26]. Taken together, these results demonstrate that pwMS with mild and severe disabilities benefit from the MIR by significantly and clinically improving their perceived and actual functional mobility.

Effects of MIR on physical activity and gait in daily life

Physical activity in daily life

Consistent with the literature, pwMS with mild walking disability do more steps per day (~ 5000 steps/day, Table 2) than pwMS with severe disability (~ 2500 steps/day, Table 2) [14, 39]. Daily step count reliably reflects unsupervised daily-life walking behavior in pwMS. An increase of about 800 steps/day indicates clinically meaningful progress following an intervention [35]. While mildly disabled pwMS showed a rising trend, our cohort did not exhibit this enhancement. Then, a minor but significant increase in daily percentage locomotion is noted in mildly disabled pwMS. The intensity and duration of locomotion are also a critical aspect to consider as being related to cardiovascular health. Our results demonstrate that pwMS in the mildly disabled subgroup clearly walk more minutes at moderate intensity (mean ± std: 5.0 ± 2.9%) than pwMS with severe MS (mean ± std: 1.3 ± 2.8%, appendix 4 in supplementary material). Then, the maximum WB duration (i.e., 95th-percentile of the long tail distribution) provides information about pwMS’ ability to walk continuously for a certain time. Again, our findings highlight that mildly disabled pwMS (EDSS < 5) walk longer than severely disabled pwMS (Table 2, and Fig. 4). Consistent with prior studies, pwMS in our cohort rarely engage in uninterrupted 2-min walks, and even struggle with 1-min walks for those with severe MS disabilities (Table 2, and Fig. 4), and predominantly at low intensity [39]. Despite the daily locomotion percentage, the statistical LME models do not uncover significant rehabilitation impact on conventional PA metrics.

In addition to the above conventional PA metrics, we investigated the complexity of daily PA. Notably, the information entropy (Hn) is significantly higher for mildly disabled than severely disabled pwMS. Interestingly, the complexity of the daily PA time-series, assessed by Hn and PLZC, of mildly disabled pwMS, significantly increase after the MIR. These results may be explained by additional PA states in barcodes, most likely tied to longer WBs or higher cadence.

Gait patterns in daily life

All of the reported gait parameters (i.e., gait speed, cadence, and stride length) demonstrate highly significant differences between the two subgroups of pwMS (Fig. 4). These results confirm the evidence of using gait parameters, particularly gait speed, as discriminative features for disease severity. The usual cadence (mild disability: 95.8 ± 6.2 steps/min; severe disability: 80.1 ± 14.2 steps/min) and gait speed (mild disability: 0.7 ± 0.09 m/s; severe disability: 0.4 ± 0.16 m/s) measured in our cohort are notably lower than the reference values reported for healthy subjects on daily living (cadence: 118.86 ± 6.76 steps/min; gait speed: 1.3 ± 0.1 m/s) [41]. The 95th-percentile of the weekly speed distribution informs about an individual’s maximal performance. Our results indicate that mildly disabled pwMS are able to reach gait speed values of approximately 1.1 m/s, close to the preferred walking speed of healthy subjects, in specific free-living conditions if needed. However, severely disabled pwMS could not walk faster than about 0.6 m/s (appendix 4 in supplementary material), which might limit their participation in activities of daily living (ADLs). In the pre-post comparison, our findings indicate that while patients enhance their capacity significantly, their walking performance in free-living environment does not show significantly improvements (Table 2, Fig. 4).

Capacity vs. performance

The Fig. 5 nicely highlights important aspects of our current study, and mainly points out that pwMS do not make a direct use of their capacity improvements into their activities in daily life. Indeed, 37 out of 40 pwMS walked faster or longer during the 2MWT and 10mWT, while they did not change their walking performance in daily life. The capacity and performance measures should be considered as complementary assessments. The 2MWT, 10mWT and TUG are meaningful clinical measure and well-established tests to assess walking function [9]. In the majority of our results, the walking tests with fast speed lay at the extreme end of the gait speed distribution at home (Fig. 5), which is in line with what has been previously reported in the literature [4, 56, 57]. Previous studies already argue that supervised walking tests are a “snapshot” of walking functions measured at a specific time and on a specific day, and assess only part of the variance in daily step count. Indeed, other factors (i.e., fluctuating symptoms, such as fatigue, mood, spasticity or daily form/condition, or environmental aspects) not necessarily captured by the supervised walking tests, might highly influence daily activities [23, 52]. Unsupervised assessments conducted over several days include the best and worst behaviors in relation to the above-mentioned influencing factors.

Notably, mildly disabled pwMS are slightly more active in their daily lives after the rehabilitation period. It seems that these pwMS do not use their improved abilities to walk faster, but to walk more (i.e., increased locomotion, Hn, and LPZC). This finding is quite intuitive, since in everyday life people would rather walk at their preferred speed than exert themselves without a specific goal. These capacity improvements can be interpreted as a higher "physiological reserve" that could help pwMS to engage more in ADLs, and might also explain the higher reported self-rated health (EQ-VAS). With regards to the severely disabled pwMS, their overall walking capacity improved, but neither PA behavior nor gait pattern in daily life changed. PwMS who walk at slow speeds, both in supervised and unsupervised conditions, may need to challenge themselves in ADLs. In a survey assessing the impact of walking speed on ADLs, a majority of pwMS reported walking impairments as the most challenging aspect of their disease [60]. Particularly, slow walking speed might limit MS patients to execute basic activities such as crossing the street, or walking to the nearest shop. The ability to speed up over a short distance is of high concern for severely disabled pwMS, and leads sometimes to ADLs avoidance [60]. Despite an improvement in walking capacity, certain environmental barriers (e.g., ascents/descents, stairs, etc.) might still pose unsurmountable challenges for severely disabled pwMS.


Home assessment on multiple measurement days is a very challenging protocol, and several limitations must be acknowledged. First, for practical reasons, we asked patients to equip themselves with the IMUs each morning. Despite our efforts to properly instruct pwMS during the supervised test sessions and well-documented paper guidelines, we do not have a complete guarantee that the sensors were properly attached. It should be noted that the orientation of the sensors on the shoes is not crucial, as our algorithms are designed to be robust to different placements of the sensors [31, 32]. However, if the rubber clip is not properly attached to the laces, interfering vibrations of the sensor at heel strike can affect the signal. Despite our data quality checking procedure, we may have missed some noisy signals. Considering the large amount of data collected (more than 600 files), we believe that a few errors in the gait parameters at the stride level do not affect the overall weekly distribution and the associated mode and 95th percentile. Then, despite the comprehensive database, additional participants would have allowed stratification into three subgroups (i.e., EDSS 2–3; EDSS 3.5–4.5; and EDSS > 5). Differentiating between pwMS with low disability (EDSS < 3) and pwMS with moderate disability (EDSS: 3.5–4.5) could have provided more insight into the impact of MIR on these subgroups. Finally, other potential confounding factors (education, employment status, comorbidity, season/weather, occupation, medical leave, usage of walking aids, etc.) were not available to be included in the statistical models.

Practical implications

Exercise training has been proven to be effective in improving leg muscle strength, balance, cardio-pulmonary fitness, and walking capacity in pwMS [28, 45, 51]. This statement is in line with our findings showing significant improvements in walking capacity (as assessed by 2MWT, 10mWT, and TUG) and self-reported questionnaires (i.e., MSWS-12, FSMC, EQ-VAS) after rehabilitation. However, those walking capacity improvements do not necessarily translate into increased mobility in daily life, especially for severely disabled pwMS. Supervised clinical assessments help diagnose patients, set goals, and prescribe PA interventions. However, practitioners should not only focus on walking capacity, but also consider behavioral and psychological (e.g., motivation, or self-efficacy) factors that might influence PA behavior and walking performance in the long term [11, 36, 37]. Following inpatient rehabilitation, a personalized program [2, 21, 29], including achievable goals, barriers management strategies, motivation and self-efficacy enhancement, might be promising to motivate pwMS to increase their walking performance, such as spending more time walking, more time in moderate to vigorous PA or undertaking longer walking bouts in daily life. As evidenced in our study, mildly disabled pwMS slightly increased their PA following rehabilitation, a trend that is not observed among their severely disabled counterparts. Therefore, educational, motivational, and PA support strategies should begin early after diagnosis [15, 39]. Finally, wearable sensors could be used not only for research purposes but also as motivational tools for patients. Feedback can be implemented to help patients become aware of their daily PA and achieve their goals.


First of all, our study demonstrates that mildly disabled pwMS walk more (i.e., locomotion and #steps/day), faster (i.e., gait speed), and longer (i.e., WB duration) in daily life than severely disabled pwMS. Secondly, our study provides comprehensive evidence of the beneficial effects of a MIR on self-reported mobility, self-rated health questionnaires, and walking capacity measured with supervised walking tests in both subgroups (i.e., mildly and severely disabled pwMS). However, these improvements do not necessarily translate to home performance in severely disabled pwMS, or only marginally mildly disabled pwMS. Motivational and behavioral factors should also be considered and incorporated into treatment strategies. As a follow-up to the MIR, future studies should examine the long-term effects of a personalized home or partly home-inpatient program on mobility, PA behavior, motivation, and well-being.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to a lack of patient consent but are available from the corresponding author on reasonable request.



10-Meter walk test


2-Minute walk test


Activities of daily living


Patient's self-rated health on a vertical visual analogue scale


Fatigue Scale for Motor and Cognitive Functions


Information entropy


Inertial measurement unit


Linear mixed-effect


Light physical activity


Multidisciplinary inpatient rehabilitation


Multiple sclerosis


Twelve Item Multiple Sclerosis Walking Scale


Moderate-to-vigorous physical activity


Physical activity


Permutation Lempel–Ziv complexity


People with multiple sclerosis




Walking bout


  1. Abbadessa G, Lavorgna L, Miele G, Mignone A, Signoriello E, Lus G, et al. Assessment of multiple sclerosis disability progression using a wearable biosensor: a pilot study. J Clin Med. 2021;10:1–8.

    Article  Google Scholar 

  2. Abonie US, Hettinga FJ. Effect of a tailored activity pacing intervention on fatigue and physical activity behaviours in adults with multiple sclerosis. Int J Environ Res Public Health. 2021;18:17.

    Article  Google Scholar 

  3. Agiovlasitis S, Motl RW. Step-rate thresholds for physical activity intensity in persons with multiple sclerosis. Adapt Phys Activ Q. 2014;31:4–18.

    Article  PubMed  Google Scholar 

  4. Atrsaei A, Corrà MF, Dadashi F, Vila-Chã N, Maia L, Mariani B, et al. Gait speed in clinical and daily living assessments in Parkinson’s disease patients: performance versus capacity. NPJ Park Dis. 2021;271(7):1–11.

    Article  CAS  Google Scholar 

  5. Baert I, Freeman J, Smedal T, Dalgas U, Romberg A, Kalron A, et al. Responsiveness and clinically meaningful improvement, according to disability level, of five walking measures after rehabilitation in multiple sclerosis: a European multicenter study. Neurorehabil Neural Repair. 2014;28:621–31.

    Article  PubMed  Google Scholar 

  6. Baert I, Smedal T, Kalron A, Rasova K. Responsiveness and meaningful improvement of mobility measures following MS rehabilitation. Neurology. 2018.

    Article  PubMed  Google Scholar 

  7. Bai Y, Liang Z, Li X. A permutation Lempel-Ziv complexity measure for EEG analysis. Biomed Signal Process Control. 2015;19:102–14.

    Article  Google Scholar 

  8. Béthoux F. Fatigue and multiple sclerosis. Ann Readapt Med Phys. 2006;49:265–71.

    Article  PubMed  Google Scholar 

  9. Bethoux F, Bennett S. Evaluating walking in patients with multiple sclerosis: which assessment tools are useful in clinical practice? Int J MS Care. 2011;13:4.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Brach JS, VanSwearingen JM, Newman AB, Kriska AM. Identifying early decline of physical function in community-dwelling older women: performance-based and self-report measures. Phys Ther. 2002;82:320–8.

    Article  PubMed  Google Scholar 

  11. Casey B, Coote S, Shirazipour C, Hannigan A, Motl R, Martin Ginis K, et al. Modifiable psychosocial constructs associated with physical activity participation in people with multiple sclerosis: a systematic review and meta-analysis. Arch Phys Med Rehabil. 2017;98:1453–75.

    Article  PubMed  Google Scholar 

  12. Cohen JT. Walking speed and economic outcomes for walking-impaired patients with multiple sclerosis. Expert Rev Pharmacoecon Outcomes Res. 2014.

    Article  Google Scholar 

  13. Decavel P, Moulin T, Sagawa Y. Gait tests in multiple sclerosis: Reliability and cut-off values. Gait Posture. 2019;67:37–42.

    Article  PubMed  Google Scholar 

  14. Ehling R, Bsteh G, Muehlbacher A, Hermann K, Brenneis C. Ecological validity of walking capacity tests following rehabilitation in people with multiple sclerosis. PLoS ONE. 2019;14:1–12.

    Article  CAS  Google Scholar 

  15. Ellis T, Motl RW. Physical activity behavior change in persons with neurologic disorders: overview and examples from Parkinson disease and multiple sclerosis. J Neurol Phys Ther. 2013;37:85–90.

    Article  PubMed  Google Scholar 

  16. Fjeldstad C, Fjeldstad AS, Pardo G. Use of accelerometers to measure real-life physical activity in ambulatory individuals with multiple sclerosis. Int J MS Care. 2015;17:215–20.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Gijbels D, Alders G, Van Hoof E, Charlier C, Roelants M, Broekmans T, et al. Predicting habitual walking performance in multiple sclerosis: Relevance of capacity and self-report measures. Mult Scler. 2010;16:618–26.

    Article  PubMed  Google Scholar 

  18. Hansen BH, Kolle E, Dyrstad SM, Holme I, Anderssen SA. Accelerometer-determined physical activity in adults and older people. Med Sci Sports Exerc. 2012;44:266–72.

    Article  PubMed  Google Scholar 

  19. Hersche R, Weise A, Michel G, Kesselring J, Bella SD, Barbero M, et al. Three-week inpatient energy management education (IEME) for persons with multiple sclerosis-related fatigue: feasibility of a randomized clinical trial. Mult Scler Relat Disord. 2019;35:26–33.

    Article  PubMed  Google Scholar 

  20. ICF. World Health Organization. Towards a common language for functioning, disability and health. Int Classif 2022 :1–22.

  21. Kalb R, Brown TR, Coote S, et al. Exercise and lifestyle physical activity recommendations for people with multiple sclerosis throughout the disease course. Mult Scler J. 2020;26(12):1459–69.

    Article  Google Scholar 

  22. Kalron A, Nitzani D, Magalashvili D, Dolev M, Menascu S, Stern Y, et al. A personalized, intense physical rehabilitation program improves walking in people with multiple sclerosis presenting with different levels of disability: a retrospective cohort. BMC Neurol. 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Karle V, Hartung V, Ivanovska K, Mäurer M, Flachenecker P, Pfeifer K, et al. The two-minute walk test in persons with multiple sclerosis: correlations of cadence with free-living walking do not support ecological validity. Int J Environ Res Public Health. 2020;17:1–11.

    Article  Google Scholar 

  24. Kluge F, Del Din S, Cereatti A, Gaßner H, Hansen C, Helbostad JL, et al. Consensus based framework for digital mobility monitoring. PLoS ONE. 2021;16: e0256541.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kos D, Kerckhofs E, Nagels G, D’hooghe MB, Ilsbroukx S. Origin of fatigue in multiple sclerosis: review of the literature. Neurorehabil Neural Repair. 2008;22:91–100.

    Article  CAS  PubMed  Google Scholar 

  26. Kuendig S, Kool J, Polhemus A, Schallert W, Bansi J, Gonzenbach RR. Three weeks of rehabilitation improves walking capacity but not daily physical activity in patients with multiple sclerosis with moderate to severe walking disability. PLoS ONE. 2022;17: e0274348.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. LaRocca NG. Impact of walking impairment in multiple sclerosis: perspectives of patients and care partners. Patient. 2011;4:189–201.

    Article  PubMed  Google Scholar 

  28. Latimer-Cheung AE, Pilutti LA, Hicks AL, Martin Ginis KA, Fenuta AM, MacKibbon KA, et al. Effects of exercise training on fitness, mobility, fatigue, and health-related quality of life among adults with multiple sclerosis: a systematic review to inform guideline development. Arch Phys Med Rehabil. 2013;94:1800-1828.e3.

    Article  PubMed  Google Scholar 

  29. Learmonth YC, Motl RW. Exercise training for multiple sclerosis: a narrative review of history, benefits, safety, guidelines, and promotion. Int J Environ Res Public Health. 2021;18:13245.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Lyons GM, Culhane KM, Hilton D, Grace PA, Lyons D. A description of an accelerometer-based mobility monitoring technique. Med Eng Phys. 2005;27:497–504.

    Article  CAS  PubMed  Google Scholar 

  31. Mariani B, Hoskovec C, Rochat S, Büla C, Penders J, Aminian K. 3D gait assessment in young and elderly subjects using foot-worn inertial sensors. J Biomech. 2010;43:2999–3006.

    Article  PubMed  Google Scholar 

  32. Mariani B, Rouhani H, Crevoisier X, Aminian K. Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors. Gait Posture. 2013;37:229–34.

    Article  PubMed  Google Scholar 

  33. Mikuľáková W, Klímová E, Kendrová L, Gajdoš M, Chmelík M. Effect of rehabilitation on fatigue level in patients with multiple sclerosis. Med Sci Monit. 2018;24:5761.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Motl RW, Pilutti LA. The benefits of exercise training in multiple sclerosis. Nat Rev Neurol. 2012;89(8):487–97.

    Article  Google Scholar 

  35. Motl RW, Pilutti LA, Learmonth YC, Goldman MD, Brown T. Clinical importance of steps taken per day among persons with multiple sclerosis. PLoS ONE. 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Motl RW, Sandroff BM, Kwakkel G, Dalgas U, Feinstein A, Heesen C, et al. Exercise in patients with multiple sclerosis. Lancet Neurol. 2017;16:848–56.

    Article  PubMed  Google Scholar 

  37. Motl RW, Sandroff BM, Wingo BC, McCroskey J, Pilutti LA, Cutter GR, et al. Phase-III, randomized controlled trial of the behavioral intervention for increasing physical activity in multiple sclerosis: project BIPAMS. Contemp Clin Trials. 2018;71:154–61.

    Article  PubMed  Google Scholar 

  38. Motl RW, Zhu W, Park Y, McAuley E, Scott JA, Snook EM. Reliability of scores from physical activity monitors in adults with multiple sclerosis. Adapt Phys Activ Q. 2007;24:245–53.

    Article  PubMed  Google Scholar 

  39. Neven A, Vanderstraeten A, Janssens D, Wets G, Feys P. Understanding walking activity in multiple sclerosis: step count, walking intensity and uninterrupted walking activity duration related to degree of disability. Neurol Sci. 2016;37:1483–90.

    Article  PubMed  Google Scholar 

  40. Nilsagard Y, Lundholm C, Gunnarsson LG, Dcnison E. Clinical relevance using timed walk tests and “timed up and go” testing in persons with multiple sclerosis. Physiother Res Int. 2007;12:105–14.

    Article  PubMed  Google Scholar 

  41. Obuchi SP, Kawai H, Murakawa K. Reference value on daily living walking parameters among Japanese adults. Geriatr Gerontol Int. 2020;20:664.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Paraschiv-Ionescu A, Büla CJ, Major K, Lenoble-Hoskovec C, Krief H, El-Moufawad C, et al. Concern about falling and complexity of free-living physical activity patterns in well-functioning older adults. Gerontology. 2018;64:603–11.

    Article  PubMed  Google Scholar 

  43. Paraschiv-Ionescu A, Newman C, Carcreff L, Gerber CN, Armand S, Aminian K. Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions. J Neuroeng Rehabil. 2019;16:1–11.

    Article  Google Scholar 

  44. Paraschiv-Ionescu A, Perruchoud C, Buchser E, Aminian K. Barcoding human physical activity to assess chronic pain conditions. PLoS ONE. 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Pearson M, Dieberg G, Smart N. Exercise as a therapy for improvement of walking ability in adults with multiple sclerosis: a meta-analysis. Arch Phys Med Rehabil. 2015;96:1339-1348.e7.

    Article  PubMed  Google Scholar 

  46. Penner IK, McDougall F, Brown TM, Slota C, Doward L, Julian L, et al. Exploring the impact of fatigue in progressive multiple sclerosis: a mixed-methods analysis. Mult Scler Relat Disord. 2020;43: 102207.

    Article  PubMed  Google Scholar 

  47. Pieper C, Li T, Johnson J, Lapuerta P. Walking speed predicts health status and hospital costs for frail elderly male veterans. Artic J Rehabil Res Dev. 2005.

    Article  Google Scholar 

  48. Prigent G, Aminian K, Cereatti A, Salis F, Bonci T, Scott K, et al. A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings. Med Biol Eng Comput. 2023.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Sebastião E, Sandroff BM, Learmonth YC, Motl RW. Validity of the timed up and go test as a measure of functional mobility in persons with multiple sclerosis. Arch Phys Med Rehabil. 2016;97:1072–7.

    Article  PubMed  Google Scholar 

  50. Shah A. Fatigue in multiple sclerosis. Phys Med Rehabil Clin N Am. 2009;20:363–72.

    Article  PubMed  Google Scholar 

  51. Snook EM, Motl RW. Effect of exercise training on walking mobility in multiple sclerosis: a meta-analysis. Neurorehabil Neural Repair. 2009;23:108–16.

    Article  PubMed  Google Scholar 

  52. Streber R, Peters S, Pfeifer K. Systematic review of correlates and determinants of physical activity in persons with multiple sclerosis. Arch Phys Med Rehabil. 2016;97:633-645.e29.

    Article  PubMed  Google Scholar 

  53. Svenningsson A, Falk E, Celius EG, Fuchs S, Schreiber K, Berkö S, et al. Natalizumab treatment reduces fatigue in multiple sclerosis. Results from the TYNERGY trial; a study in the real life setting. PLoS ONE. 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Taraldsen K, Chastin SFM, Riphagen II, Vereijken B, Helbostad JL. Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: a systematic literature review of current knowledge and applications. Maturitas. 2012;71:13–9.

    Article  PubMed  Google Scholar 

  55. Terhorst L, Beck KB, McKeon AB, Graham KM, Ye F, Shiffman S. Hierarchical linear modeling for analysis of ecological momentary assessment data in physical medicine and rehabilitation research. Am J Phys Med Rehabil. 2017;96:596–9.

    Article  PubMed  Google Scholar 

  56. Van Ancum JM, van Schooten KS, Jonkman NH, Huijben B, van Lummel RC, Meskers CGM, et al. Gait speed assessed by a 4-m walk test is not representative of daily-life gait speed in community-dwelling adults. Maturitas. 2019;121:28–34.

    Article  PubMed  Google Scholar 

  57. Warmerdam E, Hausdorff JM, Atrsaei A, Zhou Y, Mirelman A, Aminian K, et al. Long-term unsupervised mobility assessment in movement disorders. Lancet Neurol. 2020;19:462–70.

    Article  PubMed  Google Scholar 

  58. Weikert M, Motl RW, Suh Y, McAuley E, Wynn D. Accelerometry in persons with multiple sclerosis: Measurement of physical activity or walking mobility? J Neurol Sci. 2010;290:6–11.

    Article  PubMed  Google Scholar 

  59. Weiss A, Sharifi S, Plotnik M, Van Vugt JPP, Giladi N, Hausdorff JM. Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer. Neurorehabil Neural Repair. 2011;25:810–8.

    Article  PubMed  Google Scholar 

  60. Yildiz M. The impact of slower walking speed on activities of daily living in patients with multiple sclerosis. Int J Clin Pract. 2012;66:1088–94.

    Article  CAS  PubMed  Google Scholar 

  61. Zhang W, Schwenk M, Mellone S, Paraschiv-Ionescu A, Vereijken B, Pijnappels M, et al. Complexity of daily physical activity is more sensitive than conventional metrics to assess functional change in younger older adults. Sensors (Basel). 2018.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Zwibel H. Health and quality of life in patients with relapsing multiple sclerosis: making the intangible tangible. J Neurol Sci. 2009;287(Suppl):1.

    Article  Google Scholar 

Download references


The authors would like to thank the patients who participated in the study. We also would like to thank Ramona Sylvester from the clinic Valens for her contributions in the data collection.


This research was funded by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Federal Ministry for Digital and Economic Affairs, and the federal state of Salzburg under the research programme COMET—Competence Centers for Excellent Technologies—in the project Digital Motion in Sports, Fitness and Well-being (DiMo).

Author information

Authors and Affiliations



Study design and planning were led by R.A. and R.G. Data acquisition was carried out by R.A. with the help of physiotherapists from the Valens Clinic. Data analysis was performed by G.P. The interpretation of the results was performed by all authors. The manuscript was written by G.P. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Anisoara Paraschiv-Ionescu.

Ethics declarations

Ethics approval and consent to participate

The study was conducted according to the Helsinki declaration, approved by the Ethical Committee of eastern Switzerland (EKOS, 2017-00728). All patients had written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prigent, G., Aminian, K., Gonzenbach, R.R. et al. Effects of multidisciplinary inpatient rehabilitation on everyday life physical activity and gait in patients with multiple sclerosis. J NeuroEngineering Rehabil 21, 88 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: