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Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study).

Shammas L, Zentek T, von Haaren B, Schlesinger S, Hey S, Rashid A - Biomed Eng Online (2014)

Bottom Line: Participants showed significant decline in step count (p = 0.008), maximum walking speed (p = 0.02) and physical activity intensity (p = 0.03) throughout the study period.Furthermore, they track disability changes better than clinical measures.Thus, they can help to develop activity based treatments for PwMS.

View Article: PubMed Central - HTML - PubMed

Affiliation: FZI Forschungszentrum Informatik, Karlsruhe, Germany. shammas@fzi.de.

ABSTRACT

Background: Limitations in physical activity are considered as a key problem in patients with multiple sclerosis (PwMS). Contemporary methods to assess the level of physical activity in PwMS are regular clinical observation. However, these methods either rely on high recall and accurate reporting from the patients (e.g. self-report questionnaires), or they are conducted during a particular clinical assessment with predefined activities. Therefore, the main aim of this pilot study was to develop an objective method to gather information about the real type and intensity of daily activities performed by PwMS in every-day living situations using an accelerometer. Furthermore, the accelerometer-derived measures are investigated regarding their potential for discriminating between different MS groups.

Methods: Eleven PwMS that were able to walk independently (EDSS ≤ 5) were divided into two groups: mild disability (EDSS 1-2.5; n = 6) and moderate disability (EDSS 3 -5; n = 5). Participants made use of an activity monitor device attached to their waist during their normal daily activities over 4 measurements. Activity parameters were assessed and compared for the time of each participant's first measurement and follow-up measurement. Furthermore, differences between both subgroups, and the correlation of activity parameters with the clinical neurological variable (EDSS) were investigated.

Results: Participants showed significant decline in step count (p = 0.008), maximum walking speed (p = 0.02) and physical activity intensity (p = 0.03) throughout the study period. Compared to the mild subgroup, moderate affected participant accumulated less number of steps (G1: 9214.33 ± 2439.11, G2: 5018.13 ± 2416.96; p < 0.005) and were slower (G1: 1.48 ± 0.19, G2: 1.12 ± 0.44; p = 0.03). Additionally, the EDSS correlated negatively with mean walking speed (r = - 0.71, p = 0.01) and steps count (r = - 0.54, p = 0.08).

Conclusions: In this study, we used a portable activity monitoring sensor to gather information about everyday physical activity in PwMS at home. We showed that objective measurements using simple 3D accelerometers can track daily physical activity fluctuation. Furthermore, they track disability changes better than clinical measures. Thus, they can help to develop activity based treatments for PwMS.

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Related in: MedlinePlus

10-meter walking test. This figure illustrates the sensors positions during the 10-meter walking test. Number of steps, steps length and the time needed to travel the distance were collected.
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Figure 3: 10-meter walking test. This figure illustrates the sensors positions during the 10-meter walking test. Number of steps, steps length and the time needed to travel the distance were collected.

Mentions: 1. 10-meter walking test was used for initial calibration, in which patients were instructed to wear the move II (one on the right side hip and two sensors on the right and left ankle) and to walk along a 10 meter flat walkway. Since gait pattern of PwMS differ in various walking speed patients were asked to walk back and forth once at their comfortable walking speed and once again at fastest walking speed. Information about stride length, time and number of steps were recorded by the physician and as raw acceleration data from the move II (see Figure 3). This information was used as training data for developing estimation models for both slow and fast walking speed.


Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study).

Shammas L, Zentek T, von Haaren B, Schlesinger S, Hey S, Rashid A - Biomed Eng Online (2014)

10-meter walking test. This figure illustrates the sensors positions during the 10-meter walking test. Number of steps, steps length and the time needed to travel the distance were collected.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3927216&req=5

Figure 3: 10-meter walking test. This figure illustrates the sensors positions during the 10-meter walking test. Number of steps, steps length and the time needed to travel the distance were collected.
Mentions: 1. 10-meter walking test was used for initial calibration, in which patients were instructed to wear the move II (one on the right side hip and two sensors on the right and left ankle) and to walk along a 10 meter flat walkway. Since gait pattern of PwMS differ in various walking speed patients were asked to walk back and forth once at their comfortable walking speed and once again at fastest walking speed. Information about stride length, time and number of steps were recorded by the physician and as raw acceleration data from the move II (see Figure 3). This information was used as training data for developing estimation models for both slow and fast walking speed.

Bottom Line: Participants showed significant decline in step count (p = 0.008), maximum walking speed (p = 0.02) and physical activity intensity (p = 0.03) throughout the study period.Furthermore, they track disability changes better than clinical measures.Thus, they can help to develop activity based treatments for PwMS.

View Article: PubMed Central - HTML - PubMed

Affiliation: FZI Forschungszentrum Informatik, Karlsruhe, Germany. shammas@fzi.de.

ABSTRACT

Background: Limitations in physical activity are considered as a key problem in patients with multiple sclerosis (PwMS). Contemporary methods to assess the level of physical activity in PwMS are regular clinical observation. However, these methods either rely on high recall and accurate reporting from the patients (e.g. self-report questionnaires), or they are conducted during a particular clinical assessment with predefined activities. Therefore, the main aim of this pilot study was to develop an objective method to gather information about the real type and intensity of daily activities performed by PwMS in every-day living situations using an accelerometer. Furthermore, the accelerometer-derived measures are investigated regarding their potential for discriminating between different MS groups.

Methods: Eleven PwMS that were able to walk independently (EDSS ≤ 5) were divided into two groups: mild disability (EDSS 1-2.5; n = 6) and moderate disability (EDSS 3 -5; n = 5). Participants made use of an activity monitor device attached to their waist during their normal daily activities over 4 measurements. Activity parameters were assessed and compared for the time of each participant's first measurement and follow-up measurement. Furthermore, differences between both subgroups, and the correlation of activity parameters with the clinical neurological variable (EDSS) were investigated.

Results: Participants showed significant decline in step count (p = 0.008), maximum walking speed (p = 0.02) and physical activity intensity (p = 0.03) throughout the study period. Compared to the mild subgroup, moderate affected participant accumulated less number of steps (G1: 9214.33 ± 2439.11, G2: 5018.13 ± 2416.96; p < 0.005) and were slower (G1: 1.48 ± 0.19, G2: 1.12 ± 0.44; p = 0.03). Additionally, the EDSS correlated negatively with mean walking speed (r = - 0.71, p = 0.01) and steps count (r = - 0.54, p = 0.08).

Conclusions: In this study, we used a portable activity monitoring sensor to gather information about everyday physical activity in PwMS at home. We showed that objective measurements using simple 3D accelerometers can track daily physical activity fluctuation. Furthermore, they track disability changes better than clinical measures. Thus, they can help to develop activity based treatments for PwMS.

Show MeSH
Related in: MedlinePlus