Background

There is to our knowledge few validated electronic tool in MS that measures the distance walked by a person with MS (PwMS). Utilising global positioning systems (GPS), WI-FI positioning and an in-built smartphone accelerometer to measure distance walked by a PwMS outdoors or indoors, could alleviate the uncertainty around using pedometers in those with gait disturbances, and is an attractive option.

Objectives

To pilot an accurate measure of distance walked using a smartphone application (mSteps) to facilitate Expanded Disability Status Scale (EDSS) measurement, indoors and outdoors, using both an MS and a control cohort.

Methods

The pilot study recruited 25 PwMS and 10 controls. mSteps utilised the iPhone’s inbuilt accelerometer and GPS functionalities to calculate the distance walked and time taken, indoors and outdoors. Due to unpredictable weather the physician monitored walk took place indoors which was fitted with location beacons to allow for WI-FI indoor positioning. The control cohort did the same walk indoors and outdoors to validate the use of the GPS functionality.

The participant was instructed to walk 25 feet, without rest, whilst the study phone was attached to their arm using a runner’s arm band and study personnel walked alongside them with a trundle wheel. Measurements were taken at 3 separate time points within a 3-month period.

95% levels of agreement between app and trundle wheel (gold standard) were calculated using the Bland-Altman repeated measures analysis. Levels of agreement, app vs trundle, were calculated for indoor measurements on both PwMS and controls with additional app vs trundle outdoor measurements for controls only. The a priori defined clinically acceptable difference was 1.52m.

Results

The 95% levels of agreement for indoor measurements on PwMS were -2.46 to 2.27m; and for controls were -2.02 to 2.71m. The 95% levels of agreement for outdoor measurements on controls were -0.45 to 0.43m.

Conclusions

The outdoor GPS functionality of mSteps is very accurate as shown by the 95% levels of agreements compared to the a priori clinically determined difference. The indoor WI-FI positioning function of mSteps however, was not accurate enough and shows that it is not reliable enough for further use. The control cohort showed the same inaccuracy indoors which eliminates the possibility that an uneven gait pattern in the MS cohort contributed to the error margin. A further validity study is being carried out, looking at a cohort of PwMS walking outdoors using mSteps and a trundle wheel.

Background

Wearable technology refers to any sensor worn on the person, which as a result makes continuous and remote monitoring available to many people with chronic diseases, including multiple sclerosis (MS). Daily monitoring seems an ideal solution either as an outcome measure or as an adjunct to support rater-based monitoring in both clinical and research settings. There has been an increase in solutions that are available and we look to identify next generation wearables.

Objectives

To identify all validated wearable solutions for PwMS and identify areas of focus for wearable solutions in multiple sclerosis.

Methods

We completed a scoping review (using the PRISMA-ScR guidelines) to summarise the wearable solutions currently available in MS.

Our search strategy utilized subject heading searches: ‘Multiple Sclerosis’ and ‘wearable electronic devices’, as well as keywords ‘wearable technology’, and ‘electronic devices’. The literature search was conducted using MEDLINE (via PubMed) and Embase (via OVID) databases. This search included articles published from database inception to 30 May 2019. Additional searches looked at frequently published authors with different devices, as well as forward and backward citation tracking of included papers.

Results

We identified 35 validated unique solutions that measure gait, cognition, upper limb function, activity, mood and fatigue with most of these solutions being phone applications. Of these, 51% looked at lower limb function with activity levels being looked at by 37% of the total solutions. There was least focus on visual, and mood solutions at 3%, closely followed by quality of life and balance at 5%. Cognition and fatigue accounted for 14% of the total.

Conclusions

Looking forward, there is a change occurring from single measure solutions to multi-measure and multi-sensor solutions, such as the Floodlight Open app, which utilises multiple sensors within a smart-phone to remotely measure gait, cognition and upper limb function. Future research should consider costs and include implementation science as part of their research and design to ensure cost of delivery strategy is also accounted for.

As development in wearable technology in MS is still on-going, we can expect to see newer wearables focusing on other areas with technology advancements that allow for more upper body and cognitive measures. There is a dearth of validated solutions available for fatigue, mood, and pain.

Background

There is to our knowledge few validated electronic tool in MS that measures the distance walked by a person with MS (PwMS). Utilising global positioning systems (GPS), WI-FI positioning and an in-built smartphone accelerometer to measure distance walked by a PwMS outdoors or indoors, could alleviate the uncertainty around using pedometers in those with gait disturbances, and is an attractive option.

Objectives

To pilot an accurate measure of distance walked using a smartphone application (mSteps) to facilitate Expanded Disability Status Scale (EDSS) measurement, indoors and outdoors, using both an MS and a control cohort.

Methods

The pilot study recruited 25 PwMS and 10 controls. mSteps utilised the iPhone’s inbuilt accelerometer and GPS functionalities to calculate the distance walked and time taken, indoors and outdoors. Due to unpredictable weather the physician monitored walk took place indoors which was fitted with location beacons to allow for WI-FI indoor positioning. The control cohort did the same walk indoors and outdoors to validate the use of the GPS functionality.

The participant was instructed to walk 25 feet, without rest, whilst the study phone was attached to their arm using a runner’s arm band and study personnel walked alongside them with a trundle wheel. Measurements were taken at 3 separate time points within a 3-month period.

95% levels of agreement between app and trundle wheel (gold standard) were calculated using the Bland-Altman repeated measures analysis. Levels of agreement, app vs trundle, were calculated for indoor measurements on both PwMS and controls with additional app vs trundle outdoor measurements for controls only. The a priori defined clinically acceptable difference was 1.52m.

Results

The 95% levels of agreement for indoor measurements on PwMS were -2.46 to 2.27m; and for controls were -2.02 to 2.71m. The 95% levels of agreement for outdoor measurements on controls were -0.45 to 0.43m.

Conclusions

The outdoor GPS functionality of mSteps is very accurate as shown by the 95% levels of agreements compared to the a priori clinically determined difference. The indoor WI-FI positioning function of mSteps however, was not accurate enough and shows that it is not reliable enough for further use. The control cohort showed the same inaccuracy indoors which eliminates the possibility that an uneven gait pattern in the MS cohort contributed to the error margin. A further validity study is being carried out, looking at a cohort of PwMS walking outdoors using mSteps and a trundle wheel.

Background

Wearable technology refers to any sensor worn on the person, which as a result makes continuous and remote monitoring available to many people with chronic diseases, including multiple sclerosis (MS). Daily monitoring seems an ideal solution either as an outcome measure or as an adjunct to support rater-based monitoring in both clinical and research settings. There has been an increase in solutions that are available and we look to identify next generation wearables.

Objectives

To identify all validated wearable solutions for PwMS and identify areas of focus for wearable solutions in multiple sclerosis.

Methods

We completed a scoping review (using the PRISMA-ScR guidelines) to summarise the wearable solutions currently available in MS.

Our search strategy utilized subject heading searches: ‘Multiple Sclerosis’ and ‘wearable electronic devices’, as well as keywords ‘wearable technology’, and ‘electronic devices’. The literature search was conducted using MEDLINE (via PubMed) and Embase (via OVID) databases. This search included articles published from database inception to 30 May 2019. Additional searches looked at frequently published authors with different devices, as well as forward and backward citation tracking of included papers.

Results

We identified 35 validated unique solutions that measure gait, cognition, upper limb function, activity, mood and fatigue with most of these solutions being phone applications. Of these, 51% looked at lower limb function with activity levels being looked at by 37% of the total solutions. There was least focus on visual, and mood solutions at 3%, closely followed by quality of life and balance at 5%. Cognition and fatigue accounted for 14% of the total.

Conclusions

Looking forward, there is a change occurring from single measure solutions to multi-measure and multi-sensor solutions, such as the Floodlight Open app, which utilises multiple sensors within a smart-phone to remotely measure gait, cognition and upper limb function. Future research should consider costs and include implementation science as part of their research and design to ensure cost of delivery strategy is also accounted for.

As development in wearable technology in MS is still on-going, we can expect to see newer wearables focusing on other areas with technology advancements that allow for more upper body and cognitive measures. There is a dearth of validated solutions available for fatigue, mood, and pain.