MS FIT App for Multiple Sclerosis

(MS FIT Trial)

Overseen By Jaeleene Wijangco, BS

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of California, San Francisco

Disqualifiers: Cognitive impairment, Visual impairment, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

Duration: 12 months

100 Participants Needed

Trial Summary

What is the purpose of this trial?

This trial tests the MS FIT app, which helps MS patients track falls, view health data, and get fall prevention tips. It targets MS patients because they often experience falls that affect their daily lives. The app uses data from health records, wearables, and surveys to monitor falls and provide personalized advice.

Do I need to stop my current medications for the trial?

The trial does not specify whether you need to stop taking your current medications. It mentions that participants can be on any MS therapy or no treatment at all.

What data supports the effectiveness of the MS FIT treatment for multiple sclerosis?

The research suggests that wearable sensors and smartphone-based assessments can help predict and manage fall risk and mobility issues in people with multiple sclerosis, which are key components of the MS FIT treatment. ^{1 2 3 4 5}

How is the MS FIT App treatment different from other treatments for multiple sclerosis?

The MS FIT App treatment is unique because it uses digital technology to deliver motor and cognitive rehabilitation remotely, allowing for continuous monitoring and self-assessment of symptoms like fatigue and balance, which are difficult to detect with traditional methods. This approach provides a more personalized and accessible way to manage multiple sclerosis compared to standard in-person treatments. ^{2 4 6 7 8}

Research Team

Riley Bove, MD

Principal Investigator

University of California, San Francisco

Eligibility Criteria

This trial is for California residents aged 18 or older with Multiple Sclerosis, who can walk with assistance but may use a wheelchair. They should have fallen before or be at risk of falling and must be able to connect to Wi-Fi at home or work. People with cognitive or visual impairments that make it hard to follow the study plan cannot join.

Inclusion Criteria

I have been diagnosed with MS according to the 2017 criteria.

I am 18 years old or older.

I am currently on MS therapy or have not received any treatment.

Exclusion Criteria

Cognitive dexterity or visual impairment that, in the opinion of the study neurologist (RB), would put the participant at risk or limit their ability to comply with the study protocol

Inability to provide informed consent

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Design

Human-Centered Design approach engaging patients, clinicians, and stakeholders to identify critical data and interventions

Not specified

Treatment

Participants use the MS FIT mobile tool intervention for fall tracking and prevention

12 months

Quarterly visits (virtual or in-person)

Follow-up

Participants are monitored for tool engagement and adherence to fall prompts

12 months

Treatment Details

Interventions

• MS FIT: Falls Insight Track

Trial Overview The study is testing an app called MS FIT designed for MS patients to track falls, see their health data, talk to their care team about fall risks, and get information on preventing falls.

Participant Groups

Group I: MS FIT: Falls Insight Track Experimental Treatment 1 Intervention

Participants in this arm will receive 12 months use of MS FIT mobile tool intervention

Who Is Running the Clinical Trial?

University of California, San Francisco

Lead Sponsor

Trials

2,636

Recruited

19,080,000+

National Library of Medicine (NLM)

Collaborator

Trials

42

Recruited

108,000+

National Institutes of Health (NIH)

Collaborator

Trials

2,896

Recruited

8,053,000+

Findings from Research

A study involving 34 individuals with multiple sclerosis tracked their movements over eight weeks using a smart-home system, revealing that 52% of falls occurred while walking, despite participants being mostly sedentary.

The analysis showed that increased pauses while walking and more complex movement patterns significantly raised the likelihood of falls, indicating that monitoring these metrics could help in developing targeted fall prevention strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Risky movement: Assessing fall risk in people with multiple sclerosis with wearable sensors and beacon-based smart-home monitoring. Kushner, T., Mosquera-Lopez, C., Hildebrand, A., et al.[2023]

Wearable accelerometers used during a 30-second chair stand test can provide valuable metrics that correlate with clinical measures of disease severity, balance confidence, and fatigue in people with multiple sclerosis (PwMS).

These accelerometer-derived metrics improved the accuracy of predicting fall risk in PwMS, achieving 74% accuracy in classifying fallers versus non-fallers, compared to 68% accuracy using standard clinical assessments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis. Tulipani, LJ., Meyer, B., Larie, D., et al.[2021]

The pilot study tested a course-based intervention using wearable activity trackers to help people with multiple sclerosis (MS) align their daily activities with symptom severity, but showed limited efficacy with only half of participants meeting their step goals for at least 50% of the days.

Participants found the intervention acceptable and expressed interest in understanding their 'sweet spot' for activity, but many dropped out, indicating a need for improvements in the practicality of daily tracking and goal-setting to enhance the intervention's effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

In search of a daily physical activity "sweet spot": Piloting a digital tracking intervention for people with multiple sclerosis. Chiauzzi, E., Hekler, EB., Lee, J., et al.[2022]

The 5 U-Turn Test (5UTT) demonstrated good-to-excellent test-retest reliability for measuring U-turn speed in people with multiple sclerosis (MS), with an intraclass correlation coefficient of 0.87, indicating it is a reliable tool for assessing gait and balance impairment.

U-turn speed from the 5UTT was significantly correlated with established in-clinic measures of walking speed and overall MS-related disability, suggesting that this smartphone-based assessment can effectively monitor mobility changes in a home setting.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

U-turn speed is a valid and reliable smartphone-based measure of multiple sclerosis-related gait and balance impairment. Cheng, WY., Bourke, AK., Lipsmeier, F., et al.[2021]

In a study involving 269 participants with Relapsing-Remitting Multiple Sclerosis, the research aimed to determine the Minimally Important Differences (MID) for two patient-reported outcome measures, the MS Walking Scale and the MS Impact Scale-29, using objective ambulation data from accelerometers.

The results showed that changes in patient-reported outcomes did not consistently align with changes in objective performance measures, suggesting that relying solely on PROs may not accurately reflect actual changes in physical performance over time.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Investigating the minimal important difference in ambulation in multiple sclerosis: a disconnect between performance-based and patient-reported outcomes? Schwartz, CE., Ayandeh, A., Motl, RW.[2022]

Digital technology tools are increasingly being integrated into clinical trials for multiple sclerosis (MS), enhancing treatment delivery and disease monitoring through methods like psychotherapy apps and robot-assisted exercises.

These technologies improve the standardization of outcome measures and enable better detection of symptoms and treatment adherence, shifting data collection from traditional paper-based methods to more efficient internet-based systems, which can also facilitate remote rehabilitation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Digital Technology in Clinical Trials for Multiple Sclerosis: Systematic Review. De Angelis, M., Lavorgna, L., Carotenuto, A., et al.[2021]

The smartphone-based 2-Minute Walking Test (s2MWT) demonstrated good concurrent validity and test-retest reliability in measuring walking distance for individuals with multiple sclerosis (MS), with an intraclass correlation coefficient (ICC) of 0.817 when compared to traditional distance markers.

Participants found the s2MWT easy to perform, indicating its potential for practical use in both clinical studies and everyday assessments for MS patients, despite some expressing pressure to achieve certain distances.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Two-Minute Walking Test With a Smartphone App for Persons With Multiple Sclerosis: Validation Study. van Oirschot, P., Heerings, M., Wendrich, K., et al.[2022]

Smartphone accelerometry is a reliable and convenient method for assessing real-life mobility in individuals with multiple sclerosis (pwMS), showing better estimates of mobility and disability compared to traditional wrist-worn accelerometers.

In a study involving 67 pwMS and 70 matched controls, smartphone data demonstrated stronger correlations with clinical tests and effectively distinguished between different disability groups, highlighting its potential as a valuable tool for monitoring mobility in both healthy individuals and those with chronic conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals. Zhai, Y., Nasseri, N., Pöttgen, J., et al.[2020]

References

1. Netherlands pubmed.ncbi.nlm.nih.gov

Risky movement: Assessing fall risk in people with multiple sclerosis with wearable sensors and beacon-based smart-home monitoring. [2023]

2. England pubmed.ncbi.nlm.nih.gov

Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis. [2021]

3. United States pubmed.ncbi.nlm.nih.gov

In search of a daily physical activity "sweet spot": Piloting a digital tracking intervention for people with multiple sclerosis. [2022]

4. England pubmed.ncbi.nlm.nih.gov

U-turn speed is a valid and reliable smartphone-based measure of multiple sclerosis-related gait and balance impairment. [2021]

5. Netherlands pubmed.ncbi.nlm.nih.gov

Investigating the minimal important difference in ambulation in multiple sclerosis: a disconnect between performance-based and patient-reported outcomes? [2022]

6. Switzerland pubmed.ncbi.nlm.nih.gov

Digital Technology in Clinical Trials for Multiple Sclerosis: Systematic Review. [2021]

7. Canada pubmed.ncbi.nlm.nih.gov

A Two-Minute Walking Test With a Smartphone App for Persons With Multiple Sclerosis: Validation Study. [2022]

8. Switzerland pubmed.ncbi.nlm.nih.gov

Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals. [2020]