Ignite Creation Date:
2024-05-06 @ 6:56 PM
Last Modification Date:
2024-10-26 @ 2:57 PM
Study NCT ID:
NCT05837949
Status:
RECRUITING
Last Update Posted:
2024-06-21
First Post:
2023-04-17
Brief Title:
Multiple Sclerosis Falls Insight Track
Sponsor:
University of California San Francisco
Organization:
University of California San Francisco
Study Overview
Official Title:
Multiple Sclerosis Falls Insight Track A Personal Health Library to Reduce Falls in Patients With Multiple Sclerosis
Status:
RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
MS FIT
Brief Summary:
The purpose of this study is to develop an application MS Falls Insight Track MS FIT which allows patients to log their falls and near falls view their MS relevant data and responses to the clinic intake survey as well as communicate with their care team about falls and receive educational material on falls prevention
Detailed Description:
Falls occur in 50 patients with multiple sclerosis MS worsen participation in daily life and increase healthcare costs To date there are no established accessible tools to evaluate and reduce fall risk MS Falls InsightTrack is a live personal health library that combines a patients falls-relevant clinical indicators from the electronic health record EHR with patient-generated data PGD from commercial wearable tools and patient-reported outcomes PROs and community-level data sociodemographic data from University of California San Francisco UCSF Health Atlas combined with MS-specific resources from the National MS Society The tool will track fallsnear-falls in real- time and report changes in status that require intervention It will offer customized action prompts to support fall reduction through a behaviorally informed approach It will be accessed in the clinic and in the patients home
Technological features The tool will be accessible extensible and scalable The investigators will use modern technologies and industry standards eg back-end Python flask framework PostgreSQL front-end HTML CSS JavaScript and d3js The tool will launch from Epic via SMART on FHIR and will communicate with patients using MyChart
Qualifications of team and setting The UCSF MS Center is a leading clinical research center in the digital space Our sub-leads are experts in all aspects of the study digital technology human-centered design implementation science health literacy with a varied and experienced Stakeholder Advisory Group
Scientific plan In Aim 1 design the investigators will use a Human-Centered Design approach engaging 20 patients with MS clinicians and stakeholders in a series of focus groups to identify the critical data devices visualizations resources workflows and accessibilitydigital divide considerations for the tool and the key interventions likely to promote the COM-B model of behavioral change to reduce fall risk
Our key outcomes will be perceived effectiveness ease of use and likability In Aim 2 evaluate feasibility investigators will deploy MS Falls Insight Track in 100 diverse adults with MS who are at risk for falls Participants will wear a Fitbit The tool will be used by patients in their homes and by clinicians during clinical encounters The investigators will use an implementation science approach Our key outcomes will be study retention tool uptake and sustained use The investigators will explore impact on fall risk In Aim 3 test generalizability investigators will conduct focus groups with patients with other conditions where falls are common Orthopedics Parkinsons Disease Geriatrics to understand additional data and design features required to promote generalizability Our key outcomes will parallel those in Aim 1
Innovation and Broader Significance MS Falls Insight Track is a unique comprehensive accessible personal health library that can be deployed in larger efficacy trials for falls reduction Beyond this clinical use case the closed-loop approach of delivering PGD to the care system and back to the patient interpreted and actionable using scalable technology represents a significant innovation that can sequentially expand the number of wearables conditions and clinics in which patients and clinical investigators can ask their own questions of PGD
Technological features The tool will be accessible extensible and scalable The investigators will use modern technologies and industry standards eg back-end Python flask framework PostgreSQL front-end HTML CSS JavaScript and d3js The tool will launch from Epic via SMART on FHIR and will communicate with patients using MyChart
Qualifications of team and setting The UCSF MS Center is a leading clinical research center in the digital space Our sub-leads are experts in all aspects of the study digital technology human-centered design implementation science health literacy with a varied and experienced Stakeholder Advisory Group
Scientific plan In Aim 1 design the investigators will use a Human-Centered Design approach engaging 20 patients with MS clinicians and stakeholders in a series of focus groups to identify the critical data devices visualizations resources workflows and accessibilitydigital divide considerations for the tool and the key interventions likely to promote the COM-B model of behavioral change to reduce fall risk
Our key outcomes will be perceived effectiveness ease of use and likability In Aim 2 evaluate feasibility investigators will deploy MS Falls Insight Track in 100 diverse adults with MS who are at risk for falls Participants will wear a Fitbit The tool will be used by patients in their homes and by clinicians during clinical encounters The investigators will use an implementation science approach Our key outcomes will be study retention tool uptake and sustained use The investigators will explore impact on fall risk In Aim 3 test generalizability investigators will conduct focus groups with patients with other conditions where falls are common Orthopedics Parkinsons Disease Geriatrics to understand additional data and design features required to promote generalizability Our key outcomes will parallel those in Aim 1
Innovation and Broader Significance MS Falls Insight Track is a unique comprehensive accessible personal health library that can be deployed in larger efficacy trials for falls reduction Beyond this clinical use case the closed-loop approach of delivering PGD to the care system and back to the patient interpreted and actionable using scalable technology represents a significant innovation that can sequentially expand the number of wearables conditions and clinics in which patients and clinical investigators can ask their own questions of PGD
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 5R01LM013396-02 | NIH | None | httpsreporternihgovquickSearch5R01LM013396-02 |