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This US multicenter, prospective cohort study aims to evaluate how MSCopilot can be seamlessly integrated into the current care pathway and identify potential optimizations to enhance its impact on both MS patients and clinicians, facilitating broader implementation. Specifically, the study will assess:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Performance of digital tests at home at day 0 (D0), D30, D60, D90, D120, D150 and D180 | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| MSCopilot Flower mobile application | Device | MSCopilot Flower includes active tests for walking, cognition, dexterity and vision, and e-questionnaires related to fatigue and Anxiety |
| Measure | Description | Time Frame |
|---|---|---|
| To assess the ease of integrating the MSCopilot dashboard into clinical workflows for neurologists and the ease of use at home for patients with multiple sclerosis. | Descriptive analysis and acceptance criteria of a minimum score of >2/4 on neurologists' questionnaires regarding the ease of integrating the MSCopilot dashboard into clinical workflows Descriptive analysis and acceptance criteria of a minimum score of >2/4 on patients' questionnaires regarding the ease of use of MSCopilot at home | For neurologists: Day 180 (+/- 30 days) and for patients: Day 1, Day 90 and Day 180 (+/- 30 days) |
| Measure | Description | Time Frame |
|---|---|---|
| To assess the integration of MSCopilot dashboard and application into routine clinical practice as well as perceived value for both HCPs and patients. | Descriptive analysis of neurologists' questionnaires, to be completed after each investigator's last visit, regarding the Health Care Profesionnals (HCPs) dashboard Descriptive analysis and acceptance criteria of a minimum score of >2/4 for each item, of neurologists' questionnaires after the last visit on the HCPs dashboard Descriptive analysis of patients' questionnaires after the last visit regarding MSCopilot app and dashboard use by the HCPs. Descriptive analysis and acceptance criteria of a minimum score of >2/4 for each item in patients' questionnaires, to be completed after the follow-up visit, regarding MSCopilot application and dashboard use by HCPs |
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Inclusion Criteria:
For patients
For HCPs:
Exclusion Criteria (for patients):
Early termination criteria (for patients):
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Jenny Feng | New Orleans | Louisiana | 70121 | United States | ||
| Robert Naismith |
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| At Day 180 ± 30days |
| To assess patients' ability to use MSCopilot at home without supervision. | Descriptive analysis of patient questionnaires Acceptance criteria of a minimum of >2/4 for each item, of patient questionnaires | Day 1 (+/- 30 days) |
| To assess patients' ability to use MSCopilot at home without supervision. | Descriptive analysis of patient questionnaires at mid-study Descriptive analysis and acceptance criteria of a minimum score of >2/4 for each item, of patient questionnaires at mid-study | Day 90 (+/-30 days) |
| To assess the need for patient support when using MSCopilot at home | Descriptive analysis of nurses or medical assistants' questionnaires (if applicable) Descriptive analysis and acceptance criteria of a minimum score of >2/4 for each item, of nurses or medical assistants' questionnaires (if applicable) | Day 180 (+/-30 days) |
| To assess patient adherence to MSCopilot use in routine clinical practice. | Descriptive analysis of the mobile application's adherence data, including: Number of performed tests, Number of performed sessions, Number of completed questionnaires. | Day 1 to Day 180 (+/-30 days) |
| To assess user behavior based on usage analytics data from the MSCopilot mobile app and the dashboard. | Quantitative analysis of real-world utilization patterns of the MSCopilot mobile app through analytics data Quantitative analysis of real-world utilization patterns of the HCPs dashboard through analytics data | Day 1 to Day 180 (+/-30 days) |
| To assess the effectiveness of HCPs onboarding/training in ensuring successful patient onboarding. | Descriptive analysis of HCPs (neurologists) questionnaires Descriptive analysis and acceptance criteria of a minimum score of >2/4 for each item, of HCPs (neurologists) questionnaires | At the end of inclusion period (90 days) |
| To assess the effectiveness of HCPs onboarding/training in ensuring successful patient onboarding. | Descriptive analysis of patient questionnaires Descriptive analysis and acceptance criteria of a minimum score of >2/4 for each item, of patient questionnaires | After patient inclusion visit (90 days) |
| To assess the adequacy of the onboarding/training process for HCPs, including neurologists, nurses, and medical assistants, focusing on clarity, satisfaction, and confidence in using MSCopilot | Descriptive analysis of HCPs questionnaires post-onboarding/training session Descriptive analysis and acceptance criteria of a minimum score of >2/4 for each item in HCPs questionnaires post-onboarding/training | After onboarding/training session (Day 0) |
| To assess the variances in user behavior and adherence to MSCopilot use according to socio-demographic factors and EDSS scores. | Descriptive analysis of how socio-demographic factors and EDSS scores affect patient behavior and adherence to MSCopilot mobile application. | Day 1 to Day 180 (+/-30 days) |
| To assess the variances in user behavior and adherence to MSCopilot use according to socio-demographic factors and EDSS scores. | Descriptive analysis of how HCPs socio-demographic factors affect their behavior toward MSCopilot dashboard. | Day 1 to Day 180 (+/-30 days) |
| St Louis |
| Missouri |
| 63110 |
| United States |
| Gabriel Pardo | Oklahoma City | Oklahoma | 73104 | United States |
| Leorah Freeman | Austin | Texas | 78712 | United States |
| ID | Term |
|---|---|
| D009103 | Multiple Sclerosis |
| ID | Term |
|---|---|
| D020278 | Demyelinating Autoimmune Diseases, CNS |
| D020274 | Autoimmune Diseases of the Nervous System |
| D009422 | Nervous System Diseases |
| D003711 | Demyelinating Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |
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