1
|
Henderson K, Reihm J, Koshal K, Wijangco J, Sara N, Miller N, Doyle M, Mallory A, Sheridan J, Guo CY, Oommen L, Rankin KP, Sanders S, Feinstein A, Mangurian C, Bove R. A Closed-Loop Digital Health Tool to Improve Depression Care in Multiple Sclerosis: Iterative Design and Cross-Sectional Pilot Randomized Controlled Trial and its Impact on Depression Care. JMIR Form Res 2024; 8:e52809. [PMID: 38488827 PMCID: PMC10980989 DOI: 10.2196/52809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/27/2023] [Accepted: 11/24/2023] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND People living with multiple sclerosis (MS) face a higher likelihood of being diagnosed with a depressive disorder than the general population. Although many low-cost screening tools and evidence-based interventions exist, depression in people living with MS is underreported, underascertained by clinicians, and undertreated. OBJECTIVE This study aims to design a closed-loop tool to improve depression care for these patients. It would support regular depression screening, tie into the point of care, and support shared decision-making and comprehensive follow-up. After an initial development phase, this study involved a proof-of-concept pilot randomized controlled trial (RCT) validation phase and a detailed human-centered design (HCD) phase. METHODS During the initial development phase, the technological infrastructure of a clinician-facing point-of-care clinical dashboard for MS management (BRIDGE) was leveraged to incorporate features that would support depression screening and comprehensive care (Care Technology to Ascertain, Treat, and Engage the Community to Heal Depression in people living with MS [MS CATCH]). This linked a patient survey, in-basket messages, and a clinician dashboard. During the pilot RCT phase, a convenience sample of 50 adults with MS was recruited from a single MS center with 9-item Patient Health Questionnaire scores of 5-19 (mild to moderately severe depression). During the routine MS visit, their clinicians were either asked or not to use MS CATCH to review their scores and care outcomes were collected. During the HCD phase, the MS CATCH components were iteratively modified based on feedback from stakeholders: people living with MS, MS clinicians, and interprofessional experts. RESULTS MS CATCH links 3 features designed to support mood reporting and ascertainment, comprehensive evidence-based management, and clinician and patient self-management behaviors likely to lead to sustained depression relief. In the pilot RCT (n=50 visits), visits in which the clinician was randomized to use MS CATCH had more notes documenting a discussion of depressive symptoms than those in which MS CATCH was not used (75% vs 34.6%; χ21=8.2; P=.004). During the HCD phase, 45 people living with MS, clinicians, and other experts participated in the design and refinement. The final testing round included 20 people living with MS and 10 clinicians including 5 not affiliated with our health system. Most scoring targets for likeability and usability, including perceived ease of use and perceived effectiveness, were met. Net Promoter Scale was 50 for patients and 40 for clinicians. CONCLUSIONS Created with extensive stakeholder feedback, MS CATCH is a closed-loop system aimed to increase communication about depression between people living with MS and their clinicians, and ultimately improve depression care. The pilot findings showed evidence of enhanced communication. Stakeholders also advised on trial design features of a full year long Department of Defense-funded feasibility and efficacy trial, which is now underway. TRIAL REGISTRATION ClinicalTrials.gov NCT05865405; http://tinyurl.com/4zkvru9x.
Collapse
Affiliation(s)
- Kyra Henderson
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jennifer Reihm
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Kanishka Koshal
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jaeleene Wijangco
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Narender Sara
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Nicolette Miller
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Marianne Doyle
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Alicia Mallory
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Judith Sheridan
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Chu-Yueh Guo
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lauren Oommen
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Katherine P Rankin
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Stephan Sanders
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Anthony Feinstein
- Department of Psychiatry, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Christina Mangurian
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Riley Bove
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| |
Collapse
|
2
|
Henderson K, Reihm J, Koshal K, Wijangco J, Miller N, Sara N, Doyle M, Mallory A, Sheridan J, Guo CY, Oommen L, Feinstein A, Mangurian C, Lazar A, Bove R. Pragmatic phase II clinical trial to improve depression care in a real-world diverse MS cohort from an academic MS centre in Northern California: MS CATCH study protocol. BMJ Open 2024; 14:e077432. [PMID: 38401894 PMCID: PMC10895222 DOI: 10.1136/bmjopen-2023-077432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 01/25/2024] [Indexed: 02/26/2024] Open
Abstract
INTRODUCTION Depression occurs in over 50% of individuals living with multiple sclerosis (MS) and can be treated using many modalities. Yet, it remains: under-reported by patients, under-ascertained by clinicians and under-treated. To enhance these three behaviours likely to promote evidence-based depression care, we engaged multiple stakeholders to iteratively design a first-in-kind digital health tool. The tool, MS CATCH (Care technology to Ascertain, Treat, and engage the Community to Heal depression in patients with MS), closes the communication loop between patients and clinicians. Between clinical visits, the tool queries patients monthly about mood symptoms, supports patient self-management and alerts clinicians to worsening mood via their electronic health record in-basket. Clinicians can also access an MS CATCH dashboard displaying patients' mood scores over the course of their disease, and providing comprehensive management tools (contributing factors, antidepressant pathway, resources in patient's neighbourhood). The goal of the current trial is to evaluate the clinical effect and usability of MS CATCH in a real-world clinical setting. METHODS AND ANALYSIS MS CATCH is a single-site, phase II randomised, delayed start, trial enrolling 125 adults with MS and mild to moderately severe depression. Arm 1 will receive MS CATCH for 12 months, and arm 2 will receive usual care for 6 months, then MS CATCH for 6 months. Clinicians will be randomised to avoid practice effects. The effectiveness analysis is superiority intent-to-treat comparing MS CATCH to usual care over 6 months (primary outcome: evidence of screening and treatment; secondary outcome: Hospital Anxiety Depression Scale-Depression scores). The usability of the intervention will also be evaluated (primary outcome: adoption; secondary outcomes: adherence, engagement, satisfaction). ETHICS AND DISSEMINATION University of California, San Francisco Institutional Review Board (22-36620). The findings of the study are planned to be shared through conferences and publishments in a peer-reviewed journal. The deidentified dataset will be shared with qualified collaborators on request, provision of CITI and other certifications, and data sharing agreement. We will share the results, once the data are complete and analysed, with the scientific community and patient/clinician participants through abstracts, presentations and manuscripts. TRIAL REGISTRATION NUMBER NCT05865405.
Collapse
Affiliation(s)
- Kyra Henderson
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Jennifer Reihm
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Kanishka Koshal
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Jaeleene Wijangco
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Nicolette Miller
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Narender Sara
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Marianne Doyle
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Alicia Mallory
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Judith Sheridan
- Patient Stakeholder, University of California San Francisco, San Francisco, California, USA
| | - Chu-Yueh Guo
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Lauren Oommen
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Anthony Feinstein
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Christina Mangurian
- Department of Psychiatry, University of California San Francisco, San Francisco, California, USA
| | - Ann Lazar
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Riley Bove
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| |
Collapse
|
3
|
Block VJ, Koshal K, Wijangco J, Miller N, Sara N, Henderson K, Reihm J, Gopal A, Mohan SD, Gelfand JM, Guo CY, Oommen L, Nylander A, Rowson JA, Brown E, Sanders S, Rankin K, Lyles CR, Sim I, Bove R. A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack. JMIR Hum Factors 2024; 11:e49331. [PMID: 38206662 PMCID: PMC10811573 DOI: 10.2196/49331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/14/2023] [Accepted: 10/19/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Falls are common in people with multiple sclerosis (MS), causing injuries, fear of falling, and loss of independence. Although targeted interventions (physical therapy) can help, patients underreport and clinicians undertreat this issue. Patient-generated data, combined with clinical data, can support the prediction of falls and lead to timely intervention (including referral to specialized physical therapy). To be actionable, such data must be efficiently delivered to clinicians, with care customized to the patient's specific context. OBJECTIVE This study aims to describe the iterative process of the design and development of Multiple Sclerosis Falls InsightTrack (MS-FIT), identifying the clinical and technological features of this closed-loop app designed to support streamlined falls reporting, timely falls evaluation, and comprehensive and sustained falls prevention efforts. METHODS Stakeholders were engaged in a double diamond process of human-centered design to ensure that technological features aligned with users' needs. Patient and clinician interviews were designed to elicit insight around ability blockers and boosters using the capability, opportunity, motivation, and behavior (COM-B) framework to facilitate subsequent mapping to the Behavior Change Wheel. To support generalizability, patients and experts from other clinical conditions associated with falls (geriatrics, orthopedics, and Parkinson disease) were also engaged. Designs were iterated based on each round of feedback, and final mock-ups were tested during routine clinical visits. RESULTS A sample of 30 patients and 14 clinicians provided at least 1 round of feedback. To support falls reporting, patients favored a simple biweekly survey built using REDCap (Research Electronic Data Capture; Vanderbilt University) to support bring-your-own-device accessibility-with optional additional context (the severity and location of falls). To support the evaluation and prevention of falls, clinicians favored a clinical dashboard featuring several key visualization widgets: a longitudinal falls display coded by the time of data capture, severity, and context; a comprehensive, multidisciplinary, and evidence-based checklist of actions intended to evaluate and prevent falls; and MS resources local to a patient's community. In-basket messaging alerts clinicians of severe falls. The tool scored highly for usability, likability, usefulness, and perceived effectiveness (based on the Health IT Usability Evaluation Model scoring). CONCLUSIONS To our knowledge, this is the first falls app designed using human-centered design to prioritize behavior change and, while being accessible at home for patients, to deliver actionable data to clinicians at the point of care. MS-FIT streamlines data delivery to clinicians via an electronic health record-embedded window, aligning with the 5 rights approach. Leveraging MS-FIT for data processing and algorithms minimizes clinician load while boosting care quality. Our innovation seamlessly integrates real-world patient-generated data as well as clinical and community-level factors, empowering self-care and addressing the impact of falls in people with MS. Preliminary findings indicate wider relevance, extending to other neurological conditions associated with falls and their consequences.
Collapse
Affiliation(s)
- Valerie J Block
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, CA, United States
| | - Kanishka Koshal
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Jaeleene Wijangco
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Nicolette Miller
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Narender Sara
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Kyra Henderson
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Jennifer Reihm
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Arpita Gopal
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, CA, United States
| | - Sonam D Mohan
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Jeffrey M Gelfand
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Chu-Yueh Guo
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Lauren Oommen
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Alyssa Nylander
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - James A Rowson
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Ethan Brown
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Stephen Sanders
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Katherine Rankin
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| | - Courtney R Lyles
- University of California San Francisco Division of General Internal Medicine, Zuckerberg San Francisco General Hospital, San Francisco, CA, United States
- Center for Vulnerable Populations, University of California San Francisco, San Francisco, CA, United States
| | - Ida Sim
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Riley Bove
- Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, San Francisco, CA, United States
| |
Collapse
|