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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] [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.
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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
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