A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack.
A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack.
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跌倒在多发性硬化症(MS)患者中很常见，会导致受伤、害怕跌倒和丧失独立性。尽管有针对性的干预(物理治疗)可以起到帮助作用，但患者报告不足，临床医生对此问题处理不足。患者产生的数据，结合临床数据，可以支持跌倒的预测，并导致及时的干预(包括转诊到专门的物理治疗)。为了具有可操作性，这些数据必须有效地提供给临床医生，并根据患者的具体情况定制护理。本研究旨在描述多发性硬化症跌倒跟踪系统(MS-Fit)设计和开发的迭代过程，确定这一闭环应用程序的临床和技术特征，旨在支持简化跌倒报告、及时跌倒评估以及全面和持续的跌倒预防工作。利益攸关方参与了以人为中心的双钻石设计过程，以确保技术特征与用户的需求保持一致。患者和临床医生的访谈旨在利用能力、机会、动机和行为(COM-B)框架获得有关能力阻滞剂和助推器的洞察，以促进随后与行为改变轮的映射。为了支持普适性，来自其他与跌倒相关的临床疾病(老年病学、骨科和帕金森病)的患者和专家也参与其中。在每一轮反馈的基础上重复设计，并在常规临床访问中测试最终的模型。30名患者和14名临床医生的样本提供了至少1轮反馈。为了支持跌倒报告，患者倾向于使用RedCap(研究电子数据捕获；范德比尔特大学)建立的简单的双周调查，以支持自带设备访问-带有可选的附加上下文(跌倒的严重性和位置)。为了支持跌倒的评估和预防，临床医生青睐具有几个关键可视化小部件的临床仪表板：按数据捕获时间、严重性和背景编码的纵向跌倒显示；旨在评估和预防跌倒的全面、多学科和循证的行动清单；以及患者社区本地的MS资源。收文篮消息提醒临床医生严重跌倒。该工具在可用性、亲和性、有用性和感知有效性方面得分很高(基于健康IT可用性评估模型评分)。据我们所知，这是第一款采用以人为中心的设计的Fall应用程序，旨在优先考虑行为改变，并在患者在家访问的同时，在护理点向临床医生提供可操作的数据。MS-FIT通过嵌入电子健康记录的窗口简化了向临床医生提供的数据，与5权方法保持一致。利用MS-FIT进行数据处理和算法，可将临床医生的工作量降至最低，同时提高护理质量。我们的创新无缝集成了真实世界患者生成的数据以及临床和社区层面的因素，增强了自我护理能力，并解决了跌倒对MS患者的影响。初步研究结果表明，跌倒的相关性更大，延伸到与跌倒及其后果相关的其他神经疾病。
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. 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. 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. 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). 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.
影响因子：
2.7
作者：
Brown, Ethan G.;Schleimer, Erica;Bledsoe, Ian O.;Rowles, William;Miller, Nicolette A.;Sanders, Stephan J.;Rankin, Katherine P.;Ostrem, Jill L.;Tanner, Caroline M.;Bove, Riley
通讯作者：
Bove, Riley
影响因子：
5.1
作者：
Adams SD;Kouzani AZ;Tye SJ;Bennet KE;Berk M
通讯作者：
Berk M