MS Falls InsightTrack (MS FIT): A personal health library to reduce falls in patients with MS

项目总结 背景资料。50%的多发性硬化症(MS)患者会摔倒，日常生活和生活的参与能力会恶化 增加医疗成本。到目前为止，还没有既定的、可获得的工具来评估和降低跌倒风险。 MS Falls InsightTrack是一个实时的个人健康图书馆，它结合了患者与跌倒相关的临床指标 (来自电子健康记录，EHR)和来自商业可穿戴设备的患者生成数据(PGD)和 患者报告的结果(PRO)和社区层面的数据(来自加州大学旧金山分校健康地图集的社会人口学 结合来自国家MS协会的MS特定资源)。该工具将跟踪真实的跌落/接近跌落- 时间和报告需要干预的状态更改。它将提供定制的操作提示以支持秋季 通过在行为上知情的方法减少。它将在诊所和患者家中访问。 技术特征。该工具具有可访问性、可扩展性和可伸缩性。我们将使用现代技术和 行业标准(例如，后端：Python、FASK框架、PostgreSQL；前端：HTML、CSS、JavaScript和 D3.js)。该工具将从Epic通过SMART On FHIR启动，并将使用MyChart与患者交流。 参赛队伍和场地的资格。加州大学旧金山分校多发性硬化症中心是数字化领域领先的临床研究中心 太空。我们的副主管是研究的各个方面的专家(数字技术、以人为中心的设计、 实施科学、健康素养)和一个不同和经验丰富的利益攸关方咨询小组。 科学的计划。在目标1(设计)中，我们将使用以人为中心的设计方法，让20名患者参与 MS、临床医生和利益相关者在一系列焦点小组中，以确定关键数据、设备、可视化、 该工具的资源、工作流程和可访问性/数字鸿沟考虑因素，以及可能的关键干预措施 推广行为改变的COM-B模式，以降低跌倒风险。我们的主要成果将被感知 有效性、易用性和亲和性。在目标2(评估可行性)中，我们将部署MS Falls InsightTrack 在100名有跌倒风险的不同类型的多发性硬化症患者中。参赛者将穿着Fitbit Ultra。将使用该工具 由患者在家中和临床医生在临床会诊期间。我们将使用实施科学 接近。我们的主要成果将是研究保留、工具吸收和持续使用。我们将探索对 跌倒的风险。在目标3(测试概括性)中，我们将对患有其他疾病的患者进行重点小组研究，在 跌倒是常见的(骨科、帕金森氏病、老年病)，以了解其他数据和设计 促进通用性所需的功能。我们的主要成果将与目标1的成果相媲美。 创新和更广泛的意义。MS Falls InsightTrack是一个独特的、全面的、可访问的个人 健康图书馆，可以部署在更大的减少跌倒的疗效试验中。除了这个临床用例之外， 将PGD传递到护理系统并返回给患者的闭环方法，可解释和可操作的， 使用可扩展技术，代表着一项重大创新，可以按顺序扩展 可穿戴设备、条件和诊所，患者和临床研究人员可以在其中询问他们自己的PGD问题。

PROJECT SUMMARY Background. 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 patient's falls-relevant clinical indicators (from the electronic health record, EHR) with patient-generated data (PGD) from commercial wearables and patient-reported outcomes (PROs) and community-level data (sociodemographics from UCSF Health Atlas combined with MS-specific resources from the National MS Society). The tool will track falls/near-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 patient's home. Technological features. The tool will accessible, extensible and scalable. We will use modern technologies and industry standards (e.g back-end: Python, flask framework, PostgreSQL; front-end: HTML, CSS, JavaScript and d3.js). 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), we 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 accessibility/digital 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 likeability. In Aim 2 (evaluate feasibility), we will deploy MS Falls InsightTrack in 100 diverse adults with MS who are at risk for falls. Participants will wear a Fitbit Ultra. The tool will be used by patients in their homes and by clinicians during clinical encounters. We will use an implementation science approach. Our key outcomes will be study retention, tool uptake and sustained use. We will explore impact on fall risk. In Aim 3 (test generalizability) we will conduct focus groups with patients with other conditions where falls are common (Orthopedics, Parkinson's 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 InsightTrack 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.