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

MS Falls InsightTrack (MS FIT)：减少多发性硬化症患者跌倒的个人健康图书馆

基本信息

批准号：
10669143
负责人：
Riley Marie Browne Bove
金额：
$ 34.32万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10669143
关键词：
Adoption Adult Atlases Back Behavior Behavioral Behavioral Model Chronic Clinic Clinical Clinical Investigator Clinical Research Clinical Trials Clinical effectiveness Communication Communities Data Development Devices Diamond Effectiveness Electronic Health Record Ensure Fall prevention Fast Healthcare Interoperability Resources Feedback Focus Groups Geriatrics Grant Health Health Care Costs Health system Home Incidence Individual Industry Industry Standard Infrastructure Institution Intervention Interview Libraries Life Measures Modernization Monitor Morbidity - disease rate Multiple Sclerosis Nature Neighborhoods Orthopedics Outcome Parkinson Disease Participant Patient Outcomes Assessments Patient Recruitments Patient Self-Report Patients Phase Process Pythons Qualifying Recommendation Reporting Resources Risk Self Management Series Severities Societies Socioeconomic Status Technology Testing Time Validation Visualization Weather Work care systems clinical decision-making clinical encounter clinical practice clinical risk clinically relevant cohort compliance behavior design digital digital health digital technology effectiveness evaluation effectiveness measure efficacy trial experience fall risk falls fitbit flasks health determinants health literacy high risk human centered design implementation science innovation insight multidisciplinary multimodal data multiple sclerosis patient point of care prevent primary outcome prospective prototype provider behavior shared decision making social health determinants sociodemographics tool uptake usability walkability wearable device wearable sensor technology

项目摘要

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.

项目摘要背景福尔斯发生在>50%的多发性硬化症（MS）患者中，使日常生活参与度恶化，增加医疗费用。到目前为止，还没有建立的，可访问的工具来评估和减少跌倒风险。 MS福尔斯InsightTrack是一个实时的个人健康库，它结合了患者的跌倒相关临床指标 (from电子健康记录（EHR）与来自商业可穿戴设备的患者生成数据（PGD），患者报告的结局（PRO）和社区水平数据（来自UCSF健康地图集的社会人口统计数据与来自国家MS协会的MS特定资源结合）。该工具将跟踪真实的中的福尔斯/接近福尔斯- 时间和报告需要干预的状态变化。它将提供定制的行动提示，以支持下跌减少通过行为知情的方法。它将在诊所和患者家中使用。技术特点。该工具将是可访问的，可扩展的和可扩展的。我们将使用现代技术，行业标准（例如后端：Python、flask framework、PostgreSQL;前端：HTML、CSS、JavaScript和 d3.js）。该工具将通过FHIR上的SMART从Epic启动，并将使用MyChart与患者进行沟通。团队和设置的资格。UCSF MS中心是一个领先的数字临床研究中心，空间我们的副领导是研究各个方面的专家（数字技术，以人为本的设计，实施科学、卫生知识普及）与一个多样化和经验丰富的利益攸关方咨询小组。科学计划。在目标1（设计）中，我们将使用以人为本的设计方法，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，以确定关键数据，设备，可视化，该工具的资源、工作流程和可获得性/数字鸿沟考虑因素，以及可能的关键干预措施推广COM-B行为改变模型，以降低跌倒风险。我们的主要成果将被视为有效性、易用性和可爱性。在目标2（评估可行性）中，我们将部署MS福尔斯InsightTrack 在100名不同的MS成人中，他们有福尔斯的风险。参与者将佩戴Fitbit Ultra。该工具将用于由病人在家中和临床医生在临床上遇到。我们将使用一种实现科学 approach.我们的主要成果将是研究保留，工具吸收和持续使用。我们将探讨影响坠落风险在目标3（测试可推广性）中，我们将对患有其他疾病的患者进行焦点小组，福尔斯是常见的（骨科、帕金森病、老年病），以了解其他数据和设计促进普遍性所需的功能。我们的主要成果将与目标1中的成果平行。创新和更广泛的意义。MS福尔斯洞察跟踪是一个独特的，全面的，可访问的个人健康库，可以部署在更大的有效性试验，以减少福尔斯。除了这个临床用例之外，将PGD输送到护理系统并返回给患者的闭环方法，可解释且可操作，使用可扩展的技术，代表了一项重大创新，可以依次扩大可穿戴设备、条件和诊所，患者和临床研究者可以提出自己的PGD问题。