HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes

HEALing LB3P：分析生物力学、生物和行为表型

基本信息

批准号：
10406064
负责人：
Gwendolyn A Sowa
金额：
$ 17.62万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
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：
2021-09-23 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10406064
关键词：
Acceleration Activities of Daily Living Administrative Supplement Algorithms Back Biological Biomechanics Characteristics Chronic low back pain Classification Clinic Clinical Clinical Data Clinical assessments Complement Complex Data Data Analyses Data Set Development Ecological momentary assessment Environment Event Foundations Frequencies Goals Hip Joint Hip region structure Home Impairment Intervention Label Lateral Lifting Low Back Pain Measurement Medical Imaging Medicine Motion Movement Musculoskeletal Diseases Outcome Pain Parents Participant Patient Outcomes Assessments Patients Performance Phase Phenotype Physical Function Research Rotation Severities Structure Symptoms Testing Thigh structure Training Universities Validation Vertebral column Visual Walking Work behavioral phenotyping classification algorithm clinical examination cost effective data standards deep learning algorithm experience falls functional disability healing improved insight interest kinematics machine learning algorithm motion sensor multimodality optimal treatments pain patient parent grant performance based measurement predictive modeling supervised learning treatment planning

项目摘要

ABSTRACT Chronic Low Back Pain (CLBP) is a complex multi-factorial condition, as well as the most prevalent painful musculoskeletal disorder worldwide. Identifying the optimal treatment for CLBP on a patient-specific basis is an important and unresolved challenge in medicine. Tailoring interventions according to patient movement characteristics may improve clinical outcomes. Patients with CLBP are heterogenous in terms of their symptoms, clinical exam findings, and conventional medical imaging results. For most patients, the optimal treatment plan is unknown, therefore it is challenging for the clinician to prescribe an appropriate and cost- effective course of treatment. One important clinical characteristic that can be used for classification is severity of physical impairment (problems in lumbar spine structure and function) and resulting activity limitation (difficulty executing activities). A common approach to assess the impact of physical impairment is using patient-reported outcomes (PROs), wherein patients rate their perceived ability to perform various activities in their usual environment. PROs are subjective and discrepancies have been observed between how patients score PROs and how they perform activities when observed in the clinic. It is advantageous to complement PROs with objective performance-based measures of physical function. Therefore, the overall hypothesis of the Biomechanical Core of the parent grant is that including patient-specific spine biomechanics in predictive models improves our ability to characterize CLBP patients. To that end, the purpose of this administrative supplement is to expand upon Specific Aim 2 of the Biomechanical Core, which is to characterize lumbopelvic kinematics during functional tasks and daily activities using wearable (inertial) motion sensors. Specifically, this work will aim to develop deep (machine) learning algorithms that can correctly identify and characterize motions of the lumbar spine during both clinical and field assessments. During the clinical assessments, participants will be asked to perform functional tasks while wearing inertial measurement units (IMUs). Collected data will be used to develop and train machine learning algorithms to identify tasks of interest such as activities of daily living and aberrant/painful motions. The deep learning algorithms developed will be used to label lumbar motion data collected continuously during field assessment in patients' homes over a 7-day testing period. The supplemental data will be compared with the standard data analyses approaches proposed for the overall study and included with the LB3P phenotyping. Moreover, the deep learning algorithms will serve as the foundation for the development of ecological momentary interventions that are responsive to patient's real-world functional impairments related to CLBP.

抽象的慢性腰痛 (CLBP) 是一种复杂的多因素病症，也是最常见的疼痛全世界的肌肉骨骼疾病。根据患者具体情况确定 CLBP 的最佳治疗方法是医学上重要且尚未解决的挑战。根据患者的运动情况定制干预措施特征可能会改善临床结果。 CLBP 患者的病情各不相同症状、临床检查结果和常规医学影像结果。对于大多数患者来说，最佳的治疗计划未知，因此临床医生很难制定适当且成本低廉的方案。有效的疗程。可用于分类的一项重要临床特征是严重程度身体损伤（腰椎结构和功能问题）以及由此导致的活动限制（执行活动困难）。评估身体损伤影响的常用方法是使用患者报告的结果（PRO），其中患者评价他们在以下方面进行各种活动的感知能力他们平时的环境。 PRO 是主观的，并且观察到患者之间的差异对 PRO 进行评分以及他们在诊所观察时如何进行活动。有利于互补 PRO 具有基于客观表现的身体机能测量。因此，总体假设为母基金的生物力学核心是将患者特定的脊柱生物力学纳入预测中模型提高了我们描述 CLBP 患者特征的能力。为此，本行政补充内容是对生物力学核心的具体目标 2 进行扩展，即表征腰盆腔使用可穿戴（惯性）运动传感器进行功能任务和日常活动期间的运动学。具体来说，这工作目标是开发能够正确识别和表征的深度（机器）学习算法临床和现场评估期间腰椎的运动。在临床评估过程中，参与者将被要求在佩戴惯性测量单元（IMU）的同时执行功能任务。收集的数据将用于开发和训练机器学习算法，以识别感兴趣的任务，例如如日常生活活动和异常/疼痛的动作。所开发的深度学习算法将被使用对患者家中 7 天的现场评估期间连续收集的腰部运动数据进行标记测试期。补充数据将与提出的标准数据分析方法进行比较用于整体研究并包含在 LB3P 表型分析中。此外，深度学习算法将服务于作为开发响应患者的生态瞬时干预措施的基础与 CLBP 相关的现实世界功能障碍。