HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes

HEALing LB3P：分析生物力学、生物和行为表型

• 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
• 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

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 慢性下腰痛（CLBP）是一种复杂的多因素病，也是最普遍的疼痛 全球肌肉骨骼疾病。在特定于患者的基础上确定CLBP的最佳治疗方法是 医学中的重要且尚未解决的挑战。根据患者运动调整干预措施 特征可以改善临床结果。 CLBP患者在其上是异质的 症状，临床检查结果和常规医学成像结果。对于大多数患者，最佳 治疗计划尚不清楚，因此，临床医生开出适当且成本 - 有效的治疗过程。可用于分类的一个重要临床特征是严重性 身体障碍（腰椎结构和功能问题）以及由此产生的活动限制 （难以执行活动）。评估身体障碍影响的常见方法是使用 患者报告的结局（PRO），其中患者对他们的感知能力进行了各种活动的能力 他们通常的环境。优点是主观的，并且在患者之间观察到差异 在诊所观察到时，他们如何进行专业人士以及他们的执行活动。补充是有利的 具有基于客观绩效的身体功能措施的专业人士。因此，总体假设 父授予的生物力学核心是在预测中包括特定于患者的脊柱生物力学 模型提高了我们表征CLBP患者的能力。为此，此管理的目的 补充是扩展生物力学核心的特定目标2，即表征Lumbopelvic 使用可穿戴（惯性）运动传感器的功能任务和日常活动过程中的运动学。具体来说，这是 工作将旨在开发可以正确识别和表征的深（机器）学习算法 临床和现场评估期间腰椎的运动。在临床评估中， 将要求参与者在穿着惯性测量单元（IMU）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，以确定感兴趣的任务 作为日常生活和异常/痛苦动作的活动。开发的深度学习算法将被使用 在7天内，在现场评估期间连续收集的腰运动数据在现场评估期间连续收集 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LB3P表型。此外，深度学习算法将服务 作为对患者的反应的生态瞬时干预措施的基础 与CLBP有关的现实世界功能障碍。