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.

摘要 慢性腰痛（CLBP）是一种复杂的多因素疾病，也是最常见的疼痛 肌肉骨骼疾病在患者特定的基础上确定CLBP的最佳治疗方法是一个 医学中重要且尚未解决的挑战。根据患者运动量身定制干预措施 特征可以改善临床结果。CLBP患者在其 症状、临床检查结果和常规医学成像结果。对于大多数患者来说，最佳 治疗计划是未知的，因此，临床医生开出适当且成本低廉的处方是一项挑战， 有效疗程。可用于分类的一个重要临床特征是严重程度 身体损伤（腰椎结构和功能的问题）和导致的活动限制 （执行活动困难）。评估身体损伤影响的常用方法是使用 患者报告结局（PRO），其中患者对他们在以下方面进行各种活动的感知能力进行评级： 通常的环境。PRO是主观的，在患者如何 评分PRO以及在临床观察时他们如何执行活动。有利于补充 具有客观的基于性能的身体功能测量的PRO。因此，总体假设 生物力学核心的母基金是，包括患者特异性脊柱生物力学的预测 模型提高了我们描述CLBP患者的能力。为此，本行政 补充是对生物力学核心的特定目标2进行扩展，该目标2旨在描述腰骨盆 使用可穿戴（惯性）运动传感器在功能任务和日常活动期间进行运动学分析。具体来说， 工作的目标是开发深度（机器）学习算法，可以正确识别和表征 在临床和现场评估期间腰椎的运动。在临床评估期间， 参与者将被要求在佩戴惯性测量单元（伊穆斯）的同时执行功能性任务。 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， 日常生活活动和异常/疼痛运动。开发的深度学习算法将用于 标记在患者家中进行7天的现场评估期间连续收集的腰椎运动数据 测试期。补充数据将与提出的标准数据分析方法进行比较 用于整个研究，并包括在LB 3 P表型中。此外，深度学习算法将 作为发展生态瞬时干预的基础， 与CLBP相关的真实世界功能障碍。