HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes

摘要 慢性下腰痛(CLBP)是一种复杂的多因素疾病，也是最常见的疼痛 世界范围内的肌肉骨骼疾病。根据患者的具体情况确定CLBP的最佳治疗方法 医学界重要且悬而未决的挑战。根据患者的行动量身定做干预措施 这些特征可能会改善临床结果。慢性阻塞性肺疾病的患者在他们的 症状、临床检查结果和常规医学成像结果。对于大多数患者来说，最理想的 治疗计划尚不清楚，因此对临床医生来说，开出合适的、费用低廉的处方是具有挑战性的。 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(腰椎结构和功能问题)和由此导致的活动受限 (执行活动困难)。评估身体损伤影响的一种常用方法是使用 患者报告的结果(PRO)，其中患者对他们感知的执行各种活动的能力进行评级 他们通常的环境。利弊是主观的，观察到的患者之间的差异 给专业人士打分，以及他们在临床观察时如何进行活动。互补是有好处的。 支持以客观表现为基础的身体机能测量。因此，总体假设是 父母基金的生物力学核心是将患者特定的脊柱生物力学包括在预测中 模型提高了我们对慢性阻塞性肺疾病患者的特征。为此，这种行政管理的目的是 补充内容是对生物力学核心的具体目标2进行扩展，即确定腰盆的特征 使用可穿戴(惯性)运动传感器在功能任务和日常活动中的运动学。具体地说，这 工作的目标是开发能够正确识别和表征的深度(机器)学习算法 在临床和现场评估中腰椎的运动。在临床评估期间， 参与者将被要求在佩戴惯性测量单元(IMU)的情况下执行功能任务。 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 作为日常生活活动和异常/痛苦的运动。将使用开发的深度学习算法 将在患者家中进行现场评估期间连续收集的腰椎运动数据标记为7天 测试期间。补充数据将与建议的标准数据分析方法进行比较。 用于整体研究，并包括在LB3P表型中。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的基础 与CLBP相关的真实世界功能障碍。

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.