The Role of Learning in Chronic Musculoskeletal Pain

目的和目的：尽管慢性疼痛很普遍，但我们仍然不知道为什么有些人会得慢性疼痛，而有些人不会。一个有影响力的观点是，大脑中通常允许我们适应伤害并从中恢复的过程被过度使用，这意味着疼痛被夸大了，而且持续的时间超出了必要的范围。这种“大脑学习不良”假说以各种形式存在，是慢性肌肉骨骼疼痛的流行模型。然而，目前证据有限，因为缺乏衡量和量化学习所需的足够工具。该项目通过实施一套新颖的实验工具来解决这个问题，该工具基于对学习如何在大脑中工作的基本科学理解。我们将使用这些工具在两个相辅相成的纵向研究中研究结果：i)最近出现的下腰痛和ii)纤维肌痛患者开始进行多学科治疗(MDT)计划。这些工具还将在APDP和更远的地方公开传播。在过去的一年里，我们与牛津和剑桥的有疼痛生活经验的人、物理治疗师、临床医生和工程师建立了独特的合作伙伴关系，以创建用于疼痛相关学习评估的核心工具包。这是一套人们在家玩的平板电脑游戏。人们还可以使用平板相机来记录特定的物理练习，我们用这些练习来重建和量化疼痛对运动的影响。通过在开始时使用患者主导的设计，这些工具是用户友好的、引人入胜的，并最大限度地提高了可访问性。这为在广泛的临床队列中检验适应不良学习假说提供了手段。要收集的数据：我们将采取3个整合的工作流程：i)开发和分发开放式数据分析平台，以配合实验工具包。ii)对140名新近发作的(急性)下腰痛患者进行队列数据收集，以预测12个月后的预后；ii)对80名接受NHS MDT计划的纤维肌痛患者进行数据收集。这两个临床队列的目的都是寻找学习指标预测临床结果的能力，并用神经成像验证发现。所有数据都将通过ADPD数据中心开放。潜在的好处：主要的科学成果将是确定和描述学习如何与慢性疼痛结果相关。这一点很重要，因为这种机制直接意味着治疗目标，这可以通过非药物干预(认知和身体康复)来实现。因此，这为APDP和合作伙伴之间的治疗创新提供了跳板。这些好处是由患者主导的工具设计促进的。实际上，它们易于实施，只需要最少的专业知识，并提供开放的数据分析管道和协作支持(如果需要)。这将建立一个总部位于英国的网络，将利用整个APDP的创新和专业知识。此外，这一基础设施产生的数据为生物信息学和相关应用(如临床分层和结果预测)开辟了新的机会。遗留和可持续。收集的数据的本质是基于一套通用工具，将成为一个随着时间推移而增长的数据库的种子。这是自我维持的，因为数据库的统计‘能力’随着更多数据的增加而增加，从而允许对单个数据集与广泛的其他条件进行比较分析。这些工具还提供了技术支柱，随着新的见解、工具和技术的出现，可以随着时间的推移而开发和改进。实际上，一旦这些系统到位，它们就会随着时间的推移而有机地增长。这很可能在新疗法中实现，最早可能在5年内实现，因为它有可能与现有的以技术为基础的治疗方法相结合。

Aims and objectives: Despite its prevalence, we still don't know why some people get chronic pain, and others do not. One influential idea is that the processes in the brain that normally allow us to adapt to an injury and recover from it, are used excessively, meaning that pain is exaggerated and prolonged beyond what is necessary. This 'maladaptive brain learning' hypothesis, in its various forms, is a popular model of chronic musculoskeletal pain. However, evidence is currently limited by the lack of sufficient tools required to measure and quantify learning. The project addresses this by implementing a novel set of experimental tools based on a basic science understanding of how learning works in the brain. We will use these tools to study outcomes in two complementary longitudinal studies: i) recent onset lower back pain and ii) fibromyalgia patients embarking on a multidisciplinary treatment (MDT) program. These tools will also be disseminated openly across the APDP and further afield.Over the last year, we have established a unique partnership with people with lived experience of pain, physiotherapists, clinicians and engineers at Oxford and Cambridge, to create a core toolkit for pain-related learning evaluation. This is a set of tablet games that people play at home. People can also use their tablet camera to record specific physio exercises which we use to reconstruct and quantify the impact of pain on movement. By using a patient-led design at the outset, the tools are user-friendly, engaging, and maximise accessibility. This provides the means to test the maladaptive learning hypothesis in a broad range of clinical cohorts.Data to be collected:We will pursue 3 integrated workstreams: i) Development and dissemination of an open data analysis platform to accompany the experimental toolkit.ii) Data collection in a cohort of 140 patients with recent onset (acute) lower back pain, presenting via NHS GP services, to predict outcomes at 12 monthsiii) Data collection in a cohort of 80 patients with fibromyalgia undergoing an NHS MDT program. Both clinical cohorts aim to look for the ability of learning metrics to predict clinical outcomes, and validate the findings with neuroimaging. All data will be made open via the ADPD datahub. Potential benefits: The main scientific outcome will be the identification and characterisation of how learning correlates with chronic pain outcomes. This is important because such mechanisms directly imply treatment targets, which can be realised using the non-pharmaceutical interventions (cognitive and physical rehabilitation). This therefore provides a springboard for treatment innovation across the APDP and partners. These benefits are facilitated by the patient-led design of the tools. Practically, they are easy-to-implement, require minimal expertise, and come with open data analysis pipelines and collaborative support if required. This builds a UK-based network that will capitalise on the innovation and expertise across the whole APDP. Furthermore, the data generated by this infrastructure opens up new opportunities for bioinformatics and related applications (e.g. in clinical stratification and outcome prediction). Legacy and sustainability.The very nature of data collected, being based on a common set of tools, will seed a database that will grow over time. This is self-sustaining because the statistical 'power' of the database increases as more data is added, permitting comparative analyses of individual datasets to a wide-range of other conditions. The tools also provide a technological backbone that can be developed and refined over time, as new insights, tools and techniques become available. In effect once the systems are in place, they grow organically over time. This is likely to be realised in new therapies, potentially as early as 5 years, given its potential to be integrated with existing technology-based treatment methodologies.