An Implementation and Biobehavioral Study of TMJMD

描述（由申请人提供）：由于慢性颞下颌关节和肌肉疾病（TMJMD）患者的巨大经济成本和传统的不良结局，治疗急性状态的患者变得非常重要，以防止这些更慢性的残疾问题。这是过去两个资助项目的目标。最初项目的结果分离出成功预测慢性发展的风险因素，准确率为91%。开发了一种统计算法，用于第二个项目，以筛选出“高风险”患者。然后将这些患者随机分配到早期干预组或非干预组。一年的随访评估记录了早期干预的治疗效果和成本效益。这些结果对有效的早期干预和显著的医疗保健成本节约这一普遍的疼痛和残疾问题具有重大意义。对于目前提出的项目，我们计划实施这一治疗方案，以评估其在更多的社区为基础的牙科实践的有效性。这是为了响应NIH的要求，实施循证治疗方法，在控制的临床环境中开发，在社区的不同实践的“真实的世界”。急性TMJMD患者将从两个社区诊所招募。根据我们的“风险”筛选算法，高风险患者将被随机分配到两组之一（n=225/组）：早期生物行为干预组或注意力控制组。据推测，在一年的随访中，相对于“高危”早期干预患者，注意力控制“高危”患者将显示出更多的慢性TMJMD问题。该项目的另一个目的是评估新技术的潜在效用，这些新技术将导致在检测TMJMD患者之间的个体差异方面更加精确。评估的指标包括咀嚼能力、皮质醇水平和其他生物心理社会结果。这代表了全面理解TMJMD大脑行为模式的下一步。此外，将使用结构方程建模方法。这种多层次，多系统的方法还没有被应用到更好地了解TMJMD的生物心理社会基础。该项目的这一部分的结果将极大地有助于刺激未来的研究，从而更好地了解TMJMD，以及更好地定制处方治疗方案。

DESCRIPTION (provided by applicant): With the great economic costs and traditionally poor outcomes among chronic temporomandibular joint and muscle disorder (TMJMD) patients, it has become important to treat patients in the acute state, in order to prevent these more chronic disability problems. This has been the goal of two past funded grant projects. Results of the initial project isolated risk factors that successfully predicted the development of chronicity, with a 91% accuracy rate. A statistical algorithm was developed which was used in the second project to screen out "high-risk" patients. These patients were then randomly assigned an early intervention or non-intervention group. One-year follow-up evaluations documented the treatment efficacy and cost effectiveness of early intervention. These results have major implications for effective early intervention and significant health-care cost savings for this prevalent pain and disability problem. For the present proposed project, we plan to implement this treatment program in order to evaluate its effectiveness in more community-based dental practices. This is in response to NIH's request for the implementation of evidence-based treatment approaches, developed in controlled clinical settings, to the "real world" of diverse practices in the community. Acute TMJMD patients will be recruited from two community clinics. Based upon our "risk" screening algorithm, high-risk patients will be randomly assigned to one of two groups (n=225/group): an early biobehavioral intervention or an attention-control group. It is hypothesized that the attention control "high-risk" patients will display more chronic TMJMD problems, relative to the "high-risk" early intervention patients, at one-year follow-up. Another aim of the project is to evaluate the potential utility of new technologies that will lead to even greater precision in detecting individual differences among TMJMD patients. Measures evaluated include chewing performance, cortisol levels, and other biopsychosocial outcomes. This represents the next step in comprehensively understanding brain-behavior patterns in TMJMD. Also, structural equation modeling methodology will be used. Such a multi-level, multi-systems approach has not been applied to better understand the biopsychosocial underpinnings of TMJMD. Results from this component of the project will greatly aid in stimulating future research leading to the better understanding of TMJMD, as well as better tailoring of prescribed treatment regimens.