Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Duke Clinical Research Institute, Duke University, Durham, NC, USA.
Clin Orthop Relat Res. 2022 Feb 1;480(2):313-324. doi: 10.1097/CORR.0000000000002082.
Negative mood is an important risk factor for poor clinical outcomes among individuals with musculoskeletal pain. Screening for negative mood can aid in identifying those who may need additional psychological interventions. Limitations of current negative mood screening tools include (1) high response burden, (2) a focus on single dimensions of negative mood, (3) poor precision for identifying individuals with low or high negative mood levels, and/or (4) design not specific for use in populations with orthopaedic conditions and musculoskeletal pain.
QUESTIONS/PURPOSES: (1) Can item response theory methods be used to construct screening tools for negative mood (such as depression, anxiety, and anger) in patients undergoing physical therapy for orthopaedic conditions? (2) Do these tools demonstrate reliability and construct validity when used in a clinical setting?
This was a cross-sectional study involving outpatients having physical therapy in tertiary-care settings. A total of 431 outpatients with neck (n = 93), shoulder (n = 108), low back (n = 119), or knee (n = 111) conditions were enrolled between December 2014 and December 2015, with 24% (103 of 431) seeking care after orthopaedic surgery. Participants completed three validated psychological questionnaires measuring negative mood, resulting in 39 candidate items for item response theory analysis. Factor analysis was used to identify the dimensions (factors) assessed by the candidate items and select items that loaded on the main factor of interest (negative mood), establishing a unidimensional item set. Unidimensionality of an item set suggests they are assessing one main factor or trait, allowing unbiased score estimates. The identified items were assessed for their fit to the graded item response theory model. This model allows for items to vary by the level of difficulty they represent and by their ability to discriminate between patients at different levels of the trait being assessed, in this case, negative mood. Finally, a hierarchical bifactor model where multiple subfactors are allowed to load on an overall factor was used to confirm that the items identified as representing a unidimensional item set explained the large majority of variance of the overall factor, providing additional support for essential unidimensionality. Using the final item bank, we constructed a computer adaptive test administration mode, and reduced item sets were selected to create short forms including items with the highest information (reliability) at targeted score levels of the trait being measured, while also considering clinical content.
We identified a 12-item bank for assessment of negative mood; eight-item and four-item short-form versions were developed to reduce administrative burden. Computer adaptive test administration used a mean ± SD of 8 ± 1 items. The item bank's reliability (0 = no reliability; 1 = perfect reliability) was 0.89 for the computer adaptive test administration, 0.86 for the eight-item short form, and 0.71 for the four-item short form. Reliability values equal to or greater than 0.7 are considered acceptable for group level measures. Construct validity sufficient for clinical practice was supported by more severe negative mood scores among individuals with a previous episode of pain in the involved anatomical region, pain and activity limitations during the past 3 months, a work-related injury, education less than a college degree, and income less than or equal to USD 50,000.
These newly derived tools include short-form and computer adaptive test options for reliable and valid negative mood assessment in outpatient orthopaedic populations. Future research should determine the responsiveness of these measures to change and establish score thresholds for clinical decision-making.
Orthopaedic providers can use these tools to inform prognosis, establish clinical benchmarks, and identify patients who may benefit from psychological and/or behavioral treatments.
负面情绪是影响肌肉骨骼疼痛患者临床结局的一个重要风险因素。对负面情绪进行筛查可以帮助识别那些可能需要额外心理干预的患者。目前负面情绪筛查工具的局限性包括:(1)高反应负担;(2)仅关注负面情绪的单一维度;(3)识别低或高负面情绪水平个体的精度较差;以及/或(4)设计不适用于骨科疾病和肌肉骨骼疼痛人群。
问题/目的:(1)项目反应理论方法能否用于构建接受骨科治疗的患者的负面情绪(如抑郁、焦虑和愤怒)筛查工具?(2)这些工具在临床环境中使用时是否具有可靠性和结构有效性?
这是一项横断面研究,涉及在三级护理机构接受物理治疗的门诊患者。共有 431 名患有颈部(n = 93)、肩部(n = 108)、下背部(n = 119)或膝关节(n = 111)疾病的门诊患者在 2014 年 12 月至 2015 年 12 月期间入组,其中 24%(103 名/431 名)在骨科手术后就诊。参与者完成了三个经过验证的测量负面情绪的心理问卷,产生了 39 个候选项目进行项目反应理论分析。因子分析用于确定候选项目评估的维度(因素),并选择加载到主要感兴趣因素(负面情绪)上的项目,建立一个单一维度的项目集。项目集的单一维度表明它们正在评估一个主要因素或特征,允许进行无偏的分数估计。确定的项目根据其代表的难度水平和区分处于评估特征不同水平的患者的能力进行拟合度评估,在这种情况下,是区分负面情绪。最后,使用允许多个子因素加载到总体因素上的分层双因素模型,确认被确定为代表单一维度项目集的项目解释了总体因素的大部分方差,为基本的单一维度提供了额外的支持。使用最终的项目库,我们构建了一个计算机自适应测试管理模式,并选择了减少的项目集来创建短表单,包括在测量的特征的目标分数水平上具有最高信息(可靠性)的项目,同时还考虑了临床内容。
我们确定了一个用于评估负面情绪的 12 项库;开发了八项和四项简短形式,以减少行政负担。计算机自适应测试管理使用 8 ± 1 个项目的平均值 ± 标准差。项目库的可靠性(0 = 无可靠性;1 = 完美可靠性)在计算机自适应测试管理中为 0.89,在八项简短形式中为 0.86,在四项简短形式中为 0.71。等于或大于 0.7 的可靠性值被认为是组水平测量的可接受值。有足够临床实践结构有效性的支持是基于在涉及的解剖区域有过疼痛发作的个体、过去 3 个月的疼痛和活动限制、与工作相关的伤害、受教育程度低于大学学位以及收入低于或等于 50000 美元的个体中更严重的负面情绪评分。
这些新开发的工具包括短形式和计算机自适应测试选项,可用于可靠和有效地评估门诊骨科人群的负面情绪。未来的研究应确定这些措施对变化的反应能力,并为临床决策制定分数阈值。
骨科医生可以使用这些工具来告知预后、建立临床基准,并识别可能受益于心理和/或行为治疗的患者。