基于因子分析构建的中段跟腱病健康数据驱动模型。
Data driven model of midportion achilles tendinopathy health created with factor analysis.
机构信息
Delaware Tendon Research Group, Department of Physical Therapy, University of Delaware, 540 S College Ave, Newark, Delaware, 19713, USA.
School of Health Sciences, University of Iceland, Reykjavík, Iceland.
出版信息
BMC Musculoskelet Disord. 2022 Aug 3;23(1):744. doi: 10.1186/s12891-022-05702-1.
BACKGROUND
Achilles tendinopathy is a complex injury and the clinical presentation spans multiple different domains: physical and psychological symptoms, lower extremity function and tendon structure. A conceptual model of Achilles tendon health comprising these domains has been proposed in the literature. The aim of the study was to fit a model of Achilles tendinopathy using factor analysis and compare that to the conceptual model. An inclusive approach using a wide range of variables spanning multiple potential domains were included.
METHODS
Participants (N = 99) with midportion Achilles tendinopathy were assessed with variables representing symptoms, physical function, tendon structure, metabolic syndrome, and psychologic symptoms. A Kaiser-Mayer-Olkin index was used to determine suitable variables for a subsequent exploratory factor analysis.
RESULTS
A model emerged with an acceptable fit to the data (standardized root mean square of residuals = 0.078). Five uncorrelated factors emerged from the model and were labelled as biopsychosocial, lower extremity function, body size, load tolerance, and tendon structure. The total explained variance was 0.51 with the five factors explaining 0.14, 0.12, 0.10, 0.08, and 0.07 respectively. The results differed from the conceptual model as the factors of psychological variables and metabolic variables did not emerge from the analysis.
CONCLUSION
A data driven model of Achilles tendon health supports assessment of the clinical presentation over multiple domains. As the factors are uncorrelated, the results of assessment of, for example, tendon structure should not be expected to be associated with lower extremity function or biopsychosocial limitations. The results suggest that the Patient Reported Outcomes Measurement Information System, counter-movement jump height, body mass index, pain with hopping, and the tendon cross-sectional area can evaluate the five factors, respectively.
TRIAL REGISTRATION
Registered on clinicaltrials.gov (Medicine NL of.
CLINICALTRIALS
gov [Internet], 2018), ID number NCT03523325.
背景
跟腱病是一种复杂的损伤，其临床表现跨越多个不同领域：身体和心理症状、下肢功能和跟腱结构。文献中提出了一个包含这些领域的跟腱健康概念模型。本研究的目的是使用因子分析建立跟腱病模型，并将其与概念模型进行比较。采用广泛的变量，涵盖多个潜在领域的包容性方法。
方法
对中段跟腱病患者（N=99）进行评估，评估变量包括症状、身体功能、跟腱结构、代谢综合征和心理症状。使用 Kaiser-Mayer-Olkin 指数确定适合后续探索性因子分析的变量。
结果
模型与数据拟合良好（标准化残差均方根=0.078）。模型中出现了五个不相关的因子，分别命名为生物心理社会、下肢功能、体型、负荷耐受和跟腱结构。总解释方差为 0.51，五个因子分别解释 0.14、0.12、0.10、0.08 和 0.07。结果与概念模型不同，因为心理变量和代谢变量的因子没有从分析中出现。
结论
基于数据的跟腱健康模型支持对多个领域的临床表现进行评估。由于这些因子不相关，因此不应期望评估例如跟腱结构的结果与下肢功能或生物心理社会限制相关。结果表明，患者报告的结果测量信息系统、反向跳跃高度、体重指数、跳跃疼痛和跟腱横截面积可以分别评估这五个因子。
试验注册
在 clinicaltrials.gov（荷兰医学研究组织）注册（[互联网]，2018 年），注册号为 NCT03523325。
临床试验
[互联网]，2018 年），注册号为 NCT03523325。
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