Diagnosis of underlying physical fitness trait of adults’ selfreported physical function

Physical function;physical fitness;diagnostic modeling;DINA;self-reported questionnaire;CDM;PROMIS

Yang, Yan

关键词: Physical function;    physical fitness;    diagnostic modeling;    DINA;    self-reported questionnaire;    CDM;    PROMIS;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/101123/YANG-DISSERTATION-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Physical function decline is a common trajectory with aging. Physical function questionnaires have been widely used in occupational/physical therapy to assess adults’ physical function. However, most research only focuses on physical function itself. Rich patterns or profile information of the underlying physical fitness embedded in physical function questionnaires were overlooked. In addition, the associations between chronic diseases and physical fitness trait deficiencies are still less well known. Therefore, the main purpose of this study is to apply a diagnostic model that can help diagnose adults’ underlying physical fitness traits based on their self-reported physical function.This study invited three experts in the areas of physical function and physical fitness to develop a Q-matrix to be used in the diagnostic model development. Three diagnostic models were employed with the developed Q-matrix to an existing dataset (Patient-Reported Outcomes Measurement Information System [PROMIS], N = 942, 51.17% women). The most appropriate diagnostic model was selected and participants’ physical fitness trait statuses were estimated. Descriptive analyses and multiple logistic regressions were conducted to identify associations between physical fitness trait deficiencies and age/chronic diseases. A secondary data source of physical function questionnaires and performance-based fitness tests from a Chinese Parkinson’s disease group (N = 45, 60% women) was used to cross-validate the diagnostic model. The results showed that the prevalence of several physical fitness trait deficiencies gradually increased with aging, even after controlling for other demographic variables including sex, race, education level completed, marital status, family income, body weight status, and chronic diseases. The odds of all five physical fitness trait deficiencies were significantly higher in obese individuals than in normal weight individuals. This study found significant relationships between some chronic diseases and physical fitness trait deficiencies. The validation results confirmed that the diagnostic model selected by this study can be used to diagnose people with deficiencies in lower-body muscular strength, upper-body flexibility, aerobic endurance, balance, and fine motor skill. This study contributes to exercise intervention and program design by paying attention to stressing the importance of physical fitness diagnoses.