Exploratory and confirmatory factor analyses identify three structural dimensions for measuring physical function in community-dwelling older adults

Abstract

Background: Physical function is a strong indicator of biological age and quality of life among older adults. However, the results from studies exploring the structural dimensions of physical function are inconsistent, and the measures assessed vary greatly, leading to a lack of comparability among them. This study aimed to construct a model to identify structural dimensions that are suitable and best assess physical function among community-dwelling adults 60-74 years of age in China.

Method: This study was conducted in 11 communities in Shanghai, China, from May to July 2021. A total of 381 adults 60-74 years of age were included in the study. Measured physical function data were used in factor analyses. Data collected from individuals were randomly assigned to either exploratory factor analysis (EFA) (n = 190) or confirmatory factor analysis (CFA) (n = 191). The statistical software used in the study was SPSS for EFA and AMOS for CFA. To test the properties of the structural dimension model of physical function, various fit indices, convergent validity, and discriminant validity were calculated.

Results: The EFA results derived seven indicators in three factors, with 58.548% of the total variance explained. The three factors were mobility function (three indicators), which explained 26.380% of the variance, handgrip strength and pulmonary function (two indicators), which explained 19.117% of the variance, and muscle strength (two indicators) which explained 13.050% of the variance. The CFA indicated that this model had an acceptable fit (χ²/df ratio, 2.102; GFI, 0.967; IFI, 0.960; CFI, 0.959; and RMSEA, 0.076), and the criteria for convergent validity and discriminability were also met by the model.

Conclusion: The constructed structural dimension model of physical function appeared to be a suitable and reliable tool to measure physical function in community-dwelling adults aged 60-74 years in China. The structural dimension indicators identified by this model may help sports medicine experts and healthcare providers offer more targeted interventions for older adults to reverse or slow the decline of physical function and to offer actionable targets for healthy aging in this population.

Keywords: Factor analyses; Handgrip strength; Mobility function; Muscle strength; Older adults; Physical function; Pulmonary function.

Figure 1. Schematic diagram of the relationship between physical function measures and metric factors in older adults.

Values next to arrows indicate standardized path coefficients. MF represents mobility function; HSPF, handgrip strength and pulmonary function; MS, muscle strength; MWS, maximum walking speed; UWS, usual walking speed; TUG, Timed Up and Go; HS, handgrip strength; CST, 30-s chair stand test; ACT, 30-s arm curl test, and e1–e7, exogenous variables 1–7.