Variables explaining health-related quality of life in community-dwelling older adults.

BACKGROUND AND PURPOSE

Although health-related quality of life (HRQL) has been linked to numerous factors in older adults, limited or conflicting studies have investigated variables explaining HRQL in healthy, community-dwelling older adults. The purpose of this study was to determine whether physical activity, gait speed, balance, strength, endurance, and flexibility were associated with HRQL in healthy, community-dwelling older adults.

METHODS

Participants of this cross-sectional, correlational research design study included residents of a senior living community, aged 60 years and older who were independent in at least unlimited household ambulation. These residents participated in tests of physical activity, gait speed, balance, strength, endurance, flexibility, and HRQL (Medical Outcomes Study Short-Form Health Survey, SF-36). The physical (PCS) and mental (MCS) component summary scores of the SF-36 were calculated.

RESULTS

Data were collected on 84 participants (mean [SD] age = 78.6 (5.9) years, 54.8% women). Significant correlations were found between the PCS and fast gait speed (FGS) (r = 0.43; p < .001), the Fullerton Advanced Balance Scale (r = 0.44; p < .001), 8-ft up-and-go (r = -0.34; p = .002), and chair stand (r = 0.37; P = .001). Only body mass index (BMI) (r = 0.30; p = .007) was significantly correlated with MCS. Forward stepwise linear regression analyses were conducted, controlling for age, sex, and BMI, to identify factors associated with the PCS and MCS. In the model using PCS as the dependent variable, FGS accounted for 26% of the variance (R2 change) in PCS over and above age, sex, and BMI (R2 change = 0.03); for the full model, F = 5.37, p = .001. In the regression analysis using MCS as the dependent variable, only the 8-ft up-and-go was retained (R2 change = 0.06) over and above age, sex, and BMI (R2 change = 0.16); for the full model, F = 3.71, p = .01.

DISCUSSION

Fast gait speed, balance, and lower body strength were associated with the PCS of the SF-36; however, FGS was the only variable that uniquely contributed to the variance in the PCS. Body mass index was associated with the MCS; however, only balance uniquely contributed to the variance in the MCS. Physical activity was not associated with the PCS or MCS.

CONCLUSIONS

The results of this study support the assessment of FGS in community-dwelling older adults to gain insight into physical health status. Interventions directed toward FGS, balance, and BMI may contribute to optimum HRQL in this population.