PURPOSE: The purpose of this study was to identify from the International Classification of Functioning model, factors influencing quality of life in elderly persons and to describe the concrete pathway of influence and the power of each variable. METHODS: The sample included 334 elders who lived in 5 districts of D Metropolitan City. A structured questionnaire was used and the collected data were analyzed for fitness, using the AMOS 18.0 program. RESULTS: This model was concise and extensive in predicting the quality of life of elders. The research verified the factors influencing quality of life for elders as direct factors such as activity of daily living (ADL) (β=.13, t=2.47), leisure activity (β=.55, t=5.04), social disengagement (β=-.25, t=-2.25), and depression (β=-.62, t=-10.86). Indirect factors including economic status (γ=.17, p=.009), type of residence (γ=.19, p=.004), ADL (γ=.12, p=.027) were important factors in predicting quality of life for elders. These variables explained 75.6% of variance in the prediction model. CONCLUSION: The findings indicate a need for the nursing scientific community to develop intervention programs considering these variables to improve the quality of life for elders.
PURPOSE: The purpose of this study was to identify from the International Classification of Functioning model, factors influencing quality of life in elderly persons and to describe the concrete pathway of influence and the power of each variable. METHODS: The sample included 334 elders who lived in 5 districts of D Metropolitan City. A structured questionnaire was used and the collected data were analyzed for fitness, using the AMOS 18.0 program. RESULTS: This model was concise and extensive in predicting the quality of life of elders. The research verified the factors influencing quality of life for elders as direct factors such as activity of daily living (ADL) (β=.13, t=2.47), leisure activity (β=.55, t=5.04), social disengagement (β=-.25, t=-2.25), and depression (β=-.62, t=-10.86). Indirect factors including economic status (γ=.17, p=.009), type of residence (γ=.19, p=.004), ADL (γ=.12, p=.027) were important factors in predicting quality of life for elders. These variables explained 75.6% of variance in the prediction model. CONCLUSION: The findings indicate a need for the nursing scientific community to develop intervention programs considering these variables to improve the quality of life for elders.