Zahra Mosallanezhad1, Gholam Reza Sotoudeh2, Göran Jutengren3, Mahyar Salavati4, Karin Harms-Ringdahl5, Lena Nilsson Wikmar6, Kerstin Frändin7. 1. Department of Physiotherapy, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran; Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Iranian Research Centre on Aging, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. Electronic address: zmosallanezhad@yahoo.com. 2. Department of Health Sciences, Mid Sweden University, Sundsvall, Sweden; Sina Trauma and Surgery Research Center (STSRC), Sina General Hospital, Tehran University of Medical Sciences, Tehran, Iran. 3. Department of Work Life and Social Welfare, University of Borås, Sweden. 4. Department of Physiotherapy, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. 5. Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. 6. Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Academic Primary Healthcare Centre, Stockholm County Council, Stockholm, Sweden. 7. Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Abstract
BACKGROUND AND AIM: Health status is an independent predictor of mortality, morbidity and functioning in older people. The present study was designed to evaluate the link between socioeconomic status (SES), physical activity (PA), independence (I) and the health status (HS) of older people in Iran, using structural equation modelling. METHODS: Using computerized randomly selection, a representative sample of 851 75-year-olds living in Tehran (2007-2008), Iran, was included. Participants answered questions regarding indicators of HS, SES and also PA and I through interviews. Both measurement and conceptual models of our hypotheses were tested using Mplus 5. Maximum-likelihood estimation with robust standard errors (MLR estimator), chi-square tests, the goodness of fit index (and degrees of freedom), as well as the Comparative Fit Index (CFI), and the Root Mean Square Error of Approximation (RSMEA) were used to evaluate the model fit. RESULTS: The measurement model yielded a reasonable fit to the data, χ2=110.93, df=38; CFI=0.97; RMSEA=0.047, with 90% C.I.=0.037-0.058. The model fit for the conceptual model was acceptable; χ2=271.64, df=39; CFI=0.91; RMSEA=0.084, with 90% C.I.=0.074-0.093. SES itself was not a direct predictor of HS (β=0.13, p=0.059) but it was a predictor of HS either through affecting PA (β=0.31, p<0.001) or I (β=0.57, p<0.001). CONCLUSION: Socioeconomic status appeared to influence health status, not directly but through mediating some behavioral and self-confidence aspects including physical activity and independence in ADL.
BACKGROUND AND AIM: Health status is an independent predictor of mortality, morbidity and functioning in older people. The present study was designed to evaluate the link between socioeconomic status (SES), physical activity (PA), independence (I) and the health status (HS) of older people in Iran, using structural equation modelling. METHODS: Using computerized randomly selection, a representative sample of 851 75-year-olds living in Tehran (2007-2008), Iran, was included. Participants answered questions regarding indicators of HS, SES and also PA and I through interviews. Both measurement and conceptual models of our hypotheses were tested using Mplus 5. Maximum-likelihood estimation with robust standard errors (MLR estimator), chi-square tests, the goodness of fit index (and degrees of freedom), as well as the Comparative Fit Index (CFI), and the Root Mean Square Error of Approximation (RSMEA) were used to evaluate the model fit. RESULTS: The measurement model yielded a reasonable fit to the data, χ2=110.93, df=38; CFI=0.97; RMSEA=0.047, with 90% C.I.=0.037-0.058. The model fit for the conceptual model was acceptable; χ2=271.64, df=39; CFI=0.91; RMSEA=0.084, with 90% C.I.=0.074-0.093. SES itself was not a direct predictor of HS (β=0.13, p=0.059) but it was a predictor of HS either through affecting PA (β=0.31, p<0.001) or I (β=0.57, p<0.001). CONCLUSION: Socioeconomic status appeared to influence health status, not directly but through mediating some behavioral and self-confidence aspects including physical activity and independence in ADL.