OBJECTIVE: There is strong evidence for prevalent physical inactivity among persons with multiple sclerosis (MS). By comparison, very little is known about natural occurring change in physical activity over time. Such inquiry is important for identifying the rate, patterns, and predictors of change for the design and delivery of behavioral interventions in this population. The present study conducted latent growth modeling (LGM) and latent class growth analysis (LCGA) for understanding the rate, patterns, and predictors of change in physical activity over a 24-month period among persons with MS. METHODS: On three occasions each separated by 12 months, persons (n = 269) with relapsing-remitting MS (RRMS) completed a battery of questionnaires that included assessment of physical activity behavior. Data were analyzed using Mplus 3.0. RESULTS: The LGM indicated that a linear model provided a good fit to the data (χ2 = 3.94, p = .05, CFI = .987, SRMR = .025), but the slope (Ms = 0.8) was nonsignificant (p > .05) and indicated no change in physical activity over time. LCGA identified a 2-class solution, and, based on the Lo-Mendell-Rubin likelihood ratio test, this model fit the data better than the 1-class solution. The 2-class solution consisted of low-active (∼80%) and high-active (∼20%) persons, but there was no change in physical activity over time per group. Sex and disability, but not age and disease duration, were predictors of being in the low active class. CONCLUSIONS: There was prevalent physical inactivity, but little interindividual and intraindividual change over 24 months in this cohort of persons with RRMS. Such results identify the importance of behavior interventions, perhaps early in the disease process wherein physical inactivity originates.
OBJECTIVE: There is strong evidence for prevalent physical inactivity among persons with multiple sclerosis (MS). By comparison, very little is known about natural occurring change in physical activity over time. Such inquiry is important for identifying the rate, patterns, and predictors of change for the design and delivery of behavioral interventions in this population. The present study conducted latent growth modeling (LGM) and latent class growth analysis (LCGA) for understanding the rate, patterns, and predictors of change in physical activity over a 24-month period among persons with MS. METHODS: On three occasions each separated by 12 months, persons (n = 269) with relapsing-remitting MS (RRMS) completed a battery of questionnaires that included assessment of physical activity behavior. Data were analyzed using Mplus 3.0. RESULTS: The LGM indicated that a linear model provided a good fit to the data (χ2 = 3.94, p = .05, CFI = .987, SRMR = .025), but the slope (Ms = 0.8) was nonsignificant (p > .05) and indicated no change in physical activity over time. LCGA identified a 2-class solution, and, based on the Lo-Mendell-Rubin likelihood ratio test, this model fit the data better than the 1-class solution. The 2-class solution consisted of low-active (∼80%) and high-active (∼20%) persons, but there was no change in physical activity over time per group. Sex and disability, but not age and disease duration, were predictors of being in the low active class. CONCLUSIONS: There was prevalent physical inactivity, but little interindividual and intraindividual change over 24 months in this cohort of persons with RRMS. Such results identify the importance of behavior interventions, perhaps early in the disease process wherein physical inactivity originates.
Authors: Valerie J Block; Riley Bove; Chao Zhao; Priya Garcha; Jennifer Graves; Andrew R Romeo; Ari J Green; Diane D Allen; Jill A Hollenbach; Jeffrey E Olgin; Gregory M Marcus; Mark J Pletcher; Bruce A C Cree; Jeffrey M Gelfand Journal: JAMA Netw Open Date: 2019-03-01
Authors: Elisabeth Anens; Lena Zetterberg; Charlotte Urell; Margareta Emtner; Karin Hellström Journal: BMC Neurol Date: 2017-12-01 Impact factor: 2.474