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Study Design and Results
Overview Phase 1, Factorial survey of older adults health information seeking
Purpose (Quantitative Results)
An interdisciplinary team investigated the contextual factors that explain the health information seeking (HIS) decisions of older adults in northeastern and Appalachian Ohio. The specific research questions in phase 1 of the this 2 part, 3 year study were how do the: a) knowledge, skill and access variables and b) demographic characteristics of the subject affect the selection of health information, attending a meeting about health education and consulting with family.
MethodsA factorial survey of older adults was conducted and the subjects in phase1 were 60 years of age and lived in one of 8 counties in northeastern Ohio. Half the subjects were solicited from a library randomly selected in each county and the other half of the subjects were selected from a senior nutrition site that was randomly selected in each county.
The factorial survey design combines the use of vignettes with sample survey procedures, allowing for a large number of realistic vignettes that are mathematically determined based on independent variables that have multiple levels. This complexity is in contrast to simple 2x2 or 2x3 designs. In this study each respondent received a set of unique vignettes to judge in order to causally test the factors that significantly impact the respondents’ decisions. Each vignette contained 10 independent variables (7x3x7x3x2x3x4x2x2x2 factors) related to skills, access and knowledge. Each subject judged the dependent variables (DV) noted in the purpose. The central analysis for model building was use of the vignette variables as predictors of the dependent variables. Additionally, subject characteristics were used as predictors.
AnalysisThe unit of analysis for the regression is the vignette and the number of vignettes for analysis in this study were 900 (450 subjects X 2 vignettes each). Regression analysis was used to provide the effect sizes, statistical significances, and variances explained for each of the 3 models that explored the effect of the independent variables on the 3 dependent variables. Dummy coding was used for analysis of all of the IVs on the DVs.
SampleAverage age 72 (SD 8)
68% female/44% married
51% HS education; 11.5% less than HS; 27.1% some college/degree
45% don’t use a computer regularly or at all; 50% don’t use internet regularly or at all
25% don’t use library regularly or at all
Health Problems: High blood pressure 55%; Diabetes 18%; Cancer 13%; Stroke 7% and 15% reported no health problems
Stated they exercise regularly 62% and 74% ate healthy 74%
Results: All 3 of the regression models that examined the access, knowledge, and skills variables for their impact on the 3 dependent variables were significant. Model 1: R=.357; R2=.127; R2adj=.102; F (25, 849) =4.956, p<0.000. Model 2: R=.297; R2=.088; R2adj=.062; F (25, 850) =3.298, p<0.000. Model 3: R=.221; R2=.049; R2adj=.021; F (25, 849) =1.738, p<0.014. The significant variables varied slightly across all models, but health issue was a significant predictor across all 3 models.
In Model 1 the 10 variables and their levels were dummy coded for the analysis and of the 25 variables entered into the regression 8 were significant in 2 categories. all of the health issues (healthy eating, exercise, diabetes, high blood pressure, cancer and stroke) were significant in predicting use when compared to the base category of smoking. Both pamphlets and video for home use were significant predictors of likelihood of use when compared to the base category of computer use at the location. Finally, church was significantly less likely to impact the use of information when compared to the base category of a school.
In Model 2 all of the health issues (healthy eating, exercise, diabetes, high blood pressure, cancer and stroke) were significant in predicting the likelihood to attend a meeting when compared to the base health category of smoking cessation.
A meeting that was 30 minutes in length was significantly more likely to result in judgment of likelihood to be attended than a meeting that lasted 60 minutes. A meeting led by a registered nurse was significantly less likely to be judged likely to attend as compared to the base category of doctor. While the coefficients of other professionals (librarian and pharmacist) were not significant in predicting attendance as compared to a doctor, these coefficients were negative as compared to physicians, which was the base category for comparison on this IV. Last a meeting that was 10 miles from home was determined to be significantly less likely to be attended than one that was within walking distance.
In Model 3 all of the health issues (healthy eating, exercise, high blood pressure, cancer and stroke) except diabetes were significant in predicting the likelihood to attend a meeting when compared to the base health category of smoking cessation.
Next an analysis of the impact of demographic characteristics showed that age was the only significant predictor among these characteristics. Age was significantly and inversely related to each DV; as age increased the likelihood of reading pamphlets, watching videos and using the computer decreased. As age increased likelihood of attending a meeting or talking to family and friends before a meeting decreased.
Last one significant interaction effect was found. Respondents were more significantly likely to go to a meeting or talk with family and friends at a Senior Center than at the Library. Little difference between sites was observed for acquiring information.