Joops place menu for diabetics
According to national data for —, diabetes was twice as likely to affect AI men and women as compared to white men and women [ 2 , 3 ]. Higher rates of obesity also disproportionately affect both AI men compared to white men These disparities underscore the need for well-informed interventions designed for AIs. Improving diet and increasing physical exercise are widely endorsed approaches for reducing diabetes risk [ 4 , 5 , 6 , 7 , 8 ].
However, the evidence for this proposition is ambiguous since some studies have found a link between perceived risk and adoption of healthy behaviors [ 12 , 13 ], while others have not [ 14 , 15 ]. One reason for these conflicting results is explained by the Risk Perception Attitude RPA framework [ 16 ], which hypothesizes that individual self-efficacy beliefs modify the behavioral effects of risk perception.
Substantial research has found that self-efficacy modifies the effect of risk perception on health behaviors related to the prevention of HIV [ 17 ], HPV [ 18 ], cancer [ 19 , 20 , 21 ], diabetes [ 19 , 22 ], as well as behaviors related to nutrition [ 23 ], and smoking [ 24 ]. Several studies indicate that numeracy skills affect interpretations of risk [ 27 , 28 ]. For example, understanding numeric nutrition information, such as caloric intake and appropriate serving sizes, can be a burden for those with low numeracy skills, in turn, leading to higher BMI [ 29 ].
Numeracy was associated with greater knowledge and healthier diabetes related behaviors in a large sample of AIs with diabetes. Since very little is known about numeracy and its role in accurately perceiving diabetes risk, we propose exploratory analyses to examine its role in the RPA framework.
Although considerable research has addressed diabetes among AIs, few studies have explored AI perceptions of diabetes risk or the association of these perceptions with personal and demographic factors, including cultural identity [ 22 , 32 ]. The only study using the RPA framework with AIs proposed that the framework could be enhanced by attending to cultural characteristics that may influence both perceptions of risk, self-efficacy, and behavioral intention.
The RPA framework has been used to predict knowledge and information seeking, as well as behavioral outcomes [ 17 , 19 , 20 , 22 , 23 , 35 ]. The primary focus of this paper is to apply the framework in the context of prevention of diabetes among AIs. First, we examine whether we can predict diabetes knowledge, which includes awareness of its primary risk factors; diet; exercise; and genetics. Second, we examine whether it can predict active engagement in behaviors that prevent diabetes which include those related to diet and exercise.
Both the disposition to adopt a particular behavior and active engagement in that behavior can be measured with reference to five stages of change: precontemplation, contemplation, preparation, action, and maintenance. In the first stage, people do not even consider engaging in the behavior, while in the fifth stage, they regularly practice it [ 36 , 37 ].
The limited available evidence suggests that risk perception has a complex relationship with health behaviors for AIs [ 22 , 32 ]. This study is innovative as the first study to explore the impact of risk perception and self-efficacy on diabetes prevention related knowledge and behavior in AI communities. Furthermore, we advanced the RPA theory by examining the potential contributions of numeracy and cultural characteristics in predicting risk perception both of which may be important contributors to accurate predictions using the RPA framework.
We hypothesized that we could use self-efficacy and perception of risk to predict knowledge about diabetes risk factors and stage change for diabetes preventing behaviors of diet and exercise. We predict that together, higher perception of diabetes risk and higher self-efficacy, increase the stage of change a person is in and are associated with higher levels of knowledge.
Methods Data collection This cross-sectional study included a convenience sample of men and women all self-identified members of the same Northern Plains Tribe recruited at two separate community events on a Northern Plains reservation. The first event was a local craft fair in December ; the second was a powwow in September Many survey respondents recruited other tribal members to take the survey. After demonstrating eligibility being a local tribal member over 18 years of age without diagnosed diabetes and providing informed consent, participants completed the survey, which required about 15—25 min.
Measures Participants filled out a self-administered, item survey to measure the main constructs of the RPA framework: perceptions of diabetes risk and self-efficacy. The survey also included items related to the outcomes we intended to predict, diabetes knowledge and readiness to engage in diabetes preventing behaviors, along with selected covariates demographics, numeracy skills, cultural identity, and objective risk of diabetes.
Demographics Demographic data included age, sex, marital status, and educational attainment. Education was coded into less than high school, high school, or college or vocational degree. Marital status was coded as married versus not. The diet measure includes five barriers to healthful eating, such as having to rethink their entire way of nutrition, not having support from others and having to make a detailed plan.
The exercise measure includes five barriers to carrying out their intentions to exercise, such as feeling depressed, tense, worried, tired or busy. Each item has four possible responses, ranging from 1 for "very uncertain" to 4 for "very certain. Formation of four risk perception attitude groups We used perceived risk and self-efficacy scores to classify respondents into four RPA groups, separately for diet and for exercise.
We used the approach of Rimal et al. Sum scores for perceived risk and diet and exercise self-efficacy had very good internal consistency Cronbach alpha: 0. Perceived risk had low correlation with self-efficacy diet 0. Plots of sum-scores did not present 4 clusters, so rather than using clustering methods, we used principal components analyses to divide participants into RPA groups.
Again, separate analyses were undertaken for diet and exercise. Self-efficacy and perceived risk were combined in a dataset and dimensionality was assessed using a scree plot. The two leading eigenvalues were 4. The two leading principal components formed a biplot with quadrants representing each risk classification. Participants in the upper right quadrant were classified as responsive see fig.
Separate classifications were created based on self-efficacy for diet and exercise. Stages of change We used validated stages of change measures to assess the adoption of, or intentions to adopt, diet and exercise behaviors [ 36 , 37 ]. Stage of change for exercise was measured with a single item on regular exercise, defined as planned exercise intended to increase fitness and performed 3—5 times per week for 20—60 min per session.
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