Judgment Formation towards Health Risk Behaviors Concerning Obesity: An Integration Information Theory Approach

A sample of 80 secondary students was required to take an information integration theory study to explore judgment formation toward health risk behavior regarding obesity. Here, twelve social scenarios containing a simulated actor were implemented (vignettes) having in mind a three factor experimental factor design (diet, weight and physical activity). Subjects had to read each vignette and provide an answer by marking ten points anchored scale to provide judgment on actors’ possible health risk outcome. Results showed that study participants valuated diet as the most relevant factor, followed by the description of weight and finally followed by the factor of physical activity. They impose systematic thinking to integrate different sources of information provided by factor manipulation in the vignettes by using a cognitive summative rule. Implications of this study result to clinical intervention in obesity as well as for theoretical considerations of cognitive models of health risk behavior are discussed in the present article.


Introduction
Individuals impose consciously or unconsciously systematic thinking on every day health behavior [1] [2].These health cognitions (thoughts and feelings) de-termine our judgment formation toward risk health behaviors and have an enormous impact on our wellbeing [3].
Firmly grounded in the cognitive tradition, theories of health like Ajzen's Theory of Planned Behavior (TPB; [4]), Health Belief Model (HBM; [5]), and Protection Motivation Theory (PMT; [6]) seek to explain self-care and predict risk health behavior based on health cognitions [7] [8] [9].However, these models are more oriented to determine behavior prediction rather than to specify cognitive processing of health cognitions.As suggested by Anderson ([10], p. 128): "These two directions, namely, prediction of behavioral outcomes and understanding of cognitive processes, impose generally different constraints on strategy and tactics of investigation.Hence they usually interfere with each other."In turn, this lack of cognitive specification limits prediction power of adherence to healthy behavior.For instance, Hernandez et al. [2] have shown that cognitive specification of systematic thinking underlying judgment formation toward health risk behaviors in people with diabetes allows specific intervention on medication schedules and dietary behavior.Specifically, these authors found that diabetics tend to use a weighted summative cognitive rule to integrate sources of information on medication schedules, stress factors and exercise.
Here, metamemory aspects related to schedule medication obtained higher valuation followed by stress and exercising.Thus understanding (diagnosis) rather than prediction is preferred to empower intervention.They used an information Integration Theory (IIT) approach to identify this cognitive processing style in diabetics and the current study regarding obesity seeks for the same cognitive specification.

Information Integration Theory and Obesity
Obesity is related to several diseases (e.g., diabetes, stroke, and high blood pressure; [11] and it leads to early death [12] and cognitive decline [13].What are the cognitive causes to enforce, maintain and to eliminate obesity are at a center of an extensive research effort [14] [15].Dieticians, nutritionist, and physicians, stress the importance of healthier diets and a less sedentary life style.However, even when many obese people know that they should eat less and healthier and that they should exercise more, their efforts to reduce weight are unsuccessful in the long run.For example, many dieters are able to lose some weight in the short term but they frequently end up regaining more weight than they initially lost [16].Here it is argued that this is so due to a specific cognitive ruled mechanism that contributes to maintain obesity by implicit biased valuation of information regarding unhealthy food intake and exercising.As it was pointed out previously cognitive specification of cognitive processing parameters underlying this mental ruled behavior can be achieved by using an IIT approach. Basic assumptions of the IIT cognitive approach postulate that relevant stimuli (Si) are extracted from an environment and psychologically represented through a valuation process (V) with cognitive coefficients (ψi).Here, a person Information valuation (V) of sources of information depends on perception of events whereas information integration (I) relates to generalizable systematic (algebraic) cognitive processing of sources of information (Si) across a sample of individuals.This cognitive algebraic behavior can be specified by IIT functional measurement methodology (FMIIT) as described in the method section.This is relevant because cognitive specification of systematic thinking deepens our knowledge about people beliefs.For instance, there is robust evidence suggesting that people tend to follow an average integration rule to attitude formation and change rather than a summatory of products like in the Fishbein and Ajzen's expectancy-value model [10] [17].
Regarding perception of risk health behavior and obesity, no cognitive algebra specification research exists.Again, this is so, since TPB/TRA, PMMT and HBM approaches on this topic focuses on prediction rather than on understanding judgment formation cognitive mechanisms.The following study provides initial empirical research to cognitive specification of mental mechanisms underlying judgment formation toward health risk behavior in obesity.

Method
In order to explore judgment formation on health risk behavior a three factor IIT cognitive algebra design was implemented: 3 (Diet: Good, regular and bad) × 2 (weight normal, overweighed) × 2 (physical activity: sedentary, active).

Participants
A sample of 80 third grade secondary education students (50% female and 50% male) were considered in this study.Their age ranged between 13 and 16 years old (M = 13.8 years old, SD = 1.29).All of them belonged to a city located at the north of Mexico (Matamoros Tamaulipas) and five percent of this sample reported to be in a weight control program.Using the n'Query advisor statistical software, the number of participants was estimated by considering a 0.75 significance level having a 90% statistical power (80 participants).Here, a nonprobability convenience sampling approach was considered where young adolescents (an age period where many obesity problems arise) were representative of a population having overweight problems.In Mexico one out of five adolescents have overweight problems and one out of ten adolescents have obesity problems.

Instruments and Materials
Considering the current factorial design experiment, twelve scenarios were created describing an individual habit feeding behavior as well as her/his physical appearance.Thus vignettes consisted of three sources of information (independent variables) and at the end of each scenario a question was presented asking the participant how likely it was that the described scenario actor 's health is at risk.Then a 10-point scale ranged was introduced (0 to 10 pints).This scale was left anchored with a label "Not risk at all" and right anchored "Completely at risk": "Fabiola is a 14-year-old teenager having a normal weight.However, her feeding behavior is bad since she really does not follow a healthy diet.---o----o----o----o----o----o----o----o----o----o Completely at risk.

Procedure
Participants were tested individually; they were required to read each of the 12 scenarios and rate, on a 10-point scale, the probability of a person being at health risk.Scenarios were randomly presented on printed paper cards (vignettes).The required time to complete the study takes around 30 minutes.

Results
A global mixed ANOVA 3 (Diet) × 2 (Weight) × 2 (physical activity) was carried out over the 80 participants' raw scores with a significance level stablished at p < 0.001.This analysis was carried out by having in mind that no significant main effects was obtained on gender regarding health risk perception, F (1, 78) = 2.22, p = 0.13, η 2 = 0.02.However, it is worth to mention that female participants obtained a lower punctuation (M = 4.7) than males (M = 5.0).Furthermore, the ANOVA was carried out by considering participants weight attributes.Here, no differences on risk perception judgment formation could be found (Individuals slightly obese vs. healthy weighted vs. really overweighed individuals) [F (1, 77) = 1.44, p = 0.001, η 2 = 0.03].
A general description from the obtained experiment data shows that adolescents' risk perception level judgement was moderately high (M = 5, DS = 0.84).Also, as it can be noticed from Table 1.The most relevant factor to participants' judgment formation (higher valuation) relates to diet information source (η 2 = 0.72) followed by the weight factor (η 2 = 0.65) and finally followed by the frequency of physical activity factor (η 2 = 0.58).
No significant interactions were found among interactions underlying judgment formation.However, and most interestingly, judgment to health risk perception in this population seems to follow a cognitive summative rule to integrate sources of information regarding diet, weight and physical activity.This is graphically illustrated in Figure 2.

Discussion
The aim of this research was to investigate judgment formation towards obesity as a health risk behavior.As it can be noticed from this study results, exercising had the lowest ponderation by the study population.Benefits of exercising are well documented especially when obesity is under scrutiny [18].However, nowadays, exercise is severely discouraged by technological developments (e.g.use techs like cellphone, video plays, tablets) and malicious food intake satisfies more to commercial interests than people fitness.Gollwitzer and Sheeran [19] have found that significant behavior changes to favor exercising are achieved when positive pro-exercise cognition was combined with encouragement to form implementation intentions and to visualize the obstacles that stand in your way, Oettingen [20].It looks like study participants prefer to consider diet formulations to cope with obesity rather than exercising no matter the weight condition.
Accordingly, risk perception about health threatens due to obesity should follow an implicit cognitive rule defined as: PR DietWd WeightWw Physical ActivityWpa = + + where perceived risk (PR) results from orthogonal contributions of weighted (Wij) factor values and their parameter estimation can vary according to health intervention programs, cultural diet backgrounds, etc. (reflecting different valuation processes).Even though, the same information integration summative cognitive rule is expected to be maintained on different conditions.
However, factor contribution in this PR cognitive rule might vary by including other relevant health cognitions.For instance, inclusion of motivational [21] or affective [1] considerations could specify new orthogonal factor contribution if they are included on social scenarios represented by vignettes like the ones used in this study.
More research is needed to explore these possibilities.It is evident that by using an IIT approach to explore rationale underlying unhealthy behavior, more predictive behavior power can be achieved on cognitive prediction models like the ones appointed before, for instance, stablishing a relation between healthy cognitive ruled behavior of a health belief and the HBM model predictions.

Figure 1 .
Figure 1.A study three factor IIT diagram (Activity, weight and diet) describing a possible cognitive algebraic integration of psychological evaluated information of food, feeding behavior an exercise (Modified from Anderson, 2009).
Moreover, she is sedentary since she barely exercises (1 or 2 times a week)."By considering the above scenario, to what extent do you think Estela's health is at risk?No risk at all o-

Figure 2 .
Figure 2. Interaction graph showing a cognitive summative rule to integrate diet, weight and physical activity information.Here, diet factor was valuated as the most relevant, followed by the weight factor and finally by the frequency of physical activity factor.

Table 1 .
ANOVA statistical results obtained from participants' perceived risk health behavior.