Abstract
Aim
The health of the Hungarian population is not as good as it could be according to the socioeconomic development of the country. Since unhealthy behavior is widespread in the population, behavior change seems to be an appropriate tool to improve health in Hungary. To develop effective interventions, it is first necessary to identify homogeneous groups of people from the point of view of behavior change, i.e., healthstyles. Our aim was to develop a suitable survey methodology and segmentation procedure for identifying healthstyles in Hungary. In this article we present the results of our methodological developments.
Subjects and methods
Several blocks of questions were developed based on the COM-B model, which synthesizes the most recognized behavior change models. The questions cover knowledge about and attitude to health, somatic and mental health status, subjective well-being, psychological characteristics, health behaviors, social support, media consumption, health-related information seeking and socio-demographic characteristics. Nationally representative two-stage samples of schoolchildren and adults were drawn. Iterative weighting was applied, which was supplemented with a design weight correcting the design effects. For segmentation, latent cluster analysis was used.
Results
Questionnaires based on the COM-B model were developed and administered to schoolchildren and adults. Several numerical and graphical methods were developed to explore the statistical characteristics of the segmentation models obtained by latent cluster analysis.
Conclusions
The developed survey methodology and segmentation methods were successfully applied to produce statistically stable clusters in both schoolchildren and adults. The statistical methods chosen for segmentation and validation of clusters seem suitable to identify healthstyles.
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Notes
The database contains data about 4438 schools in the 2014–2015 school year. Based on the database the number of fifth classes was 4556, the number of seventh classes was 4680, and the number of ninth classes was 4755.
Social Renewal Operational Program 6.1.3./B/12/1 “Developing public health communication”
For a more comprehensive description of the statistical viewpoint of the model-based framework, see Sterba (2009).
Due to the high number of variables involved (more than 100), application of imputation methods was excluded for theoretical reasons.
The software version is R 3.2.1., and the computations were performed on a 64-bit Windows 2012 R2 server.
The ‘poLCA’ package (version 1.4.1) was built under R version 3.2.2.
In the sense of interpretation, this nature of the LCA method makes the results similar to those that come from a factor model.
Technically, the model selection in LCA aims to specify the number of clusters.
Identification of many separate clusters would be counterproductive, and technically it would be too difficult to overview and interpret all of them.
To describe the axes, a table containing the most important variables defining the axes belongs to each plot, which is not described in this article.
The adjusted standardized residual is the Pearson residual divided by the standard deviation of all residuals.
The positive values indicate that the given attribute is typical of a cluster, and in contrast the negative values characterize the cluster by the lack of a given attribute. If the absolute value exceeds two, then we can reasonably say that the given attributes are statistically significant. The higher the absolute value is, the more significant the attributes are. It is important to note that significant values do not mean exclusivity; they denote just the relative differences from the expected values.
As it can validate the model externally.
Of course, the identification of the different health-style clusters is not always so obvious as in the example above. (A detailed discussion of these models is not part of this article. We would like to present them in a different publication in the future.)
Symmetric and asymmetric Goodman-Kruskall λ, Theil’s uncertainty coefficient and Cramer’s V (Liebetrau 1983).
We have also displayed some of these values on the plots above.
The disadvantage of the applied simulation method is its time consumption as it was a function of the amount of the input elements. In general, the requested amount of time was more than 1 h per iteration, and 25 iterations were made for each parameter set.
For the purpose of comparability of the different models, the deviation was normalized to the expected value of the deviation given by the complete random reclassification. For example, the fifth-grade children’s model consists of three clusters with approximately the same size; therefore, the rate of misclassification in a completely random reclassification is approximately 66%—but depends on the sizes of each cluster. (Generally speaking, the expected value of the deviation is a function of the number of clusters and the distribution of the cluster sizes. Without the normalization, the comparison of the different stability models may lead to an erroneous conclusion.)
As the two examined attributes (rate of omitted variables or observations) interact with each other, the dots are partial values, as is the displayed non-linear model fitted on these partial values.
For the purpose of evaluating the results, investigating the transition function of the deviations is necessary as it can be interpreted as an indicator of the sensibility.
Approximately 0.75 is the expected value of the relative deviation from the simple random model omitting 75% of the cases in contrast to omitting 75% of variables—the corresponding values do not exceed 0.4 in either case.
The final model for the seventh-grade children is incomparably less stable than the others. One possible assumption is that the confusion of responses was related to the stage of personality development (Candida 2013), but to declare it with certainty we need more investigations.
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The Healthstyle Survey was implemented within the Social Renewal Operational Program “Development of public health communication” project (grant no. 6.1.3.B-12/1-2013-0001) funded by the European Social Fund.
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All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
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Informed consent was obtained from all individual participants included in the study.
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Balku, E., Tóth, G., Nárai, E. et al. Methodology for identification of healthstyles for developing effective behavior change interventions. J Public Health 25, 387–400 (2017). https://doi.org/10.1007/s10389-017-0799-y
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DOI: https://doi.org/10.1007/s10389-017-0799-y