Introduction
Analyzing diabetes mellitus type 2 in a target group of 500 patients, defined by a targeted sample, can prove relevant common and individual features and identify specific prerequisites for the development of the disease.
Statement of Purpose
The goal is to define the features of patient information to provide data on the general course of the illness and its manifestations following the criteria of age, sex, BMI, and experimental data.
Research Questions
As research tools, relevant questions are applied:
- Is there a correlation between age, gender, BMI, and experimental data on hemoglobin levels?
- What personal data correlates with the experimental data the most?
The research purpose is determined based on the presented parameters for evaluation, which should be analyzed in the context of the impact on the final experimental data.
Hypotheses and Justification
- H1: There is a direct correlation between age and experimental data on hemoglobin levels.
- H2: There is a direct correlation between gender and experimental data on hemoglobin levels.
- H3: There is a direct correlation between BMI and experimental data on hemoglobin levels.
- H0: There are no correlations between individual demographic parameters and experimental data on hemoglobin levels.
The alpha level is 0.05 (5%); the figure is optimal for avoiding errors in the evaluation (Moore et al., 2017).
This study will help complement the knowledge about the relationship between demographics and diabetes mellitus type 2 risk. In addition, skills in working with statistical tools will be improved. The outcomes of the research will contribute to identifying effective tools for assessing available indicators and the ways to analyze them.
Studying the prevalence of diabetes mellitus type 2 among different demographic groups helps highlight relevant risk factors (Aamir et al., 2019). As the critical elements to take into account, both individual demographic factors and common morbidity aspects should be considered (Carey et al., 2018).
Theoretical Framework
To answer the research questions posed and prove or disprove the proposed hypotheses, a relevant theoretical framework should be selected. As a potentially viable model, the algorithm proposed by Pal et al. (2018) can be considered. According to the authors, this framework is based on a model developed by Corbin and Strauss and implies implementing a self-management program designed to address the crucial aspects of disease control (Pal et al., 2018). The parameters used for assessment relate to the emotional, medical, and role specificities demonstrated by patients, and these factors are essential to take into account to assess the individual and general features of the manifestations of diabetes mellitus type 2 (Pal et al., 2018). Following this framework can help identify the impacts that are specific to the selected sample so that to correlate their outcomes with relevant drivers that increase the threat of disease. Individual criteria included in such a self-management program can help answer the questions about possible correlations between the given variables and uncover habits and behaviors that are directly related to health risks, thereby highlighting both unique and general features revealed through testing HgBA1C indicators.
The proposed theoretical framework is appropriate for this study because individual demographic characteristics are the key data used for the assessment. As an alternative model that also involves the disease in question and its manifestations may be the one offered by Dendup et al. (2018). Its essence lies in the use of environmental characteristics, which, as stated, affect exposure to diabetes mellitus type 2, for instance, air pollution, increased noise levels, and some other external factors (Dendup et al., 2018). However, in the context of the variables involved, such a framework is irrelevant. The main parameters utilized for the assessment relate to demographic characteristics, which, in turn, are associated with lifestyles and individual health indicators. Environmental impacts, in this case, do not play a significant role. Therefore, the aforementioned self-management framework is a more appropriate theoretical background to address specific personal parameters and make the correct conclusions regarding the hypotheses given.
In addition to the usefulness of the framework that addresses self-management factors, one can note the interest of the study participants themselves in engaging in such a methodology. Based on the comparison of clinical data with individual indicators, credible recommendations can be given regarding lifestyle changes. Moore et al. (2017) note that applying statistics to real settings contributes to building a valid assessment process, which is important in the context of health performance research. Because all involved members of the control group have confirmed diabetes mellitus type 2, compiling assistance programs for them is of critical relevance due to the need to mitigate negative health effects. Obtaining objective outcomes and proving the proposed hypotheses is crucial for the participants and may help them adapt to life with a chronic illness.
Conclusion
Thus, the framework that is based on the concept of self-management supplemented by relevant factors to take into account is a useful background to apply to the study in question and perform the research process following valid methodology.
References
Aamir, A. H., Ul-Haq, Z., Mahar, S. A., Qureshi, F. M., Ahmad, I., Jawa, A., Sheikh, A., Raza, A., Fazid, S., Jadoon, Z., Ishtiaq, O., Safdar, N., Afridi, H., & Heald, A. H. (2019). Diabetes Prevalence Survey of Pakistan (DPS-PAK): Prevalence of type 2 diabetes mellitus and prediabetes using HbA1c: A population-based survey from Pakistan. BMJ Open, 9(2), e025300.
Carey, I. M., Critchley, J. A., DeWilde, S., Harris, T., Hosking, F. J., & Cook, D. G. (2018). Risk of infection in type 1 and type 2 diabetes compared with the general population: A matched cohort study. Diabetes Care, 41(3), 513-521.
Dendup, T., Feng, X., Clingan, S., & Astell-Burt, T. (2018). Environmental risk factors for developing type 2 diabetes mellitus: A systematic review. International Journal of Environmental Research and Public Health, 15(1), 78.
Moore, D. S., Notz, W. I., & Fligner, M. (2017). The basic practice of statistics (8th ed.). W. H. Freeman.
Pal, K., Dack, C., Ross, J., Michie, S., May, C., Stevenson, F., Farmer, A., Yardley, L., Barnard, M., & Murray, E. (2018). Digital health interventions for adults with type 2 diabetes: Qualitative study of patient perspectives on diabetes self-management education and support. Journal of Medical Internet Research, 20(2), e8439.