DNP Capstone Projects Paper
Evaluation |
Evaluation Plan |
| Population | African American females are at more risk of developing prediabetes than other populations and races. The target population for this evaluation would include African American females aged between 30 and 60 years. Prediabetes is more common in women than men of corresponding ages (Centers for Disease Control and Prevention, 2020). Therefore, the population for this project would be African American females aged between 30 and 60 years with clinically diagnosed prediabetes |
| Intervention(s) | The intervention will be the use of metformin in the management of prediabetes. This will be compared with lifestyle modifications including dietary management and physical exercise. Metformin is a biguanide that is used to treat patients with confirmed diabetes and manage prediabetes in primary care. Its adverse effects are mild and the risk of hypoglycemia is low. Lifestyle modifications have been applied to many chronic non-communicable diseases such as diabetes and cardiovascular diseases. |
| Outcome(s) | The outcome to be measured will be the effect of the intervention on the quality of glucose control. Glucose control in prediabetes can be impacted upon by various interventions including medications and nonpharmacological interventions. The desired outcome to be measured would be a reduction in glucose levels. However, the quality and quantity of such reduction would important in assessing the effectiveness of interventions. The body characteristics that place the patient at risk of diabetes progression such as body weight would also be measured secondarily. |
| Sources of Evidence-Based Data to support the evaluation plan | The data will be collected from previously conducted research studies. Studies comparing the interventions of interest would be searched and evaluated. Previous evidence-based studies provide first-hand data for secondary evaluation and further enhancement of evidence-based practice (EBP). This data would be collected from five database libraries inclusion PubMed, CINHAL, Medline, Embase, and Cochrane libraries. Reviews and systematic reviews would not be included in the sources of primary data for this evaluation. |
| Outcome Measures/Measurement Tools (What is collected and how?) | The outcomes would be measured through assessment of the quantitative levels of control of blood glucose. The best measures of such control would be assessed by measurement of HbA1c. HbA1c measures chronic quality control of plasma glucose (Fayyaz et al., 2019). Assessment of baseline before administration of interventions would ensure an accurate basis of evaluation. The change in A1c from the baseline would the evaluated quantitatively. This will be achieved by comparison of differences of the outcomes from the baseline over a minimum period of three months of interventions.
HbA1c is the quantitative measure of glycated hemoglobin. The measurement is not affected by any diurnal changes. However, the assessment of the results would best be assessed after three months. The resulting quantitative data predict the possibility of diabetic complications such as neuropathy and retinopathy (Schnell et al., 2017). Persistently high A1c levels from the baseline measurement to the final assessment levels predict a poor prognosis of the patients on the particular intervention. An increment of the values from the baseline will show a negative association and poor outcomes of the intervention of interest. A reduction in levels of HbA1c from the baseline would show the effectiveness of the intervention. The impact of confounders would greatly impact the measurement. The undesired patient factors may also reduce or increase plasma glucose leading to good or poor glycemic control over the three months. Measurement of A1c levels confers better accuracy and specificity than the direct assessment of plasma glucose (Owora, 2018). Its application in prediabetes assessment is invaluable (Kam-On Chung et al., 2017). The direct assessment of random or fasting plasma glucose is best for acute settings where glucose levels are affected by short-term changes and metabolic alterations. Measurement of body mass index (BMI) will assess the quantitative and qualitative impact of teaching lifestyle modification. The baseline and final measurements will be acquired as well. Alongside comparisons of A1c levels, the impact of both interventions would be assessed through BMI calculations. Personal patient characteristics such as ethnicity, race, and genetics are expected to impact the reliability of this tool in assessing change. |
| Data Sources (Who is data collected from?) | The source of the primary data would come from randomized control trials on African American participants with prediabetes. This evaluation will acquire secondary data from primary research studies and analyze them systematically. The eligibility of such studies will be evaluated using the PRISMA model (Tawfik et al., 2019). The Cochrane Collaboration’s Tool for Assessing the Risk of Bias would be used in the assessment of these studies. |
| Data Collection (When is data collected and by whom?) | The data would be collected over four months starting with article searching, screening, and critical appraisal. The researcher will commence by setting the inclusion and exclusion criteria that meet the desired clinical issue of interest. |
| Data Analysis (How will you analyze the data?) | Data analysis would be carried out by metanalysis of findings from the primary research finding. This is because the main sources of the research evidence would be randomized control trials (RCTs) (Ahn & Kang, 2018). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model that would be used in the data collection and sampling will enhance metanalysis (Campbell et al., 2020). The resultant research evidence would have the highest level of evidence from the metanalysis. |
References
- Ahn, E., & Kang, H. (2018). Introduction to systematic review and meta-analysis. Korean Journal of Anesthesiology, 71(2), 103–112. https://doi.org/10.4097/kjae.2018.71.2.103
- Campbell, M., McKenzie, J. E., Sowden, A., Katikireddi, S. V., Brennan, S. E., Ellis, S., Hartmann-Boyce, J., Ryan, R., Shepperd, S., Thomas, J., Welch, V., & Thomson, H. (2020). Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ (Clinical Research Ed.), 368, l6890. https://doi.org/10.1136/bmj.l6890
- Centers for Disease Control and Prevention. (2020, August 7). Prevalence of prediabetes among adults. Cdc.Gov. https://www.cdc.gov/diabetes/data/statistics-report/prevalence-of-prediabetes.html
- Fayyaz, B., Rehman, H. J., & Minn, H. (2019). Interpretation of hemoglobin A1C in the primary care setting. Journal of Community Hospital Internal Medicine Perspectives, 9(1), 18–21. https://doi.org/10.1080/20009666.2018.1559432
- Kam-On Chung, J., Xue, H., Wing-Hang Pang, E., & Chuen-Chu Tam, D. (2017). Accuracy of fasting plasma glucose and hemoglobin A1c testing for the early detection of diabetes: A pilot study. Frontiers in Laboratory Medicine, 1(2), 76–81. https://doi.org/10.1016/j.flm.2017.06.002
- Owora, A. H. (2018). Commentary: Diagnostic validity and clinical utility of HbA1c tests for type 2 diabetes mellitus. Current Diabetes Reviews, 14(2), 196–199. https://doi.org/10.2174/1573399812666161129154559
- Schnell, O., Crocker, J. B., & Weng, J. (2017). Impact of HbA1c testing at the point of care on diabetes management. Journal of Diabetes Science and Technology, 11(3), 611–617. https://doi.org/10.1177/1932296816678263
- Tawfik, G. M., Dila, K. A. S., Mohamed, M. Y. F., Tam, D. N. H., Kien, N. D., Ahmed, A. M., & Huy, N. T. (2019). A step-by-step guide for conducting a systematic review and meta-analysis with simulation data. Tropical Medicine and Health, 47(1), 46. https://doi.org/10.1186/s41182-019-0165-6