Analyzing Quantitative Research Designs In Sleep Apnea

Analyzing Quantitative Research Designs in Sleep Apnea and Stroke Studies

When conducting research, it is necessary that the researcher not only know how to find the sources needed to answer the question that they have created but also how to analyze that information to understand which research design was used. Doing so will allow the researcher to provide the evidence needed to support or reject the question being asked. Quantitative research is the investigation of phenomena that lends themselves to precise measurement and quantification, often involving a controlled design (Polit & Beck, 2017). This discussion will look at two different quantitative studies and the qualities that make them so.

Paper For Above instruction

The first study, conducted by Boulos et al. (2017), investigates the effectiveness of using home sleep apnea testing (HSAT) to detect obstructive sleep apnea (OSA) in patients who have experienced a stroke or transient ischemic attack (TIA). The study aims to determine whether early detection and treatment of OSA can improve poststroke recovery outcomes. It employs a prospective observational design, specifically a single-center study, which is appropriate for examining the natural progression of a condition without experimental manipulation. This design enables the researchers to observe the relationship between OSA and stroke recovery in a real-world setting over time.

In this study, the independent variable is whether patients have had a stroke or TIA, and the primary outcome is the efficiency of HSAT in diagnosing OSA post-event. The prospective nature indicates that the researchers followed patients over time from the initial stroke or TIA to assess the impact of early OSA detection. Since the study aims to observe natural occurrences—namely, the presence of OSA and recovery outcomes—without intervention, an observational design aligns well with the research question. To analyze data, the researchers utilized t-tests, Wilcoxon rank sum tests, and multivariate logistic regression, which are suitable for comparing groups and controlling confounding variables (Boulos et al., 2017). The findings suggest that HSAT can expedite OSA diagnosis, potentially leading to better recovery outcomes, thereby supporting the study's therapeutic implications.

The second study, by Nair et al. (2019), explores whether sleep apnea serves as a risk factor for stroke by comparing functional outcomes between patients with and without sleep apnea after an ischemic stroke. This study employs a prospective cohort design, which is particularly effective for etiological investigations where the goal is to assess causality or association. The cohort design is considered one of the strongest methods for studying cause-and-effect relationships when randomization is not feasible, as is the case here, since assigning sleep apnea status would be unethical and impractical (Polit & Beck, 2017).

Participants were diagnosed with sleep apnea based on questionnaires such as the sleep disordered questionnaire, Berlin questionnaire, and Epworth Sleepiness Scale. The researchers then tracked functional recovery using Barthel scores at baseline and three months post-stroke. By comparing the functional outcomes of patients with and without sleep apnea, the study aimed to establish a potential causal link between sleep apnea and stroke prognosis. The analysis involved repeated measures ANOVA, which allows for assessing differences in outcomes over time within and between groups. The results showed that patients without sleep apnea demonstrated greater functional improvements, indicating that sleep apnea may increase the risk of poorer stroke outcomes (Nair et al., 2019).

In conclusion, understanding and selecting the appropriate research design are crucial for generating valid and reliable evidence. The first study’s observational prospective design suits its exploratory goal of assessing HSAT’s effectiveness in stroke patients, while the second’s cohort design adeptly investigates the etiological role of sleep apnea in stroke recovery. Misapplication of research designs can undermine validity, emphasizing the importance of aligning methodological choices with research questions. Both studies demonstrate how quantitative methods—through observational frameworks—can elucidate cause-and-effect relationships and inform clinical practices, especially in complex health phenomena such as sleep disorders and cerebrovascular diseases (Polit & Beck, 2017).

References

  • Boulos, M. I., Elias, S., Wan, A., Im, J., Frankul, F., Atalla, M., & Murray, B. J. (2017). Unattended Hospital and Home Sleep Apnea Testing Following Cerebrovascular Events. Journal of Stroke & Cerebrovascular Diseases, 26(1), 143–149.
  • Nair, R., Radhakrishnan, K., Chatterjee, A., Gorthi, S. P., & Prabhu, V. A. (2019). Sleep Apnea-Predictor of Functional Outcome in Acute Ischemic Stroke. Journal of Stroke & Cerebrovascular Diseases, 28(3), 807–814.
  • Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Wolters Kluwer.
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  • Bliwise, D. L., et al. (2019). Sleep in cerebrovascular disease: Pathophysiology and management. Sleep Medicine Clinics, 14(2), 227–242.

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