Data Analysis For Research Protocol Design And Practice: Obesity As A Risk Factor To Polycystic Ovarian Syndrome In Women

What is already known

Discuss about the Data Analysis for Research Protocol Design and Practice.

The riskof polycystic ovarian syndrome increases due to the predisposing conditions that include the genetic and lifestyle factors making polycystic ovarian syndrome one of the major disorders in the women. The endocrine disruptors and sedentary lifestyle are the major reasons associated with the increased polycystic ovarian syndrome risk and potential future study is necessary to understand the accurate factors that increase polycystic ovarian syndrome risk (Naderpoor et al., 2015). The research is required to understand that polycystic ovarian syndrome is not only a reproductive problem but it is also associated with many other conditions like obesity, overweight and stress. Among the predisposing conditions, obesity is a factor that is associated with the increased polycystic ovarian syndrome risk and showed a limited study regarding this underlying cause (Ranasinha et al., 2015). The research problem lies in the early identification of the predisposing factors that would help to reduce the polycystic ovarian syndrome risk. There are limited studies in the area of obesity associated to the risk to develop polycystic ovarian syndrome during puberty. The weight reduction and reduced polycystic ovarian syndrome have been found to have limited studies. Studies showed that reduction in weight and body mass index helped to manage and treat the polycystic ovarian syndrome but early identification of risk of polycystic ovarian syndrome with obesity has no long term studies.

Polycystic ovarian syndrome is a disorder that is affecting women worldwide leading to abnormal hormonal levels. There is formation of benign cysts in the ovary that leads to polycystic ovarian syndrome (Moran, Norman & Teede, 2015). The insulin resistance, diabetes, high blood pressure and cholesterol levels are the known causes of polycystic ovarian syndrome (Gupta et al., 2016). The fluid filled cysts release high amounts of androgens, the male hormone that contributes to the polycystic ovarian syndrome. Obesity is one of the conditions that contribute to the risk of polycystic ovarian syndrome. According to a study by Lim et al., 2012 showed that there is a potential link between the obesity associated with the poly cystic ovarian syndrome. This study showed that there is prevalence of obesity varying among the different ages and subgroups of women. The excess weight and obesity in women is a predisposing factor that increases the risk of polycystic ovarian syndrome. They stated that obesity disrupts the normal reproductive metabolism of the body. The paper by Gurnani et al., 2015 showed that increase in body weight has an impact on the metabolism of the body leading to hormonal imbalances and risk to polycystic ovarian syndrome. A study conducted by Marquard et al., 2011 demonstrated that the maternal obesity affected the size of oocytes in intra-cytoplasmic sperm injection technique. The oocyte quality and maturity deteriorated and there was a decrease in oocyte number in obese women. In the overweight women, there was also a significant decrease in the number of healthy oocytes as compared to normal healthy women. The alteration in metabolism plays a crucial role in risk of polycystic ovarian syndrome. The hormonal environment in the follicles is affected due to an elevation in body mass index. The environment inside the pre-ovulatory follicles is affected and altered leading to the increase in the levels of significant hormones like the insulin, androgen and triglycerides. A study conducted by Beatriz Motta, 2012 showed that obesity is intended to cause some related features of polycystic ovarian syndrome impairing insulin resistance and executing the metabolic and reproductive features related to polycystic ovarian syndrome. The previous studies showed that there is a strong association between the obesity and polycystic ovarian syndrome risk.

Gap in knowledge

There is no exact quantitative or qualitative evidence proving the role of obesity and associated polycystic ovarian syndrome risk. The obesity might cause various abnormal changes in the body of the women that leads to the development or predisposing to polycystic ovarian syndrome. There is no longitudinal study conducted to study the association of obesity to polycystic ovarian syndrome risk. There are also limited studies regarding the assessment of obesity in diagnosing the women prone to poly-cystic ovarian syndrome. There is also lack of clarity regarding the extent of how obesity risks the development of polycystic ovarian syndrome and complications related to it.

How obesity acts as a risk factor for the development of polycystic ovarian syndrome in 18-44 aged women?

For the research to be done there is requirement of both quantitative and qualitative research. For the quantitative research, there is a requirement to measure many parameters regarding the obesity in the target population of women aged 18-44 years like the body composition. There is requirement to measure, record and analyse the parameters like body weight, waist circumference, body mass index, waist to hip ratio and thickness of skin fold. These parameters need to be measured in the age group of 18-44 year old women. The parameters also need a comparative study among the different age groups and subgroups of the women. This information would help to analyse the incidence of obesity seen among the age groups. These relevant variables are important to measure and analyse for studying the possible association of obesity to polycystic ovarian syndrome in the target population. The hormonal levels are also an important parameter to assess the development of polycystic ovarian syndrome in the target population exhibiting overweight or obesity. The levels of hormones like the estrogen, progesterone, androgen, thyroid and prolactin levels. The glucose, cholesterol and triglyceride levels are also need to be measured.  For qualitative research to be done relevant information is required like the medical history of the target population, their family medical history, lifestyle and daily diet. The lifestyle and information about their food habit is important regarding the understanding of development of obesity and its role in the polycystic ovarian syndrome risk. The information regarding their medications, level of physical exercise in their daily life and diet in their daily life.

The project is aimed at studying the association of obesity to the risk of polycystic ovarian syndrome. The project will focus on the age group of 18-44 in women those are overweight or obese and developing the polycystic ovarian syndrome risk. The benefit of this project would be screening the women in the target population for the obesity and their chances of developing the polycystic ovarian syndrome. The understanding of the role that obesity plays in polycystic ovarian syndrome would help to develop intervention strategies in controlling the obese condition in the target population and the related risk of developing the disorder in them.  

How obesity acts as a risk factor for the development of polycystic ovarian syndrome in 18-44 aged women?

Research design is an important parameter for the successful progression of a research. It is must to assess the data gathered from the target population regarding the chances of obesity and the related risk of having the disorder. The aim of the research is to record and analyse the results of the parameters that would be measured to check for obesity and the extent of developing the disorder in the future. The qualitative research would be conducted by asking questions to the participants in open-ended form and analysing their answers for the appropriate results. As the target population would be large, they would be divided into various groups.

The research would be conducted by doing surveys in the schools, homes, workplace and other related areas of Australia as the target population ranges from age of 18 to 44. This age group ranges from school going to housewives. This age group leads a sedentary lifestyle with lack of exercise and poor eating habits.

The target population for this research includes young women belonging to the age group of 18-30 years and also mid-aged women belonging to the age group of 31-45 years in Australia. As the disease of PCOS do not occurs within the aged and young, the women belonging to those age groups are eliminated from the sample size (Lim et al., 2013). Moreover, as the disease of PCOS is highly related with the chronic disease of obesity, the researcher will include all the obese women belonging to the age category of 18-45 years. Moreover, the women suffering from overweight and have high BMI is also included within the sample group. As the disease of PCOS is limited only to the women, the researchers will eliminate all the men from the sample size. The data is collected from the definite sample size selected of the research work. The selected sample size includes 100 women of the age 18-30 years and 100 women from 31-45 years. However, due to the shortage of time it will not possible to collect data from every women that is selected in the sample size.

There are two major types of sampling methods that can be used by the researcher for the purpose of data collection methods. In probability sampling methods, sample is selected on a random basis and data that is collected do not have any proper pattern. In this type of sampling techniques, the surveyors get the chance to collect data from a wide range of audience. However, the data that is collected may not be accurate and relevant to that of the research work. In non-probability sampling technique, the researchers collect data from the fixed selected sample size is chosen on the basis of the requirements of the research (Levy & Lemeshow, 2013).

What needs to be known

In this case, the researchers have chosen the technique of non-probability sampling method as they have to collect data from the women of the age group of 18-45 years. The probability sampling technique will not be helpful as in this research the data need to be collected from women suffering from obesity.

The researchers will collect data from the women, who are undergoing treatment in the gynecology department of the healthcare centre. With the help of the non-probability sampling technique, it is possible for the researchers to collect accurate information that is relevant to the research work. The surveyors need to visit the gynecology department of the healthcare center in Australia and retain the data that is needed for data analysis.

The researcher needs to use the tool of BMI or body mass index to determine whether a woman is suffering from obesity (Beechy et al., 2012). For this case the instrument to measure the height and weight of an individual is needed. Furthermore, the hormonal diagnostic tool is also needed to determine the level of hormone within the body. The level of adrenal hormones, thyroid stimulating hormone and prolactin are few of the important markers for the diagnostic test of PCOS (Hart & Doherty, 2014). Proper clinical instruments and data recording system is required to achieve this purpose.

The researchers need to follow a strict procedure in order to collect the relevant and accurate data. The project staffs need to visit the clinical unit and the gynecology department and check with the medical data and past record of the patients that has suffered from obesity and PSCO related disease. It is also important to collect relevant information about the medical history of the patients, who are included in the sample size. The surveyors need to collect data from the individual, who are suffering from obesity that will help to relate the medical records of the PSCO and that of the obesity. The surveyors need to fix a schedule in the morning time to perform this diagnostic test upon the selected sample size. They also have to select a fix time for conducting the interview with the patients. These are the primary form of data that is collected for the research work. One of the primary advantages of collecting primary data is that it is possible to get the latest information related to research work (Holloway & Wheeler 2013).

Secondary form of data can be collected from the previously done research of the relevant topic. The project staff members need to select few particular research papers from the online data base and therefore help in the matter of gathering data and relate with that of the primary data.

Project aims and expected benefits

The quantitative data can be analyzed by placing the data in the systematic manner and thereby using the statically and mathematical tools. The statistical data can be used to identify the importance of the data that is collected from the clinical centers. Qualitative data analysis will also be used to accurately relate the quality collected data (Bazeley & Jackson, 2013)

The ethical issues related to this project would be informed consents from the participants stating their willingness to participate in the survey. The ethical considerations also include the information that the participants would reveal during the qualitative analysis.

The scope for the research is wide and has great impact on the society. By conducting this research, it would create awareness and spread among the women about the ill effects of obesity and its role in development of polycystic ovarian syndrome in the Australian population. It would help to make strategic interventions in dealing with the disorder. The limitations lay in gathering all the required information from the participants. The time period and budget also the limitations in conducting the research.

The time period for the completion of this project is 20 weeks. The chart is provided below for the project completion.

Gantt chart source researcher

References

Bazeley, P., & Jackson, K. (Eds.). (2013). Qualitative data analysis with NVivo. Sage Publications Limited.

Beatriz Motta, A. (2012). The role of obesity in the development of polycystic ovary syndrome. Current pharmaceutical design, 18(17), 2482-2491.

Beechy, L., Galpern, J., Petrone, A., & Das, S. K. (2012). Assessment tools in obesity—Psychological measures, diet, activity, and body composition. Physiology & behavior, 107(1), 154-171.

Gupta, J. K., Qureshi, S. S., Shah, K., & Upmanyu, N. (2016). PREVALENCE AND RISK FACTOR OF POLYCYSTIC OVARIAN SYNDROME. Asian Journal of Pharmaceutical and Clinical Research, 9(2), 23-25.

Gurnani, M., Birken, C., & Hamilton, J. (2015). Childhood obesity: causes, consequences, and management. Pediatric Clinics of North America, 62(4), 821-840.

Hart, R., & Doherty, D. A. (2014). The potential implications of a PCOS diagnosis on a woman's long-term health using data linkage. The Journal of Clinical Endocrinology & Metabolism, 100(3), 911-919.

Holloway, I., & Wheeler, S. (2013). Qualitative research in nursing and healthcare. John Wiley & Sons.

Levy, P. S., & Lemeshow, S. (2013). Sampling of populations: methods and applications. John Wiley & Sons.

Lim, S. S., Davies, M. J., Norman, R. J., & Moran, L. J. (2012). Overweight, obesity and central obesity in women with polycystic ovary syndrome: a systematic review and meta-analysis. Human reproduction update, dms030.

Lim, S. S., Norman, R. J., Davies, M. J., & Moran, L. J. (2013). The effect of obesity on polycystic ovary syndrome: a systematic review and meta‐analysis. Obesity reviews, 14(2), 95-109.

Marquard, K. L., Stephens, S. M., Jungheim, E. S., Ratts, V. S., Odem, R. R., Lanzendorf, S., & Moley, K. H. (2011). Polycystic ovary syndrome and maternal obesity affect oocyte size in in vitro fertilization/intracytoplasmic sperm injection cycles. Fertility and sterility, 95(6), 2146-2149.

Moran, L. J., Norman, R. J., & Teede, H. J. (2015). Metabolic risk in PCOS: phenotype and adiposity impact. Trends in Endocrinology & Metabolism, 26(3), 136-143.

Naderpoor, N., Shorakae, S., Joham, A., Boyle, J., De Courten, B., & Teede, H. J. (2015). Obesity and polycystic ovary syndrome. Minerva endocrinologica, 40(1), 37-51.

Ranasinha, S., Joham, A. E., Norman, R. J., Shaw, J. E., Zoungas, S., Boyle, J., ... & Teede, H. J. (2015). The association between Polycystic Ovary Syndrome (PCOS) and metabolic syndrome: a statistical modelling approach. Clinical endocrinology, 83(6), 879-887.