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Critical Analysis of Real Time Associations

Introduction

The critiquing of the article is objectively evaluating and reviewing the research to highlight its strength and limitation that is required for identifying the way best practice currently is to be formulated for ensuring well-being of individuals. In this study, the critical analysis of the article “Real-Time Associations Between Glucose Levels and Fatigue in Type 2 Diabetes: Sex and Time Effects” by Fritschi et al. (2020) is to be presented. The critical analysis is to be executed by following the Holland and Rees critiquing framework. This is because the framework allows detailed questioning of each aspect of the research in a minute way and it is the best available critiquing framework for quantitative study (Holland and Rees, 2010).

Focus

The topic of concern in the study by Fritschi et al. (2020) is the relation of fatigue and glucose level fluctuations in Type-2 diabetes in regard to sex and time period. The topic head presented is clarified and it allows to easily identify and measure the key variables of study. It is evident as from the topic the key variables that are concerned with the research such as “type-2 diabetes and glucose level”, “fatigue due to diabetes”, gender and diabetes” and others are easily identified. The topic presented in significant for research because the cause of fatigue in type-2 diabetes patient that interferes with their ability and strength to perform everyday activities and manage personal care is not identified (Singh et al., 2016). Thus, understanding through real-time data if glucose level fluctuations in type-2 diabetes that occur with time is responsible for fatigue along with the way it differs in among gender is important to determine the interventions to be taken to control the condition and ensure the well-being of patients.

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Background

In the study by Fritschi et al. (2020), the researchers argued the topic to be worthwhile by mentioning that the actual cause of fatigue that is common in type-2 diabetes patient is unknown. However, anecdotally, it is found that glucose level in type-2 diabetic condition has relation to cause fatigue in the patients. Moreover, it is found that sex is related to fatigue in patient suffering from diabetes with males reporting reduced fatigue compared to women. Thus, understanding the physiological factor such as glucose level responsible for causing fatigue and its differential expression across sex makes the study important to be executed. In the study, the researchers did not express through statistical values to what extent fatigue is widespread among diabetes patients and way it impact them. The author mentioned it to be common through descriptive analysis. This leads to fail in explaining the seriousness of the topic. Moreover, the key variables though identified are not separately defined by the researchers as they may have considered it is to be known among the public. It creates limitation for the study as it cannot be understood by common people who have no idea regarding medical terms such as fatigue, type-2 diabetes and others.

Aim

The aim of the study by Fritschi et al. (2020) is to use technological intervention for examining real-time momentary relationship between fatigue and glucose level in type-2 diabetes patients by week, day and time along with exploring the way the relationship differed by gender. This indicates that a clarified aim of the study is been presented which is significant as it informs the reader regarding the key facts to be explored in the study along with primary understand if the topic is related with their interest of research (Gusenbauer and Haddaway, 2021).

Methodology

In the study by Fritschi et al. (2020), a quantitative approach has been taken to execute the research. The correlational research design is followed to execute the study which is highly relevant to the aim of the study. This is because correlational research is non-experimental study in which the researcher measures two variables to assess their statistical relationship with one another without influence from any extraneous variable (Apuke, 2017). In this study, the correlation between two key variables that are fatigue expression and glucose level fluctuation in type-2 diabetes patient with respect to time and gender is been tried to be assessed as the main aim indicating the research method to be suitable for the study.

Tool of Data collection

The participants were made to make three visits for six days in the exercise and diabetes laboratory in the University of Illinois where their baseline data (depression and fatigue) is measured. They are made to execute 6 min walk test and data is gathered. In mentioning the method of data collection in the study, no previous studies are highlighted that has assured the methods it to be accurate and reliable. Moreover, the study by has not mentioned any context regarding if the data collection is piloted. However, the instruments and methods used to collect baseline and experimental data from the selected participants are mentioned to be reliable and valid. This is evident as Patient-Reported Outcomes Measurement Information System (PROMIS) and computerized adaptive testing (CAT) is mentioned to be used in gathering data regarding selected participants. The CAT is mentioned to be valid instrument for data collection as it helps in accurately refining respondent score through analysis on computer. Moreover, the researcher has personally approved that all the PROMIS assessment and CAT scores reported demonstrate strong validity across population recruited in the study (Fritschi et al., 2020).

The tool used for real-time glucose measurement is Medtronic CGMS iPro®2 continuous glucose monitoring system. The researchers mentioned to tool to be used because it does not present any visual real-time reports of blood glucose level due to which the participants remain unaware of their blood glucose fluctuation, in turn, making them avoid executing any aggressive changes within their diabetes management overtime (Fritschi et al., 2020). It helps in avoid the development of manipulation of data during the study execution. Thus, the rationale for the tool is presented, but no alternative tool been considered for use or mentioned by the researchers. This creates limitation for the readers in the study to determine if the specific toll is the sole way of gathering the mentioned data or other tools could have been used that would have led to better evaluation as well as collection of the determined data in the study.

Data Analysis Method

The study by Fritschi et al. (2020) mentioned in detail the process of data analysis. This is evident as the researchers mentioned they executed statistical analysis of data by using IBM SPSS version 24 along with performed descriptive analysis of data through Mann–Whitney U test, independent t-test and chi-square test. The advantage of presentation of descriptive analysis is that it helps to ease the visualization of the data and allow the facts to be presented by the researcher in meaningful as well as understandable manner to interpret them as per the raised question in the study (Titov et al., 2018). The advantage of statistical analysis is that it helps in presenting clear correlational patterns between different variable and ensures to increase their reliability along with representativeness (Pasquetto et al., 2019). The researchers Fritschi et al. (2020) also mentioned they used the GEE model over ordinary least square regression because the GEE model helps them to maintain greater independence in data analysis, allows flexible handling of time varying data (Zarei et al., 2019). The study also presented detailed context of each data analysis method to be executed and the reason behind them being selected. It acts as strength for the study because it presents effective rationale behind each method been chosen and prove its validity in the research.

Sample

The inclusion and exclusion principle of data collection is used in recruiting participants in the study (Fritschi et al., 2020). This acts as strength for the study because inclusion criteria help to ensure recruitment of participants to be done in consistent, uniform, objective and reliable manner whereas exclusion criteria help in identify the ineligible factors to be avoided in the study. Thus, they help in deciding the factor for recruitment in such a way that they are co-founder of the outcome parameter (Molinuevo et al., 2017). On the basis of the principle, the participants are recruited from a large Midwestern city in the USA through use of inter-based bulletin boards and flyer till December 2014 from September 2010. A total of 164 participants are selected and among them 157 could be screened for the study based on the eligibility mentioned through inclusion and exclusion criteria. The study by Fritschi et al. (2020) purposive sampling is used in selecting the participants as they were selected based on the inclusion and exclusion criteria considered solely by the researchers. The use of the sampling was advantageous because it allows to make homogenous selection of participants who are critical to the research (Serra et al., 2018). However, the limitation faced with its use is that low level of reliability is face and generalisation of finding are hard to be established (Schreier, 2018). No information regarding bias faced in sampling was mentioned in the study.

Ethical Considerations

In the study by Fritschi et al. (2020), effective ethical considerations are followed to ensure the study is developed morally and without violation of legal principles related to research. This is evident as informed consent from the participants in the study are taken prior to its execution and no personal data of them were shared to ensure their confidentiality. Moreover, all the methods used in the study was ethically approved by the institutional review board of the University.

Main Findings

The key findings were seen to appropriately relate with the aim. This is evident as the findings mentions that glucose level and fatigue were high in women compared to men which was one aspect of results to be identified according to the aim. The findings also mentioned that baseline glucose level (HbA1c) and self-reported fatigue are not related. The average level of fatigue by day and time of the day was found to be higher in women compared to men (p< .05). Glucose and fatigue level were significantly related in women with type-2 diabetes but not with men.

Conclusion and Recommendation

The study by Fritschi et al. (2020) did presented a clarified conclusion regarding the study in relation to the aim. The conclusion mentioned glucose exacerbations affects fatigue level in women but not in men in respect to real-time data. However, no clear recommendations was made regarding which individual are to execute what role in implicating the study in practise. This acts as weakness for the study as it lacks to provide overview regarding the way findings are to be further used in practise.

Strength and Limitation

The strength of the study is that it used real-time measures for multiple days to determine and assess the participant's experiences in regard to fatigue and glucose level which helps in overcoming issues faced with retrospective data collection method. The other strength is that repeated and multiple assessment of the data helped to ensure reliability of the results as it allowed to capture psychological or biological phenomenon as they happen during real conditions. However, the limitation of the study is missing valuable data out of hindered participants selection who did not entered their fatigue score. The other weakness is that participants with time expressed less responsiveness in sharing data that led to loss of certain valuable information to the collected and limited enriched execution of the study. The other limitation is faced during recording of glucose score where few data for it was lost as a result of interval of time from the introduction of first sensor to the second.

Application to practice

The results relate to nursing practice in the sense that nurses to lower fatigue level in women who are suffering from type-2 diabetes are to focus on controlling blood glucose level to normal. This is because in them the glucose level and fatigue are inversely related to be found in the study. The study results also mentioned that in clinical practice the presence of fatigue in men with type-2 diabetes is considered mainly to be present due to increased depression that is to be controlled by nurses.

Conclusion

The above discussion mentions that the study presented an important topic as fatigue in type-2 diabetes patients is seen to interfere with their ability to manage own care and perform everyday activities. The study did not explain the extent of the problem and briefly highlighted what actions are taken in previous studies to relate fatigue with glucose level fluctuations in tyupe-2 diabetes individuals. The study used correlation research design and performed purposive sampling in selecting participants. A well-defined finding in the study is presented that meets the aim of the research. It explained that fatigue level is related with glucose fluctuation level in women suffering from type-2 diabetes and not in men. For men, the depression is related to fatigue level in type-2 diabetes. The study was done ethically and strength along with limitation were appropriately mentioned.

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References

Apuke, O.D., 2017. Quantitative research methods: A synopsis approach. Kuwait Chapter of Arabian Journal of Business and Management Review, 33(5471), pp.1-8.

Fritschi, C., Park, C., Quinn, L. and Collins, E.G., 2020. Real-Time Associations Between Glucose Levels and Fatigue in Type 2 Diabetes: Sex and Time Effects. Biological research for nursing, 22(2), pp.197-204.

Gusenbauer, M. and Haddaway, N.R., 2021. What every researcher should know about searching–clarified concepts, search advice, and an agenda to improve finding in academia. Research Synthesis Methods, 12(2), pp.136-147.

Holland, K. and Rees, C., 2010. Nursing evidence-based practice skills. Oxford University Press.

Molinuevo, J.L., Rabin, L.A., Amariglio, R., Buckley, R., Dubois, B., Ellis, K.A., Ewers, M., Hampel, H., Klöppel, S., Rami, L. and Reisberg, B., 2017. Implementation of subjective cognitive decline criteria in research studies. Alzheimer's & Dementia, 13(3), pp.296-311.

Pasquetto, I.V., Borgman, C.L. and Wofford, M.F., 2019. Uses and reuses of scientific data: The data creators’ advantage. Issue 1.2, Fall 2019, 1(2).pp.23-56.

Schreier, M., 2018. Sampling and generalization. The Sage handbook of qualitative data collection, pp.84-98.

Serra, M., Psarra, S. and O’Brien, J., 2018. Social and physical characterization of urban contexts: Techniques and methods for quantification, classification and purposive sampling. Urban Planning, 3(1), pp.58-74.

Singh, R., Teel, C., Sabus, C., McGinnis, P. and Kluding, P., 2016. Fatigue in type 2 diabetes: impact on quality of life and predictors. PLoS One, 11(11), p.e0165652.

Titov, N., Dear, B., Nielssen, O., Staples, L., Hadjistavropoulos, H., Nugent, M., Adlam, K., Nordgreen, T., Bruvik, K.H., Hovland, A. and Repål, A., 2018. ICBT in routine care: a descriptive analysis of successful clinics in five countries. Internet interventions, 13, pp.108-115.

Zarei, A.R., Shabani, A. and Mahmoudi, M.R., 2019. Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model. Science of The Total Environment, 661, pp.711-722.

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