Effect of Physical Training on Quality of Life and Oxygen Consumption - Assignment Example

Marek Klocek, Aleksandra Kubinyi, Bogumila Bacior and Kalina Kawecka-Jaszcz (2005) contributed to the cause of cardiologists with their study on the ““Effect of physical training on quality of life and oxygen consumption in patients with congestive heart failure.” The Poland-based study, however, had certain errors in its research flow, method, and structure which could render their cause null. That is why, this essay will scrutinize the study to check whether the randomized trial the paper authors conducted are in line with CONSORT Statement 2001 Checklist. Title and Abstract It was good that the working title of their report was itself the primary aim indicated in the abstract. The authors were apparently determined to be consistent throughout. However, they failed to narrow down the research title, which was too general. The abstract specifically said that the patients were 42 men but the title overlooked this. Also, though the title may be understandable to readers of varying backgrounds, the abstract looked informal and deviated from the usual academic tone. It had sub-headings, some of which were composed of phrases instead of recognized sentences. The abstract’s opening line “Aim: To assess changes in quality of life (QoL) and oxygen consumption produced by two different patterns of physical training in patients with congestive heart failure (CHF)” was certainly a phrase. Though the abstract gave an accurate synopsis of the report, it was just puzzling why such informality was allowed to be published by professors and by an international journal. The report did mention how participants were allocated into three groups as part of the intervention process, but failed to specify the randomization technique used. Introduction The introduction part was not very direct in conveying its message to readers, whoever they may be. There was never any mention for whom the report was intended – whether it was for aspiring cardiologists, for physical therapists, for patients with CHF beyond the study’s jurisdiction, or for nearly anyone interested in the research. Moreover, the scientific explanations provided in the introduction lacked statistical backing, which would have strengthened the need or the rationale for conducting the research (University of Guelph, n.d.). For instance, the authors merely explained the meaning of congestive heart failure and its association with high mortality the way a medical dictionary would. The meaning did not prove the necessity for employing physical exercises to improve the QoL and oxygen consumption of patients with CHF. Not everyone knows that CHF is one of the leading causes of mortality and myocardial infarction (heart attack). If the paper authors had placed statistics, such as the total number of patients suffering from CHF all over the world (22 million people world-wide per recent studies), then the figures would emphasize the need for physical training as a means to improve QoL and oxygen absorption since readers now had concrete evidence of CHF’s association with high mortality (American Heart Association, cited in Cardioxyl, 2009). The authors also reiterated that quality of life was “a very important area for therapeutic intervention” and maximum oxygen uptake was “a good predictor of prognosis in patients with CHF” without presenting further evidence of such claims. Perhaps, if not statistics, they could have at least concretely mentioned the previous studies that used QoL or VO2 (the maximum amount of oxygen that an individual can make use of during intense exercise) as indicators. Yet what was more ironic was the authors’ statement that “there is no clear evidence what type of exercise training may lead to achieve more favorable effects.” One might wonder about the dependability of the study results if the authors disregarded exercise type from their study’s scope. Methods All 42 participants came from the male sex who were suffering from NYHA II and II class chronic heart failure but “stable and with left ventricular ejection fraction below 40% at echocardiographic (ECG) examination performed no later than one month before inclusion, and lesser than 65 years of age.” The criteria for inclusion were practical given the likelihood of accidents in the course of physical training. However, for 22 million persons with CHF around the world, 42 men as sample population was too inadequate to be able to make valid and representative generalizations of the trial results. There must be sample population (N) large enough so that "reasonably small" sampling errors can be disregarded. It is pointless to gather data with a very small sample because the margin of error enlarges and leads to imprecision (StatSoft, n.d.). The interventions employed by Klocek et al. divided the nature of physical training into three groups. Group A performed physical exercises having constant workload, mostly in the form of cycling. Group B performed varied physical exercises with an increasing workload. The last group, C, was the control group. With only 42 participants, the interventions lessened the likelihood of more representative results. 42 was already that small a number to represent 22 million or so patients with CHF, how much more 14. The training duration was also brief. The authors themselves admitted that they did not know whether beyond 6 months the patients’ quality of life and maximum oxygen uptake would still improve. Hence, the interventions used in the study had no sustainable aims but were only good for the short term. Research objectives were repeated in almost every page and was even hinted at in the report’s working title, so there was no problem with the clarity. Unfortunately, the authors were vague about how they arrived at their sample size. Kocek et al. were keen about the speedy calculations of QoL and Vo2 – not to mention the speedy calculation and analyses of the results from the Psychological General Well-being Index (PGWB), the Subjective Symptoms Assessment Profile (SSA-P), and the ECG and cardiopulmonary exercise tests. Besides the lengthy calculation, such measures were not ideal for calculating QoL and oxygen absorption rates. PGWB and SSA-P entailed personalized answers that could be colored with exaggeration or dishonesty. This consequently affects the accuracy of QoL as indicator. ECG and cardiopulmonary tests were good instruments in measuring cardiac activities, as well as determining blood oxygen consumed after exercise (Cardiac Risk in the Young, 2003). However, VO2 is usually measured through Balke, Bleep, lab or self-calculated tests and blood oxygen and maximum oxygen uptake are certainly different entities, meaning ECG and cardio exams alone are deficient in measuring VO2 (Barder, 2004-2009). The randomization of the trial, including its sequence generation, allocation concealment, and implementation, were all questionable in form and substance. To begin with, all the participants were concentrated on one area, which was the Cardiac Department of the Medical College of Jagiellonian University. Just because the trial was originally university-based, that certainly did not prevent the paper authors from getting patients beyond the Jagiellonian University as there were many patients with CHF strewn about in Krakow, Poland. The authors seemed to conduct the study out of personal expediency because they chose the sample population from within their school. They could have exerted more effort albeit linking with patients from other hospitals was a hassle. The authors mentioned that someone performed the randomization, but did not specify if the person was an author or an outsider specially commissioned for the task. It was also vague whether the trials were done simultaneously or at different dates. All the same, the lack of variety in the backgrounds of the participants implied that they may not have had an “equal and independent chance of selection” (Kumar, 2005, p.169). As for blinding, it was difficult to blind participants who were in a state of activity. Placebo treatments would only hinder them from engaging in all-out physical training despite the participants’ full awareness. Since it was difficult to establish bias with the kind of treatment they were getting, the participants were faced with the problem of “nocebo” instead. If the participants disbelieved that physical training was making them feel better, then the treatment would take a turn for the worse. This had something to do with the psychosomatic conditions of the client while he was being trained, regardless of which randomized group he belonged (Rajagopal, 2007). Since the study used descriptive statistics, the numerical data were neatly and simply presented in tabular format and on a categorical basis. The results from the PGWB, SSA-P, ECG and cardio exams were segregated from one another and summarized in an orderly fashion. Yet no matter how simple the statistical method was, a little miscalculation could affect the rest of the data (Trochim, 2006). Results The participant flow could be discerned through the tabulated statistics. From there, it could be seen how each group had fared compared to the other when they took the PGWB, SSA-P and the ECG and cardio exams. It was the result analyses that were insufficient – one of the disadvantages when using descriptive statistics. The authors mentioned that “mean PGWB total index was similar in groups A and B,” but did not explain why this was so. Neither the recruitment period nor the manner through which participants were recruited was known. It did say that the trial was done between 1999 and 2002 at one centre. Kocek et al. may have thought sharing the information was insignificant or they had some agreed secrecy. However, if researchers really seek truth, then “concealment in any kind of research” is unnecessary (Goodman, 1996, p.134). In fairness also, the baseline demographics and clinical characteristics of each group were impressive presentation-wise. The PGWB total index graph in page 327 particularly showed a good comparison of the baseline data and the performance of each group during the six-month period. It was hard to pinpoint the accuracy rate of the outcomes. The study nevertheless addressed issues of multiplicity by using various analyses: multiple analyses of variance (ANOVA) to calculate the groups’ time difference, the Scheffe test for logical errors, and a multiple linear regression analyses which could account for unknown parameters to establish the relationship of variables. This was a very scientific move, though very reliant on technology (i.e. using Statistica 5.1). Anyhow, this technological dependence also helped them instantly identify important adverse events in each intervention group. Discussion The authors’ interpretation of their results was repetitive and self-conscious, as though they wanted to prove how consistent they were from beginning to end. However, they also tried to explain to readers that their study was by no means very accurate. There were statements that indicated the research had flaws, and though the authors appeared down-to-earth and modest, the statements downplayed their rationale and efforts for embarking on the study to begin with. For example, the Kocek et al. admitted that “it is not known whether the described training models would have a similar beneficial effect on exercise tolerance and quality of life in women with heart failure” or that “the favorable effects of physical training will be maintained in men with CHF” since the study was only for 6 months. The authors undermined the external validity of their study with such admissions. They were able to establish cause-effect relationships between physical training and QoL and VO2, but such cause and effects were only good for 6 months and for males who qualified the criteria they established (Mitchell & Joley, 2007, p.48). In the context of current evidence, the report needed to expound more on its claims. The authors did not make use of the statistics or secondary data already contained in the sources they cited. They were merely repeating the claims of their sources. For instance, they said “Several studies have shown that chronic heart failure patients suffer from depression, anxiety and moderate-to severe emotional distress.” One might ask “What then?” The authors did not indicate who or where these studies were conducted and what were the results, so readers could better understand their relevance. The authors had the habit of presenting open-ended and narrative claims which made one wonder if they qualified as evidence. Conclusion The research in isolation was already a promising one, but its deviation from the CONSORT checklist made it look unreliable. Kocek et al. still had a lot of opportunities to refine the research such as providing information on all necessary items and paying attention to research procedures in future. Reference List Barder, O., 2004-2009. FAQ: VO2 max. [Online] Running for Fitness. Available at: http://www.runningforfitness.org/faq/vo2.php [Accessed 5 January 2010]. Cardiac Risk in the Young (CRY), 2003. SAD sudden arrhythmic death syndrome: tests. [Online] London, England: CRY. Available at: http://www.sads.org.uk/cardiac_tests.htm [Accessed 5 January 2010]. Cardioxyl Pharmaceuticals, 2009. Congestive heart failure. [Online] North Carolina: Cardioxyl Pharmaceuticals. Available at: http://www.cardioxyl.com/product_pipeline/CXL-1020/congestive_heart_failure.php [Accessed 4 January 2010]. Goodman, N., 1996. Secrecy in research. Journal of Medicine, 11 July, 335 (2), pp. 134-136. Klocek, M., Kubinyi, A., Bacior B., & Kawecka-Jascz, K, 2005. Effect of physical training on quality of life and oxygen consumption in patients with congestive heart failure. International Journal of Cardiology, 103, pp. 323-329. Kumar, R., 2005. Research methodology: a step-by-step guide for beginners. Malaysia: Sage Publications. Mitchell, M. & Jolley, J., 2007. Research design explained. Stamford Connecticut: Cengage Learning Inc. Rajagopal, S., 2007. The nocebo effect. [Online] Priory Lodge Education. Available at: http://priory.com/medicine/Nocebo.htm [Accessed 5 January 2010]. StatSoft, n.d. Power analysis. [Online] StatSoft Electronic Statistics Textbook. Available at: http://www.statsoft.com/textbook/power-analysis/ [Accessed 5 January 2010]. Trochim, W., 2006. Descriptive statistics. [Online] Research Methods Knowledge Base. Available at: http://www.socialresearchmethods.net/kb/statdesc.php [Accessed 5 January 2010]. University of Guelph, n.d. Using a scientific research journal article to write a critical review. [Online] Ontario: University of Guelph. Available at: http://www.lib.uoguelph.ca/assistance/writing_services//components/documents/ scientific_review.pdf [Accessed 4 January 2010]. Read More