EPID6430 Advanced Epidemiology

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EPID6430 Advanced Epidemiology

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EPID6430 Advanced Epidemiology

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Course Code: EPID6430
University: The University Of Newcastle

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Country: Australia

Question:
Application of the epidemiological approach to critically evaluate evidence is a key skill in the health sciences. Assignment 3 asks students to draw on their accumulated knowledge of concepts and skills in epidemiology to critically appraise the association between an exposure and an outcome as reported in a peer-reviewed epidemiological study. 
Task
Assignment will Involve students choosing a peer-reviewed journal article from 2 or 3 options given by the UG and assessing the extent to which there is a causal association between the main exposure and outcome. 
 
Prospective study of the associations between television watching and car riding behaviors and development of depressive symptoms.
Answer:

Introduction
Ideally, every researcher and physicians are familiar with the overall effect a particular finding from a published journal can have on the entire medical practice (Edwards & Loprinzi, 2016). There are various journals that have been published regarding sedentary behaviors and their effects (Adamson, Yang, & Motl, 2016). In essence, the aspect of sedentary behavior often defined by the seating or rather reclining posture has been hugely recognized as a risk factor particularly regarding negative health impact different as well as distinct from physical activities (Ku, Fox, & Chen, 2016). Consequently, there are various growing evidences ranging from observational point of view to experimental research supporting the connection between sedentary behaviors to the overall poor health outcomes including cancer, obesity, diabetes, and hypertension (Otsuka, Nishii, Amemiya, Kubota, Nishijima, & Kita, 2016). In this light, there are a various organization as well as countries that are set to give recommendations concerning limiting the aspect of sedentary time in both youth and adult (Commissaris et al., 2016). This paper intends to closely appraise the focused article on the aspect of longitudinal association that lies between the sitting behavior as well as the overall risks of developing symptoms related to depression (Elhai, Levine, Dvorak, & Hall, 2016).
Study Design
Ideally, there is various study designs that authors tend to adhere to with an aim of presenting their gathered data and finally arriving at a result. For this research study, the author used a rather analytical study design and in specific the RCTs design strategy. The author utilized the ACLS which is referred to as a prospective study of men as well as women who have undergone an examination by the Cooper Clinic in Dallas, TX (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). The patients were gathered as they came to the hospital to be examined for preventative medicine as well as for counseling regarding lifestyle behaviors. While there was the use of the RCTs study design, the author did not clearly state if it was used in the research. In essence, RCTs often acts as the cornerstone for the evidence-based medicine (EBM). Based on the rules and regulations of a Good Clinical Practice (GCP), they no doubt exhibit various strengths despite the fact that they as well present some weaknesses. The rigorous methodology that was used by the author allows the research to avoid biasness to some extent as a result of the confounding factors through a control group of both women and men (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). In this light, the study design is deemed suitable for this kind of research involving sedentary behaviors (Loprinzi & Sng, 2016). The study was of adequate length in the overall experimentation. However, there were enough visits for the collection of data as the author stated that surveys were administered every 4 to 5 years with an aim of collecting additional data.
NB: Before critical appraisal, I no doubt consider this study strong and, therefore, relevant for the topic at hand.
Methods
Subject/Participants/Patients/Population
For the purpose of presenting recent analysis, the author used from participants particularly those that responded to the 1982 survey and thus it was used as the baseline measure. Notably, the data was used as the baseline because it was the first time that type of information on the overall sedentary behaviors was gathered. (Ellingson, Meyer, & Cook, 2016)  In this light, the survey made use of 11,972 participants while returning the 1982 survey having close to 77 percent response rate (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). As a selection criterion, the author used both the exclusion and inclusion criteria.  Consequently, the study excluded about 1,830 people who were thought to have met some specific criteria.
Those that were excluded were thought to have met the following threshold: coronary bypass operation (n=560), stroke (n=43), cancer (n=476), those that often depressed or were feeling generally sad (n=406), age <18 or rather age>100 (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). The inclusive criteria selected over a legible participant of about 4,802 where they completed they were asked to complete the 10-item Center for the Epidemiological Studies Depression Scale (CES-D 10) between the year 1990 and 2005 (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). Apparently, this became the final analytical sample for the study at hand. This showed there was some sort of selection bias despite the fact that RCTs tend to avoid such scenarios of biases.  However, the sample size was large enough to be analyzed statistically.
In essence, the participant in the study was requested to recall their average hours they have been spending watching television as well as driving their cars in recent times (Hoare, Milton, Foster, & Allender, 2016). As a result, the current study categorized for the sedentary behavior was divided into three types where all were rounded up to nearest one hour (Yang, Shin, Li, & An, 2017). The author used a similar approach in previous studies thus arriving at some conformity of the outcomes. The study assessed the depressive symptoms using the CES-D 10 after the baseline visit. In this light, the study asked all the participants to respond to at least ten items from a period ranging from less than one day to about 5 to 7 days (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). The set time for the response was adequate thus allowing those involved to give accurate responses in the long run (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). The survey used up to eight CES-D items that were set specifically to assess various aspects of depression, while they maintained a rather two reverse scored assessing a happy mood for the participants. In this light, the CES-D 10 was validated as having a good reliability for the overall population. Although the use of the CES-D 10 is considered as a screening measure instead of a diagnostic tool, it is regarded to showcase the presence of rather elevated depressive signs and symptoms in the current study as explained by the author (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015).  
Baseline Measures
The study started with the same baseline of the population where the demographic, socioeconomic status, as well as the health-related questions, included the aspect of gender, age, race, education, marital status employment status and the overall presence of hypertension as well as diabetes. Due to the fact that the article was written based on the research that was done for the first time, there were no valid prognosis factors outlined by the author. The study assessed the physical activity by the use of questions where the participants were asked to report their overage time they spend doing moderate as well as moderate and vigorous activities. The study further classified the MVPA into rather three groups, that is <2.5, 2.5–4.9, as well as ≥5 hours per week (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). Additionally, the study included other covariates such as smoking, alcohol intakes, and participants body mass index. The survey indicated variation in groups that were assessed at the beginning of the trial. As a result, these differences were identified as the confounders for the overall outcomes. The study as well as assessed the cardiorespiratory fitness by the use of a rather maximum treadmill exercise test. While there were some variations, the study did not identify these as a limitation to the intended outcome. More importantly, the study was not in a position of assessing changes particularly in the sitting time that happened during the available long time follow-ups. Additionally, some participants were thought to have changed their TV or rather car driving period. Follow Up and Accountability In essence, the study utilized exclusion and inclusion strategy, therefore, accommodating most of the participants who had passed the required threshold. Nonetheless, there were questions on whether the participants or rather the subjects were accounted for at the very end of the study. As a result of the missing CES-D 10 data at the baseline, various people especially those who were reported depressed or feeling sad at the baseline were excluded from the study on the basis of mental health concerns (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). In this regards, there is the likelihood that there are some individuals at the baseline who had highly depressive conditions were included unintentionally in the analysis sections (Liao, Shibata, Ishii, & Oka, 2016). Notably, this indicated that the current study was not accountable to some extent especially on the side of the participants. Essentially, this limitation led to some unintended changes in the outcome. Nonetheless, it was clear that a small number of women had limited the ability of the experiment to conduct a rather gender-specific analysis, thus raising questions on the accountability of the entire participants to the study (Zahl, Steinsbekk, & Wichstrøm, 2017). However, the study did not reveal the exact number of those that were not accounted for as a result of some factors. Ethical Approval In most cases, carrying out an ethical approval of the participants before they take part in a particular research or experiment of any kind is imperative (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). In essence, this is because virtually all researches that involve human beings have some extent of risks involved. In this light, the purpose of an ethical approval is to make sure that both the participants a well as the researchers are protected. Baring in mind that the participants are required to have enough details to make rather informed and overall autonomous decisions, the current research excluded those people that are under the age of 18 and those who are above the age of 100 years (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). Analysis and validity Determining the validity of an analysis in a research is imperative especially if the purpose of the study is to be used for a critical subject such as clinical research. The data for the current research was analyzed by the use of SAS, version 9.3 setting alpha at p<.05 (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). Based on the nature of the current study and the fact that it used a large sample size of the population, the method used for analysis was valid. The statistical method characterized the baseline of the study by using a rather depressive symptoms status. The author described the statistical method sufficiently this analyzing the data expansively. Apparently, differences in covariates were analyzed by the use of Student t-tests as well as the chi-square tests. The analysis assessed the linear trends that were associated with sedentary behavior particularly with the risk of depressive symptoms (Suchert, Hanewinkel, & Isensee, 2016). Additionally, the article performed a rather stratified analysis across the entire MVPA groups with an aim of assessing whether MVPA modified the overall connotation between sedentary behavior as well as the depressive symptoms (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). However, it was not clear whether his trial was used in testing new methods of measurements as well as analysis. Due to the fact that the magnitude of the connotation from the current analysis was similar to the main findings that were reported by the altering self-reported MVPA, it was not reported in the manuscript. The Validity of the Results Primarily, assessing the overall validity of a research's results encompasses addressing three main issues. First is whether the study tends to ask a clear and valid clinical question (Corvey, Menear, Preskitt, Goldfarb, & Menachemi, 2016). Notably, this explores the intensity in which the article defines the population of interest, the overall nature of the interventions used, and the expected clinical outcomes. The clinical question presented in the current study is partly defined. Despite the fact that the outcome of interest-the associations between television watching and car riding behaviors and development of depressive symptoms- is quite obvious while the interventions are clear, the article is to some extent less clear regarding the population of interest as well as the standard care for sedentary behavior (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). The entire study population was not recruited at a rather standardized procedure but instead on the basis that anyone below the age of 18 and those that were above 100 years was excluded from participating in the experiment. The second question looks at whether the study's design is appropriate to the clinical question at hand (Garcia, Cox, & Rice, 2017). Depending on the overall nature of a particular treatment or rather test, there is those studying design that may seem appropriate to the question instead of others. While there is various study design that could have been used, the nature for the intervention at hand called for the application of randomized trials. Despite the fact that the intervention was going, it was possible to interpret the average effect or sedentary behaviors on the participants. The third question seeks to look at whether the study had been conducted in a rather methodological sound way. Notably, this part explores whether there are additional biases introduced apart from those introduced by the design (Gunnell et al., 2016). Due to the fact that the study was undertaken for the first time, there was no baseline that could be compared to the involved groups (Patterson, Malone, Lozano, Grandner, & Hanlon, 2016). It is not clear whether there are additional biases in the study as the author fails to showcase them in the experiment and in the result. What Were the Results? The results of the finding were as follows, over the average ranging up to a follow up of 9.3 years, there were about 568/4802 individuals who were thought to have developed rather depressive symptoms (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015). Notably, mainly younger, female, as well as a rather long time spending in a driving car, watching the car, and overall sedentary behavior, developed depressive symptoms. The study associated the time spent watching television and driving car to developing depressive symptoms. Moreover, the study found out that those participants who watched TV for more than 10 hours in every week had 42 percent more chances of developing depressive symptom than those individuals that spent less than 5 hours doing the same (Sui, Brown, Lavie, West, Pate, Payne, & Blair, 2015).  In general, the study was in a position of linking sedentary behaviors such as long hours of watching TV and riding a car to developing depressive symptoms. References Adamson, B. C., Yang, Y., & Motl, R. W. (2016). Association between compliance with physical activity guidelines, sedentary behavior and depressive symptoms. Preventive medicine, 91, 152-157. Commissaris, D. A., Huysmans, M. A., Mathiassen, S. E., Srinivasan, D., Koppes, L. L., & Hendriksen, I. J. (2016). Interventions to reduce sedentary behavior and increase physical activity during productive work: a systematic review. Scandinavian journal of work, environment & health, 42(3), 181-191. Corvey, K., Menear, K. S., Preskitt, J., Goldfarb, S., & Menachemi, N. (2016). Obesity, physical activity and sedentary behaviors in children with an autism spectrum disorder. Maternal and child health journal, 20(2), 466-476. Edwards, M. K., & Loprinzi, P. D. (2016, August). Effects of a sedentary behavior–inducing randomized controlled intervention on depression and mood profile in active young adults. In Mayo Clinic Proceedings (Vol. 91, No. 8, pp. 984-998). Elsevier. Elhai, J. D., Levine, J. C., Dvorak, R. D., & Hall, B. J. (2016). Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use. Computers in Human Behavior, 63, 509-516. Ellingson, L. D., Meyer, J. D., & Cook, D. B. (2016). Wearable technology reduces prolonged bouts of sedentary behavior. Translational Journal of the American College of Sports Medicine, 1(2), 10-17. Garcia, J. M., Cox, D., & Rice, D. J. (2017). Association of physiological and psychological health outcomes with physical activity and sedentary behavior in adults with type 2 diabetes. BMJ Open Diabetes Research and Care, 5(1), e000306. Gunnell, K. E., Flament, M. F., Buchholz, A., Henderson, K. A., Obeid, N., Schubert, N., & Goldfield, G. S. (2016). Examining the bidirectional relationship between physical activity, screen time, and symptoms of anxiety and depression over time during adolescence. Preventive medicine, 88, 147-152. Hoare, E., Milton, K., Foster, C., & Allender, S. (2016). The associations between sedentary behaviour and mental health among adolescents: a systematic review. International journal of behavioral nutrition and physical activity, 13(1), 108. Ku, P. W., Fox, K. R., & Chen, L. J. (2016). Leisure-time physical activity, sedentary behaviors and subjective well-being in older adults: An eight-year longitudinal research. Social Indicators Research, 127(3), 1349-1361. Liao, Y., Shibata, A., Ishii, K., & Oka, K. (2016). Independent and combined associations of physical activity and sedentary behavior with depressive symptoms among Japanese adults. International journal of behavioral medicine, 23(4), 402-409. Loprinzi, P. D., & Sng, E. (2016). The association of changes in screentime sedentary behavior with changes in depression symptomology: prospective pilot study. J Behav Health, 5(3), 140-4. Otsuka, T., Nishii, A., Amemiya, S., Kubota, N., Nishijima, T., & Kita, I. (2016). Effects of acute treadmill running at different intensities on activities of serotonin and corticotropin-releasing factor neurons, and anxiety-and depressive-like behaviors in rats. Behavioural brain research, 298, 44-51. Patterson, F., Malone, S. K., Lozano, A., Grandner, M. A., & Hanlon, A. L. (2016). Smoking, screen-based sedentary behavior, and diet associated with habitual sleep duration and chronotype: data from the UK Biobank. Annals of Behavioral Medicine, 50(5), 715-726. Suchert, V., Hanewinkel, R., & Isensee, B. (2016). Screen time, weight status and the self-concept of physical attractiveness in adolescents. Journal of adolescence, 48, 11-17. Sui, X., Brown, W. J., Lavie, C. J., West, D. S., Pate, R. R., Payne, J. P., & Blair, S. N. (2015, February). Associations between television watching and car riding behaviors and development of depressive symptoms: a prospective study. In Mayo Clinic Proceedings (Vol. 90, No. 2, pp. 184-193). Elsevier. Yang, Y., Shin, J. C., Li, D., & An, R. (2017). Sedentary behavior and sleep problems: a systematic review and meta-analysis. International journal of behavioral medicine, 24(4), 481-492. Zahl, T., Steinsbekk, S., & Wichstrøm, L. (2017). Physical activity, sedentary behavior, and symptoms of major depression in middle childhood. Pediatrics, e20161711. Free Membership to World's Largest Sample Bank To View this & another 50000+ free samples. 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