Sampling procedures and sample size estimation
Dsicuss about the Effect of physical exercise on workplace social.
This is because a sample size of 34 is not representative of the general population. The larger the sample size drawn from the population, the higher the precision. The authors have not indicated how they arrived at sample size 34 out of a population of 460 desk-based police employees in Tasmania in a number of metropolitan regions. Maybe a sample size calculation formula would have been indicated in the article to enable readers to understand the sample collection process (Malterud et al., 2016). However, the method used for sampling was the random sampling method which gives each member of the study population an equal probability and opportunity to be selected as a sample representing the population (Csikszentmihalyi & Larson, 2014). Random sampling therefore offers a non-biased sampling strategy, although the sampling error can occur making the sample size to fail to be a representation of the general population under study (Chen et al., 2018). If the sample is not a representation of the population, this might affect the process of drawing conclusions (Gao et al., 2015). Moreover, since the desk-based police in Tasmania were drawn from different metropolitan sectors, this could have problems during survey and hence a sampling bias.
This is because out of the 34 randomly selected participants, they were grouped into two groups; the control group (n=17), and the intervention group (n=17). Moreover, the selected participants agreed to fill the informed consent form before the study started, an indication that they all committed themselves to take part in the study ((Pedersen et al., 2014). The authors also indicate that there was 100% adherence rate and that there was no participant from either the control or the intervention group who withdrew from the study.
This section can be rated as moderate. This is because, most of the sampling procedures were carried out well except for the sample size estimation (Muhib et al., 2016). Moreover, the researchers divided the sample into two; the control and the intervention groups, consisting of equal number of participants. The two groups would make it easy to make a comparison between the desk-based employees engaging in regular physical activities and those who do not, thus making meaningful conclusions (Morgan et al., 2016).
Yes, the study was described as randomized. Yes the method described was appropriate for this study. The random method was applied in the selection of 34 participants from a total of 460 people. In order to obtain two classes of research process, randomization was also used in the assigning of the sample into either an intervention or control group, by use of the replacement method (Thomas et al., 2015). Additionally, in an effort to monitor the behaviors of the study participants, the research team also made random telephone checks to make sure that the participants were only engaging in the activities that they had been allocated and not any other new activities. A randomized control reduces the possibility of treatment allocation bias during the assigning of various treatments (Faber et al., 2015).
Randomization and comparison of control group
This section can be rated as strong (1). This is because the researchers used a randomized control trial research design in order to lower possibility of bias when testing a new treatment. Moreover, from the article, it is clear that randomization practices were applied in all sections where appropriate and this enabled the researchers to make a clear and concise conclusion about the research findings by comparing the two treatment groups; the intervention and the control groups (Kennes et al., 2015).
While there could be variations among the study participants, the authors did not indicate the differences between them. Such important differences or confounding variables include gender, age, marital status, education and health status, all of which could have significant effects to the results of this study (Lo et al., 2017). It would be important if the researchers indicated these confounder variables among the selected samples so as to understand the extent at which they affect both the dependent as well as the independent variables.
This section can be rated as weak (3). This is because, while the findings can suggest that prolonged sitting on the desk is associated with weight gain, a control group can support this statement by making a comparison with the intervention group (von Thiele Schwarz et al., 2016). However, there are confounding variables which could affect the outcomes of this research such as the amount of food taken, gender variation, education status, age and many more (Jakobsen et al., 2015). There is nothing of this sought which was mentioned at the start of the intervention yet they have significant effect on the outcomes. For instance, if the men were middle aged, while the women were over above fifty years, then age would be a confounding variable which would have a direct effect on the weight gain among these desk-based employees. This is a poor strategy and it could lead to some form of bias.
When these employees selected a physical activity that they chose to engage in, it was upon them to make a decision on how they were to engage in it (Jakobsen et al., 2014). The measurement or assessment of the physical activity was done by the use of a Software which was blinded. In this case, upon the completion of the physical activity chosen, the participants were asked to record the time they used to engage in the physical activity and this enabled the software to log the daily progress which was presented in the form of graphs. This enabled the participants to evaluate the time they spent out of their chairs and the expended calories during the respective activity. Therefore the assessor (software) was not aware of the intervention methods and hence no possibility of bias and errors (Hróbjartsson et al., 2014).
Importance of Blinding and Study Design
The study participants were aware of the research question in this study. This is because before the study commenced, they attended an orientation session. In this orientation, the participants provided an informed consent based on research ethics, and the consent outlines the purpose of the research, as well as the methods to be used so that they can make a choice on whether to participate or not (Grady, 2015). Moreover, the study procedures were discussed to the participants followed by the completion of self-reported energy expenditure at their places of work. During the orientation, the participants were also taught on the negative effects associated with prolonged sitting.
This section can be rated as strong (1). This is because the effects of blinding were considered during the setting up this study. In most cases, bias occurs either intentionally or unintentionally. The researchers were justified to inform the participants concerning the research question and the significance of this study for their health. This made the participants to be committed with the research since it was significant to their health, and to reduce the possibility of withdrawal. This is observed by the fact that there was one hundred percent adherence by the participants. Moreover, the assessors were blinded to avoid the possibility of their influence on the outcomes. This ensures that the information collected is free from bias and it is a true representation of the observations and responses from the participants.
The data collection tools were shown to be valid because the researchers used a combination of two methods, the OPAQ and OSPAQ, which have been previously used as separate methods (Jancey et al., 2014). Since each of these methods has its own limitations, a combination of the two was used in order to overcome the limitations since it’s possible to separate the various physical activities and thus collect separate information for each (Josefsson et al., 2014).
The data collection tools were reliable since they did not have any bias to the obtained findings. An updated formula was formulated in order to calculate the daily energy usage at the workplace including the BMR in the Harris-Benedict equation. In this case, the participants were required to report the average number of hours that were spent in various physical exercise such as standing, sitting, walking and heavy labor. The use of the software to record data was justified because this tool ensured that the participants did not skip the activity. This software was installed in the computers of the intervention group such that during a deactivation, a new screen would pop up with a message to remind the participants to stand and engage in a brief burst physical activity (Malik et al., 2014). Once the data was fed to the system, the software generated entries and graphs of each individual, and hence compliance to the treatment in the intervention group.
Limitations of Confounding variables
This section can be rated as strong (1). This is because the study managed to avoid cases of drop outs, although the participants had initially been allowed to do so upon need. The authors indicates that there was 100% adherence to the study up to the end. This means the data for the control and intervention group were all obtained, and analysis made to make meaningful conclusions about the relationship between physical exercise and health.
This is because out of the 34 study participants, seventeen were allocated to the intervention group where they were to participate in various physical activities for a specified period of time.
The consistency of the intervention was measured because, the participants were required to feed the period of time that they took in the respective intervention into the software which in turn performed an analysis in form of a graph. Moreover, the software was set to give a pop up screen which required the participants to engage in the selected physical activity before being allowed to proceed with the normal duties on their computers.
The study subjects received only the intended interventions, that is, the type of physical activities that they chose for a specified period of time. Initially, they viewed some video demonstrations on how to perform the exertime activity of choice. The type of exertime chosen did not matter but any time that the employees in the intervention group were not seated, this was considered as exertime and the software would be recording the progress. This is an indication that there is no point in time when the subjects ever received a contamination or co-intervention.
The use of the individual unit of allocation in analysis is an indication of the fact that each of the selected participant has similar features as that of the general population. This therefore allows for proper randomization of the participants and prevents the bias among confounding variables, in addition to creating a blinding effect. The unit of analysis was group analysis because, the data obtained from individuals was analyzed as group data either for the intervention or the control group.
The obtained data was analyzed using the PASW data analysis software and results were presented in the form of descriptive statistics. Initially, the authors have used the Crinbach alpha coefficient in order to test this reliability of the obtained data. From this article, it is indicated that a 2X2 mixed ANOVA design was used to test whether there were significant differences on the dependent variables at a critical level of alpha being 0.05. According to Cramer et al., (2016), the ANOVA is crucial in the determination of whether there are any significant differences in an outcome between two groups. In this case, the outcome of the engagement in physical activities was compared against the control group who did not engage in physical activities. However, while the data was not subjected to a priori power test analysis, I am not well conversant with this test as well as its significance in data analysis (Curtis et al., 2015).
Data Collection Methods
While the interventions given are beneficial to the health of people, it is possible that in the process of engaging in the interventions (physical activities), the participants received some health benefits as well (Andersen et al., 2015). This is indicated by the fact that the participants explained that the exertime software enabled them to stand for an additional seven to eight minutes. Thus the e-health passive prompting WHWI lowered the sitting behaviors and hence improved energy expenditure and improved health as well (Jakobsen et al., 2015).
References
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