Evaluation of adherence to Strobe items

The authors have clearly stated the sample size used and they used a sample size of 775. This sample size is big enough to warrant significant statistical results.

In terms of the statistical methods used, the authors failed to mention the statistical methods they used. For instance, the report does not mention the descriptive analysis performed nor the inferential statistics. However, the authors have just presented the results. I was able to pick out Chi-Square test as having been used by the authors to test for the association between variables.

No mention of adjustment of possible confounders by the authors. So in short, the authors have failed to adhere with strobe 12a requirements. This requirements needs the authors to document the statistical methods used and why.

The authors did not mention the methodology of analysing the subgroups. However, in the results section presented in tables 1, 2 and 3, the authors report the subgroups of male and female separately as well as those of different age groups. This was not however mentioned in the statistical methods. This means that item 12b was not complied with.

The authors did not mention about how they went about dealing with the missing data. The results shows that there is evidence of missing data, however, this is not acknowledged anywhere in the report. It is therefore difficult for the reader to ascertain the level of bias that could arise as a result of the missing data. In conclusion the authors failed to comply with item 12c of the strobe.

From the study process, this was a cross-sectional study. The authors adequately addressed how the sampling was done thereby addressing item 12d of the strobe. However, the readers are left wondering why the data collection was restricted to some days of the week only. This could potentially result to bias in the results.

The authors did not mention anything to do with sensitivity analysis.

a) Report number of individuals at each stage of study – eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up and analyses

The authors did mention the proportion of participants in the study. They gave the number of lecturers and students involved in the study-this is in compliance with strobe 12a. However, there was no breakdown of those who dropped from the study.

The authors did not give the reasons for non-participant-this is not documented anywhere in the study. This clearly violates strobe 13b.

Descriptive analysis of data

There is no flow diagram indicating the response. Even though this is not a major risk to bias by the reader, it is in contraction with Strobe 13c.

a) Give characteristics of study participants (e.g. demographic, clinical , social) and information on exposures and potential confounders

Table 1 gives the descriptive statistics of the study participants. For instance, it gives the proportion of female and male participants in the study as well as their ages, ethnicity and income.

Strobe 14b has not been complied with by the authors as could be seen in the report. There are some missing data but the authors did not mention h=on how the missing data was handled. Failure to acknowledge the missing data is wrong since the reader is not informed of potential bias and how this was mitigated.

Strobe 14c is irrelevant in this study based on the fact that this is a cross-sectional survey.

Strobe 15 is well complied with as could be seen in table 3 where the authors documented the findings on adverse effects. This helps the reader to gauge on generalizability of the results.

The authors reported their response rate which was 71.5%. This is important as it shows how representative the results are from the target sample and that the used questionnaire is performing as intended. In terms of analysis of data, the researchers used both descriptive analysis and inferential analysis. The descriptive statistics reported include the mean, median, standard deviation, minimum and maximum. Other measures of interest such as frequencies and proportions for the measures were also reported by the authors. These statistics were correctly reported and presented in the study by the authors.

For the inferential analysis, the authors only used Chi-Square test of association. Chi-Square test of association is important in fishing out whether there is any significant association between two variables (normally categorical or nominal in nature). It was not appropriate for the authors to conduct Chi-squared tests for each transport mode. Transport mode being a variable itself it was advisable for the authors to have conducted a Chi-Square test between say transport mode and gender and not a Chi-squared test for each transport mode as reported by the authors. The authors did however make correct reporting of the p-values. They correctly and appropriately stated and reported the p-values. For instance, the authors did report significance association between the variables when the p-values were found to be less than 5% level of significance.

Inferential analysis of data

The authors clearly gave the boundaries for converting the numeri variables such as age, year in college etc. This clearly shows that strobe 16b has ben complied with.

The results shows that the odd ratios (the estimates of the relative risk). This complies with strobe 16.c.

The report has only presented subgroup analysis but not any other analysis. There is no any other statistical methodology apart from the Chi-Square tests. This means that strobe 17 is not complied with.

                                                                                       Figure 1: Histogram for SED

The above graph shows the histogram of the self-reported alcohol consumption per. The graph shows that the data is skewed to the right.

                                                                                      Figure 2: Boxplot

Figure 2 above shows the boxplot for the self-reported sedentary hours per week. As can be seen, there two groups do not seem to have a normally distributed data for the SED. There are also presence of outliers in the dataset as can be seen from the plots.

A bar chart was plotted to visualize on the type of transport used by the participants. As can be seen in figure 1 below, majority used passenger bus (45.4%, n = 123) as a means of transport. Those who drove themselves were the second majority (29.5%, n = 80). Those who used other means of transport were represented by (25.1%, n = 68).

                                                                            Figure 3: Transport type

Sample size

Another bar chart was plotted to visualize on the type of licence possessed by the participants. As can be seen in figure 2 below, majority used passenger bus (45.4%, n = 123) as a means of transport. Those who drove themselves were the second majority (29.5%, n = 80). Those who used other means of transport were represented by (25.1%, n = 68).

                                                                                Figure 4: licence type

Is there significant difference between the average MVPA for the male and female participants?

The following hypothesis was tested;

This was tested at 5% level of significance.

The results are provided below;

As can be seen, the p-value is 0.1473 (a value greater than 5% level of significance), the null hypothesis is therefore not rejected and we conclude that the mean MVPA is not different for the male and female participants.

We fitted a linear regression model that sought to predict the logMVPA based on the respondents self-reported sedentary hours per week (sed), number of activities attended in the past month (activities) and dummy variable for the male.

The value of R-Squared is 0.096; this means that only 9.6% of the variation in the dependent variable (logMVPA) is explained by the three independent variables in the model. The remaining close to 90% of the variation is explained by factors outside the model (error term).

The model was however found to be significant when it comes to predicting the applications (F(3,96) = 17.102, p = 0.000).

Out of the three independent variables only one (sed) was found to be significant in the model.

• The coefficient for the sed is -0.04; this means that a unit increase in the respondents self-reported sedentary hours per week would result to the logMVPA decreasing by 0.04. Similarly, a unit decrease in the respondents self-reported sedentary hours per week would result to the logMVPA increasing by 0.04.

Considering the significant independent variable only, we the regression model constructed as follows;

The first research question sought to find out whether there is significant difference in the mean MVPA for the male and female participants. Results showed that mean MVPA is not different for the male and female participants.

The second research question sought to determine the significant factors that influence the logMVPA. Results showed that only one variable (respondents self-reported sedentary hours per week) significantly influences the change in logMVPA.