Statistical Analysis
The statistical analysis that was done for this pilot study can be split up in four parts.
The first step was aligning the data pertaining to movement with the data regarding urine production. The first hurdle that had to be crossed was that of the discrepancy in data collection moments for both urine production as well as that of movement. As mentioned above, participants were asked to collect eight urine samples. Contrary to the urine sampling, participants were asked to fill in hourly updates regarding their majority movement of the past hour, resulting in 24 inputs of data for the same day regarding movement. The solution to this was clubbing three inputs of data of movement. Here the range of movements no longer went from 0-4, but now went from 0-12. Because of this, there was now a symmetry in the data input of urine samples as well as that of movement.
The second step was to change the database format in its entirety. The database was in a wide format. This had to be changed to a long format, as otherwise, the analysis that had to be done would have been statistically challenging. As such every participant in the study had 16 different data entries each pertaining to 1 of 8 timeslots of both days.
The third step was to test the hypothesis by conducting a basic statistical test such as the Kruskal Wallis and the Mann Whitney U test. To successfully conduct these tests, movement was divided into three categories, defined as 1,2 and 3. They respectively stood for less, moderate and more movement and physical activity. A p-value of <0,05 was considered as statistically significant.
The fourth and final step was the main statistical analysis. Mixed model statistics were used to analyze the impact of movement on urine production. Here diuresis, osmolality, creatinine and sodium were compared with movement to find any form of association. Parameters of fixed effects were estimated using maximum likelihood estimation and reported as standardized regression coefficients (β) with their respective standard error. A p-value <0.05 was considered as statistically significant. These tests were conducted for the entire day as well as day and night separately. Factors such as “BMI” and “age” were defined as confounders and included in the mixed model.
Statistical analysis was carried out using SPSS 24.