The testing of an ordinal scale requires non-parametric statistical tests. Mean and standard deviation are invalid parameters for descriptive statistics whenever data are on ordinal scales, as are any parametric analyses based on the normal distribution.
The report of Allen & Seaman, 2007 describes a number of possible tests:
Nonparametric procedures—based on the rank, median or range—are appropriate for analyzing these data, as are chi-squared statistics.
Notably, Kruskall-Wallis models can be used to replace a standard parametric analysis of variance, as it is based on the ranks and not the means of the responses. Given these scales are representative of an underlying continuous measure, one recommendation is to analyze them as interval data as a pilot prior to gathering the continuous measure.
However, non-parametric tests are notoriously low in statistical power. There is a way of making an ordinal Likert scale like the ones you use continuous by using a ruler or slider, see the following figure (taken from Allen & Seaman, 2007):
This trick makes it continuous and normal parametric tests can be used, ramping up statistical power substantially. It is a lot more work though to analyze the data. Especially when the subjects indicate their perceived pain/depression by physically putting a mark on a paper ruler, as you have to manually measure the responses. A digital slider can make your life easier. If you are planning to do hundreds of subjects you should carefully think about the possible options.