Subject A wore the device for the first week and subject B wore the device for the second week. While wearing the device, we collected data on our smartphone app. After two weeks, we analyzed the data by putting the data into charts and graphs so that we could make the necessary comparisons. In addition to just measuring the activities we did, we also kept track of our weight throughout the week to see if our weight changes correlates with the level of activities. In order to keep the results as accurate as possible, we kept several variables constant, such as our diet, water intake, exercise levels, etc.
Results.
Data Analysis.
For the experiment, there were some constants that had to be taken care of because they could mess up the experiment if not kept the same. The first constant was that Subject A and Subject B had to try to keep their diet as constant as possible for the days observed. Calories eaten per day were recorded and kept around the same amount each day. This was done for seven days each for both subjects. Another constant was the bowel movement, which was usually done every two days for both subjects. Both of the subjects weighed themselves every morning around the same time and recorded the data to be looked at until the end of the experiment. We concluded a lot of findings by looking at the data for Subject A. By looking at the graphs and the data tables, you can conclude that Subject A had a slight weight loss for the days following a day of unusually high caloric consumptions. Subject A's second day was an outlier because the subject had a heavy meal. The data did not reveal significant findings. The reason that Subject A had a slight weight loss is due to the fact that she gradually increased her steps. By looking at Subject B's data, you can clearly see that it had no pattern and that the data is not usable. We analyzed the conversion from steps to miles on the FItbit, which was accurate.