Users' smart devices automatically tracked certain metrics. Based on a user's preference settings and technology on the smart device, anything could be tracked. Data recorded could be obtained when a user went for a walk with having steps and distance recorded, minutes and types of activity performed, or time spent in bed.
The more frequent a user wore their device, the more data was provided. This gave users to access to a large dataset to analyze.
These smart devices readily produced a vast amount of information for users and allowed them to monitor their habits.
As we can see below, the total steps taken in April and May show that a user can go about his/her day without needing to count each step. Although the data below shows the users' sum of steps, each user had provided steps for each day.
Similar to the calculations recorded for users' steps taken in April and May, the distance was also measured. This information gave users a different assessment of how much movement was done.
The graph below shows the total minutes a user was active. The total active minutes were added up from the four categories supplied: sedentary minutes, lightly active minutes, fairly active minutes, and very active minutes. We can see that the smart devices supply our users' with another metric that could be analyzed.
Another metric that the smart devices provided was how much time a user was asleep while they were in bed. Although it is unknown how this data was obtained, there were more users interested in tracking their sleeping habits than uninterested users.
The graph below shows how one user's submission of his/her heart rate shows that the smart device had consistently recorded data. With this, we can see that each day contains a minimum of 60,000 data points, excluding the last day since only a portion of the day's data was given.