Personal Data Collection

Personal data collection is somewhat different from scientific data gathering. Personal data collection is usually less formal and does not happen in a laboratory under controlled conditions. People collect data in the real world where there can be interruptions, bad network connectivity, or limited access to a computer. Users are not necessarily data experts, so when something goes wrong (as it inevitably will), they might not know how to adjust. Therefore, we have to make data collection as simple as possible for the user. It should be unobtrusive, intuitive, and easy to access so that it is more likely that data collection becomes a part of the daily routine.

Working Data Collection into Routine

This is one of the main reasons I chose Twitter as YFD's data proxy from phone or computer to the database. Twitter allows users to post tweets via several outlets. The ability to post tweets via mobile phone lets users log data from anywhere their phones can send SMS messages, which means they can document something as it happens and do not have to wait until they have access to a computer. A person will most likely forget if she has to wait. Accessibility is key.

One could accomplish something similar with email instead of Twitter since most mobile phones let people send SMS to an email address, and this was in fact the original implementation of YFD. However, we go back to data collection as a natural part of daily routine. Millions of people already use Twitter regularly, so part of the challenge is already relieved. People do use email frequently as well, and it is possible they are more comfortable with it than Twitter, but the nature of the two is quite different. On Twitter, people update several times a day to post what they are doing. Twitter was created for this single purpose. Maybe a person is eating a sandwich, going out for a walk, or watching a movie. Hundreds of thousands tweet this type of information every day. Email, on the other hand, lends itself to messages that are more substantial. Most people would not email a friend to tell them they are watching a television program—especially not every day or every hour.

By using Twitter, we get this posting regularity that hopefully transfers to data collection. I tried to make data logging on YFD feel the same as using Twitter. For instance, if someone eats a salami sandwich, he sends a message: "ate salami sandwich." Data collection becomes conversational in this way. Users do not have to learn a new language like SQL. Instead, they only have to remember keywords followed by the value. In the previous example, the keyword is ate and the value is salami sandwich. To track sleep, a user simply sends a keyword: goodnight when going to sleep and gmorning when waking.

In some ways, posting regularity with PEIR was less challenging than with YFD. Because PEIR collects data automatically in the background, the user just has to start the software on his phone with a few presses of a button. Development of that software came with its own difficulties, but that story is really for a different article.

Asynchronous data collection

For both PEIR and YFD, we found that asynchronous data collection was actually necessary. People wanted to enter and upload data after the event(s) of interest had occurred. On YFD, people wanted to be able to add a timestamp to their tweets, and PEIR users wanted to upload GPS data manually.

As said before, the original concept of YFD was that people would enter data only when something occurred. That was the benefit and purpose of using Twitter. However, many people did not use Twitter via their mobile phone, so they would have to wait until a computer was available. Even those who did send SMS messages to Twitter often forgot to log data; some people just wanted to enter all of their data at the end of the day.

Needless to say, YFD now supports timestamps. It was still important that data entry syntax was as close to conversational as possible. To accommodate this, users can append the time to any of their tweets. For example, "ate roast chicken and potatoes at 6:00pm" or "goodnight at 23:00." The timestamp syntax is to simply append "at hh:mm" to the end of a tweet. I also found it useful to support both standard and military time formats. Finally, when a user enters a timestamp, YFD will record the most recent occurrence of the time, so in the previous "goodnight" example, YFD would enter the data point for the previous night.

PEIR was also originally designed only for "in the moment" data collection. As mentioned before, Campaignr runs on a user's mobile phone and uploads GPS data periodically (up to every 20 seconds) to our central server. This adds up to hundreds of thousands of data points for a single user who runs PEIR every day with very little effort from the user's side. Once the PEIR application is installed on a phone, a user simply starts the application with a couple of button presses. However, almost right from the beginning, we found we could not rely on having a network connection 100% of the time, since there are almost always areas where there is no signal from the service carrier. The simplest, albeit naive, approach would be to collect and upload data only when the phone has a connection, but we might lose large chunks of data. Instead, we use a cache to store data on a phone's local memory until connectivity resumes. We also provide a second option to collect data without any synchronous uploading at all.

The takeaway point is that it is unreasonable to expect people to collect data for events at the time they happen. People forget or it is inconvenient at the time. In any case, it is important that users are able to enter data later on, which in turn affects the design of the next steps in the data flow.

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