LogFrog DB provides two methods of exporting your data. You can e-mail the data or upload to your Google account and view using Google Spreadsheet.
When you use the e-mail option, the recipient receives a nice chart of the data. Entries that are grouped together in the text log are grouped together by color in the e-mailed log. Because the data is embedded in the e-mail as HTML tables and not as an attachment, you cannot save the data outside the e-mail without a work around. You can print the data, but if using web based e-mail you may end up printing the entire web page and not just the data. With an e-mail client like Outlook you can print the e-mail alone. If you are printing from a web based e-mail portal and the color is not printed, you need to change the browser settings to print background colors. Being able to see the data with its colored backgrounds helps you better analyzed the data.
If you wish to digitally save the data outside an e-mail, find the option to view original or unformatted data. You will see pure text without formatting. Scroll down to where the table information starts and select from the <table> tag to the end of the document and copy the text. Open notepad or other plain text editor and paste. Save the file with a .html extension. When you click on the file your default browser will display the information with all color backgrounds intact. Of course all this could be avoided if the exported data was sent as an attachment and not embedded. Maybe in the next version this change will be made. If it does I will update this review.
The other method of exporting data is to your Google account. Once you configure LogFrog DB with your Google account information and approve the required permissions, you backup your data to your Google documents folder from the LogFrog DB export page. After you perform the backup you can see the file in your Google Documents section. When you click on the file, a Google Spread sheet opens and you see a mess. The data is all there, but hard to use unless you are a spread sheet guru. That's OK because there are some nifty instructions provided to create a very useful time line graph.
Follow the instructions provided, which are only a few steps, and you are presented with an interactive chart. On the right of the chart you see a series of data point descriptions. These descriptions are generated from the data groups we saw in the apps text logs. If a glucose reading was bound to a carb entry because it was taken two hours or less after the carb entry, the exact time difference is proved and a corresponding label is placed on the charts time line. If you exercised and recorded a glucose level an hour later, a tag is created to show you the affect the exercise had on your blood sugar. If you recorded taking insulin 30 minutes before that same glucose reading that is also included in the same tag. I found this chart very useful. The only issue I have is I am not familiar enough with Google Spread sheets to know how to export this chart with this useful graph intact. I can download the spreadsheet so I guess someone skilled in a desktop spreadsheet application can duplicate the chart. The data is all there. Since Google is not going anywhere, keeping my data in Google Documents is about as safe and reliable of a location I can think of myself.
LogFrog DB on my iPhone has been a valuable tool for helping me manage my diabetes. Regular blood glucose testing is very important for any diabetic. Recording those readings and events that affect glucose levels can greatly improve your understanding of what steps you need to take to improve your health.
When entering data is inconvenient, cumbersome or boring, I have a hard time recording the information I know I should. This app is not inconvenient, not cumbersome and not boring.
LogFrog DB Website (opens new window)
In the next two articles we’re going to discuss the concept of "normal" blood sugar. I say concept and put normal in quotation marks because what passes for normal in mainstream medicine turns out to be anything but normal if optimal health and function are what you’re interested in.