ClickDiary: Online Tracking of Health Behaviors and Mood

Background Traditional studies of health behaviors are typically conducted using one-shot, cross-sectional surveys. Thus, participants’ recall bias may undermine the reliability and validity of the data. To capture mood changes and health behaviors in everyday life, we designed an online survey platform, ClickDiary, which helped collect more complete information for comprehensive data analyses. Objective We aim to understand whether daily mood changes are related to one’s personal characteristics, demographic factors, and daily health behaviors. Methods The ClickDiary program uses a Web-based platform to collect data on participants’ health behaviors and their social-contact networks. The name ClickDiary comes from the platform’s interface, which is designed to allow the users to respond to most of the survey questions simply by clicking on the options provided. Participants were recruited from the general population and came from various backgrounds. To keep the participants motivated and interested, the ClickDiary program included a random drawing for rewards. We used descriptive statistics and the multilevel proportional-odds mixed model for our analysis. Results We selected 130 participants who had completed at least 30 days of ClickDiary entries from May 1 to October 31, 2014 as our sample for the study. According to the results of the multilevel proportional-odds mixed model, a person tended to be in a better mood on a given day if he or she ate more fruits and vegetables, took in more sugary drinks, ate more fried foods, showed no cold symptoms, slept better, exercised longer, and traveled farther away from home. In addition, participants were generally in a better mood during the weekend than on weekdays. Conclusions Sleeping well, eating more fruits and vegetables, and exercising longer each day all appear to put one in a better mood. With the online ClickDiary survey, which reduces the recall biases that are common in traditional one-shot surveys, we were able to collect and analyze the daily variations of each subject’s health behaviors and mood status.

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