Mental health problems are highly prevalent among college
student populations and likely increasing in frequency
and severity. The present study, named DemonicSalmon, investigates how social anxiety and depression symptoms manifest in the daily life of 72 students over a two-week study period.
Participants completed a baseline laboratory session, at least 14 days of smartphone monitoring and ecological momentary assessments (EMA), and a follow-up session approximately two weeks later. During the monitoring period, both active EMA assessments and passive mobile sensing data (e.g., GPS, accelerometer) were acquired through an application called Sensus [20] that was built in-house.
Sensus was installed on students’ personal smartphones for the duration of the monitoring period. Sensus was programmed to prompt participants up to 6 times per day between 9:00 AM and 9:00 PM, occurring at random times (RT prompts) within six 2-hour windows; thus, prompts could occur between 2 minutes and nearly 4 hours apart. Each RT prompt collected a set of information about the participant’s momentary state (e.g., momentary positive and negative mood). Sensus was also programmed to deliver an End of Day (EOD) prompt at 10pm every day that included questions from the RT prompts but also different summary questions about the individual’s day (e.g., overall positive and negative mood for that day). It should be noted that Sensus was also programmed to deliver after-call surveys (after any incoming or outgoing calls). However, due to the low base rate of calls made in our sample this data is sparse and will not be discussed further.
In addition to delivering self-report prompts, Sensus also passively collected GPS location data every 150 seconds, accelerometer data (1 HZ), and call and text logs over the course of the study.
The DemonicSalmon dataset has been made publicly available on the web to enhance collaborations. Please use the following reference to cite our data:
![DemonicSalmon study design][1]
[1]: https://photos.app.goo.gl/nsEOcWoKh7wlXPuo1
