I had an opportunity to revisit an article by Burns and colleagues that looks at using data from smartphones (they have a nice appendix of all the data they can get from each phone) to predict things that might trigger episodes of depression. Of course, the data don't contain any specific measures of depression. In order to get those, the researchers had to.... surveys. Once they had those, then they could find the associations with the censor data from the phone. Then they could deliver interventions through the phone.
There are 38 sensors on the phone. The phone delivers data quite frequently. So even a small number of phones (n=8 in this trial) there was quite a large amount of data generated. A bigger trial would have even more data. So this seems like a big data application.
And, in this case the "organic" data from the phone need some "designed" (i.e. survey) data in order to be useful.
This is also interesting in that the smartphone is delivering an intervention -- not just a survey. I've seen other applications that use smartphones to provide health- or mental health-related interventions. It might be that survey methodologists have a role to play in helping to design these kinds of studies.
There are 38 sensors on the phone. The phone delivers data quite frequently. So even a small number of phones (n=8 in this trial) there was quite a large amount of data generated. A bigger trial would have even more data. So this seems like a big data application.
And, in this case the "organic" data from the phone need some "designed" (i.e. survey) data in order to be useful.
This is also interesting in that the smartphone is delivering an intervention -- not just a survey. I've seen other applications that use smartphones to provide health- or mental health-related interventions. It might be that survey methodologists have a role to play in helping to design these kinds of studies.
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