A new study published in JMIR Mental Health has revealed that searches relating to Depression and its treatment spike at different times of the day. The data shows that searches around depression peak between 11pm and 4am daily, showing a clear focus overnight. The reason for this isn’t immediately clear but may tell us more about the day to day lives of patients. With depression a growing problem, these insights may help us to recognize depression earlier. And, with that, to help people access treatment.
Depression and Diurnal Variation.
It is common knowledge that the symptoms of depression vary in severity over the day. The classical picture of waking up ‘sad’ and ‘feeling better’ later is one of just many. But little has been done to actually quantify this in real terms over a large population. The researchers reviewed search data of key terms around ‘depression’ to elucidate a pattern. They found four peaks between 11pm and 4am, and relative ‘troughs’ between 5am and 10pm. Essentially, people were trying to learn more about depression and take ‘online tests’ during the night.
It’s hard to directly claim what this may mean without considering multiple factors. The first is the simple issue of time availability, with daily work commitments limiting free time to search. The second is that more people are likely to sit on computers overnight, increasing the likelihood of searching for anything. However, the team’s statistical analysis shows a clear difference between searches at different times, suggesting something significant. What this may mean in actuality will require further study. But it could be very useful information indeed.
Why May People Search For Depression At Night?
Aside from the reasons discussed above, it may be that this pattern is well explained by what we already know. Depression is known to be associated with anxiety and sleep disturbance. We also know that with depression, in many cases people feel better later in the day. It is very possible, with a number of assumptions, that the combination of these two factors may explain the findings. If people feel more energized later in the day and overnight, and cannot sleep due to their symptoms, there would be peaks in activity. The data would seem to support this idea.
However, further research is needed to compare these findings with the actual patients themselves. It would not be fair to assume without more supporting evidence. The data itself does not reveal whether those looking for ‘depression’ related information were actually suffering from depression, suspicious of being depressed, or simply interested in the condition. There is a lot of conjecture. But what the study does provide is strong evidence that people are more active in learning about depression at night.
Given what we know about depression, this may provide a new way of recognizing depression in the undiagnosed, and helping to monitor treatment success in patients.
A New Way Forward
Depression is a growing problem and early intervention is key in its treatment. This new data shines light on the daily lives of patients and the undiagnosed. It may be that recognizing the patterns tells us not only more about the nature of depression but could provide an early warning system for those at risk. But how this may work in practice is another question altogether. The first step may be interviews with those concerned, establishing the nature of their condition. Only then could a suggestion be made that ‘search engine’ data be instrumental in earlier diagnosis.
So what do you think? Could search engine data help doctors diagnose depression earlier? Are you concerned about privacy? Is there a happy medium between? Let us know in the comments.
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The opinions expressed in this article are those of Dr. Janaway alone and may not represent those of his affiliates. Featured image courtesy of Flickr. If you are concerned about your health please see your local healthcare provider. Sources available within main text via hyperlinks. If you find new information that contradicts this article, or feel that an error has been made, please do let me know via email@example.com. I will be happy to hear from you.