Abstract
Human sleep/wake cycles follow a stable circadian rhythm associated with hormonal, emotional, and cognitive changes. Changes of this cycle are implicated in many mental health concerns. In fact, the bidirectional relation between major depressive disorder and sleep has been well-documented. Despite a clear link between sleep disturbances and subsequent disturbances in mood, it is difficult to determine from self-reported data which specific changes of the sleep/wake cycle play the most important role in this association. Here we observe marked changes of activity cycles in millions of twitter posts of 688 subjects who explicitly stated in unequivocal terms that they had received a (clinical) diagnosis of depression as compared to the activity cycles of a large control group (n = 8791). Rather than a phase-shift, as reported in other work, we find significant changes of activity levels in the evening and before dawn. Compared to the control group, depressed subjects were significantly more active from 7 PM to midnight and less active from 3 to 6 AM. Content analysis of tweets revealed a steady rise in rumination and emotional content from midnight to dawn among depressed individuals. These results suggest that diagnosis and treatment of depression may focus on modifying the timing of activity, reducing rumination, and decreasing social media use at specific hours of the day.
Highlights
Human sleep/wake cycles follow a stable circadian rhythm associated with hormonal, emotional, and cognitive changes
Like most mammals[9], humans experience circadian rhythms involving hormonal, behavioral, and cognitive changes that lead to stable sleep-wake cycles, even when individuals are disconnected from natural d aylight[10,11] or travel across time zones
We find that the activity levels of depressed individuals, like those of a random sample, fluctuate reliably according to a well-defined circadian rhythm as was shown previously[40]
Summary
Comparing hourly Twitter activity levels for two cohorts of respectively “Depressed” and “Random” individuals, we find significant differences in the activity patterns of depressed Twitter users vs. a random sample. The latter difference corresponds to an interesting daily peak of highest activity at 9 PM which occurs in both cohorts This peak of activity levels may correspond to a period of time after dinner and before bedtime which individuals use for recreational social media u se[49]. Social media data offers distinct opportunities for this field of research It allows for the construction of large cohorts to be analyzed with higher statistical power with respect to changes in circadian rhythms, and second, the fact that the tweets are analyzed ex post hoc ensures that the results are not influenced by the Hawthorne e ffect[50]. The analysis of social media and related mobile communication data might shed light on how or whether these platforms affect public health at a global scale[34,49,56]
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