ArticleScientific reports2020
Depression alters the circadian pattern of online activity.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 44 citations in OpenAlex.
- Naturalistic Online Language as a Marker of Depression in Midlife and Older Adults: Computational Text Analysis of Bluesky and Reddit Posts.Journal of medical Internet research · 2026Article
- Temporal Dynamics of Influenza-Associated Anxiety Symptom Linguistic Markers on Weibo (2023-2024): Observational Study.JMIR infodemiology · 2026Observational
- Anxiety and Depression are Associated with More Distorted Thinking on Social Media: A Longitudinal Multi-Method Study.Cognitive therapy and research · 2025Article
- Article
- Approaches to Tailoring Between-Session Mental Health Therapy Activities.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024Article
- Association between physical frailty, circadian syndrome and cardiovascular disease among middle-aged and older adults: a longitudinal study.BMC geriatrics · 2024Article
- Article
- Article
- The Potential of Chatbots for Emotional Support and Promoting Mental Well-Being in Different Cultures: Mixed Methods Study.Journal of medical Internet research · 2023Article
- Text as signal. A tutorial with case studies focusing on social media (Twitter).Behavior research methods · 2023Article
- Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response.NPJ digital medicine · 2023Article
- Investigating the Role of Context in the Delivery of Text Messages for Supporting Psychological Wellbeing.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023Article
- "I Haven't Been Diagnosed, but I Should Be"-Insight Into Self-diagnoses of Common Mental Health Disorders: Cross-sectional Study.JMIR formative research · 2023Article
- Computational analyses identify addiction help-seeking behaviors on the social networking website Reddit: Insights into online social interactions and addiction support communities.PLOS digital health · 2022Article
- Sociodemographics and Transdiagnostic Mental Health Symptoms in SOCIAL (Studies of Online Cohorts for Internalizing Symptoms and Language) I and II: Cross-sectional Survey and Botometer Analysis.JMIR formative research · 2022Article
- Using language in social media posts to study the network dynamics of depression longitudinally.Nature communications · 2022Article
- The Mind After Midnight: Nocturnal Wakefulness, Behavioral Dysregulation, and Psychopathology.Frontiers in network physiology · 2022Article
- Predicting subjective well-being in a high-risk sample of Russian mental health app users.EPJ data science · 2022Article
- Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders.Frontiers in psychiatry · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.