Subtopic Deep Dive
Insomnia Epidemiology
Research Guide
What is Insomnia Epidemiology?
Insomnia epidemiology examines the prevalence, incidence, risk factors, and population patterns of insomnia across demographics, age groups, and comorbidities using cohort studies and meta-analyses.
Studies report insomnia prevalence at 10-30% in adults, with higher rates in elderly populations (Foley et al., 1995; 1598 citations). Key tools include the Insomnia Severity Index for case detection (Morin et al., 2011; 4322 citations). Ohayon (2002; 3654 citations) summarizes known gaps in global epidemiology.
Why It Matters
Insomnia epidemiology informs public health screening and policies, as seen in COVID-19 impacts on medical workers (Zhang et al., 2020; 1704 citations). Longitudinal data links sleep disturbances to psychiatric disorders in young adults (Breslau et al., 1996; 1829 citations), guiding comorbidity interventions. Actigraphy enables large-scale studies of sleep patterns in elderly communities (Ancoli-Israel et al., 2003; 2675 citations), supporting targeted prevention.
Key Research Challenges
Heterogeneity in Diagnostic Criteria
Studies use varying definitions of insomnia, complicating prevalence comparisons (Ohayon, 2002). Morin et al. (2011) validate ISI but note population detection limits. Meta-analyses struggle with inconsistent thresholds.
Longitudinal Causal Inference
Cross-sectional data dominate, hindering causality between short sleep and obesity (Cappuccio et al., 2008; 2032 citations). Breslau et al. (1996) provide rare longitudinal evidence for psychiatric links. Confounders like age persist.
Underrepresentation of Subpopulations
Elderly complaints are documented (Foley et al., 1995), but global and minority data gaps remain (Ohayon, 2002). Actigraphy aids but PSG validation is resource-intensive (Ancoli-Israel et al., 2003).
Essential Papers
The Insomnia Severity Index: Psychometric Indicators to Detect Insomnia Cases and Evaluate Treatment Response
Charles M. Morin, Geneviève Belleville, Lynda Bélanger et al. · 2011 · SLEEP · 4.3K citations
These findings provide further evidence that the ISI is a reliable and valid instrument to detect cases of insomnia in the population and is sensitive to treatment response in clinical patients.
Epidemiology of insomnia: what we know and what we still need to learn
Maurice M. Ohayon · 2002 · Sleep Medicine Reviews · 3.7K citations
The Role of Actigraphy in the Study of Sleep and Circadian Rhythms
Sonia Ancoli‐Israel, Roger J. Cole, Cathy Alessi et al. · 2003 · SLEEP · 2.7K citations
In summary, although actigraphy is not as accurate as PSG for determining some sleep measurements, studies are in general agreement that actigraphy, with its ability to record continuously for long...
Meta-Analysis of Short Sleep Duration and Obesity in Children and Adults
Francesco P. Cappuccio, Frances Taggart, Ngianga-Bakwin Kandala et al. · 2008 · SLEEP · 2.0K citations
Cross-sectional studies from around the world show a consistent increased risk of obesity amongst short sleepers in children and adults. Causal inference is difficult due to lack of control for imp...
Sleep disturbance and psychiatric disorders: A longitudinal epidemiological study of young Adults
Naomi Breslau, Thomas Roth, León Rosenthal et al. · 1996 · Biological Psychiatry · 1.8K citations
Mental Health and Psychosocial Problems of Medical Health Workers during the COVID-19 Epidemic in China
Wen-rui Zhang, Kun Wang, Lu Yin et al. · 2020 · Psychotherapy and Psychosomatics · 1.7K citations
<b><i>Objective:</i></b> We explored whether medical health workers had more psychosocial problems than nonmedical health workers during the COVID-19 outbreak. <b><...
Short- and long-term health consequences of sleep disruption
Goran Medić, Micheline Wille, M. Hemels · 2017 · Nature and Science of Sleep · 1.6K citations
Sleep plays a vital role in brain function and systemic physiology across many body systems. Problems with sleep are widely prevalent and include deficits in quantity and quality of sleep; sleep pr...
Reading Guide
Foundational Papers
Start with Ohayon (2002; 3654 citations) for epidemiology overview, then Morin et al. (2011; 4322 citations) for ISI detection, and Foley et al. (1995; 1598 citations) for elderly prevalence baselines.
Recent Advances
Study Zhang et al. (2020; 1704 citations) for COVID impacts and Medić et al. (2017; 1604 citations) for sleep disruption consequences post-2015.
Core Methods
ISI scoring (Morin et al., 2011), actigraphy over sleep logs (Ancoli-Israel et al., 2003), longitudinal cohorts (Breslau et al., 1996), and meta-regression for risks (Cappuccio et al., 2008).
How PapersFlow Helps You Research Insomnia Epidemiology
Discover & Search
Research Agent uses searchPapers and exaSearch to find epidemiology papers like Ohayon (2002), then citationGraph reveals clusters around Morin et al. (2011; 4322 citations) and findSimilarPapers uncovers related prevalence studies.
Analyze & Verify
Analysis Agent applies readPaperContent to extract prevalence rates from Foley et al. (1995), verifies meta-analysis claims with verifyResponse (CoVe), and uses runPythonAnalysis for pooling odds ratios from Cappuccio et al. (2008) with GRADE grading for evidence strength.
Synthesize & Write
Synthesis Agent detects gaps in longitudinal data via gap detection, flags contradictions between cross-sectional obesity links (Patel & Hu, 2008), and Writing Agent uses latexEditText, latexSyncCitations for ISI-focused reviews, plus latexCompile and exportMermaid for comorbidity flowcharts.
Use Cases
"Run meta-regression on insomnia prevalence by age from cohort studies."
Research Agent → searchPapers('insomnia epidemiology cohorts') → Analysis Agent → runPythonAnalysis(pandas meta-regression on extracted rates from Foley 1995, Ohayon 2002) → CSV export of age-stratified ORs with p-values.
"Draft LaTeX review on ISI validation in population studies."
Research Agent → citationGraph(Morin 2011) → Synthesis Agent → gap detection → Writing Agent → latexEditText(structured abstract), latexSyncCitations(Ohayon 2002), latexCompile → PDF with prevalence tables.
"Find GitHub code for actigraphy analysis in insomnia epidemiology."
Research Agent → paperExtractUrls(Ancoli-Israel 2003) → Code Discovery → paperFindGithubRepo → githubRepoInspect → validated R script for sleep-wake scoring from elderly data.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ insomnia epidemiology papers, chaining searchPapers → citationGraph → GRADE grading for prevalence meta-synthesis. DeepScan applies 7-step analysis with CoVe checkpoints to verify Ohayon (2002) gaps against recent cohorts like Zhang et al. (2020). Theorizer generates hypotheses on COVID-era insomnia trends from Breslau et al. (1996) longitudinal patterns.
Frequently Asked Questions
What defines insomnia epidemiology?
It studies prevalence, risk factors, and patterns of insomnia via population surveys and cohorts, as in Ohayon (2002) and Foley et al. (1995).
What are main methods?
Cohort studies, actigraphy (Ancoli-Israel et al., 2003), and validated scales like ISI (Morin et al., 2011) assess cases; meta-analyses pool cross-sectional data (Cappuccio et al., 2008).
What are key papers?
Morin et al. (2011; 4322 citations) on ISI; Ohayon (2002; 3654 citations) on epidemiology overview; Breslau et al. (1996; 1829 citations) on psychiatric links.
What open problems exist?
Causal inference needs more longitudinal data (Ohayon, 2002); subpopulations underrepresented (Foley et al., 1995); pandemic effects require follow-up (Zhang et al., 2020).
Research Sleep and related disorders with AI
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Part of the Sleep and related disorders Research Guide