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Record W4411679074 · doi:10.17241/smr.2025.02824

Prevalence and Associated Factors of Insomnia in Adult Psychiatric Patients: A Hospital-Based Study

2025· article· en· W4411679074 on OpenAlexaff
Jhowhar Datta, Mohammad Tariqul Alam, Taiyeb Ibna Zahangir, Ahsan Aziz Sarkar, Abdullah Muhammad Fariduzzaman, Bijoy Dutta

Bibliographic record

VenueSleep Medicine Research · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsPsychiatryInsomniaPsychiatric hospitalMedicinePsychologyClinical psychology

Abstract

fetched live from OpenAlex

Background and Objective Insomnia is highly prevalent in psychiatric disorders, yet its patterns and predictors vary across diagnoses and cultural contexts. This study examined the prevalence and associations of insomnia in adult psychiatric patients in an outpatient setting.Methods A cross-sectional study was conducted among 380 adult psychiatric patients at a tertiary hospital. Insomnia was assessed using the Structured Clinical Interview for DSM-5 Sleep Disorders–Revised. Sociodemographic, clinical, and lifestyle variables were assessed, and logistic regression analyses were used to identify predictors of insomnia.Results The prevalence of insomnia was 44.2%, with the highest rates observed in trauma-and stressor-related disorders, followed by depressive disorders. Schizophrenia spectrum disorders, obsessive compulsive disorder, and conversion disorder showed significantly lower odds of insomnia compared to depressive disorders. Key predictors of insomnia included lower educational level (0–5 years: adjusted odds ratio [aOR]=5.60, p=0.015), low socioeconomic status (aOR=6.57, p=0.044), comorbid physical illness (aOR=3.81, p=0.005), and prolonged screen time (aOR=8.17, p<0.001).Conclusions Insomnia affects nearly half of adult psychiatric patients, with considerable variation across diagnostic categories. These findings underscore the need for targeted sleep assessment and interventions within psychiatric services, especially in low resource settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.373
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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