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Research has sought to understand insomnia through identification of subtypes, yet age of onset has received limited focused empirical attention. This investigation aimed to detect clinically distinct age of insomnia onset subgroups utilising latent profile analysis (LPA). Participants were 618 adults, aged 18-71 years (M = 28.94, SD = 11.06), with insomnia. Participants completed a survey assessing insomnia natural history and causal attributions; sleep disturbance and impairment; pre-sleep arousal and threat monitoring; stress, mental and physical health; and social functioning. LPA was performed on age of insomnia onset. Binary logistic regression analyses were performed to evaluate associations between clinical measures and early versus late onset insomnia with statistical adjustment for chronological age and sex. Results showed a two-class model (Class 1: n = 547, 88.5%, M = 19.21 years, range = 0-34 years; Class 2: n = 71, 11.5%, M = 43.49 years, range = 35-68 years) was optimal for forming insomnia age of onset subgroups. Bodily arousal and developmental (e.g., childhood experiences, traumatic events) contributors to insomnia onset, greater overall and cognitive pre-sleep arousal, later bedtime and rise time, greater depressive symptoms, and endorsement of lifetime major depressive disorder, migraine, and arthritis were significant indicators of early onset insomnia subgroup membership. Hormonal contributors (e.g., ageing, menopause) to insomnia onset and maintenance, and more positive global mental health were significant indicators of late onset insomnia subgroup membership. Findings suggest the relevance of mindfulness-based, acceptance-based, and trauma-focused adaptations of cognitive-behavioural therapy for early onset insomnia, and management of ageing-related hormonal changes for late onset insomnia.
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http://dx.doi.org/10.1111/jsr.70103 | DOI Listing |
J Clin Psychol
September 2025
Department of Psychology, Sapienza University of Rome, Rome, Italy.
Objectives: Adverse childhood experiences (ACEs) are established risk factors for developing depression in adulthood, although the mechanisms of this association are yet to be fully elucidated. In this study, we tested whether insomnia (i.e.
View Article and Find Full Text PDFFront Cardiovasc Med
August 2025
Department of Medicine, Hualien Armed Forces General Hospital, Hualien City, Taiwan.
Background: The association observed between mental stress and metabolic syndrome (MetS) has varied across studies and may be confounded by physical activity (PA) and fitness status.
Method: This study included a military cohort of 2,854 participants in Taiwan who were not taking any medications and were free of baseline MetS. The Brief Symptoms Rating Scale (BSRS-5) includes five domains-depression, anxiety, hostility, insomnia, and interpersonal sensitivity-measured on a five-point Likert-type scale ranging from 0 to 4, with a maximum score of 20.
Sleep Adv
July 2025
Department of Pediatrics, Division of Child Neurology, Doernbecher Children's Hospital, Oregon Health and Science University, Portland, OR, United States.
Study Objectives: Conduct a multidimensional analysis of sleep perception, objective sleep, and neuropsychiatric wellbeing in individuals with subacute concussion compared to controls.
Methods: Thirty-one recently concussed individuals completed the Pittsburgh Sleep Quality Index, Insomnia Severity Index, and Patient-Report Outcomes Measurement Information System measures of depression, anxiety, stress, and cognitive function. Concussion symptom severity scores (Sports Concussion Assessment Tool) were obtained from participants' health records.
Sleep Adv
July 2025
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA 02118, United States.
The mismatch between rising sleep need and the fluctuating ability to fall asleep underlies insomnia-the most common sleep disorder-yet remains poorly understood. While sleep need increases steadily with time awake, sleep propensity-the likelihood of transitioning from wake to sleep-follows a bimodal pattern, peaking in the mid-afternoon, dipping in the evening, and rising again near bedtime. Building on our previously developed wave model of sleep dynamics, we extend this homeostatic framework to the waking period and show that it predicts the observed bimodal sleep propensity curve.
View Article and Find Full Text PDFFront Public Health
September 2025
Neurosciences Axis, Centre de Recherche du Centre Hospitalier Universitaire (CRCHU) de Québec-Université Laval, Québec City, QC, Canada.
Introduction: Preventive measures have been implemented in hospitals during COVID-19, but how these guidelines affected mental health among healthcare workers (HCWs) remains to be determined. On another note, reliable psychological and blood-based markers are needed to promptly identify HCWs at-risk to develop distress. Extracellular vesicles (EVs) originating from brain cross the blood-brain barrier and are detectable in blood, giving them a highly valuable potential for biomarker discovery.
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