Population research
Sleep and circadian patterns
Sleep schedules, duration, satisfaction, circadian timing and environmental or occupational influences across the lifespan.
Scientific priorities
Understanding sleep
in the context of health.
The Foundation’s research interests span sleep and wakefulness, circadian rhythms, mental and neurologic health, chronic disease and the methods needed to study them across populations and over time.
Research themes
A clinical and population perspective.
Clinical characterization
Insomnia and related complaints
The frequency, persistence and consequences of insomnia symptoms, nonrestorative sleep and sleep dissatisfaction, considered alongside medical and psychiatric context.
Wakefulness
Hypersomnolence and narcolepsy
Excessive sleepiness, sleep attacks, sleep inertia and narcolepsy-related phenomena, with attention to symptom overlap, diagnostic boundaries and everyday impairment.
Mental health
Mood, cognition and function
Relationships between sleep-wake disturbances, depression, anxiety, attention, cognitive complaints and functional change.
Aging and chronic disease
Multimorbidity and disability
Sleep and wakefulness in relation to neurologic and medical conditions, treatment, independence and health burden over time.
Public health
Work, environment and disparities
Work schedules, occupational demands, environmental conditions and differences in access to care or exposure to sleep-related risks.
Methods
Characterizing change over time.
Longitudinal research can examine the onset, persistence, remission and recurrence of symptoms and disorders. Repeated assessment also makes it possible to study changes in treatment, comorbidity and functioning.
Methodological priorities include population sampling, clinical definitions, structured interviewing, cross-national comparability and the distinction between association and causal interpretation.
Explore the Sleep-EVAL scientific recordQuestions that guide the work
- How are sleep-wake symptoms distributed across populations?
- Which symptoms persist or change over time?
- How do clinical context and comorbidity shape the phenotype?
- What are the consequences for cognition, function and disability?
Clinical inference
Ad-Infer and structured phenotyping.
Ad-Infer is an evolving inferential system that uses domain knowledge, including the Sleep-EVAL knowledge base, to support clinical assessment and longitudinal research. Within Ad-Infer, EVAL-KBS combines Type-2 causal reasoning, Zadeh fuzzy logic, neural learning and Bayesian belief updating. Its architecture incorporates Hopfield neural mechanisms and continues to evolve through new learning and language capabilities.
An integrated large language model supports questioning, clarification and interpretation. The interaction provides feedback about questions asked or skipped, comprehension, refusal and responses. Ad-Infer re-evaluates this evidence: it can strengthen a clinical hypothesis or redirect the differential diagnosis. The inference engine retains diagnostic authority throughout this bidirectional process.
This methodology provides a topic for scientific and educational exchange. Maurice M. Ohayon personally holds all rights in Ad-Infer and its associated intellectual property. EVAL Research Institute holds no ownership rights in Ad-Infer.