The Sleeper Advantage:
Elevating Peak Performance For High Achievers Though Optimized Sleep Andrew Spector, MD, FAASM, FAAN Professor of Neurology Duke Athletics Sleep Optimization Consultant
Disclosures • Royalties from Oxford University Press for Navigating Life with Restless Legs Syndrome • Consultant/Speaker: Noctrix Health, Inc • Advisory Board: Apnimed, Inc; Eisai, Inc • Phrasing on some slides was assisted by ChatGPT
Outline Sleep as a performance constraint
Why high achievers don’t sleep well
Principles of sleep optimization in high-demand contexts
Attention & Focus
Decision-Making and Judgment
Emotional Regulation & Social Performance
Learning and Skill Acquisition
Physical Resilience
Acute and subacute sleep loss
Cognitive performance
0 hours 4 hours 6 hours 8 hours
Van Dongen et al, 2003
Sleep Deprivation Is Associated With Decreased Cortical Activity 18FDG PET Study of Healthy, Sleep-Deprived Adults, Showing Decreased Metabolism in
the Thalamus, Prefrontal Cortex, and Inferior Parietal Cortex
FDG, fluorodeoxyglucose; PET, positron emission tomography Thomas M et al. J Sleep Res. 2000;9:335-352.
Motor Learning Requires Sleep
Tucker et al, 2017
Sleep Improves Language Acquisition
Schimke et al, 2021
Sleep loss reduces physical resilience
Garbarino et al, 2021
Sleep loss summary • Sleep deprivation, both acute and chronic, must be minimized to avoid degradation of – Attention and focus – Decision-making and judgment – Emotional regulation and social performance – Learning and skill acquisition – Physical resilience
• Even minor impediments and can have major consequences at the elite level
Sprint speed • Sleep restriction to 4 hours x 1 • 30-minute afternoon nap vs no nap
• 20-m sprint time fell from 3.971s to 3.878s • 2.3% improvement
Waterhouse et al, 2007
500m Speed Skating
Had Jordan Stolz skated 2.3% slower, his time would be 34.55, finishing in 8th place
Serving Accuracy • Varsity M+W Tennis players • 1 week of baseline sleep • 1 week of 9 hours of sleep (incl naps)
• Sleep increased ~2h/day • Serve accuracy increased from 35.7% to 41.8% • 6.1% improvement
Schwartz and Simon 2015
Gold Medal Match
Djokovic won 101 points Alcaraz won 90 points A 6% swing would mean 96-95 points.
Shooting Accuracy • 2-4 weeks of baseline sleep • 5-7 weeks of sleep extension • Average sleep increase 111 minutes
• Free throw percentage increased 9% • 3-point shooting increased 9.2%
Mah et al, 2011
Semifinal Game
USA 3-point shooting: 50% Serbia 3-point shooting: 38.5% A 9% improvement in 3-point shooting would yield 9 more points (and victory)
Reaction Times
Reaction Times
Reaction Times
Baseline
One night sleep deprivation
13.4% slower after sleep deprivation Taheri and Arabameri 2012
Gold Medal Match
Average time to react to a penalty kick is 0.3s 13% faster would be 0.26s, 13% slower is 0.34s
Outline Sleep as a performance constraint
Why high achievers don’t sleep well
Principles of sleep optimization in high-demand contexts
Barriers to good sleep Other people and the environment
Individual-level factors
Time constraints and circadian disruption
Barriers to good sleep Other people and the environment
Individual-level factors
Time constraints and circadian disruption
Roommates and Bedpartners • Different sleep schedules • Alarm clocks • Personal conflicts • Snoring • Nocturnal awakenings • Sleepwalking • Pets • Children
This Photo by Unknown Author is licensed under CC BY-NC-ND
Environment • Inadequate curtains • Uncomfortable mattresses • Room temperature • Noisy neighbors • LED lights
Barriers to good sleep Other people and the environment
Individual-level factors
Time constraints and circadian disruption
Schedules • Night games/shifts/performances • Early meetings • Sleep interruptions
This Photo by Unknown Author is licensed under CC BY
Travel-related sleep disruptions Logistics – Long flights, delays
Circadian – Jet lag, light exposure
Physiology – Dehydration, inflammation
Routine – Meal timing, lack of exercise
Cognitive – Anticipatory stress
West coast teams are highly likely to beat the spread at home West coast teams beat the spread >50% of the time even on the East coast East coast teams are unlikely to beat the spread playing West coast teams, and even less likely when playing away games.
Smith et al. 1997
Circadian Advantage • Night games advantage West Coast teams regardless of the time zone. • Night games on West Coast increase the advantage • East Coast teams gain the advantage with early games. This Photo by Unknown Author is licensed under CC BY-NC-ND
Barriers to good sleep Other people and the environment
Individual-level factors
Time constraints and circadian disruption
Behaviors • Device use in bed • Social media consumption • Binge watching • Watching late-ending games • Substance use
Health • Physical injuries/discomfort • Concussions • Medications • Mental health • Sleep disorder
Outline Sleep as a performance constraint
Why high achievers don’t sleep well
Principles of sleep optimization in high-demand contexts
Key sleep optimization principles
Performancefocused sleep education
Multilevel sleep assessments Individual System Organizational culture
Sleep disorder management
Proactive travel and circadian planning
Performance monitoring and feedback
Key sleep optimization principles
Performancefocused sleep education
Multilevel sleep assessments Individual System Organizational culture
Sleep disorder management
Proactive travel and circadian planning
Performance monitoring and feedback
Sleep 101
Borbély, 1982
Sleep hygiene education • Avoid binge watching shows at night • Cover LED lights from chargers
• Keep the room cool • Stop caffeine by lunch • No social media at bedtime
Ages 14-25
Logan and McClung 2019
ATHLETIC PERIOD
Lack et al., 2008
Practice/Game Times ATHLETIC PERIOD
Lack et al., 2008
Key sleep optimization principles
Performancefocused sleep education
Multilevel sleep assessments Individual System Organizational culture
Sleep disorder management
Proactive travel and circadian planning
Performance monitoring and feedback
Individual sleep assessments
Positive sleep culture
Key sleep optimization principles
Performancefocused sleep education
Multilevel sleep assessments Individual System Organizational culture
Sleep disorder management
Proactive travel and circadian planning
Performance monitoring and feedback
Proactive travel management
Environmental protection
Circadian compensation • Move the circadian phase – Shift sleep schedule and other zeitgebers in advance of trip • Protect the sleep opportunity – Skip optional events in favor of sleep • Optimize alertness
– Strategic napping – Pre-event caffeine
Caffeine •
Caffeine is banned by NCAA
– Urine limit is 15mcg/mL • ~500mg of caffeine consumed – Caffeine pill = 200mg – 20 oz Starbucks can reach 400mg
– Home brewed drip coffee 80-100mg •
Avoid ”energy drinks” – Too much caffeine – Potential for other banned substances
•
Diuretic effect is minimal and balanced by consuming caffeine in liquid. This Photo by Unknown Author is licensed under CC BY-SA
•
Caffeineinformer.com
Melatonin Purpose • Help shift circadian rhythm • Used during periods of travel, not regularly Timing • Can begin use prior to travel • Many will use incorrectly if not counseled Trade offs
• Nightmares • Oversedation
Key sleep optimization principles
Performancefocused sleep education
Multilevel sleep assessments Individual System Organizational culture
Sleep disorder management
Proactive travel and circadian planning
Performance monitoring and feedback
Performance monitoring • Sleep metrics alone are insufficient • Performance is the outcome of interest
• Optimization requires feedback – Feedback is bidirectional • Trade-offs must be evaluated
Actionable Feedback
Monitoring
Evaluation
Final Thoughts
This Photo by Unknown Author is licensed under CC BY-NC
Conclusions Sleep is a performance system, not just health behavior Attention, judgment, learning, and physical resilience are all sleep-dependent Impairment can be subtle, cumulative, and poorly perceived
High achievers don’t fail at sleep, they operate under constraints Schedules, travel, culture, and expectations shape sleep more than motivation
Optimization differs from routine sleep care The goal is not perfect sleep, it’s performance protection when ideal sleep isn’t possible
Flexibility is more important than rules Real-life constraints will always take precedence
Optimization is iterative
Acknowledgements • Dr. Neeraj Kaplish • Dr. Fauziya Hassan • Dr. Punitha Vijayakumar • Tara Siesel, ATC – Associate Director of Athletic Medicine, Duke
• Drs. Jeff Bytomski and Kenzie Johnston, – Team physicians, Duke
Contact me www.AndrewSpectorMD.com X & LinkedIn: @AndrewSpectorMD
Recognizing Cardiovascular Risk in Central Disorders of Hypersomnolence
Sonja G. Schütz, MD, MS, MS 9/18/26
Conflicts of Interest 1. I do not have any potential conflicts of interest to disclose, OR x
2. I wish to disclose the following potential conflicts of interest:
Type of Potential Conflict
Details of Potential Conflict
Grant/Research Support
Oura Oy Health, Apnimed
Consultant Speakers’ Bureaus Financial support
Other x
3. The material presented in this lecture has no relationship with any of these potential conflicts, OR
4. This talk presents material that is related to one or more of these potential conflicts, and the following objective references are provided as support for this lecture:
Outline • Narcolepsy – Definitions
– Pathophysiology • Cardiovascular Disease in Narcolepsy – Epidemiology – Possible causal pathways • Treatment considerations
3
Narcolepsy: Definitions
4
Narcolepsy – Diagnostic Criteria
Type 1 Narcolepsy
Type 2 Narcolepsy
Daily periods of irrepressible need to sleep
Daily periods of irrepressible need to sleep
MSL <8min, 2 SOREMPs, and cataplexy
MSL <8min, 2 SOREMPs, no cataplexy
- OR Low CSF hypocretin-1
No other better explanation
No other better explanation
Westphal 1877; ICSD-3-TR
5
Clinical Characteristics
Kwon 2024
6
Cataplexy
Scammell 2015
7
Narcolepsy: Pathophysiology
8
Orexin/Hypocretin
9
Orexin/Hypocretin
Krahn 2022 10
Cardiovascular Disease in Narcolepsy
11
How common is Cardiovascular Disease in Narcolepsy? • Prevalence: BOND study
Black 2017 12
How common is Cardiovascular Disease in Narcolepsy? • Incident cardiovascular disease: CV-BOND Study
Ben-Joseph 2023
13
What are potential connections between narcolepsy and CVD?
Orexin deficiency
Sleep disruption
Comorbidities
Medications
14
What are potential connections between narcolepsy and CVD?
Orexin • Autonomic function • Orexin: BP and HR • Orexin deficiency: nondipping BP and HR
Grimaldi 2012 15
What are potential connections between narcolepsy and CVD?
Orexin • Autonomic function • Orexin in animals: BP and HR • Orexin deficiency in animals and humans: nondipping BP and HR • Myocardial function and remodeling • Orexin deficiency in animals: Increased cardiac scarring
Bastianini 2011, Perez 2015
16
What are potential connections between narcolepsy and CVD?
Orexin deficiency
Sleep disruption
• Nondipping BP, HR • Cardiac remodeling
Comorbidities
Medications
17
What are potential connections between narcolepsy and CVD?
Sleep disruption • Increased mean blood pressure and heart rate • Atherosclerotic lesion formation
Badran 2025, Carreras 2014
18
What are potential connections between narcolepsy and CVD?
Orexin deficiency
Sleep disruption
• Nondipping BP, HR • Cardiac remodeling
• Increased BP, HR • Atherosclerosis
Comorbidities
Medications
19
What are potential connections between narcolepsy and CVD? Comorbidities Comorbidity
Odds Ratio (95% CI)
Obesity
2.3 (2.2, 2.5)
Diabetes
1.8 (1.7, 1.8)
Sleep Apnea
18.7 (17.5, 20.0)
Restless Legs Syndrome
8.9 (7.7, 10.3)
Black 2016, Gudka 2022 20
What are potential connections between narcolepsy and CVD?
Orexin deficiency
Sleep disruption
• Nondipping BP, HR • Cardiac remodeling
• Increased BP, HR • Atherosclerosis
Comorbidities
Medications
• Metabolic • Sleep Disorders
21
Treatment Considerations
22
Pharmacological Treatments Medication
CVD AEs
Modafinil
HTN, tachycardia, arrhythmias, cardiomyopathy, heart failure
Armodafinil
HTN, tachycardia, ischemic T-wave changes
Solriamfetol
HTN, tachycardia, chest pain
Methylphenidate
HTN, tachycardia, MI, arrhythmia, sudden cardiac death, stroke
Amphetamine
HTN, tachycardia, MI, cardiomyopathy, sudden cardiac death
Pitolisant
Tachycardia, QT prolongation, arrhythmias
Sodium Oxybate
HTN, high sodium
Mixed oxybate salts
-
Oveporexton
TBD
Adapted from Jennum 2021, and Grandner 2025
23
Expert Panel Recommendations
1. Recognize the risk of CVD in patients with narcolepsy 1. Recognized risk of co-morbid sleep disorders 2. Monitor BP, weight, waist circumference, lipids, A1C as appropriate 2. Reduce the risk of hypertension and CVD in narcolepsy 1. Counsel on the AHA’s “Life’s Essential 8” 2. Treat other CVD risk factors, co-morbid OSA 3. Tailor pharmacotherapy on the basis of risk for HTN and CVD 3. Reduce sodium intake to lower risk of hypertension and CVD in narcolepsy
24
Summary
25
Summary • Narcolepsy – Definitions: Narcolepsy type 1 vs type 2 – Pathophysiology: Orexin deficiency in type 1 narcolepsy • Cardiovascular Disease in Narcolepsy – Epidemiology: Increased risk for stroke, heart failure, atrial fibrillation, myocardial infarction, cardiac arrest
– Connections: Orexin deficiency (nondipping BP, increased cardiac scarring); sleep disruption; comorbidities; treatment • Treatment considerations: most treatment options have potential adverse CVD effects
26
THANK YOU!
Questions? schuetzs@umich.edu
Decoding RSWA for Assessment of RBD Raman Malhotra, MD Professor of Neurology Department of Neurology Washington University, St. Louis
Conflicts of Interest X
1. I do not have any potential conflicts of interest to disclose, OR 2. I wish to disclose the following potential conflicts of interest: Type of Potential Conflict
Details of Potential Conflict
Grant/Research Support Consultant Speakers’ Bureaus
Financial support Other 3. The material presented in this lecture has no relationship with any of these potential conflicts, OR 4. This talk presents material that is related to one or more of these potential conflicts, and the following objective references are provided as support for this lecture:
REM sleep behavior disorder Early descriptions: “He was thrusting his sword in all directions, speaking out loud as if he were actually fighting a giant. And the strange thing was that he did not have his eyes open, because he was asleep and dreaming that he was battling the giant…He had stabbed the wine skins so many times, believing that he was stabbing the giant, that the entire room was filled with wine…”
Other Early Accounts of RBD: James Parkinson
Drs. Mark Mahowald and Carlos Schenck
ICSD-3R Criteria- RBD A. Repeated episodes of sleep-related vocalization and/or complex motor behaviors. B. These behaviors are documented by polysomnography to occur during REM sleep or, based on clinical history of dream enactment, are presumed to occur during REM sleep. C. Polysomnographic recording demonstrates REM sleep without atonia (RSWA). D. The disturbance is not better explained by another sleep disorder, mental disorder, medication, or substance use. ICSD - 3R - 2023
Case: 28 year old female reports dream enactment behavior several times over the last 3 months. Her bed partner reports that she is fighting off someone who is trying to attack her. She was already studied with an inlab PSG which did not show OSA or PLMD, but had two epochs of REM sleep without atonia. She is otherwise healthy with a past medical history of only mild asthma and depression. She is currently on paroxetine.
Comes to your clinic: She was told she has REM sleep behavior disorder and that she is at high risk for developing Parkinson’s disease, and wanted another opinion.
What will we “decode” this session ? • Mimics and Differential Diagnosis of RBD • Measurement/Diagnosis of RBD • Prognosis and future risk of neurodegeneration with RBD
RBD Clinical Symptoms: ➢ episodes of dream enactment behavior ➢ minor hand movements to jumping out of bed
➢ recall of dream content (typically) ➢ aggressive/violent dream content (not a reflection of wake behavior) ➢ being chased/defending oneself ➢ vocalizations are loud/aggressive ➢ sleep quality usually not affected but there is a risk for injuries to the patient or bed partner
Epidemiology • Mean age of onset = 63 years old (several years to diagnose) • Male> female when over 50 years old (M=F < 50) • 0.6% of the population with RBD (with PSG diagnosis), higher if only use history or questionnaire • Some history of dream enactment alone once in their life is as high as 98% (Nielsen T, et al, Sleep 2009) • RBD is seen commonly in Parkinson’s disease (30%), dementia with Lewy bodies (80%) and multiple system atrophy (90%)
Questionnaires ▪ RBD Questionnaire- Hong Kong (RBDQ-HK) ▪ 13 item measure ranging from 0-100 ▪ > 18, PPV of 86%, NPV 83%
▪ RBD Screening Questionnaire (RBDSQ) ▪ 10 item screen ▪ >4 score has a sensitivity of 90%, specificity of 87%
▪ The Mayo Sleep Questionnaire (MSQ) includes one question on RBD and the RBD Single Question Screen with similar sensitivities and specificities *Li SX, et al. Validation of a new REM sleep behavior disorder questionnaire (RBDQHK). Sleep Med. 2010 Jan;11(1):43-8. *Stiasny-Kolster K, et al. Diagnostic value of the REM sleep behavior disorder screening questionnaire in Parkinson's disease. Sleep Med. 2015 Jan;16(1):186-9.
Single Questions: MSQ: “Have you ever seen the patient appear to ‘act out
his/her dreams’ while sleeping? (punched or flailed arms in the air, shouted or screamed)” RBD1Q: “Have you ever been told, or suspected yourself, that
you seem to ‘act out your dreams’ while asleep (for example, punching, flailing your arms in the air, making running movements, etc.)?”
Polysomnography’s role in RBD? • Clinical history and questionnaires are not specific enough and pick up too many mimics. • Subjective recall is poor (may not have bed partner or may not recall) • Up to 44% do not recall DEB (Cesari, Sleep, 2022) • PSG can also miss REM sleep without atonia (night to night variability, absence of REM sleep, artifact, early in disease) • Upper extremities tend to have more RSWA than lower extremities so should add upper extremity leads if evaluating parasomnia
Arm Leads on PSG • Flexor digitorum superficialis is typically used/added
AASM Scoring: REM sleep without atonia (RWA) Sustained muscle activity (tonic) – an epoch of REM with >50% EMG tone as compared to NREM sleep Phasic activity- 2X higher amplitude for ½ of the epoch (broken into 10 three second miniepochs)
AASM Scoring Manual:
Many challenges of scoring RSWA: • How many epochs needed for an RBD diagnosis? • What is the interscorer reliability of REM sleep without atonia? • What is the sensitivity of seeing RSWA on a single night of PSG? • Are there better automated scoring techniques such as REM atonia index?
Other Scoring Criteria • Montreal scoring • SINBAR • REM atonia index • Mayo • Other
Sleep Innsbruck Barcelona (SINBAR)
• Mentalis, flexor digitorum superficialis, extensor digitorum brevis • Count any EMG activity during REM Sleep in 3 second mini-epochs for phasic elevation • > 32% was specific for RBD
REM Atonia Index
Ferri, et al, Sleep Medicine, 2010
REM Atonia Index REM atonia index
Meta-analysis from 2023
Actigraphs or other technology Colman, K, et al, Parkinsons Dis, 2025.
Mimics of RBD • NREM parasomnias (they can have recall of dream) • (Castelnova A, Neurosci Biobehav Rev 2024)
• • • • • • •
OSA causing dream enactment or “pseudo-RBD” GERD causing arousals from REM sleep PLMD causing arousals from REM sleep (Gaig C, Sleep, 2017) Seizures/epilepsy Sleep-related dissociative disorder Parasomnia overlap disorder Trauma associated sleep disorder (RBD/PTSD)
• Narcolepsy
How to differentiate? • Timing of event during sleep • Past history of parasomnias • Age of onset
• What happens if you try to wake them up? • Do they leave the room/bedroom?
Risk of neurodegenerative condition: Age <50 years – 5-, 10-, and 14-year conversion rates of 0, 1.6, and 1.6 percent, respectively Age 50 to 60 years – 5-, 10-, and 14-year conversion rates of 2.5, 9, and 9 percent, respectively Age 61 to 70 years – 5-, 10-, and 14-year conversion rates of 6, 22, and 36 percent, respectively Age >70 years – 5-, 10-, and 14-year conversion rates of 15, 67, and 84 percent, respectively
Antidepressant use and RSWA • 12% of patients on antidepressants had RSWA • Is it unmasking underlying pathology or unrelated? • Is depression an early sign of neurodegenerative disease?
• Most antidepressants can cause this (besides buproprion) • Stopping antidepressant can reverse symptoms and RSWA (but can take several weeks)
Age of presentation and Narcolepsy • Among adults less than 40 years old, RBD is more likely due to antidepressant use or narcolepsy. • Up to 50% of narcoleptics have abnormally high REM sleep motor activity (different pathophysiology) • Young RBD can be from autism, epilepsy, ADHD, tumors
Skin biopsies looking for alpha-synuclein • None found in narcolepsy type 1, but 87% in iRBD. • Lower risk of this finding in patients on antidepressants (93% versus 30%) Biscarini F, Pizza F, Vandi S, Incensi A, Antelmi E, Donadio V, Ferri R, Liguori R, Plazzi G. Biomarkers of neurodegeneration in isolated and antidepressantrelated rapid eye movement sleep behavior disorder. Eur J Neurol. 2024 Jun;31(6)
Disorders Associated with RBD: Continuum, Aug 2023
Early symptoms: • Alternative tap test and subtle changes in gait • Visuospatial deficits/executive dysfunction
• Color identification/facial expression recognition • Hyposmia • Autonomic dysfunction (heart rate variability, orthostatic hypotension) and specifically constipation
Diagnostic Tests: • FDG-PET shows occipital hypometabolism • In vivo PET demonstrates reduced acetylcholinesterase activity in superior temporal cortex, cingulate cortex, and dorsolateral prefrontal cortex • Genetic tests can also be helpful with certain gene mutations causing a more rapid rate of phenoconversion (glucocerebrosidase or GBA mutation) and others that have less risk (LRRK-2 mutation) • Cardiac 123I-MIBG scintigraphy helpful to identify early multiple system atrophy
DAT-SPECT scans • Decrease in dopamine uptake in the putamen • Used in conjunction with age over 70 and constipation • CSF alpha-synuclein positivity (measured by RTQuIC)
Parkinson disease and RBD • More malignant course and aggressive disease • More cognitive impairment, quality of life, postural instability, and gait dysfunction (freezing gait and falls)
Predicting phenoconversion: • Older age • Family history of dementia • Subtle motor or cognitive dysfunction
• Non-use of antidepressants
RBD Management • Safety precautions • • • • • •
Treat other underlying sleep disorders Avoid triggers Clonazepam and/or melatonin(immediate release) Pramipexole Rivastigmine in RBD with mild cognitive impairment Bed alarms, acetylcholinesterase inhibitors, sodium oxybate have also been shown to help
Treatments for RBD- AASM CPG • Conditional Use FOR: • Clonazepam • Melatonin • Pramipexole • Transdermal rivastigmine (with MCI or PD)
• Do NOT use deep brain stimulaton (DBS) • Higher RISK for future development of neurological disease • -should advise patient Howell M, Avidan AY, Foldvary-Schaefer N, et al. Management of REM sleep behavior disorder: an American Academy of Sleep Medicine clinical practice guideline. J Clin Sleep Med. 2023;19(4):759–768.
WAYNE STATE UNIVERSITY
SLEEP MEDICINE • YEAR IN REVIEW 2026
Editor’s Picks (But I Love All My Children Equally) Ten studies from the year — design, findings, and what each one allows us to do
M. Safwan Badr, MD, MBA • Wayne State University School of Medicine Department of Internal Medicine
WAYNE STATE UNIVERSITY
How I Am Grading These Papers A readiness taxonomy — the question is not 'is it a good study' but 'what is the next legitimate move'
Hypothesis-generating
Ready for translational study
Ready for implementation
Association without a design that can support causal or clinical action. Observational, exploratory, unreplicated, or confounded by indication.
Credible effect with a plausible mechanism, but tested in a setting or with a measure that does not yet map onto practice.
Design and setting adequate to change what we do on Monday — including negative trials that justify stopping something.
Next move: replication, a design that addresses confounding, or mechanistic work.
Next move: pragmatic replication in a realworld setting, or quantification against clinical decision thresholds.
Next move: define the local protocol, thresholds, and the population it does not apply to.
• 1 Medicaid PSG geography • 3 Mild SDB progression • 6 Sleep timing and mortality • 9 GLP-1RA and CV risk • 10 OSA–MetS by age
• 7 Self-viewing apnea videos • 8 Sleep duration and neurocognitive testing
• 2 Smartwatch OSA detection • 4 Zolpidem for acclimatization (de-adopt) • 5 CBT-I component delivery
Assignments are my judgment, not the authors' claims.
2
WAYNE STATE UNIVERSITY
STUDY 1
Hypothesis-generating
Geography of Pediatric Polysomnography in Medicaid McLaughlin CC, et al. J Clin Sleep Med. 2026;22:128
Question: How does pediatric PSG utilization vary geographically and by neighborhood composition in the USA?
Design
Cross-sectional observational study of administrative claims
Data source
T-MSIS Analytic Files, Medicaid/CHIP, 2017–2019; 49 states + DC
Population
46.1 million children ages 0–18 with ≥1 utilization claim; 95.5 million person-years
Exposure
ZIP-code RUCA urbanicity and ACS race/ethnicity majority category
Outcome
Age-adjusted attended overnight PSG rate per 10,000 (CPT 95782–95811)
Analysis
Jenks mapping at PUMA level; Poisson regression with clustered error variance
Design caution: Ecologic design: neighborhood race, not individual. RI excluded; VT excluded from ZIP/PUMA analyses. McLaughlin CC, Hawke JL, Boss EF, et al. J Clin Sleep Med. 2026;22:128.
3
WAYNE STATE UNIVERSITY
Geography of Pediatric Polysomnography in Medicaid
50.1 PSGs per 10,000 person-years nationally
4×
state variation: Kansas 23 → Michigan 93
McLaughlin CC, Hawke JL, Boss EF, et al. J Clin Sleep Med. 2026;22:128.
+9.7 per 10,000 metropolitan vs isolated rural
Hypothesis-generating
+23.2
per 10,000 for ≥90% White vs ≥50% other races
4
WAYNE STATE UNIVERSITY
STUDY 1
Hypothesis-generating
Geography of Pediatric Polysomnography in Medicaid What it means INTERPRETATION
• Fourfold variation reflects supply and guideline disagreement, not disease prevalence. • Absolute differences are small — metro vs isolated rural <0.1%. Do not oversell.
• Michigan is the national high-utilization outlier: good access or overuse?
HYPOTHESIS-GENERATING
Link PSG rates to lab capacity and downstream outcomes. Utilization alone cannot distinguish under-use from over-use.
McLaughlin CC, Hawke JL, Boss EF, et al. J Clin Sleep Med. 2026;22:128.
5
WAYNE STATE UNIVERSITY
STUDY 2
Implementation
Smartwatch Detection of Moderate-to-Severe OSA Alavi A, et al. J Clin Sleep Med. 2026;22:150
Question: Can an FDA-cleared consumer smartwatch identify moderate-to-severe OSA against in-lab PSG?
Design
Prospective diagnostic accuracy study, STARD-reported (NCT06603441)
Population
152 adults enrolled, 147 completed 2 in-lab PSG nights (1,850 h of PSG sleep)
Index test
Galaxy Watch 6 Sleep Apnea Feature — PPG 25 Hz, SpO₂ 1 Hz, estimated AHI
Reference
In-lab PSG, four definitions: AHI 3%/arousal, OAHI 3%/arousal, AHI 4%, OAHI 4%
Decision rule
Two-night rule: positive only if both nights met threshold; ≥3 at-home nights between
Key secondary
Performance stratified by PSG-derived hypoxic burden (cut-point 87.1 %min/h)
Design caution: Enriched cohort inflates PPV. Single site. 57 of 147 lacked ≥1 valid watch night. Alavi A, Costa E, Matsumoto MMS, et al. J Clin Sleep Med. 2026;22:150.
6
WAYNE STATE UNIVERSITY
Wayne State University | Detroit, Michigan
7
WAYNE STATE UNIVERSITY
STUDY 2
Implementation
Smartwatch Detection of Moderate-to-Severe OSA Main findings
0.94
94.1%
94.9%
100%
AUROC vs PSG OAHI 4%
sensitivity at default eAHI ≥15 (spec 66.7%)
specificity at optimized eAHI 25.95 (sens 82.4%)
sens and spec in the high hypoxicburden group
Reading the numbers • OAHI 4% showed strongest concordance; Pearson r = 0.87 (225 night-level pairs). • Perfect discrimination in high-HB stratum — best performance where CV risk is highest.
• 8 of 13 false positives: PSG disagreed night-to-night, not device error. • Stable across sex, BMI, ethnicity, and skin tone strata.
Alavi A, Costa E, Matsumoto MMS, et al. J Clin Sleep Med. 2026;22:150.
8
WAYNE STATE UNIVERSITY
STUDY 2
Implementation
Smartwatch Detection of Moderate-to-Severe OSA What it means INTERPRETATION
• A screening and triage tool, not a diagnostic — not cleared to diagnose OSA. • Hypoxic burden reframes the story: the watch detects desaturation dose, the CV-relevant metric.
• Threshold is a program policy decision — choose deliberately before deploying.
READY FOR IMPLEMENTATION
Usable now for triage with a defined threshold, known-OSA exclusion, and a plan for the ~40% with invalid nights.
Alavi A, Costa E, Matsumoto MMS, et al. J Clin Sleep Med. 2026;22:150.
9
WAYNE STATE UNIVERSITY
STUDY 3
Hypothesis-generating
Progression of Mild SDB Under Watchful Waiting Kirkham EM, et al. J Clin Sleep Med. 2026;22:106 — PATS secondary analysis
Question: Which children with mild SDB observed without surgery progress over 12 months?
Design
Secondary, exploratory (not prespecified) analysis of one RCT arm; STROBE-reported
Parent trial
PATS — 458 children aged 3.0–12.9 randomized to early adenotonsillectomy vs watchful waiting
Analysis cohort
234 children in watchful waiting with supportive care; mean age 6.2 y, 47% female, 28% Black
Entry criteria
Snoring ≥3 nights/week with obstructive AHI < 3 events/h
Outcome 1
PSG progression: 12-month oAHI ≥ 3 events/h
Outcome 2
Symptom persistence/progression: 12-month PSQ-SRBD ≥ 0.33
Design caution: Exploratory, unadjusted, wide CIs. Race proxies unmeasured social exposures. Kirkham EM, Ishman S, Garetz S, et al. J Clin Sleep Med. 2026;22:106 (PATS secondary analysis).
10
WAYNE STATE UNIVERSITY
STUDY 3
Hypothesis-generating
Progression of Mild SDB Under Watchful Waiting Main findings
13%
57%
18%
0.42
progressed on PSG to oAHI ≥ 3 (20/150)
had symptom persistence or progression (110/192)
of symptomatic children also progressed on PSG
OR for symptom progression with grade III–IV tonsils
Reading the numbers • PSG progression: Black race OR 2.71 (1.03–7.17); higher PSQ-SRBD OR 1.74 (1.03–3.08). • Symptom predictors: asthma OR 2.70, ADHD OR 3.73, smoke exposure OR 2.41.
• Counterintuitive: larger tonsils predicted lower symptom progression. • Symptoms and physiology dissociate — most symptomatic children never crossed the PSG threshold.
Kirkham EM, Ishman S, Garetz S, et al. J Clin Sleep Med. 2026;22:106 (PATS secondary analysis).
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WAYNE STATE UNIVERSITY
STUDY 3
Hypothesis-generating
Progression of Mild SDB Under Watchful Waiting What it means INTERPRETATION
• An oAHI threshold is the wrong surgical trigger — 57% stay symptomatic, only 13% progress on PSG. • Repeat PSG in mild SDB has low yield for changing management (relevant to Study 1).
• Asthma, ADHD, smoke exposure identify watchful-waiting failures — a no-cost triage heuristic.
HYPOTHESIS-GENERATING
Predictors need prospective validation; the race association needs decomposition into measurable exposures.
Kirkham EM, Ishman S, Garetz S, et al. J Clin Sleep Med. 2026;22:106 (PATS secondary analysis).
12
WAYNE STATE UNIVERSITY
STUDY 4
Implementation
Zolpidem for CPAP Acclimatization Pisalnoradej P, et al. J Clin Sleep Med. 2025;21(11):1831–1837
Question: Does one week of zolpidem 10 mg improve CPAP adherence during acclimatization?
Design
Randomized, double-blind, placebo-controlled crossover trial (CONSORT crossover extension)
Population
30 randomized, 28 analyzed; CPAP-naive adults, mean age 54.5 y, 64% severe OSA
Intervention
Zolpidem 10 mg vs identical placebo, 30 min before bed for 1 week, then crossover
Washout
24 hours (zolpidem half-life ~2 h; 10–12 h total elimination)
Primary outcome
Average CPAP usage, hours per night, from device SD-card download
Secondary
% nights ≥4 h; subgroup analyses by sex, severity, and arousal index
Design caution: Underpowered (n†28). High adherence in both arms (Hawthorne effect). Arousal threshold not measured. Pisalnoradej P, Banhiran W, Kasemsuk N. J Clin Sleep Med. 2025;21(11):1831–1837 (NCT06084130).
13
WAYNE STATE UNIVERSITY
STUDY 4
Implementation
Zolpidem for CPAP Acclimatization Main findings
+0.18 h
+2.60%
p = .87
0
usage difference, per protocol (CI −0.39 to 0.75)
nights ≥4 h (CI −5.65 to 10.87)
no treatment sequence effect
serious adverse events (dizziness 7.1%)
Reading the numbers • ITT: 5.14 vs 4.70 h/night (MD 0.43 h, CI −0.62 to 1.49, P = .40). • No signal in any subgroup.
• ~25% CPAP intolerance, consistent with real-world rates. • Unanalyzed: 5 discontinued CPAP on placebo vs 2 on zolpidem.
Pisalnoradej P, Banhiran W, Kasemsuk N. J Clin Sleep Med. 2025;21(11):1831–1837 (NCT06084130).
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WAYNE STATE UNIVERSITY
STUDY 4
Implementation
Zolpidem for CPAP Acclimatization What it means INTERPRETATION
• Clean negative: zolpidem should not be prescribed routinely for CPAP acclimatization. • Consistent with Bradshaw 2006; eszopiclone benefits may reflect its longer half-life (6–9 h).
• Arousal index ≠ arousal threshold. The hypothesis survives; unphenotyped treatment fails.
READY FOR IMPLEMENTATION
De-adopt routine hypnotics. Redirect to mask fitting and CBT-I. A phenotyped arousal-threshold trial remains warranted.
Pisalnoradej P, Banhiran W, Kasemsuk N. J Clin Sleep Med. 2025;21(11):1831–1837 (NCT06084130).
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WAYNE STATE UNIVERSITY
STUDY 5
Implementation
Dismantling CBT-I in Older Adults O'Hora KP, et al. J Clin Sleep Med. 2025;21(10):1679–1695
Question: Are the cognitive and behavioral components of CBT-I individually as effective as the full package in older adults?
Design
Randomized three-arm dismantling trial — the first in older adults
Population
128 older adults with insomnia disorder
Arms
Cognitive therapy (CT) vs behavioral therapy (BT) vs full CBT-I
Primary outcome
Insomnia Severity Index at posttreatment and 6-month follow-up
Secondary
Sleep diaries, fatigue, dysfunctional beliefs about sleep, cognitive arousal, stress
Analysis
Split-plot linear mixed models; Benjamini-Hochberg correction; linear regression for predictors
Design caution: Self-reported primary outcome; no attention-control arm. Not powered for equivalence. O'Hora KP, Morehouse AB, Freidman L, et al. J Clin Sleep Med. 2025;21(10):1679–1695 (NCT02117388).
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WAYNE STATE UNIVERSITY
STUDY 5
Implementation
Dismantling CBT-I in Older Adults Main findings
d = −3.14
d = −2.85
d = −2.68
P = .63
CBT-I effect on ISI at 6 months
BT alone at 6 months
CT alone at 6 months
adjusted, for between-group difference in ISI improvement
Reading the numbers • All three arms improved and held gains at 6 months; no differences in remission. • Components moved their mechanistic targets as expected, yet outcomes converged.
• Baseline ISI was the only predictor of response (higher severity → greater gain). • Large effect sizes partly reflect the uncontrolled within-subject design.
O'Hora KP, Morehouse AB, Freidman L, et al. J Clin Sleep Med. 2025;21(10):1679–1695 (NCT02117388).
17
WAYNE STATE UNIVERSITY
STUDY 5
Implementation
Dismantling CBT-I in Older Adults What it means INTERPRETATION
• Single component = full package → insomnia care needs fewer, less specialized providers. • Mechanistic targets diverged; outcomes converged — different routes, same destination.
• Especially relevant in older adults, where sedatives carry fall and hospitalization risk.
READY FOR IMPLEMENTATION
BT alone is a reasonable first step; full CBT-I reserved for non-responders.
O'Hora KP, Morehouse AB, Freidman L, et al. J Clin Sleep Med. 2025;21(10):1679–1695 (NCT02117388).
18
WAYNE STATE UNIVERSITY
STUDY 6
Hypothesis-generating
Sleep Timing and Mortality in Older Adults Wang J, et al. J Clin Sleep Med. 2025;21(10):1709–1722 — Guangzhou Biobank
Question: Independent of duration, is there an optimal sleep timing for mortality risk?
Design
Prospective cohort study, Guangzhou Biobank Cohort Study
Population
18,129 adults, median age 65 years; recruited 2008–2012, followed to July 2022
Follow-up
Median 12.4 years; 213,534 person-years; 2,997 deaths
Exposure
Sleep midpoint (bedtime-to-waketime), categorized early / intermediate / late
Outcomes
All-cause and cardiovascular mortality
Analysis
Cox models with restricted cubic splines; 4-SNP CVD genetic risk score for effect modification
Design caution: Self-reported single-baseline timing; no actigraphy. Early timing may reflect reverse causation. One Chinese urban cohort. Wang J, Li YR, Jiang CQ, et al. J Clin Sleep Med. 2025;21(10):1709–1722.
19
WAYNE STATE UNIVERSITY
STUDY 6
Hypothesis-generating
Sleep Timing and Mortality in Older Adults Main findings
1.18
1.13
~11 PM
~6 AM
aHR all-cause mortality, early vs intermediate (CI 1.07–1.30)
aHR all-cause mortality, late vs intermediate (CI 1.01–1.26)
bedtime associated with lowest risk
wake time associated with lowest risk
Reading the numbers • U-shaped associations for all timing metrics; nadir midpoint ~2:30 AM. • Stronger associations in males, older adults, and high CVD genetic-risk strata.
• Early timing carried larger risk than late — opposite of the ‘night owl’ narrative. • Effect sizes modest (13–18%); late-group CI barely excludes 1.0.
Wang J, Li YR, Jiang CQ, et al. J Clin Sleep Med. 2025;21(10):1709–1722.
20
WAYNE STATE UNIVERSITY
STUDY 6
Hypothesis-generating
Sleep Timing and Mortality in Older Adults What it means INTERPRETATION
• Adds timing as a mortality-relevant dimension alongside duration. • Gene×timing interaction: timing matters most where CV susceptibility is high.
• Self-reported, single-cohort data do not support prescribing a bedtime.
HYPOTHESIS-GENERATING
Needs objective timing replication and Mendelian randomization to address reverse causation.
Wang J, Li YR, Jiang CQ, et al. J Clin Sleep Med. 2025;21(10):1709–1722.
21
WAYNE STATE UNIVERSITY
STUDY 7
Translational
Self-Viewing of One's Own Apnea Videos Kim KT, Son N-H, Cho YW. J Clin Sleep Med. 2026;22:7
Question: Does showing patients video of their own apneas improve PAP adherence beyond standard CBT?
Design
Prospective, parallel-group, open-label randomized controlled trial, single center (South Korea)
Population
228 treatment-naive adults with AHI ≥ 15; mean age 52.6 y, 13.6% female
Intervention
10–20 min viewing of self-recorded PSG apnea segments after standard CBT vs CBT alone
Delivery
Sleep technician showed pre-selected clips with no coaching, same visit, no extra appointment
Primary outcome
Good adherence: ≥4 h/night on ≥70% of nights over 90 days
Ascertainment
Device-recorded; 0% attrition across the 90-day period
Design caution: Open-label, fixed block size. Terminated early (power ~66%). Non-specific contact effects cannot be excluded. Kim KT, Son N-H, Cho YW. J Clin Sleep Med. 2026;22:7 (KCT0005250).
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WAYNE STATE UNIVERSITY
STUDY 7
Translational
Self-Viewing of One's Own Apnea Videos Main findings
89.5% vs 78.1%
+11.4%
+9.05 d
+0.55 h
good adherence, intervention vs control
absolute difference (CI 1.94–20.86)
more days with ≥4 h use (CI 2.29– 15.81)
mean daily usage (CI 0.13–0.98)
Reading the numbers • Residual AHI identical between groups — a behavioral, not physiologic, effect. • Control adherence 78% — unusually high; reflects tertiary best-case conditions.
• 68 eligible patients declined randomization to get the video — strong patient signal. • CI lower bound 1.94%: a trivially small true effect is compatible.
Kim KT, Son N-H, Cho YW. J Clin Sleep Med. 2026;22:7 (KCT0005250).
23
WAYNE STATE UNIVERSITY
STUDY 7
Translational
Self-Viewing of One's Own Apnea Videos What it means INTERPRETATION
• Near-zero cost, no extra visit, effect size comparable to elaborate adherence programs. • “Seeing is believing” converts abstract disease into a felt experience — generic video has not.
• Best-case setting is the main threat; real-world effect is unknown.
READY FOR TRANSLATIONAL STUDY
Pragmatic multi-site replication with lower baseline adherence and 6–12 month follow-up. Easy to pilot locally now.
Kim KT, Son N-H, Cho YW. J Clin Sleep Med. 2026;22:7 (KCT0005250).
24
WAYNE STATE UNIVERSITY
STUDY 8
Translational
Sleep Duration and Neurocognitive Test Performance Aderman MJ, et al. J Clin Sleep Med. 2026;22:91 — CARE Consortium
Question: Does the prior night's sleep affect concussion neurocognitive testing at baseline and after injury?
Design
Retrospective cohort, four US military service academies, 2014–2022
Population
21,938 cadets and midshipmen enrolled (26% female); 1,429 with post-concussion testing
Exposure
Self-reported sleep duration the night before each evaluation
Outcome
Computerized neurocognitive test domains — verbal and visual memory, visual motor speed, reaction time
Analysis
Univariate and multivariable linear regression; two-way ANOVA by hours slept × timepoint
Context
Cadets average under 5.5 h of sleep during the academic year
Design caution: Self-reported, single-night exposure. Very large n inflates significance. No prior sleep-debt control. Aderman MJ, Roach MH, Malvasi SR, et al. J Clin Sleep Med. 2026;22:91.
25
WAYNE STATE UNIVERSITY
STUDY 8
Translational
Sleep Duration and Neurocognitive Test Performance Main findings
β = 0.052
β = 0.045
β = 0.024
β = −0.022
visual motor speed per hour of sleep (p < .001)
visual memory per hour (p < .001)
verbal memory per hour (p = .003)
reaction time per hour (p < .001)
Reading the numbers • Associations held in multivariable models and at post-injury timepoints. • Direction uniformly favorable: more sleep, better scores on every domain.
• Per-hour effects are small relative to clinical recovery thresholds. • Near-worst-case population may amplify effect while limiting generalizability.
Aderman MJ, Roach MH, Malvasi SR, et al. J Clin Sleep Med. 2026;22:91.
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WAYNE STATE UNIVERSITY
STUDY 8
Translational
Sleep Duration and Neurocognitive Test Performance What it means INTERPRETATION
• If the “personal baseline” moves with last night’s sleep, it is a noisy reference. • Both errors run in opposite directions: deprived baseline → early clearance; deprived post-injury → prolonged restriction.
• The practical question: does the shift ever cross a return-to-duty threshold?
READY FOR TRANSLATIONAL STUDY
Quantify effect against reliable-change indices; test whether documenting prior-night sleep changes return-to-duty decisions.
Aderman MJ, Roach MH, Malvasi SR, et al. J Clin Sleep Med. 2026;22:91.
27
WAYNE STATE UNIVERSITY
STUDY 9
Hypothesis-generating
GLP-1 Receptor Agonists and Cardiovascular Risk in OSA Ahn J, et al. J Clin Sleep Med. 2026;22:102 — TriNetX
Question: Do GLP-1 receptor agonists reduce cardiovascular events in patients with OSA and obesity?
Design
Propensity score-matched retrospective cohort, TriNetX Global Collaborative Network
Population
Adults with OSA and BMI > 30 diagnosed 2010–2021, without prior HF, PH, or MI
Exposure
Semaglutide, liraglutide, or dulaglutide within 1 year of OSA diagnosis (tirzepatide excluded)
Cohorts
18,774 users vs 847,137 non-users → 18,523 matched pairs (greedy nearest neighbor)
Primary outcome
Newly diagnosed heart failure over 3 years
Secondary
Pulmonary hypertension, all-cause death, ischemic stroke, acute MI
Design caution: No AHI, no PAP data, no weight trajectory. 82% had T2DM — severe indication confounding. Ahn J, Song J, Admire K, et al. J Clin Sleep Med. 2026;22:102.
28
WAYNE STATE UNIVERSITY
STUDY 9
Hypothesis-generating
GLP-1 Receptor Agonists and Cardiovascular Risk in OSA Main findings
0.76
0.67
0.76
0.57
HR heart failure (CI 0.71–0.82)
HR pulmonary hypertension (CI 0.59–0.75)
HR acute myocardial infarction (CI 0.67–0.86)
HR all-cause mortality (CI 0.51– 0.63)
Reading the numbers • Weakest signal: ischemic stroke HR 0.90 (0.82–0.99). • Each agent reduced HF risk: liraglutide 0.73, dulaglutide 0.75, semaglutide 0.83.
• KM curves separated early — authors attribute to direct CV benefit beyond weight loss. • HR 0.57 exceeds any GLP-1 RCT result — a red flag for residual confounding.
Ahn J, Song J, Admire K, et al. J Clin Sleep Med. 2026;22:102.
29
WAYNE STATE UNIVERSITY
STUDY 9
Hypothesis-generating
GLP-1 Receptor Agonists and Cardiovascular Risk in OSA What it means INTERPRETATION
• Biologically plausible, but the magnitude (HR 0.57 for mortality) is the problem. • Missing: OSA severity and PAP adherence — the variables that matter most here.
• The sleep medicine question: do GLP-1RAs work with PAP, instead of it, or against it?
HYPOTHESIS-GENERATING
Needs a target-trial emulation with PAP data and an OSA-specific CV outcomes RCT.
Ahn J, Song J, Admire K, et al. J Clin Sleep Med. 2026;22:102.
30
WAYNE STATE UNIVERSITY
STUDY 10
Hypothesis-generating
Age Modifies the OSA–Metabolic Syndrome Link Pejovic S, et al. J Clin Sleep Med. 2025;21(8):1371–1378 — Penn State Adult Cohort
Question: Is the association between OSA and metabolic syndrome modified by age — and does it hold in nonobese adults?
Design
Cross-sectional analysis of a population-based cohort with sampling weights
Population
1,741 adults aged 20–88 from the Penn State Adult Cohort; 1,292 nonobese (BMI < 30)
Sleep measure
One night of attended 8-hour laboratory polysomnography; OSA defined as AHI ≥ 15 events/h
Outcome
Metabolic syndrome (≥ 3 of 5 modified NHLBI/AHA components) and each component separately
Effect modifier
Age dichotomized at 60 years after a significant OSA × age interaction
Analysis
Logistic regression adjusted for race, sex, smoking, alcohol, and sampling weight
Design caution: Cross-sectional. Waist/HDL unavailable; BMI and total cholesterol substituted. Tables 1 and 2 report inconsistent OSA n (82 vs 149). Pejovic S, Vgontzas AN, Fernandez-Mendoza J, He F, Li Y, Bixler EO. J Clin Sleep Med. 2025;21(8):1371–1378.
31
WAYNE STATE UNIVERSITY
STUDY 10
Hypothesis-generating
Age Modifies the OSA–Metabolic Syndrome Link Main findings
3.14
1.34
5.99
8.70
OR for MetS, age < 60 (CI 1.60– 6.15)
OR for MetS, age ≥ 60 (CI 0.65– 2.77, NS)
OR hypercholesterolemia, age < 60 (CI 2.36–15.21)
OR hypertension in NONOBESE age < 60 (CI 2.28–33.21)
Reading the numbers • Pooled OR 2.02 (1.22–3.36); age split more than doubles it in the young and erases it in the old. • Under 60: all components significant (ORs 3.85–5.99). Over 60: none (all CIs cross 1.0).
• Only obesity remained significant over 60, and attenuated (OR 6.03 → 2.47). • In nonobese age <60: all 15 OSA patients had high cholesterol (OR incalculable) — cells are thin.
Pejovic S, Vgontzas AN, Fernandez-Mendoza J, He F, Li Y, Bixler EO. J Clin Sleep Med. 2025;21(8):1371–1378.
32
WAYNE STATE UNIVERSITY
STUDY 10
Hypothesis-generating
Age Modifies the OSA–Metabolic Syndrome Link What it means INTERPRETATION
• Young OSA is metabolic; older OSA is anatomic — consistent with Edwards 2014 phenotype data. • Even nonobese adults show metabolic derangement with OSA — it is not purely anatomic.
• Screen aggressively for MetS in young OSA patients; do not apply the same calculus at 70.
HYPOTHESIS-GENERATING
Wide CIs and small cells. Longitudinal follow-up and mechanistic phenotyping by age are the next steps.
Pejovic S, Vgontzas AN, Fernandez-Mendoza J, He F, Li Y, Bixler EO. J Clin Sleep Med. 2025;21(8):1371–1378.
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WAYNE STATE UNIVERSITY
The Year in One Table Ten studies sorted by what they license us to do #
Study
Design
Headline
2
Smartwatch OSA detection
Prospective diagnostic accuracy
AUROC 0.94; perfect discrimination at high hypoxic burden
Implement
4
Zolpidem for CPAP acclimatization
Crossover RCT
No adherence benefit
De-adopt
5
CBT-I dismantling in older adults
3-arm RCT
Components equal the full package
Implement
7
Self-viewing apnea videos
RCT, single center
+11.4% good adherence at 90 days
Translate
8
Sleep duration and neurocognitive testing
Retrospective cohort
Prior-night sleep shifts baseline scores
Translate
1
Medicaid pediatric PSG geography
Cross-sectional claims
Fourfold state variation in utilization
Generate
3
Mild SDB under watchful waiting
Exploratory RCT secondary
57% stay symptomatic; 13% progress on PSG
Generate
6
Sleep timing and mortality
Prospective cohort
U-shaped risk; nadir 11 PM–6 AM
Generate
9
GLP-1RA and cardiovascular risk
PS-matched claims cohort
HR 0.76 heart failure, 0.57 mortality
Generate
10
OSA and metabolic syndrome by age
Cross-sectional cohort
OR 3.14 under age 60; null at 60 and above
Generate
Verdicts are my assessment of readiness, not the authors' conclusions.
Verdict
34
WAYNE STATE UNIVERSITY
FOUR THINGS TO TAKE HOME
1
2
3
4
Consumer wearables have crossed into triage Not diagnosis, and not for known OSA — but a device that discriminates perfectly at high hypoxic burden belongs in a referral pathway, with a threshold we choose deliberately.
Two negatives are as useful as the positives Zolpidem does not improve acclimatization, and CBT-I components work as well as the whole. Both let us spend less to get the same result.
One AHI threshold, several different diseases OSA at 40 tracks metabolic syndrome; at 70 it tracks airway mechanics. Mild pediatric SDB separates symptoms from physiology entirely. The number is the same; the disease is not.
Big claims data generate questions, not answers A 43% mortality reduction from a matched claims cohort is a reason to design a trial, not a reason to change a prescription.
WAYNE STATE UNIVERSITY
Thank you Questions & discussion
M. Safwan Badr, MD, MBA | Wayne State University School of Medicine
Obesity Management: Treating OSA Comprehensively Jonathan Gabison, MD, FAAFP, DABOM Associate Professor, Department of Family Medicine Associate Director of Michigan Medicine Obesity Fellowship Clinical Content Expert, Michigan Collaborative for Type 2 Diabetes (MCT2D) University of Michigan
Conflicts of Interest x
1. I do not have any potential conflicts of interest to disclose, OR 2. I wish to disclose the following potential conflicts of interest:
Type of Potential Conflict
Details of Potential Conflict
Grant/Research Support Consultant Speakers’ Bureaus
Financial support Other x
3. The material presented in this lecture has no relationship with any of these potential conflicts, OR 4. This talk presents material that is related to one or more of these potential conflicts, and the following objective references are provided as support for this lecture:
OBESITY AND SLEEP: A BIDIRECTIONAL RELATIONSHIP
Figorilli, Michela et al. “Obesity and sleep disorders: A bidirectional relationship.” Nutrition, metabolism, and cardiovascular diseases : NMCD vol. 35,6 (2025): 104014. doi:10.1016/j.numecd.2025.104014
SLEEP DISORDERS INCREASE HUNGER
Antza, Christina et al. “The links between sleep duration, obesity and type 2 diabetes mellitus.” The Journal of endocrinology vol. 252,2 125-141. 13 Dec. 2021, doi:10.1530/JOE-21-0155
OBJECTIVES 1. Redefine obesity as a chronic disease 2. Review current anti-obesity pharmacotherapy options 3. Identify appropriate candidates and choosing medications
4. How to manage side effects, titration, and follow-up 5. Collaborate effectively between specialties
CASE 1: MELANIE 39 year-old woman PMH: PCOS, hypertension, depression, and chronic pain syndrome CC: Fatigue Vitals: BP 142/70, HR 79, 272 lbs, BMI 41
Labs: A1c 5.4% Hgb 12.4 TSH: 2.73
HPI: She sleeps 8 hours every night but always feels tired!
Medications: Metformin 500 mg bid (PCOS) HCTZ 25 mg qd (HTN) Duloxetine 60 mg QD Pregabalin 50 mg bid Lipid Panel: HDL 41, Non-HDL 160, TGs 215 AST / ALT 44 / 64
WHAT IS OBESITY?
Obesity is defined as a chronic, relapsing, multifactorial, neurobehavioral disease, where an increase in body fat promotes adipose tissue dysfunction and abnormal fat mass physical forces, resulting in adverse metabolic, biomechanical, and psychosocial health consequences.
Adapted from Obesity Algorithm®, Obesity Medicine Association®
CO-MORBIDITIES ASSOCIATED WITH OBESITY
Yuen et al. Obesity Week 2016. Oct 31-Nov 4 2016. New Orleans: T-P-3166
“SICK FAT” DISEASE OR ADIPOSOPATHY
Poonam P et al. Curre Res Diabetes & Obes J 2021; 14(1): 555879. DOI:10.19080/CRDOJ.2021.14.555879
REVERSE DYSFUNCTIONAL FAT (ADIPOSOPATHY)
WHY DOES THIS MATTER? 1 Metabolic Health Metabolic health is not defined by BMI alone. You can be unhealthy at any BMI, and metabolically healthy at higher BMIs 2 Lifestyle Recommendations apply to every patient. Nutrition, movement, and sleep are cornerstones for all, regardless of weight.
3 Think “metabolic risk”, not just “obesity.” Lifestyle changes should be prioritized if co-morbidities exist. 4 Obesity Medications build on lifestyle, they don’t replace it Medications are more effective with solid lifestyle foundations.
5 It’s okay not to prescribe. If labs are normal, lifestyle is balanced, and weight is stable, then weight loss medications may be unnecessary.
As little as 5% weight loss improves health Adapted from Garvey. J Clinical Endo and Met., 2022, slide credit Dina Griauzde MD
OUR GOALS – THE TREAT TO HEALTH APPROACH 1.
To reverse or improve their co-morbidities 2. Sustainability 3. To feel good in the process
CASE 1: MELANIE 33 year-old woman PMH: PCOS, hypertension, depression, and chronic pain syndrome CC: Fatigue HPI: She sleeps 8 hours every night but always feels tired! Vitals: BP 142/70, HR 79, 272 lbs, BMI 41 1.
She has Metabolic Syndrome (Waist Circ, BP, TGs, HDL)
2. Inflammatory co-morbidities (fatigue, PCOS, chronic pain)
Labs: A1c 5.4% Hgb 12.4 TSH: 2.73
Lipid Panel: HDL 41, Non-HDL 160, TGs 215 AST / ALT 44 / 64
Treatment Options 20-40% weight loss
10-20% weight loss
5-25% weight loss
5-15% weight loss
2-5% weight loss
Yanovski & Yanovski, 2024
LIFESTYLE INTERVENTIONS – 4 PILLARS
Eating habits
Movement
Sleep
Mental Health
LIFESTYLE INTERVENTIONS – 4 PILLARS Eating habits
Movement
Sleep
Mental Health
1.2-1.6 g/kg protein
Walking, dancing, stretching
Aim for 7-8 hours
Depression/Anxiety
Good sleep hygiene
Motivation
Screen for OSA (STOPBANG) and treat if applicable
History of eating disorders?
20-30g fiber Food tracking
Avoid sugarsweetened beverages
Small incremental changes are better than no movement 5 minutes is better than nothing, 10 minutes is better than 5 minutes
Food as a coping mechanism
Patient Resources
Expert Guidance
Prevent.MCT2D.org
Clinical Content Experts and Patient Advisory Board
7 Diabetes Prevention Strategies
The AOM (Anti-Obesity Medication) Rules 1.
Consider them lifelong medications. For now, the evidence shows weight regain once stopping them
2.
They are expensive – if you cannot afford them out of pocket, we need to confirm your insurance covers them
3.
All studies included lifestyle changes
Expected Weight Loss with FDA Approved Medications
1Wilding et al., NEJM, 2021
5PI_Xenical-brand_FINAL.PDF
2Gadde et al., Lancet, 2011
6Surmount Trial 2022
3Greenway et al., Lancet, 2010
7Bays et al, Obesity Pillars. 2022
4Pi-Sunyer, NEJM, 2015
8Wharton S, et al. N Engl J Med. 2025
BACK TO OUR CASE: MELANIE 1.
Metabolic Syndrome with high cardiometabolic risk (waist circumference, BP, TGs, HDL)
2.
Symptoms tied to insulin resistance and inflammation (fatigue, chronic pain)
3.
We discussed a plan centered around lifestyle improvement at our initial visit and order a sleep study.
PCOS,
3. Sleep Study confirms Severe Obstructive Sleep Apnea (AHI 47) 4. CPAP Intolerance: “I felt claustrophobic in my mask!” “I couldn’t fall asleep!” 5. “Can we try that medication I have heard so much about?”
Tirzepatide and Obstructive Sleep Apnea
AHI
% Weight Loss
Blackman A, Malhotra A, Patel SR, et al. Tirzepatide for the Treatment of Obstructive Sleep Apnea and Obesity. New England Journal of Medicine. 2024;391(1):13-26.
INCRETINS (LIRAGLUTIDE, SEMAGLUTIDE, TIRZEPATIDE)
Nauck et al., 2021
Incretins (semaglutide, liraglutide, tirzepatide)
Contraindications
Potential side effects
Potential secondary benefits
Medullary thyroid cancer (personal or FH) MEN2 syndrome Pancreatitis Gastroparesis hx
Nausea and vomiting Bloating Diarrhea and constipation Fatigue Depression / suicidal ideation
Improved A1c OSA treatment (tirzepatide)
Risks of Sarcopenia
•
Sarcopenia = low muscle mass, impaired muscle function
•
Vulnerable populations = post-menopausal women, older adults
•
Associated with falls, fractures, frailty, mortality Locatelli et al., 2024
Mitigating Sarcopenia Risk Aerobic and Resistance Training1
Adequate Protein Intake (1.2-1.6 g/kg/day) 1.
Muscle Preservation (Younger and Older Adults) • Higher protein intake maintains lean mass and supports muscle retention2
2.
Sarcopenia Prevention (Older Adults) • 1.0-1.3 g/kg/day reduces muscle loss and preserves function3
1Locatelli et al., 2024 2Nunes et al., 2022 3 Nowson & O’Connell, 2015
Weight Regain with Discontinuation STEP-4 - Semaglutide
-5.0% at 68-wks
-10.6% at 20-wks
-17.4% at 68-wks
Rubino et al., 2021
Weight Regain with Discontinuation SURMOUNT4 - Tirzepatide
Rubino et al., 2021 Aronne et al., 2024
MEDICARE AND MEDICAID: THE COMORBIDITY BYPASS Fastest path to coverage. Qualifying comorbidities bypass step therapy across payers. Lead with the diagnosis, not the BMI.
MASH (sema)
OSA (tirzepatide)
ASCVD (Sema)
Metabolic-dysfunction steatohepatitis. Biopsy proven or Fibroscan with F2/F3
Moderate to severe obstructive sleep apnea. AHI/REI > 15
Prior MI, stroke, or PAD.
Anchor trial
Anchor trial
Anchor trial
ESSENCE (semaglutide)
SURMOUNT-OSA (tirzepatide)
SELECT (semaglutide)
BACK TO OUR CASE: MELANIE 1.
Insurance approved Tirzepatide, so we initiated treatment
2. She developed significant nausea and vomiting on the first dose 3. We trialed anti-nausea strategies (Ondanesetron) without meaningful improvement 4. After 6 weeks of persistent symptoms, she chose to discontinue the medication
HOW TO DECIDE WHICH MEDICATION TO START? 1.Cost 2.Obesity Phenotypes 3.Consider Secondary Benefit 4.Consider Contraindications
OBESITY PHENOTYPES • N=450
Hungry Brain (16%) •
Abnormal satiation
• 27% with 2+ phenotypes • 15% unknown phenotype Hungry Gut (18%)
Emotional Hunger (18%) • Hedonic eating
•
Abnormal satiety
Slow Burn (12%) •
Decreased metabolic rate
Acosta A, Camilleri M, Abu Dayyeh B, et al. Selection of Antiobesity Medications Based on Phenotypes Enhances Weight Loss: A Pragmatic Trial in an Obesity Clinic [published correction appears in Acosta etSpring). al. Obesity., 2021 doi: 10.1002/oby.23236.] [published correction appears in Obesity (Silver Spring). 2022 Jul;30(7):1521. doi: 10.1002/oby.23498.]. Obesity (Silver Obesity (Silver 2021 Sep;29(9):1565-1566. Spring). 2021;29(4):662-671. doi:10.1002/oby.23120
CHOOSING AN ANTI-OBESITY MEDICATION When treatment was matched to phenotype: 79% of patients lost ≥10% of their body weight over 12 months
Hungry brain
Phenterminetopiramate
Acosta et al. Obesity., 2021
Emotional hunger Naltrexone/ bupropion
Hungry gut Liraglutide Semaglutide (Tirzepatide)
Slow burn
Phentermine
When treatment was not matched to phenotype: Only 34% of patients lost ≥10% of their body weight
WHAT BARRIERS ARE YOU HAVING TO THE LIFESTYLE INTERVENTIONS?
Starting A Medication
Naltrexone / Bupropion (Contrave) Contraindications
Potential side effects
Potential secondary benefits
Uncontrolled HTN Seizure disorder Eating disorder Chronic opioid use MAOi use within 14 days
GI upset Headache Insomnia Dry mouth
Smoking and/or alcohol cessation Improved mood / depression Reduced chronic pain
[!] Suicidal behavior and ideation Worsening mood / depression
Phentermine / Topiramate (Qsymia) Contraindications
Potential side effects
Potential secondary benefits
Pregnancy* Glaucoma Hyperthyroidism MAOi use within 14 days
Insomnia Paresthesia Dry mouth Dizziness Constipation Mood changes / anxiety Tachycardia Increased blood pressure
Reduced migraines Reduced seizures
[caution] History renal stones Uncontrolled HTN Cognitive concerns
[!] Reduced efficacy of OCPs
*2 forms of birth control + monthly negative pregnancy test
Topiramate is a teratogen (increased risk of fetal cleft defects)
VIVUS, 2022
Phentermine Monotherapy
Lewis et al., 2019
CONSIDER CO-MORBIDITIES – DOUBLE BENEFIT! OTM Option Bupropion/Naltrexone Phentermine/Topiramate
Co-Morbidities Depression ADHD Smoking Cessation Alcohol Cravings Migraines Seizures
Phentermine
ADD/ADHD
Incretins
T2DM Prediabetes Cardiovascular disease HFpEF Renal disease Sleep Apnea
CONSIDER HARMS OTM Option Phentermine Topiramate (Qsymia) Contrave (Bupropion/Naltrexone)
Incretins (Wegovy/Saxenda/Zepbound)
Contraindications Heart Failure, Arrhythmia,CAD Uncontrolled HTN, hyperthyroidism, glaucoma, H/o SUD, uncontrolled anxiety Nephrolithiasis, Reproductive Considerations (Birth Control) Uncontrolled HTN, seizures, bulimia, anorexia, chronic opioid use Personal or FHx of Medullary Thyroid Cancer or MEN2 Syndrome, Hx of Gastroparesis
SUMMARY Obesity is a chronic, relapsing disease – Driven by adipose hypertrophy and inflammation, not willpower. These processes underpin many obesity-related co-morbidities. Lifestyle is the foundation – Eating, movement, sleep, and mental health form the core of treatment for every patient
Focus on sustainable change – Use % weight loss as a biomarker for improved metabolic health, not just the number on the scale Medications are powerful tools – When appropriate, they enhance satiety, reduce cravings, and support long-term success Choose meds thoughtfully – Consider secondary benefits, harms, phenotype, and cost when selecting treatment.
WHAT SLEEP CLINICIANS CAN DO Address obesity as a chronic disease, not a behavioral issue Screen for weight-related eating patterns - evening cravings, nighttime eating, irregular meal timing Identify metabolic risk during routine sleep evaluations - hypertension, dyslipidemia, prediabetes, MASLD Reinforce how sleep quality affects appetite and energy – Better sleep improves hunger signals, mood and daytime activity Offer weight management referral when appropriate – Especially for patients with OSA, CPAP intolerance, or persistent daytime fatigue
HOW SLEEP CLINICIANS SUPPORT MEDICATION SUCCESS • Monitor sleep-related side effects • Reinforce gradual titration and adherence • Emphasize lifestyle is key to overall success • Coordinate with primary care/obesity clinicians when symptoms change or weight loss stalls
Email: gabisonj@med.umich.edu
The Sleep, Pain, Affect and Opioid Tetrad Michael T. Smith, Ph.D. 9.18.26
Objectives 1)
Describe the inter-relationships between sleep, chronic pain, Affect, and Reward System Dysfunction
2)
Discuss experimental and prospective studies demonstrating how sleep causally disrupts these systems in the fronto-mesolimbic brain
3)
Discuss treatment implications and future directions to develop multi-targeted, mechanistically driven interventions
Public Health Impact of Chronic Pain, Depression and Drug Use Years Lived with Disability (YLDs) Global 2021 (LANCET 2024): 1. 2. 3. 5. 6. 24.
Low back pain Depression Headache MSK Anxiety Drug Use (ETOH 22)
Rates of Co-Occurrence are High Problematic Opioid Use
• All mutually reciprocate risk for the other • Co-occurrence compounds morbidity
21-29%
• Co-occurrence increases treatment resistance
Chronic Pain
• None are typically cotreated systematically
20.9%
56%-24%
Depression ~8.3%
50-88%
40-88%
Goya%2520The%2520Sleep%2520of%2520Reason%2520Produces%2520Mosters%25201798
pain
Chronic Insomnia 10-15% Wong J. Intern Med (2022)
Nutt. dialogues clin neuro (2008)
Witkiewitz, Alcol, Clin. Exp. Res (2018)
Hilederink Psychosom. (2012)
Pinheiro ACR (2025)
De La Rosa., Pain, 2024
Epidemiologic studies increasingly implicate poor sleep as a risk factor of opioid misuse NHANES Data in the US. (N=10,685); Short et. al. Addictive Beh. (2023). UK BIOBank Data ( N = 444,039 Opioid Free Individuals)
• Models adjusted for: demographics, baseline pain, BMI, psychiatric illness, nonopioid substance use, surgeries, medical (including sleep) comorbidity, baseline hsCRP Chen et. al. Sleep (2023)
Poor Sleep Predicts Incident Chronic pain and Poor Pain Outcomes •
At least 5 prospective epidemiological studies controlling for depression and othe psychosocial risk factors show that over 1-3 years poor sleep [Gupta (2007); Mikkelsson (1999); Bonvanie (2016); Sanders (2016); Harrison (2014);] Smith et. al. (2008)
1) 2) 3)
•
Confers 2-3 fold risk of new onset chronic pain (pain free to start) Linked to persistence and progression of emergent musculoskeletal pain Predicts progression from regional to widespread pain disorder
Restorative sleep linked to 3- fold pain remission rate [Davies (2008)]
• Effects of poor sleep on developing chronic musculoskeletal pain and pain severity are more pronounced in females (Bonvanie (2016) ; Zhang (2012)
OPPERA Study (N=2453) Sanders, Maixner, et al., JOP, 17(6) 2016)
High Co-Prevalence of Multiple Sleep d/o in Chronic Pain. Pain patients have overlapping risk factors for sleep apnea: obesity, older age
% Overall Sample N = 53 Primary Insomnia (PI) 26% SI (TMD/Psych) 9.5% No DX 32%
68% ICSD Sleep D/O
OSA, 28.4% (73% mild range)
Heat Pain Threshold – Ventral Forearm Mean, 95% Confidence Intervals 44.5
Degrees Centigrade
Rates of ICSD / RDC Sleep Disorder Diagnoses in TMD
44 43.5 43 42.5 42 41.5 41 40.5 40
No DX n = 17
PI n = 14
OSA n =15
RDC BRUX n=9
RDC BRUX, 17.3 % 43% > 2 disorders 13% RDC good sleepers
RLS / PLMS 7.6% Other SD 7.6%
• PI, OSA, & Sleep Bruxism all associated with
Clinical Pain Severity (BPI), P<.05 Smith et al., Pain 2009
Insomnia is not just a symptom of depression and chronic pain: Shared Risk Factor for Both Breslau et al., (1996) Insomnia Associated with 4-FOLD
5 4
Gender Adjusted
3
Increased Risk of New Onset MDD
2
Controlling for History of Depression: 2-Fold RISK
• • • •
3-Year longitudinal, Young Adults 1007 subjects
1 0
NO INSOMNIA
>2 WKS INSOMNIA
Insomnia Increases Risk of New Onset MDD and Recurrence Insomnia Attenuates Treatment Response (Franzen & Buysse Dialogue Clin Neurosci, 2008) Increases Time to Remission (Boland et al. J. Affect Dis (2020)) Residual insomnia after depression RX increases relapse (Dombrovski 2008)
Baglioni et al. J. Affect Disord. (2011).
Baglioni et al. J. Affect Disord. (2011).
Does CBT-I Work in the Context of Chronic Pain and is it Analgesic? • Effect Size of CBT-I on Insomnia was large SMD = .89 Post treatment and .56 on Follow Up (Probability of better sleep was 81% at Post and 71% at FU
SMD on Pain = .22, P=.01 N=12., (P=.006) SMD on Depression = .44 N=8, P=.01
Selvanathan et al. SMR 2021
Does CBT-I Work in the Context of MDD and Does it Improve Depression? Systematic Review and Meta-Analysis: 19 RCTs and 4808 Participants
• Effect Size on Insomnia Remission was large [OR 3.57 (2.48-5.14)] Effects on Depression Response (50% reduction); [OR 2.28 (1.67-3.12)
Depression Response: CBT-I = 32% vs Control = 17% Sensitivity Analyses Removing Sleep Item = minimal effect OR = 2.32 Insomnia Remission:
CBT-I = 26% vs Control = 9%
Furukawa et al J. Aff. Dis. *(2024)
Insomnia Often Fails to Respond to Antidepressants or Psychological Treatments for Pain •
•
AD Effects on Pain are SmallModerate for Duloxetine (high dose)
AD Effects on mood were small –
• •
Floor Effects (most studies excluded low mood or those with mental health disorder. Major Gap!
AD Effects on Sleep are Small Mirtazapine understudied (Ottman et. al. Rheumatology (2018); Wang (2014)
•
Effects of CBT-P on Pain are small (SMD ~.2 , e.g,, Williams, Cochrane 2020). –
Same Effect size as for CBT-I on pain
Network Meta-Analysis of Antidepressants for Pain N= 176 RCT; 28,664 subjects
Effects of Top Ranked (n > 200) Antidepressants (all categories, TCAs, SSRIs, SNRIs, etc.) Pain Intensity Drug
SMD
RCTs #
N
Duloxetine High Duloxetine Low Milnacipran High Milnacipran
-.37 -.31 -.22 -.22
14 18 2 4
1925 2727 823 943
Mood Duloxetine
-.16
26
4873
Milnacipran High
-.13
5
1753
Mirtazapine
-.50
1
204
Sleep
Birkinshaw, Cochrane (2023)
Duloxetine High
-.14
6
1491
Duloxetine S Milnacipran High Milnacipran S
-.21 -.03 -.06
11 1 1
2615 797 799
What are the modifiable mechanisms by which sleep disturbance contributes to clinical pain and depression? •
We don’t have a good understanding of the molecular factors involved ➢
•
Sleep MOR Study Ongoing
We don’t have a clear sense of which aspects of sleep architecture and microstructure drive hyperalgesia or mood disturbances ➢
Most of the studies used Total Sleep Deprivation so architecture is unclear
What is the Mechanistic Substrate Underling the Sleep-Pain-Depression-Addiction Tetrad? Is sleep causally linked Mesolimbic Reward and Pain pathways
Nociplastic Pain: the Role of Sleep and Depression
PAG RVM Mesolimbic System
• SD ↓ D2 receptor BP in Striatum (Volkow J. Neuro, 2008;2012)
• SD ↓ NAc, Insula, ↑ S1 activity during pain •
Peripheral Nociceptors Dorsal Horn of Spine
(Krause, Walker J. Neuro (2019) Lateral Hypothalamus OREXIN neurons →VTANac → (Nac (vS) & DLPFC (Harris, Nature (2005)
Depression
Adapted from Reid & Finan (2022); Haack & Sethna CSMR (In Press)
Could insomnia and/or sleep loss be a causal factor in chronic nociplastic pain? Experimental Sleep Loss Literature • Multiple Forms of Sleep Loss Induce Next Day Hyperalgesia to Noxious Pressure and Thermal Stimuli (e.g., Moldofsky (1975) Lentz & Landis (1999) Roehrs (2006); Onen (2001); Kundermann (2004); Haack (2005), Smit (2007); Ablin and Clauw (2013)
• What About Central Pain Modulatory Mechanisms? 1) Does it Impair Pain Inhibitory Capacity •
Descending opioiderics circuit modulated by 5HT and Norepinephrine
•
CPM Predicts the transition from acute to chronic pain (Wilder-Smith (2010), Granovsky (2013)
•
Poor sleep is correlated with impaired CPM in chronic pain, including TMD [Edwards & Smith (2009), Prent (2023); Lee (2013), Petrov (2015)
2) Does it Enhance Spinal and Supraspinal Fascilitation? •
NMDA receptor process sensitizes second order neurons in dorsal horn (Eide 2000)
•
Predict development of chronic postsurgical pain (Petersen (2015)
Experimental Sleep Disruption (Forced Awakenings) Impairs Pain Inhibitory Capacity
• Forced Awakenings (FA): Insomnia sleep Loss pattern with ~50% sleep loss— not sleep restriction — impairs endogenous pain inhibition (CPM). • Multiple Replications ➢ ➢ ➢ ➢
Iacovides (2017), Eichhorn (2017), Haack (2023), Hima (2023)]
Smith e al., Sleep, 2008
Experimental Sleep Disruption Facilitates Pain – and it Diverges by Sex (weighted pin prick stimulator (512N, 1 second ISI)
Mixed model Experiment (N=79 (46 Females): Within subjects factor sleep condition (FA vs US, 2 nights); Between subject factor = drug (morphine vs. placebo), N=79 (46 Females)
Sleep Condition by Sex Interaction, controlling for menstrual phase, race, age, BMI (P=.02)
Replication ➢
Haack (2018) Smith & Irwin et al. Sleep (2019)
Similarly Experimental Sleep Loss Disrupts Affective Functioning Meta-Analytic Studies • •
The most robust effect of sleep loss is blunted positive mood (anhedonia)! But negative mood and emotion regulation are impacted too.
Rank
Dimension of affective functioning
Hedges g
1
Positive mood (anhedonia)
−.94
2
Negative mood
+.45
3
Adaptive emotion regulation
−.32
4
Emotional reactivity to stimuli
−.18
5
Maladaptive emotion regulation
+.14 (n.s.)
Tomaso, Sleep (2021); Palmer, Psych. Bull. (2023)
Experimental Studies of Affective Pain Modulation: A potential mechanism linking sleep, affect (mood, depression) and chronic pain Affect tunes pain up and down through the fronto-mesolimibic descending circuit substrate
Positive-affect analgesia: present after sleep, gone after forced awakening
• Positive affect inhibits pain — the brake (PAPM) • Negative affect amplifies pain — the gas (NAPM) Rhudy et al., Curr Opin Psychiatry 2002 · Psychophysiology 2005
Sleep disruption disables the brake — so more pain gets through. APM is impaired in insomnia, depression, and chronic pain populations (Rosello, psychosom. (2015), Rhudy, Pain (2013), Kamping (2013) US = undisturbed sleep · FA = forced awakening · Finan & Smith, Sleep 2017 · N=45
High Negative Affective Pain Modulation and Insomnia Increase Risk for Escalating Pain over 1-Year Older Adults With (red) and Without (blue) Insomnia • Free From Chronic Pain at Baseline • 2-week pain diaries, quarterly • Only one trajectory climbs over a year — insomnia plus high negative-affective pain modulation. The other three stay flat.
NAPM × insomnia × time: p<.001. The link strengthens over 12 months in insomnia; absent in good sleepers (g=0.48 at 6 mo).
Smith et al. (prelim) · APM endotoxin study, placebo arm · adults 50–76 · n=60 Endotoxin 1R01AG057750, Smith)
The Surgical Gateway — Opioid Analgesia, Abuse Liability and Sleep Disturbances Surgical procedures remain a real point of exposure. The run-up to surgery is where sleep matters.
100M+
90%
1 in 5
surgeries per year in the US
of surgical patients receive an opioid prescription
still using opioids at 3+ months
Preoperative insomnia is common — and predicts opioid use and developing chronic post-surgical pain ~40% of total-knee patients have preoperative insomnia, and insomnia predicts perioperative opioid consumption — a modifiable target before the gateway opens Hill 2017 · Sing 2022 · Owens, Pain Med 2023 · Ho 2023 · Luo 2019
Experimental forced awakenings decrease the effects of morphine analgesia (.08 mg/kg, I.M.) N= 100 male and female •
•
Findings replicate pre-clinical data (Alexandre et al., Nat. Med., 2017)
No Sex Differences observed
Sleep Condition X Drug Interaction = P<.05 (Morphine effect @ US; P<.001; Morphine effect at FA; P=.39)
Results controlling for menstrual phase, race, age, bmi
Smith & Irwin et al. R01: Scientific Reports (2021)
FA shifts opioid reward – a Sex specific signal N= 100 male and female
The same disruption raises how “high” morphine feels — in men under forced awakening most of all.
Morphine raised how “high” participants felt (p<.001). The sleep×sex interaction is significant — men under FA report the largest high. (US = blue, FA = red.)
A behavioral abuse-liability precursor — set by sleep.
Results controlling for menstrual phase, race, age, bmi
Smith & Irwin et al. R01: Scientific Reports (2021)
Sleep MOR: Imaging the Fronto-mesolimbic Opioid System Directly µ-opioid receptor PET study of sleep disruption. [¹¹C]carfentanil PET, N=68 randomized to three inpatient nights; PET scan + opioid challenge on Day 3.
NIH 1U01 HL150568 (Smith)
How we measured the opioid system AM after the second sleep manipulation night, subjects competed a 90 Minute PET Scan conducted PM subject completed a 270 minute placebo controlled, dose escalating opioid (hydromorphone) challenge. Receptor imaging
Opioid challenge
Resting + pain-evoked [¹¹C]carfentanil → MOR availability & release
Cumulative hydromorphone → analgesia & drug “high”
Manipulation check: the conditions produced the intended sleep disruption pattern
Each disruption did what it was designed to do — in opposite ways. FA — the insomnia analog: deep sleep ↓~36%, REM ↓~50%, continuity broken. FRAG — the apnea analog: total sleep time preserved, but fragmented into brief arousals.
Schematic hypnograms; stage totals = study manipulation-night means.
Objective 1: Evaluate the Effect of Sleep Condition on Resting and Pain-Evoked MOR BP in Mesolimbic, Pain and Reward Circuits (6 ROIs: DLPFC, Anterior Cingulate, Amygdala, Insula, PAG, vStriatum) Co Primary outcomes: 1) Resting and Pain Evoked Endogenous Opioid Release [Percent Change (Resting Binding Potential-Pain Binding potential/Resting BP X 100) FA ↓ resting availability
FRAG ↑ pain-evoked release (women)
Resting MOR availability ↓ in ventral striatum under FA (** vs US)
Pain-evoked release ↑ under FRAG in women (FRAG×sex p=.0007)
Basal BP: Omnibus Mancova: Basal BP: Wilks’ λ = 0.625, F(12,96) = 2.12, p = .022. (region p < .0001; age p < .001, sex p = .036, BMI p = .020; race ns.) ; FA < US in vs P=.0085 Percent Change BP, Null MANCOVA; Significant Pre-planned SEX XvS X FRAG interaction (zubieta also found sex diffences in NA (J Neurscience (2002)
Objective 2: Evaluate the Effect of Sleep Condition on Opioid Analgesia and Abuse Liability (High and Liking) The two disruptions diverge on behavior — FA blunts pain relief, FRAG amplifies reward. SEX X Sleep Condition X Time FA ↓ analgesia
AUC Ancova, P=.02
Hydromorphone analgesia ↓ ~37% on-drug under FA
FRAG ↑ drug “high” (sex-split)
Sex X Sleep Condition X Time P=008
Drug “high” ↑ — men under FA, women under FRAG
FA suppresses pain relief; FRAG amplifies reward — the behavioral double dissociation. Smith et al. · MOR-PET RCT (in preparation)
3-Group ANCOVA LOG AUC, P=.023 Adjusted for V2 AUC, age, sex, race, BMI
Exploratory Objective 3: Do morning MOR alterations couple with PM Analgesia Attenuation or Drug High Ratings?
VS release tracks the drug “high” If fragmentation amplifies reward by driving µ-opioid release, then release should predict the “high” — Sex X Sleep Condition X Time P=008 and it does.
More ventral-striatal release → a bigger “high.” The coupling is carried by men; in women it appears specifically under fragmentation. vS release×sex p=.0001 (the carrier) · women panel descriptive, FRAG r=+0.67 · V2-adjusted, on-drug window · MOR-PET RCT (in prep)
Exploratory Objective 3: Do morning MOR alterations couple with Analgesia Attenuation or Drug High Ratings?
So What Predicts Opioid Analgesia?
The analgesia deficit tracks objective vigilance (PVT) — not the receptor, and not sleepiness. Points to arousal / adenosine — caffeine should rescue it; more opioid won’t. Smith et al. · MOR-PET RCT (in prep) · PVT D2A pooled FA+FRAG r≈−.38, p≈.003 · cf. Alexandre 2017, Elmenhorst 2017
How do we translate this emerging mechanistic knowledge into improved treatments and develop novel prevention approach for chronic pain?
Data from our RCT of CBT-I in TMD may shed light on how to take a mechanistic approach moving forward. (Full Sample N= 130 Women with Insomnia (Age 37.6(11))
CBT-I fixes the sleep N=44 N=44
CBT-I produced a large drop in insomnia severity vs. education — its proximal target was hit.
ISI at mid-treatment (V6): b = −4.29, p = .001, d = 0.56 — the a-path.
Both EDU and CBT demonstrated comparable reduction in Pain @ 3 Months The two disruptions diverge on behavior — FA blunts pain relief, FRAG amplifies reward. SEX X Sleep Condition X Time
Both arms improve and converge — CBT-I shows no pain advantage over education on average.
…but it doesn’t beat education on pain
The null hid a conditional effect What are the mechanistic drivers of CBT-I’s effects on Pain? Do Improvement differential improvements Positive Affect Predict Reduce Pain? • Baseline QST Profiling on Mechanical Temporal Summation of Pain • Prior Work suggests that early CBT-I treatment gain in sleep predict 6-month pain reduction in KOA pain
CBT-I raised positive affect, and PA gains reduce pain — but only in highly sensitized patients. IMM = −0.315, p = .038 · high wind-up indirect = −0.525, p = .046 · ~1 in 5 patients above the J–N threshold · n=60
A Promising Emerging Treatment for Chronic Nociplastic (central sensitization) Pain Emotional Awareness and Expression Therapy [(EAET) (Lumley et al] • Intensive 8- session protocol Component
What It Involves
1. Education & Rationale 2. Emotional Awareness 3. Emotional Expression 4. Putting It Into Practice
Pain Neuroscience. Stress and emotions can cause and amplify pain; decoupling threat value of pain Notice and name avoided feelings; connect them to people and life events Safely express emotions like anger, grief, and fear; Develop PA Assertive appropriate expression in relationships; Foster PA
Population
vs CBT
Primary Endpoint
Reference
N
p (vs CBT)
Lumley, Pain, 2017
230 Fibromyalgia
d = 0.35–0.37 (WPI, FM) 6-mo FU
Yarns, Pain Med, 2020
53 Older veterans, MSK
η²p = 0.13–0.16 (large)
Post & 3-mo < .05
Yarns, JAMA NO (202)4
126 Older veterans, MSK
ΔBPI −1.59; OR 21.5 (≥30% ↓)
Post-tx
0.02 (≥50% ↓)
< .001
Mark Lumley
The opportunity: fix the sleep, and the treatment may work even better
EAET and CBT lower pain — but mainly in better sleepers. In poor sleepers (dashed), the benefit largely vanishes.
So we planning a study to test a sleep-first sequence: optimize sleep (CBT-I, with daridorexant as needed) before EAET, in older adults with chronic pain and insomnia.
Get the mechanism right → expand who responds. Treatment and prevention. Preliminary data: CBT vs EAET × baseline sleep quality · the Sleep + EAET trial (in development)
Data from Lumley et. al, Pain 2017)
Future Directions 1.
CBT-I Needs to Be Optimized for Pain and Depression •
Need mechanistic driven adaptive designs to test components in sequence
•
Novel hybrid interventions need to be developed and tested ➢
Combining CBT-I with Morning Bright Light Treatment (Smith & Burgess)
(NIH: AG085712
➢
Develop hybrids combining CBT-I with psychological RX for pain and depression
2. Continue Mechanistic Studies (Carfentanil PET Imaging) 3. Test Newer Sedative Hypnotics in Depression, Chronic pain and OUD DORAs (Huhn) 4. Develop Sleep-Related Biomarkers that Predict Treatment Response
Future Directions: EEG signatures that predict pain — and that we may be able to target
Slow-wave activity (deep sleep) Lower nocturnal slow-wave (delta) activity predicts higher next-day pain in TMD.
Sleep spindles A large, prospective signature over 5 years — and sexspecific.
Women · slow-spindle DENSITY ~67% less likely severe pain · ~19% more likely pain-free
Could augmenting deep sleep — pharmacologically or with acoustic stimulation — reduce pain?
Reid et al., J Pain 2023 · female TMD
Men · slow-spindle DURATION ~66% less likely severe pain · ~21% more likely pain-free Fast spindles: no effect · SHHS n=5,804 · Reid et al., Sleep 2026 (advance access)
Different signatures, different sexes — the path to biomarker-guided, precision pain care.
Four take-homes
1 2
3 4
Sleep is causal — not just a symptom It is mechanistically involved in chronic pain and opioid addiction, converging on shared reward / affective circuitry.
The tetrad is reciprocal Sleep, pain, and affective dysregulation, and addiction interact — and that loop is what drives treatment resistance.
Treatment must target all three …but we still lack precision about WHICH dimension of sleep and architecture to target, and in whom.
Mechanism is the opportunity Studying these interacting mechanisms is where the next generation of treatment — and prevention — will come from.
Thank you Johns Hopkins Behavioral Medicine Laboratory & Collaborators Jennifer Haythornthwaite, PhD
Patrick Finan, PhD
Robert Edwards, PhD
Traci Speed, MD
Luis Buenaver, PhD
Sheera Lerman, PhD
Claudia Campbell, PhD
Molly Attwood, PhD
Janelle Letzen, PhD
Jeongwi An, PhD
Janelle Coughlin, PhD
Lynn Nakad, PhD
Matthew Reid, PhD
Helen Burgess, PhD.
Funded by the NIH · NIDA · NIA · NINDS · NIAMS · NIDCR · NHLBI …and the patients and participants who make this work possible.
References Epidemiology & risk
Affect & affective pain modulation
Sanders, Maixner et al. OPPERA. J Pain 2016 (N=2453). Runge et al. 2024, systematic review / meta-analysis. Chen et al. UK Biobank. Sleep 2023 (N=444,039). Short et al. NHANES. Addict Behav 2023. Hill 2017; Luo 2019; Sing 2022; Owens 2023; Ho 2023 (surgical).
Tomaso et al. Sleep 2021 (meta-analysis). Palmer et al. Psychol Bull 2023 (meta-analysis). Rhudy et al. Curr Opin Psychiatry 2002; Psychophysiol 2005. Finan & Smith. Sleep 2017 (PA analgesia; N=45). Mun, Weaver … Finan, Smith. J Pain 2022 (PMID 34839028).
Pain inhibition & sensitization
Triad & treatment stratification
Smith et al. Forced-awakening CPM model (N=32). Edwards & Smith. Clin J Pain 2009 (TMD DNIC; N=53). Smith, Remeniuk … Irwin. Sleep 2019 (N=79). Reid et al. J Pain 2023 (SWA → next-day pain, TMD). Reid et al. Sleep 2026 (spindles; SHHS N=5,804).
Franzen & Buysse 2008; Dombrovski 2008. Smith et al. TMD CBT-I trial; cf. Selvanathan 2021. Smith et al. APM endotoxin study, placebo arm (prelim). Lerman et al. Sleep Medicine 2022 (I-SSD; N=128). EAET: Lumley 2017 (Pain); Yarns 2024 (JAMA Netw Open).
Substrate / mesolimbic system
Opioid axis
Reid & Finan 2022 (schematic). Volkow et al. 2008, 2012. Krause, Walker et al. 2019. Apkarian 2025; Dagher & Cahill 2024.
Smith, Irwin et al. Scientific Reports 2020 (N=100). Alexandre et al. Nat Med 2017. Elmenhorst et al. 2017. Smith et al. [¹¹C]carfentanil MOR-PET RCT (in prep).
Shaping PLATO:
Bringing the Patient Voice to the Forefront of Care Douglas Kirsch, MD, CPE, FAAN, FAASM
Medical Director, Sleep Medicine, Atrium Health Clinical Professor, Dept. of Neurology, Wake Forest University
Conflicts of Interest 1. I do not have any potential conflicts of interest to disclose, OR
x
2. I wish to disclose the following potential conflicts of interest:
Type of Potential Conflict
Details of Potential Conflict
Grant/Research Support Consultant
Apnimed, Carelon, Huxley Medical, Incannex
Speakers’ Bureaus
Financial support Other x
3. The material presented in this lecture has no relationship with any of these potential conflicts, OR 4. This talk presents material that is related to one or more of these potential conflicts, and the following objective references are provided as support for this lecture:
Learning Objectives
Upon completion of this course attendees should be able to: Recall the pros and cons of current OSA assessment tools Comprehend the importance of the use of PROs in patient care Recognize how a PRO can be utilized for long term patient management.
4
Agenda • Introduction • Background • Methodology • Results • Conclusions and Next Steps
Introduction OVERTURE
The Journey Starts with a Single Patient • 69-year-old male patient with HTN, DM, Afib Sleepy at times during the daytime Fragmented sleep
• Diagnosed with OSA by PSG: AHI 26/h, Min O2 82% • Placed on PAP therapy
PAP Adherence
PAP Adherence
Medicare 2008 • The patient reported improved symptoms • Less sleepy • Sleep improved • Blood pressure improved • However, Medicare refused to pay for PAP • The machine was returned • Pt lost to follow up
Act I BACKGROUND
Medicare Changes • CMS began requiring Positive Airway Pressure (PAP) adherence, specifically defining it as using the device for at least 4 hours per night on 70% of nights within a 30-day period, on November 1, 2008 • This was one of the first time in history a therapy was required to be used and tracked for ongoing coverage of that therapy
• It caused a fair amount of consternation in the Sleep Medicine community
AASM Responses • The AASM went to CMS twice (that I was aware of) to discuss it. • I was present at CMS 2018 and a prior visit occurred in 2013 • CMS declined to change their viewpoint (and made some subtle hints at not great outcomes if we pushed harder for change, at least in 2018)
• The AASM had several groups working on a response to this change since 2008.
AASM Response • One AASM task force focused on alternative models to getting a patient coverage for a PAP machine • What mattered to patients? • Was it hours of PAP use or symptomatic changes? • The only questionnaire tool being required was the Epworth SS – but that only measures situational sleepiness and isn’t relevant to non-sleepiness symptoms.
Thus, An Idea Was Born • Was there a patient-reported questionnaire tool that had appropriate characteristics? 1) Freely available to any providers without need for a dispensation from a University or other rights-holder - The Epworth has been broadly used but requires a license
2) Encapsulates the patient experience of a broader symptom set of OSA Could this new tool be potentially used to justify PAP use in patients with less than 4h/70% adherence in Medicare patients? https://epworthsleepinessscale.com/licenses/
Does That Tool Already Exist?
Gamaldo 2018
Gamaldo 2018
Central Point of Gamaldo 2018 •“No single tool met all the TF’s objective criteria and subjective evaluation for clinical validity and feasibility to be recommended by the AASM.”
Gamaldo 2018
Gamaldo et al. (2018) Conclusions To fulfill this unmet need, researchers in the field of sleep medicine should pursue the development and validation of OSA screening and OSA outcome assessment tools, ideally with the following features: • 1. 10 or less questions (subjective symptoms or objective physical findings, alone or in combination) written at or below the 5th gradeschool reading level • 2. Completion in less than 5 minutes by any member of the health care team
Gamaldo 2018
Gamaldo et al. (2018) Conclusions • 3. Tiered system for completion, scoring, and interpretability: • (1) patient-reported • (2) bed partner-reported • (3) provider-reported measures • 4. Compatibility with electronic health record platform for future monitoring of clinical outcomes and analysis • 5. Capability of patient self-tracking or capability to monitor progress
• 6. Availability as an application platform with electronic scoring or paper format with easy manual scoring Gamaldo 2018
Gamaldo et al. (2018) Conclusions • 7. Availability in the various languages that represent communities with high OSA presence
• 8. Adaptability to general and sleep patient populations • 9. Adaptability for specific, at-risk OSA populations (eg, stroke patients, atrial fibrillation) or those individuals with unique occupational or public health risk (eg, commercial drivers, pilots) • 10. Available to the public
Gamaldo 2018
What I Told the AASM Board
Act 2 AASM Intervention
From CMS • Ensuring patients and families are engaged as partners in their care—a CMS goal—can also be an effective way to measure the quality of patient care. • Although patient reports of their health and experience with care are not the only items that should undergo measurement, they are an important component. • Historically CMS used surveys to collect patient-reported data, but the continued development of the infrastructure allows more timely collection and use of alternative collection methods (e.g., using mobile devices) of these data.
https://mmshub.cms.gov/about-quality/types/proms/overview
PRO Defined: • CMS defines a PRO as any report of the status of a patient’s health condition or health behavior coming directly from the patient, without interpretation of the patient’s response by a clinician or anyone else. • Self-reported patient data provide a rich data source for outcomes. This definition reflects the key areas: • Health-related quality of life (including functional status) • Symptoms and symptom burden (e.g., pain, fatigue) • Health behaviors (e.g., smoking, diet, exercise)
https://mmshub.cms.gov/about-quality/types/proms/overview
https://mmshub.cms.gov/about-quality/types/proms/overview https://mmshub.cms.gov/
CMS Conclusion re: PRO and Pt. Mgmt. • The overarching principle is these measures should consider the patient foremost. Quality measures designed to capture performance on PROs should be : • Psychometrically sound—In addition to the usual validity and reliability criteria, the measure developer should consider cultural and language considerations, and patients’ burden of responding. • Person-centered—Quality measures should reflect collaboration and shared decision-making with patients. Patients become more engaged when they can give feedback on outcomes important to them. • Meaningful—Quality measures should capture impact on health-related quality of life, symptom burden, and achievement of personal goals. • Amenable to change—Outcomes of interest must be responsive to specific health careservices or intervention. • Implementable—Data collection directly from patients involves challenges of burden to patients, health literacy of patients, cultural competence of measured entities, and adaptation to computer-based platforms. Evaluation should address how to manage these challenges. https://mmshub.cms.gov/
AASM Partnership for a PRO • What I naively thought would happen: • The AASM would convene an expert group for a weekend • A base tool would be developed based on expert consensus • The tool would be tested by expert group and then • What actually happened: • The AASM Board determined that the best course of action was to hire a external tool-building company to help us through the building and psychometrics process
Dancing Through Data
Overall ICON Project Design • The study was done in 3 parts: • Part 1: Concept Elicitation and Cognitive Interviews (Qualitative methods) • Part 2: Pilot Testing (Quantitative methods)
• Part 3: Development of scoring algorithm and psychometric validation (Quantitative methods).
Part I: Concept Elicitation • Participants in the concept elicitation interviews were recruited through sleep clinic sites and a patient advocacy group, the American Alliance for Healthy Sleep (AAHS), and received a $100 gift card for their participation. • Adults at least 18 years of age who had a new diagnosis of OSA in the past 6 months based on an in-laboratory or home sleep apnea test with OSA as the major cause of their sleep disturbance were eligible for an interview. • Key exclusion criteria were unstable psychiatric disorder, severe chronic medical condition, active substance use disorder or current participation in a clinical trial.
• The concept elicitation interviews were up to 60 minutes long, conducted by telephone or electronic video platform, and audio recorded. • Experienced interviewers who conducted the interviews received training on OSA and on the semistructured interview guide specifically developed for the concept elicitation interviews.
Statistics – Concept Elicitation
Demographic Category Age (years) Mean (SD*) Range Gender Male Female Race/Ethnicity Caucasian Asian African American American Indian or Alaskan Native Highest Level of Education High school Bachelor’s degree Post-graduate degree Work Status Full time Part time Retired OSA Treatment Continuous positive airway pressure (CPAP) Using Treatment as Prescribed Yes No Body Mass Index (BMI) 25.0 – 29.9 kg/m2 (Overweight) > 30.0 kg/m2 (Obese) Missing
n (%) 52.8 (14.82) 26-74 11 (73.3%) 4 (26.7%) 12 (80.0%) 1 (6.7%) 1 (6.7%) 1 (6.7%) 5 (33.3%) 4 (26.7%) 6 (40.0%) 9 (60.0%) 4 (26.7%) 2 (13.3%) 15 (100.0%) 11 (73.3%) 4 (26.7%)
6 (40.0%) 8 (53.3%) 1 (6.7%)
Draft PRO – PLATO 43Q • Following development of the draft PLATO (43Q), cognitive interviews were conducted with 14 patients with OSA in the United States in rounds of 4-5 participants per round. • During cognitive interviews, participants completed an electronic device familiarity form to provide context for their responses to usability questions and completed the draft ePLATO on their handheld electronic devices. • Participants were asked what the instructions asked them to do and about the 7-day recall period in the instructions, and how they interpreted or what they considered in responding to each item.
• Other topics discussed included the ease of completion, the relevancy of each item, potential missing concepts relevant to OSA, the suitability of the frequency or severity scale used in the PRO, and response options, ease of responding or choosing between response options, and item redundancies.
Part 2: Pragmatic Testing • Sleep medicine specialists and advanced practice providers (n=12) from geographically dispersed sleep clinics in the US (n=10) piloted the PLATO 31Q in their practices to assess its ease of use, comprehensiveness, relevance, applicability, utility, and patient acceptability.
• Each participating provider had 5 patients with OSA complete the PLATO 31Q during routine in-person (at the clinic) or telemedicine visits. • Upon completion of the ePLATO by patients, the clinicians held brief informal discussions with their patients to gauge reactions to, and the value of, the ePLATO in clinical practice.
Part 2: Pragmatic Testing • After administering the ePLATO to patients and obtaining their feedback, clinicians completed the PRAgmatic Content and face validity test (PRAC-test), a survey developed for clinicians to provide feedback on the utility and content of the ePLATO for use in their clinical practices.
Part 3: Psychometric Validation Survey • A prospective non-interventional study was conducted by Opinion Health Limited, a health-care market research company
• The study included adults who self-reported that they had OSA and aimed to start PAP therapy within 6 weeks after baseline (n=560), and a small sample of adults without sleep problems (n=40). • Responses were collected using online surveys over a period of 8 weeks including baseline, retest (2 weeks later) and follow-up assessments (8 weeks after baseline). • Data assessments were conducted between January 25, 2023, and April 19, 2023.
Psychometric Validation Survey • In the psychometric validation survey, the revised ePLATO (31 items as developed in parts 1 and 2 of this study) was administered at baseline, retest, and follow-up.
• Further, at baseline and follow-up, addition assessments included the - Visual Analogue Scale (VAS) from the European Quality of Life - the Epworth Sleepiness Scale - the 10-item version of the Functional Outcomes of Sleep Questionnaire (FOSQ-10) - two short forms on Sleep Disturbance and Sleep-Related Impairment, respectively, from the Patient-Reported Outcomes Measurement Information System (PROMIS) - the Patient Health Questionnaire-8 (PHQ-8).
Psychometric Validation Survey • Additionally, information gathered: - “How severe is your OSA?” according to the apnea-hypopnea index (AHI) - Body mass index (BMI) were gathered from adults with OSA only. • Patient-reported Global Impression of Severity (PGI-S, at all assessments) and Change (PGI-C, at retest and follow-up) were used - PGIS: “How would you rate your OSA symptoms over the past 7 days?” with 5 response options from “Very mild” to “Very severe” - PGI-C: “Compared to the start of the study, how are your OSA symptoms today?” with 7 response options from “Much worse” to “Much better”).
Act III What are the Results?
Getting to the Meat of the Matter
Shift to the Short Form • Selection of items for the PLATO-11 was based on the psychometric properties of the items and clinical expertise. • To develop the scoring algorithm and investigate the domain structure for the ePLATO SF: • 1) Exploratory factor analysis (EFA) was conducted • 2) Psychometric validation included investigation of reliability (internal consistency and test-retest reliability), construct validity (convergent, divergent, and known-groups validity), and responsiveness to change. • 3) meaningful change thresholds were derived (on individual- and group-levels) and the scoring algorithm was verified.
You are being asked to complete this questionnaire to help your healthcare provider understand your recent sleep-related experiences in the past 7 days. Please indicate the answer to each question that best describes you and your experiences. A. In the past 7 days, about how often did you experience the following during the day? Scale: Never (0 Days), Rarely (1-2 Days), Sometimes (3-4 Days), Often (5-6 Days), Always (Every Day) 1. I felt tired 2. I felt sleepy 3. I felt exhausted 4. I felt irritable 5. I had morning headaches 6. I nodded off in the middle of participating in an activity 7. I nodded off when I was sitting quietly 8. It was difficult for me to concentrate For the following questions, please consider your night-time experience over the past 7 nights. B. In the past 7 nights, about how often did you experience the following? Scale: Never (0 Nights), Rarely (1-2 Nights), Sometimes (3-4 Nights), Often (5-6 Nights), Always (Every Night) 9. I was told that I snored 10. I had difficulty staying asleep C. For the following question, please consider your overall sleep experience in the past 7 nights. 11. Thinking about the past 7 nights, how would you rate your sleep quality? NRS: 0= Poor to 10= Excellent
Part A - Daytime
Part B - Nighttime
Part C – Overall Sleep Quality
After 7 years (starting in 2018)…
Naming PLATO Potential Names
Acronym
Obstructive Sleep Apnea Patient-Reported Outcomes Patient-Reported Outcomes for OSA AASM Patient-Reported Outcomes for Sleep Apnea AASM Patient-Reported Outcomes OSA Patient Longitudinal Apnea Tool for Outcomes
OSA-PRO PROSA APROSA AASM PRO OSA-PLATO
Patient-reported Longitudinal Assessment Tool for OSA Patient Longitudinal Outcomes Tracking OSA Clinical Outcomes Reporting OSA Clinical Outcomes Assessment Tracking Longitudianal OSA Outcomes Monitoring OSA Patient Assessment OSA Patient Assessment Longitudinal OSA Patient-reported Outcomes Reporting Tool OSA Clinical Outcomes Reporting Tool
PLATO PLOT OSA CORE OSA COAT LOOM OPA OPAL OSA PORT OSA CORT or OSA COURT
Strengths of the PLATO Focus
Patient-centered, specifically developed for patients with OSA
Relevancy
Based on an OSA population experience, tested in clinical practice
Permission
Publicly available for clinical use
Format
Paper and electronic, easy to integrate into an electronic health record
Scoring
Manual or electronic
Ease of Completion
11 items, mean completion time of 4 minutes
Validation Population
Large, diverse population of patients with OSA undergoing treatment
Readability
5th Grade
Languages
English, Spanish
Validation
Demonstrated content validity, construct validity, reliability; excellent measurement properties
Comprehensiveness
Daytime and nighttime symptoms, sleep quality assessment
Based on data from baseline to follow-up; RD=Responder Definition; CDF=Cumulative Distribution Curve; CID=Clinically Important Difference; SD=Standard Deviation.
Downsides to PLATO • Some narrowness of symptoms: more fatigue/sleepiness focused, no nocturia • Scoring is not quite as clean as some tools
Meaningful Change (Change over Time) • Based on the present study, a Responder Definition (RD) threshold of 20 at the individual level (change over time in an individual) focusing on improvement was recommended for the 11-item short form total score (observed RD range 4-26). • That is, if a patient scored 40 at the first assessment and 20 at the follow-up assessment, results of the present study suggest considering this improvement by 20 points as meaningful to the patient.
Meaningful Change (Group Differences) • Based on the present study, a Clinical Important Difference (CID) of 15 at the group-level (change over time in means for one group, and differences between groups) focusing on improvement was recommended for the 11-item short form total score (observed CID range 5-28). • That is, if a group of patients has a mean of 40 at the first assessment and the same group of patients has a mean of 25 at a follow-up assessment, results of the present study suggest considering this improvement by 15 points as meaningful to the patients (within-group comparison). • Further, if one group of patients has a mean of 40 and another group of patients has a mean of 25, results of the present study suggest considering the second group of patients as meaningfully improved compared to the first group (between-groups comparison).
Conclusions and Next Steps: FINALE
Next Steps for PLATO • The PRO questionnaire itself and the validation process was published in JCSM • https://link.springer.com/article/10.5664/jcsm.11790
• The AASM aid in the validation process. • Will likely occur via a grant application process via the AASM Foundation • EMR Integration • Atrium-WF is working to integrate into EPIC
Where Can I Get It? • The Questionnaire is freely available for non-commercial use • It is available through the MAPI Trust • The questionnaire is free when you make an account • It is available in English and Spanish • https://eprovide.mapi-trust.org/instruments/patientreported-longitudinal-assessment-tool-for-osa-11-items
MAPI Trust
Thanks to: • AASM BOD of 2018-19 for approving this project and subsequent boards for not shutting it down • ICON for all the design/scientific help • The OSA Questionnaire Committee • Charlene Gamaldo • Charles Davies • Fariha Abbasi-Feinberg • Carol Rosen • Sherene Thomas (AASM)
https://tenor.com/view/you-actually-did-it-glinda-wicked-you-actually-pulled-it-off-universal-pictures-gif-6574140661907002111
Summary • 1) Currently available OSA questionnaires do not meet all the criteria necessary for widespread use • The ESS is necessary currently, but not broad enough
• 2) The AASM set out to build a tool that met as many criteria as possible • Took a minute, but now accomplished • Two tools, a longer one perhaps for non-clinical use and the shorter one for dayto-day use
• 3) Next step is to validate the use of the tool and to integrate it into practice • 4) Then, perhaps another trip to see the Wizard with some PRO data
Questions?
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Ronald Gavidia-Romero, MD, MS
Conflicts of Interest ✔ 1. I do not have any potential conflicts of interest to disclose, OR 2. I wish to disclose the following potential conflicts of interest: Type of Potential Conflict
Details of Potential Conflict
Grant/Research Support Consultant Speakers’ Bureaus
Financial support Other 3. The material presented in this lecture has no relationship with any of these potential conflicts, OR 4. This talk presents material that is related to one or more of these potential conflicts, and the following objective references are provided as support for this lecture:
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Objectives • Understand the role of sleep medicine scales. • Recognize the clinical purpose and appropriate use of commonly utilized adult sleep questionnaires. • Differentiate between screening tools and those used for monitoring severity.
• Interpret the basic scoring and severity categories by identifying score ranges, cutoffs, and clinically significant changes.
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
Role of Sleep Medicine Scales • Screening • Standardize symptom burden • Tracking response • Improve communication between the healthcare team • Not diagnostic
Role of Sleep Medicine Scales
AASM ICSD-3 RT, 2023.
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
Pittsburgh Sleep Quality Index • Purpose: To assess subjective sleep quality and sleep disturbances over the prior month. • This is a screening questionnaire. • Structure: self-rated 19 items, distributed in 7 components: a. b. c. d. e. f. g.
Subjective sleep quality Sleep latency Sleep duration Habitual sleep efficiency Sleep disturbances Use of sleeping medication Daytime dysfunction
Pittsburgh Sleep Quality Index • Open-ended and Likert-type questions. • Each component has a score range of 0- 3.
Buysse et al., 1989.
Pittsburgh Sleep Quality Index • Global score range: 0-21 points. • Cut-off > 5 points (no categories). • No minimal clinically important difference. • Time for completion: 5-10 minutes. • Main clinical use: global sleep quality (nonspecific or multifactorial sleep complaints).
Buysse et al., 1989; Mollayeva et al., 2016.
Pittsburgh Sleep Quality Index • Strengths: a. b. c. d.
Easy to administer Multi symptom assessment Use in clinic and research Good sensitivity and specificity
• Limitations: a. b. c. d. e.
•
Subjective Not diagnostic Confounders Medication may inflate the score despite sleep improvement No severity scores available
The PSQI is a copyrighted instrument. https://www.sleep.pitt.edu/psqi
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
Insomnia Severity Index • Purpose: To measure the patient’s perceived severity of insomnia symptoms and related impairment. • This is a screening, severity, and responsemonitoring tool. • Structure: 7 items, Likert-type a. b. c. d.
Difficulty falling asleep Difficulty staying asleep Problems waking too early Satisfaction or dissatisfaction with current sleep pattern e. Interference of sleep problem with daytime functioning f. Noticeability of impairment attributed to the sleep problem g. Worry or distress about the sleep problem
Developed by Charles M. Morin and originally described in “Insomnia: Psychological Assessment and Management.” New York: Guilford Press; 1993
Insomnia Severity Index • Each item with a score range of 0-4. • Recall period: 2 weeks.
• Total score range: 0-28 points. • Cut-offs • Minimal clinically important difference: a. Reduction of 6 points in primary insomnia b. Reduction ≥8 points for treatment response in insomnia clinical research.
• Time for completion: 2-5 minutes. • Main clinical use: initial insomnia assessment, triage, therapy response, research outcome measure. Morin, 1993; Bastien et al., 2001; Morin et al., 2011; Yang et al., 2009.
Insomnia Severity Index • Strengths: a. b. c. d. e.
Brief and easy to administer Measures nighttime symptoms and daytime impact Provides baseline for follow-up Sensitive to treatment-related change Provides severity categories
• Limitations: a. b. c. d. e.
•
Subjective No quantitative sleep metrics Does not identify cause Confounders Should not replace Consensus Sleep Diary
https://eprovide.mapi-trust.org/isi-insomnia-severityindex/#conditions
The ISI is a copyrighted instrument.
Morin, 1993; Bastien et al., 2001; Morin et al., 2011; Schutte-Rodin et al., 2008; Morin & Benca, 2012; Carney et al., 2012.
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
Epworth Sleepiness Scale • Purpose: To measure a person’s general level of daytime sleepiness or average sleep propensity in daily-life situations. • This is a screening, severity, and responsemonitoring tool.
• Structure: 8 items, Likert-type. • Each receives a 0-3 score: 0: Would never doze 1: Slight chance of dozing 2: Moderate chance of dozing 3: High chance of dozing
Epworth Sleepiness Scale • Single total score 0-24. • Recall period: “in recent times” • Cut-offs • Minimal clinically important difference: 2 points (OSA).
• Time for completion: 2-5 minutes. • Main clinical use: triage, evaluation of hypersomnolence, monitoring therapy response.
Johns, 1991; Crook et al., 2019, Kendzerska et al., 2014.
Epworth Sleepiness Scale • Strengths: a. b. c. d. e.
Brief and easy to administer and score Measures sleepiness across multiple situations Widely recognized Baseline and longitudinal tracking High sensitivity and specificity
• Limitations: a. b. c. d.
•
Subjective (recall bias) No assessment of fatigue, mood, cognitive fog, or physical exhaustion Modest correlation with objective measures Confounders
The ESS is a copyrighted instrument. Johns, 1991; Roehrs & Roth, 2010; Pagel, 2009; Slater & Steier, 2012.
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
International Restless Legs Syndrome Study Group Rating Scale • Purpose: To assess the severity and impact of restless legs syndrome (Willis-Ekbom disease) symptoms. • This is a severity and response-monitoring tool. • Structure: 10 items, Likert-type. • Each receives a 0-4 score: a. 0 (None), 1 (Mild), 2 (Moderate), 3 (Severe), 4 (Very severe) b. 0 (No symptoms), 1 (Complete or almost complete relief), 2 (Moderate relief), 3 (Mild relief), and 4 (No relief) c. 0 (never), 1 (Occasionally), 2 (Sometimes), 3 (Often), and 4 (Very often) Walters et al., 2003; Winkelman et al., 2016; Silber et al., 2021.
International Restless Legs Syndrome Study Group Rating Scale • Single total score 0-40. • Recall period: “the past week” • Cut-offs • Minimal clinically important difference: 3 points.
• Time for completion: 5-10 minutes. • Main clinical use: baseline and therapy monitoring, suspect augmentation, clinical trial outcome measure, communication across the care team.
Allen et al., 2014; Silber et al., 2021; Walters et al., 2003; Garcia-Borreguero et al., 2014; Winkelman et al., 2016. .
International Restless Legs Syndrome Study Group Rating Scale • Strengths: a. b. c. d. e.
Brief and easy to administer and score Measures RLS and its impact on sleep, daytime function, and mood. Widely recognized Baseline and longitudinal tracking High internal consistency and interrater reliability
• Limitations: a. b. c. d. e.
•
Subjective (recall bias) Not diagnostic Does not distinguish from other limb disorder Does not identify augmentation Confounders
https://eprovide.mapi-trust.org/instruments
The IRLS is a copyrighted instrument.
Allen et al., 2014; Silber et al., 2021; Hening et al., 2009; Walters et al., 2003; Garcia-Borreguero et al., 2014; Winkelman et al., 2016. .
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
Morningness-Eveningness Questionnaire • Purpose: To assess an individual’s circadian preference, or chronotype, along a continuum from morningness to eveningness. • This is a screening tool. • Structure: 19 items, Likert and time scale type.
• Items received weighted response options (not identical across all).
Morningness-Eveningness Questionnaire • Single total score 16-86. • Recall period: unspecified. • Cut-offs: 16-30 and 70-86. • Minimal clinically important difference: not universally accepted.
• Time for completion: 10-15 minutes. • Main clinical use: Chronotype assessment, circadian rhythm disorders, tailoring counseling for sleep, work, or school schedule, shift work vulnerability, or light exposure. • For monitoring circadian rhythm, sleep diary and actigraphy are better. Horne & Östberg, 1976; Morgenthaler et al., 2007; Auger et al., 2015.
Morningness-Eveningness Questionnaire • Strengths: a. b. c. d.
Brief and easy to administer and score Provides intuitive categories. Widely recognized in research and clinical settings Good internal consistency
• Limitations: a. b. c. d. e.
•
Subjective (self-reported) Not diagnostic Schedules may influence response Influenced by demographics such as age Confounders
The IRLS is a copyrighted instrument. Adan et al., 2012; Morgenthaler et al., 2007; Auger et al., 2015.
https://eprovide.mapi-trust.org/instruments
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
REM Sleep Behavior Disorder Screening Questionnaire • Purpose: To screen for symptoms suggestive of REM sleep behavior disorder. • This is a screening tool (clinical and research).
• Structure: 10 items, Yes = 1, and No = 0 questions:
REM Sleep Behavior Disorder Screening Questionnaire • Single total score 0-13. • Recall period: unspecified, could be lifetime, but modified in research. • Cut-off: ≥5 points. • Minimal clinically important difference: not universally accepted. • Time for completion: 2-5 minutes. • Main clinical use: triage, injury risk identification, prodromal of neurodegenerative disorders, and research recruitment.
Stiasny-Kolster et al., 2007; Boeve, 2010; Högl et al., 2018; Postuma et al., 2019; ; Iranzo et al., 2014
REM Sleep Behavior Disorder Screening Questionnaire • Strengths: a. b. c.
Brief and easy to administer and score High sensitivity Could be used in different settings
• Limitations: a. b. c. d. e.
•
Patient awareness/recall dependent Not diagnostic False positives with other disorders May not distinguish other parasomnias Confounders: medications/substances, and other sleep disorders
https://eprovide.mapi-trust.org/instruments
The questionnaire is a copyrighted instrument. Stiasny-Kolster et al., 2007; Boeve, 2010; Högl et al., 2018; Winkelman & James, 2004.
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
Disturbing Dream and Nightmare Severity Index • Purpose: To quantify disturbing dream and nightmare frequency, awakenings, intensity, and perceived severity. • This is a screening, severity, and response monitoring tool.
• Structure: 5 items, first multistep, time, and Likert scale type: a. Frequency of nights with disturbing dreams or nightmares b. Number of disturbing dreams or nightmares c. Frequency of awakenings from disturbing dreams or nightmares d. Perceived severity of the nightmare problem e. Intensity of disturbing dreams or nightmares Gieselmann et al., 2019.
Disturbing Dream and Nightmare Severity Index • Single total score 0-37. • Recall period: week, month, year, and lifetime. • Cut-off: ≥10 points. • Minimal clinically important difference: not universally accepted.
• Time for completion: 1-3 minutes. • Main clinical use: triage, nightmare disorder, PTSD-related nightmares, trauma-related sleep complaints.
Krakow et al., 2002; Gieselmann et al., 2019; Morgenthaler et al., 2018
Disturbing Dream and Nightmare Severity Index • Strengths: a. b. c.
Brief and easy to administer Excellent reliability and validity Measures nightmare burden (monitoring)
• Limitations: a. b. c. d.
•
Subjective and recall bias Not diagnostic May not distinguish other parasomnias Confounders: medications/substances, and other sleep disorders (increase or mimics)
The questionnaire is copyrighted, but its use is less strict. Krakow et al., 2002; Gieselmann et al., 2019; Morgenthaler et al., 2018
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician Outline • Role of Sleep Medicine Scales • Pittsburgh Sleep Quality Scale • Insomnia Severity Index • Epworth Sleepiness Scale • International Restless Legs Syndrome Study Group Rating Scale • Morningness-Eveningness Questionnaire • REM Sleep Behavior Disorder Screening Questionnaire
• Disturbing Dream and Nightmare Severity Index • Consensus Sleep Diary
Consensus Sleep Diary • Purpose: To prospectively capture the patient’s sleep timing, sleep continuity, sleep duration, and subjective sleep quality. • This is a response monitoring tool. • Structure: Core version has 9 items, and the Expanded version has additional items (naps, medications, substance use, exercise, and other factors).
Carney et al., 2012; Schutte-Rodin et al., 2008; Auger et al., 2015; Edinger et al., 2021
Consensus Sleep Diary
Carney et al., 2012
Consensus Sleep Diary • Single total score not available. • Recall period: after waking. • Cut-off: not available. • Minimal clinically important difference: not universally available.
• Time for completion: 1-3 minutes each morning. • Main clinical use: assessment/follow-up of insomnia, circadian rhythm disorders, insufficient sleep, irregular sleep schedules, pre-MSLT.
Carney et al., 2012; Schutte-Rodin et al., 2008; Smith et al., 2018; ; Auger et al., 2015.
Consensus Sleep Diary • Strengths: a. b. c.
d. e.
Day-by-day Prospective data Captures night-to-night variability Low cost, low technology, and easy to integrate into clinical workflows Promotes patient education Central to CBT-I
• Limitations: a. b. c. d. e.
Subjective Does not measure other sleep disturbances Estimates may vary in people with sleep state misperception Adherence (completed retrospectively) May increase clock watching or sleep-related anxiety
Carney et al., 2012; Schutte-Rodin et al., 2008; Auger et al., 2015; Edinger et al., 2021
Consensus Sleep Diary • Limitations: e. Not diagnostic f. Confounders
•
The questionnaire is copyrighted, but free for non-profit.
Carney et al., 2012; Schutte-Rodin et al., 2008; Morin & Benca, 2012; Sateia, 2014; American Academy of Sleep Medicine, 2023.
Take Home Messages • Sleep questionnaires standardize symptom and communication assessment but do not replace clinical judgment.
• Select the tool based on the patient’s primary sleep complaint. • Know what each score means—and what it does not mean. • Use questionnaire results to flag risk and prioritize followup.
• Repeat key scales over time to monitor response. •
Interpret results in context.
Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician
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Validated Scales in Sleep Medicine: Essential Knowledge for Every Clinician
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