DELAYED SLEEP PHASE SYNDROME CHARACTERIZES CIRCADIAN DISORDER IN PATIENTS WITH ACTIVE SLE
Notice bibliographique
Résumé
PV053 / #179 Poster Topic: AS06 - Comorbidities Background/Purpose Poor sleep quality is a common complaint of patients with SLE. Although chronic sleep disruption is known to drive circadian rhythm disorders, the effects of poor sleep quality have not yet been elucidated in SLE. Actigraphy is a validated approach to objectively assess 21 sleep variables and motor activity using a noninvasive accelerometer. In addition, actigraphy can characterize circadian dysfunction by assessments of activity. We examine the relationship of actigraphy data from patients with SLE with 1) disease activity and 2) subjective patient-reported outcome measures of sleep quality. Methods Seventy-six consented subjects from the Washington University Lupus Center with classified SLE were enrolled. Participants wore a wrist-mounted actigraph (Micro Motionlogger, Ambulatory Monitoring Inc, Ardsley, NY) for 1 week. Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Patient Reported Outcomes Measurement Instrument System (PROMIS)-Sleep Related Impairment (SRI), and PROMIS-Sleep Disturbance (SD) survey instruments were administered to measure subjective sleep quality. SLEDAI-2000 Responder Index-50 (S2K RI-50) assessed disease activity (>4, active SLE). Actigraphy data were analyzed using Action W (Ambulatory Monitoring Inc), and circadian variables were derived using ClockLab (Actimetrics, Wilmette, IL). Unpaired Student t tests (2-sided, α < 0.05) were used to compare sleep quality and circadian dysfunction in patients with active vs inactive disease. Pearson correlation coefficient was used to assess correlation of actigraphy and circadian variables with subjective sleep quality. Statistical analyses were performed using SPSS Statistics (IBM, Armonk, NY). Results No differences in actigraphic measures of sleep quality (eg, total sleep duration, percent sleep, wake after sleep onset, etc.) were observed in active vs inactive disease. Active SLE was associated with phase-dependent circadian variables including bedtime, acrophase (peak of circadian activity), M start (beginning of most active hours), and M start – waketime (p=0.01, discrepancy between natural circadian rhythm and actual activity pattern) (Table 1). Table 1. PROMIS-SRI and PROMIS-SD showed no correlation with actigraphy or circadian measures, while PSQI and ESS correlated moderately with % sleep and rho counts (activity during sleep period), and ESS additionally showed modest correlation with measures such as sleep efficiency, sleep and wake episodes, and MESOR (measure of mean activity level) (Table 2). Table 2. Conclusions Changes in circadian phase, but not sleep quality, is associated with SLE disease activity, with a phase delay in those with active disease. The ESS was the PRO that most highly associated with several actigraphy-assessed sleep parameters, including sleep efficiency and fragmentation. Circadian dysfunction may be an underlying cause for other widely experienced symptoms of SLE including cognitive dysfunction and fatigue. Future work will focus on examining this relationship.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».