IS PHYSICAL ACTIVITY (PA) BEHAVIOR ANOTHER CASUALTY OF DEPRIVED NEIGHBORHOODS IN PERSONS WITH SYSTEMIC LUPUS ERYTHEMATOSUS (SLE)?
Notice bibliographique
Résumé
PV076 / #248 Poster Topic: AS10 - Environment and SLE Background/Purpose SLE, a chronic inflammatory, autoimmune disease with pervasive self-reported fatigue, negatively impacts most individuals with SLE. Because of its recognized benefits in improving oxygen capacity and endurance, physical activity (PA) is one modifiable lifestyle behavior to help reduce fatigue in patients with SLE who are typically not physically active and afraid to exercise. Recent research suggests a role for Social Determinants of Health (SDOH) in identifying environmental impact on medical care and conditions. The Area Deprivation Index (ADI), a composite score which ranks geographical areas of the US based on environmental living conditions, measures neighborhood resources and can estimate SDOH. Lupus Intervention Fatigue Trial (LIFT) is a 12-month phase II randomized, parallel group, single blind 2-arm investigator-initiated trial, comparing effectiveness of a motivational interviewing program intervention combining diet and physical activity strategies vs an educational program control arm to reduce fatigue in persons with lupus ( NCT02653287 ). This ancillary analysis assessed the correlation between objectively measured PA behavior and ADI disadvantage of the participant’s home at the LIFT baseline visit. Methods All LIFT participants met criteria: ≥18 years of age, BMI: 18-40 kg/m2, ambulate household distances (50 ft), classification criteria for SLE per American College of Rheumatology (ACR) or Systemic Lupus International Collaborating Clinics (SLICC). Accelerometers were worn for 7 consecutive days during waking hours to measure daily PA minutes and were categorized as sedentary minutes (<100 counts), light PA (LPA) minutes (100-2019 counts), and moderate-vigorous PA (MVPA) minutes (2020+ counts). Valid PA monitoring was defined as at least 4 days of 10+ hours of accelerometer wear. Weekly MVPA minutes were calculated as mean daily MVPA minutes times 7. The ADI uses nine-digit zip codes to calculate a national ranking score on a scale from 0-100 (low to high deprivation rank). Descriptive statistics were calculated, and the associations between ADI and PA minutes (all categories) were estimated using Spearman correlation. Results Among 160 LIFT participants, mean (SD) age was 43.3 (13) years; 92% identify as female with 52% participants self-identifying as White, 33% Black/African American, 8% Asian, 1% Hawaiian/Pacific Islander, and 5% unknown/not reported; 95% having some college education. The mean (SD) for the following were BMI 27 (5), [disease activity measure] SLEDAI 2.9 (3.1), SLICC/Damage 1.2 (1.6), and ADI 34.7 (21.4), respectively. The total PA daily mean (SD) minutes were 262.3 (83.4); mean (SD) daily minutes category were sedentary 582.8 (92.8), LPA 242.2 (75.5), and MVPA 20.1 (19.6). Thirty-two percent met PA guidelines (≥150 minutes/week of MVPA). Mean daily MVPA minutes correlated with ADI rank, r= -0.20, p= 0.01 (Table 1). Table 1. Spearman Correlations Between PA Measures and ADI in Baseline LIFT Participants (n=160) Conclusions This analysis documents that only 32% of LIFT participants met weekly PA guidelines of > 150 minutes of MVPA at baseline. Mean MVPA minutes were negatively correlated with ADI rank suggesting those participants living in higher ADI deprivation areas had lower MVPA minutes. Future analyses are planned to assess changes in PA between baseline & 6-month visit in the intervention and control groups in LIFT by ADI rank or if other interventions are necessary.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».