ArticleRMD open2023
Assessment and personalised advice for fatigue in systemic lupus erythematosus using an innovative digital tool: the Lupus Expert system for the Assessment of Fatigue (LEAF) study.
Article in RMD open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Efficacy of lifestyle interventions in the management of systemic lupus erythematosus: a systematic review of the literature.Rheumatology international · 2024Pooled it
- Pooled it
- Observational
- Disease-Specific Associations of Physical Activity in Patients with Rheumatoid Arthritis and Systemic Lupus Erythematosus.Neuroimmunomodulation · 2026Article
- Levels of physical activity in a large international cohort of patients with systemic lupus erythematosus.Lupus science & medicine · 2025Article
- Late-onset Systemic Lupus Erythematosus.Rheumatology international · 2025Review
- ActiLup: is it feasible? High-intensity interval training in systemic lupus erythematosus patients with fatigue: protocol for a prospective, monocentric proof-of-concept study.BMJ open sport & exercise medicine · 2025Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFatigue is reported as the most prevalent symptom by patients with systemic lupus erythematosus (SLE). Fatigue management is complex due to its multifactorial nature. The aim of the study was to assess the usefulness of an innovative digital tool to manage fatigue in SLE, in a completely automated manner.
methodsThe «Lupus Expert System for Assessment of Fatigue» (LEAF) is free digital tool which measures the intensity and characteristics of fatigue and assesses disease activity, pain, insomnia, anxiety, depression, stress, fibromyalgia and physical activity using validated patient-reported instruments. Then, LEAF automatically provides personalised feedback and recommendations to cope with fatigue.
resultsBetween May and November 2022, 1250 participants with SLE were included (95.2% women, median age 43yo (IQR: 34-51)). Significant fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue <34) was reported by 78.9% of patients. In univariate analysis, SLE participants with fatigue were more likely to be women (p=0.01), perceived their disease as more active (p<0.0001), had higher levels of pain (p<0.0001), anxiety (p<0.0001), depression (p<0.0001), insomnia (p<0.0001), stress (p<0.0001) and were more likely to screen for fibromyalgia (p<0.0001), compared with patients without significant fatigue. In multivariable analysis, parameters independently associated with fatigue were insomnia (p=0.0003), pain (p=0.002), fibromyalgia (p=0.008), self-reported active SLE (p=0.02) and stress (p=0.045). 93.2% of the participants found LEAF helpful and 92.3% would recommend it to another patient with SLE.
conclusionFatigue is commonly severe in SLE, and associated with insomnia, pain, fibromyalgia and active disease according to patients' perspective. Our study shows the usefulness of an automated digital tool to manage fatigue in SLE.
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