Background: Mobile applications (apps) are a resource for information on lifestyle and nutrition which are associated to improved outcomes in inflammatory arthritis. Objective: The aim of this study was to explore whether targeted lifestyle counselling via an app improves disease activity in arthritis patients. Methods: Patients with rheumatoid arthritis (RA), spondyloarthritis (SpA), psoriatic arthritis (PsA) were randomized to 12 weeks of lifestyle counselling via an app (Mida, Midaia GmbH, Germany) pertaining to a healthy Mediterranean Diet, physical activity, and mental health. Disease activity was measured with specific instruments by a blinded physician and categorized (remission, low, moderate, high). Dietary adherence was assessed by the Mediterranean Diet Adherence Screener (MEDAS). Mixed effects logistic regression adjusted to baseline disease activity, age, and sex were calculated. Results: Of 158 patients included (73% female, 53.3 ± 11.7 years), 74 were in the active counselling group (ACG). All showed improvement in low disease activity or remission. ACG patients had an odds ratio (OR) of 2.8 (95%-CI 1.1–7.2, p = 0.035), while OR in the control group was not significant OR = 2.1 (0.9–5.0, p = 0.097). The control group was less likely to reach a MEDAS >= 4 (OR = 0.16 (0.03–0.77), p = 0.02), while this was not seen in the ACG (OR = 0.54 (0.06–4.63), p = 0.6). Patients in the ACG showed a tendency towards improved adhesion to a Mediterranean Diet (MEDAS) (β = 0.35 (−0.05–0.74), p = 0.086). This tendency was not observed in the control group (β = 0.09 (−0.29–0.46), p = 0.64). Conclusions: Individualized lifestyle and dietary counselling via app may help to improve disease control in inflammatory arthritis patients.

Introduction/objectives: Patient Global Assessment of Disease Activity (PtGADA), pain, and Routine Assessment of Patient Index Data 3 (RAPID3) are core patient-reported outcomes in inflammatory arthritis. We examined whether digital self-management engagement was associated with their long-term trajectories. Method: This retrospective cohort included 3513 adults using a certified digital self-management application. Patients were grouped by completion of 0, 1–2, or ≥3 structured self-management plans. Longitudinal models with patient-level cluster-robust standard errors estimated post-baseline change trajectories. Sensitivity analyses addressed differential attrition, measured confounding, and reverse causality. Results: Of 2895 patients with baseline PtGADA, 1026 (35.4%) contributed post-baseline observations. Patients completing ≥3 plans had more favourable PtGADA, pain, and RAPID3 trajectories than zero-plan patients (group-effect p =6.2×10 ⁻⁵ , p =0.0055, and p =0.0044, respectively), without diverging slopes. At weeks 12–48, PtGADA improved by 11.1–12.1 points and pain by 7.2–7.3 points in the high-engagement group. After attrition weighting, PtGADA remained significant, RAPID3 retained a significant joint group effect, and pain was no longer significant. Conclusions: Higher engagement was most robustly associated with more favourable long-term PtGADA trajectories; support for pain was directionally similar but statistically more limited, and RAPID3 findings were the least consistent of the three outcomes. These results describe an engagement-associated phenotype rather than a causal treatment effect and support its evaluation as a potential marker of sustained digital engagement in future prospective stratification studies. Key Points • In this real-world cohort of 3513 patients with inflammatory arthritis, higher digital self-management engagement was most robustly associated with more favourable PtGADA trajectories; the association with pain was directionally similar but less robust, while RAPID3 findings were less consistent. • Engagement-group trajectories were approximately parallel, and the findings remained directionally consistent in analyses examining sensitivity to differential follow-up, measured baseline differences, and reverse-causality concerns, although statistical significance varied by outcome and landmark. • The engagement-outcome association was directionally consistent across multiple sensitivity analyses addressing measured confounding and differential follow-up, although temporal exposure assignment could not be established and residual reverse-causality bias cannot be excluded. • Digital self-management engagement, operationalised by action-plan completion, represents a potential marker of sustained digital engagement for future prospective stratification studies, rather than a validated tool for baseline risk stratification.

Fatigue is a burdensome symptom in inflammatory arthritis (IA), yet meaningful longitudinal change remains poorly characterized in real-world settings. To characterize fatigue trajectories, quantify clinically meaningful improvement/worsening, and identify baseline predictors of fatigue outcomes in a digital IA cohort. We analyzed Midaia digital application data from patients with rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, or mixed IA with baseline Brief Fatigue Inventory (BFI) data. Fatigue trajectories were modelled via cluster-robust longitudinal regression, with secondary landmark analyses classifying BFI improvement/worsening using a prespecified pragmatic 1.0-point BFI change threshold at weeks 12 (W12), 24 (W24), and 52 (W52). Predictors were examined using logistic regression, machine learning with area under the curve (AUC) and model interpretation, and inverse probability weighting (IPW) for loss to follow-up. The cohort (2,660 patients; 3,436 valid BFI observations; 485 with follow-up) was 78.3% female, mean age 45.6 ± 12.9 years, baseline BFI 5.2 ± 2.0. The unadjusted BFI slope was − 0.017 points/week (95% CI − 0.025 to − 0.008; p < 0.001; corresponding to − 0.20 points by W12, below the MCID); the IPW-adjusted slope was attenuated to − 0.003 points/week, suggesting that the crude estimate was sensitive to differential follow-up. At W12, 26.8% improved and 25.6% worsened, with similar proportions at W24 and W52. Higher baseline BFI predicted W12 improvement (OR 1.54) and lower worsening (OR 0.62); higher baseline Patient Global Assessment of Disease Activity predicted worsening (OR 1.02). Machine-learning discrimination was modest (AUC 0.651–0.720). Diet/nutrition engagement was associated with higher improvement (OR 1.78) and lower worsening (OR 0.45). Fatigue trajectories in IA were heterogeneous. Landmark improvement/worsening proportions were unchanged after IPW, whereas mean improvement was attenuated. Baseline fatigue severity dominated prediction; action-plan associations are hypothesis-generating.