Digital assessment of real-world physical activity in Pulmonary Hypertension: A Systematic Review and Meta-Analysis
Brehm, S.; Fiengo Tanaka, L.; Majeed, Y.; Barnikel, M.; Le Roux, C.; Ghiani, A.; Jansen, C.-P.; Jaeger, S. U.
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BackgroundThe assessment of daily-life physical activity (DLPA) using wearables in patients with pulmonary hypertension (PH) can provide information on real-world function, potentially enhancing the evaluation of disease progression. Research QuestionWhat is the existing evidence on sensor-based DLPA assessment in patients with PH and its quality? Study Design and MethodsWe searched MEDLINE and Embase from inception to January 13, 2026, extracting data on devices, DLPA outcomes, and associations with clinical outcomes. We obtained pooled estimates through random-effects models and assessed evidence quality using a customized tool. ResultsWe identified 33 studies (29 adult, 4 pediatric) including 1,257 patients mainly with pulmonary arterial hypertension (PAH), followed by chronic thromboembolic PH (CTEPH), and only rarely with PH due to lung diseases and/or hypoxia. Participants were predominantly female, WHO functional class II-III. Most studies investigated step count and time spent in different physical activity levels, but showed substantial heterogeneity in devices and their utilization. The meta-estimate was 4,811 daily steps. A moderate positive correlation was found between daily step count and six-minute walking distance (6MWD) (r=0.59, 95%CI 0.47-0.69); a weak positive correlation was found between time spent in moderate-to-vigorous physical activity and 6MWD (r=0.38; 95% CI 0.26-0.49). Inconsistent wear-time definition, non-wear reporting and temporal misalignment of DLPA may compromise validity and comparability. InterpretationWearable-based DLPA assessment in PH is feasible, though high-quality evidence remains scarce. Future research should standardize procedures, terminology, and reporting of DLPA outcomes. Concordance with established measures such as the 6MWD, and their ability to predict clinical outcomes and disease progression need to be demonstrated.
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