IPAH is a rare but serious condition in which the arteries in the lungs gradually narrow, increasing pressure and eventually leading to heart failure. Early symptoms such as fatigue and shortness of breath are often vague, which makes diagnosis difficult. Confirming the disease typically requires an invasive procedure known as Right Heart Catheterisation, contributing to delays in detection.
To explore non-invasive alternatives, researchers analyzed long-term physical activity and heart rate data collected through smartphones and wearable devices. The study included 109 participants in the UK, spanning individuals diagnosed with IPAH, those with other conditions, and healthy controls. Researchers examined up to eight years of historical data to identify early patterns linked to the disease.
The results were promising. A machine learning model trained on activity and heart rate trends before diagnosis was able to distinguish IPAH patients from others with strong accuracy, achieving a high ROC AUC score. When combined with questionnaire data collected via a smartphone app, the model’s performance improved even further.
However, when tested on a similar group in the United States, the model showed lower accuracy - highlighting differences between populations and the need for further refinement. Even so, the study demonstrated that passively collected digital health data can capture subtle physiological changes well before clinical diagnosis.
Notably, activity levels recorded by wearables also aligned with results from the six-minute walk test, a standard method used to assess physical capacity in patients. This suggests that such data could complement existing clinical tools.
Overall, the findings indicate that smartphone-based monitoring could support earlier detection and ongoing risk assessment for IPAH. While the study was limited in size, researchers emphasized the need for larger trials to confirm these results and explore real-world applications.
Looking ahead, continuous data from everyday devices may become an important tool in identifying serious heart and lung conditions before symptoms become severe enough to require medical attention.
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