D28-06 Digital Phenotyping of Physical Activity in Pulmonary Arterial Hypertension Using Functional Data Analysis: Findings From the Actiph Study

Background

Physical activity and fatigue patterns in pulmonary arterial hypertension (PAH) are heterogeneous and incompletely captured by traditional clinical assessments. Actigraphy provides objective, continuous measurement of real-world activity. ACTiPH is an ancillary study of the Pulmonary Hypertension Association Registry (PHAR) that uses actigraphy to objectively characterize physical activity patterns in PAH. We applied functional data analysis to identify behavioral activity phenotypes and compared their clinical characteristics, risk profiles, and health-related quality of life (HRQoL).

Methods

Participants wore the ActiGraph hip based accelerometer during waking hours for 7 consecutive days, with wear periods within 14 days of a standardized PHAR assessment. Minute-level tri-axial accelerometry data were collected, yielding 10,000 minutes of data per participant. To address the high dimensionality and temporal structure of these data, minute-level activity counts were analyzed using functional principal component analysis. The leading components captured variation in overall activity amplitude (PC1), timing of peak activity (PC2), and activity fragmentation or midday fatigue (PC3). Unsupervised k-means clustering was applied to FPCA scores to identify distinct activity phenotypes. Clinical characteristics, patient-reported outcomes, and PH risk scores were compared across clusters using Kruskal-Wallis and chi-square tests. Analyses were restricted to the baseline actigraphy assessment per participant.

Results

Among 504 participants, four behavioral clusters were identified: Cluster 1 (low activity, early peak), Cluster 2 (low activity, delayed peak), Cluster 3 (moderate activity, early peak), and Cluster 4 (high activity, late peak). Lower activity clusters (Clusters 1 and 2) were older (60-66 vs. 53-55 years;p=0.01) and less likely to be married (36-48% vs. 53-54%; p = 0.0008), with higher proportions reporting never being married or being widowed or divorced. Primary PH diagnosis differed across clusters (p = 0.007), with connective tissue disease-associated PAH more common in lower activity clusters and idiopathic PAH more frequent in higher activity clusters. Functional status and risk profiles varied significantly, with more active clusters showed a lower frequency of elevated EmPHasis-10 scores, a greater proportion with higher SF-12 physical component scores, longer six-minute walk distances, and a lower proportion classified as higher risk by REVEAL Lite 2.0 and COMPERA 2.0 (all p < 0.0001).

Conclusion

Digital phenotyping using functional data analysis identifies distinct activity patterns in PAH associated with differences in clinical risk, function, and HRQoL. Wearable monitoring offers complementary insights beyond clinic-based assessments and may inform personalized care. Ongoing work will evaluate prognostic significance and temporal stability. Ongoing analyses are evaluating the prognostic significance of these phenotypes and the temporal stability of cluster membership over time.

This abstract is funded by: NHLBI – R01 – HL159997 (PI: Kawut), NHLBI – K23 – HL169930 (PI: Minhas)