Evidence map›Paper›PMID 41882158›Full record

ArticleNPJ cardiovascular health2026

Assessing the feasibility of using smartphone data to identify risk of idiopathic pulmonary arterial hypertension.

Juan A Delgado-SanMartin, Merve Keles, Niamh Errington, Narayan Schuetz, Anders Johnson, Varsha Gupta, Steve Hershman, Mark Toshner, Martin R Wilkins, David G Kiely and 4 more

Abstract read
In one paragraph

Article in NPJ cardiovascular health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Remote exercise assessment in pulmonary hypertension.Current opinion in pulmonary medicine · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Juan A Delgado-SanMartinNational Heart and Lung Institute, Imperial College London, London, UK.
Merve KelesNational Heart and Lung Institute, Imperial College London, London, UK.
Niamh ErringtonNational Heart and Lung Institute, Imperial College London, London, UK.
Narayan SchuetzDepartment of Medicine, Stanford University, Stanford, CA, USA.
Anders JohnsonDepartment of Medicine, Stanford University, Stanford, CA, USA.
Varsha GuptaAgency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Steve HershmanUniversity of Texas, Austin, TX, USA.
Mark ToshnerDepartment of Medicine, University of Cambridge, Cambridge, UK.
Martin R WilkinsNational Heart and Lung Institute, Imperial College London, London, UK.
David G KielyNational I/HPAH Cohort Study, Cambridge, UK.
Roger ThompsonNational I/HPAH Cohort Study, Cambridge, UK.
Euan AshleyDepartment of Medicine, Stanford University, Stanford, CA, USA.
Dennis WangNational Heart and Lung Institute, Imperial College London, London, UK.
Allan LawrieNational Heart and Lung Institute, Imperial College London, London, UK. a.lawrie@imperial.ac.uk.

Funding

Academy of Medical Sciences APR7\1002British Heart Foundation FS/18/13/33281British Heart Foundation FS/18/52/33808British Heart Foundation RE/18/4/34215British Heart Foundation SP/18/10/33975Medical Research Council MR/K020919/1
6 · The paper itself

Abstract

Idiopathic pulmonary arterial hypertension (IPAH) is a progressive, life-limiting condition often diagnosed late due to non-specific symptoms and requirement of invasive right heart catheterisation. This pilot study explores the feasibility of using real-world physical activity data from wearable devices and a smartphone app (My Heart Counts) to aid earlier detection. We analysed up to eight years of retrospective data from 109 UK participants, including patients with IPAH, disease controls, and healthy individuals. A classifier trained on pre-diagnostic activity and heart rate, distinguished individuals with IPAH from healthy and disease controls with an ROC AUC of 0.87, improving to 0.94 with in-app questionnaire input. Validation in a matched US cohort yielded an ROC AUC of 0.74. Wearable-derived metrics correlated with clinical 6MWD supporting their potential to complement traditional risk assessment. These pilot findings suggest that digital health tools may support earlier detection and remote monitoring of IPAH warranting larger scale studies.

Identifiers

PMID41882158
PMCPMC13018193

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.