Evaluating Heartfelt’s predictive utility in heart failure: insights from the FOOT multicentre trial

Background Heart failure (HF) poses a global health challenge, with traditional management methods contributing to high readmission and mortality rates. This observational study evaluates the Heartfelt device for remote, autonomous monitoring of peripheral oedema, aiming to improve HF management. Methods Conducted from February 2020 to June 2022 across five UK NHS trusts, the FOOT Study ( NCT04072744 ) enrolled 31 patients, with 26 actively monitored. The study assessed the Heartfelt device’s predictive accuracy, data collection frequency, consistency versus traditional weighing (standard care), lead time for intervention and event prediction sensitivity. Results The Heartfelt device showed a notable lead time of 13 days for HF decompensation prediction, offering more consistent and frequent data than traditional methods. Among the monitored patients, six HF events were observed. Patient feedback was largely positive, with several participants choosing to keep the device poststudy. Conclusions Offering improvements over traditional methods, the Heartfelt device could enhance early HF decompensation detection through continuous oedema monitoring. This study highlights the transformative potential of remote monitoring in HF management, suggesting a need for further research to refine alert systems and assess long-term impacts. Trial registration number NCT04072744 .

Authors

Institutions

Publication Details

Journal
BMJ Innovations
Published
2026-03-17
DOI
https://doi.org/10.1136/bmjinnov-2025-001394
Primary Topic
Heart Failure Treatment and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating Heartfelt’s predictive utility in heart failure: insights from the FOOT multicentre trial

Lisa Gallagher, Hayley McKie, Gemma McCafferty, louise clayton et al.
BMJ Innovations
Heart Failure Treatment and Management
article

Evaluating Heartfelt’s predictive utility in heart failure: insights from the FOOT multicentre trial

Lisa Gallagher, Hayley McKie, Gemma McCafferty, louise clayton, Debbie Hughes, Susan Gent, Karen Hann, Matthew Dewhurst, Philip J. Keeling, Sara Pick, Iain Matthews, Ganesan Kumar, William Nicolson
article en

Abstract

Background Heart failure (HF) poses a global health challenge, with traditional management methods contributing to high readmission and mortality rates. This observational study evaluates the Heartfelt device for remote, autonomous monitoring of peripheral oedema, aiming to improve HF management. Methods Conducted from February 2020 to June 2022 across five UK NHS trusts, the FOOT Study ( NCT04072744 ) enrolled 31 patients, with 26 actively monitored. The study assessed the Heartfelt device’s predictive accuracy, data collection frequency, consistency versus traditional weighing (standard care), lead time for intervention and event prediction sensitivity. Results The Heartfelt device showed a notable lead time of 13 days for HF decompensation prediction, offering more consistent and frequent data than traditional methods. Among the monitored patients, six HF events were observed. Patient feedback was largely positive, with several participants choosing to keep the device poststudy. Conclusions Offering improvements over traditional methods, the Heartfelt device could enhance early HF decompensation detection through continuous oedema monitoring. This study highlights the transformative potential of remote monitoring in HF management, suggesting a need for further research to refine alert systems and assess long-term impacts. Trial registration number NCT04072744 .

BMJ Innovations
North Tees and Hartlepool NHS Foundation Trust (GB), Luton and Dunstable University Hospital NHS Foundation Trust (GB), Northumbria Healthcare NHS Foundation Trust (GB), University Hospitals of Leicester NHS Trust (GB), Torbay and South Devon NHS Foundation Trust (GB)
Good health and well-being
Openalex Percentile: Top 28%
Heart Failure Treatment and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.