Evidence map›Paper›PMID 41743190›Full record

ArticleEuropean heart journal. Digital health2026

Long-term lifestyle monitoring adherence in patients after cardiac intervention: a prospective observational trial.

Wilhelmina Francisca Goevaerts, Nicole Catharina Christina Wilhelmina Tenbült-Van Limpt, Sebastiaan André Goossen, Max Valentin Birk, Yunjie Liu, Marta Regis, Rutger Willem Maurice Brouwers, Yuan Lu, Willem Johan Kop, Hareld Marijn Clemens Kemps

Abstract read
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In one paragraph

Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Wilhelmina Francisca GoevaertsDepartment of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0002-4310-6046
Nicole Catharina Christina Wilhelmina Tenbült-Van LimptDepartment of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0002-9021-9895
Sebastiaan André GoossenDepartment of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0001-6946-7336
Max Valentin BirkDepartment of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0002-1490-2086
Yunjie LiuDepartment of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0003-3467-7725
Marta RegisDepartment of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0003-4306-8673
Rutger Willem Maurice BrouwersDepartment of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0002-8409-2357
Yuan LuDepartment of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0003-2480-2051
Willem Johan KopDepartment of Medical and Clinical Psychology, Center of Research on Psychological Disorders and Somatic Diseases, Tilburg University, Tilburg, The Netherlands.ORCID https://orcid.org/0000-0003-3141-4815
Hareld Marijn Clemens KempsDepartment of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0003-0272-4355

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Lifestyle behaviours are important predictors of morbidity and mortality in patients with cardiovascular disease. However, structured lifestyle monitoring is insufficiently integrated into clinical practice. This study evaluated dropout and long-term adherence to an eHealth system for self-monitoring lifestyle behaviours in patients with cardiovascular disease. Methods and results: Patients undergoing a cardiac intervention used an eHealth system (web application with integrated health watch and chatbot) to monitor physical activity, nutrition, stress, and sleep for 1 year. The primary outcome was dropout, defined as system disengagement. Secondary outcomes included adherence (percentage of prescribed health watch wear time and chatbot responses) and usability. The predictive value of demographic, clinical, and psychosocial factors was examined using logistic regression models. Of 100 patients (mean age 61.6 ± 10.4 years; 88% male; 45% percutaneous coronary intervention, 55% other intervention), there were 43 dropouts; most (27; 63%) occurred in the first quarter, with participation burden being the most cited reason (51%). Health watch adherence was higher than chatbot adherence (80.7% (66.6-90.3%) vs. 60.8% (30.7-82.7%), Conclusion: Long-term lifestyle monitoring of multiple health-related behaviours is feasible after cardiac intervention, highlighting its potential for integration into clinical practice. Patient engagement could be enhanced by targeting subgroups at risk of low adherence, particularly in the early phase, and by reducing self-reporting burden while improving usability.

Indexed as

AdherenceCardiac rehabilitationHealth behaviour changeSelf-monitoringUsabilityWearable electronic devices

Identifiers

PMID41743190
PMCPMC12930194

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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 OpenQuestion graph.