Evidence map›Paper›PMID 42242832›Full record

ArticleBMJ open respiratory research2026

Development of an artificial intelligence prediction model for moderate-to-severe COPD exacerbations using continuous multiple unobtrusive sensors: protocol of a multicentre prospective observational study.

Roger Vásquez-Andrade, Victoria Alcaraz-Serrano, Joren Buekers, Pasquale Bufano, Marco Laurino, Alessandro Celi, Henrik Watz, Joaquim Gea, Nicola Carbonaro, Alessandro Tognetti and 1 more

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in BMJ open respiratory research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06172712 (COPD Exacerbation Modelling Study Using Daily-life Data From Unobtrusive Sensors - the TOLIFE Clinical Study A), which is not on this map. Not yet cited in PubMed.

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

NCT06172712 unknown statusnot on this map

COPD Exacerbation Modelling Study Using Daily-life Data From Unobtrusive Sensors - the TOLIFE Clinical Study A

TypeobservationalSponsorBarcelona Institute for Global HealthRan2024 to 2025Enrolled150ConditionsChronic Obstructive Pulmonary Disease (COPD)ArmsVariations in daily life activity signals detected by unobtrusive sensors
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

11 authors.

Roger Vásquez-AndradeBarcelona Institute for Global Health, Barcelona, Spain.ORCID http://orcid.org/0009-0005-1693-6895
Victoria Alcaraz-SerranoBarcelona Institute for Global Health, Barcelona, Spain.
Joren BuekersBarcelona Institute for Global Health, Barcelona, Spain.
Pasquale BufanoInstitute of Clinical Physiology, National Research Council, Pisa, Italy.
Marco LaurinoInstitute of Clinical Physiology, National Research Council, Pisa, Italy.
Alessandro CeliDepartment of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy.
Henrik WatzVelocity Clinical Research Germany, Ahrensburg, Germany.
Joaquim GeaServei de Pneumologia, Hospital del Mar, Hospital del Mar Research Institute, Barcelona, Spain.
Nicola CarbonaroDepartment of Information Engineering, University of Pisa, Pisa, Italy.
Alessandro TognettiDepartment of Information Engineering, University of Pisa, Pisa, Italy.
Judith Garcia-AymerichBarcelona Institute for Global Health, Barcelona, Spain judith.garcia@isglobal.org.ORCID http://orcid.org/0000-0002-7097-4586

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionExacerbations, impaired health-related quality of life (HRQoL) and reduced exercise capacity increase the risk of hospitalisations and death in chronic obstructive pulmonary disease (COPD). However, their monitoring relies on in-person assessments, potentially delaying early care. While smart sensing technologies can enable remote monitoring, their use in predicting disease worsening remains limited. The TOLIFE Clinical Study A (CSA) aims to develop an artificial intelligence (AI) model integrating clinical data with smart sensing devices data to predict exacerbation onset and changes in HRQoL, dyspnoea and exercise capacity in people with COPD. METHODS AND ANALYSES: TOLIFE CSA is a longitudinal observational study that will recruit 150 clinically stable people with COPD from three clinical sites in Spain, Italy and Germany. Over 1 year, participants will attend quarterly in-person visits to collect clinical data, while being continuously monitored using six unobtrusive smart sensing devices collecting daily metrics calculated from triaxial acceleration, angular velocity, photoplethysmogram, sound intensity, changes in latitude and longitude, ambient light intensity, biomechanical pressure and respiratory airflow parameters. Clinical outcomes are exacerbation onset through medical records; 3-month changes in HRQoL through the COPD Assessment Test and the Clinical COPD Questionnaire; 3-month changes in dyspnoea severity through the modified Medical Research Council Dyspnoea Scale; and 6-month changes in functional exercise capacity through the 6-minute walk test. We will train, internally validate and test AI-based models (Random Forests, XGBoost, multilayer perceptrons, cumulative link model and standard classification model) to predict clinical outcomes. ETHICS AND DISSEMINATION: Ethical approval was issued for all sites by the Ethical Commission (EC) of the Medical Association of Schleswig-Holstein (Bad Segeberg; vote 074/23 ff), EC of the Tuscany Region-North West Area (Pisa; vote CET10/2023) and EC of Parc de Salut Mar (Barcelona; vote 2023/11230). All participants will sign a written informed consent. TRIAL REGISTRATION NUMBER: NCT06172712.

Indexed as

Artificial IntelligenceDyspneaExercise TolerancePulmonary Disease, Chronic ObstructiveRemote Patient MonitoringDisease ProgressionGermanyHumansItalyLongitudinal StudiesMulticenter Studies as TopicObservational Studies as TopicPrediction AlgorithmsProspective StudiesQuality of LifeSeverity of Illness IndexCOPDCOPD ExacerbationsExerciseTelemedicine

Identifiers

PMID42242832
PMCPMC13239523

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Registered trials

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.