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Record W4413023100 · doi:10.3389/fdgth.2025.1640585

Digital health technologies for improving the management of people with chronic obstructive pulmonary disease

2025· review· en· W4413023100 on OpenAlexafffund
Hye Yun Park, Sunga Kong, Mangyeong Lee, Hyein Ryu, Yoko Hamakawa, Fabrizio Luppi, Janice M. Leung

Bibliographic record

VenueFrontiers in Digital Health · 2025
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersCanada Research ChairsGlaxoSmithKline
KeywordsTelehealthPsychological interventionPulmonary diseaseTelemedicineMedicineDigital healthCOPDHealth carePandemicWearable computerDisease managementIntensive care medicineIsolation (microbiology)Coronavirus disease 2019 (COVID-19)DiseaseMedical emergencyNursingComputer scienceHealth management systemAlternative medicinePathologyInfectious disease (medical specialty)Political science

Abstract

fetched live from OpenAlex

Advances made in digital health in recent years have the potential to improve the care of patients living with chronic obstructive pulmonary disease (COPD) for whom substantial disability still exists. In particular, telehealth and telerehabilitation programs, wearable devices, and apps have been studied as novel methods of providing care to COPD patients who may have limited access to clinical centers or who may benefit from an increased level of monitoring. Many of these interventions gained traction during the COVID-19 pandemic when mandated social isolation required the rapid implementation of remote care models. While these digital health interventions have since demonstrated promise in delivering care to otherwise isolated communities, the ongoing need for more evidence proving their positive impact on important clinical outcomes remains a barrier to their full implementation. How to best integrate digital health solutions into existing care models requires greater consideration of the technological, financial, and labor demands such solutions may entail.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.314
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Digital HealthSame topicChronic Obstructive Pulmonary Disease (COPD) ResearchFrench-language works237,207