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A Feasibility and Pilot Randomized Clinical Trial Assessing the Integration of Digital Remote Patient Monitoring Within Pulmonary Rehabilitation

2025· article· en· W4410269319 on OpenAlexaff
Evelyn Etruw, Virginia Huynh, Heather Sharpe, Desi P. Fuhr, E. Bok, Robert W. Dubois, Ronald W. Damant, Michael K. Stickland

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsMedicineRandomized controlled trialPulmonary rehabilitationRehabilitationPhysical therapyIntensive care medicineMedical physicsPhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

Abstract Rationale: Chronic respiratory diseases, like chronic obstructive pulmonary disease (COPD), require self-monitoring and disease management, yet traditional methods are hindered by cognitive decline, low health literacy, and symptom underreporting. Pulmonary rehabilitation (PR) improves outcomes but has diminishing long-term effects. Integrating digital remote patient monitoring (DRPM) into PR permits continuous symptom tracking and early exacerbation detection and may facilitate better adherence to disease management behaviours. This study evaluated the feasibility of combining PR with DRPM and will inform future trials assessing clinical efficacy. Methods: Participants in this cluster-randomized trial were assigned to Standard-PR or PR+DRPM, with both groups completing 16 PR sessions over 6 or 8 weeks, then a 12-week post-PR follow-up. Using a Wi-Fi-enabled, Bluetooth-connected device, the PR+DRPM group monitored five resting vital signs daily – blood pressure, heart rate, oxygen saturation, body temperature and weight. Daily adherence was tracked automatically via device connectivity, with missed readings prompting compliance calls. DRPM data were linked to the patient's electronic health record. PR providers monitored the data remotely and contacted participants if readings exceeded pre-set thresholds. Feasibility outcomes included recruitment, retention, adherence, acceptability, adverse events, and staff workload. Secondary outcomes (exercise capacity, quality of life, physical activity and self-efficacy) were analyzed using linear mixed models. Results: The recruitment rate was 94% (n=78, 55% male, mean age = 68.4 ± 11, mean FEV1 = 59.1 ± 22.7). Retention was 77% post-PR and 67% at 12-week follow-up, with similar rates between groups. 94% of PR+DRPM participants attended all PR sessions compared to 86% in Standard-PR. Participants’ adherence to daily vitals monitoring was 90% during PR and 89% at follow-up; 163 compliance calls were made when readings were missed. 100% of participants would recommend DRPM to other patients, and 92% agreed that their health information was kept private. Ten participants reported minor and transient adverse events not related to DRPM. Staff workload increased by ∼45 minutes per participant (attending 496 notifications for abnormal data) over the trial. Secondary outcomes did not differ between the two groups. Conclusion: PR+DRPM is feasible and well-received by participants based on high adherence to daily readings. However, the high number of compliance calls to address missed readings suggests that DRPM may significantly burden staff. Addressing staff workload will be essential in planning a larger trial to examine the efficacy, cost-effectiveness, and scalability of DRPM for sustainable use in managing COPD and other chronic respiratory diseases.

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)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.061
GPT teacher head0.431
Teacher spread0.369 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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