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Record W4392966110 · doi:10.3389/frsip.2024.1362754

Feasibility, functionality, and user experience with wearable technologies for acute exacerbation monitoring in patients with severe COPD

2024· article· en· W4392966110 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Signal Processing · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersMcGill University Health CentreMcGill University
KeywordsExacerbationWearable computerCOPDMedicineCopd exacerbationWearable technologyIntensive care medicineComputer scienceHuman–computer interactionEmbedded systemInternal medicineAcute exacerbation of chronic obstructive pulmonary disease

Abstract

fetched live from OpenAlex

Background: The increasing interest in remote patient monitoring technologies in patients with chronic obstructive pulmonary disease (COPD) requires a phased and stepwise investigative approach, which includes high-risk clinical subgroups who stand to benefit most from such innovations. Methods: Patients aged > 40 with spirometry-confirmed COPD presenting with a current acute exacerbation (ECOPD) were recruited from a tertiary centre Day Hospital in this prespecified feasibility study. Heart rate, respiratory rate, oxygen saturation, skin temperature, and daily activity and overnight sleep quality parameters were collected remotely by a wearable biometric wristband and ring for 21 consecutive days. “Total ambulatory wear time” and “percent of useable data” for eligible vital sign parameters were calculated. Correlation and agreement between cardiorespiratory vital sign data were performed using Spearman’s correlation rho and the Bland-Altman test, respectively. User experience was measured with end-of-study System Usability Scale (SUS) questionnaires. Results: Nine participants (mean age 66.8 ± 8.4 years, 22% female, mean FEV 1 1.4L (34.1% predicted), with “severe” (56%) or “very severe” (44%) COPD) experiencing a current ECOPD were included. Wear time was 94% (wristband) and 88.2% (ring) of the total ambulatory study period. Wristband-obtained data (every 1 min, artefact-free) revealed 99.2% and 98.6% of all heart rate and temperature data, respectively, was useable, whereas only 17.6% of all respiratory rate data was useable. Ring-obtained data (every 5 min, “average” and “good” quality) revealed 84.5% of all heart rate data was useable. Cross-sectional analyses with nurse-obtained vital signs revealed correlation coefficients of 0.56 ( p = 0.11) and 0.81 ( p = 0.0086) for wristband-obtained and ring-obtained heart rate, respectively, and only 0.15 ( p = 0.74) for wristband-obtained respiratory rate, without evidence of systematic/proportional bias. Longitudinal heart rate and respiratory rate inter-device analyses demonstrated correlations of 0.86 ( p < 0.001) and 0.65 ( p < 0.001), respectively. Finally, end-of-study SUS scores were 86.4/100 (wristband) and 89.2/100 (ring). Conclusion: Older adults with severe/very severe COPD experiencing a current ECOPD were capable of autonomous physiological data collection/upload/transmission from their home environment over several weeks using sophisticated wearable biometric technology, with favourable user experiences. Cross-sectional and longitudinal comparative results call into question the paradigm of single sets of infrequent/interval vital sign checks as the current “gold-standard” in frontline clinical practice.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.301
Teacher spread0.282 · 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