The Performance of Digital Monitoring Devices for Oxygen Saturation and Respiratory Rate in COPD: A Systematic Review
Bibliographic record
Abstract
Healthcare access and delivery for individuals with chronic obstructive pulmonary disease (COPD) who live in remote areas or who are susceptible to contracting communicable diseases, such as COVID-19, may be a challenge. Telehealth and remote monitoring devices can be used to overcome this issue. However, the accuracy of these devices must be ensured before forming healthcare decisions based on their outcomes. Therefore, a systematic review was performed to synthesize the evidence on the reliability, validity and responsiveness of digital devices used for tracking oxygen saturation (SpO2) and/or respiratory rate (RR) in individuals with COPD, in remote settings. Three electronic databases were searched: MEDLINE (1996 to October 8, 2020), EMBASE (1996 to October 8, 2020) and CINAHL (1998 to October 8, 2020). Studies were included if they aimed to evaluate one or more measurement properties of a digital device measuring SpO2 or RR in individuals with COPD. Six-hundred and twenty-five articles were identified and after screening, 7 studies matched the inclusion criteria; covering 11 devices measuring SpO2 and/or RR. Studies reported on the reliability (n = 1), convergent validity (n = 1), concurrent validity (n = 2) and predictive validity (n = 2) of SpO2 devices and on the convergent validity (n = 1), concurrent validity (n = 1) and predictive validity (n = 1) of RR devices. SpO2 and RR devices were valid when compared against other respiration monitoring devices but were not precise in predicting exacerbation events. More well-designed measurement studies are needed to make firm conclusions about the accuracy of such devices.Supplemental data for this article is available online at https://doi.org/10.1080/15412555.2021.1945021 .
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".