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Record W2949018184 · doi:10.2196/13920

Technology-Enabled Self-Monitoring of Chronic Obstructive Pulmonary Disease With or Without Asynchronous Remote Monitoring: Protocol for a Randomized Controlled Trial

2019· article· en· W2949018184 on OpenAlexafffundvenue
Vess Stamenova, Rebecca Yang, Katrina Engel, Kyle Liang, Florence van Lieshout, Elizabeth Lalingo, Angelica Cheung, Adam Erwood, Maria Radina, Allen Greenwald, Payal Agarwal, Aman Sidhu, R. Sacha Bhatia, James Shaw, Roshan Shafai, Onil Bhattacharyya

Bibliographic record

VenueJMIR Research Protocols · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity Health NetworkMarkham Stouffville HospitalUniversity of TorontoWomen's College Hospital
FundersOntario Centres of Excellence
KeywordsMedicineCOPDRandomized controlled trialVital signsOxygen therapyPhysical therapyEmergency medicineInternal medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is the third leading cause of mortality worldwide. Reducing the number of COPD exacerbations is an important patient outcome and a major cost-saving approach. Both technology-enabled self-monitoring (SM) and remote monitoring (RM) programs have the potential to reduce exacerbations, but they have not been directly compared with each other. As RM is a more resource-intensive strategy, it is important to understand whether it is more effective than SM. OBJECTIVE: The objective of this study is to evaluate the impact of SM and RM on self-management behaviors, COPD disease knowledge, and respiratory status relative to standard care (SC). METHODS: This was a 3-arm open-label randomized controlled trial comparing SM, RM, and SC completed in an outpatient COPD clinic in a community hospital. Patients in the SM and RM groups recorded their vital signs (oxygen, blood pressure, temperature, and weight) and symptoms with the Cloud DX platform every day and were provided with a COPD action plan. Patients in the RM group also received access to a respiratory therapist (RT). The RT monitored their vital signs intermittently and contacted them when their vitals varied outside of predetermined thresholds. The RT also contacted patients once a week irrespective of their vital signs or symptoms. All patients were randomized to 1 of the 3 groups and assessed at baseline and 3 and 6 months after program initiation. The primary outcome was the Partners in Health scale, which measures self-management skills. Secondary outcomes included the St. George's Respiratory Questionnaire, Bristol COPD Knowledge Questionnaire, COPD Assessment Test, and modified-Medical Research Council Breathlessness Scale. Patients were also asked to self-report on health system usage. RESULTS: A total of 122 patients participated in the study, 40 in the SC, 41 in the SM, and 41 in the RM groups. Out of those patients, 7 in the SC, 5 in the SM, and 6 in the RM groups did not complete the study. There were no significant differences in the rates of study completion among the groups (P=.80). CONCLUSIONS: Both SM and RM have shown promise in reducing acute care utilization and exacerbation frequencies. As far as we are aware, no studies to date have directly compared technology-enabled self-management with RM programs in COPD patients. We believe that this study will be an important contribution to the literature. TRIAL REGISTRATION: ClinicalTrials.gov NCT03741855; https://clinicaltrials.gov/ct2/show/NCT03741855. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13920.

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)0.034
metaresearch head score (Gemma)0.032
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.083
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.032
Meta-epidemiology (narrow)0.0070.003
Meta-epidemiology (broad)0.0140.007
Bibliometrics0.0030.004
Science and technology studies0.0030.004
Scholarly communication0.0050.004
Open science0.0040.002
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0830.012

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 head0.058
GPT teacher head0.450
Teacher spread0.393 · 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
GenreProtocol

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

Citations13
Published2019
Admission routes3
Has abstractyes

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