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Record W3216226724 · doi:10.2196/30782

Evaluation of myCOPD Digital Self-management Technology in a Remote and Rural Population: Real-world Feasibility Study

2021· article· en· W3216226724 on OpenAlexvenueno aff
Rowena Cooper, Adam Giangreco, Michelle M. Duffy, Elaine Finlayson, Shellie Hamilton, Mahri Swanson, Judith Colligan, Joanna Gilliatt, Mairi McIvor, Elizabeth K. Sage

Bibliographic record

VenueJMIR mhealth and uhealth · 2021
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationComputer scienceGeographyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common, costly, and incurable respiratory disease affecting 1.2 million people in the United Kingdom alone. Acute COPD exacerbations requiring hospitalization place significant demands on health services, and the incidence of COPD in poor, remote, and rural populations is up to twice that of cities. OBJECTIVE: myCOPD is a commercial, digital health, self-management technology designed to improve COPD outcomes and mitigate demands on health services. In this pragmatic real-world feasibility study, we aimed to evaluate myCOPD use and its clinical effectiveness at reducing hospitalizations, inpatient bed days, and other National Health Service (NHS) resource use. METHODS: myCOPD engagement and NHS resource use was monitored for up to 1 year after myCOPD activation and was compared against health service use in the year prior to activation. A total of 113 participants from predominantly remote and rural communities were recruited via community-based care settings, including scheduled home visits, outpatient appointments, pulmonary rehabilitation, and phone or group appointments. There were no predetermined age, disease severity, geographical, or socioeconomic inclusion or exclusion criteria. RESULTS: Out of 113 participants, 89 activated myCOPD (78.8%), with 56% (50/89) of those participants doing so on the day of enrollment and 90% (80/89) doing so within 1 month. There was no correlation between participant enrollment, activation, or myCOPD engagement and either age, socioeconomics, rurality, or COPD severity. Most active participants used at least one myCOPD module and entered their symptom scores at least once (79/89, 89%). A subgroup (15/89, 17%) recorded their symptom scores very frequently (>1 time every 5 days), 14 of whom (93%) also used four or five myCOPD modules. Overall, there were no differences in hospital admissions, inpatient bed days, or other health service use before or after myCOPD activation, apart from a modest increase in home visits. Subgroup analysis did, however, identify a trend toward reduced inpatient bed days and hospital admissions for those participants with very high myCOPD usage. CONCLUSIONS: Our results suggest that neither age, wealth, nor geographical location represent significant barriers to using myCOPD. This finding may help mitigate perceived risks of increased health inequalities associated with the use of digital health technologies as part of routine care provision. Despite high levels of activation, myCOPD did not reduce overall demands on health services, such as hospital admissions or inpatient bed days. Subgroup analysis did, however, suggest that very high myCOPD usage was associated with a moderate reduction in NHS resource use. Thus, although our study does not support implementation of myCOPD to reduce health service demands on a population-wide basis, our results do indicate that highly engaged patients may derive benefits.

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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.058
GPT teacher head0.427
Teacher spread0.368 · 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 designObservational
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

Citations23
Published2021
Admission routes1
Has abstractyes

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