Acceptance, Adherence and Dropout Rates of Individuals With COPD Approached in Tele-Monitoring Interventions: A Systematic Review
Bibliographic record
Abstract
Background:To assess the benefits of telemonitoring (TM) on decreasing exacerbations and emergency room visits with COPD, it is important to evaluate the factors that impact acceptance and successful adherence to TM. The objective of this study was to conduct a systematic review of TM studies with COPD, to evaluate the (1) acceptance, adherence, and dropout rates, and (2) identify the reasons for dropout. Methods:Included studies were randomized control trials and observational single arm pre-post trials that evaluated TM with COPD. A systematic search was performed in CINAHL, MEDLINE (Ovid), Cochrane library, and Embase databases. The Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines was used to guide the selection process. Two independent reviewers retrieved titles, abstracts, and full texts, and completed data extractions. Acceptance rate refers to the number of participants who consented to enroll in TM studies over the number approached. Adherence rate refers to the total participants who completed TM over the number who started TM. Dropout rate refers to the number of participants who dropped out over the number who consented. All rates were calculated as percentages. Results:Among 1,460 abstracts identified, 90 articles underwent full-text review and 33 articles were eligible and included. Twenty-seven were randomized controlled trials, and six were pre-post studies. Acceptance rate for all included studies was an average of 52% and ranged from 6% to 100%. The average adherence rate was 77% and ranged from 41% to 100%. The average dropout rate was 22% and ranged from 2% to 58%. Across the 33 studies, 369 participants reported reasons for dropout. TM related reasons for dropouts included technical difficulties (33%), complicated TM system (31%), and time constraints (9%). Patient-related reasons included hospitalization (37%), deceased (18%), lack of interest to continue (12%), and moved from study location (3%). Conclusions:The acceptance, adherence, and dropout rates of TM were variable. Most reasons for dropout were related to patient or TM features. The current review suggests optimizing the design of TM studies by considering patient-related and TM-related reasons to increase the acceptance and decrease the dropout rate in the future research. The next step will be to evaluate significant predictors of adherence and dropout rates in included studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".