Effect of monitoring adherence to regular inhaled corticosteroid (ICS) alone or in combination with a long-acting β2-agonist (LABA) using electronic methods on asthma outcomes: a narrative systematic review
Bibliographic record
Abstract
Objectives To evaluate through a systematic review the effectiveness of electronic methods in monitoring adherence to regular inhaled corticosteroids (ICS) alone or in combination with long-acting β2-agonists (LABAs) and their effect on clinical outcomes. Design A narrative systematic review. Data sources MEDLINE, EMBASE, Cochrane Database of Systematic Reviews and Web of Science were searched through up to 10 July 2022. Eligibility criteria We included peer-reviewed studies of qualitative and quantitative outcomes that compared the effect of electronic methods to routine non-electronic monitoring intervention or placebo among children and adults with asthma on medication adherence rates to regular ICS alone or in combination with LABA, asthma control and asthma exacerbations. Data extraction and synthesis Data extraction was performed according to a predetermined sheet specific to the review objectives. The risk of bias was assessed using the Cochrane Risk of Bias Tool for randomised controlled trials and the Risk of Bias in Systematic Reviews tool for systematic reviews. Meta-analysis was not possible based on the findings of the scoping search; however, a narrative review was performed to allow for the grouping of results based on asthma inhaler adherence rates, asthma control and exacerbations. Results Six articles comprising 98 studies published from 1998 to 2022 in the USA, Canada and the UK were included. Compared with the control, electronic monitoring devices (EMDs) showed a 23% adherence improvement, mean difference (MD) of 23%, 95% CI 10.84 to 34.16, p=0.0002. Asthmatic children were 1.5 times more likely to be adherent using EMDs compared with non-EMD users (RR=1.5, 95% CI 1.19 to 1.9) (p<0.001). Mobile devices and text message reminders (MHealth) showed a 12% adherence improvement (MD 12%, 95% CI 6.22 to 18.03) (p<0.0001), alongside a small to medium improvement in asthma control (standardised mean difference (SMD) 0.31, 95% CI 0.17 to 0.44), small improvement in asthma-related quality of life (SMD 0.26) (p=0.007) and variable risk reduction in asthma exacerbations for digital health (risk ratio 0.53, 95% CI 0.32 to 0.91) (p=0.02) compared with EMDs, which showed insignificant differences (risk ratio 0.89, 95% CI 0.45 to 1.75) (p=0.72). Technologies combined yielded variable adherence effects, with an SMD for eHealth of 0.41, 95% CI 0.02 to 0.79, and MD for digital health was 14.66% higher than the control, 95% CI 7.74 to 21.57. Heterogeneity between studies was significant (eHealth I 2 =98%, digital I 2 =94%). Conclusion Electronic methods improved adherence to inhaled medications in asthma. EMDs appear to be the most effective technology, followed by mHealth. The adherence improvement was associated with a small clinical improvement. There was inconsistent overlapping of terminology describing electronic methods that require standardisation. Data on the cost-effectiveness of electronic devices and their utilisation in severe asthma are lacking and require further research. PROSPERO registration number CRD42022303069.
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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 | 0.023 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".