Protocol for a systematic review assessing the role of digital health technology in optimising medication adherence in older patients with asthma or COPD
Bibliographic record
Abstract
INTRODUCTION: An estimated 262 million people lived with asthma globally in 2019. Similarly, in 2021, chronic obstructive pulmonary disease (COPD) was responsible for 3.5% million global deaths. They are usually distinct disorders, but the Global Initiative Chronic Obstructive Lung Disease (GOLD) 2024 strategy document asserts that asthma and COPD are conditions that may coexist in an individual and may require specific personalised approaches and treatments. It is acknowledged that they may share some common treatable traits and clinical features There are many challenges to manage asthma and COPD in the older population, including poor adherence to prescribed medications and poor inhaler techniques. The overall aim of this systematic review is to identify, appraise and synthesise available evidence around digital health interventions used to improve medication adherence in older people with asthma or COPD. METHODS AND ANALYSIS: This systematic review will examine studies that evaluated digital health interventions for asthma or COPD in any setting (eg, primary or secondary care). To be included, studies must be reported in English, Arabic or French and published from the year 2000 onwards. A literature search will be performed in MEDLINE via Ovid, Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL, EMBASE and PsycINFO via Ovid to identify relevant articles published since 2000 and up to December 2024. No language restrictions will be applied.The Cochrane risk-of-bias tool for randomised trials will be used to assess the quality of retrieved randomised controlled trials and quasi-experimental studies. The quality of cross-sectional, cohort and case-control studies will be assessed using the Newcastle Ottawa Scale. Mixed-methods studies will be assessed using the Mixed Methods Appraisal Tool (MMAT). The quality of qualitative studies will be assessed using the Critical Appraisal Skills Programme (CASP) qualitative checklist.Data will be synthesised using a convergent segregated approach, which involves an independent synthesis of quantitative and qualitative data leading to the generation of quantitative and qualitative evidence, which will then be integrated. ETHICS AND DISSEMINATION: Ethics approval is not applicable for this study since no original data will be collected. The results will be disseminated through a peer-reviewed publication and conference presentations. Findings will be used in a bigger project aimed to answer the question on how to embed a pharmacist-led digital health service to support older people with asthma or COPD into the NHS (National Health Service) usual care. PROSPERO REGISTRATION NUMBER: CRD42024575924.
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 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 | 0.069 | 0.111 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.026 | 0.022 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.112 | 0.014 |
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".