Digital Technologies for Monitoring and Improving Treatment Adherence in Children and Adolescents With Asthma: Scoping Review of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Inadequate pediatric asthma care has resulted in potentially avoidable unplanned hospital admissions and morbidity. A wide variety of digital technologies have been developed to monitor and support treatment adherence in children and adolescents with asthma. However, existing reviews need to be updated and expanded to provide an overview of the current state of research on these technologies and how they are being integrated into existing health care services and care pathways. OBJECTIVE: This study aims to provide an overview of the current research landscape and knowledge gaps regarding the use of digital technologies to support the care of children and adolescents with asthma. METHODS: This study was structured according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and Population, Intervention, Comparator, Outcome, and Study frameworks. Five databases (PubMed, the Cochrane Central Register of Controlled Trials, Web of Science, Embase, and PsycINFO) were systematically searched for studies published in English from 2014 onward. Two reviewers independently screened the references and selected studies for inclusion based on the eligibility criteria. Data were systematically extracted per research question, which were synthesized in a descriptive analysis. RESULTS: A wide variety of study characteristics, including the number and age of participants, study duration, and type of digital intervention, were identified. There was mixed evidence for the effectiveness of the interventions. Of the 10 studies that evaluated treatment adherence, 7 (70%) found improvements, but the evidence was inconsistent for asthma control (6/9, 67% of studies reported improvement or maintenance, but only 1 was significantly different between groups) and health outcome variables (5/9, 56% of studies found no evidence of effectiveness). The 6 studies that examined patient perceptions and assessments of acceptability and usability generally had positive findings. CONCLUSIONS: A wide range of digital interventions are being developed and evaluated to support the monitoring and treatment adherence of children and adolescents with asthma. Meta-analyses are inhibited by the use of samples with a variety of overlapping age ranges; a theoretical framework for evaluating specific age groups would aid comparison between studies. Most studies found significant evidence for improved adherence to treatment or medications, but there was mixed evidence of the impact of the digital interventions on asthma control and other health outcomes. There are gaps in the literature relating to cost-effectiveness and integration with existing clinical care pathways. This study will be necessary to determine which digital interventions for children and young people with asthma are worth supporting and adopting in the clinical care pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".