Economic Evaluation of Text-Messaging and Smartphone-Based Interventions to Improve Medication Adherence in Adolescents with Chronic Health Conditions: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The rate of chronic health conditions (CHCs) in children and adolescents has doubled in the past 20 years, with increased health care costs. Technology-based interventions have demonstrated efficacy to improving medication adherence. However, data to support the cost effectiveness of these interventions are lacking. OBJECTIVE: The objective of this study is to conduct an economic evaluation of text-messaging and smartphone-based interventions that focus on improving medication adherence in adolescents with CHCs. METHODS: Searches included PubMed MEDLINE, Embase, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Web of Science, and Inspec. Eligibility criteria included age (12-24 years old), original articles, outcomes for medication adherence, and economic outcomes. RESULTS: Our search identified 1118 unique articles that were independently screened. A total of 156 articles met inclusion criteria and were then examined independently with full-text review. A total of 15 articles met most criteria but lacked economic outcomes such as cost effectiveness or cost-utility data. No articles met all predefined criteria to be included for final review. Only 4 articles (text messaging [n=3], electronic directly observed therapy [n=1]) described interventions with possible future cost-saving but no formal economic evaluation. CONCLUSIONS: The evidence to support the cost effectiveness of text-messaging and smartphone-based interventions in improving medication adherence in adolescents with CHCs is insufficient. This lack of research highlights the need for comprehensive economic evaluation of such interventions to better understand their role in cost-savings while improving medication adherence and health outcomes. Economic evaluation of technology-based interventions can contribute to more evidence-based assessment of the scalability, sustainability, and benefits of broader investment of such technology tools in adolescents with CHCs.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".