Text Messaging and Mobile Phone Apps as Interventions to Improve Adherence in Adolescents With Chronic Health Conditions: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The number of adolescents with chronic health conditions (CHCs) continues to increase. Medication nonadherence is a global challenge among adolescents across chronic conditions and is associated with poor health outcomes. While there has been growing interest in the use of mHealth technology to improve medication adherence among adolescents with CHCs, particularly text messaging and mobile phone apps, there has been no prior systematic review of their efficacy. OBJECTIVE: The purpose of this review was to systematically evaluate the most recent evidence for the efficacy of text messaging and mobile phone apps as interventions to promote medication adherence among adolescents with CHCs. METHODS: PubMed, Embase, CENTRAL, PsycINFO, Web of Science, Google Scholar, and additional databases were searched from 1995 until November 2015. An additional hand search of related themes in the Journal of Medical Internet Research was also conducted. The Preferred Reporting Results of Systematic Reviews and Meta-Analyses guidelines were followed. Two reviewers independently screened titles/abstracts, assessed full-text articles, extracted data from included articles, and assessed their quality using Grades of Recommendation, Assessment, Development, and Evaluation criteria. Included studies were described in original research articles that targeted adherence in adolescents with CHCs (12-24 years-old). RESULTS: Of the 1423 records examined, 15 met predefined criteria: text messaging (n=12) and mobile phone apps (n=3). Most studies were performed in the United States (11/15, 73%), were randomized-controlled trials (8/15, 53%), had a sample size <50 (11/15, 73%), and included adherence self-report and/or biomarkers (9/15, 60%). Only four studies were designed based on a theoretical framework. Approaches for text messaging and mobile phone app interventions varied across studies. Seven articles (7/15, 47%) reported significant improvement in adherence with moderate to large standardized mean differences. Most of the included studies were of low or moderate quality. Studies varied in sample size, methods of adherence assessment, and definition of adherence, which prohibited performing a meta-analysis. CONCLUSIONS: The use of text messaging and mobile phone app interventions to improve medication adherence among adolescents with CHCs has shown promising feasibility and acceptability, and there is modest evidence to support the efficacy of these interventions. Further evaluation of short- and long-term efficacy and cost-effectiveness of these interventions is warranted given the early and evolving state of the science.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".