Texting and Mobile Phone App Interventions for Improving Adherence to Preventive Behavior in Adolescents: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Many preventable behaviors contribute to adolescent mortality and morbidity. Non-adherence to preventive measures represents a challenge and has been associated with worse health outcomes in this population. The widespread use of electronic communication technologies by adolescents, particularly the use of text messaging (short message service, SMS) and mobile phones, presents new opportunities to intervene on risk and preventive risk behavior, but little is known about their efficacy. OBJECTIVE: This study aimed to systematically evaluate evidence for the efficacy of text messaging and mobile phone app interventions to improve adherence to preventive behavior among adolescents and describe intervention approaches to inform intervention development. METHODS: This review covers literature published between 1995 and 2015. Searches included PubMed, Embase, CENTRAL, PsycINFO, CINAHL, INSPEC, Web of Science, Google Scholar, and additional databases. The search strategy sought articles on text messaging and mobile phone apps combined with adherence or compliance, and adolescents and youth. An additional hand search of related themes in the Journal of Medical Internet Research was also conducted. Two reviewers independently screened titles and abstracts, assessed full-text articles, and extracted data from articles that met inclusion criteria. Included studies reflect original research-experimental or preexperimental designs with text messaging or mobile phone app interventions-targeting adherence to preventive behavior among adolescents (12-24 years old). The preferred reporting items of systematic reviews and meta-analyses (PRISMA) guidelines were followed for reporting results, and findings were critically appraised against the Oxford Centre for Evidence-based Medicine criteria. RESULTS: Of 1454 records, 19 met inclusion criteria, including text messaging (n=15) and mobile phone apps (n=4). Studies targeted clinic attendance, contraceptive use, oral health, physical activity and weight management, sun protection, human papillomavirus (HPV) vaccination, smoking cessation, and sexual health. Most studies were performed in the United States (47%, 9/19), included younger adolescents (63%, 12/19), and had sample size <100 (63%, 12/19). Although most studies were randomized controlled trials (RCTs; 58%, 11/19), only 5 followed an intent-to-treat analysis. Only 6 of 19 studies (32%) incorporated a theoretical framework in their design. Most studies reported good feasibility with high acceptability and satisfaction. About half of the included studies (42%, 8/19) demonstrated significant improvement in preventive behavior with moderate standardized mean differences. As early efforts in this field to establish feasibility and initial efficacy, most studies were low to moderate in quality. Studies varied in sample size and methods of preventive behavior adherence or outcome assessment, which prohibited performing a meta-analysis. CONCLUSIONS: Despite the promising feasibility and acceptability of text messaging and mobile phone apps in improving preventive behavior among adolescents, overall findings were modest in terms of efficacy. Further research evaluating the efficacy, effectiveness, and cost-effectiveness of these intervention approaches in promoting preventive behavior among adolescents is needed.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.005 | 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".