Text Message Interventions in Adolescent Mental Health and Addiction Services: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The vast majority of adolescent mental health and substance use disorders go undiagnosed and undertreated. SMS text messaging is increasingly used as a method to deliver adolescent health services that promote psychological well-being and aim to protect adolescents from adverse experiences and risk factors critical for their current and future mental health. To date, there has been no comprehensive synthesis of the existing literature on the extent, range, and implementation contexts of these SMS text message interventions. OBJECTIVE: The objective of this scoping review was to map and categorize gaps in the current body of peer-reviewed research around the use of SMS text messaging-based interventions for mental health and addiction services among adolescents. METHODS: A scoping review was conducted according to Levac's adaptation of Arksey and O'Malley's methodological framework for scoping reviews in six iterative stages. A search strategy was cocreated and adapted for five unique databases. Studies were screened using Covidence software. The PICO (patient, intervention, comparator, outcome) framework and input from multiple stakeholder groups were used to structure and pilot a data extraction codebook. Data were extracted on study methodology and measures, intervention design, and implementation characteristics, as well as policy, practice, and research implications. RESULTS: We screened 1142 abstracts. Of these, 31 articles published between 2013 and 2020 were eligible for inclusion. Intervention engagement was the most common type of outcome measured (18/31), followed by changes in cognitions (16/31; eg, disease knowledge, self-awareness) and acceptability (16/31). Interventions were typically delivered in less than 12 weeks, and adolescents received 1-3 messages per week. Bidirectional messaging was involved in 65% (20/31) of the studies. Limited descriptions of implementation features (eg, cost, policy implications, technology performance) were reported. CONCLUSIONS: The use of SMS text messaging interventions is a rapidly expanding area of research. However, lack of large-scale controlled trials and theoretically driven intervention designs limits generalizability. Significant gaps in the literature were observed in relation to implementation considerations, cost, clinical workflow, bidirectionality of texting, and level of personalization and tailoring of the interventions. Given the growth of mobile phone-based interventions for this population, a rigorous program of large-scale, well-designed trials is urgently required.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".