Content guidance for mobile phones short message service (SMS)-based antiretroviral therapy adherence and appointment reminders: a review of the literature
Bibliographic record
Abstract
Mobile phones are increasingly being used to support health activities, including the care and management of people living with HIV/AIDS. Short message service (SMS) has been explored as a means to optimize and support behaviour change. However, there is minimal guidance on messaging content development. The purpose of this review was to inform the content of SMS messages for mobile health (mHealth) initiatives designed to support anti-retroviral therapy adherence and clinic appointment keeping in resource-limited settings. PubMed, OvidMedline, Google Scholar, K4Health's mHealth Evidence database, the mHealth Working Group project resource, and Health COMpass were searched. A request to online communities for recommendations on message content was also made. 1010 unique sources were identified, of which 51 were included. The information was organized into three categories: pre-message development, message development, and security and privacy. Fifteen of the publications explicitly provided their message content. Important lessons when developing the content of SMS were: (1) conducting formative research; (2) grounding content in behaviour change theory; and (3) reviewing proposed content with experts. Best practices exist for developing message content for behaviour change. Efforts should be continued to apply lessons learned from the existing literature to inform mHealth initiatives supporting HIV/AIDS care and treatment.
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 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".