The Acceptability of Adherence Support via Mobile Phones for Antituberculosis Treatment in South India: Exploratory Study
Bibliographic record
Abstract
BACKGROUND: India has the greatest burden of tuberculosis (TB). However, over 15% of the people on antitubercular therapy (ATT) in India are nonadherent. Several adherence monitoring techniques deployed in India to enhance ATT adherence have had modest effects. Increased adoption of mobile phones and other technologies pose potential solutions to measuring and intervening in ATT adherence. Several technology-based interventions around ATT adherence have been demonstrated in other countries. OBJECTIVE: The objective of our study was to understand the acceptance of mobile phone adherence supports for ATT using self-administered quantitative measures among patients with TB in South India. METHODS: This exploratory study was conducted at a TB treatment center (TTC) at a tertiary care center in Thrissur District, Kerala, India. We recruited 100 patients with TB on ATT using convenience sampling after obtaining written informed consent. Trained study staff administered the questionnaire in Malayalam, commonly spoken in Kerala, India. We used frequency, mean, median, and SD or IQR to describe the data. RESULTS: Of the 100 participants diagnosed with TB on ATT, 90% used mobile phones routinely, and 84% owned a mobile phone. Ninety-five percent of participants knew how to use the calling function, while 65% of them did not know how to use the SMS function on their mobile phone. Overall, 89% of the participants did not consider mobile phone-based ATT adherence interventions an intrusion in their privacy, and 93% did not fear stigma if the adherence reminder was received by someone else. Most (95%) of the study participants preferred mobile phone reminders instead of directly observed treatment, short-course. Voice calls (n=80, 80%) were the more preferred reminder modality than SMS reminders (n=5, 5%). CONCLUSIONS: Mobile phones are likely an acceptable platform to deliver ATT adherence interventions among individuals with TB in South India. Preference of voice call reminders may inform the architecture of future adherence interventions surrounding ATT in South India.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".