Mobile Health for Tuberculosis Management in South India: Is Video-Based Directly Observed Treatment an Acceptable Alternative?
Bibliographic record
Abstract
BACKGROUND: With the availability of low-cost mobile devices and the ease of internet access, mobile health (mHealth) is digitally revolutionizing the health sector even in resource-constrained settings. It is however necessary to assess end-user perceptions before deploying potential interventions. OBJECTIVE: This study aimed to assess the mobile phone usage patterns and the acceptability of mobile phone support during care and treatment in patients with tuberculosis (TB) in South India. METHODS: This exploratory study was conducted at an urban private tertiary care teaching hospital and nearby public primary-level health care facilities in Bangalore, South India. We recruited 185 patients with TB through consecutive sampling. Subsequent to written informed consent, participants responded to an interviewer-administered pretested questionnaire. The questionnaire included questions on demographics, phone usage patterns, and the benefits of using of mobile phone technology to improve health outcomes and treatment adherence. Frequency, mean, median, and SD or interquartile range were used to describe the data. Bivariate associations were assessed between demographics, clinical details, phone usage, and mHealth communication preferences using the chi-square test and odds ratios. Associations with a P value ≤.20 were included in a logistic regression model. A P value of <.05 was considered significant. RESULTS: Of the 185 participants, 151 (81.6%) used a mobile phone, and half of them owned a smartphone. The primary use of the mobile phone was to communicate over voice calls (147/151, 97.4%). The short message service (SMS) text messaging feature was used by only 66/151 (43.7%) mobile phone users. A total of 87 of the 151 mobile phone users (57.6%) knew how to use the camera. Only 41/151 (27.2%) mobile phone users had used their mobile phones to communicate with their health care providers. Although receiving medication reminders via mobile phones was acceptable to all participants, 2 participants considered repeated reminders as an intrusion of their privacy. A majority of the participants (137/185, 74.1%) preferred health communications via voice calls. Of the total participants, 123/185 (66.5%) requested reminders to be sent only at specific times during the day, 22/185 (11.9%) suggested reminders should synchronize with their prescribed medication schedule, whereas 40/185 (21.6%) did not have any time preferences. English literacy was associated with a preference for SMS in comparison with voice calls. Most participants (142/185, 76.8%) preferred video-based directly observed treatment when compared with in-person directly observed treatment. CONCLUSIONS: Although mobile phones for supporting health and treatment adherence were acceptable to patients with TB, mHealth interventions should consider language, mode of communication, and preferred timing for communication to improve uptake.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".