Smartphone Applications to Support Tuberculosis Prevention and Treatment: Review and Evaluation
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) remains a major global health problem and is the leading killer due to a single infectious disease. Mobile health (mHealth)-based tools such as smartphone apps have been suggested as tools to support TB control efforts (eg, identification, contact tracing, case management including patient support). OBJECTIVE: The purpose of this review was to identify and assess the functionalities of mobile apps focused on prevention and treatment of TB. METHODS: We searched 3 online mobile app stores. Apps were included if they were focused on TB and were in English, Spanish, or Portuguese. For each included app, 11 functionalities were assessed (eg, inform, instruct, record), and searches were conducted to identify peer-review publications of rigorous testing of the available apps. RESULTS: A total of 1332 potentially relevant apps were identified, with 24 meeting our inclusion criteria. All of the apps were free to download, but 7 required login and password and were developed for specific clinics, regional sites, or research studies. Targeted users were mainly clinicians (n=17); few (n=4) apps were patient focused. Most apps (n=17) had 4 or fewer functions out of 11 (range 1-6). The most common functionalities were inform and record (n=15). Although a number of apps were identified with various functionalities to support TB efforts, some had issues such as incorrect spelling and grammar, inconsistent responses to data entry, problems with crashing, or links to features that had no data. Of more concern, some apps provided potentially harmful information to patients, such as links to natural remedies for TB and natural healers. One-third of the apps (8/24) had not been updated for more than a year and may no longer be supported. Peer-reviewed publications were identified for only two of the included apps. In the gray literature (not found in the app stores), three TB-related apps were identified as in progress, being launched, or tested. CONCLUSIONS: Apps identified for TB prevention and treatment had minimal functionality, primarily targeted frontline health care workers, and focused on TB information (eg, general information, guidelines, and news) or data collection (eg, replace paper-based notification or tracking). Few apps were developed for use by patients and none were developed to support TB patient involvement and management in their care (eg, follow-up alerts/reminders, side effects monitoring) or improve interaction with their health care providers, limiting the potential of these apps to facilitate patient-centered care. Our evaluation shows that more refined work is needed to be done in the area of apps to support patients with active TB. Involving TB patients in treatment in the design of these apps is recommended.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".