Implementation outcomes of tuberculosis digital adherence technologies: a scoping review using the RE-AIM framework
Bibliographic record
Abstract
INTRODUCTION: Globally, tuberculosis (TB) remains one of the leading infectious causes of death, with 1.3 million deaths. Digital adherence technologies (DATs) have the potential to provide person-centred care and improve outcomes. Using the reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework, we conducted a scoping review of DAT implementations for TB treatment. METHODS: We searched seven databases for papers published between January 2000 and April 2023, using keywords for 'tuberculosis' and 'digital adherence technology'. Articles meeting prespecified inclusion criteria and containing data on RE-AIM domains were included. We defined 'reach' as comprising cellphone ownership and engagement by people with TB (PWTB) with DATs, 'adoption' as engagement by healthcare providers with DAT programmes, 'implementation' as the fidelity of the DAT programme implemented and 'maintenance' as longer-term uptake of DATs. RESULTS: Of 10 313 records, 102 contributed to the synthesis. DATs included short message service (SMS), phone, 99DOTS, video-supported therapy (VST) and pillboxes. For 'reach', across various settings, cellphone access varied from 50%-100% and 2%-31% of PWTB was excluded from accessing DATs due to technology challenges. 36%-100% of PWTB agreed to use a DAT. The weighted mean of DAT engagement over dose-days was 81% for SMS, 85% for phone, 61% for 99DOTS, 87% for pillbox and 82% for VST. Concerning 'implementation', the fidelity of DAT implementations was affected by technological issues such as cellphone coverage, DAT malfunction and provider-facing issues, including failure to initiate intensified patient management following low DAT engagement. Findings related to RE-AIM dimensions of 'adoption' and 'maintenance' were limited. CONCLUSION: Our findings suggest that the 'reach' of DATs may be limited by a cascade of barriers, including limitations in cellphone accessibility and suboptimal sustained DAT engagement by PWTB. Video and pillbox DATs have higher levels of engagement. Implementation challenges included technological and provider-facing issues. Improving implementation outcomes may be important for TB DATs to achieve a broader public health impact. PROSPERO REGISTRATION NUMBER: CRD42022326968.
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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 | 0.078 | 0.217 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".