Iterative Adaptation of a Tuberculosis Digital Medication Adherence Technology to Meet User Needs: Qualitative Study of Patients and Health Care Providers Using Human-Centered Design Methods
Bibliographic record
Abstract
BACKGROUND: Digital adherence technologies have been widely promoted as a means to improve tuberculosis medication adherence. However, uptake of these technologies has been suboptimal by both patients and health workers. Not surprisingly, studies have not demonstrated significant improvement in treatment outcomes. OBJECTIVE: This study aimed to optimize a well-known digital adherence technology, 99DOTS, for end user needs in Uganda. We describe the findings of the ideation phase of the human-centered design methodology to adapt 99DOTS according to a set of design principles identified in the previous inspiration phase. METHODS: 99DOTS is a low-cost digital adherence technology wherein tuberculosis medication blister packs are encased within an envelope that reveals toll-free numbers that patients can call to report dosing. We identified 2 key areas for design and testing: (1) the envelope, including the form factor, content, and depiction of the order of pill taking; and (2) the patient call-in experience. We conducted 5 brainstorming sessions with all relevant stakeholders to generate a suite of potential prototype concepts. Senior investigators identified concepts to further develop based on feasibility and consistency with the predetermined design principles. Prototypes were revised with feedback from the entire team. The envelope and call-in experience prototypes were tested and iteratively revised through focus groups with health workers (n=52) and interviews with patients (n=7). We collected and analyzed qualitative feedback to inform each subsequent iteration. RESULTS: The 5 brainstorming sessions produced 127 unique ideas that we clustered into 6 themes: rewards, customization, education, logistics, wording and imagery, and treatment countdown. We developed 16 envelope prototypes, 12 icons, and 28 audio messages for prototype testing. In the final design, we altered the pill packaging envelope by adding a front flap to conceal the pills and reduce potential stigma associated with tuberculosis. The flap was adorned with either a blank calendar or map of Uganda. The inside cover contained a personalized message from a local health worker including contact information, pictorial pill-taking instructions, and a choice of stickers to tailor education to the patient and phase of treatment. Pill-taking order was indicated with colors, chevron arrows, and small mobile phone icons. Last, the call-in experience when patients report dosing was changed to a rotating series of audio messages centered on the themes of prevention, encouragement, and reassurance that tuberculosis is curable. CONCLUSIONS: We demonstrated the use of human-centered design as a promising tool to drive the adaptation of digital adherence technologies to better address the needs and motivations of end users. The next phase of research, known as the implementation phase in the human-centered design methodology, will investigate whether the adapted 99DOTS platform results in higher levels of engagement from patients and health workers, and ultimately improves tuberculosis treatment outcomes.
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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 | 0.054 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".