Selection of Behavior Change Techniques for Asthma Medication Adherence Apps: Evidence-Based Design Study
Bibliographic record
Abstract
Background: Poor medication adherence is a widespread issue that causes adverse patient outcomes and is expensive for all aspects of the health care system. Developing cost-effective and scalable interventions to promote medication adherence is a key goal. Mobile apps hold promise as a mode of delivery for adherence interventions, but app design rarely takes into account the behavioral influences on nonadherence with sufficient rigor. As a result, apps may not realize their full potential in enhancing adherence. Medication nonadherence is common among adults prescribed preventer inhalers for asthma and has a variety of influences, creating a need to identify what components behavior change technique (BCT) apps should include to effectively tackle each influence. Objective: This study aimed to identify the most acceptable and practicable BCTs to include in a medication adherence app targeting factors that influence preventer inhaler adherence in adults with asthma. Methods: Key influences on preventer inhaler adherence in adults with asthma were identified based on reviews of peer-reviewed and gray literature and domain expert knowledge. These influences were then mapped to a published set of 26 mechanisms of action (MoAs) of behavior change interventions. Next, candidate BCTs to change each MoA were identified using the Theory and Techniques tool, a web-based resource that reflects almost 100 expert behavioral scientists' consensus about which BCTs are most likely to change particular MoAs. Finally, candidate BCTs were filtered by considering their potential acceptability and practicability. Results: A total of 31 influences on preventer inhaler adherence were identified and coded to 15/26 of the influences on behavior listed by the Theory and Techniques tool. The initial mapping of influences on behavior to candidate BCTs to change those influences identified 41 candidate BCTs. After considering the potential acceptability and practicability of the candidate BCTs, the number of BCTs suggested for inclusion was reduced to 24. Conclusions: Using an evidence-based approach, this study identified 24 BCTs that may be particularly useful to include in apps promoting adherence to preventer inhalers in order to target particular influences on adherence. The list can be used by app developers to improve the quality of adherence behavior change support that their apps provide or by health care decision-makers to identify which apps contain elements addressing a range of adherence difficulties.
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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 | 0.104 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".