Assessing the Medication Adherence App Marketplace From the Health Professional and Consumer Vantage Points
Bibliographic record
Abstract
BACKGROUND: Nonadherence produces considerable health consequences and economic burden to patients and payers. One approach to improve medication nonadherence that has gained interest in recent years is the use of smartphone adherence apps. The development of smartphone adherence apps has increased rapidly since 2012; however, literature evaluating the clinical app and effectiveness of smartphone adherence apps to improve medication adherence is generally lacking. OBJECTIVE: The aims of this study were to (1) provide an updated evaluation and comparison of medication adherence apps in the marketplace by assessing the features, functionality, and health literacy (HL) of the highest-ranking adherence apps and (2) indirectly measure the validity of our rating methodology by determining the relationship between our app evaluations and Web-based consumer ratings. METHODS: Two independent reviewers assessed the features and functionality using a 4-domain rating tool of all adherence apps identified based on developer claims. The same reviewers downloaded and tested the 100 highest-ranking apps including an additional domain for assessment of HL. Pearson product correlations were estimated between the consumer ratings and our domain and total scores. RESULTS: A total of 824 adherence apps were identified; of these, 645 unique apps were evaluated after applying exclusion criteria. The median initial score based on descriptions was 14 (max of 68; range 0-60). As a result, 100 of the highest-scoring unique apps underwent user testing. The median overall user-tested score was 31.5 (max of 73; range 0-60). The majority of the user tested the adherence apps that underwent user testing reported a consumer rating score in their respective online marketplace. The mean consumer rating was 3.93 (SD 0.84). The total user-tested score was positively correlated with consumer ratings (r=.1969, P=.04). CONCLUSIONS: More adherence apps are available in the Web-based marketplace, and the quality of these apps varies considerably. Consumer ratings are positively but weakly correlated with user-testing scores suggesting that our rating tool has some validity but that consumers and clinicians may assess adherence app quality differently.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.021 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".