Quality Principles of App Description Texts and Their Significance in Deciding to Use Health Apps as Assessed by Medical Students: Survey Study
Bibliographic record
Abstract
BACKGROUND: Currently, there are no binding requirements for manufacturers prescribing which information must be included in the app descriptions of health apps. OBJECTIVE: The aim of this study was to investigate how medical students perceive a selection of quality principles, intended for usage decisions in the app context, and establish whether the information presented in a sample of app descriptions is perceived as sufficient for facilitating an informed usage decision. METHODS: A total of 123 students (mean age 24.2 years, SD 3.4) participating in a 6-week teaching module covering cardiology and pulmonology at the University of Göttingen (original enrollment 152 students, response rate 80.9%) were included. Students were asked to read 3 store description texts of cardiological or pneumological apps and initially assess whether the descriptions sufficed for a usage decision. Subsequently, they were queried on their perception of the relevance of 9 predefined quality principles, formulated for usage decisions. An appraisal of whether the app description texts contained sufficient information to satisfy these quality principles followed. By means of 20 guiding questions, participants were then asked to identify relevant information (or a lack thereof) within the descriptions. A reassessment of whether the description texts sufficed for making a usage decision ensued. A total of 343 complete datasets were obtained. RESULTS: A majority of the quality principles were described as "very important" and "important" for making a usage decision. When accessed via the predefined principles, students felt unable to identify sufficient information within the app descriptions in 68.81% (2124/3087) of cases. Notably, information regarding undesired effects (91.8%, 315/343), ethical soundness (90.1%, 309/343), measures taken to avert risks (89.2%, 306/343), conflicts of interest (88.3%, 303/343), and the location of data storage (87.8%, 301/343) was lacking. Following participants' engagement with the quality principles, statistically significant changes in their assessment of whether the app descriptions sufficed for a usage decision can be seen-McNemar-Bowker test (3)=45.803919, P<.001, Cohen g=.295. In 34.1% (117/343) cases, the assessment was revised. About 3 quarters of changed assessments were seen more critically (76.9%, 90/117). Although, initially, 70% (240/343) had been considered "sufficient," this rate was reduced to 54.2% (186/343) in the second assessment. CONCLUSIONS: In a considerable number of app descriptions, participants were unable to locate the information necessary for making an informed usage decision. Participants' sensitization to the quality principles led to changes in their assessment of app descriptions as a tool for usage decisions. Better transparency in app descriptions released by manufacturers and the exposure of users to quality principles could collectively form the basis for well-founded usage decisions.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".