Prioritization of Quality Principles for Health Apps Using the Kano Model: Survey Study
Bibliographic record
Abstract
BACKGROUND: Health apps are often used without adequately taking aspects related to their quality under consideration. This may partially be due to inadequate awareness about necessary criteria and how to prioritize them when evaluating an app. OBJECTIVE: The aim of this study was to introduce a method for prioritizing quality attributes in the mobile health context. To this end, physicians were asked about their assessment of nine app quality principles relevant in health contexts and their responses were used as a basis for designing a method for app prioritization. Ultimately, the goal was to aid in making better use of limited resources (eg, time) by assisting with the decision as to the specific quality principles that deserve priority in everyday medical practice and those that can be given lower priority, even in cases where the overall principles are rated similarly. METHODS: A total of 9503 members of two German professional societies in the field of orthopedics were invited by email to participate in an anonymous online survey over a 1-month period. Participants were asked to rate a set of nine app quality principles using a Kano survey with functional and dysfunctional (ie, positively and negatively worded) questions. The evaluation was based on the work of Kano (baseline), supplemented by a self-designed approach. RESULTS: Among the 9503 invited members, 382 completed relevant parts of the survey (return rate of 4.02%). These participants were equally and randomly assigned to two groups (test group and validation group, n=191 each). Demographic characteristics did not significantly differ between groups (all P>.05). Participants were predominantly male (328/382, 85.9%) and older than 40 years (290/382, 75.9%). Given similar ratings, common evaluation strategies for Kano surveys did not allow for conclusive prioritization of the principles, and the same was true when using the more elaborate approach of satisfaction and dissatisfaction indices following the work of Timko. Therefore, an extended, so-called "in-line-of-sight" method was developed and applied for this evaluation. Modified from the Timko method, this approach is based on a "point of view" (POV) metric, which generates a ranking coefficient. Although the principles were previously almost exclusively rated as must-be (with the exception of resource efficiency), which was not conducive to their prioritization, the new method applied from the must-be POV resulted in identical rankings for the test and validation groups: (1) legal conformity, (2) content validity, (3) risk adequacy, (4) practicality, (5) ethical soundness, (6) usability, (7) transparency, (8) technical adequacy, and (9) resource efficiency. CONCLUSIONS: Established survey methodologies based on the work of Kano predominantly seek to categorize the attributes to be evaluated. The methodology presented here is an interesting option for prioritization, and enables focusing on the most important criteria, thus saving valuable time when reviewing apps for use in the medical field, even with otherwise largely similar categorization results. The extent to which this approach is applicable beyond the scenario presented herein requires further investigation.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".