Scientific appraisal of the health app review tool: A validation and usability testing protocol
Bibliographic record
Abstract
Abstract Background There is a critical need to develop and evaluate effective tools to support the quality of life for those with Alzheimer’s disease (AD). Apps focused on health and function have great potential to fulfill this need through education, health and behavior tracking, and interactive capabilities. However, current app evaluation tools lack specificity to the AD and caregiver population and fail to assess factors specific to the unique needs of AD. The Health App Review Tool (HART) was designed to characterize the features of apps and then match these features to the needs and abilities of those affected by AD. The purpose of this study is to evaluate the reliability and validity of the HART and to refine the structure and content of the HART according to findings. Method This study will be carried out across Quebec, Columbus, and Pittsburg. A sample of ≥300 health care professionals working with the AD population will be recruited to complete the HART at two time points based on one of four apps and case studies. Inter and intra‐rater reliability will be assessed according to app/case study allocation and between time points. Confirmatory factor analysis and Rasch analysis of time 1 scores will be used to assess psychometric soundness. A sample of ≥12 researchers and clinicians will participate in focus groups according to the think‐aloud qualitative approach to systematically assess each item in the HART. Feedback from both psychometric and qualitative assessments will be used to refine the HART. Result We anticipate that the current version of the HART will be have redundant items as indicated by residual correlation and that the current factor loading will indicate the need for item relocation. In addition, we anticipate qualitative feedback will indicate that branching structure and more limited item list would be preferable. Conclusion Initial investigations into the HART indicate promise as a clinically valuable instrument, however, additional development of the HART content and structure are needed prior to its widespread use. The HART is anticipated to impact the uptake and use of health and function supporting apps within the AD and caregiver population.
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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 | 0.273 | 0.295 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.015 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".