Peer Review of “A Physical Activity Mobile Game for Hematopoietic Stem Cell Transplant Patients: App Design, Development, and Evaluation”
Bibliographic record
Abstract
Comments1.This was a novel and interesting manuscript [1] on the development and user evaluation of a walking app for hematopoietic stem cell transplant (HSCT) patients.The main comment is that clarity on who comprised the usability samples (survey respondents, initial usability testers, additional usability evaluators), and who specifically the target sample for the game is (HSCT patients can be fairly diverse), would enhance the paper.2. In general, the conducted and planned usability testing seemed heavy on the expert testing and light in terms of planned testing with patients.In addition, the focus appeared to be very much on usability testing, without much acknowledgment that there would be a need in the future to test the impact on walking behavior.3.While there is no doubt that expert usability testing is important, and it is nice to see clear descriptions of the early development processes, it does not seem sufficient to then do a short usability test with patients and release the app to the public.4. It would be good to acknowledge that rigorous evaluation (including feasibility, acceptability, and measured impact on walking) would be required prior to release.I am sure that this is planned and has been considered, but perhaps a flowchart or figure/table outlining each of the development steps, the samples involved in these steps, and details on a trial exploring the impact on walking within the target sample might bring clarity. Specific CommentsThese are mostly for clarity rather than any issue with the study.Minor, but rather than "the aim of this paper," replace with "the aim of this study" or "the aim of this paper was to describe…."2. Make it clear that the paper describes only the evaluation, rather than a behavioral evaluation (ie, impact on physical activity), and that the evaluation took place with game development experts and clinicians rather than patients.This was not clear until quite far into the methodology.Some of the results (eg, "moving tiles") lack context in the abstract.
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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 | 0.032 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.102 | 0.065 |
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".