Let’s build an app for that
Bibliographic record
Abstract
The promotion of physical activity behavior change among adults with cancer is a research priority. Within this field, increasing attention is being devoted to the use of health technology, which includes mobile phones and applications, or apps, to support and deliver physical activity behavior change interventions. While building a mobile app is a popular proposal among exercise oncology researchers, little practical information exists on how this process should be done or what considerations researchers should take in collaboration with participants and industry. The present article provides an overview of recent experiences with app development in exercise oncology and outlines several recommendations for future research. Methods and Results: After forming an interdisciplinary team of researchers, industry partners, and exercise oncology program participants, an iterative, user-centered app improvement process was followed to collect feedback and make meaningful changes to an existing mobile health app for its use in exercise oncology. Participant feedback was summarized and addressed collaboratively via open discussion and detailed action plans. Changes made include enhanced introductory materials for the app and improvements to usability and personalization. Some requests remain to be addressed in future updates. Two challenges identified during the app improvement process were balancing the unique needs and priorities of all parties, as well as addressing the variable feedback from a variable population of adults with cancer. Conclusions and significance: A multidisciplinary participant-oriented app improvement process led to meaningful updates to the mobile application of interest, preparing researchers to carry out an evaluation of its effectiveness within exercise oncology. Furthermore, based on lessons learned, the research team present key recommendations to consider in future mobile app research before, during, and after the development process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.082 |
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".