Applying the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Adapt the CHAMP App for Pediatric Feeding Tube Weaning: Application and Case Report
Bibliographic record
Abstract
Background: Evidence-based tube feeding (TF) weaning involves reducing the volume of tube feeds to induce hunger, with interdisciplinary monitoring to allow for proactive medical, nutritional, and behavioral intervention as needed. This can be done outpatient; however, the current standard of care requires a high degree of medical monitoring and care coordination, which can be challenging to implement. The CHAMP App is a mobile app designed for remote patient monitoring of children born with congenital heart conditions who are at high risk for medical morbidity and mortality. The CHAMP App remote patient monitoring program would be ideally suited to improve medical monitoring and care coordination. Objective: This study aims to determine the feasibility of adapting the CHAMP App for children ready to wean from TF. Methods: We used the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework as a formative tool and conducted a case study beta test. Results: The level of complexity for the digital innovation's adaptation supported a high likelihood of success for the TF population. Most issues were simple, such as expanding the types of data that could be entered into the app, and some were more complicated, for instance, training all relevant staff to use and maintain the technology. The case study beta test was conducted with "Greyson", a 10-month old child weaning from TF (name changed for confidentiality). Once a week, the team reviewed the parent-entered data and communicated with Greyson's parents, recommending a 25% reduction in tube feeding each week. With the CHAMP App facilitating 2-way communication between the family and the team, Greyson successfully transitioned from receiving 30% of his feeds orally and 70% via tube feeding to 100% oral feedings over the course of 1 month in a home setting. Conclusions: The CHAMP App is likely to be incredibly valuable in TF weaning. The NASSS framework helped identify key considerations for adapting the CHAMP App for TF weaning at a Midwestern children's hospital. Interviews with the health care team highlighted issues like data entry expansion and staff training. The framework confirmed TF weaning as a suitable application with no major barriers. The CHAMP App successfully supported a test patient, Greyson, in weaning from his feeding tube. It may improve access, communication efficiency, and satisfaction among families and health care teams while reducing costs and enhancing safety monitoring. The app could also make TF weaning more accessible to families with lower health literacy.
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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 | 0.048 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".