Exploring Individuals’ Views and Feedback on a Nutritional Screening Mobile App: Qualitative Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Malnutrition is a major global health challenge. Worldwide, approximately 390 million adults are underweight, while 2.5 billion are overweight. The Malnutrition Universal Screening Tool (MUST) has been implemented successfully in the United Kingdom to assess the nutritional status of patients in health care settings. Currently, MUST is available as a web-based tool or as a paper-based version, However, the paper tool can lead to calculation errors, and web-based tools require internet access, limiting use in some communities. The MUST app uses clear and simple navigation and processes information precisely, so could potentially improve the accuracy and accessibility of malnutrition screening for health care professionals (HCP) in all settings. OBJECTIVE: This study aimed to explore the views of HCPs on the content, functionality, and usability of a newly developed mobile app for MUST. METHODS: We performed a qualitative study using deductive and inductive framework analysis. A series of online focus groups (~1 hour each) were conducted, exploring potential users' views on the app's content design, functionality, and usefulness, which was set in demonstration mode and not available for direct use with patients. Each focus group used a semistructured approach and predefined topic guide. Participants were recruited consecutively and United Kingdom-wide using advertisements through emails, newsletters, and on social media across appropriate local and national networks. Participants had the opportunity to look at the app on their phones before giving feedback and an on-screen demonstration of the app was provided during the focus group. Data were analyzed using deductive and inductive framework analysis. RESULTS: In total, 8 online focus groups were conducted between August 2022 and January 2023. Participants (n=32) were dietetic and nutrition HCPs or educators with experience in using MUST in clinical or community settings. Data analysis revealed three broad themes: (1) improving the app for better use in practice, (2) user experience of design, and (3) barriers and facilitators in different settings. Overall feedback for the app was positive with potential users considering it to be very useful for improving routine and accurate screening, particularly in the community, and mainly because of the automatic calculation feature, which may help with improving discrepancies. Participants generally considered the app to be for professional use only, stating that patients may find it too clinical or technical. Participants also made suggestions for app sustainability and improvements, such as incentives to complete the demographics section or the option to skip questions, and the addition of more subjective measures and instructions on measuring ulna length. CONCLUSIONS: The MUST app was positively evaluated by potential users, who reported it was user-friendly and an accessible way to screen for malnutrition risk, whilst improving the accuracy of screening and availability in community settings.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".