Developing a Digital Tool to Calculate Protein Quality in Plant-Based Meals of Older Adults: User Engagement Design Approach With End Users
Bibliographic record
Abstract
BACKGROUND: The global shift toward plant-based diets has been increasing, with more people making the transition for various reasons. In vulnerable subgroups such as older adults, the transition to plant-based diets deserves attention due to the potentially detrimental consequences of lower protein quantity and quality. OBJECTIVE: We aimed to develop a digital tool that ensures adequate protein quality in plant-based meals for older adults experiencing low protein intake through an interdisciplinary collaboration and user engagement with potential end users. METHODS: Three focus group interviews of Dutch and Danish dietitians and older adults as potential end users were conducted to identify their needs, preferences, and deal-breakers. Focus group interviews were based on a user-task-environment analysis, the Walt Disney method, the brainwriting method, and a cognitive walkthrough. The interview transcripts were analyzed with a thematic analysis. The front end and backend development of a potential tool took place in parallel and was well-synced to the focus group interviews. RESULTS: Both dietitians and older adults from Dutch and Danish sites expressed high interest in a tool that provides feedback and background information on protein quality, sustainability, and nutrients or micronutrients. The user-task-environment analysis delivered input among others that dietitians and older adults are good potential users, the tool should be functional as an app as well as a website and the tool should provide preprogrammed meals or recipes. The Walt Disney method delivered usable and realistic solutions to the 4 challenges presented. Thirty-two percent of the solutions on all themes presented with the brainwriting method appeared to be highly feasible and relevant, having the potential to be implemented in a tool. The cognitive walkthrough identified certain screens as unclear, necessitating revisions for improved understandability, for example, the need for explanation in selecting food item filters is shown in screenshot 2, with an overall usability score of 59%. CONCLUSIONS: Our user engagement design approach resulted in a prototype that ensured end users' wishes and needs, with a finetuned output tested in focus groups. We conclude that our user engagement design approach was a suitable and meaningful stepwise approach to ensure the relevance of the tool and identify potential barriers. The focus group results indicate that dietitians have a clear understanding and need for a tool to aid in meal planning for enhanced protein quality, highlighting its absence in their current resources despite increasing demands arising from the protein transition. Conversely, for older adults, the introduction of a digital tool appears less appropriate; instead, there is a necessity for foundational education on protein quality before such a tool can be effectively used. Future studies are needed to further implement the prototype into practice.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".