Prototyping the Automated Food Imaging and Nutrient Intake Tracking System: Modified Participatory Iterative Design Sprint (Preprint)
Bibliographic record
Abstract
BACKGROUND A total of 45% of older adults living in long-term care (LTC) have some form of malnutrition. Several methods of tracking food and fluid intake exist, but they are limited in terms of their accuracy and ease of application. An easy-to-use, objective, accurate, and comprehensive food intake system designed with LTC in mind may provide additional insights regarding nutritional support systems and nutritional interventions. OBJECTIVE The aim of this study was to conduct a multistage participatory iterative design sprint of a Goldilocks quality horizontal prototype for the Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) system. Specific design objectives included the following: (1) identify practice-relevant problems and solutions through user-centered participatory design, (2) mitigate feasibility-related barriers to uptake, and (3) employ user-centered technology development. METHODS A 6-stage iterative participatory design sprint was developed and executed. A total of 38 participants and advisors representing 15 distinct roles (eg, personal support worker, nurse, and dietitian) were engaged in the design sprint. Subjective workload (Raw Task Load Index), subjective usability scales, and a modified Ravden checklist were used to assess project advisors’ perceptions of the AFINI-T system prototype compared with the current method of food and fluid intake charting. RESULTS The top priorities for this system were identified as the following: ease of use, high accuracy, system reliability, ease of maintenance, and requirement of integrating with the current PointClickCare system. Data from project advisors informed design decisions leading to a Goldilocks quality horizontal prototype of the AFINI-T system. Compared with the current food and fluid intake charting system, AFINI-T was perceived to have the following: less time demands (t10.8=4.89; P<.001), less effort (t13.5=5.55; P<.001), and less frustration (t13.0=3.80; P=.002). Usability ratings of the AFINI-T prototype were high, with a subjective usability score mean of 89.2 and the highest ratings on a modified Ravden usability checklist of “very satisfactory” for 7 out of 8 sections. CONCLUSIONS The AFINI-T concept system appears to have good practice relevance as a tool for an intelligent food and fluid intake tracking system in LTC. The AFINI-T concept system may provide improvement over the current system, and advisors are keen to try the AFINI-T system. This research gives tangible examples of how the sprint method can be adapted and applied to the development of novel needs-based application-driven technology.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".