Personalised Nutrition Technologies and Innovations: A Cross-National Survey of Registered Dietitians
Bibliographic record
Abstract
BACKGROUND: Commercial technology-enabled personalised nutrition is undergoing rapid growth, yet its uptake in dietetics practice remains low. This survey sought the opinions of dietetics practitioners on personalised nutrition and related technologies to understand the facilitators and barriers to its application in practice. METHOD: A cross-section of registered dietitians were recruited in the USA, UK, Australia, Canada, Israel, Mexico, Portugal, Spain, and South Africa. The questionnaire sought their views on the risks of genetics technology, the ethics of genetic testing, the usefulness of new personalised nutrition technologies, entrepreneurism, and the perceived importance of new technologies to dietetics. Validated scales were included to assess personality (Big Five) and self-efficacy (NGSEI). The survey was available in English, Spanish, and Portuguese. Regression analyses were performed to identify factors associated with the integration of nutrigenetic testing into practice, and to identify factors associated with the perceived importance of bio-information, and mobile technology to dietetics practice. RESULTS: A total of 323 responses (response rate 19.7%) were analysed. Dietetics practitioners who had integrated personalised nutrition technology into practice perceived technologies to be less risky (p = 0.02), biotechnology to be more important (p < 0.01), and professional skills to be less important (p = 0.04) than those who had not. They were also more likely to see themselves as entrepreneurs (p < 0.01) and to perceive lower risks to be associated with technology (p < 0.01). Practitioners of nutrigenetics were lower on neuroticism (p < 0.01) and higher on self-efficacy (p < 0.01), extraversion (p < 0.01), and agreeableness (p < 0.01). A higher perceived importance of biotechnology to dietetics practice was associated with higher perceived usefulness of omics tests (p < 0.01). Perceived importance of information technology was associated with the perceived importance of biotechnology (p < 0.01). Mobile technology was perceived as important by dietitians with the highest level of education (p = 0.02). CONCLUSIONS: For dietitians to practice technology-enabled personalised nutrition, training will be required to enhance self-efficacy, address the risks perceived to be associated with new technologies, and instil an entrepreneurial mindset.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".