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Record W3012339376 · doi:10.1111/jhn.12746

Personalised nutrition technologies: a new paradigm for dietetic practice and training in a digital transformation era

2020· article· en· W3012339376 on OpenAlexaboutno aff
Mariëtte Abrahams, Nathan V. Matusheski

Bibliographic record

VenueJournal of Human Nutrition and Dietetics · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCornerstonePsychosocialPersonalizationHealth careMedical educationGerontologyMarketing

Abstract

fetched live from OpenAlex

It has recently been estimated that one in five early deaths worldwide is associated with poor dietary habits 1. Addressing this societal challenge through dietetics practice will require substantial human resource investment. However, according to the World Health Organization, there is currently a substantial shortage of healthcare workers, which is expected to increase over the coming decades 2. Because of this, it is important to develop an understanding of the potential ways that new technologies and digital tools can help to increase the impact of dietetics. This will create value not only for the individual patient, but also as a scalable approach to helping individuals develop improved dietary habits and transition to a service that is based on prevention and self-care. Patient-centred care is a cornerstone of modern dietetics practice 3. A key tenant underlying this approach is the individualisation of guidance based on the patient’s specific needs and, in a broad sense, treating the patient and not the disease 4. In parallel, personalisation has recently developed as a trend in the consumer nutrition and wellness area 5. Numerous apps, programs, platforms and plans are now aimed at delivering a personalised experience for the user based on profiling of an individual’s demographics, genotype, nutritional intake and status, anthropometrics, lifestyle behaviours, and/or preferences 6, 7. Several definitions have been put forth for personalised nutrition, including those that deal primarily with genetic differences, and others that include much broader concepts, including phenotypic, psychosocial and behavioural aspects of individualisation 8-11. In this editorial, we employ a recently proposed definition of personalised nutrition to describe approaches that ‘use individual-specific information, founded on evidence-based science, to promote dietary behaviour change that may result in measurable health benefit’ 12. By leveraging a holistic definition, we can consider how different aspects of personalisation can be of greatest benefit and can be most effectively leveraged for the individual patient, which can be appreciated by all dietitians. The scientific evidence for personalised nutrition is growing. Substantiation continues to emerge that personalised nutrition can provide added value beyond conventional approaches. In the clinical setting, there has been increased recognition of the importance of implementing nutritional screening and intervention 13. For example, individualised nutrition assessments and provision of tailored nutritional support in patients at nutritional risk have been shown to significantly improve clinical outcomes, including patient survival 14. In a broader wellness context, several important gene–diet interactions were found to influence the response to dietary weight-loss interventions in the landmark DIOGENES trial 15. More holistic approaches, leveraging personalised information based on both genotypic and phenotypic variation, have also been promising 16. A recent study in older Dutch adults found that the provision of personalised advice, based on dietary intake, genetic and physiological information, resulted in increased resiliency and motivation, and decreased body fat percentage and hip circumference 17. In the future, we expect to see even more research investment in Personalised ‘algorithm-based’ approaches. For example, clinical trials are currently underway aiming to validate a microbiome-based personalisation approach for blood sugar management 18, 19. Likewise, an ongoing collaboration between Stanford University and Massachusetts General Hospital recently published a pilot study (PREDICT) and is now conducting a large observational study (PREDICT2) to measure individual metabolic responses to foods, with the aim of developing a commercial platform 20. In the Nutrigenomics, Overweight/Obesity and Weight Management Trial (NOW Trial), the effects of a lifestyle intervention employing personalised genetic testing and behavioural advice will be compared with the same intervention with population-based advice 21. However, for many commercial platforms, their benefit has yet to be established in randomised controlled trials. Challenges still exist in terms of replicability of results, diversity of population groups included 22, as well as scientific validation and accuracy of products currently available 23. It has become clear however, that behaviour change is the common denominator underpinning successful personalised nutrition approaches for which dietitians are well versed, trained and experienced. Considering the high level of consumer interest in personalised nutrition, it is not surprising that many commercial personalised nutrition programs have arisen. However, each approach varies regarding the information that it collects about an individual, and which recommendations arise. Because of this, it is important to develop a framework for assessing whether a given personalised nutrition platform can offer real benefits, or whether some alternative should be recommended. An interdisciplinary expert group 12 recently developed a set of 10 ‘guiding principles’ for personalised nutrition that can support such an assessment (Box 1). These principles can be of equal value for those developing and for those using or implementing a technology-enabled personalised nutrition program. Using such a framework can help determine whether a personalised nutrition approach is credible and would be expected to deliver results for an individual. Despite a rapid rise in availability, the integration of digital tools into daily dietetic practice remains low amongst dietitians. In a survey of dietitians in Canada, Australia and the UK, 63% of respondents reported using a mobile health app in their practice, primarily for informational and patient self-monitoring purposes 24, yet very few are used for behaviour change 25, 26. Another study in Australia demonstrated poor eHealth readiness in terms of advocacy, although there was an improvement with respect to attitudinal, aptitude and access to eHealth readiness 27. A recent multi-national survey showed that dietitians who had adopted personalised nutrition innovations demonstrated higher levels of self-efficacy, lower perceptions of risk and higher usefulness, and also assigned a higher importance of technology to dietetic practice, compared to those who had not 28. Interestingly, dietitians who had integrated personalised nutrition technologies perceived themselves to be entrepreneurs, bringing another dimension to how we may need to address digital transformation and organisational change in a modern data-driven healthcare service 29. As a result of these advances in both science and technology, it is important for the practising dietitian to increase their awareness, knowledge, digital literacy (in terms of artificial intelligence and genomics) 30, professional skills and comfort level with respect to the digital solutions that power these personalised recommendations through big data analytics, machine learning and artificial intelligence (AI). Although the guiding principles are an excellent reference point for those aiming to develop personalised nutrition solutions, the growing role for the next generation of dietitians is clear. Digital solutions will not replace dietitians because of the crucial value that we bring in terms of personal relationship building and behaviour change 31. However, dietitians who do not adopt or sufficiently understand new technologies may run the risk of being replaced. As a profession, we need to address this new reality at all levels of personalisation. Dietitians can play an important role in new initiatives and product development to ensure that digital products are scientifically valid, inclusive, equitable, accessible, explainable and representative. The opportunities for dietitians as we move into the fourth industrial revolution are limitless and include those outlined in Box 2. In a modern healthcare system, which is transitioning to one that is participatory and personalised, we need to ensure that we are equipped with the right skills, knowledge and mindsets for this shift. These skills include inclusive leadership, developing an entrepreneurial mindset 28, 32, data management and digital literacy 30. At present, the area of tech-enabled personalised nutrition receives little attention in the dietetic curriculum 33. To our knowledge, with the exception of genomics, new technologies such as AI, machine learning and neural networks are not currently covered in the dietetic curriculum. This is concerning, considering that the recent survey cited above demonstrated that most Registered Dietitians (RDs) did not consider technology to play an important role in dietetic practice 28. However, we know that students are interested 33, which highlights that there is indeed a gap between consumer demand and current dietetic awareness. The time is right for dietitians to take the lead in the digital transformation of healthcare services, with nutrition and lifestyle playing a vital role in the prevention of noncommunicable diseases. New personalised nutrition technologies that are based on science, and are inclusive and accessible, provide new ways of delivering care and reaching key groups to support them in lasting behaviour change. Dietitians have a unique opportunity to be a guiding voice, a reality check and a key resource for the creation and delivery of new solutions and healthcare models. To become the reference professionals for a data-driven future that is already here, we need to address where we are as a profession in terms of our inclusive leadership, and ensure that our digital and entrepreneurial literacy skills are truly at the forefront of change.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.297
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations24
Published2020
Admission routes1
Has abstractyes

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