MétaCan
Menu
Retour à la cohorte
Enregistrement 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 sur OpenAlexaboutno aff
Mariëtte Abrahams, Nathan V. Matusheski

Notice bibliographique

RevueJournal of Human Nutrition and Dietetics · 2020
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueNutrition, Genetics, and Disease
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineCornerstonePsychosocialPersonalizationHealth careMedical educationGerontologyMarketing

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,629
Score d'incertitude au seuil0,560

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,034
Tête enseignante GPT0,297
Écart entre enseignants0,263 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations24
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Human Nutrition and DieteticsMême sujetNutrition, Genetics, and DiseaseTravaux en français237 207