Personalising nutritional guidance for more effective behaviour change
Open accessAbstract
Improving diet and other lifestyle behaviours has considerable potential for reducing the global burden of non-communicable diseases, promoting better health across the life-course and increasing wellbeing. However, realising this potential will require the development, testing and implementation of much more effective behaviour change interventions than are used conventionally. Evidence-based, personalised (or stratified) interventions which incorporate effective behaviour change techniques (BCT) and which are delivered digitally are likely to be an important route to scalable and sustainable interventions. Progress in developing such interventions will depend on the outcomes of research on: (i) the best bases for personalisation of dietary advice; (ii) identification of BCT which are proven to enhance intervention efficacy; (iii) suitable platforms (digital-based tools) for collection of relevant participant characteristics (e.g. socioeconomic information, current diet and lifestyle and dietary preferences) linked with intelligent systems which use those characteristics to offer tailored feedback and advice in a cost-effective and acceptable manner. Future research should focus on such interventions aiming to reduce health inequalities and to improve overall public health.
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Different people respond differently to foods and nutrients, warranting personalized approaches to nutrition interventions and services.
“It is now understood that different people respond differently to foods and nutrients, warranting personalized approaches to nutrition interventions and services [3,17-21].”
From Wearable Technology to Quantify the Nutritional Intake of Adults: Validation Study (2020) · cites this paper as [3]
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- Authors
- Celis-Morales, C., Lara, J., & Mathers, J. C.
- Affiliations
- Newcastle University (GB)
- Corresponding author
- Mathers, John C
- Venue
- Proceedings of the Nutrition Society
- Publisher
- Cambridge University Press (CUP)
- Published
- 2014-12-12
- Volume
- 74
- Issue
- 2
- Pages
- 130-138
- ISSN
- 0029-6651 (ISSN-L)
- 1475-2719
- OpenAlex
- W2128538650
- Cited by
- 130
- References
- 78
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 8 · 2023: 16 · 2022: 15 · 2021: 16 · 2020: 13 · 2019: 8 · 2018: 13 · 2017: 8 · 2016: 19 · 2015: 7
- FWCI
- 1.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of The Nutrition Society (publishedVersion)
- PubMed (publishedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Funders
- Office of Research and Development
- Scottish Government
- National Institute for Health and Care Research
- European Commission (265494)
- Public Health Agency
- Directorate for Biological Sciences
- Chief Scientist Office, Scottish Government Health and Social Care Directorate
- Medical Research Council (MR/K006312/1, G0900686)
- Engineering and Physical Sciences Research Council
- Economic and Social Research Council
- Topics
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Keywords
- behaviour change interventions, behaviour change techniques, digital health interventions, non-communicable diseases, lifestyle habits, personalized feedback, intelligent systems, health disparities, socioeconomic factors, food preferences
- MeSH terms
- Behavior Therapy, Chronic Disease, Combined Modality Therapy, Congresses as Topic, Humans, Life Style, Patient Compliance, Technology Transfer, Nutrition Policy, Internet, Nutrigenomics, Precision Medicine, Elder Nutritional Physiological Phenomena
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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