A statistical framework to interpret individual response to intervention: paving the way for personalized nutrition and exercise prescription.
Open accessAbstract
The concept of personalized nutrition and exercise prescription represents a topical and exciting progression for the discipline given the large inter-individual variability that exists in response to virtually all performance and health related interventions. Appropriate interpretation of intervention-based data from an individual or group of individuals requires practitioners and researchers to consider a range of concepts including the confounding influence of measurement error and biological variability. In addition, the means to quantify likely statistical and practical improvements are facilitated by concepts such as confidence intervals (CIs) and smallest worthwhile change (SWC). The purpose of this review is to provide accessible and applicable recommendations for practitioners and researchers that interpret, and report personalized data. To achieve this, the review is structured in three sections that progressively develop a statistical framework. Section 1 explores fundamental concepts related to measurement error and describes how typical error and CIs can be used to express uncertainty in baseline measurements. Section 2 builds upon these concepts and demonstrates how CIs can be combined with the concept of SWC to assess whether meaningful improvements occur post-intervention. Finally, section 3 introduces the concept of biological variability and discusses the subsequent challenges in identifying individual response and non-response to an intervention. Worked numerical examples and interactive Supplementary Material are incorporated to solidify concepts and assist with implementation in practice.
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There is ongoing vivid discussion on how to define critical thresholds for responders and non-responders and whether further subgroups should be established.
“There is ongoing vivid discussion on how to define these critical thresholds (Atkinson and Batterham, 2015; Swinton et al., 2018; Atkinson et al., 2019; Dankel and Loenneke, 2019) and whether further subgroups should be established (Dankel and Loenneke, 2019).”
From Dose-Response Matters! - A Perspective on the Exercise Prescription in Exercise-Cognition Research (2019) · cites this paper as Swinton et al., 2018
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Dose-Response Matters! - A Perspective on the Exercise Prescription in Exercise-Cognition Research
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- Authors
- Swinton, P. A., Hemingway, B. S., Saunders, B., Gualano, B., & Dolan, E.
- Affiliations
- Robert Gordon University (GB)
- Universidade de São Paulo (BR)
- Corresponding author
- Dolan, Eimear
- Venue
- Frontiers in Nutrition
- Publisher
- Frontiers Media SA
- Published
- 2018-05-28
- Volume
- 5
- Pages
- 41-41
- ISSN
- 2296-861X (ISSN-L)
- PMC
- PMC5985399
- OpenAlex
- W2800372031
- Cited by
- 228
- References
- 37
- Citations by year
- 2026: 31 · 2025: 31 · 2024: 23 · 2023: 31 · 2022: 25 · 2021: 29 · 2020: 28 · 2019: 24 · 2018: 6
- FWCI
- 4.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Frontiers in Nutrition (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open Access Institutional Repository at Robert Gordon University (Robert Gordon University) (submittedVersion, cc-by)
- Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo (2015/11328-2, 2017/09635-0, 2013/14746-4, 2016/50438-0, 2017/04973-4)
- Topics
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Sports Science and Education
- Keywords
- personalized nutrition, exercise prescription, measurement error, biological variation, confidence intervals, smallest worthwhile change, interindividual variability, uncertainty quantification
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