A Novel Approach to Improve the Estimation of a Diet Adherence Considering Seasonality and Short Term Variability – The NU-AGE Mediterranean Diet Experience

Enrico Giampieri, Rita Ostan, Giulia Guidarelli, Stefano Salvioli, A.M. Berendsen, Anna Brzozowska, Barbara Pietruszka, Amy Jennings, Nathalie Meunier, Elodie Caumon, Susan J. Fairweather-Tait, Ewa Sicinska, E.J.M. Feskens, C.P.G.M. de Groot, Claudio Franceschi, Aurelia Santoro

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In this work we present a novel statistical approach to improve the assessment of the adherence to a 1-year nutritional intervention within the framework of the NU-AGE project. This was measured with a single adherence score based on 7-days food records, under limitations on the number of observations per subject and time frame of intervention. The results of the NU-AGE dietary intervention were summarized by variations of the NU-AGE index as described in the NU-AGE protocol. Food and nutrient intake of all participants was assessed by means of 7-days food records at recruitment and after 10 to 14 months of intervention (depending on the subject availability). Sixteen food groups and supplementations covering the dietary goals of the NU-AGE diet have been used to estimate the NU-AGE index before and after the intervention. The 7-days food record is a reliable tool to register food intakes, however, as with other tools used to assess lifestyle dietary compliance, it is affected by uncertainty in this estimation due to the possibility that the observed week is not fully representative of the entire intervention period. Also, due to logistic limitations, the effects of seasonality can never be completely removed. These variabilities, if not accounted for in the index estimation, will reduce the statistical power of the analyses. In this work we discuss a method to assess these uncertainties and thus improve the resulting NU-AGE index. The proposed method is based on Hierarchical Bayesian Models. This model explicitly includes country-specific averages of the NU-AGE index, index variation induced by the dietary intervention, and country based seasonality. This information is used to evaluate the NU-AGE index uncertainty and thus to estimate the “real” NU-AGE index for each subject, both before and after the intervention. These corrections reduce the possibility of misinterpreting measurement variability as real information, improving the power of the statistical tests that are performed with the resulting index. The results suggest that this method is able to reduce the short term and seasonal variability of the measured index in the context of multicenter dietary intervention trials. Using this method to estimate seasonality and variability would allow one to obtain better measurements from the subjects of a study, and be able to simplify the scheduling of diet assessments.
Original languageEnglish
Article number149
JournalFrontiers in Physiology
Volume10
DOIs
Publication statusPublished - 9 May 2019

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Mediterranean Diet
Diet
Uncertainty
Food
Eating
Dietary Supplements
Life Style

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Giampieri, Enrico ; Ostan, Rita ; Guidarelli, Giulia ; Salvioli, Stefano ; Berendsen, A.M. ; Brzozowska, Anna ; Pietruszka, Barbara ; Jennings, Amy ; Meunier, Nathalie ; Caumon, Elodie ; Fairweather-Tait, Susan J. ; Sicinska, Ewa ; Feskens, E.J.M. ; de Groot, C.P.G.M. ; Franceschi, Claudio ; Santoro, Aurelia. / A Novel Approach to Improve the Estimation of a Diet Adherence Considering Seasonality and Short Term Variability – The NU-AGE Mediterranean Diet Experience. In: Frontiers in Physiology. 2019 ; Vol. 10.
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title = "A Novel Approach to Improve the Estimation of a Diet Adherence Considering Seasonality and Short Term Variability – The NU-AGE Mediterranean Diet Experience",
abstract = "In this work we present a novel statistical approach to improve the assessment of the adherence to a 1-year nutritional intervention within the framework of the NU-AGE project. This was measured with a single adherence score based on 7-days food records, under limitations on the number of observations per subject and time frame of intervention. The results of the NU-AGE dietary intervention were summarized by variations of the NU-AGE index as described in the NU-AGE protocol. Food and nutrient intake of all participants was assessed by means of 7-days food records at recruitment and after 10 to 14 months of intervention (depending on the subject availability). Sixteen food groups and supplementations covering the dietary goals of the NU-AGE diet have been used to estimate the NU-AGE index before and after the intervention. The 7-days food record is a reliable tool to register food intakes, however, as with other tools used to assess lifestyle dietary compliance, it is affected by uncertainty in this estimation due to the possibility that the observed week is not fully representative of the entire intervention period. Also, due to logistic limitations, the effects of seasonality can never be completely removed. These variabilities, if not accounted for in the index estimation, will reduce the statistical power of the analyses. In this work we discuss a method to assess these uncertainties and thus improve the resulting NU-AGE index. The proposed method is based on Hierarchical Bayesian Models. This model explicitly includes country-specific averages of the NU-AGE index, index variation induced by the dietary intervention, and country based seasonality. This information is used to evaluate the NU-AGE index uncertainty and thus to estimate the “real” NU-AGE index for each subject, both before and after the intervention. These corrections reduce the possibility of misinterpreting measurement variability as real information, improving the power of the statistical tests that are performed with the resulting index. The results suggest that this method is able to reduce the short term and seasonal variability of the measured index in the context of multicenter dietary intervention trials. Using this method to estimate seasonality and variability would allow one to obtain better measurements from the subjects of a study, and be able to simplify the scheduling of diet assessments.",
author = "Enrico Giampieri and Rita Ostan and Giulia Guidarelli and Stefano Salvioli and A.M. Berendsen and Anna Brzozowska and Barbara Pietruszka and Amy Jennings and Nathalie Meunier and Elodie Caumon and Fairweather-Tait, {Susan J.} and Ewa Sicinska and E.J.M. Feskens and {de Groot}, C.P.G.M. and Claudio Franceschi and Aurelia Santoro",
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Giampieri, E, Ostan, R, Guidarelli, G, Salvioli, S, Berendsen, AM, Brzozowska, A, Pietruszka, B, Jennings, A, Meunier, N, Caumon, E, Fairweather-Tait, SJ, Sicinska, E, Feskens, EJM, de Groot, CPGM, Franceschi, C & Santoro, A 2019, 'A Novel Approach to Improve the Estimation of a Diet Adherence Considering Seasonality and Short Term Variability – The NU-AGE Mediterranean Diet Experience' Frontiers in Physiology, vol. 10, 149. https://doi.org/10.3389/fphys.2019.00149

A Novel Approach to Improve the Estimation of a Diet Adherence Considering Seasonality and Short Term Variability – The NU-AGE Mediterranean Diet Experience. / Giampieri, Enrico; Ostan, Rita; Guidarelli, Giulia; Salvioli, Stefano; Berendsen, A.M.; Brzozowska, Anna; Pietruszka, Barbara; Jennings, Amy; Meunier, Nathalie; Caumon, Elodie; Fairweather-Tait, Susan J.; Sicinska, Ewa; Feskens, E.J.M.; de Groot, C.P.G.M.; Franceschi, Claudio; Santoro, Aurelia.

In: Frontiers in Physiology, Vol. 10, 149, 09.05.2019.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

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AU - Giampieri, Enrico

AU - Ostan, Rita

AU - Guidarelli, Giulia

AU - Salvioli, Stefano

AU - Berendsen, A.M.

AU - Brzozowska, Anna

AU - Pietruszka, Barbara

AU - Jennings, Amy

AU - Meunier, Nathalie

AU - Caumon, Elodie

AU - Fairweather-Tait, Susan J.

AU - Sicinska, Ewa

AU - Feskens, E.J.M.

AU - de Groot, C.P.G.M.

AU - Franceschi, Claudio

AU - Santoro, Aurelia

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N2 - In this work we present a novel statistical approach to improve the assessment of the adherence to a 1-year nutritional intervention within the framework of the NU-AGE project. This was measured with a single adherence score based on 7-days food records, under limitations on the number of observations per subject and time frame of intervention. The results of the NU-AGE dietary intervention were summarized by variations of the NU-AGE index as described in the NU-AGE protocol. Food and nutrient intake of all participants was assessed by means of 7-days food records at recruitment and after 10 to 14 months of intervention (depending on the subject availability). Sixteen food groups and supplementations covering the dietary goals of the NU-AGE diet have been used to estimate the NU-AGE index before and after the intervention. The 7-days food record is a reliable tool to register food intakes, however, as with other tools used to assess lifestyle dietary compliance, it is affected by uncertainty in this estimation due to the possibility that the observed week is not fully representative of the entire intervention period. Also, due to logistic limitations, the effects of seasonality can never be completely removed. These variabilities, if not accounted for in the index estimation, will reduce the statistical power of the analyses. In this work we discuss a method to assess these uncertainties and thus improve the resulting NU-AGE index. The proposed method is based on Hierarchical Bayesian Models. This model explicitly includes country-specific averages of the NU-AGE index, index variation induced by the dietary intervention, and country based seasonality. This information is used to evaluate the NU-AGE index uncertainty and thus to estimate the “real” NU-AGE index for each subject, both before and after the intervention. These corrections reduce the possibility of misinterpreting measurement variability as real information, improving the power of the statistical tests that are performed with the resulting index. The results suggest that this method is able to reduce the short term and seasonal variability of the measured index in the context of multicenter dietary intervention trials. Using this method to estimate seasonality and variability would allow one to obtain better measurements from the subjects of a study, and be able to simplify the scheduling of diet assessments.

AB - In this work we present a novel statistical approach to improve the assessment of the adherence to a 1-year nutritional intervention within the framework of the NU-AGE project. This was measured with a single adherence score based on 7-days food records, under limitations on the number of observations per subject and time frame of intervention. The results of the NU-AGE dietary intervention were summarized by variations of the NU-AGE index as described in the NU-AGE protocol. Food and nutrient intake of all participants was assessed by means of 7-days food records at recruitment and after 10 to 14 months of intervention (depending on the subject availability). Sixteen food groups and supplementations covering the dietary goals of the NU-AGE diet have been used to estimate the NU-AGE index before and after the intervention. The 7-days food record is a reliable tool to register food intakes, however, as with other tools used to assess lifestyle dietary compliance, it is affected by uncertainty in this estimation due to the possibility that the observed week is not fully representative of the entire intervention period. Also, due to logistic limitations, the effects of seasonality can never be completely removed. These variabilities, if not accounted for in the index estimation, will reduce the statistical power of the analyses. In this work we discuss a method to assess these uncertainties and thus improve the resulting NU-AGE index. The proposed method is based on Hierarchical Bayesian Models. This model explicitly includes country-specific averages of the NU-AGE index, index variation induced by the dietary intervention, and country based seasonality. This information is used to evaluate the NU-AGE index uncertainty and thus to estimate the “real” NU-AGE index for each subject, both before and after the intervention. These corrections reduce the possibility of misinterpreting measurement variability as real information, improving the power of the statistical tests that are performed with the resulting index. The results suggest that this method is able to reduce the short term and seasonal variability of the measured index in the context of multicenter dietary intervention trials. Using this method to estimate seasonality and variability would allow one to obtain better measurements from the subjects of a study, and be able to simplify the scheduling of diet assessments.

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DO - 10.3389/fphys.2019.00149

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JO - Frontiers in Physiology

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