Introduction: Accurate
measurement of dietary intake remains challenging in large-scale
nutritional studies. This study aimed to develop and evaluate both a
practical dietary assessment strategy and a computationally efficient
statistical method for estimating usual dietary intake in the German
National Cohort (NAKO Gesundheitsstudie).Methods: We
developed a blended approach using data from NAKO. During baseline
(2014–2019) and first follow-up examinations (2019–2024), up to four
24-h food lists (24 h-FLs) and one food frequency questionnaire (FFQ)
were collected. We combined these dietary intake data sources using an
adapted Multiple Source Method (MSM) and supplemented them with
estimated consumption amounts based on data from the German National
Nutrition Survey II (NVS II, 2005–2007) to generate
measurement-error-corrected estimates of dietary intake. The adapted MSM
was empirically evaluated against conventional logistic linear
mixed-effects model (LLMM), which can be computationally complex for
large datasets due to lengthy processing times. Additionally, a
simulation study evaluated how varying the number of 24 h-FLs and FFQ
assessments affected the consumption probability estimates.Results: The
adapted MSM showed high statistical agreement with LLMM (correlation
≥0.97). The usual intake of 90 EPIC-SOFT food groups, 124 nutrients, and
energy intake was estimated for 152,304 participants (75% of the
cohort) who had at least one 24 h-FL and an FFQ available. Furthermore,
the simulation showed that including repeated 24 h-FLs alongside an FFQ
improved the accuracy of individual consumption probability estimates,
particularly when only one or two 24 h-FLs were available.Conclusion: The
adapted MSM offers a computationally efficient, practical alternative
to LLMM, generated dietary intake estimates for over 150,000 NAKO
participants to support future research. By integrating repeated
24 h-FLs, an FFQ, and external consumption data, this blended approach
balances logistical feasibility with statistical precision, providing a
scalable, cost-effective framework for large-scale nutritional studies.