Beyond NutritionQuest's standard Block FFQ analysis (food groups and individual nutrients), we offer specialty scoring and analytical services: dietary indices (HEI, AHEI, DASH, MIND, Mediterranean, DII, DIS), UPF and NOVA classifications, FoodProX analytics, and specialized analyses including flavonoids. As the developers of the Block FFQ instruments for four decades, we produce these services for current studies, recently completed studies, and studies from years past.
Scoring methods most commonly requested by researchers studying diet quality and ultra-processed food consumption. Deliverables provided as machine-readable data files with documentation of methodology and food-code references.
Healthy Eating Index (HEI), Alternate HEI (AHEI), Dietary Approaches to Stop Hypertension (DASH) score, MIND diet score, Mediterranean diet scores (Trichopoulou's MDS, aMED, Panagiotakos's MedDietScore), Dietary Inflammatory Index (DII), Dietary Inflammation Score (DIS), glycemic index and load, and other validated quality indices.
Our preferred UPF scoring method. Continuous probability-based scores derived from machine-learning models trained on NHANES food composition data. More nuanced than NOVA's four-category system, providing a probability for each food rather than a categorical assignment.
Assignment of each food item to one of the four NOVA processing categories (1 unprocessed, 2 culinary ingredients, 3 processed, 4 ultra-processed). FoodProX scores of 0.7 and higher are considered equivalent to NOVA category 4 (Menchetti et al.).
Aggregate ultra-processed food consumption estimates per participant, in grams and percent of total intake. Available daily, weekly, or study-period totals.
Study-specific scoring systems, alternative ultra-processed food definitions, and other analytical work tailored to specific research questions.
Many large multi-cohort studies and consortia have used Block FFQs to collect dietary data, including the ECHO program, among others. Researchers who have obtained access to such data through the appropriate institutional or consortium processes can engage NutritionQuest to produce UPF, NOVA, FoodProX, and dietary index scoring on the Block FFQ data in those datasets.
Classification and aggregate intake metrics for researchers conducting analyses of Block FFQ data within multi-cohort datasets.
HEI, DASH, MIND, Mediterranean, DII, and other indices computed from existing Block FFQ data, ready to merge with your analytical dataset.
Tailored scoring, methodological consultation, and comparative analyses across cohorts or harmonization with other dietary instruments.
NutritionQuest does not provide or control access to consortium data. Each researcher remains responsible for obtaining data access through the relevant consortium or institutional process before engaging us for specialty scoring services.
Researchers analyzing Block FFQ data often need analytical outputs that aren't included in standard nutrient deliverables. We can produce specialized analyses drawing on our proprietary nutrient databases and decades of methodological development.
Per-class and total flavonoid intake, including flavonols, flavones, flavanones, flavan-3-ols, anthocyanidins, and isoflavones, computed using our flavonoid composition database.
NutritionQuest was the first to measure dietary flavonoid diversity, developing and publishing the DFDI, a Hill-numbers-based measure of intake diversity. Our research linked the DFDI to gut bacterial diversity, and subsequent independent research has linked flavonoid diversity to all-cause mortality. Additional publications on the DFDI are in progress.
Targeted food group categorizations for specific research questions, including fermented foods, whole grains by type, plant-based protein sources, and other custom groupings.
Studies analyzed years or decades ago may not have generated all the nutrient or food group estimates that are now available. We can re-analyze the original FFQ data to add nutrient estimates, food group values, dietary indices, or specialty measures that weren't available at the time of the original analysis.
These analyses are available for studies using any Block Food Frequency Questionnaire. Researchers with consortium data should reach out about their specific needs, since we can often produce specialized outputs for participants in datasets where we performed the original analytical work.
We've collaborated with researchers across thousands of studies for four decades. The process is straightforward.
Reach out with information about your study: which Block FFQ was used, sample size, time points, and what scoring or analyses you need. We respond with pricing and an estimated timeline.
Once you've reviewed the quote, your institution issues a purchase order for the work. That's typically all the paperwork involved.
We produce the scoring or analyses and deliver machine-readable files with documentation, ready to merge with your analytical dataset.
NutritionQuest's UPF and NOVA classifications are developed in the context of the instruments themselves, drawing on our nutrient databases, food-code mappings, portion-size models, and decades of instrument development expertise.
Our UPF and NOVA scoring uses the same NHANES food-code mappings that underpin our standard nutrient estimates. A single Block FFQ line item may name a few foods but typically maps to many NHANES food codes. Using consistent mapping ensures internal consistency across measures and prevents the over-weighting of certain foods that can occur with naive item-level NOVA approaches.
Food-code mappings, classification methodology, and handling of any edge cases, provided alongside the scored data.
As the developers of the Block FFQs, NutritionQuest is the single authorized source for UPF and NOVA values on Block FFQ data, ensuring consistency across the literature.
Authorized scoring is publishable. Unauthorized item-level classification systems applied to Block FFQ content are derivative works of NutritionQuest's copyrighted instruments and are not publishable without authorization.
The scoring services on this page fall into two technical categories. Dietary indices such as HEI, DASH, MIND, Mediterranean scores, DII, and DIS can be calculated from numerical outputs (food groups and nutrient values) using publicly available algorithms, provided the original analysis included the food group and nutrient values required by each index. We offer these as a service for researchers who would rather have us produce them than do it themselves, and we can re-analyze older datasets to add outputs that weren't originally generated.
UPF, NOVA, and FoodProX classifications are different in kind. They cannot be derived from numerical outputs alone, and require working directly with the food item content of the Block FFQ instruments themselves. That work involves NutritionQuest's copyrighted instrument content and is permitted only through authorized engagements. See our Intellectual Property Notice for the full framework.
Yes. NutritionQuest has worked with the Block instruments for four decades, and we routinely produce scoring for studies that completed their data collection years ago. We can deliver UPF, NOVA, FoodProX, dietary indices, and specialized analyses for studies regardless of when the data was collected, whether the study is ongoing, recently completed, or from the distant past. Many of our current scoring engagements are with researchers analyzing data from studies that ended long before the recent interest in ultra-processed food analysis.
Yes. NOVA classifications, UPF categorizations, and FoodProX scoring applied to Block Food Frequency Questionnaire items are derivative works of NutritionQuest's copyrighted instrument content. Their development, distribution, and publication require NutritionQuest authorization. This is important because, as the developers of the Block FFQs, we need to remain the authoritative source for scoring methods applied to our questionnaires, so that scoring is methodologically consistent across the literature. See our Intellectual Property Notice for the full framework.
Yes, in many cases. Dietary indices such as the Healthy Eating Index (HEI), DASH score, MIND diet score, Mediterranean diet scores, the Dietary Inflammatory Index (DII), and the Dietary Inflammation Score (DIS) can be calculated from food group and nutrient values using publicly available algorithms. This is possible when the standard analysis for your study generated the specific food group and nutrient outputs each index requires. Some studies (particularly older ones) may not have generated all the outputs needed for newer indices; we can re-analyze the original FFQ data to add them, or produce the index scores directly as a service.
NOVA is a four-category framework classifying foods by degree of processing (1 unprocessed, 2 culinary ingredients, 3 processed, 4 ultra-processed). FoodProX is a machine-learning framework that produces continuous probability-based scores from food composition data, providing more nuanced analysis than the four-category NOVA system. NutritionQuest offers both, with FoodProX as our preferred method given its direct grounding in NHANES food composition data and its probabilistic basis. FoodProX scores of 0.7 and higher are considered equivalent to NOVA category 4 (Menchetti et al.), so researchers using FoodProX can produce NOVA-4-equivalent classifications when needed.
Beyond UPF and NOVA scoring, we can produce specialty analyses such as flavonoid intake estimates by class, the Dietary Flavonoid Diversity Index (DFDI), which we developed and published, custom food group categorizations, and re-analyses of older studies to add nutrient or food group estimates that weren't available when the data were originally analyzed.
No. NutritionQuest does not provide or control access to consortium data. Each researcher remains responsible for obtaining data access through the relevant consortium or institutional process. Once you have access to the data, we can produce scoring and analyses on it.
Researchers planning analyses of Block FFQ data, whether for current studies or studies from years past, are encouraged to reach out. Tell us about your study and what you need, and we'll send you a quote.