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specific to the study design. Typically, the FFQ is a pencil/paper approach using


bubble-format questionnaires; however, computer-based data capture is becoming


more common. The validity of computer-based data has not been adequately


reported in the literature. Most paper FFQs have been validated against biomarkers


of dietary intake and/or calibrated through comparison to repeat 24-h recalls (Block


et al. 1990 ; Kroke et al. 1999 ). While validation studies, performed to confirm the


effectiveness of FFQs, support the use of FFQs, the correlations with biomarkers are


not strong (usually 0.4–0.6 for carotenoids and 0.3–0.6 for doubly labeled water as


an indicator of energy intake). Calibration studies, performed to adjust andfine-tune


a questionnaire, show stronger correlations with biomarkers (Johansson et al. 2002 ).


Strengths of the FFQ include ease in completion and low burden for data col-


lection and analysis, less effect on eating behavior, one time administration, and the


ability to capture change in intake over time at the level of the study population.


Weaknesses include time to complete (45–60 min); less detail/precision in cap-


turing diet information; limited information on food preparation, meal frequency,


and eating environment; requirement of literacy; and complexity in terms of cog-


nition. Report bias, especially in terms of underreporting of energy intake in obese


individuals, is well documented. Further, food lists must reflect foods commonly


consumed in the population under study. To address this issue, population-specific


FFQs have been developed (Teufel 1997 ), such as the southwestern FFQ (Taren
et al. 2000 ), Geisinger Rural Aging FFQ (Mitchell et al. 2012 ), and the Yup’ik


Western Alaska FFQ (Kolahdooz et al. 2014 ). As with other self-report methods,


the nutrient analysis of these data is dependent on the accuracy and completeness of


the nutrient database to which the line items are linked. Most FFQs use


instrument-specific software programming linked to the USDA database; however,


the frequency with which the database linkage is updated can vary across instru-


ments and each program must make certain assumptions in selecting the link that


best reflects true intake. These factors can influence the validity of the self-reported


data.


Technology-Based Dietary Assessment


Efforts to advance self-reported dietary intake have led to intensive methodological


research to develop innovative technology-driven approaches with the goal of


improving the accuracy and precision of dietary measurement. These approaches


target a reduction in respondent-associated measurement error by reducing the


burden in data capture and/or circumventing individual bias associated with


selective recall, while increasing adherence and communication. Technology-based


diet assessment automates and standardizes coding, thereby upgrading data quality,


allowing for real-time data capture and feedback. Mobile devices provide a con-


venient platform for diet research as they offer wireless communication, built in


cameras, Global Positioning Systems (GPS), accelerometers, high-speed micro-
processors and connectivity to external devices via infrared or Bluetooth (Sharp and


9 Biomarkers of Diet and Nutritional Health 175

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