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