Next Article in Journal
Obesity and Other Nutrition Related Abnormalities in Pre-Dialysis Chronic Kidney Disease (CKD) Participants
Next Article in Special Issue
Lutein Has a Positive Impact on Brain Health in Healthy Older Adults: A Systematic Review of Randomized Controlled Trials and Cohort Studies
Previous Article in Journal
Probiotic Bacillus Spores Together with Amino Acids and Immunoglobulins Exert Protective Effects on a Rat Model of Ulcerative Colitis
Article

Validation and Reliability of the Alzheimer’s Disease-Commonwealth Scientific and Industrial Research Organisation Food Frequency Questionnaire

1
School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia
2
Australian Alzheimer’s Research Foundation, Sarich Neuroscience Research Institute, Nedlands, WA 6009, Australia
3
School of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
4
School of Psychological Science, University of Western Australia, Crawley, WA 6009, Australia
*
Author to whom correspondence should be addressed.
This work formed part of Samantha Gardener’s PhD thesis.
Nutrients 2020, 12(12), 3605; https://doi.org/10.3390/nu12123605
Received: 26 October 2020 / Revised: 19 November 2020 / Accepted: 19 November 2020 / Published: 24 November 2020
(This article belongs to the Special Issue Dietary Intake and Aging Brain)

Abstract

Accuracy in measuring intake of dietary constituents is an important issue in studies reporting the associations between diet and chronic diseases. We modified a Commonwealth Scientific and Industrial Research Organisation (CSIRO) food frequency questionnaire (FFQ) to include foods of interest in the field of Alzheimer’s disease (AD) research. The aim of the current study was to determine the reliability and validity of the AD-CSIROFFQ in 148 cognitively normal older adults. The AD-CSIROFFQ was completed before and after completion of a four-day weighed food record. Of the 508 food and beverage items reported, 309 had sufficient consumption levels for analysis of reliability. Of the 309 items, over 78% were significantly correlated between the two questionnaire administrations (Spearman’s rank correlations). We used two additional methods to assess absolute nutrient intake agreement between the AD-CSIROFFQ and the weighed food records (Pearson’s correlation coefficients and Bland–Altman plots) and quintile rankings to measure group level agreement. The adequate correlations observed between questionnaire responses suggest that the AD-CSIROFFQ is reliable. All nutrient intakes were acceptable for ranking of individuals on a group level, whilst the agreement levels with respect to the weighed food records for 11 of the 46 nutrients show validity in terms of their individual level absolute intake. The AD-CSIROFFQ makes an important contribution to the tools available for assessing usual dietary intake in groups of older adults with respect to AD research.
Keywords: validation; food frequency questionnaire; diet; Alzheimer’s disease; Australia; FFQ; weighed food record; reliability validation; food frequency questionnaire; diet; Alzheimer’s disease; Australia; FFQ; weighed food record; reliability

1. Introduction

Accurate assessment of usual dietary intake is crucial to understanding the association between diet and chronic diseases; incorrect information may lead to false associations between dietary factors and disease risk and prevention. There is a need to develop and validate methods of assessing food and beverage intake to facilitate the development of guidelines for the prevention of diet-related chronic diseases.
Food frequency questionnaires (FFQs) are a relatively inexpensive method of measuring usual food intake over an extended period, particularly among large cohorts. FFQs are easy to administer with low participant burden and are therefore a more practical method to collect dietary data for large-scale studies, compared with 24-h recalls or food records which may require trained interviewers. However, dietary questionnaires need to be both specific to the population being analysed, and to the disease of interest; currently, there is no Alzheimer’s disease (AD)-specific FFQ available.
Accuracy and precision in measuring intake of individual dietary constituents are important issues in the analysis and evaluation of results from studies on the associations between diet and chronic diseases. The gold standard and most common method of validating a dietary questionnaire is through comparison of responses from the FFQ to actual food intakes documented for a representative number of days using weighed food records [1]. The reliability or precision of the tool can be estimated by administering the FFQ at two time points to the same group of people and assessing the level of agreement between the two responses [2].
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) FFQ is a quantitative questionnaire containing over 200 foods and beverages in the following categories: cereals and breads, dairy and eggs, meats and mixed dishes, chicken, fish and seafood, takeaway food, vegetables, fruit, beverages, and snacks and sweets; a paper version of the CSIROFFQ has been validated for use in the Australian population [3,4,5,6]. For the current study, a computerised version of the original FFQ was modified to include questions on foods of interest in the field of AD research with the resultant questionnaire termed the AD-CSIROFFQ. These new questions were selected based on current literature describing major dietary contributors associated with AD risk [7,8,9,10,11]. Questions were added to capture information in relation to dietary intake of chocolate type, oils (e.g., grape seed, coconut oil), wine type, green and white tea, pomegranates and pomegranate juice, types of grapes, types of berries, offal (e.g., kidney, liver, heart), types of meat used in stew, casserole, curry and goulash, types of rice, types of nuts and seeds, nutritional supplements, and herbs and spices.
The aim of this study was to determine the suitability of the AD-CSIROFFQ for use in studies of AD and ageing through assessment of (1) reliability of the AD-CSIROFFQ by comparing the responses of cognitively normal older adults when the questionnaire was administered on two occasions and (2) validity of the AD-CSIROFFQ relative to four-day weighed food records.

2. Materials and Methods

2.1. Participants

This study was undertaken by 148 older adults drawn from three cohorts in Perth, Western Australia: 88 participants from the Australian Imaging, Biomarkers and Lifestyle Study of ageing [12], 51 participants from the Western Australian Memory Study [13,14,15,16], and 9 participants from the Western Australian Participant Pool (a register of potential participants for studies to recruit from). All participants were classified as cognitively normal at the time they completed the FFQ validation study, as determined by a Mini-Mental State Examination (MMSE) score of 25 or higher.

2.2. Study Design

Participants completed an initial computerised AD-CSIROFFQ followed by a consecutive four-day weighed food record (started within the next three days) and then a repeat computerised AD-CSIROFFQ (Figure 1).

2.3. Alzheimer’s Disease Food Frequency Questionnaire

The AD-CSIROFFQ is an online FFQ with over 280 food and beverage items which includes serving sizes that can be altered by the respondent. The FFQ also contains options for each item related to food preparation and cooking techniques, as well as options for selection of specific type of the item (e.g., type of milk used, type of bread consumed, type of fats used as spread or in cooking, type of fish consumed, etc.), which yields a plethora of final options of the 280 initial food and beverage items. To minimise fatigue, the FFQ was designed to have the foods arranged into categories and automatically saves data entered, allowing participants to leave the questionnaire and return later to complete it in multiple sittings. Participants cannot move to the next section until all questions are fully answered in the current section, ensuring the questionnaire is complete. The AD-CSIROFFQ takes approximately 45 min to complete, requires access to the Internet, and assesses usual daily intake over the preceding 12 months. Grams per day intake of foods and beverages is provided following completion of the questionnaire. This intake was then analysed using FoodWorks Professional version 7.0 (Xyris Software Pty Ltd., Brisbane, Queensland, Australia.) and the AUSNUT 2007 database of Australian Foods to yield grams per day of nutrients consumed.

2.4. Four-Day Weighed Food Record

Participants attended an orientation session, during which detailed verbal and written instructions regarding the completion of the four-day weighed food records were given by an Accredited Practising Dietitian. Digital electronic kitchen scales (model 1348, Propert) weighing to the nearest gram and modular measuring cup and spoon sets were provided to each participant, as was a sample of a completed food record. Each participant was requested to record the weight of all food and beverages consumed during the four-day period from Sunday through Wednesday (at home or away from home) and to record brand names, methods of food preparation, and ingredients of recipes in a specially designed diary. In addition, participants were asked to record the amount of any leftover food at the end of a meal or snack, either at home or away from home, and to report any dietary supplements consumed. All food records were reviewed face-to-face with the participant upon completion of the four-day period to clarify details or missing information. Four-day weighed food records were analysed using FoodWorks Professional version 7.0 and the AUSNUT 2007 database of Australian Foods to yield grams per day of nutrients consumed.

2.5. Statistical Analysis

Statistical analyses were performed using the IBM Statistical Package for the Social Sciences, Version 25.0 (SPSS Inc., Chicago, IL, USA). A p-value of <0.05 determined a significant result. Mean values, standard deviations (SD), and percentages are provided for the demographics for the reliability and validation study cohort.

2.5.1. Reliability Study

The raw data for the majority of food items was not normally distributed; therefore, Spearman’s rank correlation coefficients were used to compare the correlation between food and beverage intakes from the first and second administration of the AD-CSIROFFQ.

2.5.2. Validation Study

The raw data for the majority of nutrients were not normally distributed; logarithmic transformation produced normalised data that maximised the Kolmogorov–Smirnov test statistic. Following log transformation, the nutrient data were energy-adjusted using linear regressions. Raw data were used to calculate mean and SD intakes for the 46 nutrients analysed. Bivariate Pearson’s correlation coefficients were calculated for the log-transformed and energy-adjusted nutrient data to estimate the associations between the two methods, i.e., the AD-CSIROFFQ and the weighed food record.
Agreement between both methods was assessed using Bland–Altman plots [17], which represent the individual differences between the two measurements graphed against the mean of the measurements. Interpretation of the Bland–Altman results was based on three categories: “good agreement”, “fairly good agreement”, and “poor agreement”, depending on whether the difference between the two measurements is approximately equal to 1, 2, or 3 standard deviations of the average nutrient intake, respectively.
The agreement between methods of the relative rankings of nutrient intakes was assessed by classifying subjects into quintiles and cross-tabulating, using the log-transformed and energy-adjusted data. Weighted kappa statistics (κ) were calculated to measure the strength of agreement with 0.61–0.80 being “substantial”, 0.41–0.60 “moderate”, 0.21–0.40 “fair”, and less than 0.20 “slight” [18].

3. Results

Of the cohort of 148 participants, 136 completed the two administrations of the AD-CSIROFFQ and were included in the reliability study analysis. This cohort included 44 males (32%) and had a mean and SD age of 73.19 ± 6.28 years. All 148 individuals participated in the validation study; of these, 48 were male (32%) and the mean age was 73.20 ± 6.43 years.

3.1. Reliability Study

To investigate the reliability of the AD-CSIROFFQ, we assessed the correlation of intakes of 508 food and beverage items from two administrations of the questionnaire (Table 1). Of the 508 items, 182 were highly correlated (p < 0.001), 27 were moderately correlated (p < 0.01), and 34 were weakly correlated (p < 0.05). Sixty-six items were not significantly correlated; these items comprised mostly individual fruits and vegetables, and takeaway foods for which low consumption levels were reported in both administrations of the questionnaire. Spearman’s rank correlation coefficients ranged from −0.866 for subs/wraps with seafood as the main filling (three participants reporting consumption) to 1.000 for packet soup—mushroom. One hundred and ninety-nine items could not be analysed due to their low reporting rate; 19 of these items were from the AD-related questions added to the FFQ, and included intakes of chicken and beef liver, beef and veal kidney, lamb heart, and palm oil.
Intake of herbs and spices reported following completion of the AD-CSIROFFQ on two occasions was compared using binary scores assigned to “yes” or “no” answers to questions relating to whether an individual had consumed each of 19 herbs or spices in the last fortnight. Out of a possible 2584 responses, 2083 (80.61%) matched between the two administrations. The herbs and spices most commonly inconsistent in their responses were ginger, parsley, and rosemary. Only 19 of the 136 participants had all 19 answers identical.
The intake of a total of 964 dietary supplements was reported overall; 288 were reported in one FFQ administration and not the other, whilst 338 were reported in both administrations. Of the 288 reported in only one administration, 235 had a consumption frequency of “yearly” or “rarely”. Of the 338 supplements reported in both administrations, 50 had inconsistent consumption frequency stated, 120 had different brands of supplements stated, and 22 had different supplement strength reported (only fish oil and krill oil had supplement strength requested). The final question concerning supplement intake was an open-ended question regarding intake of any other supplements not listed previously within the questionnaire (e.g., magnesium, iron, and probiotics): 37 participants stated that they consumed an additional supplement in both administrations of the questionnaire; 27 participants stated that they consumed an additional supplement in one administration of the questionnaire only.

3.2. Validation Study

Bivariate Pearson’s correlation coefficients estimating the association of the nutrient intakes between the AD-CSIROFFQ and weighed food records ranged from −0.092 (vitamin B6) to 0.570 (alcohol). Of the 46 nutrients analysed, 41 were significantly positively correlated, with 34 at the p < 0.001 level, 5 at the p < 0.01 level, and 2 at the p < 0.05 level (Table 2). Nutrients that were not correlated were vitamin B6, vitamin B12, sodium, zinc, and energy.
Agreement between both methods with respect to nutrient intake was assessed using Bland–Altman plots (“Level of Agreement”; Table 2). Six of 46 nutrients showed “good agreement”, 29 nutrients showed “fairly good agreement”, and 11 nutrients showed “poor agreement”. Example plots depicting “good”, “fairly good”, and “poor agreement” are shown in Figure 2.
Quintiles of nutrient intake levels were also compared between the AD-CSIROFFQ and the weighed food records. The ranking analysis indicated that between 54% and 88% of subjects (mean 63%) were classified into either the same or adjacent nutrient quintiles in both methods (% in same quintile + % in adjacent quintile), with the highest percentages observed for niacin, docosahexaenoic acid, eicosapentaenoic acid, and alcohol. The two methods grossly misclassified (disagreement by three or four quintiles) between 3% and 22% of subjects (average 15%), with the highest misclassification seen for water, carbohydrate, and energy. The strength of quintile agreement between the two methods was “substantial” for 4 nutrients, “moderate” for beta carotene intake, “fair” for 7 nutrients, and “slight” for the remaining 34 nutrients (Table 3).

4. Discussion

The reliability and comparative validity of the customised online AD-CSIROFFQ were assessed by comparing actual food intake data from the AD-CSIROFFQ at two administrations (reliability), and by comparing nutrient intake documented using four-day weighed food records with data generated from the first administration of the AD-CSIROFFQ (validity).
Of the 309 food and beverage items that had sufficient consumption levels for analysis of reliability, over 78% were significantly correlated between questionnaire administrations. The items lacking significant correlation were mainly takeaway foods and individual fruit and vegetable items that were consumed at low levels. These results indicate that the overall repeatability, and thus reliability, of the AD-CSIROFFQ is good.
Validity of an FFQ is the degree to which the instrument measures the diet of the subjects it was designed to study. To determine validity, we used two methods to assess agreement of absolute nutrient intake between the AD-CSIROFFQ and four-day weighed food records. First, Pearson’s correlation coefficients showed that 41 of the 46 nutrients analysed were significantly positively correlated, with 34 of these highly correlated, providing evidence that intake levels in the AD-CSIROFFQ are associated with intakes documented via the weighed food records. Second, Bland–Altman plots assessing individual agreement of nutrient intakes between the two methods indicated that 35 of the 46 nutrients showed “good” or “fairly good” agreement.
We also examined the ranking of individuals into quintiles of nutrient intake and compared quintiles between the AD-CSIROFFQ and weighed food records. The AD-CSIROFFQ demonstrated an acceptable ability to rank subjects into the same or adjacent quintile as the weighed food records. On average, for all nutrients, 63% of participants were classified into the same or adjacent nutrient intake quintile, with the highest observed agreement of 88% for alcohol intake. On average, only 15% of participants were grossly misclassified by three or four quintiles, with the lowest value of 3% again for alcohol intake. Our quintile agreement results are comparable to those reported in a previous validation study using the paper-based CSIROFFQ in 62 women aged 31–60 years, where approximately 70% of reported intakes were in the same or adjacent quintile [6]. Yet, our group level agreement results are lower than values reported in a validation study of 14-year-old adolescents (n = 785), where the percentage agreement was 80%–90%. Notably, however, the adolescent study utilised a three-day weighed food record as the reference tool, and data were assigned to tertiles rather than quintiles [3]; these methodological differences increased the likelihood of group level agreement in the adolescent study.
In the present study, due to the quintile agreement being similar to that reported in other studies [3,6], we conclude that the AD-CSIROFFQ is a suitable tool for determining high or low intakes of nutrients, thereby yielding valuable information at the group level. For epidemiological purposes, ranking of individuals may be more relevant than determining absolute intakes. FFQs are commonly used in studies to examine the association between diet and chronic diseases [2], and for these studies, the ability of the FFQ to rank individuals on usual nutrient intakes may be more important than the estimation of absolute intake. For example, dietary patterns utilise individuals’ intake rankings to investigate the association between diet and chronic diseases; dietary patterns frequently use median intake values to assign individuals to a binary value for inclusion into a dietary pattern score.
Overall, the evidence from the current study, particularly from agreement levels, leads to the recommendation that the AD-CSIROFFQ should be used selectively if measuring absolute nutrient intakes at the individual level. Specifically, our results provide evidence that the AD-CSIROFFQ measures VLC N3, alpha linolenic acid, docosahexaenoic acid, and caffeine accurately, based on good agreement and significant correlations, and a further 27 nutrients reasonably accurately, based on fairly good agreement and significant correlations; therefore, the AD-CSIROFFQ is a useful tool in this respect. Available carbohydrate, sugars, and dietary fibre are important nutrients for disease research, and whilst there was high correlation (p < 0.001) of intakes between the AD-CSIROFFQ and weighed food records, poor agreement was observed in the Bland–Altman plots, suggesting that one should proceed with caution in utilisation of the AD-CSIROFFQ for measurement of absolute intake of these nutrients.
Weighed food records are acceptable gold standards because they are thought to provide more accurate and representative food intake data within the period covered by the FFQ. It is important that the errors of both methods are independent to avoid spuriously high estimates of validity. Among the available and feasible comparison methods for validating an FFQ, diet records are likely to have the least correlated errors. The main source of error in the reference method is the possibility of a change in diet due to the measurement process itself, despite requests that participants consume their normal diet, and reassurance from all that this was the case. Conversely, the main sources of error in the questionnaire are restrictions imposed by a fixed list of foods, memory of foods consumed, interpretation of questions, and the assumption of average serving sizes for most foods. By contrast, diet records are collected prospectively and do not depend on memory. They allow a more direct assessment of portion sizes, and errors in interpretation relate to the dietitian coding the records rather than to the subjects. Agreement between the two methods of dietary data collection may be affected by reporting of items in the FFQ which were not consumed during the four-day weighed food record or were consumed by few people (e.g., specific types of fish and breakfast cereals). The day of the week may also influence the result of validation studies. In our study, the greatest proportion of records was collected on weekdays (75%) with 25% collected on a weekend day; this division was deliberate, as it is perceived that usual dietary intake occurs during the week with greater variation observed at weekends [1,2].
Irrespective of the limitations described above, the present study provides evidence of acceptable reliability and validity of the AD-CSIROFFQ as a measure of dietary intake, particularly in the AD field for Australian cohorts. The adequate correlations observed between test–retest responses suggest that the AD-CSIROFFQ is reliable. The agreement levels with respect to the weighed food records for a number of nutrients shows validity in terms of their individual intake, with all intakes being acceptable for ranking of individuals on a group level, and thus providing an acceptable assessment of long-term dietary intake in older adults. In summary, the AD-CSIROFFQ makes an important contribution to the tools available for assessing usual dietary intake in older adults with respect to AD research.

Author Contributions

Conceptualisation, S.L.G., S.R.R.-S., P.L.-W. and R.N.M.; methodology, S.L.G., S.R.R.-S., and P.L.-W.; formal analysis, S.L.G.; writing—original draft preparation, S.L.G.; writing—review and editing, S.R.R.-S., P.L.-W. and R.N.M. All authors have read and agreed to the published version of the manuscript.

Funding

Funding for the project was provided by Edith Cowan University through a School of Medical and Health Sciences Research Grant. S.R.R.-S. is supported by a BrightFocus Foundation Fellowship, USA.

Acknowledgments

The authors would like to acknowledge the support provided by Simon Gibson and Mac Coombes of CSIRO in actioning the modification of the CSIROFFQ and managing the online questionnaire throughout the study. The authors would also like to acknowledge the assistance of Sarah Tester, Sam Hodge, Tegan Hodson, Matthew Chau, and Serena MacManus in the entry of weighed food records and AD-CSIROFFQ responses into FoodWorks.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cade, J.E.; Thompson, R.; Burley, V.; Warm, D. Development, validation and utilisation of food-frequency questionnaires—A review. Public Health Nutr. 2002, 5, 567–587. [Google Scholar] [CrossRef]
  2. Cade, J.E.; Burley, V.J.; Warm, D.L.; Thompson, R.L.; Margetts, B.M. Food-frequency questionnaires: A review of their design, validation and utilisation. Nutr. Res. Rev. 2004, 17, 5–22. [Google Scholar] [CrossRef]
  3. Ambrosini, G.L.; De Klerk, N.H.; O’Sullivan, T.A.; Beilin, L.J.; Oddy, W.H. The reliability of a food frequency questionnaire for use among adolescents. Eur. J. Clin. Nutr. 2009, 63, 1251–1259. [Google Scholar] [CrossRef] [PubMed]
  4. Ambrosini, G.; Mackerras, D.; De Klerk, N.; Musk, A.; Ambrosini, G.; De Klerk, N. Comparison of an Australian food-frequency questionnaire with diet records: Implications for nutrition surveillance. Public Health Nutr. 2003, 6, 415–422. [Google Scholar] [CrossRef]
  5. Rohan, T.; Record, S.; Cook, M. Repeatability of estimates of nutrient and energy intake: The quantitative food frequency approach. Nutr. Res. 1987, 7, 125–137. [Google Scholar] [CrossRef]
  6. Lassale, C.; Guilbert, C.; Keogh, J.; Syrette, J.; Lange, K.; Cox, D.N. Estimating food intakes in Australia: Validation of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) food frequency questionnaire against weighed dietary intakes. J. Hum. Nutr. Diet. 2009, 22, 559–566. [Google Scholar] [CrossRef]
  7. Willis, L.M.; Shukitt-Hale, B.; A Joseph, J. Recent advances in berry supplementation and age-related cognitive decline. Curr. Opin. Clin. Nutr. Metab. Care 2009, 12, 91–94. [Google Scholar] [CrossRef]
  8. Solanki, I.; Parihar, P.; Mansuri, M.L.; Parihar, M.S. Flavonoid-Based Therapies in the Early Management of Neurodegenerative Diseases. Adv. Nutr. 2015, 6, 64–72. [Google Scholar] [CrossRef] [PubMed]
  9. Crichton, G.E.; Elias, M.F.; Alkerwi, A. Chocolate intake is associated with better cognitive function: The Maine-Syracuse Longitudinal Study. Appetite 2016, 100, 126–132. [Google Scholar] [CrossRef]
  10. Fernando, W.M.A.D.B.; Martins, I.J.; Goozee, K.G.; Brennan, C.S.; Jayasena, V.; Martins, R.N. The role of dietary coconut for the prevention and treatment of Alzheimer’s disease: Potential mechanisms of action. Br. J. Nutr. 2015, 114, 1–14. [Google Scholar] [CrossRef] [PubMed]
  11. Gorji, N.; Moeini, R.; Memariani, Z. Almond, hazelnut and walnut, three nuts for neuroprotection in Alzheimer’s disease: A neuropharmacological review of their bioactive constituents. Pharmacol. Res. 2018, 129, 115–127. [Google Scholar] [CrossRef] [PubMed]
  12. Ellis, K.A.; Bush, A.I.; Darby, D.; De Fazio, D.; Foster, J.; Hudson, P.; Lautenschlager, N.T.; Lenzo, N.; Martins, R.N.; Maruff, P.; et al. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: Methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int. Psychogeriatr. 2009, 21, 672–687. [Google Scholar] [CrossRef] [PubMed]
  13. Clarnette, R.M.; Almeida, O.P.; Forstl, H.; Paton, A.; Martins, R.N. Clinical characteristics of individuals with subjective memory loss in Western Australia: Results from a cross-sectional survey. Int. J. Geriatr. Psychiatry 2001, 16, 168–174. [Google Scholar] [CrossRef]
  14. Laws, S.M.; Clarnette, R.M.; Taddei, K.; Martins, G.; Paton, A.; Hallmayer, J.; Almeida, O.P.; Groth, D.M.; Gandy, S.E.; Förstl, H.; et al. APOE-epsilon4 and APOE -491A polymorphisms in individuals with subjective memory loss. Mol. Psychiatry 2002, 7, 768–775. [Google Scholar] [CrossRef]
  15. Johnson, L.; Sohrabi, H.R.; Hall, J.R.; Kevin, T.; Edwards, M.; O’Bryant, S.E.; Martins, R.N. A depressive endophenotype of poorer cognition among cognitively healthy community-dwelling adults: Results from the Western Australia memory study. Int. J. Geriatr. Psychiatry 2015, 30, 881–886. [Google Scholar] [CrossRef] [PubMed]
  16. Sohrabi, H.R.; Bates, K.A.; Rodrigues, M.; Taddei, K.; Martins, G.; Laws, S.M.; Lautenschlager, N.T.; Dhaliwal, S.S.; Foster, J.K.; Martins, R.N. The relationship between memory complaints, perceived quality of life and mental health in apolipoprotein Eepsilon4 carriers and non-carriers. J. Alzheimers Dis. 2009, 17, 69–79. [Google Scholar] [CrossRef] [PubMed]
  17. Bland, J.M.; Altman, D. Altman, Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986, 327, 307–310. [Google Scholar] [CrossRef]
  18. Altman, D.G. Practical Statistics for Medical Research; Chapman and Hall: London, UK, 1991. [Google Scholar]
Figure 1. Schematic of the study design. AD-CSIROFFQ, (Alzheimer’s disease-Commonwealth Scientific and Industrial Research Organisation Food Frequency Questionnaire).
Figure 1. Schematic of the study design. AD-CSIROFFQ, (Alzheimer’s disease-Commonwealth Scientific and Industrial Research Organisation Food Frequency Questionnaire).
Nutrients 12 03605 g001
Figure 2. Bland–Altman plots depicting examples of “good agreement” (A), “fairly good agreement” (B), and “poor agreement” (C) of nutrient intakes determined using the AD-CSIROFFQ and four-day weighed food records. The difference in intake of nutrients between the two methods is plotted against the mean intake across the two methods for each individual. Upper horizontal line shows one standard deviation above the mean intake, middle horizontal line is the mean intake, and lower horizontal line shows one standard deviation below the mean intake. Abbreviations: AD-CSIROFFQ, Alzheimer’s disease-Commonwealth Scientific and Industrial Research Organisation Food Frequency Questionnaire.
Figure 2. Bland–Altman plots depicting examples of “good agreement” (A), “fairly good agreement” (B), and “poor agreement” (C) of nutrient intakes determined using the AD-CSIROFFQ and four-day weighed food records. The difference in intake of nutrients between the two methods is plotted against the mean intake across the two methods for each individual. Upper horizontal line shows one standard deviation above the mean intake, middle horizontal line is the mean intake, and lower horizontal line shows one standard deviation below the mean intake. Abbreviations: AD-CSIROFFQ, Alzheimer’s disease-Commonwealth Scientific and Industrial Research Organisation Food Frequency Questionnaire.
Nutrients 12 03605 g002
Table 1. Associations between reported food and beverage item intake from two administrations of the Alzheimer’s disease-CSIRO Food Frequency Questionnaire.
Table 1. Associations between reported food and beverage item intake from two administrations of the Alzheimer’s disease-CSIRO Food Frequency Questionnaire.
Food or Beverage ItemSpearman’s Rank Correlation CoefficientNumber of Participants Consuming ItemFood or Beverage ItemSpearman’s Rank Correlation CoefficientNumber of Participants Consuming ItemFood or Beverage ItemSpearman’s Rank Correlation CoefficientNumber of Participants Consuming Item
VegetablesMeat and Mixed DishesDairy and Eggs
Avocado—fresh0.810 ***105Roast lamb0.558 ***76Cheese—regular variety0.688 ***95
Baked beans—canned0.730 ***92Rissole/meat patty0.600 ***63Cheese—fat reduced variety0.830 ***19
Bean sprouts0.33819Asian stir-fry0.602 ***78Cottage cheese0.674 ***33
Beetroot—canned0.813 ***81Mincemeat as a sauce0.632 ***78Ricotta cheese0.493 **27
Bok choy0.610 ***47Ham0.650 ***90Soft cheese0.585 ***68
Broccoli0.623 ***124Beef stew/casserole0.667 ***49Milk—whole milk0.776 ***35
Brussels sprouts0.794 ***68Pizza0.684 ***24Milk—reduced fat milk0.606 ***38
Cabbage0.675 ***90Steak0.689 ***105Milk—reduced fat milk + added calcium and nutrients0.4004
Capsicum0.658 ***122Roast beef/veal0.687 ***64Milk—skim/non-fat milk0.534 *20
Carrots0.526 ***131Gravy—powder prepared with water0.682 ***42Glass of milk0.686 ***38
Cauliflower0.722 ***115Pate/liver paste0.640 ***34Ice cream—regular0.757 ***59
Celery0.625 ***109Gravy—prepared from pan drippings0.641 ***28Ice cream—fat reduced0.748 *10
Coleslaw0.722***96Homemade soup—vegetable0.699 ***34Milkshake/thickshake—regular fat milk0.732 **15
Cucumber0.717 ***123Crumbed veal0.677 **20Flavoured milk—regular fat0.956 **6
Fresh fruit salad0.552 ***71Lamb chop or cutlet0.726 ***85Flavoured milk—reduced fat0.900 *5
Fried mixed vegetables0.427 **47Bacon0.734 ***93Butter0.645 ***88
Garlic0.703 ***102Pork chop0.749 ***64Margarine0.638 ***60
Gherkins/pickled onions0.737***52Roast pork/pork fillet0.751 ***57Cream—regular thickened (35%)0.848 ***36
Green beans0.569 ***113Beef curry0.758 ***19Cream—light thickened (18%)0.5003
Green peas0.570 ***117Meat pie0.785 ***25Dairy-style dessert0.2918
Green peas—canned0.5003Sausages0.797 ***90Ice block/icy pole0.4948
Lentils—dried/canned0.564 ***50Chicken stew/casserole0.790 ***15Custard—regular vanilla flavoured0.614 ***36
Lettuce0.447 ***124Continental sausage0.820 ***25Yoghurt—Greek-style natural0.902 ***32
Mushrooms—fresh0.579 ***75Homemade soup—pea and ham0.979 ***9Yoghurt—flavoured regular fat (3%)0.900 *5
Mushrooms—fried0.476 ***79Packet soup—mushroom1.000 ***2Yoghurt—flavoured low fat (.5%)0.9494
Olives0.693 ***85Frankfurters/saveloys0.9434Yoghurt—natural regular fat (4%)0.6148
Onion—fried0.559 ***49Lamb liver0.715 *8Yoghurt—natural low fat (.5%)0.5645
Onion—raw/baked/boiled0.560 ***79Beef goulash0.609 *11Yoghurt—natural reduced fat (2%)0.882 **7
Other beans—dried or canned0.650 ***57Lamb curry0.6159Boiled/poached egg0.555 ***95
Other pickled vegetables0.696 **15Lamb stew/casserole0.7166Fried egg0.633 ***61
Pumpkin0.705 ***118Canned soup—tomato0.5918Omelette/scrambled eggs0.526 ***92
Silver beet/spinach0.594 ***90Canned soup—meat and vegetable0.975 **5Cereals and Cereal Foods
Strawberries0.667 ***121Homemade soup—chicken and vegetable0.643 *14Bread rolls0.504 ***67
Sweet potato0.614 ***111Mornay dishes0.483 ***49Flat breads0.514 **32
Sweetcorn0.738 ***88Chicken curry0.782 ***30Crumpets or English muffins0.664 ***31
Sweetcorn—canned0.548 ***38Vegetarian curry/stew casserole0.342 *43Croissants0.807 ***19
Tomato—fresh0.536 ***133Homemade soup—pumpkin0.641 *11Crispbread/rice cake/cracker0.606 ***67
Tomato—fried/grilled0.523 ***77Canned soup—pea and ham0.6675Muesli bar/health bar0.852 ***43
Tomatoes—canned0.552 ***88Packet soup—vegetable−0.8663Fruit loaf/currant bread0.801 ***40
Turnip, swede0.605 **23Mincemeat dish0.08269Cereal-based sweet desserts0.682 ***22
Vegetarian stir-fry0.570 ***45Packet soup—chicken noodle0.3005Barley cooked0.858 ***16
Zucchini0.605 ***81Homemade soup—meat and vegetable−0.16810Porridge/oatmeal—unsweetened0.864 ***57
Potato—roasted0.427 ***96Canned soup—mushroom−0.5003Plain bran0.886 ***12
Jacket potatoes0.475 ***66Lamb kidney−0.0946Muesli—packaged untoasted/natural0.521 **28
Potato—mashed with milk0.708 ***94Luncheon meat/devon0.1054Muesli—homemade0.562 **28
Potato salad0.790 ***78Parmagiana0.4558Muesli—packaged toasted0.5886
Oven baked hot chips prepared at home0.615 ***35Savoury pies/pastries0.434 **39LSA mixture0.718 **15
Potato—boiled0.645 ***85Chicken, Fish, and SeafoodsSanitarium Weet-Bix0.623 *14
FruitCanned salmon—red salmon0.42210Instant noodles0.635 **16
Apple, pear—fresh baked0.763 ***121Canned salmon—pink salmon0.693 ***29Asian noodles0.643 ***34
Apricot—fresh0.614 ***66Canned salmon—Australian salmon0.3107Couscous0.589 ***33
Banana0.713 ***128Canned tuna—regular variety canned in spring water0.577 **27Plain pasta0.573 ***83
Blackberries0.604 *16Canned tuna—regular variety canned in oil0.776 **13Filled pasta0.538 *21
Blueberries0.682 ***58Canned tuna—regular variety canned in brine0.0004Packet pasta and sauce0.479 *23
Cherries0.547 ***72Canned tuna—flavoured variety canned in spring water0.7557Brown rice0.733 **18
Cranberries0.422 *29Canned tuna—flavoured variety canned in oil0.6324Fried rice0.614 ***41
Figs—fresh0.658 ***42Other canned fish0.543 ***44White rice0.637 ***91
Fruits canned in juice0.684 ***40Steamed/grilled/boiled fish—other inexpensive fish—no coating0.3536Risotto0.849 ***31
Fruits canned in syrup0.4759Steamed/grilled/boiled fish—barramundi—no coating0.6798Sweets and Snacks
Fruits canned in water0.44110Steamed/grilled/boiled fish—salmon—no coating0.776 ***23Chocolate-covered bar0.592 ***37
Green grapes—fresh0.521 ***106Steamed/grilled/boiled fish—snapper—no coating0.43610Dark chocolate0.657 ***90
Kiwifruit0.695 ***72Seafood0.491 ***69Milk chocolate0.675 ***62
Mango0.607 ***98Fried fish—salmon—no coating0.9494White chocolate0.45415
Melon0.469 ***90Fried fish—snapper —flour0.5003Vegemite, marmite, etc.0.714 ***71
Nectarine—fresh0.507 ***105Oven-baked fish—salmon—no coating0.3767Honey, jam, marmalade0.603 ***89
Orange, mandarin, grapefruit0.726 ***111Oven-baked fish—Snapper—no coating−0.5003Peanut paste0.763 ***57
Other berries0.540 *15Crumbed chicken0.522 **28Fancy sweet biscuits0.463 **52
Other dried fruit0.532 ***53Chicken breasts, thighs, or wings—without skin0.621 ***83Salted biscuits0.618 ***71
Pawpaw0.668 **17Chicken breasts, thighs, or wings—with skin0.474 *19Plain sweet biscuits0.648 ***64
Peach—fresh0.477 ***86Roast/barbeque chicken0.24042Cake/sweet muffin0.654 ***90
Pineapple—fresh0.559 ***40Chicken cooked in simmer sauce−0.00216Potato crisps/Twisties/corn chips, etc.0.655 ***48
Plum—fresh0.529 ***91Takeaway FoodsLollies, toffees0.599 **21
Pomegranates0.6938Garlic bread0.481 ***50Sweet bun/doughnut0.487 *27
Raisins, sultanas, or currants0.752 ***83Fries0.590 ***38Fruit pie or pastry or fritters0.20824
Raspberries0.424 *25Hot chips0.647 ***52Popcorn—commercial0.8663
Red/black grapes—fresh0.475 ***102Pizza0.632 ***64Sugar/honey added to cereals0.745 ***28
Watermelon0.505 ***92Meat/chicken pie0.05511Sugar/honey added to hot beverages0.763 ***28
BeveragesPastie0.640 ***30Chia seeds0.904 ***18
Milo/Quik/Ovaltine/cocoa/hot chocolate0.609 ***45Hamburgers—large0.601 **18Flax seeds0.8663
Green tea0.701 ***62Hamburgers—small0.785 **11Pumpkin seeds0.767 ***25
Herbal tea0.746 ***56Sausage roll0.707 ***34Pumpkin seeds (roasted)0.4169
Black tea0.878 ***76Deep fried seafood0.22119Sesame seeds0.796 ***20
White tea0.5004Deep fried chicken0.690 **14Sesame seeds (roasted)0.6797
Coffee0.880 ***118Deep fried battered fish0.763 ***48Sunflower seeds0.601 **28
Water/spring water0.758 ***99Potato cakes/fritters0.1958Almonds0.821 ***61
Unflavoured mineral water0.813 ***20Spring/chiko roll0.42410Almonds (roasted)0.875 ***20
Pure fruit juice0.828 ***44Fried dim sum0.7716Brazil nuts0.852 ***29
Fruit drink0.932 ***12Steamed dim sum0.36610Cashew nuts0.389 *27
Pomegranate juice0.500 3Chicken sushi rolls0.598 ***31Cashew nuts (roasted)0.625 ***30
Vegetable juice0.644 **16Meat sushi rolls0.654*13Hazelnuts0.4957
Diet fizzy drink0.676 **18Seafood sushi rolls0.553 ***49Hazelnuts (roasted)0.6386
Regular fizzy drink0.523 *23Vegetarian sushi rolls0.688 ***31Macadamia nuts0.841 ***15
Low-calorie cordial0.882 *6Wraps/subs with chicken or fish as the main filling0.538**23Macadamia nuts (roasted)0.806 **11
Regular cordial0.626 *13Wraps/Subs with beef, lamb, or pork as the main filling0.33813Other nuts0.2787
Red wine0.810 ***64Wraps/subs with sandwich meat as the main filling0.8115Peanuts (salted or unsalted, roasted)0.783 ***30
Sparkling wine or Champagne0.861 ***48Subs/wraps with seafood as the main filling−0.8663Pecans0.877 ***12
Rose wine0.719 *10MiscellaneousPine nuts0.50510
Medium white wine0.878 ***69Canola oil0.634 **20Pine nuts (roasted)0.857 **8
Sherry/port/liqueur0.874 ***22Coconut oil0.733 **13Pistachios0.737 *10
Spirits0.917 ***28Dripping/lard0.0005Pistachios (roasted)0.922 ***12
Low-alcohol beer0.862 ***28Extra virgin olive oil0.683 ***109Walnuts0.628 ***53
Regular beer0.937 ***29Olive oil0.514 ***43Walnuts (roasted)0.0607
Peanut oil−0.3087
Sunflower oil0.405 *12
Marinades and other thick sauces0.519 ***47
Thick sauces0.622 ***62
Mayonnaise—regular0.765 ***39
Mayonnaise—fat reduced0.563 **24
Salad dressing—full fat oil and vinegar style0.454 *30
Salad dressing—full fat creamy style−0.4004
Salad dressing—reduced/fat free vinegar style0.716 ***22
Bold indicates significance (* p < 0.05, ** p < 0.01, *** p < 0.001); n = 136. Abbreviations: CSIRO, Commonwealth Scientific and Industrial Research Organisation; LSA, linseeds, sunflower seeds and almonds.
Table 2. Associations between energy and nutrient intakes obtained from the Alzheimer’s disease-CSIRO Food Frequency Questionnaire and four-day weighed food records.
Table 2. Associations between energy and nutrient intakes obtained from the Alzheimer’s disease-CSIRO Food Frequency Questionnaire and four-day weighed food records.
NutrientMean WFR aSD WFR aMean FFQ aSD FFQ aPearson’s Correlation Coefficient bLevel of Agreement b,c
Energy8049.01938.011,391.63844.90.160Fairly Good
Protein84.420.2113.036.20.336 ***Fairly Good
Total fat76.625.0129.758.30.356 ***Fairly Good
Saturated fat27.410.744.021.40.438 ***Fairly Good
Trans fatty acids1.20.62.11.20.418 ***Poor
Polyunsaturated fat13.35.920.110.20.403 ***Fairly Good
Monounsaturated fat29.011.554.726.90.285 ***Fairly Good
Cholesterol274.7121.4351.0152.60.256 **Poor
Available carbohydrate192.861.7236.289.70.465 ***Poor
Sugars96.936.3134.956.20.441 ***Poor
Starch94.838.599.948.40.428 ***Fairly Good
Dietary fibre26.67.242.314.50.343 ***Poor
Thiamin3.210.22.42.10.165 *Fairly Good
Riboflavin2.53.12.71.10.335 ***Fairly Good
Niacin23.418.631.410.80.266 **Fairly Good
Niacin equivalents40.320.152.917.30.254 **Fairly Good
Vitamin C150.3200.6249.2117.30.280 **Fairly Good
Vitamin E15.315.323.211.50.386 ***Fairly Good
Alpha tocopherol11.57.819.98.90.456 ***Fairly Good
Vitamin B65.215.72.40.8−0.092Poor
Vitamin B127104.884214.46.63.6−0.032Good
Total folate535.4359.3585.1219.20.360 ***Fairly Good
Folic acid168.8180.181.999.50.357 ***Fairly Good
Dietary folate equivalents645.7437.4639.9257.10.405 ***Fairly Good
Vitamin A1172.81394.32412.71391.40.317 ***Fairly Good
Retinol334.9374.4606.2833.60.340 ***Fairly Good
Beta carotene equivalents4263.22724.110,841.86524.60.426 ***Fairly Good
Beta carotene3500.52319.58564.75258.00.430 ***Fairly Good
Sodium2079.2859.12448.41042.40.138Fairly Good
Potassium3244.5751.95241.31731.70.390 ***Poor
Magnesium397.1159.9485.6168.00.384 ***Poor
Calcium1020.7447.21146.7489.80.377 ***Fairly Good
Phosphorus1488.8337.22006.2636.90.413 ***Fairly Good
Iron11.73.915.85.40.328 ***Fairly Good
Zinc17.772.514.24.5−0.044Good
Selenium94.961.9114.969.70.561 ***Poor
Iodine173.077.0165.377.30.391 ***Fairly Good
VLC N316.8106.80.80.80.462 ***Good
Linoleic Acid11.812.616.59.10.372 ***Fairly Good
Alpha Linolenic Acid33.5265.62.31.50.192 *Good
Eicosapentaenoic Acid5.460.60.30.30.363 ***Fairly Good
Docosapentaenoic Acid0.10.10.20.10.436 **Poor
Docosahexaenoic Acid3.740.40.30.30.455 **Good
Caffeine209.1502.3137.2130.30.474 **Good
Water3380.56702.83119.32283.50.279 **Fairly Good
Alcohol10.213.610.615.00.570 ***Poor
Bold indicates statistical significance (* p < 0.05, ** p < 0.01, *** p < 0.001); n = 148. a Uses raw data. b Uses log-transformed and energy-adjusted data. c “Good agreement”: the difference between the two methods is approximately equal to one standard deviation of the average nutrient intake. “Fairly good agreement”: the difference between the two methods is approximately equal to two standard deviations of the average nutrient intake. “Poor agreement”: the difference between the two methods is approximately equal to three standard deviations of the average nutrient intake. Abbreviations: CSIRO, Commonwealth Scientific and Industrial Research Organisation; FFQ, food frequency questionnaire; SD, standard deviation; VLC N3, very long chain n-3 polyunsaturated fatty acids; WFR, weighed food record.
Table 3. Quintile percentage agreement between energy and nutrient intakes determined from the Alzheimer’s disease-CSIRO Food Frequency Questionnaire and four-day weighed food records.
Table 3. Quintile percentage agreement between energy and nutrient intakes determined from the Alzheimer’s disease-CSIRO Food Frequency Questionnaire and four-day weighed food records.
NutrientSame Quintile, %Adjacent Quintile, %Gross Misclassification, % aWeighted κ bStrength of Agreement c
Energy23.8134.0122.450.249Fair
Protein25.8531.9716.330.076Slight
Total fat23.8140.1417.010.249Fair
Saturated fat25.8534.6914.970.076Slight
Trans fatty acids30.5036.1716.310.002Slight
Polyunsaturated fat29.9336.7315.650.003Slight
Monounsaturated fat25.1734.0116.330.117Slight
Cholesterol29.2536.0514.290.005Slight
Available carbohydrate23.8136.7319.730.249Fair
Sugars25.1729.2518.370.117Slight
Starch33.3330.6112.930.000Slight
Dietary fibre24.4935.3719.050.174Slight
Thiamin27.2135.3713.610.029Slight
Riboflavin28.5734.0114.970.009Slight
Niacin25.5343.9714.890.100Slight
Niacin equivalents21.0944.2214.970.743Substantial
Vitamin C27.2134.6916.330.029Slight
Vitamin E27.8935.3713.610.017Slight
Alpha tocopherol29.0832.6214.890.007Slight
Vitamin B624.1132.6219.150.221Fair
Vitamin B1228.3733.3316.310.013Slight
Total folate29.9334.0112.930.003Slight
Folic acid36.0532.658.840.000Slight
Dietary folate equivalents27.8937.4112.240.017Slight
Vitamin A29.2533.3317.690.005Slight
Retinol31.2932.6519.050.001Slight
Beta carotene equivalents23.1344.2215.650.344Fair
Beta carotene22.7044.6814.180.422Moderate
Sodium25.1732.6518.370.117Slight
Potassium19.0536.7317.690.772Substantial
Magnesium27.2131.9712.240.029Slight
Calcium19.0540.8212.240.772Substantial
Phosphorus23.8133.3316.330.249Fair
Iron26.5335.3712.240.048Slight
Zinc21.0936.0519.050.743Substantial
Selenium34.7530.5010.640.000Slight
Iodine27.8936.7314.970.017Slight
VLC N330.6138.109.520.001Slight
Linoleic acid26.5339.4615.650.048Slight
Alpha linolenic acid23.1344.2214.970.344Fair
Eicosapentaenoic acid31.9136.8814.180.000Slight
Docosapentaenoic acid30.5036.1713.480.002Slight
Docosahexaenoic acid29.7941.1310.640.003Slight
Caffeine26.2136.5517.240.062Slight
Water29.2531.2919.730.005Slight
Alcohol44.2244.223.400.000Slight
n = 148. a Gross misclassification = disagreement by three or four quintiles. b Weighted kappa (κ) measures the degree of disagreement between the nutrient intakes from the two methods. c Strength of agreement is classified according to weighted κ as follows: 0–0.20 = slight agreement, 0.21–0.40 = fair agreement, 0.41–0.60 = moderate agreement, 0.61–0.80 = substantial agreement [18]. All results reported using log-transformed and energy-adjusted data. Abbreviations: CSIRO, Commonwealth Scientific and Industrial Research Organisation; VLC N3, very long chain n-3 polyunsaturated fatty acids.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Back to TopTop