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Simple methods to obtain a food listing and portion size distribution estimates for use in semi-quantitative dietary assessment methods

Semi-quantitative dietary assessment methods are frequently used in low income countries, and the use of photographic series for portion size estimation is gaining popularity. However, when adequate data on commonly consumed foods and portion sizes are not available to design these tools, alternative data sources are needed. This study aimed to develop and test methods to: (i) identify foods likely to be consumed in a study population in rural Uganda, and; (ii) to derive distributions of portion sizes for common foods and dishes. A process was designed to derive detailed food and recipe lists using guided group interviews with women from the survey population, including a ranking for the likelihood of foods being consumed. A rapid recall method to estimate portion sizes using direct weight by a representative sample of the survey population was designed and implemented. Results were compared to data from a 24 hour dietary recall. Of the 82 food items reported in the 24 hour recall survey, 87% were among those ranked with a high or medium likelihood of being consumed and accounted for 95% of kilocalories. Of the most frequently reported foods in the 24 hour recall, portion sizes for many (15/25), but not all foods did not differ significantly (p<0.05) from those in the portion size estimation method. The percent of portion sizes reported in the 24 hour recall between the 5th and 95th percentiles determined by the portion size distribution estimation method ranged from a low of 18% up to 100%. In conclusion, a simple food listing and ranking method effectively identified foods most likely to occur in a dietary survey. A simple method to obtain reliable portion size distributions was effective for many foods, while the approach for others should be modified. These methods are an improvement on those in current use.


Introduction 40
Dietary assessment surveys are necessary to adequately inform, design and evaluate nutrition intervention 41 programs in low-income countries. While the 24-hour dietary recall (24HR) is one of the most common 42 method used in these settings [1], it is also more resource intensive and technically challenging than other 43 methods [2,3] and this may be a limiting factor for the use of dietary surveys to inform effective nutrition 44 programs. Depending on the specific objectives of a dietary survey, simpler and less resource intensive 45 methods, including food frequency questionnaires (FFQ) or semi-quantitative (SQ) FFQ, may be 46 adequate [4]. The semi-quantitative estimation of portion sizes consumed using food photo series or atlases depicting graduated portion sizes for a variety of foods is gaining popularity, and may be used to 48 support the application of SQ-FFQs [5] or as a way of simplifying 24HR methods [6,7].

49
However, to adequately develop such SQ dietary assessment tools, some key information is required 50 beforehand. This includes, but is not limited to: (i) a listing of the foods that are commonly consumed in 51 the study population and hence should be included in the SQ tools; (ii) relevant details about the way they 52 are typically prepared or consumed, and; (iii) the distribution of usual portion sizes to select those to 53 represent in the SQ tools. Ideally, data-driven methods in the form of previously collected dietary intake 54 survey data that is quantitative, valid, and representing the same survey population and sub-population 55 groups of interest, would serve this purpose [5,8]. In low-income countries where data meeting these 56 criteria may often not be available, some form of reliable, empirically-derived preliminary data are 57 needed.

58
We have found few well-described or well-designed processes in the published literature on how to 59 collect food listing and portion size distribution data when appropriate previous survey data are not 60 available. Food listings have been derived using informal or subjective methods such as consultation with 61 food service professionals, local cook books, or restaurant and cafeteria menus, or conducting interviews 62 with cooks or chefs in households and restaurants, but without any information on the sample size,

87
This study was conducted as part of a larger study to compare dietary intake outcomes of a SQ 88 24-HR survey method and a SQ-FFQ method with a standard 24-HR method (to be reported 89 elsewhere). The food listing method used a qualitative approach with categorical ranking of the 90 likelihood of foods being consumed, while the method to derive portion size distributions used a 91 quantitative recall approach. The sampling frame was established for the larger study and was 92 used to draw sub-groups of participants for the data collection methods described here. are given in S1 and S2 Figs, respectively.

160
Information from the two interviews on the same food groups was combined in a spreadsheet 161 (one for food items and one for recipe data), and an average ranking score of foods or ingredients 162 was obtained, rounding to the higher frequency category. baked goods items that were not well addressed in the interviews but added by the researchers as 170 they were considered common in the area. From this total of 53 individual food items, 10 were 171 dropped, as one was not found in the market (i.e., apples), one was better estimated as a count 172 than portion size distribution in grams (i.e., hard candies), and 8 were similar to other items and 173 the portion size was not expected to differ between them (i.e., different meat types, and different 174 varieties of sweet potato, amaranth leaves, yams and some bananas). For the mixed dishes, 30 common ones had been identified, but 6 were dropped as portion sizes were expected to be the 176 same for very similar mixed dishes. For some mixed dishes for which primary ingredients are 177 substitutable, a mixed dish 'type' was used to represent the variations (e.g., dishes made with 178 similar types of green leaves or common beans were grouped together). sample sizes ranging from n=13 to 135, and 80% of the 15 sample sizes calculated were n<60. 189 We rationalized that n=56 data points would be adequate for most foods.

190
The portion size estimation sessions were organized in a central location of each parish. All   207 We used a multiple pass approach based on Gibson and Ferguson [17] with specific methods that 208 were previously described in detail [18]. Group 'training' sessions were held in each EA two days 209 before the 24HR interview to explain the purpose of the study, and the methods involved. They 210 were asked to use their own dishes for serving and eating their food the next day to improve 211 visual memory, and instructed on the use of picture charts to mark foods consumed.

212
Portion sizes of items consumed were estimated using methods specified for each food type.

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These included life-sized graduated photographs, weighing scales, graduated measuring 214 cylinders and play dough models, or standard weights for foods that are served as units (e.g., 215 boiled egg, bread slice) [17]. Portion sizes recorded accounted for any leftovers that were served 216 but not consumed. If multiple servings of the same food item were reported to be consumed in a 217 single eating occasion (e.g., morning, afternoon, or evening meals or snacks) these amounts were 218 combined to a single portion. All of these proxy measures were later converted to gram weights 219 of the food represented using a set of conversion factors.

Comparison of portion sizes between the PSE activity and 24HR survey
Of the foods reported with relatively high frequency in the 24HR survey (Table 2), the median portion sizes for many (15/25), but not all foods, were not significantly different from those determined in the PSDE method. For foods with medians that differed significantly, there was no systematic bias in the direction of difference. The percent of portion sizes reported in the 24HR survey that fell between the 5th and 95th percentiles determined by the PSDE method ranged from a low of 18% up to 100%. Of the foods reported with lower frequency in the 24HR survey (Table 3), results were similar. The percentage of portion sizes falling between the 5th and 95th percentiles differed markedly among the 6 food items shown here, ranging from 20 to 100%.

Discussion
We have described two relatively low cost methods that could aid the development of semiquantitative dietary assessment methods, as determined in a rural African population. A simple food and recipe listing method found that a ranking system was very effective at identifying foods that were most likely to occur in a dietary survey. A relatively simple and rapid method to obtain reliable distributions of portion sizes from a minimum sample indicated that for many foods, portion size distributions compared well with those obtained from standard 24HR methods, while several others did not.
The food listing method developed and field-tested here provides a useful, categorical method to identify foods that should be included in a food list for dietary surveys using closed lists, such as 23 FFQ and SQ-FFQ methods. The foods identified by the ranking process as having a high or medium likelihood of being consumed covered the vast majority (i.e., 95%) of the total kilocalorie intake in the 24HR survey. This is important as it is a key criteria for developing adequate FFQ/SQ-FFQ methods [5]. If the foods that were ranked here as having a low likelihood or unlikelihood of being consumed were omitted from a FFQ or SQ-FFQ survey derived from it, this would have accounted for a negligible proportion of kilocalories being missed by the survey (i.e., <1%). Although this process may be best suited to general surveys that aim to assess intakes from all foods, it could easily be adapted for use with specific food groups or foods providing specific nutrients.
In addition to use in FFQ and SQ-FFQ surveys, this food listing process could be used to support SQ-24HR methods such as those applying food photo atlases for portion size estimation. It would also be recommended to prepare for standard 24HR surveys as it allows survey designers to create prompt lists for relevant food details that should be probed for in an interview, and to predetermine the most appropriate portion size estimation method for each food likely to occur.
This is expected to enhance the training and preparation of enumerators, and possibly the quality of data collected. Very little detail or specific guidance has been provided in the literature where such food listing processes are mentioned [6,9,10,19] or recommended [20].
This food listing method would be improved by including separate likelihood rankings for foods consumed in different forms, including individual foods consumed in raw or cooked forms, foods cooked with or without oil, or as ingredients in mixed dishes so that these can be distinguished for inclusion in the survey. A small number of food items (n=5) occurring in the 24HR survey were not captured by the food listing method. These were primarily low frequency foods, but one food, sugarcane, was reported by a large percentage (i.e., 23%) of individuals.
Baked goods and some commercial beverages were also not adequately probed for during the KIs or GGIs so more careful listing and probing, particularly of processed foods or snacks, is needed, as these can be easily missed under standard food group headings. Finally, the GGI interviews were more relevant to the process as they focused on foods actually consumed in households, rather than focusing more on availability in the community as for the KIs. The latter resulted in more foods being mentioned by KIs and foods being ranked with greater likelihood of consumption than in the GGIs. Nonetheless, the KIs did serve to develop a more complete and locally relevant list of foods for use as a probing guide in the GGIs. The usefulness of combining expert consultation with ethnicity-specific details derived from the target population has been previously recommended [21].
We developed and field tested a novel method to derive portion size distributions for the purpose of developing low cost and simple portion size estimation tools, such as food photo atlases, for use in large-scale dietary surveys. The systematic nature of this method represents an improvement on those reported in the literature for similar use, as previous methods have been largely qualitative in nature, and/or the level of representativeness of the usual range of foods consumed by the target population is questionable [6,9,10,12,14,16].
Portion size estimation tools, including those using photographs, should reflect the range of amounts of foods typically consumed in the study population. A study among children [8] suggested that using age appropriate portion size options greatly reduced error in portion size estimation using photo series depicting portion sizes actually consumed by children (i.e., average of 7% error in weight estimation) compared to using the lower range of portion size photos derived for use with adults (i.e., 46% error) [22]. In an extensive review of FFQ methods [5], it was suggested that in the absence of existing survey data, researchers assigning on the FFQ 25 conduct small surveys to derive portion size data (e.g., 24HR, diet histories). However, in practice this may be impractical due to cost, time, and technical skill required and may not provide reliable distributions where samples for specific foods are less frequent. In the 24HR survey conducted in this study including 111 respondents, the majority of unique foods were reported <10 times, providing a very small sample from which to derive reliable portion size distributions. The method we tested here overcame that problem by quickly obtaining a large sample for each food deemed to be commonly consumed, as identified in the food listing activity.
This study did not aim to validate the PSDE method against a gold standard method, and the comparisons to the 24HR survey must be interpreted cautiously. The PSDE method was limited in that it relied on both short and longer term recall of portion sizes for foods consumed more than a day or week ago, and hence estimates may be distorted by memory. If some responses reflected 'usual' portion sizes, the width of distributions may be attenuated as fewer extremes might be reported. This might partially explain why at least some portion sizes reported in the 24HR for most foods were outside the 5th and 95th percentiles of the PSDE distributions.
The 24HR method used different portion size estimation tools, which included photos of small, medium and large items (e.g., vegetables or roots used as ingredients), and dry rice or play dough to estimate volumes, and each of these is then converted to edible portion amounts in grams using previously obtained conversion factors. Thus, some lack of conformity with the PSDE likely occurred due to the difference in methods and additional error that may be introduced by these conversions. In examining results for items for which the distributions were significantly different between methods, there was no apparent bias towards any one portion size estimation tool being consistently associated with low conformity. However, some foods with significantly different distributions had two or more distinct sizes available, such as bread slices from small and large loaves, mandazi (fritters), and small and large mango varieties. In the PSDE method, these were combined into one distribution. However, it's possible that in the 24HR survey, more individuals were consuming the smaller sizes of those food items and hence the distributions were more skewed to the larger sizes. The lower conformity between methods for standard unit size items (e.g., bread slices, chapatti, mandazi) supports that basing portions on unit size with options for multiples or fractions of those units is a better approach than using continuous portion weights as derived from the PSDE [8]. In the case of beef, the PSDE method accounted for an average amount of bone as part of the beef portions measured, and it's possible that the 24HR method did not distinguish between meat with or without bone. These are issues that should be considered more deeply in establishing PSE methods and the way foods are included in the PSDE method, as relevant for a particular population.
In addition to supporting the development of FFQs, SQ-FFQ, and simplified 24HRs using food photo atlases for portion size estimation, the PSDE method presented here may also find use in nutrition research and advocacy tools that use linear programming. These methods identify foods that provide, or could provide, sufficient energy or nutrients to meet dietary requirements of a target population and require portion size estimates as input. These include Optifood, primarily used to derive food-based recommendations for optimizing diets of infants and young children [23], and the Cost of the Diet tool, an advocacy tool for estimating the cost of a nutritionally adequate diet [24]. Studies using Optifood typically use 24HR surveys to obtain input data [25,26], while the Cost of the Diet tool does not currently employ a satisfactory method for obtaining usual portion size data on which the models are based; this relatively low-cost method may provide an option to improve this tool.

27
Validating these methods were beyond the scope of this study. This would require a large-scale quantitative dietary intake survey in the same population with a large enough sample reporting intakes for a wide range of foods. However, we have provided a preliminary, detailed description of methods well beyond what is currently described in the published literature. This includes a limited evaluation comparing to data collected by a quantitative 24HR in the same population, as well as recommendations for improvement. We propose that these methods be further tested and validated when opportunities arise, such as in preparation for a large-scale or national dietary surveys.

Conclusions
We have identified a gap in available, well-described methods to collect data for deriving food lists and portion size distribution estimates for use in a wide range of dietary assessment methods where existing, suitable dietary intake data are not available. This preliminary evaluation of the methods described and field-tested here, employing qualitative, semi-quantitative and quantitative methods with representative sampling, is encouraging and we recommend efforts to identify the best method of estimating portion size distributions for different food types and to validate these approaches.