Weighed food diaries (diet diaries or food records) are prospective dietary assessment methods, providing descriptions of the foods consumed and eating occasions. These methods provide excellent estimates for energy, nutrients, foods and food groups. The outcomes measured by weighed food diaries are described in Table D.2.5. Recommendations are based upon multiple days of food diary entries rather than a single day in order to account for daily variation in diet.
Table D.2.5 Dietary outcomes assessed by weighed food diaries over multiple days.
Dietary outcome | Possible to assess? |
---|---|
Energy and nutrient intake of total diet | Yes |
Intake of specific nutrients or food | Yes |
Infrequently consumed foods | Maybe |
Dietary pattern | Yes |
Habitual diet | Yes* |
Within-individual comparison | Yes* |
Between-individual comparison | Yes |
Meal composition | Yes |
Frequency of eating/meal occasions | Yes |
Eating environment | Yes |
Adult report of diet at younger age | No |
* possible when repeated measures were collected over time.
An example of a weighed food diary is displayed in Figure D.2.3. Measurement involves respondents or investigators weighing every item of food and drink consumed at the time of consumption. The following are also recorded as much as possible:
Timeframe
Procedure
Figure D.2.3 Example of weighed food diary.
Source: MRC Epidemiology
Estimates of diet can only be derived following extensive data entry. One person may have more than 50 items per day, and the number of food items in a population-based study can readily become thousands. Data entry should consider the following issues:
Each dietary entry may include the following attributes:
Outcomes can vary depending on aims, detail of information, and number of days. Outcomes extracted from diaries may be averaged across multiple days of measurement to estimate a ‘typical’ day’s consumption.
Example of dietary estimates from weighed food diaries include:
Key characteristics are described in Table D.2.6.
Strengths
Limitations
Table D.2.6 Characteristics of weighed food diaries.
Characteristic | Comment |
---|---|
Number of participants | Up to ~1000 |
Cost of development | Low |
Cost of use | Medium |
Participant burden | Very high |
Researcher burden of data collection | Medium |
Researcher burden of coding and data analysis | High |
Risk of reactivity bias | Yes |
Risk of recall bias | Minimised if diary completed at time of consumption |
Risk of social desirability bias | Yes |
Risk of observer bias | Minimised |
Participant literacy required | Yes |
Suitable for use in free-living | Yes |
Requires individual portion size estimation | No |
Considerations relating to the use of weighed food diaries for assessing diet in specific populations are described in Table D.2.7.
Table D.2.7 Diet assessment by weighed food diaries in different populations.
Population | Comment |
---|---|
Pregnancy | Suitable. |
Infancy and lactation | Requires proxy. |
Toddlers and young children | Requires proxy. Completed by parents of young children aged 7-9 years, weighed dietary records have shown good agreement with estimates of energy expenditure made by doubly labelled water [6]. |
Adolescents | Under-reporting apparent in adolescents [6]. |
Adults | Under-reporting reported in adults [7]. |
Older Adults | May require proxy depending on cognitive function. Larger size diaries can be created for children and for the elderly who do not see well. |
Ethnic groups | Requires language/cultural specificity. |
Other |
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A weighed food diary is known as a resource-intensive method. Thus, this could not be adopted readily in an epidemiological study with a large sample size. Food diaries (estimated or weighed) are realistic and conceivable to implement in a subset of a study population, although this still requires expert knowledge on data entry and further processing. Food diary data in a subset are then to be combined with dietary data of an entire study population deriving from another dietary assessment (e.g. multiple 24-h recalls, food frequency questionnaires). Unique statistical approaches are required to accurately merge multiple dietary datasets from multiple methods [9].