Table 2.
Survey, country | Dietary assessment method | Advantages of the current method | Challenges of the current method | Potential benefits of new technologies |
---|---|---|---|---|
Australian Health Survey 2011–13 (AHS), Australia | Two 24 h recalls including interviewer-assisted data collection via computerised Automated Multiple-Pass Method (AMPM) | Provides detailed intake data (additional information collected including supplement use and dietary behaviours); ease of application among those with low literacy and older adults | Expensive and time consuming; possible recall bias; trained interviewer required; respondent burden | Online FFQ – the Australian Eating Survey (AES): Lower respondent burden (as takes 15 min to complete); relatively inexpensive; does not require trained interviewers; fewer data processing requirements: provides personalised dietary feedback in real time; possibility to monitor response rates |
Danish National Survey of Diet and Physical Activity (DANSDA), Denmark 4–75 years old: 2003–2008, 2011–2013, and 6–36 months old: 2006–2007 |
Paper 7-d estimated pre-coded diary with open-answer possibilities and pre-coded answer options for the most commonly eaten foods and dishes in the Danish diet | Ease of application among those with low technology usage and older adults; ease of coding process due to precoding | Expensive and time consuming; trained interviewer required; respondent burden; slow data processing and reporting timeline; generic-level food intake information | Web-based 7-d food record (6–36 months old, 2014–2015): Lower respondent burden; richer foods and portion size options; partly automated food coding; advanced food identification and search features; automated prompts through web; personal text messages |
The Dutch National Food Consumption Survey (DNFCS), the Netherlands | Two 24 h recalls using computerised GloboDiet with trained interviewers | Provides detailed intake data (provides additional information including supplement use) | Expensive and time consuming; possible recall bias; trained interviewer required; respondent burden; slow data processing and reporting timeline; difficulty to keep up-to-date food composition database; low respondent motivation (method perceived as ‘old-fashioned’) | Barcoding technology (in combination with GloboDiet): Advanced linkage of food consumption and food composition data using artificial intelligence techniques Use of mobile applications instead of GloboDiet to achieve more advanced level of food identification |
National Diet and Nutrition Survey Rolling Programme (NDNS RP), UK | Paper 4-d estimated food diary | Provides detailed intake data (provides additional information on supplement use); ease of application among those with low technology usage and older adults; provides contextual eating information | Expensive and time consuming; trained interviewer required; large respondent burden (high motivation required); slow data processing and reporting timeline | Web-based 24 h recall: Cost-effective and time saving; less respondent burden; possible increase in response rates of those with high motivation for automated methods (e.g. teenagers); ease of data processing and reporting |