Eileen Gibney. Latest developments in technology and dietary intake assessment. UCD Institute of Food and Health, Dublin, Ireland

Transcription

1 Latest developments in technology and dietary intake assessment Eileen Gibney UCD Institute of Food and Health, Dublin, Ireland

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3 Correlation between meat intake and incidence of colon cancer (Armstrong and Doll, 1975)

4 Dietary intake assessment Need to know what people are eating; To ensure that we understand the correct relationship Use this to correctly advise individuals/populations with respect to dietary change.

5 Traditional dietary assessment methods Record (Food Diary weighed, semiweighed, estimated) Recall (24hr recall, diet history) Questionnaire (Food frequency questionnaire)

6 Traditional dietary assessment methods Limitations: Expensive Burdensome Portion size estimation

7 New dietary assessment technologies Benefits

8 Novel techniques Translation of existing techniques to online/mobile environment 24 hr recalls Food frequency questionnaires Food diaries Development of novel techniques Image capture Biomarkers

9 24 hr recalls (multiple pass) Select Meal/Snack (breakfast, lunch, snack etc.) List food and drink items Answer probe questions (e.g. bread toasted or not) Answer linked foods (e.g. burger did you have a bun with that) Portion size Review of foods + additional information (e.g. brand) Frequently forgotten foods Supplements

10 Tool name Recall method N. food and drink items Portions size estimation method Use of prompts Slimani et al. (2011) EPIC-Soft 24HR, IA, MR foods & recipes included Photographs (in a book) Household measures Standard units (in gram, piece) and standard portions Weight or volume method Reliability checking Frequently forgotten foods Linked foods Touvier et al. (2011) NutriNet- Sante 24HR, SA, MR Photographs of seven increasing portion sizes presented. Enter amount consumed Frequently forgotten foods Linked foods Daniel et al. (2013) NINA-DISH 24HR, FFQ, IA, MR 910 foods Household measures Photographs of fruits (small, medium and large) Linked foods Shin et al. (2014) CAPIS 24HR, IA, MR 3642 foods and 1886 recipes Photographs presented ½, 1, and 1½ serving sizes for each item Enter amount consumed Frequently forgotten foods Carter et al. (2015) Myfood24 24HR. SA, MR, ~45,000 UK branded and generic foods Portion size images for 5669 food items Portion size suggestions (household measures) Free entry of gram weight by participant Linked foods Additional information (i.e.: brand names)

11 Tool name Study type No. of subjects Ref method Food Groups Nutrients Ferrari et al. (2009) EPIC-Soft Validation h urinary biomarkers Correlation coefficients between means of urinary and 24HR measurements were and for nitrogen and potassium Touvier et al. (2011) NutriNet- Sante Comparison 147 Interviewer based 24HR ICC from 0.5 for fats/sauces, breakfast cereals, to 0.9 for fruits, pulses, alcoholic drinks and meat Energy-adjusted Pearson s coefficients was 0.6 for PUFAs, retinol, vitamin E to 0.9 for a wide range of nutrients Lassale et al. (2015) NutriNet- Sante Validation h urinary biomarkers Correlations between reported and true intakes were 0.61, 0.78 and 0.47 for men and 0.64, 0.42 and 0.37 for women for protein, K and Na, respectively. Foster et al. (2014) INTAKE24 Comparison 167 Interviewer based 24HR Of all foods listed 82.6% were matches, 9% omissions and 7.5% intrusions. LOA for CHO, fat, vitamin C, iron and alcohol were ; ; ; and respectively.

12 Traditional dietary assessment methods Translate well to online / mobile methods Well received by volunteers, researchers Need continuing work

13 New dietary assessment technologies Personalised Nutrition Benefits

14 Online FFQ Food4me

15 Validity of food intake tool Comparison to paper version

16 Validity of food intake tool Comparison to weighed intakes J Med Internet Res 2014;16(8):e190

17 Nutrient intakes & Personalised Nutrition Food4me project RCT examining the impact of Personalised Nutrition to change in dietary intake Used online FFQ to determine intakes, and derive feedback in target nutrients for change Personalised Nutrition Feedback System. (

18 Feedback system (Step 1) 1. Designed a gradation system for the categorising nutrient intakes from Food4Me FFQ

19 Feedback system (Step 2) 1. Designed a gradation system for categorising nutritional intakes from Food4Me FFQ 2. Created a method for prioritising 3 nutrient-related goals for subsequent feedback

20 Feedback system (Step 3) 1. Designed a gradation system for categorising nutritional intakes from Food4Me FFQ 2. Created a method for prioritising 3 nutrient-related goals 3. Constructed decision tree algorithms, linking nutritional intake data to feedback messages

21 Feedback system (Step 4) 1. Designing a gradation system for categorising nutritional intakes from Food4Me FFQ 2. Creating a method for prioritising 3 nutrient-related goals for subsequent dietary advice 3. Constructing decision tree algorithms, linking nutritional intake data to feedback messages 4. Developed feedback reports for the delivery of dietary /lifestyle advice

22 How you can increase your intake of calcium: Eat more dairy products as these are the richest sources of calcium, choose low-fat dairy products where possible Aim to have 3 portions of low-fat dairy products every day Have low-fat milk or yoghurt with breakfast cereals For a healthy dessert try low-fat yoghurt topped with fresh fruit Try to eat more green vegetables e.g. broccoli, kale and cabbage

23 Dietary intake truth or not!

24 Whats next. Lets make things easy Images Life logging Lets not ask Urinary biomarkers of intake Urinary biomarkers of dietary patterns

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27 Place meal into context

28 Life-logging Issue of privacy

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30 Dietary Patterns

31 Conclusions Technological advances in dietary intake methodology are moving fast Using technology to improve existing methods Developing novel methods which will challenge the way we collect intake data Cameras, sensors Biological fluids Need to now link these to effective dietary change strategies