Skip to main content Accessibility help
×
×
Home

Identifying usual food choices at meals in overweight and obese study volunteers: implications for dietary advice

  • Vivienne X. Guan (a1) (a2), Yasmine C. Probst (a1) (a2), Elizabeth P. Neale (a1) (a2), Marijka J. Batterham (a2) (a3) and Linda C. Tapsell (a1) (a2)...
Abstract

Understanding food choices made for meals in overweight and obese individuals may aid strategies for weight loss tailored to their eating habits. However, limited studies have explored food choices at meal occasions. The aim of this study was to identify the usual food choices for meals of overweight and obese volunteers for a weight-loss trial. A cross-sectional analysis was performed using screening diet history data from a 12-month weight-loss trial (the HealthTrack study). A descriptive data mining tool, the Apriori algorithm of association rules, was applied to identify food choices at meal occasions using a nested hierarchical food group classification system. Overall, 432 breakfasts, 428 lunches, 432 dinners and 433 others (meals) were identified from the intake data (n 433 participants). A total of 142 items of closely related food clusters were identified at three food group levels. At the first sub-food group level, bread emerged as central to food combinations at lunch, but unprocessed meat appeared for this at dinner. The dinner meal was characterised by more varieties of vegetables and of foods in general. The definitions of food groups played a pivotal role in identifying food choice patterns at main meals. Given the large number of foods available, having an understanding of eating patterns in which key foods drive overall meal content can help translate and develop novel dietary strategies for weight loss at the individual level.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Identifying usual food choices at meals in overweight and obese study volunteers: implications for dietary advice
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Identifying usual food choices at meals in overweight and obese study volunteers: implications for dietary advice
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Identifying usual food choices at meals in overweight and obese study volunteers: implications for dietary advice
      Available formats
      ×
Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Corresponding author
*Corresponding author: V. X. Guan, fax +61 02 4221 4844, email xg885@uowmail.edu.au
References
Hide All
1. Finucane, MM, Stevens, GA, Cowan, MJ, et al. (2011) National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet 377, 557567.
2. Ng, M, Fleming, T, Robinson, M, et al. (2013) Global, regional, and national prevalence of overweight and obesity in children and adults during, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384, 766781.
3. World Health Organization (2016) Obesity and Overweight – Key Facts. Geneva: WHO. http://www.who.int/mediacentre/factsheets/fs311/en/ (accessed August 2017).
4. Australian Bureau of Statistics (2015) National Health Survey: First Results, 2014–15. Canberra: ABS.
5. The Global Burden of Disease (GBD) 2015 Obesity Collaborators (2017) Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med 377, 1327.
6. National Health and Medical Research Council (2013) Clinical Practice Guidelines for the Management of Overweight and Obesity in Adults, Adolescents and Children in Australia. Melbourne: NHMRC.
7. Grima, M & Dixon, J (2013) Obesity recommendations for management in general practice and beyond. Aust Fam Physician 42, 532541.
8. Sacks, FM, Bray, GA, Carey, VJ, et al. (2009) Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N Engl J Med 360, 859873.
9. Grafenauer, SJ, Tapsell, LC, Beck, EJ, et al. (2013) Baseline dietary patterns are a significant consideration in correcting dietary exposure for weight loss. Eur J Clin Nutr 67, 330336.
10. Heymsfield, SB & Wadden, TA (2017) Mechanisms, pathophysiology, and management of obesity. N Engl J Med 376, 254266.
11. Barte, JCM, Bogt, NCWT, Bogers, RP, et al. (2010) Maintenance of weight loss after lifestyle interventions for overweight and obesity, a systematic review. Obes Rev 11, 899906.
12. Looney, SM & Raynor, HA (2013) Behavioral lifestyle intervention in the treatment of obesity. Health Serv Insights 6, HSI.S10474.
13. Gatenby, SJ (1997) Eating frequency: methodological and dietary aspects. Br J Nutr 77, S7S20.
14. Gorgulho, BM, Pot, GK, Sarti, FM, et al. (2016) Indices for the assessment of nutritional quality of meals: a systematic review. Br J Nutr 115, 20172024.
15. Gorgulho, BM, Pot, GK, Sarti, FM, et al. (2017) Main meal quality in Brazil and United Kingdom: similarities and differences. Appetite 111, 151157.
16. Leech, RM, Worsley, A, Timperio, A, et al. (2015) Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality. Nutr Res Rev 28, 121.
17. St-Onge, M-P, Ard, J, Baskin, ML, et al. (2017) Meal timing and frequency: implications for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation 135, e96e121.
18. Krebs-Smith, SM, Cronin, FJ, Haytowitz, DB, et al. (1990) Contributions of food groups to intakes of energy, nutrients, cholesterol, and fiber in women’s diets: effect of method of classifying food mixtures. J Am Diet Assoc 90, 15411546.
19. Prynne, CJ, Wagemakers, JJMF, Stephen, AM, et al. (2008) Meat consumption after disaggregation of meat dishes in a cohort of British adults in 1989 and 1999 in relation to diet quality. Eur J Clin Nutr 63, 660666.
20. Burden, S, Probst, YC, Steel, DG, et al. (2009) Identification of food groups for use in a self-administered, computer-assisted diet history interview for use in Australia. J Food Compost Anal 22, 130136.
21. Probst, YC, De Agnoli, K, Batterham, M, et al. (2009) Video-recorded participant behaviours: the association between food choices and observed behaviours from a web-based diet history interview. J Hum Nutr Diet 22, 2128.
22. Han, J, Pei, J & Kamber, M (2011) Data Mining: Concepts and Techniques. Waltham, MA: Elsevier.
23. Hearty, ÁP & Gibney, MJ (2008) Analysis of meal patterns with the use of supervised data mining techniques – artificial neural networks and decision trees. Am J Clin Nutr 88, 16321642.
24. Woolhead, C, Gibney, MJ, Walsh, MC, et al. (2015) A generic coding approach for the examination of meal patterns. Am J Clin Nutr 102, 316323.
25. Murakami, K, Livingstone, MBE & Sasaki, S (2017) Establishment of a meal coding system for the characterization of meal-based dietary patterns in Japan. J Nutr 147, 20932101.
26. Agrawal, R, Imieliski, T & Swami, A (1993) Mining association rules between sets of items in large databases. SIGMOD Rec 22, 207216.
27. Agrawal, R & Srikant, R (1994) Fast algorithms for mining association rules. Proc 20th Int Conf Very Large Data Bases, VLDB 1215, 487499.
28. Tapsell, LC, Lonergan, M, Martin, A, et al. (2015) Interdisciplinary lifestyle intervention for weight management in a community population (HealthTrack study): study design and baseline sample characteristics. Contemp Clin Trials 45, 394403.
29. World Health Organization, (1995) Physical Status: The Use and Interpretation of Anthropometry. Report of a WHO Expert Committee. Technical Report Series no. 854. Geneva: WHO.
30. Martin, GS, Tapsell, LC, Denmeade, S, et al. (2003) Relative validity of a diet history interview in an intervention trial manipulating dietary fat in the management of type II diabetes mellitus. Prev Med 36, 420428.
31. Food Standards Australia New Zealand (2008) AUSNUT 2007. Australian Food, Supplement and Nutrient Database for Estimation of Population Nutrient Intakes. Canberra: FSANZ.
32. Guan, V, Probst, Y, Neale, E, et al. (2016) The feasibility of analysing food consumption combinations from overweight and obese participants of weight loss clinical trials. CEUR Workshop Proc 1683, 16.
33. Food Standards Australia New Zealand (2014) AUSNUT 2011–13 food measures database file. http://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/classificationofsupps/Pages/default.aspx (accessed February 2016).
34. Neale, EP, Probst, YC & Tapsell, LC (2016) Development of a matching file of Australian food composition databases (AUSNUT 2007 to 2011–13). J Food Compost Anal 50, 3035.
35. Food Standards Australia New Zealand (2014) AUSNUT 2011–13. Australian Food, Supplement and Nutrient Database for Estimation of Population Nutrient Intakes. Canberra: FSANZ.
36. R Core Team (2016) R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. https://www.R-project.org/
37. Tapsell, LC, Lonergan, M, Batterham, MJ, et al. (2017) Effect of interdisciplinary care on weight loss: a randomised controlled trial. BMJ Open 7, e014533.
38. Brossette, SE, Sprague, AP, Hardin, JM, et al. (1998) Association rules and data mining in hospital infection control and public health surveillance. J Am Med Inform Assoc 5, 373381.
39. Ramezankhani, A, Pournik, O, Shahrabi, J, et al. (2015) An application of association rule mining to extract risk pattern for type 2 diabetes using Tehran Lipid and Glucose Study database. Int J Endocrinol Metab 13, e25389.
40. Pasquier, N, Taouil, R, Bastide, Y, et al. (2005) Generating a condensed representation for association rules. J Intell Inform Syst 24, 2960.
41. de Castro, JM (2009) When, how much and what foods are eaten are related to total daily food intake. Br J Nutr 102, 12281237.
42. Cho, S, Dietrich, M, Brown, CJP, et al. (2003) The effect of breakfast type on total daily energy intake and body mass index: results from the Third National Health and Nutrition Examination Survey (NHANES III). J Am Coll Nutr 22, 296302.
43. Horikawa, C, Kodama, S, Yachi, Y, et al. (2011) Skipping breakfast and prevalence of overweight and obesity in Asian and Pacific regions: a meta-analysis. Prev Med 53, 260267.
44. Sui, Z, Raubenheimer, D & Rangan, A (2017) Exploratory analysis of meal composition in Australia: meat and accompanying foods. Public Health Nutr 20, 21572165.
45. Madjd, A, Taylor, MA, Delavari, A, et al. (2016) Beneficial effect of high energy intake at lunch rather than dinner on weight loss in healthy obese women in a weight-loss program: a randomized clinical trial. Am J Clin Nutr 104, 982989.
46. Jakubowicz, D, Barnea, M, Wainstein, J, et al. (2013) High Caloric intake at breakfast vs. dinner differentially influences weight loss of overweight and obese women. Obesity (Silver Spring) 21, 25042512.
47. Bo, S, Musso, G, Beccuti, G, et al. (2014) Consuming more of daily caloric intake at dinner predisposes to obesity. A 6-year population-based prospective cohort study. PLOS ONE 9, e108467.
48. Albrecht, U (2017) The circadian clock, metabolism and obesity. Obes Rev 18, 2533.
49. Vadiveloo, M, Parker, H & Raynor, H (2017) Increasing low-energy-dense foods and decreasing high-energy-dense foods differently influence weight loss trial outcomes. Int J Obes (Lond) 42, 479.
50. Tapsell, LC, Dunning, A, Warensjo, E, et al. (2014) Effects of vegetable consumption on weight loss: a review of the evidence with implications for design of randomized controlled trials. Crit Rev Food Sci Nutr 54, 15291538.
51. Kaiser, KA, Brown, AW, Bohan Brown, MM, et al. (2014) Increased fruit and vegetable intake has no discernible effect on weight loss: a systematic review and meta-analysis. Am J Clin Nutr 100, 567576.
52. Mytton, OT, Nnoaham, K, Eyles, H, et al. (2014) Systematic review and meta-analysis of the effect of increased vegetable and fruit consumption on body weight and energy intake. BMC Public Health 14, 886.
53. Burke, BS (1947) The dietary history as a tool in research. J Am Diet Assoc 23, 10411046.
54. Lichtman, SW, Pisarska, K, Berman, ER, et al. (1992) Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. N Engl J Med 327, 18931898.
55. Smith, AF (1993) Cognitive psychological issues of relevance to the validity of dietary reports. Eur J Clin Nutr 47, Suppl. 2, S6S18.
56. Hébert, JR (2016) Social desirability trait: biaser or driver of self-reported dietary intake? J Acad Nutr Diet 116, 18951898.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

British Journal of Nutrition
  • ISSN: 0007-1145
  • EISSN: 1475-2662
  • URL: /core/journals/british-journal-of-nutrition
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Type Description Title
WORD
Supplementary materials

Guan et al. supplementary material 1
Supplementary Table

 Word (55 KB)
55 KB

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed