Validation of a Tablet Application for Assessing Dietary Intakes Compared with the Measured Food Intake/Food Waste Method in Military Personnel Consuming Field Rations

Overview

Journal Nutrients

Date 2017 Mar 8

PMID 28264428

Citations 12

Authors

Mavra Ahmed

Iva Mandic

Wendy Lou

Len Goodman

Ira Jacobs

Mary R LAbbe

Affiliations

Soon will be listed here.

Abstract

The collection of accurate dietary intakes using traditional dietary assessment methods (e.g., food records) from military personnel is challenging due to the demanding physiological and psychological conditions of training or operations. In addition, these methods are burdensome, time consuming, and prone to measurement errors. Adopting smart-phone/tablet technology could overcome some of these barriers. The objective was to assess the validity of a tablet app, modified to contain detailed nutritional composition data, in comparison to a measured food intake/waste method. A sample of Canadian Armed Forces personnel, randomized to either a tablet app ( = 9) or a weighed food record (wFR) ( = 9), recorded the consumption of standard military rations for a total of 8 days. Compared to the gold standard measured food intake/waste method, the difference in mean energy intake was small (-73 kcal/day for tablet app and -108 kcal/day for wFR) ( > 0.05). Repeated Measures Bland-Altman plots indicated good agreement for both methods (tablet app and wFR) with the measured food intake/waste method. These findings demonstrate that the tablet app, with added nutritional composition data, is comparable to the traditional dietary assessment method (wFR) and performs satisfactorily in relation to the measured food intake/waste method to assess energy, macronutrient, and selected micronutrient intakes in a sample of military personnel.

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References

Rangan A, OConnor S, Giannelli V, Yap M, Tang L, Roy R . Electronic Dietary Intake Assessment (e-DIA): Comparison of a Mobile Phone Digital Entry App for Dietary Data Collection With 24-Hour Dietary Recalls. JMIR Mhealth Uhealth. 2015; 3(4):e98. PMC: 4704908. DOI: 10.2196/mhealth.4613. View

Coughlin S, Whitehead M, Sheats J, Mastromonico J, Hardy D, Smith S . Smartphone Applications for Promoting Healthy Diet and Nutrition: A Literature Review. Jacobs J Food Nutr. 2016; 2(3):021. PMC: 4725321. View

Shay L, Seibert D, Watts D, Sbrocco T, Pagliara C . Adherence and weight loss outcomes associated with food-exercise diary preference in a military weight management program. Eat Behav. 2009; 10(4):220-7. PMC: 3936599. DOI: 10.1016/j.eatbeh.2009.07.004. View

Zhu F, Bosch M, Woo I, Kim S, Boushey C, Ebert D . The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation. IEEE J Sel Top Signal Process. 2010; 4(4):756-766. PMC: 2941896. DOI: 10.1109/JSTSP.2010.2051471. View

Sharp D, Allman-Farinelli M . Feasibility and validity of mobile phones to assess dietary intake. Nutrition. 2014; 30(11-12):1257-66. DOI: 10.1016/j.nut.2014.02.020. View

Poppitt S, Swann D, Black A, Prentice A . Assessment of selective under-reporting of food intake by both obese and non-obese women in a metabolic facility. Int J Obes Relat Metab Disord. 1998; 22(4):303-11. DOI: 10.1038/sj.ijo.0800584. View

Archer E, Hand G, Blair S . Validity of U.S. nutritional surveillance:National Health and Nutrition Examination Survey caloric energy intake data, 1971-2010. PLoS One. 2013; 8(10):e76632. PMC: 3793920. DOI: 10.1371/journal.pone.0076632. View

Arcand J, Floras J, Azevedo E, Mak S, Newton G, Allard J . Evaluation of 2 methods for sodium intake assessment in cardiac patients with and without heart failure: the confounding effect of loop diuretics. Am J Clin Nutr. 2010; 93(3):535-41. DOI: 10.3945/ajcn.110.004457. View

Bland J, Altman D . Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1(8476):307-10. View

10.

Carter M, Burley V, Nykjaer C, Cade J . 'My Meal Mate' (MMM): validation of the diet measures captured on a smartphone application to facilitate weight loss. Br J Nutr. 2012; 109(3):539-46. DOI: 10.1017/S0007114512001353. View

11.

Gan K, Allman-Farinelli M . A scientific audit of smartphone applications for the management of obesity. Aust N Z J Public Health. 2011; 35(3):293-4. DOI: 10.1111/j.1753-6405.2011.00707.x. View

12.

Bland J, Altman D . Calculating correlation coefficients with repeated observations: Part 1--Correlation within subjects. BMJ. 1995; 310(6977):446. PMC: 2548822. DOI: 10.1136/bmj.310.6977.446. View

13.

Wang Y, Gortmaker S, Sobol A, M Kuntz K . Estimating the energy gap among US children: a counterfactual approach. Pediatrics. 2006; 118(6):e1721-33. DOI: 10.1542/peds.2006-0682. View

14.

Briggs M, Rumbold P, Cockburn E, Russell M, Stevenson E . Agreement between Two Methods of Dietary Data Collection in Male Adolescent Academy-Level Soccer Players. Nutrients. 2015; 7(7):5948-60. PMC: 4517039. DOI: 10.3390/nu7075262. View

15.

Garriguet D . Under-reporting of energy intake in the Canadian Community Health Survey. Health Rep. 2009; 19(4):37-45. View

16.

McClung H, Sigrist L, Smith T, Karl J, Rood J, Young A . Monitoring energy intake: a hand-held personal digital assistant provides accuracy comparable to written records. J Am Diet Assoc. 2009; 109(7):1241-5. DOI: 10.1016/j.jada.2009.04.015. View

17.

Timon C, Astell A, Hwang F, Adlam T, Smith T, Maclean L . The validation of a computer-based food record for older adults: the Novel Assessment of Nutrition and Ageing (NANA) method. Br J Nutr. 2015; 113(4):654-64. DOI: 10.1017/S0007114514003808. View

18.

Beasley J, Riley W, Davis A, Singh J . Evaluation of a PDA-based dietary assessment and intervention program: a randomized controlled trial. J Am Coll Nutr. 2008; 27(2):280-6. DOI: 10.1080/07315724.2008.10719701. View

19.

Hedrick V, Dietrich A, Estabrooks P, Savla J, Serrano E, Davy B . Dietary biomarkers: advances, limitations and future directions. Nutr J. 2012; 11:109. PMC: 3568000. DOI: 10.1186/1475-2891-11-109. View

20.

Lieffers J, Vance V, Hanning R . Use of mobile device applications in Canadian dietetic practice. Can J Diet Pract Res. 2014; 75(1):41-7. DOI: 10.3148/75.1.2014.41. View