Evaluation of a digital method to assess evening meal intake in a free-living adult population

Figures & data

Fig. 1.  Example of images from the reference material (½ and 1 unit, respectively, of five different vegetable items from two different plate-angles).

Table 1. ‘Coding tree’ used to analyse interviews about use of the digital method

1st level code	2nd level code	3rd level code
The methodology	(1.1) Overall	(1.1.1) Funny
		(1.1.2) Easy/becomes a habit
		(1.1.3) Can be hard to remember
		(1.1.4) Bothersome/require attention
	(1.2) Time spent	(1.2.1) Does not take much time
		(1.2.2) Takes time at the beginning
	(1.3) Taking images	(1.3.1) Easy/no problem
		(1.3.2) Awkward not mixing meal components on the plate
	(1.4) Writing down recipes	(1.4.1) Easy/no problem
		(1.4.2) Annoying/bothersome
		(1.4.3) Difficult sometimes
Influence on eating
behaviour	(2.1) Influence due to practical considerations	(2.1.1) No (or minor) influence
		(2.1.2) Food quantity may be different
		(2.1.3) Food quality may be different
		(2.1.4) Eating pattern may be influenced
	(2.2) Influence due to other people watching what you eat	(2.2.1) No (or minor) influence
		(2.2.2) Embarrassing (in the beginning)
		(2.2.3) Tend to eat healthier
		(2.2.4) Awkward at restaurant visit
	(2.3) Influence due to increased consciousness on eating habits	(2.3.1) No (or minor) influence
		(2.3.2) Probably a small influence
Registration period	(3.1) Lengths of registration period	(3.1.1) Three weeks ok, but not more
		(3.1.2) A few weeks more would be fine
		(3.1.3) Even longer period would be fine
	(3.2) Registration break in the weekend	(3.2.1) No influence
		(3.2.2) Nice with a break

Table 2. Regression and cross-classification analysis between weights estimated by the digital and by the weighed record methods, respectively, for different food categories (log-transformed and per 10 MJ)

	Actual content, g	Estimated content,^a g	P-value		Spearmans correlation coefficient		Classified into same/same or adjacent quartile%		Regression slope 95% confidence intervals
Food categories	Mean (SD)	Mean (SD)	Log g	g/10 MJ	Log g	g/10 MJ	Log g	g/10 MJ	Log g	g/10 MJ
Meat products (n = 85)	210(145)	176(104)	<.0001	0.652	0.91	0.95	72/99	78/99	0.76–0.88	0.90–0.98
Fruit and vegetables (n = 58)	130(95)	116(80)	0.002	0.655	0.94	0.94	74/100	72/100	0.78–0.93	0.90–1.03
Starchy products (n = 74)	173(76)	157(76)	<.0001	0.644	0.86	0.87	68/95	65/95	0.85–1.03	0.74–0.90
Fatty products and sauce (n = 36)	58(28)	50(23)	0.021	0.107	0.83	0.87	61/100	56/100	0.61–0.93	0.58–0.93
Total weight (n = 88)	462(152)	404(138)	<.0001	0.006	0.83	0.95	63/93	78/99	0.85–1.08	0.86–0.99

^aAverage of the two image analysts’ weight estimations.

Table 3. Regression and cross-classification analysis between values estimated by the digital and by the weighed record methods, respectively, for total energy intake per meal, energy density, and macronutrient distribution (n = 88)

	Weighed record		Digital method^a
Nutrients	mean	(SD)	mean	(SD)	P-value	Spearmans correlation coefficient	Classified into same/same or adjacent quartile,%	Regression slope, 95% confidence intervals
Energy (Kj)	2220	(744)	1970	(691)	<0.001	0.89	64/98	0.83–1.03
Energy density (kJ/100g)	493	(131)	501	(132)	0.074	0.95	78/99	0.89–1.02
Fat (E%)	32.8	(11.5)	33.2	(11.7)	0.564	0.97	82/100	0.94–1.04
Carbohydrate (E%)	45.2	(13.3)	44.7	(13.3)	0.358	0.96	77/100	0.89–1.02
Protein (E%)	21.7	(6.8)	21.7	(6.5)	0.961	0.95	77/100	0.84–0.97

^aAverage of the two image analysts’ weight estimations.

^bCorrelation and regression analysis on log-transformed data.

Fig. 2.  Bland–Altman plots showing the mean bias (middle line) and limits of agreement (top and bottom lines) between the digital and weighed record methods, respectively, for energy density and macronutrients.