Validation of an early warning food record chart: Comparison of weighed intake vs intake recorded on the food chart

Killen, C.; McDougall, T.; Wilson, J. Jeffrey

doi:10.1016/j.clnesp.2017.07.065

Cited by 1 publication

(2 citation statements)

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“…18,19 When a percentage intake of the whole tray is recorded, this visual estimation method can overor under-estimate intake, resulting in a significant degree of subjectivity. [20][21][22] In addition, without the nutrient analysis of that specific meal (set of individual selections), the manual 5PVS is not able to provide individual nutrient analysis data.…”

Section: Introductionmentioning

confidence: 99%

“…The method provides an overall visual estimate of percentage of the total meal tray consumed (0%, 25%, 50%, 75%, 100%), or occasionally an estimation of food groups or individual food items 18,19 . When a percentage intake of the whole tray is recorded, this visual estimation method can over‐ or under‐estimate intake, resulting in a significant degree of subjectivity 20–22 . In addition, without the nutrient analysis of that specific meal (set of individual selections), the manual 5PVS is not able to provide individual nutrient analysis data.…”

Section: Introductionmentioning

confidence: 99%

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Validation of an electronic food intake tool and its usability and efficacy in the healthcare setting

Maunder

Marshall

Syed

et al. 2021

J Human Nutrition Diet

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Background: Accurate dietary intake data are critical to nutrition care planning. Commonly used food record charts (FRC) are paper-based, time consuming, require nutrient analysis estimations, and may provide limited accuracy. The present study aimed to validate Mobile Intake ® (MI) (an electronic food intake tool incorporating the five-point visual scale and providing automatic nutrient analysis) for usability and efficacy in quantifying dietary intake in the healthcare setting. Methods: Two research stages within two tertiary hospitals included: (1) examining criterion validity and efficiency of dietary intake quantification using FRC and MI compared to the gold standard weighed food record (WFR) in a controlled environment and (2) comparing efficiency and effectiveness of FRC and MI in usual care conditions. Results: In Stage 1, dietary intake was calculated (n = 90) with a significant difference across all methods (FRC, MI and WFR) for energy (p = 0.04), but not between MI and WFR (p = 1.00). The time taken for MI (40 s) was significantly less than FRC (174 s) and WFR (371 s) (p < 001). In Stage 2, dietary intake was determined (n = 210) using FRC and MI. Sufficient data to complete dietary analysis were available for 35% of meals from FRC compared to 98% from MI. Calculated mean daily energy intake (4764 ± 1432 kJ vs. 6636 ± 2519 kJ, p = 0.002) and mean daily protein intake (62.9 ± 12.7 g vs. 78.5 ± 22.2 g, p = 0.007) were significantly lower with FRC compared to MI. Average time to complete MI was 14.4 seconds. Conclusions: MI demonstrates efficacy as an accurate measure of dietary intake compared to WFR, as well as usability, providing faster, more accurate and comprehensive real-time intake data in practice than FRC.

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