Accuracy and Cost-effectiveness of Technology-Assisted Dietary Assessment Comparing the Automated Self-administered Dietary Assessment Tool, Intake24, and an Image-Assisted Mobile Food Record 24-Hour Recall Relative to Observed Intake: Protocol for a Randomized Crossover Feeding Study

Measures of nutrition status and health for weight-inclusive patient care: A narrative review

In healthcare, weight is often equated to and used as a marker for health. In examining nutrition and health status, there are many more effective markers independent of weight. In this article, we review practical and emerging clinical applications of technologies and tools used to collect non-weight-related data in nutrition assessment, monitoring, and evaluation in the outpatient setting. The aim is to provide clinicians with new ideas about various types of data to evaluate and track in nutrition care.

Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study

BACKGROUND

Technology-assisted 24-h dietary recalls (24HRs) have been widely adopted in population nutrition surveillance. Evaluations of 24HRs inform improvements, but direct comparisons of 24HR methods for accuracy in reference to a measure of true intake are rarely undertaken in a single study population.

OBJECTIVES

To compare the accuracy of energy and nutrient intake estimation of 4 technology-assisted dietary assessment methods relative to true intake across breakfast, lunch, and dinner.

METHODS

In a controlled feeding study with a crossover design, 152 participants [55% women; mean age 32 y, standard deviation (SD) 11; mean body mass index 26 kg/m2, SD 5] were randomized to 1 of 3 separate feeding days to consume breakfast, lunch, and dinner, with unobtrusive weighing of foods and beverages consumed. Participants undertook a 24HR the following day [Automated Self-Administered Dietary Assessment Tool-Australia (ASA24); Intake24-Australia; mobile Food Record-Trained Analyst (mFR-TA); or Image-Assisted Interviewer-Administered 24-hour recall (IA-24HR)]. When assigned to IA-24HR, participants referred to images captured of their meals using the mobile Food Record (mFR) app. True and estimated energy and nutrient intakes were compared, and differences among methods were assessed using linear mixed models.

RESULTS

The mean difference between true and estimated energy intake as a percentage of true intake was 5.4% (95% CI: 0.6, 10.2%) using ASA24, 1.7% (95% CI: -2.9, 6.3%) using Intake24, 1.3% (95% CI: -1.1, 3.8%) using mFR-TA, and 15.0% (95% CI: 11.6, 18.3%) using IA-24HR. The variances of estimated and true energy intakes were statistically significantly different for all methods (P < 0.01) except Intake24 (P = 0.1). Differential accuracy in nutrient estimation was present among the methods.

CONCLUSIONS

Under controlled conditions, Intake24, ASA24, and mFR-TA estimated average energy and nutrient intakes with reasonable validity, but intake distributions were estimated accurately by Intake24 only (energy and protein). This study may inform considerations regarding instruments of choice in future population surveillance. This trial was registered at Australian New Zealand Clinical Trials Registry as ACTRN12621000209897.

Newly developed dietary assessment tools for lutein and zeaxanthin are correlated with 24-hour diet recalls, but are not a valid measure of intake in Australian and United Kingdom adults

Habitual dietary intake measurement of carotenoids lutein and zeaxanthin (L/Z) has often been omitted or attempted with tools of unknown validity in past research. It was hypothesized that the dietary assessment tool, the L/Z screener, developed as part of this study, would be valid with agreement within 0.25 mg/day when compared against multiple 24-hour diet recalls in healthy Australian and United Kingdom adults. Two screeners with 91 food items were developed, 1 with a recall timeframe of a month and the other a week. Over 4 weeks, 56 Australian and 47 United Kingdom participants completed 4 weekly screeners, 2 monthly screeners, and eight 24-hour diet recalls. Validity was assessed through Bland-Altman plot analysis. L/Z intake measured by all tools was significantly correlated, with correlation coefficients from 0.58 to 0.83. Despite these correlations, the screeners were not valid, with poor Bland-Altman plot agreement when compared with the diet recalls. The Australian weekly screener performed best, demonstrating a mean difference of 0.51 mg/day and 95% limits of agreement between -1.46 mg/day and 2.49 mg/day of L/Z intake. Baby spinach, broccoli, and pumpkin provided the greatest proportion of L/Z intake. The low validity may be explained by high rates of misestimation or missed capture of moderate to high L/Z containing foods such as baby spinach. Prior research reliant on correlational statistics for L/Z tool validity should be interpreted with caution, and future screener development should prioritize accurate capture of high contribution foods.

No Effect of Breakfast Consumption Observed for Afternoon Resistance Training Performance in Habitual Breakfast Consumers and Nonconsumers: A Randomized Crossover Trial

BACKGROUND

Pre-exercise meal frequency is commonly believed to impact exercise performance, but little is known about its impact on resistance training.

OBJECTIVE

This study investigated the impact of breakfast consumption on afternoon resistance training performance in habitual breakfast consumers and nonconsumers.

DESIGN

A randomized, crossover study was conducted in Lubbock, TX between November 2021 and May 2022.

PARTICIPANTS

Thirty-nine resistance-trained male (n = 20) and female (n = 19) adults (mean ± SD age 23.0 ± 4.7 years) who habitually consumed (≥5 d/wk; n = 19) or did not consume (≥5 d/wk; n = 20) breakfast completed the study.

INTERVENTION

After the establishment of 1-repetition maximums at the first visit, participants completed 2 additional visits, each of which included 4 sets of barbell back squat, bench press, and deadlift, using 80% of their 1-repetition maximum after either consuming breakfast and lunch or the same food at lunch only.

MAIN OUTCOME MEASURES

Repetitions, along with average and peak average concentric velocity and power, were measured for all repetitions throughout each exercise session. Visual analog scales were used to assess feelings of fatigue, energy, focus, hunger, desire to eat, and fullness throughout each exercise session.

STATISTICAL ANALYSES PERFORMED

Data were analyzed using linear mixed-effects models.

RESULTS

No interactions or main effects involving condition or habitual breakfast consumption were observed for resistance training outcomes, although sex differences were noted. Male participants performed significantly fewer repetitions on sets 2, 3, and 4 (P < .014) for total repetitions, on sets 2 and 4 for barbell back squat (P < .023), and set 4 for deadlift (P = .006), with no observed differences between sexes for bench press repetitions. Male participants displayed reductions in average power across all sets and exercises except deadlift.

CONCLUSIONS

These data suggest that alterations in pre-exercise meal frequency may not influence afternoon resistance training performance provided similar total nutritional intake is consumed.