Development and Pilot-Testing of a Smartabase-Delivered Food Frequency Monitoring Tool
Published On: February 20, 2021Categories: Research
Kevin Iwasa-Madge, MHSC, RD, CSCS completed a Bachelors degree at the University of Guelph in Applied Human Nutrition, and a Masters degree in Nutrition Communication at Ryerson University. Kevin is a former member of the National Wrestling Team, a registered dietitian working with the Canadian Sport Institute Ontario, a certified strength and conditioning specialist, ISAK – Level 2 accredited anthropometrist, and a coach.
Appropriate dietary intake can promote health and sport performance1, and is determined by food choices, and eating behaviours such as timing and frequency of meals, and portion sizes of food consumed2–6. Dietary intake is difficult to quantify for a variety of reasons: eating behaviours are transient, influences on food choices are vast and constantly changing, nutrient content and density of food is variable, accurately determining portion sizes of food intake is difficult, and desirability bias in reporting is an ongoing concern7–10. Furthermore, the novelty of record keeping itself can alter dietary intake11. For these reasons, variability in actual and reported day-to-day intake can be quite large, complicating nutrition assessments.
Many available tools such as diet
recalls and food logs require training to perform correctly and have high
participant and practitioner burden, making them impossible for most non-nutrition
practitioners to use, and unrealistic for nutritionists to use on a daily basis7,9. In practical cases,
nutritionists may attempt to quantify dietary intake by assessing intake over a
short period of time using one of the available tools, and hope that it is
representative of overall dietary intake. Subjective daily nutrition monitoring
is possible as part of an Athlete Self-Report Measures12 monitoring program. However, subjective
daily nutrition monitoring is an indicator of the athlete’s perception of their
diet’s nutritional adequacy, and effective use will depend on the athlete’s
nutrition knowledge. Research suggests that many athletes have poor nutrition
knowledge13–15. Additionally, since a
subjective score is based on both perception of intake and perception of
requirement, sensitivity to change in intake may be low.
It is common practice in health care settings to implement regular, sensitive screening processes, followed by more specific assessments where needed16. Relatively specific dietary intake assessments and subjective daily nutrition monitoring are available, but in many cases, neither are sensitive to change in intake. There is a need for an objective, easy-to-use tool that is sensitive to changes in intake and can be implemented on a daily basis.
A Food Frequency Monitoring Tool
(FFMT) that can be accessed on a smartphone via Fusion Sport’s athlete
management system Smartabase App17 to monitor frequency-based
changes in dietary intake has been developed. The FFMT operates on the
assumption that intra-athlete portion sizes will remain relatively consistent,
and therefore do not need to be recorded. This assumption was made to reduce
the time required to complete the FFMT. Evidence of inter-day reproducibility
of ad libitum energy intake during a single meal supports this assumption18,19. The FFMT is a simple form
within Smartabase that consists of eight possible feeding opportunities, each
with the same list of food groups that could be selected in a Select-All-That-Apply
checkbox format (Figure 1). Foods were grouped based on macronutrient profile
to reduce the length of the list of food. Responses can be exported from
Smartabase into Excel for analysis (Figure 2a-d).
The development of the FFMT was
prompted by the need for an objective, easy-to-use monitoring tool. To
determine the utility of the FFMT, its validity and sensitivity to screen for
changes in intake needs to be established. The purpose of this paper is to
describe and discuss the results of a pilot-test comparing the FFMT to food
logs from four competitive, adolescent female rugby players.
Data were analyzed from four competitive,
Canadian, adolescent female rugby players participating in a Nationally and
Provincially funded academy training program in Ontario, Canada. All subjects
were 16 years of age, with mean body mass of 62.8kg (+/- 3.2kg), mean standing
height of 164.3cm (+/- 5.5cm), and mean Body Mass Index of 23.3kg/m2
(+/- 1.1kg/m2). All subjects worked with a Registered Dietitian,
were relatively healthy, had no apparent barriers to dietary intake, and
consumed a balanced, omnivorous diet.
Pilot testing was conducted through
a retrospective design, and consent from all athletes included in the analysis was
retroactively acquired upon the writing of this paper. Data collected with the
FFMT and food logs between January 29th and February 24th,
2020, were reviewed. Data obtained from the FFMT on the same days as the food
logs were included in the analysis, while data from days where only one tool
was completed were discarded. In total, the data included in the analysis came
from four athletes across 23 days. Subjects had been instructed by a Registered
Dietitian on how to properly complete both the FFMT and food logs prior to
completion. To standardize use, athletes were instructed to do the following:
not adjust dietary intake from “normal”, only report on feeding opportunities
where food was consumed, and only select food groups when the food consumed met
a minimum size threshold of approximately the size of their hand (e.g. milk in
coffee should not have been recorded). Responses at each feeding were coded
into a protein bolus (PB) and/or carbohydrate bolus (CB) based on macronutrient
profile of the foods selected. The total number of PB and CB for each day is
recorded. Specific feeding opportunities, food groups, and coding criteria used
in the FFMT can be found in Table 1. Food
logs were reviewed with athletes and analyzed in ESHA Food Processor20 to determine daily protein
intake and daily carbohydrate intake. To correct for inter-individual
differences in bolus size when pooling data, average PB size and average CB
size were calculated for each athlete using the number of PB and CB from the
FFMT and daily protein intake and daily carbohydrate intake from the food logs,
respectively. For each athlete, PB size and CB size were multiplied by their
number of PB and CB, respectively, to determine a calculated-protein intake
(CPI) and calculated-carbohydrate intake (CCI). Calculated intake values from
each athlete were then pooled for comparison to corresponding intake values
from the food logs.
Using a Pearson Correlation
Coefficient to compare pooled data, the number of PB and protein intake from
food logs, and the number of CB and carbohydrate intake from food logs were
analyzed. Using an Intraclass Correlation Coefficient, the CPI and protein
intake from food logs, and the CCI and carbohydrate intake from food logs were analyzed.
The number of PB and CPI, and the number of CB and CCI, were also compared for
each athlete individually using a Pearson Correlation Coefficient.
To determine sensitivity of the
calculated intake values to screen for variance in intake values determined by
the food logs, the average protein intake and carbohydrate intake were
calculated from pooled data. From there, delta values were created by taking
the individual calculated intake values, and individual intake values from the
food logs, and subtracting the average. Scatter plot quadrants were created
using the individual delta values for calculated intake, and intake from food
logs. Points located in quadrant 1 or 3 (top right or bottom left), show delta
values for both calculated intake and intake from food logs that were directionally
consistent (i.e. both positive or both negative), indicating a “true positive”
screening (i.e. the FFMT captured changes that occurred on the food logs). Points
located in quadrant 2 or 4 (top left or bottom right), show delta values for calculated
intake and intake from food logs that were not directionally consistent,
indicating a “false negative” screening (i.e. the FFMT did not capture changes
that occurred on the food logs). This analysis was repeated at 5% incremental
increases (up to 20%) in the minimum threshold delta value required to be considered
a change from the average. Values below the threshold were considered no change
from the average. Specificity of the tool was not calculated.
Daily completion time of the FFMT
was approximately one to two minutes. Individual data and descriptive
statistics can be found in Table 2 and 3. The number of PB and protein intake
from food logs, and the number of CB and carbohydrate intake from food logs, were
both non-significantly correlated. Calculated-protein intake and protein
intake from food logs were moderately and significantly correlated at 0.68 (p
< 0.01) (Figure 3), while CCI and carbohydrate intake from food logs were
moderately and significantly correlated at 0.45 (p < 0.05) (Figure 4). Intra-individual
correlation of the number of PB and CPI, and the number of CB and CCI, were
both 1.0 for each athlete.
For CPI sensitivity, 19 of 23 scatter
plot points were located in quadrant 1 or 3 (Figure 5), giving the FFMT an 83%
sensitivity at a 0% change-threshold. If a 5% or 10% change-threshold is
utilized, sensitivity is 86%. At a 15% and 20% change-threshold, sensitivity
drops to 78% and 63%, respectively. Delta values for CPI and protein intake from
food logs can be found in Table 4. For CCI sensitivity, 18 of 23 scatter plot points
were located in quadrant 1 or 3 (Figure 6), giving the FFMT a 78% sensitivity
at a 0% change-threshold. If a 5% change-threshold is utilized, sensitivity is
76%. At a 10%, 15%, and 20% change-thresholds, sensitivity drops to 63%, 58%,
and 42%, respectively. Delta values for CCI and carbohydrate intake from food
logs can be found in Table 5.
The FFMT was developed to fill
the need for an objective tool that is sensitive to changes in dietary intake,
while pilot testing sought to explore the use and sensitivity of this newly
developed tool. Calculated intake values derived from the FFMT were moderately
and significantly correlated with intake values from food logs. Calculated-protein
intake sensitivity was >80% when using change-thresholds below 15%, while
CCI sensitivity was >70% when using change-thresholds below 10%. The FFMT is
easy-to-use and time-efficient, and appears to be relatively sensitive to intra-individual,
frequency-based changes in protein and carbohydrate intake under certain
Due to the declining sensitivity
of the FFMT as the threshold for change increased during pilot-testing (Figure 7
and 8), the FFMT may be more appropriate for athletes with a low inter-day
variance in intake. Acquiring a large enough baseline on an athlete to reduce
Standard Error of Measurement (SEM) at a 95% Confidence Interval below 15% of the
average number of PB may be necessary to identify real change with good
sensitivity. In this study, 15% of the average number of PB was 0.6, and 15% of
the average protein intake was 21g. For most athletes, reducing SEM below 15%
will require a baseline average from at least one week of FFMT use, while
athletes with a larger inter-day variation may require two weeks or more to
confidently establish a SEM below 15%. The FFMT has poorer sensitivity to
changes in carbohydrate intake compared to protein intake. Reducing SEM below
10% of the average number of CB is likely necessary to identify real change while
maintaining relatively good sensitivity. In this study, 10% of the average
number of CB was 0.4, and 10% of the average CHO was 34g. For most athletes, reducing
SEM below 10% will require a baseline average from at least two weeks of
intake, while athletes with a larger inter-day variation in intake may require
one month or more to confidently establish a SEM below 10%. It should be noted
that there will be a greater incidence of false negatives when monitoring for
change in carbohydrate intake compared to protein intake.
Both the FFMT and subjective daily nutrition monitoring can be completed quickly and on a daily basis. However, the FFMT may be a more appropriate tool for daily use than subjective daily nutrition monitoring for athletes who have not yet received sufficient sport nutrition education (e.g. adolescent athletes), as evidence shows some lack of understanding of sport nutrition requirements prior to education21. However, sport nutrition knowledge appears to improve after several years of education22, suggesting that subjective daily nutrition monitoring may be appropriate for educated athletes. Fortunately, frequency-based nutrition recommendations are practical in adolescent populations due to the low food autonomy of many adolescents23,24, making the FFMT appropriate for monitoring these changes. As adolescent athletes get older and “graduate” from nutrition education programs, recommendations related to portion sizes may be more appropriate, and a transition to subjective daily nutrition monitoring may be warranted.
This study was limited by the
sample size and demographics. As with all dietary intake data, it may be
subject to desirability bias and reporting error. Food logs are not the gold
standard for assessing dietary intake, and were only used as a criterion
measure for practical reasons. The FFMT requires the use of Smartabase or
another customizable athlete management system. Due to inter-individual
differences in PB and CB size, and the use of CPI and CCI to establish
sensitivity, the tool is not ideal for quantifying total protein or carbohydrate
intake on its own, or for comparing protein or carbohydrate intake between
athletes. If there is reason to think that PB size or CB size for an athlete
has changed, such as following a nutritionist-recommended change to portion
sizes, a new baseline is necessary to maintain reliability. Future research
that utilizes larger sample sizes, different demographics, and validates the
FFMT’s ability to capture intentional changes in intake should be performed.
The FFMT can be a practical and
time-efficient option to monitor for intra-individual, frequency-based changes
in protein and carbohydrate intake. The FFMT can either be used on its own for monitoring
purposes, or “calibrated” by pairing with periodic use of diet recall or food logs
to determine average PB and CB size. For those who do not have access to Smartabase
or another customizable athlete management system, data entry and coding in
Excel may be a viable approach for a limited number of athletes. The FFMT is an
ideal alternative to subjective daily nutrition monitoring for adolescent
athletes with low nutrition knowledge and/or food autonomy, or in any situation
where a frequency-based recommendation has been made. For non-nutrition
practitioners such as Sport Coaches, Strength and Conditioning Coaches,
Therapists, etc. who do not have the training or time to accurately perform diet
recalls or analyze food logs, the FFMT is a viable option to monitor frequency-based
changes in protein intake and/or carbohydrate intake during key periods such as
weight-loss, injury-recovery, hypertrophy training, or high-volume training.
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