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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM).
https://doi.org/10.1109/HNICEM57413.2022.10109490
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Conference Paper
Virtual Dietitian as a Precision Nutrition Application for Gym
and Fitness Enthusiasts: A Quality Improvement Initiative
Manuel B. Garcia a *, Teodoro F. Revano, Jr. b, Pocholo James M. Loresco c,
Renato R. Maaliw III d, Ryan Michael F. Oducado e, Kadir Uludag f
a Educational Innovation and Technology Hub, FEU Institute of Technology, Philippines
b College of Computer Studies and Multimedia Arts, FEU Institute of Technology, Philippines
c College of Engineering, FEU Institute of Technology, Philippines
d College of Engineering, Southern Luzon State University, Lucban, Quezon, Philippines
e College of Nursing, West Visayas State University, Iloilo City, Philippines
f Institute of Psychology, Chinese Academy of Sciences, Beijing, China
* Correspondence:
Manuel B. Garcia, Educational
Innovation and Technology Hub,
FEU Institute of Technology.
mbgarcia@feutech.edu.ph
How to cite this article:
Garcia, M. B., Revano, T. F.,
Loresco, P. J. M., Maaliw III, R.
R., Oducado, R. M. F., & Uludag,
K. (2023). Virtual Dietitian
Application as a Web-Based
Nutrition Support Service for
Gym and Fitness Enthusiasts: A
Quality Improvement Study.
2022 IEEE 14th International
Conference on Humanoid,
Nanotechnology, Information
Technology, Communication and
Control, Environment and
Management (HNICEM).
https://doi.org/10.1109/
HNICEM57413.2022.10109490.
Article History:
Received: 15 Sep 2022
Revised: 11 Nov 2022
Accepted: 15 Nov 2022
Published: 1 May 2023
Abstract:
The Virtual Dietitian (VD) application is a nutrition knowledge-based system that
generates personalized meal plans in accordance with the one-size-does-not-fit-all
concept of precision nutrition. A subset of the population that was not involved in its
four-part developmental study was gym and fitness enthusiasts despite them being
important target users. As part of our quality improvement (QI) plan, we initiated a two-
phase user testing to inform modifications to VD. We recruited a total of 30 users with
prior experience in nutrition applications. In phase 1, they used the current version of
VD for a week and answered a mixed-form questionnaire afterward. We used the same
questionnaire from our previous study, which is composed of System Usability Scale
(SUS) items and open-ended questions. After months of system modification, the same
set of users evaluated again the new VD version after another week of use. A paired-
sample t-test showed a statistically significant difference in SUS scores before (SUS =
79) and after (SUS = 82) modifying VD based on the suggestions of the participants (p =
0.005). Some new features include water tracker and reminder modules, Google Fit
integration, and other nutrition support services (e.g., teleconsultation with registered
dietitians). Although further refinements to VD are still needed, we were able to
incorporate a QI initiative typically employed by healthcare organizations into software
development for a better and improved personalized nutrition application.
Keywords:
Nutrition Research, Quality Improvement, Precision Nutrition, Dietetics, Knowledge-
Based System, Fitness
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM)
https://doi.org/10.1109/HNICEM57413.2022.10109490
INTRODUCTION
The field of nutrition has been increasingly interested in how food affects human health
and well-being (Galekop et al., 2021; Jinnette et al., 2021; Kirk et al., 2021). Founded upon the
one-size-does-not-fit-all concept, one relatively new area of study is precision nutrition (PN).
According to the proposed definition by the American Nutrition Association, PN (sometimes
referred to as personalized nutrition or individualized nutrition) is a field that “leverages human
individuality to drive nutrition strategies that prevent, manage, and treat disease and optimize health”
(Bush et al., 2020). Therefore, one of the ultimate goals of PN is to develop effective, dynamic,
and tailored nutritional recommendations that conform to inter-individual variability (e.g., food
preferences and eating behaviors, deep phenotyping, physical activity, gut microbiome, genetic
profile, and social determinants of health) in response to nutrition (Harper et al., 2021; Morand et
al., 2020; Murphy et al., 2022; Walther et al., 2019). Figure 1 illustrates this variability in the PN
plate (De Toro-Martín et al., 2017). Unfortunately, PN as a field of research is still in its infancy
and not many studies have been conducted in this area (Pigsborg & Magkos, 2022).
In physical fitness, adequate nutrition plays an irreplaceable role in the effective
performance of exercise (Aoi et al., 2006). The balance between nutritional needs and intake
assumes paramount importance in conditioning, avoidance of injury, recovery from fatigue after
exercise, muscle repair, and the overall improvement of athletic performance. With the close
relationship between physical fitness and energy intake (Genton, 2011), gym and fitness
enthusiasts (i.e., people who exercise regularly) must be familiar with their macronutrient
requirements and the effects of intake before, during, and after exercise. Diet choices are also
considerable and various factors influence these decisions. Some variables include nutrition
knowledge, attitude, culture, religious beliefs, affordability, availability, dietary restrictions,
preference, social environment, and more (Chen & Antonelli, 2020; Kamphuis et al., 2015;
Pollard et al., 2002; Sobal & Bisogni, 2009). Unfortunately, gym-goers were found to have a low
level of nutrition knowledge, especially when compared with athletes (Calella et al., 2021).
Following the PN concept, it is insufficient to adhere to any generic dietary plan since an optimal
macronutrient distribution compliant with the total daily energy expenditure is vital to achieving
fitness goals (Garcia, 2019; Garcia & Garcia, 2023; Genton, 2011; Ostendorf et al., 2019).
In recent years, there have been growing studies looking into the development and
utilization of computer systems and mobile applications to improve nutrition behavior (e.g.,
Garcia, 2019). According to a systematic review (Paramastri et al., 2020), nutrition applications
are associated with increased nutrition knowledge, and using various platforms (e.g., computer,
mobile, smartphone, and internet technologies) promotes the attainment of diet and weight goals.
One example is the two-arm parallel randomized controlled trial with a three-month intervention
and six-month maintenance program called “TXT2BFiT” (Partridge et al., 2016). The primary
strategy is to send motivational text messages to nurture behavior transformation around weight
maintenance. This multi-component lifestyle program conforms with PN by personalizing
coaching calls as well as text messages according to gender and stage of change. After the trial
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM)
https://doi.org/10.1109/HNICEM57413.2022.10109490
period, it was found that participants prefer self-monitoring applications capable of personalizing
nutrition services. Thus, it is noticeable that modern nutrition applications attempt to incorporate
the PN concept. For instance, the Virtual Dietitian (VD) application was purposely designed to
generate meal plans based on users’ preferences and restrictions (Garcia et al., 2021b). The core
functionality of VD utilizes a forward chaining algorithm as a method of reasoning to filter
thousands of recipes based on the nutritional values of all foods and ingredients. VD also
distributes the macronutrients and micronutrients tailored to the needs of users. Despite the
advancements and numerous nutrition applications, there is a concern that their utilization the
context of dietary health may inadvertently foster detrimental habits and unhealthy eating
behavior (McKay et al., 2019). Therefore, continuous quality improvement of nutrition
applications should be the ethos of any nutrition policy and public health initiative.
Figure 1: The Precision Nutrition Plate
In healthcare, quality improvement (QI) is an important part of quality management to
ensure high-quality care for patients. This approach presents an avenue for the assessment and
refinement of existing methodologies, fostering a more encompassing and efficacious delivery of
healthcare services. Nutrition research likewise utilizes QI initiatives (Garcia et al., 2023;
Kavanagh et al., 2022; Li et al., 2015). Following the call for further exploration of nutrition
applications in improving diet and health (Paramastri et al., 2020), we commenced a two-phase QI
initiative with the target subset of the population (i.e., gym and fitness enthusiasts). This QI study
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM)
https://doi.org/10.1109/HNICEM57413.2022.10109490
was performed on VD – a nutrition application that follows the conception of PN (Garcia et al.,
2021b). As a generic tool, the sample in this study was not consulted during the development
stage of VD despite their low level of nutrition knowledge indicating the potential value of such an
application for them (Calella et al., 2021). The primary goal of this study was to determine
nutrition application features needed by this sample and modify VD to serve their needs. To the
best of our knowledge, this is the first QI analysis undertaken with a nutrition application
grounded on PN. We hope to provide further evidence in the existing thread of discussions not
only in nutrition research but also in application development.
METHODS AND MATERIALS
This paper is a sequel to a four-part developmental study that started with the
construction of a nutrition application prototype called Plan-Cook-Eat (PCE) (Garcia, 2019).
Following the growing trend of PN, we developed PCE to generate tailored dietary prescriptions
based on a person’s total daily energy expenditure. PCE ensures that there is an optimal
distribution of macronutrients (protein, fat, and carbohydrate) in meals throughout the day.
Despite the overall positive ratings, registered dietitians mentioned that PCE lacked more
nutrition-related features that integrate evidenced-based dietetics while application users
demanded more flexibility in generating their daily meal plans (e.g., incorporating personal food
preferences and restrictions). These shortcomings led to a mixed-methods needs analysis for a
larger and smarter nutrition application (Garcia et al., 2020). Accordingly, it was learned that
people generally lack the essential nutrition knowledge and dietary compositions to make smarter
food choices. Nonetheless, they expressed their willingness to embrace a healthier lifestyle with
the assistance of a nutrition application. This confirmation initiated the design and development
of VD to assuage the inadequate nutrition problems (Garcia et al., 2021b). Inspired by a
knowledge-based information system, VD uses a forward chaining algorithm to generate
personalized meal plans tailored to individuals’ nutritional needs, goals, preferences, and
restrictions. Unlike PCE, VD is strictly anchored on the Nutrition Care Process, which is used by
nutrition professionals to assess, diagnose, treat, and monitor their patients. This final version
was evaluated by experts and target users in terms of quality, acceptability, and usability (Garcia
et al., 2021a). Various features have been recommended by the evaluators for future versions
although a series of testing and evaluations have already been conducted. The most recent
evaluation is evidence that there are remaining refinements to be done in VD.
As part of our mission to continuously improve the usefulness and quality of VD, we
performed a two-phase QI study following the protocol used in a web-based health application for
nutrition therapy in primary care (Kavanagh et al., 2022). Our target participants were fitness and
gym enthusiasts who were invited via social media platforms using snowball and purposive-
convenience sampling techniques. Unlike the basis of the protocol which had two separate
samples, our QI study relied on the same participants for both phases. An instructional guide was
sent to the participants after submitting an informed consent form. To begin the first phase,
participants were asked to use VD daily for seven days to assess the usability of the current
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM)
https://doi.org/10.1109/HNICEM57413.2022.10109490
version and recommend missing features that they require for a nutrition application. On a side
note, they were not mandated to follow the personalized meal plans generated by VD. A mixed-
form questionnaire was delivered within the application after a seven-day trial period. This
questionnaire sought to assess the system usability of the current version of VD and identify new
potential features based on the viewpoints of the target sample. Similar to the last two sprints of
VD (Garcia et al., 2021a, 2021b), we used the System Usability Scale (SUS) and open-ended
questions such as “Which features of VD do you like?” and “What feature(s) would you like to see
added in VD?”. All evaluations from Phase 1 were completed on March 31, 2022, with a total of 30
participants. Despite a low number of participants, it is still more than the acceptable sample size
(n = 20) for usability testing with a quantitative analysis (Nielsen, 2012). Given our sample size,
we sorted all responses to the open-ended questions manually according to the most requested
features and reported by at least three participants. As undertaken by another QI study, we
included representative quotations to improve the credibility of the findings (Kavanagh et al.,
2022). In terms of SUS, we calculated the scores of this ten-item questionnaire according to the
published instruction (Brooke, 1996). Accordingly, the range of scores is 0 to 100 and the
acceptable score is higher than 70. Afterward, we considered all responses and conducted a series
of sprints for three months to modify VD (June 1 to August 1, 2022). In Phase 2, we recruited the
same set of participants and presented the modified VD that complies with some of their
recommendations. Participants were instructed to use VD daily again for another week and assess
its usability. We completed the Phase 2 on August 19, 2022. Finally, we utilized a paired sample t-
test to test the statistical difference of SUS scores between Phase 1 and Phase 2.
RESULTS AND DISCUSSION
All participants (n = 30) provided feedback on both phases. Most of them were 21 to 30
years old (n = 19, 63.33%) and living with family (n = 22, 73.33%) in an upper middle-income class
(between ₱76,669 to ₱131,484; n = 20, 66.67%). Their physical activity was active (daily or
intense exercise 3-4 times/week; n = 14, 46.67%) and their nutritional status was overweight (BMI
≥ 25 and < 30 kg/m2; n = 19, 63.33%). Although all participants have experience with three to four
nutrition applications (n = 25, 83.33%), only four participants use them regularly (13.33%).
A. Which features of VD do you like?
When asked which VD features they like, most participants selected the meal plan diary
and generator (n = 23, 76.67%). As shown in Figure 2, this two-in-one feature tracks what users
eat and automatically generate daily meal plans according to several variables: (1) preferences
such as diet plans, cooking techniques, and cuisines; (2) restrictions such as food allergies and
dietary practices based on religion; (3) body image goals such as losing, maintaining, or gaining
weight; and (4) food-based dietary guidelines. A regular exercise routine complemented by
healthy eating is fundamental to maintaining good health and well-being (Aoi et al., 2006; Genton,
2011). In terms of treatment options for weight loss, exercise with a healthy diet is better than
exercise or diet alone (Clark, 2015). For beginners who lack the financial means to avail the
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM)
https://doi.org/10.1109/HNICEM57413.2022.10109490
services of registered dietitians, this feature presents a cost-free avenue to encourage the
adoption of healthy eating habits and engagement in physical exercise. Nutrition application users
are better at maintaining dietary and physical activity behaviors than non-users (Wang et al.,
2016). The remaining participants (n = 7, 23.33%) favored the flexibility of the automated meal
planner augmenting the personalized dietary recommendations (see Figure 3 for the settings
module). Some of the excerpts from the qualitative feedback are as follows:
• The app is great because it not only allows you to track what you eat but also recommends
meal plans. I do not need to consult with dietitians often. [P6]
• I like the feature of the meal planner that allows me to select my preferred cuisines. I would not
probably eat the meals if they are not to my taste. [P22]
Figure 2: Meal Plan Diary and Generator
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
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https://doi.org/10.1109/HNICEM57413.2022.10109490
What feature(s) would you like to see added in VD?
Feedback from Phase 1 opened opportunities to enhance the functionality and
applicability of VD. We tagged each response manually and ranked it according to the most
requested feature. However, we did not include all the suggested features because some of them
were out of the scope of VD. For example, seven participants (23.33%) asserted that the
“combination of nutrition and workout application would be more useful” [P12]. Although we
acknowledge that this is a good feature idea, particularly for this sample (i.e., gym and fitness
enthusiasts), we concluded that we should stick with features that every user could benefit from.
More importantly, we believe that adding “daily routine workout guides” [P19] deviates far from
the primary goal of VD (i.e., to create personalized meal plans for any individual).
Nutrition Coaching (Teleconsultation)
The most requested feature was remote nutrition coaching or teleconsultation (n = 22,
73.33%). This suggestion is consistent with what has been found in a randomized controlled trial
where participants valued phone coaching calls the most, and that text and email messages were
found helpful in achieving their goals (Partridge et al., 2016). In the nutrition literature, there is
already well-established evidence supporting the acceptability, usefulness, and benefits of remote
consultation (Farid, 2020; Kaufman-Shriqui et al., 2021; Singh et al., 2021). One possible reason
behind this suggestion is the current COVID-19 pandemic. This feature idea is also a reminder
that no technology can replace human experts like registered dietitians. Although we modified VD
to have this feature, it is still unclear how to invite and compensate dietitians. Some of the
feedback regarding this feature are as follows:
• Regardless of the countless available nutrition apps on the market, I still prefer talking to
real experts. [P2]
• My friend who is a dietitian does not provide me with a lot of meal options unlike this
website. Still, I would like to consult him regarding the calories and nutrients and doing it
within the app seems a good idea. [P25]
• With COVID and other diseases going around now, an online consultation feature would
be useful. [P26]
Water Tracker and Reminder
Next to teleconsultation is the water tracker and reminder (n = 19, 63.33%). In our
defense, we did not include it in the initial version because plain water is calorie-free, which
means that it will not affect the computation of total daily energy expenditure. Nevertheless,
water is the main constituent of the human body, a participant in all biochemical reactions (e.g.,
digestion), and a vital component of nutrition (Kleiner, 1999). In general, proper fluid intake is
imperative for the human body to function at its very best. For people under intense exercise, the
sweating that occurs leads to a loss of water that can then weaken thermoregulation as well as the
circulatory system. Thus, water replenishment is essential to prevent a decline in athletic
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM)
https://doi.org/10.1109/HNICEM57413.2022.10109490
performance (Aoi et al., 2006) and should always be part of the diet record (Kleiner, 1999). In
addition to the meal diary, we also included a reminder feature to notify users of when to drink
water. Some of the participants noted the following:
• If there is food, why there should be no water? I think it should be included in the
application. [P3]
• I drink a lot of water during exercise but not on my rest days. A reminder to drink water is
what I need. [P14]
Figure 3: Food Preferences Settings: Cuisines, Techniques, Diet Plans
Wearable Technology Integration
Although not as many as the recommendations for the water tracker and reminder and
teleconsultation, the next feature idea was integrating wearable technology (n = 8, 26.67%).
Recently, there is an increasing interest in wearable activity trackers and a systematic review
learned that they affect physical activity (Ferguson et al., 2022). Since integrating different
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM)
https://doi.org/10.1109/HNICEM57413.2022.10109490
wearable technologies require a lot of time and resources, we agreed to focus on Google Fit.
Following our position on mixing nutrition and workout applications, only metrics related to
nutrition were included in the dashboard. The participants noted the following during their
evaluation:
• It would be helpful to possibly include the data from my smartwatch. I use Google Fit to
track and record all of my data especially the calories burned. [P20]
• It would be nice to have a more informative dashboard where all health-related data are
available. If possible, include data from wearable devices. [P21]
Other Feature Idea Recommendations
Unlike the first three recommendations that were applied to the new VD version, there
were other challenging feature ideas that we were not able to address but are good features to
consider in the future. For instance, participant 21 urged a “gamification feature to encourage
people to eat healthy foods”. One idea is to award points for each kilogram people lose or gain.
Nonetheless, integrating this feature demands a major application update and a proper
gamification strategy. Another potential new feature is diet programs to be chosen as a “package
rather than generating meal plans daily” [P13]. However, the completion of this feature requires
the expertise of registered dietitians. Total daily energy expenditure may also be a barrier to
creating meal packages. A 3000-calorie meal package is only valid for people that require the
energy of around 3000 calories. Finally, two participants did recommend nutrition guides with
visual examples [P2, P15]. In the next version of VD, we will be incorporating this feature to
strengthen the dissemination of nutrition knowledge.
System Usability Scale: Before and After VD Modification
In Phase 1, participants rated VD with good usability scores (M = 79.20, SD = 4.103). This
score is less than what was given by regular users in the previous evaluation (Garcia et al., 2021a).
However, it is expected since the needs of this study’s sample were not taken into consideration.
This suspected inadequacy is the reason why we initiated a QI study. In Phase 2, participants graded
the latest version of VD with excellent usability scores (M = 83.16, SD = 2.249). Scores from Phase
2 were statistically higher than Phase 1, according to paired samples t-test: t(29) = 3.095, p = .042.
It means that the modifications accomplished for the new version helped increase the application's
usability. This finding implies that application developers may perform a QI study to improve their
artifacts even though this methodology is only common in the field of healthcare. The primary
strength of this paper is that VD has already been a subject of a series of evaluations. Future QI
studies may have to perform a more extensive modification, especially for newly-developed
applications. Nevertheless, the commitment to consistent software updates (e.g., fixing bugs or
adding new features) is a vital process in software development.
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM)
https://doi.org/10.1109/HNICEM57413.2022.10109490
CONCLUSION
In this study, we initiated a two-phase QI initiative to improve VD and address the needs
of gym and fitness enthusiasts. To our knowledge, this study was the first QI analysis undertaken
with a nutrition application grounded on PN. Borrowing this process from the healthcare field
allowed us to discover specific features needed by our target users. Additionally, it statistically
improved the usability of VD after another sprint of system modification. It underlines the
significance of involving users in the development lifecycle to guarantee the availability of
necessary features. For a nutrition tool like VD that offers vital health services, continuous quality
improvement should be the ethos of any nutrition policy.
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2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information
Technology, Communication and Control, Environment and Management (HNICEM)
https://doi.org/10.1109/HNICEM57413.2022.10109490
RELATED RESEARCH
Conference Paper
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Garcia, M. B., Mangaba, J. B., & Tanchoco, C. C. (2021). 2021 IEEE 13th International Conference on Humanoid,
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Book Chapter
Teaching Physical Fitness and Exercise Using a Computer-Assisted Instruction: A
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Garcia, M. B., Yousef, A. M. F., de Almeida, R. P. P., Arif, Y. M., Happonen, A., & Barber, W. (2023). Handbook of
Research on Instructional Technologies in Health Education and Allied Disciplines.
https://manuelgarcia.info/publication/public-health-intervention-cai
LET'S COLLABORATE!
If you are looking for research collaborators, please do not
hesitate to contact me at mbgarcia@feutech.edu.ph.
ABOUT THE CORRESPONDING AUTHOR:
Manuel B. Garcia is a professor of information technology and the founding
director of the Educational Innovation and Technology Hub (EdITH) at FEU
Institute of Technology, Manila, Philippines. His interdisciplinary research interest
includes topics that, individually or collectively, cover the disciplines of education
and information technology. He is a licensed professional teacher and a proud
member of the National Research Council of the Philippines – an attached agency
to the country’s Department of Science and Technology (DOST-NRCP).
