SousChef: Mobile Meal Recommender System
for Older Adults
David Ribeiro
, João Machado
, Jorge Ribeiro
, Maria João M. Vasconcelos
Elsa F. Vieira
and Ana Correia de Barros
Fraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 4200-165 Porto, Portugal
REQUIMTE, Institute of Engineering of Porto, Polytechnic Institute of Porto, Porto, Portugal
CRPG - Gaia Vocational Rehabilitation Centre, Vila Nova de Gaia, Portugal
Keywords: Recommender System, Nutrition, Intelligent Companions, Mobile Health Monitoring Older Adults,
Personalized Interfaces.
Abstract: Nowadays, following a healthy diet is a challenge, either due to the large variety of food and ingredient
combination possibilities or due to the lack of knowledge required to make healthy choices. This problem is
even more patent amongst older adults. Although some recommender systems and applications have been
proposed with aim to monitor calorie consumption and/or to suggest healthy recipes to general consumers,
no similar solution was yet presented focused on older adults’ needs. In this work, a mobile meal
recommender system, SousChef, for this target group is presented. This system is capable of creating a
personalized meal plan based on the information provided by the user, including the anthropometric
measures, personal preferences and activity level. The nutritional recommendations and the application was
thought and designed for older adults, presenting friendly user interfaces and following the guidelines of a
nutritionist. Tests with users were conducted in order to ascertain recipe and nutritional plan suitability as
well as usability of the mobile application. Results showed that more than 70% of the older adult
participants were satisfied with the meal plan suggestions and with the simplicity of the SousChef
According to the literature improved nutrition is a
major cause of increased lifespan in the last two
centuries (Le Couteur et al., 2016; Bunker, 2001).
Poor diet, in contrast, is the main risk factor for
death and disabilities in developed nations (Murray
et al., 2013). Although the exact definition of elderly
age group is controversial, for high-resourced
countries the World Health Organization have
accepted the chronological age of 65 years as a
definition of 'elderly' or older person (World Health
Organization, 2010). An ageing population tends to
have a higher prevalence of chronic diseases,
physical disabilities, mental illnesses and other co-
morbidities (Saka et al., 2010). Thus, in order to
promote long-term biological effects, continuing
efforts to increase the relevance and effectiveness of
nutritional recommendations have been made
(World Health Organization, 2002). When looking
at older demographics, several studies refer that
older adults often struggle with making the right
decisions regarding meal preparation, healthy diets
or groceries shopping. Studies also suggest that
many older adults neglect nutrition and are more
inclined to do so if they happen to live alone (Ramic
et al., 2011). Furthermore, under financial
restrictions, which older adults often find themselves
in, balancing healthy eating habits with money
saving can become a complicated task (World
Health Organization, 2002; Ministry of Health,
Taking into account the previous facts and
acknowledging that nowadays technologies are
always present and may be used to assist people, in
this work we present SousChef, a mobile meal
recommender system to assist older adults by
providing a nutrition companion to guide them into
making wise decisions regarding food management
and healthy eating habits.
The meal recommender system was developed to
create personalized nutritional plans according to the
Ribeiro, D., Machado, J., Ribeiro, J., Vasconcelos, M., Vieira, E. and Barros, A.
SousChef: Mobile Meal Recommender System for Older Adults.
DOI: 10.5220/0006281900360045
In Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2017), pages 36-45
ISBN: 978-989-758-251-6
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
information provided by the user, namely, the
personal preferences, activity level and the
anthropometric measurements (weight and height).
The system intelligence was built with the
professional knowledge and participation of a
nutritionist, who formulated the diet plans and
healthy meals suggestions, and took part in the final
system validation.
The present paper is organized as follows:
section 2 presents the related work; in section 3 the
system is described while in section 4 the mobile
application is presented; in section 5, the user testing
results are shown; finally, conclusions and future
work are drawn in section 6.
Regarding mobile technologies related to food and
nutrition, several studies may be found in the
literature addressing issues such as: recommender
systems (Aberg, 2006; Mika, 2011; Sezgin and
Ozkan, 2013; Hazman and Idrees, 2015; Espín et al.,
2015), social interaction (Terrenghi et al., 2007),
menu generation (Kuo et al., 2012) or cooking
assistance for users with specific impairments, such
as in memory (Tran et al., 2005) or language (Tee, et
al., 2005). As this work presents a recommender
system, we will briefly detail the related work in this
In (Adomavicius and Tuzhilin, 2005), the authors
performed a survey of the State-of-the-Art on
recommender systems and identified three main
types of systems based on the employed
methodologies: content-based, collaborative and
hybrid recommender systems. The challenges
related to the design and implementation of
nutritional recommender systems are discussed in
(Mika, 2011). This author identifies the uncertainty
of nutritional information of recipes or foods, or the
missing or incorrect data from food recording
measurements as the main challenges and suggests
ways to tackle them. In (Sezgin and Ozkan, 2013) a
literature review on Health Recommender Systems
(HRS) is presented: studies have demonstrated that
HRS have branched out in different fields of health
industry and HRS applications have been
increasingly embedded in the health service systems.
Challenges and opportunities in HRS are also
addressed in this paper.
In (Åberg, 2006) the author has focused
specifically on older adults and their nutritional
needs, designing a recommender system with user
interfaces designed to consider the specific needs of
the user group. The recommendations generated by
this system are based on parameters that go beyond
nutritional needs, such as taste or available food
items at the person’s home. However, the
description of the system says nothing about recipes
prepared specifically by dieticians, it did not allow
multiple users and it was not designed to
accommodate specific medical conditions.
Moreover, the user interface, given the time of that
work, does not consider current mobile contexts of
In (Hazman and Idrees, 2015), a prototype for a
healthy nutrition expert system for children is
proposed that considers all stages of the child, their
growth stage, gender and health status. A case study
is presented and a web application was developed
however, the validation of the knowledge for the
proposed system is still needed.
Recently, (Espín et al., 2015) presented
NutElcare, a semantic recommender system that
provides healthy diet plans for the older adults. It
claims to retrieve reliable and complete information
from expert sources as nutritionists, gerontologists
as well as knowledge from information systems and
nutritional databases and with that information aim
to assist older people to take advantage of these tips
and make their own diet plans.
Regarding commercially available ICT
applications designed for non-professionals, there
are different options, such as EatThisMuch
, Lose It!
, Lifesum
, or Nutrino
Among other features, in general all of these
applications offer the ability to monitor calories
consumption based on the manual input of food
from a database. Other features can be provided,
such as step-by-step guidance to prepare meals, a
shopping list or the use of social interactions through
social networks and gamification. Among these
applications, only Nutrino and EatThisMuch are
capable of creating personalized meals plan.
The mobile meal recommender system that we
are presenting could be a good solution for older
people, providing them healthy and personalized
dietary plans, which are suitable to their individual
requirements (based on age, sex, activity level,
weight and height) and dietary preferences.
SousChef system is specifically addressed to this
SousChef: Mobile Meal Recommender System for Older Adults
target group because consider the nutrient intake
recommendations for older persons (age > 65)
regarding (1) Energy: 1.4-1.8 multiples of the basal
metabolic rate to maintain body weight at different
levels of physical activity; (2) Protein intake of 0.9-
1.1 g/kg per day, the equivalent of 15-20% of the
daily energy; (3) Lipids intake of 30% of the daily
energy in sedentary older persons and 35% for
active older persons; consumption of saturated fats
should be minimized and not exceed 8% of energy,
and (4) Carbohydrates intake of 55 to 60% of the
daily energy (World Health Organization, 2002).
Moreover, dietary recommendations using the
“food-based dietary guideline approach” (Wahlqvist,
2002) were also taken into account. SousChef
system can be a remarkable advantageous system
compared to other applications available in the
The SousChef system is composed of a central cloud
server and a mobile application which is the user
interface for the system. The cloud server is
responsible for centrally storing the information of
the system and making it accessible through web
service application programming interfaces (APIs).
Its easy accessibility also facilitates the integration
of information from other sources, which is
demonstrated through the integration with Fitbit
cloud services to retrieve users’ activity
measurements measured by Fitbit
Given the computational demands for the
creation of meal recommendation, the superior
processing capability of the server when compared
to mobile devices also makes it a more suitable
option. The generation of recommendations can be
triggered by the mobile devices through web service
The recommendations are created considering
information from different sources: personal
information provided using the user interface,
activity data through Fitbit devices and nutritional
information from the food composition database.
Work performed on top of the database will be
described in the following subsection.
3.1 Food and Recipe Database
The development of the SousChef system was based
on the Portuguese Food Composition database
elaborated by INSA (Ministério da Saúde, 2006).
This database contains the nutritional composition of
over a thousand products.
For the purpose of the system, the ingredients
that are not suitable for direct consumption (e.g. raw
chicken meat) and the specific ingredients (e.g. salty
chips) which are not considered healthy for the
target group were selectively removed by the
nutritionist. Moreover, combinations of recipes and
ingredients were also created by the nutritionist to
ensure that SousChef’s recommendations are not
only suitable for every meal of the day and for the
target population, but also culturally acceptable. In
this system context, a recipe does not refer to the
instructions to prepare the meal, but rather to a
combination of ingredients and respective quantities.
Multiple combinations were created for each meal of
the day: 40 combinations for breakfast, mid-morning
and mid- afternoon snacks; 20 combinations for
supper and 340 combinations for main dishes. Each
combination for a meal comprises the list of
ingredients and respective quantities based on a
daily intake of 2000 kcal.
3.2 Recommender System
The implemented recommender system is a content-
based recommender system and mainly employs
information retrieval techniques (Adomavicius and
Tuzhilin, 2005). In order to create a personalized
weekly meal plan, there are three main steps:
calculation of nutritional requirements, selection of
food items for each meal and scaling the meals to
match the user's caloric needs (Figure 1). The
algorithm was designed in collaboration with a
The Estimated Energy Requirement (EER) is
calculated from the predictive Harris-Benedict
equations (Long et al., 1979) equations used to
estimate the Basal Metabolic Rate (BMR) by
multiplying with the physical activity level (PAL)
(National Research Council, 1989). BMR is
calculated based on a user's energy expenditure, age,
sex, weight, height and PAL is calculated based on
the Total Energy Expenditure measured using Fitbit
devices. The daily caloric need is then distributed
across meals as follows: 15% for each breakfast,
middle morning and middle afternoon snack meals;
25% for each lunch and dinner and 5% for supper
(Candeias et al., 2005).
ICT4AWE 2017 - 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health
Figure 1: Overview of Meal Plan Recommender System.
After estimating the user’s caloric needs, the next
step consists in selecting the most appropriate
ingredient combinations for each meal considering a
given context. The meal planning context consists of
the user’s personal information, including nutritional
needs and food preferences, as well as meal planning
history (in order to enable dietary diversity in the
resulting plan).
Considering the previously created
combinations as candidates, for each meal of the
week being planned, a two phase selection process
will determine the most suitable candidate for that
meal. The first phase consists of applying restriction
rules, which filters candidates that are not suitable
for a given meal and context. Multiple rules have
been implemented for removing candidates: limiting
repeated recipes in the same week by removing
candidates which have been used in planning more
than twice; removing any candidates that include
comfort food; filtering candidates that include
ingredients users are allergic to.
The next phase consists in selecting the most
suitable candidate from the ones that were not
filtered. This is performed by calculating a score S
for each candidate c according to different criteria
measured by different heuristic functions H
. Each
heuristic function evaluates the meal planning
context, which meal is being planned and a single
candidate and assigns a suitability score between 0.0
and 1.0 to each candidate. Different heuristic
functions have been implemented to reflect the
criteria identified by the nutritionist. One of them
consists in favouring main dish combinations with
the same soup in four consecutive meals (about one
litre). The idea behind this criteria is allowing users
to cook soup for several days. The heuristic function
checks if the series of one litre was met. If so the
same value (0.5) is assigned to every candidate.
Otherwise, 0 is assigned to candidates with a
different soup and 1 to the others. Another criteria is
to favour meat dishes for lunch and fish for dinner in
order to have lighter dinners. For main dishes, the
candidates meeting the criteria are assigned the score
1 and 0 to the others. All candidates for other meals
are assigned 0.5. Finally, another heuristic takes the
food preferences of users into consideration. Using
the application, users are able to provide ratings to
ingredients from 0 to 4, which are normalized into a
value between 0 and 1. The preference for a
candidate is calculated by combining the preference
for each of its composing ingredients whenever
available or the value 0.5 instead. The final score, S,
for a candidate is also calculated as the average of
the values calculated by each heuristic function.
The chosen candidate for each meal being
planned is the one with highest suitability score S.
The approach followed by this algorithm benefits the
scalability of the system, since it facilitates the
inclusion of new restrictions and heuristics to
consider new criteria and data to provide users with
better recommendations. It also enables in the future
to use different weights to each heuristic function for
each user, personalizing the recommendations even
Once the ingredient combination has been
chosen for each meal, the final step of the planning
process consists in scaling the ingredient quantities
in order to match the energy requirements of that
particular individual. The previously calculated
caloric requirements for a given meal are compared
with the total energy of the ingredients in the chosen
candidate’s plan. If the difference is higher than an
acceptable deviation, the quantity of the ingredients
will be scaled to suit the requirements. To ensure
that daily nutrient requirements are maintained, only
ingredients from specific categories will be scaled:
cereals, fruit and legumes, dairy products, meat, fish,
eggs and oil. The new quantity for each scalable
ingredient is then calculated using a weighted
average so the quantity of more caloric ingredients
change more than others, therefore reducing the
changed amount in grams of the overall
To make the interaction between the food
recommender system and the end-user possible, a
mobile application was designed. The current
version of the mobile application is organized in
three different components: the meal plan, the
grocery list, and the activity monitoring.
Supporting these different components, there is a
profile that aggregates the various settings and
preferences used to tune the system. The profile
contains anthropometric data, food-related
SousChef: Mobile Meal Recommender System for Older Adults
preferences, and activity profile. Regarding the
food-related preferences, two levels of control are
provided: food restrictions, and dietary
considerations. Food restrictions are hard constraints
that remove food from the recommendation engine.
That is, some food added to the food restrictions list
will not be returned in the meal recommendation.
Dietary considerations work with a similar principle,
but on a group level, therefore, restricting an entire
set of products, and consist of a predefined list of
diets that can be followed by the user (e.g.
vegetarian, lactose free). For instance, if the lactose
free diet is selected all the products with lactose will
be removed.
4.1 Meal Plan
The Meal Plan section is the centre of the entire
system from a technical perspective but also from
the point of view of the user. The Meal Plan fulfils
two main goals: to generate new meal plans, and to
track the user’s food intake. In the application, the
Meal Plan section is responsible for presenting all
the information related to nutrition.
The Meal Plan (Figure 2) is the place to record
everything that the user eats throughout the day.
There are two different ways to log information into
the food diary: 1) Automatic food recommendations;
2) Manual input.
Automatic food recommendation: The
fundamental feature of this system is the ability to
generate personalized meals plan according to the
needs of the user.
Manual input: Although the system is in theory
capable of generating an entire meal plan, in
practice, the recommendations generated by the
system might not be enough to cover all the different
scenarios involved in a typical diet. To address this
issue, it is possible to manually record additional
information in the diary. New entries can be added
based on a search query on the food database that
also powers the food recommender engine.
To complement the food diary, which provides a
quick overview of the user’s diet, each recipe or
ingredient has a dedicated view to display additional
information and actions (Figure 3). The nutritional
information of a product is one of the aspects that
are provided in this user interface. Besides the
energy value in calories, there is also the macro and
micro nutrients information. Basic mechanisms for
editing the product and the diary (for example,
removing products form the diary) are also provided.
The user is able to control the recommender engine
by removing ingredients from the list of approved
products. In this case, products added to that list are
entirely ignored by the system. However, the control
level is rather limited since users are only able to
make binary decisions. In order to provide the user
with additional control of the system, another feature
particularly important in terms of personalization is
the ability to personally rate the products both
recipes and ingredients. Whereas in the former case
the system works with hard constraints that remove
products from the recommender engine entirely, in
the latter case the user input is used to influence the
weight of a product in the engine. The rating system
used in this evaluation contains three different
values, which are used to adjust the weight of the
product when a new plan is generated. From the
perspective of the user, this translates to (Figure 3,
right): 1) “I don’t like it that much”, 2) “I like it”,
Figure 2: Meal Plan.
Figure 3: Ingredient view.
and 3) “I love it”. For instance, a product rated with
the first score will not stop being recommended to
ICT4AWE 2017 - 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health
the user, but since it has a lower weight, it will be
recommended less often.
4.2 Grocery List
Although the Grocery List is not the focus of the
SousChef system, it can nevertheless be a valuable
tool in the above mentioned goal to assist food
management. The goal of the Grocery List
component is to help the user with shopping related
activities. This component takes on a physical
grocery list, but augments it with new capabilities
(Figure 4). One of the main features is the
integration with the Meal Plan component and the
ability to automatically add products to the list based
on the ingredients of a recipe. There is also the
option to manually add new products to the list
based on a database search.
Since the goal of the Grocery List is to assist
users in their shopping activities, there was the
concern to design a user interface that would be easy
to use in the wild, e.g. in the supermarket. Therefore,
one characteristic of the system is the ability to work
offline, so that the user does not need an Internet
connection to access his or her grocery list while in
the supermarket. Users are also able to mark
products as bought as they go, and that information
will be synchronized the next time there is an
Internet connection. Moreover, products are
organized by aisles to make the shopping process in
the store more efficient.
Figure 4: Grocery List.
4.3 Activity Monitoring
Along with nutrition, an important part of a healthy
lifestyle is a person’s activity level. The Activity
Monitoring used in the SousChef system presents
the users with metrics related to their activity
(Figure 5). The system is able to collect and display
calories burned, steps and active time. For each of
these metrics, daily objectives can be set in order to
raise awareness to their healthy physical activity
Given that one of the inputs required by the
system in order to generate new meals is the level of
activity of the user, the Activity Monitoring
component is responsible for collecting user activity
data and feeding those data to the recommender
engine. In order to do that the system can be
connected to an activity monitoring device such as a
Fitbit bracelet. The information gathered from such
devices is then converted to a scale that classifies the
level of activity as belonging to one of five levels,
ranging from “sedentary” to “extremely active”. The
advantage of using a wearable device such as Fitbit
is that the user activity information can be collected
seamlessly, without direct input, with a reasonable
level of accuracy, and updated automatically to our
system. However, in order to free the system from
dependence on external devices or systems, it is also
possible to manually insert this information in the
user profile. In such scenario, the charts with the
user activity information would not be used.
Figure 5: Activity monitoring.
5.1 Algorithm Validation
In this section the results of the tests performed with
older adults regarding the recommender system and
the mobile application itself are presented and
discussed. For both tests, the project was explained
to the subjects and informed consents were obtained.
SousChef: Mobile Meal Recommender System for Older Adults
The questionnaires and protocols were designed to
be applied to older people.
In order to ascertain user’s quality perception of
the meal recommender system, a first set of tests
were performed with 16 subjects, 8 men and 8
women, with a mean age of 70 ± 4.2 years, mean
height of 1.64 ± 0.07 m and a mean weight of 76 ±
12 Kg. Besides the anthropometric measures to
insert in the system, participants were also asked
about their preferences concerning ingredients and
their activity level. This information was then
submitted to the system in order to generate a
personalized weekly plan composed of six meals per
day. Afterwards, participants were asked questions
regarding the recommended meals, daily plans and
the weekly plan itself, which they were asked to
evaluate on a 4 point Likert scale, ranging from 1
(Strongly disagree) to 4 (Strongly agree).
For each meal, participants were asked whether
they could eat the suggested recipe and if they
considered the recipe to be adequate for that meal.
Regarding the daily plans, participants were asked if
they felt the daily plan was adequate for them.
Finally, for the weekly plan, participants were asked:
if they would follow the generated weekly plan; if
they would use the application to create a meal plan,
should this application be available to them; to rate
the sentence “I do not like the system, therefore I
would not use it”. It should be noted that the last two
questions are actually the inverse of each other. This
was done in order to minimize biases in the subjects’
personal perception of the questions. In addition to
these questions, the subjects were asked, by
answering yes/no, if the generated weekly plan was
good for them. Furthermore, throughout the
questionnaire, the subjects’ comments and
suggestions were also annotated.
Regarding the results for meals, the subjects
were, in general, agreeable with the recommended
meals. Concerning the case if they could eat the
suggested meal 60.86% of the subjects strongly
agreed, 26.04% agreed, 8.48% disagreed and 4.62%
strongly disagreed. To the question about recipe
adequacy to the meal 52.98% of the subjects
strongly agreed, 22.02% agreed, 20.69% disagreed
and 4.31% strongly disagreed. In Figures 6 and 7,
the answers are grouped by meal type.
Considering Figure 6, it can be seen that
participants felt that the recipes for mid-afternoon
and mid-morning meals were not as conforming to
their dietary restriction as were the recipes allocated
to the remaining meals (in this case, it was due to the
recommendation of too many recipes with pork
sausage). When it comes to Figure 7, it can be seen
that the participants overwhelmingly felt that the
recipes allocated to the mid-morning meal were not
a good fit. Through the observation of the
participants’ comments on the suggested recipes for
the mid-morning meal, the cause for this misfit is
due to the fact that they either felt that the recipes
consisted of too much food or that this meal was not
necessary. It should also be noted that some subjects
felt that the recipes for dinner consisted of too much
Figure 6: Scores for per meal regarding if the subject
could eat the suggested meal.
Figure 7: Scores regarding recipes adequacy for the meal.
Considering the results about the adequacy of the
daily plans, Figure 8 shows that the most disliked
daily plans where those for Sundays. Taking into
account the participants’ comments throughout the
tests, the cause of this was due to the fact that in this
day the participants usually eat recipes that are not
eaten in other days of the week or that they do not
have as many as meals in this day as they do in the
other days of the week. Regarding all evaluations of
the judged adequacy of each daily plan, 6.25% were
graded with 2 (disagree), 41.07% with 3 (agree) and
52.68% with 4 (strongly agree).
Concerning the weekly personalized plan, as
depicted in Figure 9, it is possible to conclude that
the majority of the participants would follow such a
plan (more than 70%) and also use an application
like this one, with 20% of the subjects disagreeing.
Furthermore, 50% of the participants felt that the
ICT4AWE 2017 - 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health
weekly plan was good for them. Figure 8 shows that,
in general, the participants had a positive experience
with the system.
Figure 8: Scores for daily plan adequacy.
Figure 9: Meal plan and SousChef acceptance scores.
5.2 User Interface Validation
In parallel to the validation of the outputs of the
recommender engine, we were also evaluating the
user interface. The evaluation of the SousChef
application was executed through the assistance of
usability tests with a low-fidelity prototype of the
user interface. The primary objectives of these tests
were to: 1) identify navigation and flow issues; 2)
evaluate the rating system used for the recipes; 3)
explore different data visualization methods with
elderly users.
Usability test sessions took place at a local day-
care centre, and five participants over 65 years were
recruited to participate. All of the participants had
taken part in previous usability tests with
smartphones or tablets. For the testing material, a
Motorola Moto G with a 5-inch screen and running
Android was used. An interactive prototype
developed with InVision was used in order to
simulate behaviours and flows.
Participants were required to complete a total of
seven small tasks. Task success, errors, deviations
and assistances were collected in order to evaluate
the performance of the users. Standard usability tests
were not used at this point because, rather than
general levels of usability and satisfaction, the
authors were seeking to identify specific problems
with the user interface, information architecture and
the mechanics of interaction, which could be
improved in future iterations. Task analysis was a
better fit for the purpose. On a user level, three
participants completed six out of the seven tasks
successfully; one performed five tasks successfully;
and the other participants only completed four tasks.
The main problems were identified in Task 3, which
required participants go back to the previous page by
using the back button. And on Task 6, which
required participants to view the meal plan for the
entire week. This was a critical navigation issue that
resulted from the lack of affordances of the button to
change the week.
In terms of data visualization, there were a few
issues with the charts used. First, the measurement
unit was not clear; the labels were also not readable
enough; and the daily objective plotted on the chart
was not self-explanatory.
All of these issues will be addressed in the next
iteration of the application.
In this work a mobile meal recommender system,
named SousChef, was presented having as target
audience older adults. SousChef is intended to act as
a nutrition companion that guides older adult users
into making wise decisions regarding food
management and healthy eating.
Although in the literature, recommender systems
were presented and different ICT applications are
available, we have found no similar mobile-based
solution designed with a focus on older adults’
Tests to the recommender system and mobile
application were made with older adults, to infer, on
the one hand, the adequacy and quality of the meal
plans suggested and, on the other hand, to test the
usability of the mobile application itself for this
specific audience. The user testing results were very
satisfactory with more that 70% of the participants
considering following the meal plan suggestions and
use an application like the one presented. On the
usability side, results were also satisfactory for the
first prototype and we will keep iterating the current
solution and testing new features.
SousChef: Mobile Meal Recommender System for Older Adults
As future work, new features are to be
implemented, such as choosing the number of meals
to be planned per day and setting recurrent meals.
Another interesting feature to implement in the
system would be to take into consideration the
profile of more than one person. Also, a study with
more participants will allow a more detailed analysis
of the quality of the recommendations in terms of
the nutritional requirements of the participants.
This paper reports the first version of the system
and respective user mobile application, which brings
some limitations. Due to its stage of development, it
has not yet been possible to conduct a full-fledged
evaluation of the system. Because people have many
deeply rooted habits and beliefs related to food, we
are aware that a thorough evaluation on the usability,
usefulness and acceptance of the system may only
be done in a longitudinal study in users’ real context.
The authors are aware of this current limitation.
However, in this regard, the strength of the work
relies on it having involved older adult users from
onset, which allows to identify and eliminate
potential barriers to acceptance and use of SousChef.
We would like to acknowledge the financial support
from North Portugal Regional Operational
Programme (NORTE 2020), Portugal 2020 and the
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