=Paper=
{{Paper
|id=Vol-2559/paper4
|storemode=property
|title=Improving Sleeping Habits: Preliminary Experiments in Barcelona and Lleida
|pdfUrl=https://ceur-ws.org/Vol-2559/paper4.pdf
|volume=Vol-2559
|authors=Eloisa Vargiu,Carme Zambrana,Adriano Targa,Ferran Barbe
|dblpUrl=https://dblp.org/rec/conf/aiia/VargiuZTB19
}}
==Improving Sleeping Habits: Preliminary Experiments in Barcelona and Lleida==
https://ceur-ws.org/Vol-2559/paper4.pdf
Improving Sleeping Habits: Preliminary
Experiments in Barcelona and Lleida
Eloisa Vargiu1 **[0000−0002−3840−628X]? , Carme
Zambrana1??[0000−0003−2625−1900] , Adriano Targa2 , and Ferran
Barbe2,3[0000−0002−2340−8928]
1
Eurecat, Centre Tecnlogic, eHealth Unit, Barcelona, Spain
{name.surname}@eurecat.org
2
Institut de Recerca Biomedica (IRBlleida), Lleida
febarbe.lleida.ics@gencat.cat, adrianotargads@gmail.com
3
CIBERES, Madrid, Spain
Abstract. Due to intrinsic (e.g., daily-life habits) and extrinsic (e.g.,
environmental change) factors, people are far to have a healthy life and,
thus, there is an increase of chronic diseases, mental disorders, and pre-
mature death. In this paper, we propose a solution to improve lifestyle
habits in terms of sleeping activity through an intelligent system that
monitors the sleeping and further habits together with environmental
data and provides personalized recommendations and nudges. Our so-
lution aims to study how to improve sleeping habits of citizens, inves-
tigating if and how the environment impacts the sleeping activity, as
well as how it may be influenced by bad lifestyle habits. To perform a
feasibility study of the proposed solution, on May we started recruiting
volunteers in Barcelona (from Eurecat) and in Lleida (from IRBLleida).
Volunteers were asked to wear their activity tracker 24/7, to weight once
a week, and to answer the selected questionnaires at the end of the week.
The day of the inclusion, we also took note of the comfort of the bed of
each volunteer, as well as her/his address to take into consideration the
environmental factors. Preliminary experiments clearly show the need
for improving sleeping habits in the population and, thus, of intelligent
solutions like the one presented in this paper.
Keywords: Sleeping activity · Life-style · Activity monitoring · Envi-
ronmental monitoring.
1 Introduction
To live longer, healthier and more active, people at any age must follow sim-
ple and clear suggestions that cover the 3 main pillars of health: nutrition,
?
Corresponding author.
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
??
These authors contributed equally to the work
2 E. Vargiu et al.
physical-, and sleeping-activity. Unfortunately, due to the intrinsic (e.g., daily-
life habits) and extrinsic (e.g., environmental change) factors, people are far to
have a healthy life and, thus, there is an increase of chronic diseases, mental
disorders, and premature death. While a lot of effort has been done to support
good habits on nutrition providing personalized diet and continuous follow-up
and to encourage and monitor physical activity, sleeping activity is still an un-
taught and unconsidered problem. In this paper, we propose a solution aimed at
improving sleeping habits through an intelligent system that monitors the sleep-
ing activity together with environmental data and lifestyle habits and provides
personalized recommendations and nudges. In particular, the proposed system
focuses on the following research questions:
Q1 How we may improve sleeping habits of citizens?
Q2 How the environment influences and impacts the sleeping activity of citizens?
Q3 How bad lifestyle habits influence and impact the sleeping activity of citi-
zens?
The work presented in this paper is part of the CarpeDiem project (Collabo-
rative and Adaptive Recommender for PErsonalized DIEt Management), aimed
at providing intelligent and automatic support to people who want to follow a
diet to lose weight, or to maintain a healthy lifestyle (in a comprehensive way,
taking into account nutrition, physical activity, and sleep). CarpeDiem target
users are elderly people who need to follow healthy habits, including sleeping
better.
To study the feasibility of the solution and to start creating the machine-
learning models that will allow to send personalized recommendations to the final
users, on May we started collecting data from volunteers in Barcelona and Lleida
(Spain). Accordingly, in this paper we present the overall idea of the solution,
currently under development, and the preliminary experiments performed to
study its feasibility.
The rest of the paper is organized as follow. Section 2 illustrates the under-
lying problem and its relevance for the population. In Section 3, we describe the
overall solution for monitoring sleeping activity, environmental data, and lifestyle
habits. Section 4 presents the preliminary experiments we made in Barcelona and
Lleida with volunteers. Finally, Section 5 ends the paper with conclusions and
future directions.
2 The Problem
The Center for Disease Control and Prevention (CDC) in the United States,
within the Healthy People 2020 policy, has considered that sleeping little is a
public health problem. In fact, according to a recent CDC study, more than a
third of adult Americans do not get enough sleep on a regular basis [7]. Insuf-
ficient sleep is not exclusively a problem of the United States, it also affects
other industrialized countries such as the United Kingdom, Japan, Germany, or
Canada [1]. Regarding Catalonia, the average number of hours of the overall
Improving Sleeping Habits... 3
population is 7.1 hours at night, very close to the lower limit recommended by
clinicians (i.e., 7 hours at night). At Catalan level, a survey made in 2015 [3]
performed using the SATED questionnaire [2] confirms that sleeping duration is
age dependent. Moreover, they found that the SATED score is lower for elderly
people than citizens of other age groups, meaning that their sleep is worse.
According to recent studies, the proportion of people who sleep less than
the recommended hours (i.e., between 7 and 9) continuously increases and it is
associated to lifestyle factors related to a modern society, such as psycho-social
stress, unbalanced diet, lack of physical activity, and excessive use of electronic
media, among others [8]. This is alarming since it has been found that lack of
sleep is associated with negative social and health outcomes.
The association of lack of sleep with a series of negative social and health
outcomes is becoming worrisome. Insufficient sleep duration has been linked to
seven of the fifteen leading causes of death in the United States. The existing
evidence suggests that the relationship between sleep time and health, outside
of the normal window (7-9 hours), both below and above, is associated with a
high risk of stroke, coronary heart disease, hypertension, obesity, type 2 diabetes
and mortality [11] [12]. For instance, Gallicchio and Kalesan [5] report that the
combined relative risk (RR) for all-cause mortality for sleep insufficiency is 1.10
(95% CI = [1.06, 1.15]).
The link between sleep duration and mortality represents that a citizen who
sleeps on average less than 6 hours per night has a mortality risk 13% higher
than an individual who sleeps between 7 and 9 hours, which is the window that
is considered as the healthy amount of sleep. Furthermore, a person who sleeps
between 6 and 7 hours per night has a 7% increased risk of death, including
all causes of death such as fatal car accidents, strokes, cancer, or cardiovascular
disease.
Finally, sleeping insufficient time reduces productivity in the workplace: work-
ers who sleep less than 6 hours per day have an average productivity loss of more
than 2.4 percentage points due to absenteeism, calculated on those workers who
sleep between 7 and 9 per day. Those who sleep on average between 6 and 7
hours continue to generate a loss of productivity greater than 1.5 percentage
points compared to those of seven to nine hours. To put these numbers in per-
spective, assuming that there are 250 business days in a given year, this means
that a worker who sleeps less than 6 hours loses about 6 business days per year
more than a worker who sleeps 7 to 9 hours. A person sleeping for 6 to 7 hours
loses an average of 3.7 more business days per year [9].
3 The Proposed Solution
The aim of this work is improving sleeping habits in the population. Thus,
we propose an automatic and intelligent system that empowers and supports
the citizens giving them personalized recommendations and nudges. In order to
ensure a clinical relevance, the actual set of recommendations and nudges will
4 E. Vargiu et al.
be defined by the clinical personnel. Personalization will be then reached by
monitoring the citizens and studying their usual behavior.
Three main categories of factors that impact sleeping habits will be consid-
ered:
1. number of hours;
2. sleep efficiency, calculated as the ratio between the number of slept hours
and the total number of hours in bed per night;
3. the satisfaction of the citizen about her/his sleep calculated through the
standard questionnaire SATED.
Fig. 1. The proposed solution at a glance.
The proposed solution, sketched in Figure 1, works with a set of data coming
from different sources:
– The citizen wears a wristband 24/7 and the sleep activity is monitored in
terms of: total number of sleeping hours, number of sleeping hours during
the night, number of the sleeping hours during the day, number of naps,
time when going to sleep, wake-up time, and sleep efficiency. Moreover, the
data regarding the physical activity in terms of total number of steps and
vigorous activity before going to sleep are constantly monitored.
– The citizen is asked to monitor her/his weight once a week in order to take
under control her/his Body Mass Index (BMI) and to avoid reaching obesity.
Improving Sleeping Habits... 5
– To build the user’s profiles, the day of the inclusion, the citizen is asked
to answer to some questionnaires regarding her/his life-style habits. The
following questionnaires will be used: SATED, about the satisfaction of the
citizen regarding her/his sleep; Caffeine, regarding the number of cups of
coffee or teas drunk on average during the week; Smoke, on the number
of cigarettes smoked on average during the week; and Use of technology,
concerning the number of minutes spent on average using the smartphone
or a tablet before going to sleep.
– The smartphone of the citizen is used to detect the luminosity of the bedroom
during the sleeping time.
– The citizen will be asked regarding the comfort of the bed, since especially
vulnerable or at risk citizens often sleep on truly old or uncomfortable beds.
– To be aware regarding seasonality, daylight hours, and further environmental
data, we will rely to the Dark Sky API4 . It allows to look up the weather any-
where on the globe, returning (where available): weather conditions, minute-
by-minute forecasts out to one hour, hour-by-hour and day-by-day forecasts
out to seven days, hour-by-hour and day-by-day observations going back
decades, weather alerts, and humidity.
– To monitor the data about the air quality and noise pollution in Barcelona,
the Open Data service by the Barcelona Municipality (Open Data BCN)
will be used5 .
The system will collect and fuse the heterogeneous data coming from all the
data sources and, through machine learning techniques, will build the models to
cluster and classify citizens according to their habits.
First, the profile of the citizen is calculated adopting data mining and ma-
chine learning approaches relying on Knowledge Data Discovery (KDD) models
[4] as well as User Data Discovery (UDD) models [6]. A static profile will be
created considering gender and age together with data coming from the ques-
tionnaires. That profile will be dynamically and continuously updated by con-
sidering the data gathered from the wristband, the changes in the BMI, if any,
and the environmental data.
Once built the profile, the citizen will be automatically clustered according
to fixed groups of habits defined considering the intersections among the three
categories: number of sleeping hours (less than 7 hours, between 7 and 9 hours,
more than 9 hours), sleep efficiency (less than 50%, between 50% and 95%, more
than 95%), and satisfaction about the sleep (by using the 5 levels of the SATED
questionnaire: 1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Usually, 5 = Always).
For each cluster, the clinicians will define a suitable set of recommendations and
nudges with the final goal of moving all the citizens in the healthiest cluster,
corresponding to a number of hours between 7 and 9, a sleep efficiency higher
than 95%, and a satisfaction of 5 (i.e., always satisfied). In the case a citizen
does not belong to that cluster because s/he is not compliant with one or more
4
https://darksky.net/dev
5
https://opendata-ajuntament.barcelona.cat/en/
6 E. Vargiu et al.
of the categories, the system asks to choose the category s/he wants to improve
(monthly goal).
Recommendations and nudges will be sent to the citizen on daily (short-
term), weekly (medium-term), and monthly (long-term) basis. Recommenda-
tions and nudges on daily basis will be based on the number of slept hours, the
efficiency, the performed physical activity, and will take into account anomalies
in environmental factors specific of the considered day (e.g., high temperatures).
Taking particular reference to the category the citizen wants to improve, on
weekly basis the system will send specific questions or request of filling a ques-
tionnaire in case of anomalies (e.g., in case of a decrease of the number of hours,
the Caffeine questionnaire may be sent to be filled). Finally, monthly based ones
will take into account the changes in habits during the month, the change from
a cluster to another, if any, and will consider similarities among citizens in order
to send recommendations and nudges according to a collaborative filtering ap-
proach [10]. At monthly basis the citizen will be also asked to confirm or change
the category s/he want to improve, depending on the results of the past month.
From a technological point of view, the citizen will interact with the system
through a simple and user-friendly app installed in her/his smartphone and
that will automatically send recommendations and nudges as push notifications.
Through the app, the citizen will be also able to set-up her/his profile, answer
questionnaires, pair her/his smartphone and smart-scale (if any), or to manually
input the weight (e.g., after weighting at the pharmacy). The app will also allow
to capture the luminosity of the bedroom during the sleeping hours.
4 Preliminary Experiments
The success of the proposed solution strictly depends on the gathered data.
In fact, profiling, recommendations and nudges definition, clustering, and per-
sonalization cannot be achieved without a preliminary phase in which data are
collected and used to build all the models. Thus, we started on May recruiting
volunteers in Barcelona (from Eurecat) and in Lleida (from IRBLleida) to profile
the users and to study the feasibility of the proposed solution.
4.1 The Collected Dataset
A total of 30 volunteers has been recruited in Barcelona and Lleida. Volunteers
were asked to wear their activity tracker 24/7, to weight once a week, and to
answer the selected questionnaires at the end of the week. The day of the inclu-
sion, we also took note of the comfort of the bed of each volunteer, as well as
her/his address to take into consideration the environmental factors.
Due to technical problems and adherence issues, 6 of the 30 volunteers
dropped-out before finishing the first month of the study. Thus, the analysis
of the data have been done using the data of 24 volunteers (38.43 ± 11.46 years
old; 15 females; and 23.09 ± 3.66 BMI). The preliminary study started on May
and will finish in December.
Improving Sleeping Habits... 7
Fig. 2. Distribution of the volunteers in each group for the three categories (Sleeping
Hours, Efficiency and Satisfaction) at inclusion time.
At inclusion time, we first analysed the distribution of our population among
the three categories:
– Sleeping Hours
1. less than 7 hours;
2. between 7 and 9 hours (the healthiest group);
3. more than 9 hours.
– Efficiency
1. less than 50%;
2. between 50% and 95%;
3. more than 95% (the healthiest group).
– Satisfaction
1. Never;
2. Rarely;
3. Sometimes;
4. Normally;
5. Always (the healthiest group).
Figure 2 shows the percentage of volunteers in each group for the three
categories. As shown, all the volunteers slept less than 7 hours, which, as stated
above, is risky for their health. Regarding the efficiency, the 70% of the volunteers
are in the healthiest group, whereas the 30% are in the group between 50% and
95%. As for the satisfaction, the majority of the volunteers answered sometimes
or normally with about 40% of the answers in each. The rest of the volunteers are
in the Always group (about 8%) and in the Rarely one (only a 4%). It is worth
noting that nobody belongs to the Never group. This first analysis performed
at inclusion time shows the need of the proposed solution to improve sleeping
8 E. Vargiu et al.
Fig. 3. Distribution of the volunteers in the following four sets: less than 25% of the
weeks in the recommended group, between 25% and 50% of the weeks in the recom-
mended group, between 50% and 75% of the weeks in the recommended group, and
more than 75% of the weeks in the recommended group.
habits with the final goal to move the volunteers to the healthiest group of each
category, specially in case of Sleeping Hours and Satisfaction.
As a second step, for each category, we computed the percentage of weeks
that each volunteer belonged to the healthiest group. To better visualise the
distribution, for each category we considered four sets (see Figure 3): less than
25% of the weeks, between 25% and 50% of the weeks, between 50% and 75% of the
weeks, and more than 75% of the weeks. As shown, the majority of the volunteers
are in the set less than 25% of the weeks for all the categories: about 58% for
Sleeping Hours, 41% for Efficiency, and 91% for Satisfactions. Results clearly
show that there is a urgent need for a solution to improve sleeping activity of
citizens considering all the three categories. In fact, at least in our population,
the percentage of people that sleeps the right number of hours, with a high
efficiency, and is satisfied of their sleep is very low (about 16%, 20%, and 4% for
Sleeping Hours, Efficiency, and Satisfactions, respectively).
Finally, we analysed the dynamics of the changes among groups in a monthly
basis. To visualize it a Sankey Diagram is used: the left column display the groups
from which the volunteer moved and the right column the groups to which the
volunteer moved. The links between them are represented with arcs that have a
width proportional to the number of times this transition occurred.
Figure 4 shows the dynamics of the Sleeping Hours category. As shown, all
the volunteers are in the groups less than 7h and between 7h and 9h, being the
majority of them in the first group (about 72%). The volunteers tend to stay in
the same group. Only a small percentage changed from less than 7h to between
7h and 9h (about 11%), and vice versa (about 27%). In this case, the proposed
Improving Sleeping Habits... 9
Fig. 4. Dynamics of the Sleeping Hours category.
solution will give support to move all the volunteers from the less than 7h to the
between 7h and 9h group. It is worth noting that, once a volunteer reaches the
healthiest group, the system will give support to do not change the group.
Figure 5 shows the dynamics of the Efficiency category. As shown, all the
volunteers are in the groups between 50% and 95% and more than 95%, and
they are balanced (about 51% and 49%, respectively). In this case, the tendency
is either stay in the same group (about 60%) or change to the other one (about
40%). Seemly to the Sleeping hours category, our system will work to change
this dynamic and move all the volunteers from between 50% and 95% group to
the more than 95% one. Once a volunteer reaches the healthiest group, s/he will
be supported by the system to stay in it.
Figure 6 shows the dynamics of the Satisfaction category. As shown, the
majority of the volunteers are in the groups Sometimes and Normally (about
the same number in each, i.e. 43% and 49%, respectively), and a small percentage
in the group Rarely (about 8%). The volunteers from the Rarely group used to
stay in the same group (about 67%) or to move to the Normally one (about
33%). On the contrary, volunteers belonging to Sometimes and Normally groups
move to their consecutive groups: Rarely, Sometimes, and Normally (about 12%,
47% and 41%, respectively) and Sometimes, Normally and Always (about 26%,
10 E. Vargiu et al.
Fig. 5. Dynamics of the Efficiency category.
68% and 5%, respectively), respectively. Let us note that, only volunteers from
the Normally group moves to the Always one (about 5%)6 .
4.2 Daily Monitoring
Recommendations and nudges on daily basis will be based on the number of
slept hours, the efficiency, the performed physical activity, and will take into
account anomalies in environmental factors specific of the considered day (e.g.,
high temperatures).
The aim of our system is to encourage the user to Keep going if s/he is
improving with respect to “yesterday” or suggests to Keep following the recom-
mendation to improve in case of worsening. Recommendations could be provided
with respect to the sleeping habits (e.g., You should go to sleep every night at the
same hour and before 12 am) and/or the performed physical activity (e.g., Do
not perform high-intensity exercises 2 hours before going to sleep). Regarding the
environmental factors, the system informs: when the city is suffering from high
temperature and suggests to the user to keep the bedroom temperature to the
recommended one (between 16-18◦ C); when the air quality is low and suggests
the user to not perform vigorous activity outdoors; and when the noise level of
her/his street is too high and suggests to keep closed the windows during night.
6
The group Always appears only in the right column because the month which a
volunteer was in the group Always was the last one of the study
Improving Sleeping Habits... 11
Fig. 6. Dynamics of the Satisfaction category.
Figure 7 shows an example of collected data of a volunteer (52 years old; male
and who lives in Barcelona) and potential messages that the proposed system
should send to the final user. Let us note that during the selected days (the week
from June 24th to June, 30th) a high temperature was measured in Barcelona.
4.3 Weekly Check-Up
In order to support the user in changing her/his sleeping habits, the proposed
system will compute a summary, weekly. Based on it, nudges and recommenda-
tions will be provided. In case a change greater than ±2 hours, 5% in efficiency,
and/or ±3 points in satisfaction is detected, the system will send a requests to fill
the questionnaires in order to recalculate the profile and find potential anomalies
that could have affected the sleeping activity. Figure 8 shows an example of 3
consecutive weeks of a volunteer (26 years old; female): her evolution in terms
of the three categories and the suggested messages to give her support.
4.4 Monthly Clustering
At the beginning of a new month, the system analyses all the collected data to
cluster the users considering the three selected categories. In so doing, the system
verifies if the user changed the cluster s/he belongs to, or not. In case of a positive
change, the system sends an awards to the user and suggests the new goal to be
12 E. Vargiu et al.
Fig. 7. Example of daily monitoring during a week in which in Barcelona a high tem-
perature was measured.
followed during then next month. In case of a negative change or not changing,
the system recommends to still work on the same goal during the next month.
In both cases, the users can accept or reject the received suggestion and choose
the goal to be achieved. Figure 9 shows an example of 2 consecutive months for
a volunteer (26 years old; female) in which efficiency has been improved while
the satisfaction still needs to be enhanced.
5 Conclusions and Future Work
In this paper, we presented a novel solution aimed at improving sleeping activi-
ties. Although the research and development is at its early stage, the feasibility
study performed right now shows how the research questions listed in the Intro-
duction will be addressed.
Q1. How we may improve sleeping habits of citizens? The proposed
solution is a recommender system that will be built according to clinical evi-
dence and with the supervision of the clinical team of the Sleep Unit at the IRB
Improving Sleeping Habits... 13
Fig. 8. Example of weekly check-up and messages during 3 consecutive weeks.
in Lleida. The overall set of recommendations and nudges will be defined by
clinicians of that Unit and will be fully personalized according to the typology
of the final user. The system will automatically send the recommendations and
nudges to the citizens on daily base (short-term recommendations), on weekly
base (medium-term recommendations), and on monthly base (long-term recom-
mendations).
Q2: How the environment influences and impacts the sleeping ac-
tivity of citizens? It has been shown in the literature that the main envi-
ronmental factors that influence and impact the sleeping activity are: daylight
hours, seasonality, luminosity, humidity, pollution, noise, and comfort of the bed.
The proposed solution will consider all these factors, their influence on sleeping,
and will define recommendations taking into account the peculiarities of each
citizen according to the environment in which s/he lives.
Q3: How bad lifestyle habits influence and impact the sleeping ac-
tivity of citizens? Having bad lifestyle habits has an impact on the overall
sleeping activity. In particular, clinicians recommend to not perform the follow-
ing tasks just before going to bed: take a coffee, smoke, eat, perform vigorous
physical activity, and use the smartphone or a tablet. In addition, citizens have
to control their weight, in order to avoid obesity which could cause sleeping
problems. The final system will send recommendations and nudges on this kind
of tasks to improve citizens behavior and will sporadically monitor coffee in-
take, smoking, eating, and smartphone usage, in case of detected anomalies with
respect to the usual sleeping behavior. Moreover, physical activity will be con-
tinuously monitored and suitable suggestions given accordingly.
14 E. Vargiu et al.
Fig. 9. Example of monthly clustering during 2 consecutive months.
As for the future work, as soon as 6 months of data will be collected from all
the volunteers, we will build the models for sending daily, weekly, and monthly
recommendations and nudges. Clinicians in IRBLleida are currently working
on the definition of them. At the beginning of next year, the system will be
integrated in the CarpeDiem app and the pilot will start with new volunteers
from both Barcelona and Lleida, as well as further towns in the Catalonia Region.
6 Acknowledgments
This work was financially supported by the Catalan Government through the
funding grant ACCI-Eurecat (CarpeDiem project).
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