Predicting Subjective Well-Being by Smartphone Usage Behaviors
Yusong Gao
1
, He Li
2
and Tingshao Zhu
1
1
Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
2
The 6th Research Institute of China Electronics Corporation, Beijing 100083, China
Keywords: Smartphone Usage Behaviours, Subjective Well-Being, Mental Health.
Abstract: Subjective Well-Being (SWB) refers to how people experience the quality of their lives, thus to acquire
people’s SWB levels timely and effectively is very important. Self-report and interviewing are mostly used
techniques for assessing SWB, but cannot be done in time. This study aims to predict one’s SWB levels by
smartphone usage behaviours. We collect users’ smartphone usage and self-reported subjective well-being,
and found that several usage behaviours correlate with SWB, especially for females. For example,
smartphone users with higher SWB scores tend to use more communicating apps, play more games and read
more, but take fewer photos. Based on these findings, we trained a predicting model of user’s SWB based
on smartphone usage behaviours, and the accuracy is up to 62%. The result indicates that SWB can be
identified based on smartphone usage fairly well.
1 INTRODUCTION
Subjective well-being (SWB) means how people
experience the quality of their lives, comprising
longer-term levels of pleasant affect, lack of
unpleasant affect, and life satisfaction (Angner, 2010;
Diener, 1994). SWB focus on how a person
evaluates his/her own life, including emotional
experiences of pleasure versus pain in response to
specific events and cognitive evaluations of what a
person considers a good life (Diener, 2000).
Positive psychologists have done much research
on SWB (
Lyubomirsky, 2001), and they claimed that
the pursuit of happiness is regarded as one of the
most valued goals in almost every culture. Some
studies found that health and SWB influence each
other, as good health leads to greater happiness
(Okun, 1984), and a number of studies also found
that positive emotions and optimism have a
beneficial influence on health (Frey, 2011).
As SWB plays an important role in both mental
and physical health of people, it is critical to acquire
either individual or population SWB level. Currently,
SWB is generally measured by using self-reported
questionnaires, in which participants fill in the
related scales to get the SWB levels. However, this
method has three disadvantages.
1. It’s not convenient to collect SWB on large
population. It may take a lot of manpower
and material resource, and it is prone to
human errors in the process of questionnaires.
2. It is not easy to identify the driving force of
SWB level, if only SWB is presented. We
cannot identify any detailed personal
conditions or daily behaviours that can
explain his or her mental state.
3. Sometimes, it is difficult to acquire SWB in
time. Self-reported method is always time-
consuming, and conditions may have some
changes after the data process (i.e.,
questionnaire results) has been totally
completed.
In order to avoid these disadvantages, we
propose to predict SWB based on users’ phone
usage behaviours. Since a smartphone is built on a
mobile operating system with advanced computing
capability, it becomes an indispensable part of our
daily lives as an attractive tool for communication
and interpersonal interaction. IDC (the Internet Data
Center) statistics show that in the first quarter of
2013, total global mobile phone sales to 418.6
million, in which smartphones to 216.2 million,
accounting for 51.6% of the total mobile phone sales
volume.
There are various definitions of phone usage
behaviour, some researchers take it as part of the
functions of mobile phones, such as telephone,
calendar, SMS using frequency, etc. (Coen et al.,
2003). Suh et al. took some specific phone features
317
Gao Y., Li H. and Zhu T..
Predicting Subjective Well-Being by Smartphone Usage Behaviors.
DOI: 10.5220/0004800203170322
In Proceedings of the International Conference on Health Informatics (HEALTHINF-2014), pages 317-322
ISBN: 978-989-758-010-9
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
as phone usage behaviour, such as mobile e-
commerce usage (Suh et al., 2003). Falaki et al.
described it as users’ interactions with the device
and the applications used (Soikkeli, Karikoski and
Hammainen, 2011). In this paper, we investigate the
interactions between users and their devices to
explore individual’s phone usage behaviour, such as
frequency of telephone, SMS, application use, phone
switch habits, wallpaper changing, GPS using, etc.
Since smartphone is now a very powerful
platform, it is possible to record various interactions
between users and devices, ranging from the most
basic communication functions to a variety of third-
party applications use, such as the frequency of
making calls, sending text messages, App using,
GPS using. This data intensive platform provides a
new opportunity, as it allows us to study users’
various phone usage behaviours.
In order to record user's usage behaviour
automatically, we have developed an Android
application named MobileSens (Li et al., 2013; Guo
et al., 2011). This application can record smart
phone usage and upload data to the server. At the
same time, the application also allows the user to fill
a questionnaire and upload the answers to the server.
In this paper, we recruited 106 participants using
smartphones to record phone usage and SWB scores.
We analysed relations between user’s phone usage
and SWB, and found that it is possible to predict
SWB by phone usage behaviours with an accuracy
of 62%.
The rest of the paper is organized as following:
Section 2 introduces some related works conducted
by other researchers. We describe the details of our
data in Section 3. Section 4 mainly describes the
process of feature extraction, and the results will be
thoroughly discussed in Section 5. Section 6
concludes our work in this paper and gives a brief
discussion on future work.
2 RELATED WORKS
Much research has been conducted to investigate the
users’ psychological characteristics and their usage
of Internet and social media, such as Youtube, blogs,
Facebook. It is reported that Internet and social
media usage can reflect psychological characteristics
(Back et al., 2010; Biel et al., 2011; Counts and
Stecher, 2009; Stecher, 2008; Yeo, 2010).
Since smartphones become one kind of
ubiquitous communication and interpersonal
interaction tools nowadays, smartphone usage may
reflect our mood and mental health. Traditional
researches mostly used questionnaires to acquire
users’ phone usage preference, and then analysed
these data with the psychological scale scores. Butt
pointed out that mobile phone supports interpersonal
interaction, and concluded that psychological theory
can explain patterns of mobile phone use (
Butt and
Phillips, 2008). Ehrenberg analysed mobile IM
(instant messaging, IM) application use, and found
agreeable and low self-esteem have a negative
relationship with IM-related applications usage
among teenagers (Ehrenberg and Juckes, 2008).
With a self-designed questionnaire assessing
problematic mobile phone use, Billieux found
impulsivity played a specific role in mobile phones
usage (Billieux and Van der Linden, 2008).
Chittaranjan found some aggregated features
obtained from smartphone usage data can indict the
Big-Five traits (Chittaranjan and Blom, 2011). Gross
et al. reported that time spent on-line was not
associated with dispositional or daily well-being.
However, the closeness of instant message
communication partners was associated with daily
social anxiety and loneliness in school, above and
beyond the contribution of dispositional measures
(Gross et al., 2002).
Some research have been done to investigate
mobile phone usage and psychological
characteristics. Chittaranjan installed a data
collection procedure into Nokia N95, collected 117
participants’ phone use logs during 17 months,
including text messaging, applications, phone card
use logging (Chittaranjan et al., 2011). They found
several aggregated features obtained from
smartphone usage can be indicators of the Big-Five
traits. LiKamWa developed an iPhone software
system to record phone use data and remained user
of noting down their mood regularly (LiKamWa,
2013). He found smartphone use patterns fluctuate
as mood changes, and he built a smartphone
software system which can infer user’s mood by
how the smartphone is used.
In this study, we explore relationship between
smartphone usage and SWB using data collected
automatically. Differing from studies mentioned
above, we intend to predict users’ SWB based on
smartphone usage, thus to acquire the user’s SWB
timely and accurately.
3 METHODS
After MobileSens has been implemented, this study
was carried out by three steps. Firstly, we recruited
106 participants who owned Android smartphones
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and also use phones in daily life. During the
experimental time (one month), all the participants
were required to fill out the questionnaire at least
once, and should keep the phone network
connectivity when phone use records were
uploading. Secondly, we extract 48 phone use
behavioural features, including SMS, telephone use
and wallpaper changed frequency, GPS use
frequency, etc. We analysed the relationships
between these features and user’s well-being by
calculating the correlation coefficients. Finally, we
extract features and build a prediction model using
data mining method.
3.1 Participants
We recruited participants by advertising on social
networks. Finally, 98 participants between 18 and 32
years (mean 23.5, SD=2.48) were finally included in
the data analysis; 61.22% were male, and 38.78%
were female. Most of the participants are college
students and postgraduates, and Fig. 1 shows
detailed education levels of the participants.
Figure 1: Participants Education level distribution.
3.2 Data Collection
MobileSens consists of two modules, one is for
collecting phone usage, and the other is for filling
the questionnaire, which can get usage data and the
corresponding questionnaire results at the same time.
Its framework is shown in Fig. 2.
Figure 2: MobileSens Modules.
After one user installed MobileSens into their
Android devices, most of their interactions with the
device were recorded in the Android Sqlite, and
these data would upload to our server later.
MobileSens recorded 14 categories of information,
as listed in Tab. 1.
3.3 Psychological Scale
In our study, SWB is assessed by Satisfaction With
Life Scale (SWLS), which was developed to assess
subjective feelings of well-being (Diener et al.,
1985). SWLS is a single scale that has been used by
UNESCO (The United Nations Educational,
Scientific and Cultural Organization), the CIA (the
Table 1: Categories of Phone usage information and details.
Type Record Content
activity application log creating, starting, resuming, stopping, and exiting of activity application
application package log adding, changing, and removing package
calling log state, number, contact, and direction of calling
configuration log configuration change information (e.g., font, screen size, and keyboard type)
contact log adding, changing, and deleting of contacts
date changed log changing of system date and time
GPS log user’s locale, altitude, latitude, longitude and direction of movement
headset log plugging in headset or not
power connected log connecting or disconnecting the power
power log powering on smartphone or not
screen log state of the screen
service application log creating, starting, and deleting service application
sms log state, content, and contacts of SMS
wallpaper log changing wallpaper
PredictingSubjectiveWell-BeingbySmartphoneUsageBehaviors
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Central Intelligence Agency), the New Economics
Foundation, and the WHO (the World Health
Organization) to measure how one views his or her
self-esteem, well-being and overall happiness with
life (
Diener, 1999
). Among the various components
of subjective well-being, SWLS focuses on assess
global life satisfaction and does not tap related
constructs such as positive affect or loneliness.
SWLS items are rated on a 7-point scale: 0 =
strongly disagree, 1 = disagree, 2 = slightly disagree,
3 = neither agree nor disagree, 4 =slightly agree, 5 =
agree, 6 = strongly agree. Five items are shown as
follows.
1. In most ways my life is close to my ideal.
2. The conditions of my life are excellent.
3. I am satisfied with my life.
4. So far I have gotten the important things I
want in life.
5. If I could live my life over, I would change
almost nothing.
The distribution of participants’ SWB scores in
this study is shown in Tab. 2.
Table 2: The mean, standard deviation and range of SWLS
scores in this study.
Mean StdDev Range
Male 14.600 6.239 4~30
Female 15.395 5.299 7~28
Total 14.908 5.877 3~30
4 FEATURES EXTRACTION
After data collection, we then extract several
features from smartphone usage. There are three
types of features extracted as shown in Fig. 3, such
as the frequency of some basic smartphone functions
usage. We classify Apps according to Wandoujia
Android market (http://www.wandoujia.com/apps),
which is very popular and comprehensive in China.
Figure 3: The extracted feature classification and details.
48 features are extracted from phone usage data
by MobileSens running as a background service.
Therefore, these features were objective and
captured various aspects of communication and
applications use on the phone.
5 DATA ANALYSIS
5.1 Correlation Analysis
In this section, we run statistical analysis to examine
the correlation between smartphone usage and SWB
across genders.
In psychology literature, Pearson’s correlation
coefficient is commonly used as a bounded measure
of correlation, or linear dependence between two
variables. For two random variables X and Y, it is
given by

co
v
X,
Y
 
(1)
where cov(X,Y) is the covariance between the
random variables X and Y. r=1 denotes a positive
sloped linear relationship, and r=-1 denotes a
negative one. Values in-between -1 and 1 indicate
sub-linear relationships between the variables.
We compute the Pearson’s correlation coefficient
between the Well-being and the smartphone
features. We enlist parts of the correlation analysis
results where p<0.05 in Table 3.
Table 3: Correlations between Well-being and parts of
Smartphone use features.
Female Male Total
Communication
App using
0.371* 0.234* 0.251*
Strategy Game 0.320* 0.004 -0.002
Reading App using 0.299* -0.099 0.063
Camera using -0.311* -0.242* -0.251*
Competitive Game -0.050 0.265* 0.197*
Browser using 0.368* -0.027 0.124
*p < 0.05
Table 3 shows that users with high SWB tend to
use communication Apps (MSN, weixin, feixin)
more frequently, but less camera Apps usage,
especially for female participants. Besides, all the
users with higher SWB are more likely to play
games, with females prefer strategy games while
males prefer competitive games. In addition, females
with higher Well-being may use reading Apps and
browsers more frequently, which is not significant
on male participants.
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5.2 Prediction Model
To identify SWB based on smartphone usage
behaviours, we train prediction models by machine
learning.
To achieve better performance, we need to get
more meaningful features at first. In this study, we
use StepWise regression as a feature extraction
method. StepWise Regression algorithm is a
regression-based algorithm for automatic filtering
features, and this selection method is applicable to a
variety of other models (Kwak and
Choi, 2002). It is
a forward selection algorithm, which involves
starting with no variables in the model, testing the
addition of each variable using a chosen model
comparison criterion, adding the variable (if any)
that improves the model the most, and repeating this
process until none improves the model. In this study,
we have adopted StepWise regression algorithm for
feature selection. The selection result shows that
communicate apps usage, certain types of game
(strategy games are for female and competitive
games are for male), and photo-taken frequency are
the most important variables for predicting SWB.
After feature extraction, we built prediction
models on WEKA. WEKA contains a collection of
visualization tools and algorithms for data analysis
and prediction model, together with graphical user
interfaces (Witten and
Frank, 2005).
In this study, we choose pace regression to build
the prediction model in WEKA. Pace regression is
one kind of linear regression algorithm, to create
parametric linear relationship between independent
variables and dependent variable (Wang, 1999).
Compared with other algorithms, pace regression
performs much better in this study. To make the
trained model more general, we use four criterions to
measure the quality of prediction model.
1. Correlation Coefficient (CORR)
,
(X )(Y )
cov( X, Y)
XY
XY XY
EXY
 


(2)
2. Mean Absolute Error (MAE):
ii
1
1
ˆ
(x ) y
n
i
MAE f
n

(3)
3. Root Mean Squared Error (RMSE):
2
ii
1
1
ˆ
((x) y)
n
i
RMSE f
n

(4)
The results of CORR, MAE and RMSE are
shown in Table 4.
Table 4: Results of the prediction model.
CORR MAE RMAE
Female 0.62 3.49 4.23
Male 0.34 4.82 5.89
Total 0.39 4.76 5.92
We can see that the prediction model for females
obviously get a much higher accuracy than models
for males or total users. The reason for the result
hasn’t been found up to now, but we have supposed
that females interact with their smartphones more by
using more kinds of Apps and spending more time
on phones than male users. Therefore, we can
predict females’ SWB more easily based on females’
rich datasets.
6 CONCLUSION
AND FUTURE WORK
This paper mainly investigates how smartphone
usage correlates with SWB, and further develops a
SWB prediction model. We intend to provide an
alternative method to measure subjective well-being
based on smartphone usage behaviours.
The result demonstrates that users with different
SWB level behave differently on their smartphones.
Users with higher SWB would like to use more
communication Apps but less camera Apps,
especially for females users. Besides, all the users
with higher well-being are more likely to play
games, female users prefer to strategy games and
males play competitive games more. In addition,
females with higher Well-being use reading Apps
and browsers more frequently, which does not
appear on male users.
In the near future, we plan to tune the
performance of prediction model by developing new
behavioural features, and take into account short
message and phone voice. We also intend to
implement SWB predicting App, to get more
feedback from real users.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the generous
support from National High-tech R&D Program of
China (2013AA01A606), National Basic Research
Program of China (973 Program 2014CB744600),
PredictingSubjectiveWell-BeingbySmartphoneUsageBehaviors
321
Key Research Program of CAS (KJZD-EW-L04),
Strategic Priority Research Program
(XDA06030800) and 100-Talent Project
(Y2CX093006) from Chinese Academy of Sciences.
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