Integrating Person-to-Person Social Support in Smartphone Apps for
Promoting Physical Activity
Bojan Simoski, Michel Klein, Aart T. van Halteren and Henri Bal
Dept. of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Keywords:
Mobile Health, Physical Activity, Social Support.
Abstract:
The epidemic of physical inactivity is a major health hazard in the modern society, therefore creating effec-
tive and innovative health programs and interventions is important. This paper presents a novel approach in
which person-to-person social support is incorporated in mHealth interventions for increasing physical activ-
ity. Social support is already used as a behaviour change technique in mHealth apps for influencing physical
activity, but mostly offered virtually. While virtually-offered social support is efficient, we believe that person-
to-person communication, based on personal coaching, might open a new way of influencing the inactive users
and their motivation. Responding to this, we developed an Android application that facilitates physically inac-
tive users to connect with a real life coach to receive person-to-person social support. This paper explains the
motivation behind the system’s design decisions and discusses the potential of social support in mHeatlh apps.
In addition, we present the design of the evaluation study in which the hypotheses and research questions will
be evaluated.
1 INTRODUCTION
Physical inactivity is identified as one of the major
hazards related to many health problems including
cancer, diabetes and heart diseases. Consequently,
physical inactivity is the 4th global risk for mortal-
ity in the world, being responsible for over 3 mil-
lion deaths annually, as reported by the World Health
Organization [WHO] (2009). WHO recommends at
least 150 minutes of moderate-intensity aerobic phys-
ical activity throughout the week for adults between
16-64 years. Alternatively, this age group should do
at least 75 minutes of weekly vigorous-intensity ex-
ercises, or a combination between the two (WHO,
2017). Unfortunately, these guidances are not met
by a significant proportion of the population (Hallal
et al., 2012), resulting in a common interest of de-
signing efficient health programs and interventions.
Meanwhile by 2018, over a one third of the world
population is projected to own a smartphone, bring-
ing the estimated total of users to almost 2.53 bil-
lion (Statista, 2017). As of June 2017, there were
more than 3 million available smartphone applica-
tions (apps) at Google Play Store, almost 100000 of
them were categorized as health & fitness apps (App-
Brain, 2017). Apps are undoubtedly popular, easy to
reach and offer cost-effective interventions, therefore
they could be considered as promising tool for influ-
encing human health.
van den Dool et al. (2017) have shown that 31% of
the Dutch population uses electronic tools for sports
and moving activities, out of which the apps were the
most popular tool with 61% of total electronic tools
usage. However, there is a remarkable contrast in
tools usage between the sufficiently physically active
users and the insufficiently physically active users,
that we will refer to as inactives in the remaining part
of the paper. The usage of tools among active people
varies from 32% to 55%, depending on the type of
active person, compared with just 12% of inactives,
that have used any electronic tool for their sport ac-
tivities. In order to indulge inactives to use health &
fitness apps, there is a necessity to better understand
their needs. van den Dool (2015) has investigated
the behaviour of this population group, and proposed
several guidances that inactives might find useful re-
lated to goal setting, skills, social support and per-
sonal coaching, as detailedly explained in Section 3.
Following these guidances, we have created an
Android app that targets inactives and promotes
physical activity intervention predominantly via real-
life social support and having a personal motivator
(coach). Social support is proven as a powerful tool
for influencing people’s behavior change and is al-
Simoski, B., Klein, M., Halteren, A. and Bal, H.
Integrating Person-to-Person Social Support in Smartphone Apps for Promoting Physical Activity.
DOI: 10.5220/0006644504970504
In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 497-504
ISBN: 978-989-758-281-3
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
497
ready applied in some health & fitness apps, but
mostly offered virtually: via forums, online social
networks or chat messages. While virtually-offered
social support is efficient and was already linked with
increasing physical activity among users, we believe
that person-to-person communication, based on social
support and personal coaching, could bring additional
benefit for the inactives and their motivation. There-
fore, in this work the virtually-offered social support
was joined by an additional layer, one of real-life so-
cial support - our app promotes group exercises and
support where dyad, a group of two people, can do
joint exercises, indulge in real life communication and
motivation. In the dyad, one individual being suffi-
ciently physically active, is a personal motivator for
the other individual, namely the insufficiently physi-
cally active person. The dyad members should have
strong mutual social ties, being either friends, col-
leagues, family members or partners.
The goal in the first phase of this long term
project, is to investigate the effectiveness and accep-
tance of applying (real-life) social support in mo-
bile health (mHealth) interventions for increasing the
physical activity among inactives. In addition, we
will obtain the physical activity level (PAL) trend-
lines, and determine if there is an increase of phys-
ical activity for our participants over the experimental
period. Finally, we would like to investigate the corre-
lations between the PAL trendlines and the perceived
social support via our app.
The remainder of the paper is organized as fol-
lows: Section 2 discusses the potential of using so-
cial support for physical activity interventions, espe-
cially in physical activity apps. In Section 3 we ex-
plain the motivation behind the Social Coaching app
design choices, and give an extensive description of
the app’s modules. The evaluation plan is presented
in Section 4, where we describe the planned experi-
mental setup, the data nature and collection, followed
by the analysis plan where we define our hypotheses.
Finally, Section 5 ends the paper with a discussion.
2 BACKGROUND
This section starts by giving overview of previous re-
search studies that explored the implementation of so-
cial support in physical activity apps. As real-life
social support is often omitted when designing the
apps features, we continue by explaining the influence
that the social environment and real life contact might
have on people’s physical activity behavior. Finally,
we present one way in which person-to-person com-
munication could be integrated in apps, by explaining
the concept of social accountability.
Social support is a complex term and has be con-
ceptualized and defined from multiple perspectives.
From the mHealth perspective, a valid social sup-
port interpretation is specified in the taxonomy con-
structed by Abraham and Michie (2008), that pro-
posed a set of standardized definitions of the tech-
niques most commonly used in behaviour change in-
terventions. Social support, one of the 26 techniques
described, is defined as ”prompting consideration of
how others could change their behaviour to offer the
person help or (instrumental) social support, includ-
ing buddy systems and/or providing social support”
(Abraham and Michie, 2008, p.4). The presence and
effectiveness of social support as a behaviour change
technique in physical activity apps has already been
investigated. Matthews et al. (2016) investigated the
pervasive technologies used in physical activity apps,
concluding that social support is moderately repre-
sented, mostly via social comparison, social learn-
ing and competition. These techniques refer to com-
paring behavior and results between users, observing
and learning from behavior of other users, and finally
competing with other users. Another study (Bort-
Roig et al., 2014) has identified social support net-
working as one of the most effective behavior change
strategies for encouraging physical activity. King
et al. (2013) tested the effectiveness of different mo-
tivational apps (analytic, social and affective) for in-
creasing physical activity at users, and found the so-
cial app, being based on social comparison and social
normative feedback, as most effective.
A common approach for the above examined apps
is that they offer social support virtually, mostly via
online social networks like Twitter, Facebook, or by
creating online support communities. While applying
virtual social support has already been shown to be
effective, the potential of adapting the real-life social
support via the social environment in mHealth inter-
ventions is usually left out. The social environment
could offer social interactions that are missed in vir-
tual social support, for example, observational learn-
ing. Observational learning for physical activity can
be achieved by exercising with others and observing
their behavior, that was shown useful to build positive
social norms for physical activity (St
˚
ahl et al., 2001).
People having low social support from the per-
sonal environment are more than twice as likely to
exhibit sedentary behavior than those whose personal
environment was highly supportive and motivational
(St
˚
ahl et al., 2001). Connecting with the nearest sur-
roundings matter: having supportive partner, family
and/or friends all contribute to increased physical ac-
tivity (Eyler et al., 1999; Sternfeld et al., 1999). Social
HEALTHINF 2018 - 11th International Conference on Health Informatics
498
support interventions based on ”buddy” systems, hav-
ing a ”contract” with others to achieve a certain level
of physical activity, initiating walking or other types
of sport groups, showing confidence in one’s ability,
were all associated with increasing levels of physical
activity (Kahn et al., 2002; Sternfeld et al., 1999).
Social accountability refers to ”a person’s aware-
ness of another person’s goal and rendering him-
self/herself responsible to the goal’s successful ful-
fillment” (Chen et al., 2014, p.1). Personal coach-
ing, where the coach observes and supervises the goal
progress set by another person (coachee), could be
one way of applying social accountability in mHealth
interventions. This type of coach-coachee relation-
ship could be used to integrate person-to-person com-
munication in physical activity apps. Commercial ap-
plications are already using social accountability to
help users achieve goals. CommitTo3 (2015) is an
app in which users build social accountability groups,
and try to motivate themselves in fulfilling three daily
goals, by sharing their own progress and looking at
other team members progress. In the GoalSponsor
(2012) app, users appoint their own ”accountability
buddy” to monitor and share their progress with. The
HealthyTogether project explores mutual accountabil-
ity in a gamification mobile app, their results show-
ing that users improved their physical activity by 15%
when using the app compared with when they were
exercising alone (Chen and Pu, 2014).
3 THE SOCIAL COACHING APP
We present a ’Social Coaching’ app that offers a per-
sonalized social support experience for inactives. As
mentioned in Section 1, our app design is inspired by
the set of requirements introduced by van den Dool
(2015), that suggested several approaches that might
attract inactives to be more physically active, the most
relevant for our app being paraphrased as follows:
1. Include skills that the inactives already possess or
introduce them with less complicated new skills.
2. Define goals that are individual and where any
progress is good enough.
3. Apply social support in interventions as this tech-
nique can keep users motivated for longer time
and comfortable during the exercise.
4. Have a personal coach as this can be helpful to
overcome the initial obstacles.
The following subsections discusses the app func-
tionalities, and the technology behind the app.
3.1 Social Aspects between Dyad
Members
The dyad is composed by two individuals having
strong mutual social ties: family members, partners,
friends or colleagues. The dyad members are:
Coachee - user that does not meet the minimum
150 minutes/week WHO requirement. Supported
by the motivator and using the app, the coachee
wants to improve its physical activity.
Motivator - user that does meet the minimum 150
minutes/week WHO requirement. The motivator
should help the coachee in the process of becom-
ing more physically active.
As shown in Section 2, the social environment and
having supportive partners, family or friends were all
associated with increasing physical activity, therefore
socially tied dyads are preferred. The dyad is teamed
before participants are joining the experiment, it is a
responsibility of the coachee to decide whom to team
up with - preferably the coachee will choose a sup-
portive person as the motivator.
The relation between our dyad members is implic-
itly based on the social accountability factor intro-
duced in the previous section - even though the ex-
ercise results are team based, there is one dyad mem-
ber, the coachee, that wants to accomplish a goal, and
another dyad member, the motivator, that is aware of
this goal and tracks the goal progress.
The motivator should be a person who is already
physically active, therefore has awareness of the ben-
efits of being fit. Driven by the positive effects of ob-
servational learning for physical activity as discussed
in Section 2, we assume that having a ”role model”
motivator type can be helpful for the inactives.
3.2 Exercise Types
Following the first requirement (related to skills), the
app promotes and records dyad’s walking and / or run-
ning exercise sessions. Walking and running are cho-
sen as physical activities because they do not require
any specific equipment, are budget friendly and have
significant health effects if performed regularly.
The users could obviously do other types of phys-
ical activity during their day. In order to have this
information, the app supports manual logging of any
other physical activity types, as we want to take in
consideration all the activities that might influence
user’s PAL.
Integrating Person-to-Person Social Support in Smartphone Apps for Promoting Physical Activity
499
3.3 Goal Setting
The dyad sets weekly goals expressed in number of
meters of weekly walk and/or run. Before confirming
the goal, the app gives a pop-up message showing the
approximate total time in minutes for achieving that
weekly goal. Considering the second requirement (re-
lated to goals), explicit goal recommendation is inap-
propriate. The goals are set by the coachee, according
to the ambition for the upcoming week, and possi-
bly, upon consulting the motivator. The app implic-
itly influences the coachee’s goal setting strategy by
showing the WHO recommendation of 150 minutes
of moderate-intensity physical activity per week as a
reference point for the user. We have used this partic-
ular reference point, as the app promotes walking and
running, that are both considered moderate-intensity
exercise types, making them a suitable choice for the
inactives. Users can track their goals on the app’s
home screen where we visualize pie charts of the cur-
rent weekly progress, presenting the percentage of the
remaining/accomplished goal. Moreover, in order to
support a timeline of the physical activity progress,
the app has a history screen that provides the dyad
members with a retrospective view of their weekly
walking / running goals accomplishments and their
individual exercise session records over the weeks.
3.4 Contract Agreement
Before they are allowed to exercise, the dyad mem-
bers needs to sign’ an exercise contract. This vir-
tual contract is imagined as defining the available free
slots by both team members, during the week, that
would ideally be dedicated for performing team exer-
cise. The time slots should bring more responsibility
at both the motivator and the coachee ”to stick to the
plan”, as having a behavioural contract for a certain
amount of exercise was already related to increase of
physical activity (Kahn et al., 2002). The dyad mem-
bers can initiate exercise at any time, regardless the
time slots that are defined in the contract agreement.
3.5 The Exercise Session
The exercise session is imagined as team activity,
where the motivator and coachee walk or run together.
The exercises can be initiated by both the motivator
and the coachee, upon mutual agreement. These ex-
ercises are an opportunity for the motivator to support
and teach the coachee. During the exercise session,
the app shows the progress made by the coachee in
time and meters passed, to both members of the dyad.
Therefore, the motivator is able to real-time track the
progress that the coachee is making and give instant
feedback, while the latter can simply ”forget” about
the phone and relax on actually performing the ex-
ercise. After each exercise, the app displays a short
questionnaire to the coachee, related to satisfaction
with the exercise and the team communication dur-
ing the exercise. When exercising together is not pos-
sible, the coachee can individually perform the exer-
cise session, and the motivator will be notified about
the end result.
Figure 1: Social Coaching app screenshots. Left screenshot
shows the different menu options, related to weekly goal
setting, initiating new exercises or logging extra exercises.
The right screenshot displays the home screen, where the
user can reflect on his weekly goal progress and see infor-
mation about upcoming exercises.
3.6 Chat Feature
The dyad can communicate to each other using the
chat feature. Having the messaging feature can be
beneficial for the dyad to ease and enrich their com-
munication. The frequency of messages exchanged
was already linked to increase of physical activity
(Chen and Pu, 2014).
3.7 App Communication with
End-users
The app has a rather passive communication with the
users. This is a conscious design decision, since we
aimed at keeping the app interaction with the users at
minimum, as our goal is to investigate how the real-
life social support works for the coachee. However,
the app does communicate using notifications, and
there are several of them, mostly directed toward the
motivator. Sending these notifications, the app can be
HEALTHINF 2018 - 11th International Conference on Health Informatics
500
Figure 2: Architecture of the Social Coaching app. The app is supported by SWAN, an open source framework for building
context aware Android apps. SWAN enables offline real-time sensor data sharing between smartphones via Bluetooth, a
feature used for the exercise sessions of the app. In addition, the cloud layer is used to store and retrieve the app’s data.
considered as a rudimental virtual coach for the mo-
tivator: it reminds the motivator to send motivational
messages to the coachee in case this was not done yet
during the day, it reminds to initiate new exercise in
case when the dyad has a free slot assigned for that
particular day, and finally it notifies if the dyad did
not do the planned exercise.
3.8 Technologies and Tools
The Social Coaching app is an Android based app.
We chose the Android platform due to its big market
share and wide acceptance among smartphone users.
The app is supported by a back-end API system, built
using the Play Framework and deployed as a Heroku
cloud application. The REST API calls are used
to store and retrieve information to/from the cloud
database (Heroku Postgres database), that contains all
the app related data, except the chat messages. To de-
ploy the chat we used Google Firebase and stored the
chat messages in the Firebase Realtime Database.
One of the biggest challenges we faced during the
app development was how to build an exercise track-
ing system, that can work in offline mode, has low
battery consumption and enables real-time sharing of
exercise progress between the coachee and the moti-
vator. We have used the open-source SWAN project
(SWAN, 2010), a framework for developing context-
aware Android apps, in order to manage the smart-
phone’s sensor data processing during the exercises.
SWAN runs as a background service and can be
simultaneously accessed by multiple Android apps
on the phone. SWAN creates powerful abstractions
for managing the otherwise complex (Android) sen-
sor APIs: it supports smartphone sensors, software
sensors (e.g. open-source weather API data) or exter-
nal sensors connected to the smartphone (e.g. wear-
ables data via Bluetooth). SWAN offers more than 20
predefined sensors, moreover the developer can eas-
ily plug-in any new sensor to the framework accord-
ing to the application needs. As example, in order to
satisfy our application requirements, we have manu-
ally created the Distance Covered sensor that uses the
smartphone’s accelerometer sensor data obtained dur-
ing the exercise and translates it to number of steps,
using the Pedometer (2013) app’s open source step
counter algorithm. The number of steps are then rep-
resented in meters depending on the walk/run stride
length of the coachee.
Context-aware apps communicate with SWAN us-
ing the SWAN API to register and unregister sen-
sor expressions. These expressions are build using
the domain specific language of SWAN: the SWAN-
Song. In our app we use the following sensor expres-
sion for calculating the distance covered during the
exercise session, at the coachee phone:
Integrating Person-to-Person Social Support in Smartphone Apps for Promoting Physical Activity
501
self@distancecovered:meters
where the first component indicates the source of
the sensor - self means that we get the data locally
from the smartphone. The second component defines
the sensor, which is the Distance Covered sensor in
our case. Finally, the third component specifies the
value of interest within the chosen sensor, in our case
we are interested in the number of meters, obtained
from the Distance Covered sensor.
On the motivator side, during the joint exer-
cises, we want to show the real-time progress of the
coachee. SWAN supports Bluetooth-enabled data
sharing between phones, as simple as registering a
sensor expression in the following manner:
coachee_BT_ID@distancecovered:meters
here the source of the sensor is the coachee’s Blue-
tooth MAC address, meaning that we are interested
in getting the meters of the Distance Covered sensor
from the coachee’s smartphone. After the Bluetooth
connection is established, the motivator will be able
to see the coachee’s real-time progress on the screen.
Once the expressions are registered, they are eval-
uated on every new sensor event, using the Evaluation
Engine. The Evaluation Engine sends broadcast mes-
sages to the app, whenever it needs to notify sensor
data changes. The sensor data is read until an un-
register event is called, for example, when our users
finish an exercise session.
4 EVALUATION PLAN
4.1 Experimental Setup
The proposed app in this paper will be evaluated in
a user experiment, to test if promoting real-life social
support via mHealth apps can be considered as an use-
ful tool for physical activity interventions. There is no
control group (i.e. dyads without the app), as the aim
of the experiment is broader than comparing the ef-
fectiveness of using such a system with face-to-face
coaching.
The goal of our experiment is to investigate the
feasibility of real-life social support via an app. We
will do this in a small user study of approximately 50
participants, i.e. at least 25 dyads, in which we both
look at the user experiences and the change in physi-
cal activiity. At the time of writing this paper, we have
three dyads that have already started the experiment.
The ongoing recruitment is done via online tools like
websites, forums, social media, and by recruiting at
the university campus. The participants should be
between 16-64 years old, similar to the age group
specified by WHO in Section 1, be healthy enough
to perform physical activities and possess an Android
smartphone.
In order to get additional step-based physical ac-
tivity data, each participant will be given an activity
tracker, a Fitbit One device. This is a sufficiently
reliable activity tracker for continuous measurement
of physical activity (Takacs et al., 2014; Paul et al.,
2015), and will be used during the whole experiment
duration, as a support tool besides the apps.
Before the experiment, the participants will an-
swer online questionnaires regarding team relation-
ship; personality traits; and current physical activity
status, motivation and goals. The overall participa-
tion will take 5 weeks, with one week of assessment
period and four weeks of intervention period.
For the assessment week, the participants will in-
stall the ActivityLogger app, a simple physical activ-
ity diary app, where they will log their individual ex-
ercises over the period of 7 days. We will use the
data obtained from this app, combined with the activ-
ity tracker data, to determine the pre-intervention PAL
for each participant. This approach will give us more
reliable and accurate baseline for determining the ini-
tial PALs of the participants, compared to using phys-
ical activity self-assessment questionnaires like IPAQ
(Lee et al., 2011). Using the initial PAL values of our
participants, we can test if the dyad indeed consists of
one sufficiently active person and one insufficiently
active person.
After the dyads are confirmed, the participants
will enter the four weeks of intervention period, dur-
ing which they will use the Social Coaching app, sup-
ported by the activity tracker. At the end of the in-
tervention period, the participants will be asked to fill
another set of questionnaires, regarding their satisfac-
tion of using the app as a tool for increasing their
physical activities, focusing on the social support con-
text of the app.
4.2 Data Collection
We are interested in gathering relevant data regarding
physical activity and perceived social support. The
physical activity data is collected via the Android
apps, and the activity tracker, and enables us to calcu-
late the PAL of each participant. The pre-intervention
PAL value will be calculated combining the data from
the ActivityLogger app and the activity tracker. Dur-
ing the intervention period, the PAL will be obtained
on weekly basis, by gathering the data from the Social
Coaching app and the activity tracker. Using these
PAL values we will calculate the PAL trendlines of
each participant, which will be then used in the anal-
ysis. The activity tracker enables data extraction in
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502
spreadsheets, where we will obtain daily information
about number of steps, distance in km, floors climbed;
and number of lightly, fairly and very active minutes
over the day. The ActivityLogger app will provide in-
formation about exercise type, duration and date. The
Social Coaching app gives data about exercise type,
time spent in exercise, number of meters, date for the
walking and running sessions. In addition, this app al-
lows the user to log extra exercises (besides walking
and running), in a similar way as in the ActivityLog-
ger app, therefore an additional data about exercise
type, duration and date, will be obtained for them.
The Social Coaching app, will further give us in-
sights about the perceived social support. The social
support factor will be calculated by quantifying the
communication patterns between dyad members, and
by using the app questionnaire responses. We have
identified several suitable data sources for calculat-
ing the perceived social support: the frequency of
joint exercises; frequency of chat messages and senti-
ment analysis of the messages; weekly questionnaires
about dyad satisfaction sent as notification; after ex-
ercise satisfaction questionnaire sent as a notification
to the coachee.
Additional data will be collected via online ques-
tionnaires both before and after the experiment, as ex-
plained in the previous subsection.
4.3 Analysis Plan
The multidimensionality of the collected data will
give an opportunity for data analysis from different
perspectives. Calculating the individual PAL trend-
lines, as explained in Section 4.2, will help us de-
termine if there was an increase of physical activity
at the participants, during the experimental period.
We will test the significance of the PAL trends us-
ing Mann-Kendall (MK) trend tests, that can statis-
tically access trend presence in a variable over time.
Moreover, we will investigate the correlation between
the PAL trends and the perceived social support over
time. For this, we have defined several hypotheses:
H1. Inactives that show satisfaction of the per-
ceived social support will show an increase in
PAL trends.
H2. Inactives that show dissatisfaction of the per-
ceived social support will not show an increase in
PAL trends.
H3. High frequency and positive sentiment of ex-
changed messages between dyad members, will
result with increase in PAL trends at inactives.
H4. Low frequency and negative/neutral senti-
ment of exchanged messages between dyad mem-
bers, will not result with increase in PAL trends at
inactives.
These conditions will be tested by performing MK
non-parametric tests for statistical dependence.
Finally, we would like to investigate the ac-
ceptance of applying (real-life) social support in
mHealth interventions for increasing the physical ac-
tivity among inactives. In order to answer this, the
participants will be given questionnaires regarding
app usability and acceptance, with special emphasis
on the (real-life) social support context of the app.
5 DISCUSSION
This paper presents a novel approach in which person-
to-person social support is incorporated in mHealth
interventions for increasing physical activity. In or-
der to fight the epidemic of physical inactivity, in-
novative and effective tools which are accepted by
end-users are required. Real-life social support via
personal coaching has the potential to motivate inac-
tives, but is hardly used in mHealth apps. In this work
we focused on explaining the design decisions behind
the system and we discussed the potential of social
support in mHeatlh apps. Furthermore, we have in-
troduced our evaluation plan in which the hypotheses
and research questions will be evaluated.
There are several considerations for future work,
inspired by the current limitations of our research, that
we would like to discuss. The social context play key
role in this research, and we can imagine incorporat-
ing different social interactions, in addition to the cur-
rent dyad relationship, for example:
create groups of arbitrary number of members, for
example enable the coachee to have more than one
motivator.
match with an anonymous person instead of a per-
son from the social circle.
instead of having a motivator, match inactive per-
sons with each other.
With these combinations, we could test different
types of social interactions, which is important in or-
der to find the ideal combinations of motivators and
coachees.
One important aspect that could influence the per-
ceived social support by people, is the effect of social
comparison. Not all people get motivated by having
a role model that performs better then they do. Some
individuals gets more motivated by comparing them-
selves with others performing worse (downward so-
cial comparison), and other individuals are motivated
Integrating Person-to-Person Social Support in Smartphone Apps for Promoting Physical Activity
503
by comparing themselves with someone who is better
then they are (upward comparison). We would like to
consider this theory when thinking about supporting
new social ties types.
Finally, the focus of this research is on the inac-
tives, but in order to have an effective intervention
that is based on social support, we should consider
the satisfaction of the motivators as well. We have
build the current app under the assumption that the
motivators will use the app in order to help their ac-
quaintances, but the question is what will keep them
motivated to use the app for a longer period? One pos-
sible approach is to extend the app by implementing
gamification elements, i.e having rewards, trophies,
challenges, or the possibility to compare their perfor-
mance with the performance of other motivators.
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