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Opportunities for Smart & Tailored Activity Coaching

Authors:
Harm op den Akker at Roessingh Research and Development
Harm op den Akker
Randy Klaassen at University of Twente
Randy Klaassen
Rieks Op den Akker at University of Twente
Rieks Op den Akker
Valerie Margaret Jones at University of Twente
Valerie Margaret Jones
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This short paper describes how emerging technologies can be used to augment the effectivenes of activity coaching applications through tailoring.
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Opportunities for Smart & Tailored Activity Coaching
Harm op den Akker
Roessingh Research and Development
Telemedicine group
h.opdenakker@rrd.nl
Randy Klaassen
University of Twente
Human Media Interaction
r.klaassen@utwente.nl
Rieks op den Akker
University of Twente
Human Media Interaction
h.j.a.opdenakker@utwente.nl
Valerie M. Jones
University of Twente
Telemedicine group
v.m.jones@utwente.nl
Hermie J. Hermens
Roessingh Research and Development
Telemedicine group
h.hermens@rrd.nl
Abstract
This short paper describes how emerging technologies
can be used to augment the effectivenes of activity coaching
applications through tailoring.
1 Introduction
Technology aided coaching on healthy behavior is
widely regarded as a promising paradigm to aid in the pre-
vention of chronic diseases and the process of healthy age-
ing in general. In order to encourage physical activity in
patients suffering from chronic disease, as well as healthy
adults, many different coaching systems have been devel-
oped. Typically these consist of an activity sensor and
some form of coaching application delivered either through
a web portal, smartphone or the sensor itself. We present
our model of tailoring as a framework for discussing key
areas in which such activity coaching applications can be
improved. Tailoring is the process whereby a system ad-
justs its communication to a specific user. We concretize
this definition by considering four communication proper-
ties: intention, timing, content, and representation. In our
case, a typical intention would be to either inform about
the benefits of physical activity, or to provide information
on the user’s daily progress towards a goal. Timing defines
the moment at which the system chooses to initiate an in-
teraction. Content consists of the chosen words in a verbal
communication, or values displayed in a graphical represen-
tation of progress. Given these four properties, the goal of
tailoring is to increase the system’s likelihood of conveying
its intention by matching the timing, content, and represen-
tation to the user in his specific context. Based on the work
by Hawkins et al. [2] and our own literature study [6], we
identified six different forms of tailoring and matched them
to the communication properties. In this model (Figure 1),
feedback is used to present the user with information about
himself. Inter-human interaction provides support for in-
teraction with other real humans. Adaptation “attempts to
direct messages to individuals’ status on key theoretical de-
terminants...” [2]. User targeting “attempts to increase at-
tention or motivation by conveying that the communication
is designed specifically for you” [2]. Context awareness is
the notion of tailoring a communication based on external
information. Self learning can be used to enhance other tai-
loring techniques through adapting to the user by learning
from his reactions to previous communications.
2 Key Areas for Improvement
The model of tailoring presented is based on an analy-
sis of the state of the art of tailoring in real-time activity
coaching systems [6]. Combined with research into emerg-
ing smart technologies as well as our many years of expe-
rience in deploying physical activity coaching systems to
various patient populations, we have identified opportuni-
ties for future research directions in six key areas related to
activity coaching.
I. Smart Sensing. Use sensor data fusion to combine ac-
celerometer data from the activity sensor and location data
from the smartphone to provide accurate activity classifi-
cation, increasing the accuracy of energy estimation algo-
rithms and providing additional context to the virtual coach.
II. Adaptive Goal Setting. Employ activity data gath-
ered from the user to learn a user-specific — challenging but
achievable — goal, and define a balanced individual pattern
that can prompt the user to increase his activity at times dur-
ing the week and day where it is most suitable for him.
III. Adaptive Reminding. Find the opportune moment
Timing
Intention
Representation
Content
Motivation
Strategies
Adaptation
Static
Tailoring
Dynamic
Tailoring
Context Awareness
User targeting
Self Learning
Inter-Human Interaction
Feedback
Figure 1. The relationships between tailoring techniques and the communication model properties.
The layering describes how certain techniques can be used to augment others.
for motivational cues by analysing the user’s response to
those messages in relationship to current contextual factors,
increasing the possibility of favourable response while re-
ducing the risk of information overload and interruption ir-
ritability [3]. The proof of this self-learning approach is
given in [5].
IV. Personalized Message Generation. Motivational
messages can be tailored to psychological constructs (adap-
tation) or the user’s environment (context awareness) and
preferences. Natural language generation techniques can be
used to generate varying and relevant messages.
V. Advanced HCI. To increase perceived intelligence of
a smart coaching system, embodied (conversational) agents
offer an interesting opportunity as HCI-paradigm. As Bick-
more et al. showed [1], ECAs can have a positive effect on
perceived relationship with a software agent.
VI. Pervasive Coaching. As humans interact with many
different devices during the day, cross media systems offer
the opportunity for the activity coach to travel with the user
across those devices. Depending on the needs and context
of the user, coaching can thus be provided on the most suit-
able device (e.g. smartphone, PC, smart television) [4].
3 Conclusions
We have identified six areas where smart technologies
can be applied to tailor various aspects of an individualised
activity coach. Location-aware activity-type sensing (I) and
self-learning individual goal setting algorithms (II) should
form the basis for providing awareness of physical activity
as well as obtainable goals. The generation of motivational
messages can benefit from complex pattern analysis to de-
termine an optimal timing (III) and content (IV) of mes-
sages for the user in his current context. Language gen-
eration tools can alleviate the problem of repetitiveness in
natural language interaction between user and coach. The
presentation of an intelligent coach can use advanced HCI
methods — e.g. the use of ECA’s (V) — that can migrate
with the user through various devices in order to optimally
use the available interaction resources at the user’s current
location (VI). Based on our analysis we formulated a model
for smart tailoring of feedback and attempted to improve
coaching strategies. In different experiments we developed
and implemented technologies that aim to find an optimal
timing for motivational messages [5], systems for context-
aware message generation and intelligent embodied agents
which travel with the user across multiple devices [4]. From
own experience, as well as the state of art [6], we see fu-
ture research directions in the use of more advanced context
sensing and the application of machine learning technolo-
gies as the way towards an autonomous, adaptive and indi-
vidualised coaching agent.
References
[1] T. W. Bickmore and R. W. Picard. Establishing and maintain-
ing long-term human-computer relationships. ACM Transac-
tions on Computer-Human Interaction, 12(2):293–327, 2005.
[2] R. P. Hawkins, M. Kreuter, K. Resnicow, M. Fishbein, and
A. Dijkstra. Understanding tailoring in communicating about
health. Health Education Research, 23(3):454–466, 2008.
[3] J. Ho and S. S. Intille. Using context-aware computing to
reduce the perceived burden of interruptions from mobile de-
vices. Proceedings of the SIGCHI conference on Human fac-
tors in computing systems CHI 05, Portland,:909, 2005.
[4] R. Klaassen, R. op den Akker, T. Lavrysen, and S. van Wis-
sen. User preferences for multi-device context-aware feed-
back in a digital coaching system. Journal on Multimodal
User Interfaces (to appear), 2013.
[5] H. op den Akker, V. M. Jones, and H. J. Hermens. Pre-
dicting Feedback Compliance in a Teletreatment Application.
In Proc. of the 3rd Int. Symposium on Applied Sciences in
Biomedical and Communication Technologies, Rome, 2010.
[6] H. op den Akker, V. M. Jones, and H. J. Hermens. A literature
review of real-time, tailored coaching systems for physical ac-
tivity. User Modeling and User-Adapted Interaction, Special
Issue on Personalization and Behaviour Change (submitted),
2013.
... According to [21] and [22], the success of this type of systems is based on their capacity 157 of adaptation, which is the notion of tailoring a communication on the basis of external 158 information [22] and involves attempts to increase attention or motivation by conveying 159 personalized communication" [22]. ...
... According to [21] and [22], the success of this type of systems is based on their capacity 157 of adaptation, which is the notion of tailoring a communication on the basis of external 158 information [22] and involves attempts to increase attention or motivation by conveying 159 personalized communication" [22]. ...
... According to [21] and [22], the success of this type of systems is based on their capacity 157 of adaptation, which is the notion of tailoring a communication on the basis of external 158 information [22] and involves attempts to increase attention or motivation by conveying 159 personalized communication" [22]. ...
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... Some work has gone further and used current trajectory towards a goal to intervene. For instance, they can notify a user if they are not on track to meet their goal by a certain time frame if they keep that trajectory, or are consistently not meeting daily goals [1,12,22,34,38]. However, these methods do not consider a holistic view of the many important behaviors that lead to a goal being met, and make simplistic assumptions about linear progressions. ...
... Some researchers have sought to assign adaptive fitness goals based on a particular user's historical data, in order to improve a goal that is too difficult or too easy. These typically employ simple algorithms that raise goals that the user is easily achieving, and lower goals that the user is struggling to achieve [1,12,22,34,38]. MyBehavior, in a study of 17 users, suggested exercise or actions to take, but not goals [42]. ...
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Activity tracking apps often make use of goals as one of their core motivational tools. There are two critical components to this tool: setting a goal, and subsequently achieving that goal. Despite its crucial role in how a number of prominent self-tracking apps function, there has been relatively little investigation of the goal-setting and achievement aspects of self-tracking apps. Here we explore this issue, investigating a particular goal setting and achievement process that is extensive, recorded, and crucial for both the app and its users' success: weight loss goals in MyFitnessPal. We present a large-scale study of 1.4 million users and weight loss goals, allowing for an unprecedented detailed view of how people set and achieve their goals. We find that, even for difficult long-term goals, behavior within the first 7 days predicts those who ultimately achieve their goals, that is, those who lose at least as much weight as they set out to, and those who do not. For instance, high amounts of early weight loss, which some researchers have classified as unsustainable, leads to higher goal achievement rates. We also show that early food intake, self-monitoring motivation, and attitude towards the goal are important factors. We then show that we can use our findings to predict goal achievement with an accuracy of 79% ROC AUC just 7 days after a goal is set. Finally, we discuss how our findings could inform steps to improve goal achievement in self-tracking apps.
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... Research work in [13] defines a framework for discussing key areas in which such activity tracking applications can be improved. It considers four properties (see Fig.1): intention, timing, content, and representation. ...
... Focused Research Model -adapted from[13]. ...
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Mobile health tracking applications are in abundance over the last years, with applications such as fitness tracking, photo diet tracking for weight loss and diet reporting applications. These applications are used for self-assessment, dynamic feedback and motivation of the users, while some more recent studies revealed that the data from these applications can be beneficial for experts in advising their clients. In this work, a real-time nutrition and fitness tracking web platform is proposed that enables experts to track the progress of their clients (even) in real-time or based on their work schedule, so as to establish a more holistic view of clients' progress. The platform aims to support experts in providing the best guidance and advice on a nutrition and fitness plan. The web platform was developed, used and evaluated by both clients and experts, which showcased the importance of this real-time expert-user interaction.
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... Time scope includes, for example, the duration of the intervention (short-term vs. long-term; [13] and the frequency and duration of the coaching interactions (speed coaching vs. longer conversation; [13]). Organization of coaching includes, for example, the communication channel (e.g., in person, telephone, web [25][26][27] and additional voluntary interventions including environmental modifications (e.g., walking tracks outside a company; [28]). Further parameters comprise the underlying theory (e.g., selfdetermination theory, SDT, [29]; transtheoretical model of change, TTM, [30]; social cognitive theory, SCT, [31]) and the question of which and how many behavior change techniques (BCTs) are applied [32]. ...
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A literature review of real-time, tailored coaching systems for physical activity
  • Jan 2013
  • H Op Den Akker
  • V M Jones
  • H J Hermens
H. op den Akker, V. M. Jones, and H. J. Hermens. A literature review of real-time, tailored coaching systems for physical activity. User Modeling and User-Adapted Interaction, Special Issue on Personalization and Behaviour Change (submitted), 2013.