=Paper=
{{Paper
|id=Vol-1582/13Rutjes
|storemode=property
|title=Understanding Effective Coaching on Healthy Lifestyle by Combining Theory- and Data-driven Approaches
|pdfUrl=https://ceur-ws.org/Vol-1582/13Rutjes.pdf
|volume=Vol-1582
|authors=Heleen Rutjes,Martijn C. Willemsen,Wijnand A. IJsselsteijn
|dblpUrl=https://dblp.org/rec/conf/persuasive/RutjesWI16
}}
==Understanding Effective Coaching on Healthy Lifestyle by Combining Theory- and Data-driven Approaches==
https://ceur-ws.org/Vol-1582/13Rutjes.pdf
Understanding Effective Coaching on Healthy Lifestyle
by Combining Theory- and Data-driven Approaches
Heleen Rutjes, Martijn C. Willemsen, Wijnand A. IJsselsteijn
Human-Technology Interaction, Eindhoven University of Technology
{h.rutjes,m.c.willemsen,w.a.ijsselsteijn}@tue.nl
Abstract. New wearable technologies such as health watches and smartphones
provide rich data and give the opportunity to learn about the user’s preference,
traits, states and context. Our research aim is to uncover principles of effective
coaching and to deliver personalized e-coaching applications in the domain of
healthy lifestyle, by using both psychological theory and data science tech-
niques. We believe the synergy of both fields will result in a deeper understand-
ing of effective coaching. Theory provides plausibility criteria and ‘behavioral
templates’ which help to understand the meaning of data. On the other hand,
data can fine tune theory, especially in terms of studying individual differences
and tailoring. Our research plans include a review of literature, interviews with
health coaches and studying real life coaching data to generate hypotheses.
Next, we plan to use adaptive tools in user trials, where both data-driven and
theory-driven approaches can be combined.
1 Background and Research Questions
There is a strong link between behavior and health [1]. Having a healthy lifestyle,
e.g., regular physical activity and healthy eating, prevents many diseases. E-coaching
can play a role in supporting people to achieve their health goals and changing or
maintaining their healthy behavior.
New wearable technologies such as health watches and smartphones create new
opportunities to learn more about a user’s preference, psychological state, personality
and environment [2]. Insights about variances on these aspects between and within
users are easier to obtain now the trend is to have bigger and richer data created by
following users on many aspects over time.
Our research aim is to uncover principles of effective coaching and to deliver per-
sonalized e-coaching applications in the domain of healthy lifestyle. We combine two
worlds: psychological theories of coaching, persuasion and behavior (change) on the
one hand, and data science and machine learning techniques on the other hand. We
expect this synergy will result in more and deeper insights in effective coaching.
Many psychological theories are formulated on a high level of abstraction, which
makes the transfer to application domains, e.g. behavior change interventions, not
trivial. However, these theories do provide plausibility criteria and ‘behavioral tem-
plates’ which help to understand the meaning of data, or ‘pointers’ that tell us where
Copyright © by the paper’s authors. Copying permitted for private and academic
purposes.
In: R. Orji, M. Reisinger, M. Busch, A. Dijkstra, A. Stibe, M. Tscheligi (eds.): Pro-
ceedings of the Personalization in Persuasive Technology Workshop, Persuasive
Technology 2016, Salzburg, Austria, 05-04-2016, published at http://ceur-ws.org
Understanding Effective Coaching on Healthy Lifestyle by Combining Theory- and Data- 27
driven Approaches
to look for in the data. Theory can inform the relevant frameworks and constraints
needed to formulate working hypotheses that may direct the data mining process and
help separate relevant from irrelevant information. Tailoring to users also follows
from understanding data, for example by recognizing behavioral patterns, habits and
teachable moments.
This brings us to our research questions: (1) What are the critical parameters for ef-
fective (e-)coaching? And (2) what aspects are most important to tailor to, in order to
improve the effectiveness of (e-)coaching?
In the current early phase of this research, we wish to understand coaching in a
broad sense. It includes many aspects, e.g. persuasion, behavior change, personal
contact and type of recommendations. We propose to differentiate between:
─ what does the coach say or recommend,
─ how and
─ when does he do that.
What the coach recommends is sometimes left by psychologists as ‘domain
knowledge’, but we argue it is potentially critical for effective coaching. Also, it can
be important content for tailoring. In the field of recommender systems, much is
known about procedures how to obtain proper recommendations [3]. How the coach is
communicating / persuading / motivating / coaching includes many forms, e.g. ‘tone
of voice’, communication styles, principles of persuasion (e.g. [4]) and behavior
change techniques (e.g. [5]). When the coaching occurs is in literature often referred
to as opportune, interruptible or teachable moments [2].
Within these fields, the effectiveness of coaching interventions is often extensively
studied. For example, in a meta-analysis, the effectiveness of 26 behavior change
techniques is studied in the domains of physical activity and healthy eating [6]. Meta-
regression enables identification of effective components of behavior change inter-
ventions, for example the authors found that ‘self-monitoring’ is the most effective
technique. However, there is criticism on the simplicity that is used in this study, for
example [7] state that there is more nuance needed in why and how certain behavior
change techniques do or do not work. On top of that, we would like to argue that we
should take even one more step back, and investigate the way in which the what, how
and when relate to each other and which of those components is most critical for ef-
fective coaching.
Tailored behavior change interventions are proven to be more effective than stand-
ard interventions [8,9,10]. It should be noted that tailoring can be manifested on many
levels [11], ranging from calling the user by his name to using the tone of voice that
suits the user’s personality or current mood best. Although tailoring is shown to be
effective, the mechanisms underlying this increased effectiveness are still ill-
understood, and require further exploration.
In literature, several models are presented with determinants of behavior (e.g.
awareness, social support, attitude, perceived barriers, self-efficacy) which are indi-
cated as important aspects for tailoring. (e.g. [12,13]). The authors of the latter study
even present a computational model, which is a good start to make it practically feasi-
28 Understanding Effective Coaching on Healthy Lifestyle by Combining Theory- and Data-
driven Approaches
ble as an e-coach application. Still, the quantification and validation of these models
is limited. Having rich data sets, machine learning techniques can help to overcome
this problem.
The ever growing presence of smartphones allows us to collect ecological valid da-
ta, since it brings the lab into our lives, as stated in Millers’ [14] ‘Smartphone Psy-
chology Manifesto’ and in IJsselsteijns’ [15] ‘Psychology 2.0’. Seizing this oppor-
tunity is inevitable to bring the psychology of coaching and behavior change to the
next level.
To conclude, data can help to fine tune theory, a practical application provides a
good test for the value of the theory. But, looking at data only can also burden us with
spurious correlations or other flaws. For this, good theory can offer a solution. By
combining both theory and data, we aim for understanding the critical parameters in
coaching, including the important tailoring aspects. We hope to map the what, how
and when of the coaching to the relevant user characteristics to make coaching appro-
priate for anyone anywhere at any moment.
2 Research Plans and Methodology
First, we explore the literature for the current state of the art. We interview health
coaches and perform a thematic analysis on this data. To complete, real life coaching
data from field trials from Philips Research1 will be used for inspiration. By those
means, hypotheses will be formulated about effective coaching.
Note that coaching principles obtained from the interviews with human health
coaches cannot be generalized to e-coaches automatically. We need to take into ac-
count the differences – and the consequent advantages and challenges – between hu-
man and e-coaches.
After this exploration, we move forward to a more confirmatory phase. Field trials
will be used to check the hypotheses on real life data. When using adaptive tools to
influence behavior, both data-driven and theory-driven approaches can be combined.
Possible future research questions are:
─ If certain user characteristics or context aspects happen to be important to tailor the
coaching on, how can those be measured? Which sensors are needed? What kind of
data processing (e.g. affective computing) is needed?
─ What is the optimal balance (in terms of user experience) between asking questions
to the user versus deducing information from the data? How can the uncertainty of
predictions be used explicitly in machine learning techniques, to prompt questions
at the right moment?
1
This research is part of a collaboration between Philips Research Eindhoven and the
Data Science Centre of the Eindhoven University of Technology.
Understanding Effective Coaching on Healthy Lifestyle by Combining Theory- and Data- 29
driven Approaches
3 References
1. Institute of Medicine: Health and Behavior: The Interplay of Biological, Behavioral, and
Societal Influences. National Academies Press, Washington (2001)
2. Pejovic, V., Musolesi, M.: InterruptMe: Designing Intelligent Prompting Mechanisms for
Pervasive Applications. In: Proceedings of the 2014 ACM International Joint Conference
on Pervasive and Ubiquitous Computing, pp. 897–908. (2014)
3. Konstan, J. A., Riedl, J.: Recommended for you. IEEE Spectrum, 49, 54-61 (2012)
4. Cialdini, R. B.: Influence: Science and Practice. Allyn and Bacon: Boston (2001)
5. Michie, S., Ashford, S., Sniehotta, F. F., Dombrowski, S. U., Bishop, A., French, D. P.: A
refined taxonomy of behaviour change techniques to help people change their physical ac-
tivity and healthy eating behaviours: the CALO-RE taxonomy. Psychology and Health. 26,
1479–1498 (2011)
6. Michie, S., Abraham, C., Whittington, C., McAteer, J., Gupta, S.: Effective techniques in
healthy eating and physical activity interventions: a meta-regression. Health Psychology.
28, 690–701 (2009)
7. Peters, G.-J. Y., de Bruin, M., Crutzen, R.: Everything should be as simple as possible, but
no simpler: towards a protocol for accumulating evidence regarding the active content of
health behaviour change interventions. Health Psychology Review. 9, 1–14 (2013)
8. Kaptein, M. C.: Personalized persuasion in Ambient Intelligence. Eindhoven University of
Technology, Eindhoven (2012)
9. Krebs, P., Prochaska, J. O., Rossi, J. S.: A meta-analysis of computer-tailored interven-
tions for health behavior change. Preventive Medicine. 51, 214–221 (2010)
10. Noar, S. M., Benac, C. N., Harris, M. S.: Does tailoring matter? Meta-analytic review of
tailored print health behavior change interventions. Psychological Bulletin. 133, 673–693
(2007)
11. op den Akker, H., Jones, V. M., Hermens, H. J.: Tailoring real-time physical activity
coaching systems: a literature survey and model. User Modeling and User-Adapted Inter-
action. 24, 351–392 (2014)
12. Honka, A., Kaipainen, K., Hietala, H., Saranummi, N.: Rethinking Health: ICT-Enabled
Services to Empower People to Manage Their Health. IEEE Reviews in Biomedical Engi-
neering, 4, 119–139 (2011)
13. Klein, M., Mogles, N., van Wissen, A.: Intelligent mobile support for therapy adherence
and behavior change. Journal of Biomedical Informatics. 51, 137–151 (2014)
14. Miller, G.: The Smartphone Psychology Manifesto. Perspectives on Psychological Sci-
ence. 7, 221–237 (2012)
15. IJsselsteijn, W. A.: Psychology 2.0: towards a new science of mind and technology. Eind-
hoven University of Technology, Eindhoven (2013)