1. ONTOLOGICAL MODELING OF
MOTIVATIONAL MESSAGES FOR
PHYSICAL ACTIVITY COACHING
C. VILLALONGA1,3, H. OP DEN AKKER2, H. HERMENS1,2, L.J. HERRERA1,
H. POMARES1, I. ROJAS1, O. VALENZUELA1, O. BANOS1,3
1CITIC, UNIVERSIDAD DE GRANADA, SPAIN
2ROESSING R&D, NETHERLANDS
3CENTER FOR MONITORING AND COACHING, UNIVERSITY OF TWENTE, NETHERLANDS

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CENTER FOR MONITORING AND COACHING
UNIVERSITY OF TWENTE
The future coach
Model based reasoning
& Interactive learning
Context aware Feedback
via various devices
Context information
Self learning from
responses
Wearable sensors
Multimodal
Sensing
Technology
- Wearables
- Textiles
- Ambient sensors
- ESM
Human Behaviour Modeling
- Artificial intelligence
- Probabilistic reasoning
Human-Computer
Interaction
- Haptic interfaces
- (Emboddied) Virtual agents
- NLP https://www.utwente.nl/ctit/cmc/
Application domains:
Frailty, Stroke, Diabetes,
Parkinson’s, Alzheimer’s, Autism,…

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E-COACHING
WHY IS IT THAT CHALLENGING?
 Major efforts have been mainly targeted
at improving the quantification part
(oversimplification vs exaggeration)
 “Generic” motivational messages (one-
size-does-not-fit-all)
 Shallow recommendations (explanation
of goals, how to reach them,
implications, etc.)
 We are still learning…

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MOTIVATIONAL MESSAGES FOR E-COACHING
MAIN CHALLENGES
 Represent the principal, and perhaps more natural,
means for translating behavioral findings into easy-to-
follow and realizable recommendations (actions)
 KEY challenges:
 Generation of relevant messages tailored to the
performance, needs and characteristics of each
specific user [Noar2007]
 Fostering the diversity of the messages to
increase adherence and make the coaching system
more realistic and trustworthy [opdenAkker2015]
Noar et al. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychological bulletin 133, 4 (2007),
673.
op den Akker et al.Tailored motivational message generation: A model and practical framework for real-time physical activity coaching. Journal of
Biomedical Informatics 55 (2015), 104-115.

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MOTIVATIONAL MESSAGES FOR E-COACHING
PROPOSED “SOLUTION”
Generic
“handcrafted”
messages
(health/wellness
guidelines,
repositories,etc.)
Ontological
mapping and
categorisation
Automatic
retrieval of
tailored messages
“You should walk
more”
“Eat an apple
every morning”
E-Coach1
E-Coach2
E-Coach3
Thesaurus
(lexicon)

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ONTOLOGY OF MOTIVATIONAL MESSAGES
THE MODEL
Intention
Content

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CATEGORISATION OF MESSAGES
TYPE OF ACTION
 Depending on the type of action:
 Sedentary
 Mild
 Vigorous
Reference guidelines: 2011 Compendium of Physical Activities

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CATEGORISATION OF MESSAGES
TYPE OF ACTION (EXAMPLE)
“You have not walked enough today”
does not contain information about the intensity of the action walking
Reference guidelines: 2011 Compendium of Physical Activities

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CATEGORISATION OF MESSAGES
TIME
 Categorization of the messages depending on the part of the day:
 Morning(-related)
 Afernoon(-related)
 Evening(-related)
 Night(-related)

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CATEGORISATION OF MESSAGES
TIME (EXAMPLE)
“Why don’t you go for a walk early in the morning?”
contains explicit information about the time the action takes place
(Explicit semantics)

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CATEGORISATION OF MESSAGES
TIME (EXAMPLE)
“It is important that you have breakfast to replenish your
supply of glucose”
does not contain explicit information about the time the action takes place
(Implicit semantics)

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RETRIEVAL OF MESSAGES
ACTION SPECIFIC
Messages recommending the action walking
“Why don’t you go for a
walk early in the
morning?”
“What about
going for a walk
in the park?”

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RETRIEVAL OF MESSAGES
TYPE OF ACTION AND TIME
Messages recommending actions of type mild and
performed in the morning
“Why don’t you go for a
walk early in the
morning?”

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CONCLUSIONS
This work contributes with:
 A new ontology to facilitate the modelling of physical activity motivational
messages according to various categories, e.g. sedentary, mild or
vigorous activities, and retrieved based on the preferences, needs and
context of the user
 The ontology not only models the message intention and its components,
e.g. argument, feedback or follow-up, but also its content, i.e. action,
place, time or object required to perform the recommended activity
 Additional information not explicitly present on the messages can be
inferred from the ontology by applying reasoning techniques and used to
enhance the message retrieval process