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Ontological Modeling of Motivational Messages for Physical Activity Coaching

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Smart coaching systems are named to play a central role in both prevention and intervention strategies for behavioral change. While relevant progresses have been made in terms of automatic and continuous monitoring of behavioral aspects, e.g. amount and variety of physical activity, coaching and feedback techniques are still in an infancy stage. Current smart coaching strategies are mostly based on handcrafted messages which hardly personalize to the needs, context and preferences of each user. In order to make these recommendations more realistic, engaging and effective more flexible and sophisticated strategies are needed. This paper presents an ontology-based approach to model personalizable motivational messages for promoting healthy physical activity. The proposed ontology not only models the message intention and its components, e.g. argument, feedback or followup, but also its content, i.e. action, place, time or object required to perform the recommended activity. Through this ontology the messages can also be categorized into multiple classes, e.g. sedentary, mild or vigorous activities, and retrieved based on the preferences, needs and context of the user. 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.

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Ontological Modeling of Motivational Messages for Physical Activity Coaching

  1. 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
  2. 2. 8/6/17 2 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,…
  3. 3. 8/6/17 3 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…
  4. 4. 8/6/17 4 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.
  5. 5. 8/6/17 5 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)
  6. 6. 8/6/17 6 ONTOLOGY OF MOTIVATIONAL MESSAGES THE MODEL Intention Content
  7. 7. 8/6/17 7 CATEGORISATION OF MESSAGES TYPE OF ACTION  Depending on the type of action:  Sedentary  Mild  Vigorous Reference guidelines: 2011 Compendium of Physical Activities
  8. 8. 8/6/17 8 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
  9. 9. 8/6/17 9 CATEGORISATION OF MESSAGES TIME  Categorization of the messages depending on the part of the day:  Morning(-related)  Afernoon(-related)  Evening(-related)  Night(-related)
  10. 10. 8/6/17 10 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)
  11. 11. 8/6/17 11 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)
  12. 12. 8/6/17 12 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?”
  13. 13. 8/6/17 13 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?”
  14. 14. 8/6/17 14 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

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