Professorship Personalised Digital Health:

1. Professorship Personalised Digital Health: Working on self management with technology and data science Hilbrand Oldenhuis

2. Health: ‘Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity’ (WHO, 1948). Who is healthy…? What is the aim of health care…?

3. Health: Now: ‘The ability to adapt and self manage in the face of physical, emotional and social challenges of life’ (Huber et. al., 2011). flexible, dynamic Illness or constraint does not necessarily mean ‘unhealthy’

4. Health: Coreconcepts: • Self management • Focus at functioningin daily life instead of on illness and constraints • Resilience • Multidisciplinarity: Healthcare and socialwork more and more intertwined

5. Focus at self management: • How to stimulate self management? eHealth: ‘an attempt to enhance health or health service delivery through use of modern information technology and electronic communication resources’ (Glasgow & Solomon, 2014)

6. Healthcare costs in the Netherlands: • 1972: 8% Gross Domestic Product • 2010: 13% GDP • 2040: 19-31% GDP Causes: • More care • Better care • More expensive care Technology as solution?

7. Focus at self management : • eHealth is promisingbecause of 2 (related) reasons: • Patient/consumer is lessdependent of (health care) professionals forobtaining relevant information

8. Example: www.thuisarts.nl

9. Example: self-tracking devices

10. Focus at self management: eHealth is promisingbecause of 2 (related) reasons: • Patient/consumer is lessdependent of (health care) professionals forobtaining relevant information • eHealth tools can support patients/consumers (‘personalized’) tobehavehealthy and in doingso make themlessdependent of (health care) professionals in the short as well as in the long run Data science and Persuasivetechnology

11. Behavior Model (Fogg, 2009)

12. Focus at self management: Next step: Data mining and data science: searchingformeaningfulpatterns in personal data Result: personally relevant feedback, obtainingmeaningfulinsightsconcerning health status and behavior, just-in-time tailored feedback EcologicalMomentary Assessments and Interventions (EMA and EMI)

13. Focus at self management: • Just-in-time tailored feedback canbeobtained in twoways: • Controlled discovery of knowledge: with a certain goal doel. • Uncontrolled discovery of knowledge: without a certain goal (Fawcett, 2015). ‘Based on data about your eating pattern it is unlikely that you are allergic for gluten. There is a possibility that carbohydrates and caffeine influence your productivity in the afternoon. You may consider the following test to investigate this suggestion.’

14. Controlleddiscovery of knowledge: • Based on questions like: • Whycan I hardlyresistthetemptationtoeatunhealthy snacks in theafternoon? • Why do I sleep badly?

15. Controlleddiscovery of knowledge:

16. Uncontrolleddiscovery of knowledge: • Augemberg’s Lifestream dataset: 15 variables, 6 months • Several correlations…

17. Personalized Digital Health

18. Core project PDH Development of ‘virtual coach’: ‘a computational system that assists the user to support behavior that is desired to improve health or well-being’

19. Example: eatingbehavior(Spanakis, Weiss, Boh, Lemmens & Roefs, 2017) Input: data gatheredby user: cravingfor (un)healthy food, emotion, location, activity, time (av. 10 times a day) Process: machine learningalgorithms(see: https://www.itacademy.nl/evenementen/it-academy-colleges-data-sciencefor Dutch speakingstudents) Output: • Warningsignalsfor users: ‘DANGER’ • Forminggroups of users based on theaggregated data on the basis of which new users quicklycanbeappliedto a certaingroup in order toobtaintailored feedback (‘evening at home’, ‘outdoors/social’, ‘circumstances-driven’, ‘veryoccasional’, ‘after-activity’, ‘unhealthy-cravingssatisfaction’)

20. Example: physicalactivity(Yom-Tov, Feraru, Kozdoba, Mannor, Tennenholtz& Hochberg, 2017) Diabetes patients: physicalactivityvery important Increase in number of interventionsby means of smartphones and/or web based But stilllittlebased on ‘personalizedlearningalgorithmtotailormessagestoindividuals’ In thisstudy: whichmessage does most likelyincreasethe next day’sactivitypattern?

21. Example: physicalactivity(Yom-Tov, Feraru, Kozdoba, Mannor, Tennenholtz& Hochberg, 2017) 3 month-period in which different types of feedback werebeingoffered • Negative feedback: ‘Youneedtoexercisetoreachyouractivity goals. Pleaseremembertoexercisetomorrow.’ • Positive feedback relatedtoself: ‘You have so far acieved N% of yourweeklyactivity goal. Yourexercise level is in accordancewithyour plan. Keep up thegoodwork.’ • Positive feedback relatedtoothers: You have so far acieved N% of yourweeklyactivity goal. You are exercising more thantheaverage person in yourgroup. Keep up thegoodwork.’ • No message

22. Example: physicalactivity(Yom-Tov, Feraru, Kozdoba, Mannor, Tennenholtz& Hochberg, 2017) 3 month-period in which different types of feedback were being offered Afterwards: learning algorithm decides which type of feedback is most effective based on activity level relative to an individual’s goal Result: more activity and better health-related measures in experimental group

23. Integration of Knowledge Domains Personalization oftriggers: context &timing Experimentswith Coaching Strategies

24. Coaching Strategy Platform Data Storage Personalized Feedback (Advice, EMA, Goal) Lifestyle & Health Data Platform Analysis Clustered Big Data Storage Big Data Analysis (Predictive, Change, Classification, …) Collect Compare Filter Summarize Time Series Coaching Feedback Rules Query Engine (MapReduce) Learnfrompreviouspatterns Feedback/EMA Templates LOG BOOK

25. Applications at HUAS: Functional Fitness Monitor forfiremen (prof. Johan de Jong)

26. Holistic approach (physical-mental-social) • Test- and measurement technology individual • Monitor-feedback + coaching-effect measures (week on, week off) • Zephyr (HF-HRV-BF) • Actigraph (physical activity + sleep) • Digital questionnaires BORG/mindfulness…(smartphone) (privacy, feasibility, pilot)

27. Fit for Sustainable Employability (Het Nieuwe Werken HG) Personalized physical activity coaching: a machine learning approach (submitted) personalized-coaching 10.000 Personalized Model Algorithm Training Corresponding author: T.B. Dijkhuis, HUAS

28. Applications: • ‘Fit for sustainable employability (FIT4SE) • Predictive modelling of employees’ resilience using wearable technology (Herman de Vries) • Development of stress prevention app for employees working with digital screen equipment (Aniek Lentferink) • Based on self-tracking (heart rate, experience sampling) and e-coaching

29. Applications: • ‘Fit for sustainable employability (FIT4SE) • Living lab ‘Healthy Workplace’ (http://www.healthy-workplace.nl/): ‘real-life’ office in which a lot of data is being gathered (behavior, environment, performance) (Justin Timmer, Marion Dam, Jan Gerard Hoendervanger) • How can we make sense of the data? And how does that improve employees’ sustainable employability?

30. Applications: Focus on professionals: • Development of app LIV formental health care professionals (Jessica van der Staak) • Based on positivepsychology • Combinedwith data concerning life style

31. Applications: Data minimization Profiling Purposelimitation Responsibility Wrong suggestions

32. Wrong or false? • Based on the data of an employee’s smartwatch, that was offered to him by his employer, he was fired, because it turned out that he was visting the zoo instead of ‘working at home’. • By means of a Google Glass you can instantly see a person’s name, address, profession and Facebook page by simpy looking at him/her

33. Interested? Name: Hilbrand Oldenhuis Function: Professor Personalised Digital Health, Hanzehogeschool Email: h.k.e.oldenhuis@pl.hanze.nl