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: Core concepts: Self management
Focus at functioning in daily life instead of on illnes and constraints
Resilience
Multidisciplinarity: Healthcare and social work 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)
Let wel: het gaat altijd primair om het verbeteren van gezondheid, dus om het verhogen van dat vermogen. Technologie is en blijft slechts een tool…! En er staat ook niets over kosten….

6. Focus at self management :
eHealth is promising because of 2 (related) reasons:
Patient/consumer is less dependent of (health care) professionals for obtaining relevant information

7. Example: www.thuisarts.nl
Afname bezoek huisarts met 12% ( consulten per maand)
Succesfactoren: betrouwbaar, door huisartsen ontwikkeld, up to date, meer dan 90% van de huisartsen gebruikt het, eenvoudig te begrijpen
Betrouwbare informatie
Regelmatige updates
Eindgebruiker centraal
Privacy en security
Ondersteuning vanuit professionals: >90%

8. Example: self-tracking devices
Deze devices kunnen helpen om meer kennis of eigen gezondheid te verzamelen die op meer gebaseerd is dan vermoedens

9. Focus at self management:
eHealth is promising because of 2 (related) reasons :
Patient/consumer is less dependent of (health care) professionals for obtaining relevant information
eHealth tools can support patients/consumers (‘personalized’) to behave healthy and in doing so make them less dependent of (health care) professionals in the short as well as in the long run
Persuasive technology
Voorbeelden persuasieve technologie: oplichtend pad naar de trap

10. Behavior Model (Fogg, 2009)
Technologie kan op alle drie de elementen inspelen op heel persoonlijk niveau d.m.v. data science/big data: heel veel data uit verschillende bronnen, realtime verzameld. Dat moet ‘smart data’ worden op basis waarvan ’actionable insights’ geformuleerd zouden moeten worden

11. Focus at self management:
Next step:
Using data science: ‘Ecological Momentary Assessments and Interventions’

12. Personalized Digital Health
Uit lectoraatsaanvraag: Interventies binnen Personalised Digital Health worden geïndividualiseerd en effectief door een sterke koppeling tussen persuasieve technologie en Big Data binnen één feedback loop: gedrag wordt gemeten en geanalyseerd voor het bepalen van een interventie, waarna continue de volgende stappen worden bepaald op basis van geconstateerde effecten. Persuasieve technologie en Big Data zijn in dit verband onlosmakelijk met elkaar verbonden. Omdat het gaat om kenmerken van individuen neemt Big Data hier de vorm aan van Personal Data. De toepassing van Big Data vereist het gebruik van het domein van Data Science dat de kennis, tools en technieken levert om Personal Big Data te analyseren en te interpreteren.
CRUCIAAL: aansluiting vragen, problemen, context, domein van toepassing. Kennis van domein helpt bij toepassen data science.

13. 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’

14. Integration of Knowledge Domains
Personalization of triggers: context & timing
Experiments with Coaching Strategies

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

16. Professorship Personalised Digital Health
Focus:
Supporting professionals
Fit for sustainable employability (FIT4SE): focus on employees
Vulnerable groups
Tav punt 1:

17. Applications: ‘Fit for sustainable employability (FIT4SE)
Functional Fitness Monitor for firemen (Johan de Jong)

18. Functional Fitness Monitor
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)

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

20. Feedback

21. Applications: ‘Fit for sustainable employability (FIT4SE)
Personalized physical activity coaching for employees

22. 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
To encourage employees to lead a less sedentary life, the Hanze University of Applied Sciences Groningen started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The step count data served as input for a periodical coaching session. Starting from the step count data, we investigated the possibility of automating the coaching on physical activity, by enabling personalized feedback during the day on a participant's progression. The gathered step count data was used to train eight different machine learning algorithms to estimate hourly, the probability of achieving the personal, daily steps threshold. To show the practical usefulness, we constructed a Web application that demonstrates the possibility to determine whether the participant will make his goal for the day, throughout the day, by applying an individualized machine learning algorithm based on previous behavior (
Corresponding author: T.B. Dijkhuis, HUAS

23. 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

24. Applications: ‘Fit for sustainable employability (FIT4SE)
Living lab ‘Healthy Workplace’ ( ‘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?

25. Applications: Focus on professionals:
Development of app LIV for mental health care professionals (Jessica van der Staak)
Based on positive psychology
Combined with data concerning life style

26. Cooperation: Within HUAS many professorship working together with PDH:
Nursing
Sport sciences
Allied Health Care
Facility Management
New Business & ICT
User-Centred Design

27. Name: Hilbrand Oldenhuis Function: Professor Personalised Digital Health, Hanzehogeschool