Moodbuster supporting research on the mental health domain

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2 Moodbuster supporting research on the mental health domain Artur Rocha 1, on behalf of the Moodbuster Team: INESC TEC, VU Amsterdam, Univ. Limerick, GGZ ingeest 1 INESC TEC, Porto, Portugal emen Key Note Presentation September 6 th, 2018

3 Projects Driving Moodbuster ICT4Depression: features, insight E-COMPARED: features, insight STOP Depression: features, insight icare4depression on-going new features Towards Moodbuster 2.0: on-going new features Conclusions 3

4 Previous Projects Driving Moodbuster Development

5 ICT4D: short for ICT4Depression, FP7 IST [ ] User-friendly ICT Tools to Enhance Self-Management and Effective Treatment of Depression in the EU Partners: 5

6 61 bpm Heart Rate, Respiration, and Acceleration Electrodermal Activity and Heart Rate Physiological Signs Monitoring Moodbuster Web 6 Pharmacotherapy Adherence Monitoring Moodbuster App

7 Insight Body Sensors: careful! Patients do not wish to feel further stigmatized. Give us something fashionable and non-intrusive. Pharmacotherapy Adherence: not enough patients doing both psychotherapy and pharmacotherapy. Inconclusive. Web Intervention: Okay, but could be more appealing. Also, there are too many choices in an unguided setting. Some patients feel a bit lost. Mobile App: EMA is perceived as useful. Most of the CBT exercises are too complex for small screen. 7

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9 Architecture From Unguided to Blended Therapist Portal Publisher M V C S c r i p t s Reasoning Engine ICT4D Core Services W e b S e r v i c e s Moodbuster Generat or 9 Content Editor & Generator Editor Moodbuster App

10 Improved Patient Portal Simple Status Overview 6 Therapeutic Modules + Intro 10 Contents Explaining Therapies plus step-by-step Exercises

11 Improved Patient Portal Communication with Therapist Mood Chart 11 Simple access to unlocked Exerc. Cross-therapy Calendar

12 Improved Moodbuster App Mood Ratings Mood Graph Patient s Inbox Feedback 12

13 A Therapist Portal Compact overview of patients Setting Appointments 13 Viewing Patient s Activities Viewing Patient s Mood Chart

14 14 Log Analysis

15 Insight Users are generally satisfied with Moodbuster, but there s always room for improvements From an effectiveness perspective, blended produces better results (but that s for therapists to consider) Wealth of data considering therapy input, EMA, PHQ assessments and usage logs altogether Several scientific works were possible because of these data, particularly in the ML field 15

16 Pilot Implementation in Porto s Primary Care Screening, evaluation & follow-up platform Moodbuster ICT4D Core Services Publisher Generat or 16 Content Editor & Generator Editor Healthcare Professional Portal

17 Leaner Design Dashboard - Simple Status Overview 6 Therapeutic Modules + Intro 17 Contents Explaining Therapies plus step-by-step Exercises

18 Screening, assessment & treatment support Anonymous, minimal characterization Systematic screening 18 Validated Instruments (eg. PHQ-9) also for Suicide Risk management

19 Insight Project was very well received in the primary care setting Currently a hiatus, doctors, nurses and patients want more Impact on the National Plan for Depression Prevention Acknowledgment that more therapists are needed in the primary care 19

20 icare4depression Goals & Partners Assess the feasibility and acceptability of the use of blended therapy to improve the delivery of healthcare solutions for depressed adults within a pilot study in Portugal between 2017 and

21 EMA/EMI strategy Content targeted to the user (personalized) Scheduled ratings Personalizable for each patient or groups of patients Automated prompts based on personalization, soon also on context Reduced intrusiveness 21

22 22 Cross-platform Mobile app

23 23 Cross-platform Mobile app

24 24 Monitoring Portal for icare4depression App

25 25 Monitoring Portal for icare4depression App

26 26 Monitoring Portal for icare4depression App

27 27 Monitoring Portal for icare4depression App

28 Context Aware Interventions SituMan ability to recognise user generated contexts tested: Name Location (Geo) Day in week Can we present intervention snippets based on their contextual appropriateness? Time of day (Night, Morning, Afternoon, Evening) Activity (vehicle, cycle, walk, run, still) Availability for certain types of rating 28 Ariel Soares Teles, Artur Rocha, Francisco José da Silva e Silva, João Correia Lopes, Donal O Sullivan, Pepijn van de Ven, and Markus Endler. Enriching mental health mobile assessment and intervention with situation awareness. Sensors, 17(1):127, 2017.

29 Framework for Smart EMA/EMI 29 P. van de Ven, H. O Brien, R. Henriques, M. Klein, R. Msetfi, J. Nelson, A. Rocha, J. Ruwaard, D. O Sullivan, and H. Riper. ULTEMAT: A mobile framework for smart ecological momentary assessments and interventions. Internet Interventions, 9:74 81, 2017.

30 Moodbuster 2.0 Sneak Peek (under development) Focus on empowering researchers in the design of their own interventions More personalization for the end user: pages and modules can be combined and republished as new personalized treatments Publisher Generat or 30 Content Editor & Generator Editor Intervention Builder

31 31 Moodbuster 2.0 Sneak Peek (under development)

32 Conclusions Several projects influenced Moodbuster s features, many others still are There isn t a definitive way to perform therapy, research support platforms like Moodbuster have their place in the value chain Adaptability and personalization are key factors Data science has new challenges, Moodbuster is designed to address them DIFFER is on the move 32

33 Acknowledgements This communication is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project «POCI FEDER », and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia as part of project «UID/EEA/50014/2013». 33