1. mQoL: Mobile Quality of Life Lab
From Behaviour Change to QoL
Vlad Manea, Katarzyna Wac
Quality of Life Technologies Lab
University of Geneva & University of Copenhagen
www.qol.unige.ch
Workshop on Mobile Human Contributions 2018
ACM UBICOMP 2018

2. Agenda
1. Challenges and Opportunities
2. Mobile Health Applications
3. Mobile Quality of Life Lab
4. Cardiovascular Disease Risk

3. Challenges and Opportunities: Scientific Rigour
Challenges
Use of unclear Behaviour Change techniques
• Not used
• Used, but not declared
• Used and declared, but poorly implemented
Lack of basis in medical evidence
• Low evidence of study effectiveness
• Lack of sensitivity analyses
Attempts at creating a review system for mobile health apps
• No definitive review framework
Opportunities
Apps can classify Behaviour Change techniques
• Using and declaring Behaviour Change taxonomies
• Ensuring the implementation adheres to the technique
Apps can declare medical evidence used in studies
• Describing the expectations and progress of studies
• Using, e.g., textual, graphical, interactive content
• e.g., with interactive tables for chronic disease risk
• Specifying sample characteristics for studies
Apps can ensure a review process before adding studies
• Ensuring the steps above
• Strictness may lead to quality
• e.g., Apple App Store

4. Challenges and Opportunities: Holistic Assessment
Challenges
Many apps fall into one of these two approaches
Focus on general lifestyle, health, and wellbeing
• Unclear effects of behaviour change interventions
Focus on preventing or managing diseases
• Contain healthy Behaviour Change interventions
• The disease is specific
Neither approach assesses the participant holistically
Opportunities
Apps can achieve both span and depth
Using validated scales for health and Quality of Life general
assessment
• e.g., World Health Organisation’s Quality of Life assessment
Using specific guidelines from health organisations
• e.g., the American Heart Association, the European Respiratory
Society, and many others
Implementing exploratory and interventional studies
• Studies can combine general and specific assessments
• Users can choose in which specific assessments to participate

5. Challenges and Opportunities: Data Dimensionality
Challenges
Many scientific studies need data that has
• Multiple variables and data sources
• High accuracy, frequency, and continuity
Few datasets integrate multiple data sources
• e.g., device-reported + lab-reported
Many datasets use less reliable sources
• Researchers tend to default to them
• e.g., self- or proxy/observer reports
Opportunities
Mobile platforms and frameworks can obtain such data
• Combine multiple data sources: device-reported and self-
reported with lab-reported
• Prioritise data sources by accuracy, frequency, and continuity
• Collect data of types that can trace multiple behavioural
markers simultaneously
• Obtain a holistic and realistic view of human daily living
Examples of frameworks on the Apple iOS platform
• HealthKit: lab- and device-reported health data
• ResearchKit: self-reported data
• AWARE: device-reported usage data

6. Challenges and Opportunities: Data Timespan
Challenges
Disease prevention studies need data that is
• Spanning over long periods of time
• Large enough to test scientific hypotheses with confidence
Yet, many studies continue to focus on
• Short-term data collection (“only within the study time frame”)
• Often several weeks
• Small samples (“whoever we can”)
• Often in the tens
Many apps are not updated, yielding
• A feeling of outdated user experience from participants
• Gaps in data collection and participant attrition
• Loss of information about the evolution of the data in time
Opportunities
Mobile platforms and frameworks can obtain such data
• Collect the data continuously and over long periods of time
• Collect the data independent of the installed apps
• Store the data onto the device and, optionally, in the cloud
• Already achieve horizontal scalability
• Release the apps from the burden of data storage
• Allows for less, and more complex, mobile health apps
Examples of frameworks on mobile platforms
• HealthKit: health data collected on Apple iOS devices
• Fit: health data collected on Google Android devices
• Fitbit, Withings, … - health-oriented activity trackers

7. Challenges and Opportunities: Data Control
Challenges
Many mobile health apps fail to provide
• Scientific grounds for collecting each data type
• Information about data location, transfer, and access
• Easy options to pause, stop, withdraw data
• Even a privacy policy!
Opportunities
Specific studies can focus on specifying the needed
• Data sources
• Data types
A mobile app that manages the studies can
• Allow participants to sign up for studies they are interested in
• Allow participants to manage the collection of each data type
• Request consent for any data transfer outside of the device
• Pseudonymise the participants

8. Challenges and Opportunities: Operational Burden
Challenges
Research mobile health apps are treated by researchers as yet
another tool for the study due to
• The app not being the main focus of the study researchers
• Difficulties with keeping apps alive between rounds of funding
• Interest, but limited resources in focusing on app quality
However, not addressing the user experience needs leads to
• Apps falling below participants’ expectations if not met
• Users leaving the app, negatively impacting the studies
• A Meetup friend calls them… “academic” apps
Opportunities
An app can provide a standardised structure
• For characterising the samples of participants
• Under the assumption of a large participant base
• For designing and conducting studies
• Exploratory
• Interventional
• For performing common non-functional tasks
• Health data collection from, e.g., device sensor monitoring
• Self-reported data from, e.g., questionnaires
• Study and data management
• Security
• Towards a familiar experience for participants and researchers

9. Mobile Health Applications: Examples

10. Mobile Health Applications: Causes of Death

11. Mobile Health Applications: the App Gap

12. To our knowledge, there is no holistic app for researchers and smartphone users
to deploy and participate in evidence-based longitudinal, multidimensional
studies, using and generating high-resolution datasets to assess and then
change behaviours and improve QoL in the long-term.

13. mQoL | Mobile Quality of Life Lab: Overview
Motivation
Solve challenge in data collection by adding value for
participants and researchers.
Participants
Offer users the Quality of Life mobile lab as a holistic
tool for daily life exploration.
Researchers
Invite researchers to configure explorations, by
specifying motivations, models, schedules, and data.

14. mQoL | Mobile Quality of Life Lab: Data
Quality of Life self-reports
Explorations can correlate Quality of Life with behavioural marker data.
Demographic self-reports
Necessary for participant segmentation.
Medical self-reports
Necessary as input for, e.g., cardiovascular disease risk assessment models.
Performance reports
Monitoring daily life: explorations obtain behavioral marker data, e.g., physical
activity, sleep, and heart measurements.
Custom self-reports
Explorations specify questions relevant for their underlying studies.

15. Case Study on Cardiovascular Disease Risk: Scenarios

16. Case Study on Cardiovascular Disease Risk: Variables
sex, age, ethnicity, country risk, area-based index of deprivation, body mass index, total
cholesterol, HDL cholesterol, LDL cholesterol, glucose, systolic blood pressure, smoking status,
diabetes, hypertensive treatment, family history, past diseases, level of physical activity,
cardiorespiratory fitness
Dataset Large Longitudinal Has enough variables Affordable
Open data
e.g., Open Humans No Yes No Yes
Cohort data
e.g., Framingham Yes Yes Yes No
Our projects
60 seniors, 1-2 years No Yes Yes Yes

17. Case Study on Cardiovascular Disease Risk: With mQoL
Data source Device-reported (usage) Device-reported (health) Lab-reported health Self-reported
Frameworks iOS, AWARE HealthKit HealthKit EHR ResearchKit
Types country, area
systolic blood pressure (patent
pending), cardiorespiratory
fitness, level of physical
activity, body mass index
total cholesterol, HDL
cholesterol, LDL cholesterol,
glucose, family history, past
diseases
body mass index,
smoking status, diabetes,
hypertensive treatment,
family history, past
diseases

18. We Are Seeking Feedback and Collaborators
mQoL clickable prototype
Works on mobile and web
www.bit.ly/mobileQoLlab

19. Let’s Further Square the Curve!

20. Thank you
Quality of Life Technologies Lab
University of Geneva & University of Copenhagen
www.qol.unige.ch
Vlad Manea
manea@di.ku.dk
Special Acknowledgments to H2020 WellCo project (769765)

21. References
This list supplements the references of the workshop paper “mQoL: Mobile Quality of Life Lab: From Behaviour Change to QoL” by Vlad
Manea and Katarzyna Wac, presented at the Workshop on Mobile Human Contributions, in conjunction with the UbiComp Conference,
Singapore, October 8 2018.
List of all ResearchKit apps
Tourraine, Shazino Blog 2016 blog.shazino.com
Contributed to Slide 9
ResearchKit and CareKit
Apple, 2016 www.apple.com/lae/researchkit
Cause of death by age
Abajobir et al., Lancet 2017
Used in Slide 10
People count by age
De Wulf et al., Population Pyramid 2017
Used in Slide 11
European Guidelines on cardiovascular disease prevention in clinical practice
Piepoli et al., European Heart Journal 2016
Used in Slide 16
The Compression of Morbidity
Fried et al., Milbank Memorial Fund Quarterly 1983
Used in Slide 20