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mQoL: Mobile Quality of Life Lab: From Behavior Change to QoL


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Vlad Manea, Katarzyna Wac, mQoL: Mobile Quality of Life Lab:
From Behavior Change to QoL, Mobile Human Contributions: Opportunities and Challenges (MHC) Workshop in conjunction with UBICOMP, Singapore, October 2018.

Published in: Health & Medicine
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mQoL: Mobile Quality of Life Lab: From Behavior Change to QoL

  1. 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 Workshop on Mobile Human Contributions 2018 ACM UBICOMP 2018
  2. 2. Agenda 1. Challenges and Opportunities 2. Mobile Health Applications 3. Mobile Quality of Life Lab 4. Cardiovascular Disease Risk
  3. 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. 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. 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. 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. 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. 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. 9. Mobile Health Applications: Examples
  10. 10. Mobile Health Applications: Causes of Death
  11. 11. Mobile Health Applications: the App Gap
  12. 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. 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. 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. 15. Case Study on Cardiovascular Disease Risk: Scenarios
  16. 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. 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. 18. We Are Seeking Feedback and Collaborators mQoL clickable prototype Works on mobile and web
  19. 19. Let’s Further Square the Curve!
  20. 20. Thank you Quality of Life Technologies Lab University of Geneva & University of Copenhagen Vlad Manea Special Acknowledgments to H2020 WellCo project (769765)
  21. 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 Contributed to Slide 9 ResearchKit and CareKit Apple, 2016 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