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Getting most out of your SENSORS
Mixed-Methods Research Methodology Enabling Identification, Modeling
and Predicting Human ...
Agenda
▪ Who are we ?
▪ “In the Wild”
▪ Mixed-Methods
▪ Use Cases
▪ Wrangling, Modeling and Predicting
▪ Conclusive Remarks
World Health Organization | www.who.int
Who are we ?
Patients
mQoL
Plateforme flexible pour
études sur la santé
humaine
Researcher
Clinicians
Instituts
Visualization
Explorati...
Bending the Curve
Wac, K., Fiordelli, M., Gustarini, M., & Rivas, H. (2015). Quality of life technologies: Experiences fro...
“In the Wild”
In-situ design, development and evaluation
- Real-time
- Minimally intrusive
- Context/Environment is everyt...
Mixed-Methods : Quantitative & Qualitative
Mixed-Methods : Quantitative & Qualitative
“What humain aspect are we exploring ?”
“ What does the literature says about t...
Explorative, Inductive &
Hypothetico-Inductive Approach
‘digital phenotyping’
Dey, A. K., Wac, K., Ferreira, D., Tassini, K., Hong, J. H., & Ramos, J. (2011). Getting closer: An Empirical Investigatio...
De Masi, A., & Wac, K. (2018). You're Using This App for What?: A mQoL Living Lab Study. Mobile Human Contributions: Works...
Wac, K. (2018). From Quantified Self to Quality of Life, Chapter in: Digital Health: Scaling Healthcare to the World, Serie...
What About Qualitative Methodology ?
- Initial and Final interview
- ESM/EMA
- DRM
Hyewon Suh, Nina Shahriaree, Eric B. Hekler, and Julie A. Kientz. 2016. Developing and Validating the User Burden Scale: A...
Intille, Stephen et al. “μEMA: Microinteraction-based Ecological Momentary Assessment (EMA) Using a Smartwatch.” Proceedin...
Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA. A survey method for characterizing daily life experience: the day...
Adding Context to Subject Reports
InFrequent
Qualntitative
SuObjective
Memory
Sensory
Biased
Socially
Non-
Judgemental
Des...
mQoL Lab: Step-by-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable and Ubiquito...
Concrete Cases
- Sleep
- Stress
Use Case :
Sleep
“During the past month,
How often have you had
trouble sleeping because you”
Wake up in the middle
of the...
From a Wearable...
Wac, K., Montanini, L., Ryager, K. B., & Rivas, H. (2017). Digital Health Tools for Sleep Self-Manageme...
Spring Summer Fall Winter
Laghouila, S., Manea, V., Estrada, V., Wac, K. (2018). Digital Health Tools for Chronic Illness ...
...Back to Phone (ON-OFF ‘Sensor’)
N = 14: working mothers (S2-S5) and students (S11-S20), up to 6 months each (DK)
Ciman,...
Stress
Ciman, M., Wac, K., (2018). Individuals’ Stress Assessment Through Human-Smartphone Interaction Analysis, IEEE Tran...
You have the method,
what about the data ?
Wrangling, Modeling and Predicting
Best practices:
- Constant monitoring
- Testing the data as they come
- Backup rule of ...
Conclusive Remarks
- Limitations:
- Limited time (3 years max)
- Limited number of participants
(aggregated 317 subject)
-...
Exercice
Now you learned the Mixed-Method, let try to
prepare a study together using it.
EXERCISE
- Define a use case: choose a human aspect to study
- Define the data to collect with:
- Smartphone
- Wearable
- De...
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mQoL-Lab : Living Lab Infrastructure

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Related Video: https://www.youtube.com/watch?v=WbjSiPT8scI

Thank You for referencing this work, if you find it useful!

Citation of a related scientific paper:
Berrocal, A., Manea, V., De Masi, A., Wac, K., mQoL Lab: Step-By-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable And Ubiquitous Devices, 17th International Conference On Mobile Systems And Pervasive Computing (MobiSPC), August 2020.

The talk details:
Alexandre De Masi, Katarzyna Wac, Getting Most out of your SENSORS: Mixed-Methods Research Methodology Enabling Identification, Modelling and Predicting Human Aspects of Mobile Sensing “In the Wild”, 19th IEEE Conference on Sensors (IEEE SENSORS’20), October 2020.

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mQoL-Lab : Living Lab Infrastructure

  1. 1. Getting most out of your SENSORS Mixed-Methods Research Methodology Enabling Identification, Modeling and Predicting Human Aspects of Mobile Sensing "In the Wild" Quality of Life Technologies Lab University of Geneva & University of Copenhagen qualityoflifetechnologies.org Alexandre De Masi, Prof. Katarzyna Wac
  2. 2. Agenda ▪ Who are we ? ▪ “In the Wild” ▪ Mixed-Methods ▪ Use Cases ▪ Wrangling, Modeling and Predicting ▪ Conclusive Remarks
  3. 3. World Health Organization | www.who.int Who are we ?
  4. 4. Patients mQoL Plateforme flexible pour études sur la santé humaine Researcher Clinicians Instituts Visualization Exploration Modelization Smartphones Wearables Calibrated scales (PROs) EMR • • • • • • • • • Passive Data Collection Patients’ Data Mobile Data Platform What is our goal ?
  5. 5. Bending the Curve Wac, K., Fiordelli, M., Gustarini, M., & Rivas, H. (2015). Quality of life technologies: Experiences from the field and key challenges. IEEE Internet Computing, 19(4), 28-35. Fries, J. F. (2002). Aging, natural death, and the compression of morbidity. Bulletin of the World Health Organization, 80, 245-250. QoL Domains QoL Facets (13/24) Physical health Activities of daily living ✓ Dependence on medicinal substances and medical aids Energy and fatigue ✓ Mobility ✓ Pain and discomfort ✓ Sleep and rest ✓ Work capacity Psychological Bodily image and appearance Negative feelings ✓ Positive feelings ✓ Self-esteem Spirituality / Religion / Personal beliefs ✓ Thinking, learning, memory, and concentration Social relationships Personal relationships ✓ Social support ✓ Sexual activity Environment Financial resources Freedom, physical safety, and security ✓ Health and social care: accessibility, and quality Home environment Opportunities for acquiring new information and skills Participation in and opportunities for recreation / leisure activities Physical environment (pollution / noise / traffic / climate) ✓ Transport ✓ { { { { Computational Models Self-management & Behaviour Change Facilitation 1000+ Participants (mQoL Living Lab) World Health Organization | www.who.int “The World Health Organization Quality of Life Assessment (WHOQOL): development and general psychometric properties.,” Soc. Sci. Med., vol. 46, no. 12, pp. 1569–85, Jun. 1998.
  6. 6. “In the Wild” In-situ design, development and evaluation - Real-time - Minimally intrusive - Context/Environment is everything - During multiple months or years Chamberlain, A., & Crabtree, A. (Eds.). (2020). Into the Wild: Beyond the Design Research Lab. Springer International Publishing.
  7. 7. Mixed-Methods : Quantitative & Qualitative
  8. 8. Mixed-Methods : Quantitative & Qualitative “What humain aspect are we exploring ?” “ What does the literature says about this aspect ?” - Quantitative : Objective, continuous, automatic, unobtrusive, sensory based, in-context - Qualitative: Subjective, momentary, self reported, memory based, biased
  9. 9. Explorative, Inductive & Hypothetico-Inductive Approach ‘digital phenotyping’
  10. 10. Dey, A. K., Wac, K., Ferreira, D., Tassini, K., Hong, J. H., & Ramos, J. (2011). Getting closer: An Empirical Investigation Of The Proximity Of User To Their Smart Phones. In Proceedings of the ACM UBICOMP. Smartphone as a sensor 88% of the time next to us 1 /4 Quantitative
  11. 11. De Masi, A., & Wac, K. (2018). You're Using This App for What?: A mQoL Living Lab Study. Mobile Human Contributions: Workshop in conjunction with ACM UBICOMP, Singapore, October 2018. Berrocal, A., Manea, V., De Masi, A., Wac, K. Step-by-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable and Ubiquitous Devices, ACM MobileHCI 2020 (under evaluation) Smartphone Logger mQoL-log 6.6+ billion data points Quantitative
  12. 12. Wac, K. (2018). From Quantified Self to Quality of Life, Chapter in: Digital Health: Scaling Healthcare to the World, Series: Health Informatics, Springer Nature, Dordrecht, the Netherlands. Annotated wearables dataset https://doi.org/10.6084/m9.figshare.9702122 Wearables 438 wearables (2018) Quantitative
  13. 13. What About Qualitative Methodology ? - Initial and Final interview - ESM/EMA - DRM
  14. 14. Hyewon Suh, Nina Shahriaree, Eric B. Hekler, and Julie A. Kientz. 2016. Developing and Validating the User Burden Scale: A Tool for Assessing User Burden in Computing Systems. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). Association for Computing Machinery, New York, NY, USA, 3988–3999. DOI:https://doi.org/10.1145/2858036.2858448 2 /4 Qualitative: Initial & Final Interview Initial : Before the Study - Since when use of mobile, what phone(s) & provider - Specific questions depending of the human aspect researched - Phone and wearable proximity at day-night, week-weekend, home-outside, etc - Age, education, occupation, marital status, cultural background, etc - Any other comments Final : After the Study - Experience of the participants : User Burden Scale - Comments about the smartphone and wearable (HCI) - Anecdotal stories (out-of-the-scope)
  15. 15. Intille, Stephen et al. “μEMA: Microinteraction-based Ecological Momentary Assessment (EMA) Using a Smartwatch.” Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) vol. 2016 (2016): 1124-1128. doi:10.1145/2971648.2971717 3 /4 Qualitative : ESM/EMA ESM : Experience Sampling Method EMA : Ecological Momentary Assessment Registering experience at a given moment In-situ survey triggered by: - Pre-established/Random times/intervals - “Everyday around 8:00 am” - Context based: “Once the study subject arrived at work” - Enabled by : sensor data fusion (e.g., WiFi, heart rate, …) Subjective: like “an-ad-hoc diary” (no need to memorize), may miss context
  16. 16. Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA. A survey method for characterizing daily life experience: the day reconstruction method. Science. 2004 Dec 3;306(5702):1776-80. doi: 10.1126/science.1103572. PMID: 15576620. 4 /4 Qualitative : DRM DRM : Day Reconstruction Method - Specific episodes of last 24h “re-instantiated” in memory in some detail. - Indicate approximate times (and/or estimate duration) - Where, Who with, What - additional questions tailored for the study. Characteristics: - Subjective, like “cumulative diary” - May miss context if additional questions not asked - Depends on organization of memories, memory bias / memorable episodes - May depend on social desirability of answers
  17. 17. Adding Context to Subject Reports InFrequent Qualntitative SuObjective Memory Sensory Biased Socially Non- Judgemental Desirable ! ! ! !! ! ! ! ! ! !! ! !! Context-Rich Continuous Longitudinal ! !! !! ! !
  18. 18. mQoL Lab: Step-by-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable and Ubiquitous Devices A Berrocal, V Manea, A De Masi, K Wac - Procedia Computer Science, 2020 mQoL-Lab In Reality: Tech View
  19. 19. Concrete Cases - Sleep - Stress
  20. 20. Use Case : Sleep “During the past month, How often have you had trouble sleeping because you” Wake up in the middle of the night or early In the morning? Buysse, D. J., Reynolds III, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193-213.
  21. 21. From a Wearable... Wac, K., Montanini, L., Ryager, K. B., & Rivas, H. (2017). Digital Health Tools for Sleep Self-Management: Working Mothers Use Case. In 38th Annual Meeting and Scientific Sessions of the Society of Behavioral Medicine (SBM 2017), USA, 2017. User ID Hours of sleep User ID Hours of sleep Weekday Weekend Weekday Weekend 2 5.5 +/- 13 min. 6 +/- 20 min. 5 6 +/- 18 min. 8.25 +/- 15 min. 3 7 +/- 17 min. 7.3 +/- 23 min. 7 7 +/- 15 min. 8 +/- 15 min. 4 8 +/- 10 min. 6.25 +/- 30 min. 10 6 +/- 10 min. 5.75 +/- 10 min. N = 6 working mothers (DK) Up to 6 months each Study details
  22. 22. Spring Summer Fall Winter Laghouila, S., Manea, V., Estrada, V., Wac, K. (2018). Digital Health Tools for Chronic Illness and Dementia Risk Assessment in Older Adults, 39th Annual Meeting and Scientific Sessions of the Society of Behavioral Medicine (SBM 2018), USA, 2018. N = 75 seniors over 65 (HU, ES) Enrolled since January 2017 for at least 6 months Study details Currently: multivariable logistic regression models for PSQI (PRO) vs. wearable dataset (TechRO) Via a Wearable & Risk Assessment
  23. 23. ...Back to Phone (ON-OFF ‘Sensor’) N = 14: working mothers (S2-S5) and students (S11-S20), up to 6 months each (DK) Ciman, M., Wac, K. (2019), Smartphones as Sleep Duration Sensors: Validation of the iSenseSleep Algorithm, JMIR mhealth and uhealth, Vol 7, No 5. User ID Sleep duration Phone ON-OFF Remark BASIS watch [avg±std] Estimate [avg±std] * significantly diff 2 (mothers) 393 +/- 10 min. 418 +/- 14 min. 3 402 +/- 15 min. 507 +/- 16 min.* Over 4 455 +/- 13 min. 377 +/- 34 min.* Under 5 446 +/- 15 min. 444 +/- 25 min. 11 (students) 429 +/- 15 min. 481 +/- 23 min.* Over 12 473 +/- 16 min. 478 +/- 25 min. 13 377 +/- 22 min. 377 +/- 24 min. 14 450 +/- 15 min. 454 +/- 35 min. 15 482 +/- 14 min. 459 +/- 24 min. 16 478 +/- 16 min. 446 +/- 36 min. 17 409 +/- 19 min. 378 +/- 45 min.* Under 18 417 +/- 24 min. 374 +/- 16 min. 19 462 +/- 16 min. 346 +/- 25 min.* Under 20 452 +/- 29 min. 426 +/- 39 min.* Under Study details mQoL Log
  24. 24. Stress Ciman, M., Wac, K., (2018). Individuals’ Stress Assessment Through Human-Smartphone Interaction Analysis, IEEE Transactions on Affective Computing (IEEE TAC), 9(1): 51-65. N = 38 participants, 1 month (CH) Study details
  25. 25. You have the method, what about the data ?
  26. 26. Wrangling, Modeling and Predicting Best practices: - Constant monitoring - Testing the data as they come - Backup rule of three - Prepare for worst case scenario Tools : Jupyter Notebook and R for data tasks Machine learning: - KISS: Keep It Simple St***d - Oversampling, aggregation. - Time Series analysis : forecasting, classification or regression.
  27. 27. Conclusive Remarks - Limitations: - Limited time (3 years max) - Limited number of participants (aggregated 317 subject) - Future work: - Methodology paper - Longitudinal study ( 2 to n-years) with Mixed-Methods to enable predictive health.
  28. 28. Exercice Now you learned the Mixed-Method, let try to prepare a study together using it.
  29. 29. EXERCISE - Define a use case: choose a human aspect to study - Define the data to collect with: - Smartphone - Wearable - Define the content of: - Interviews - ESM/EMA - DRM -

Related Video: https://www.youtube.com/watch?v=WbjSiPT8scI Thank You for referencing this work, if you find it useful! Citation of a related scientific paper: Berrocal, A., Manea, V., De Masi, A., Wac, K., mQoL Lab: Step-By-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable And Ubiquitous Devices, 17th International Conference On Mobile Systems And Pervasive Computing (MobiSPC), August 2020. The talk details: Alexandre De Masi, Katarzyna Wac, Getting Most out of your SENSORS: Mixed-Methods Research Methodology Enabling Identification, Modelling and Predicting Human Aspects of Mobile Sensing “In the Wild”, 19th IEEE Conference on Sensors (IEEE SENSORS’20), October 2020.

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