PhD Thesis Defence: From Participation Factors to Co-Calibration of Patient- and Wearable-Reported Outcomes in Behavioural, Health, and Quality of Life Studies

Motivation
Facilitation
Co-
Calibration
PhD Defence
Vlad Manea
Quality of Life
Technologies Lab
University of Copenhagen

From Participation Factors to Co-Calibration of Patient- and Wearable-Reported
Outcomes in Behavioural, Health, and Quality of Life Studies
PhD Thesis Defence • April 14th
, 2021 • University of Copenhagen
Candidate
Vlad Manea
Københavns Universitet, Copenhagen, Denmark
Committee
Professor Nancy E. Mayo
McGill University, Montreal, Canada
Professor Bert Arnrich
Universität Potsdam, Potsdam, Germany
Professor Thomas T. Hildebrandt
Quality of Life Technologies Lab
Human-Centered Computing / Computer Science / Science
University of Copenhagen | Københavns Universitet
Supervisor
Professor Katarzyna Wac
Université de Genève, Geneva, Switzerland

12:45-13:05: Preparation
13:05-13:50: Presentation
13:50-14:00: Break
14:00-15:30: Discussion
15:30-15:45: Votation
15:45-16:00: Reception
Agenda for the Day

Agenda for the Presentation
Part 1
Motivation and Facilitation of
Human Subject Study
Participation
Part 2
Co-Calibration of Behavioural,
Health, and Quality of Life
Outcomes
Introduction
Conclusions

Primary Causes of Mortality: Chronic Diseases
Introduction
Chronic diseases
Common examples:
- Cardiovascular
- Respiratory
- Metabolic
- Digestive
- Mental
Roth, G.A., et al., 2018. Global, regional, and national age-sex-speciﬁc mortality for 282
causes of death in 195 countries and territories, 1980–2017: a systematic analysis for
the Global Burden of Disease Study 2017. The Lancet, 392(10159).

Risk Factors for Chronic Diseases: Behaviours
Introduction
Behavioural pattern Risk
Smoking habits 18.1%
Poor diet
Physical inactivity
16.6%
Alcohol consumption 3.5%
Inadequate sleep ...
{
Mokdad, A.H., Marks, J.S., Stroup, D.F. and Gerberding, J.L., 2004. Actual causes of
death in the United States, 2000. Jama, 291(10).
Roth, G.A., et al., 2018. Global, regional, and national age-sex-speciﬁc mortality for 282
causes of death in 195 countries and territories, 1980–2017: a systematic analysis for
the Global Burden of Disease Study 2017. The Lancet, 392(10159).

Introduction
From Behaviours to Reported Outcomes
Mayo, N.E., Figueiredo, S., Ahmed, S. and Bartlett, S.J., 2017. Montréal Accord on
Patient-Reported Outcomes (PROs) use series–Paper 2: Terminology proposed to
measure what matters in health. Journal of clinical epidemiology, 89.

Introduction
Patient-Reported Outcome (PRO) Example
During the past month,
how often have you had
trouble sleeping because
you wake up in the middle
of the night or early
morning?
Buysse, D.J., Reynolds III, C.F., Monk, T.H., Berman, S.R. and Kupfer,
D.J., 1989. The Pittsburgh Sleep Quality Index: a new instrument for
psychiatric practice and research. Psychiatry research, 28(2).

Challenges with Patient-Reported Outcomes
Introduction
infrequent
subjective
memory-
based
socially
acceptable
qualitative
? What happens between the visits at the doctor?
often
1-dimensional
out of
context

Measure behaviours
from daily life
Over 430 wearables
available (2018)
Mobiles & Wearables
Introduction
Wac, K., 2018. From quantiﬁed self to quality of life. In
Digital Health. Springer, Cham.
Dey, A.K., Wac, K., et al. 2011. Getting closer: an
empirical investigation of the proximity of user to their
smart phones. In Proceedings of the 13th UbiComp.
Grossman, L. and Vella, M. 2014. Never Oﬄine. TIME,
September 22.
Mobiles in our
proximity 88% time

Technology-Reported Outcome (TechRO) Examples
Introduction
Physical Activity
steps, elevation, distance, calories
Heart Rate
normal, moderate, fast, too fast
Sleep
total, in bed, score, interruptions, heart rate
Heart Activity
electrocardiogram
Behavioural Markers

What happens between the visits at the doctor?
Behaviours can be reported during daily life.
Opportunities with Technology-Reported Outcomes
Introduction
quantitative
?
objective continuous
sensor-based
in context
longitudinal
non-judgemental
frequent
multi-dimensional
✓

Mayo, N.E., Figueiredo, S., Ahmed, S. and Bartlett, S.J., 2017. Montréal Accord on
Patient-Reported Outcomes (PROs) use series–Paper 2: Terminology proposed to
measure what matters in health. Journal of clinical epidemiology, 89.
Introduction
Co-Calibrating Reported Outcomes
PROs TechROs
Emerging
?
?
Co-Calibration
Gold Standard

Introduction
A Quick Reality Check
PROs TechROs
? Why participate in studies in the ﬁrst place?

Introduction
Understanding Participation
Motivation Facilitation
? Why participate in studies in the ﬁrst place?
✓Need more research to understand participation.

Part 1
Motivation and Facilitation of
Human Subject Study Participation

Part 1
Motivation and
Facilitation of Human
Subject Study
Participation
Background
Methods
Results
Reﬂections
Contributions

Community
Absent models to assess
study participation
Studies
Limited data quality
from / to participants
Participants
Diﬀerent reasons to
participate in research
Challenges
Motivation and Facilitation of Human Studies / Background

Objectives
Motivation and Facilitation of Human Studies / Background
Review literature on
participation
1
Propose model and
framework designs
2
Extend mQoL-Lab
platform
3

Activities
Motivation and Facilitation of Human Studies / Methods
Literature review on
the motivation to
participate in studies
Information exchange
with researchers who
conduct studies
Model and framework
designs for study
participation
Software tools that
facilitate participation
in studies

Human Subject Studies
Longitudinal Studies
Health Studies
Literature Review on Participation
Motivation and Facilitation of Human Studies / Methods
Willingness factors to share
health data
Motivation factors to
participate
Human factors
Technical factors
Requirements Architectural design
Manea, V., Schnoor Hansen, M., Elbeyi, S.E. and Wac,
K., 2019. Towards personalizing participation in health
studies. In Workshop on Multimedia for Personal
Health & Health Care.
1
Manea, V. and Wac, K., 2018. mQoL: mobile quality of
life lab: from behavior change to QoL. Workshop on
Mobile Human Contributions.
2
Manea, V., Berrocal, A., De Masi, A., Møller, N.H.,
Wac, K., Bayer, H., Lehmann, S. and Ashley, E., 2019.
International workshop on longitudinal data collection
in human subject studies. In UbiComp Adjunct 2019.
3
Berrocal, A., Manea, V., De Masi, A. and Wac, K.,
2020. mQoL Lab: Step-by-Step Creation of a Flexible
Platform to Conduct Studies Using Interactive, Mobile,
Wearable and Ubiquitous Devices. MobiSPC 2020.
4
1
2
3
4
Challenges
Opportunities
Challenges
Opportunities
Electronic health records
Wearable data
Healthy participants
Diseased participants
Participants
Researchers
Data
Participants
System
Researchers
Layers
Features
Components

Factors Inﬂuencing the Motivation to Participate
Motivation and Facilitation of Human Studies / Results
Manea, V., Schnoor Hansen, M., Elbeyi, S.E. and Wac, K., 2019. Towards personalizing participation in health studies. In Workshop on Multimedia for Personal Health & Health Care.
May share electronic
health records
67% May share wearable
data from daily life
78% Altruism over health
and ﬁnancial gains?
♡/$ No model to assess
motivation
?
1 2 3 4

Factors Inﬂuencing the Motivation to Participate
Manea, V., Schnoor Hansen, M., Elbeyi, S.E. and Wac, K., 2019. Towards personalizing participation in health studies. In Workshop on Multimedia for Personal Health & Health Care.
Helping others
Family
Friends
Acquaintances
Society
Personal benefits
Health
Information
Reward
Social
Topic of study
Organ
Process
Behavior
Quality of Life
Method of study
Delivery
Effort
Artefact
Data use
1 2 3 4

Information Exchange Workshop with Domain Experts
Manea, V., Berrocal, A., De Masi, A., Møller, N.H., Wac, K., Bayer, H., Lehmann, S. and Ashley, E., 2019, September. LDC'19: international workshop on longitudinal data
collection in human subject studies. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the
2019 ACM International Symposium on Wearable Computers.
Workshop on Longitudinal Data Collection
● Contributions and discussions
● Methods, tools, and frameworks
● Collection, analysis, and interpretation
● Mobile, wearable, and ubiquitous data
● Exclusive focus on longitudinal data
● Keynote by a domain expert (UCL)
● Aﬃliations: KU, DTU, UniGe, NYU, SU
● ldc2019ubicomp.wordpress.com
1 2 3 4

Chatbot Stories Facilitating Participation
Manea, V., Schnoor Hansen, M., Elbeyi, S.E. and Wac,
K., 2019, October. Towards personalizing participation
in health studies. In Proceedings of the 4th
International Workshop on Multimedia for Personal
Health & Health Care.
Personalized Stories
Personalization with stories and
moments relevant for the
participant
1 2 3 4

mQoL Mobile App Facilitating Participation
Manea, V. and Wac, K., 2018, October. mQoL: mobile quality of life lab: from behavior change to QoL. In Proceedings of the 2018 ACM International Joint Conference and 2018
International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers.
1 2 3 4

mQoL-Lab Platform Facilitating Participation
Berrocal, A., Manea, V., De Masi, A. and Wac, K., 2020. mQoL Lab: Step-by-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable and
Ubiquitous Devices. MobiSPC 2020.
1 2 3 4

Summary
Motivation and Facilitation of Human Studies / Reﬂections
Feasibility
Personalised studies may
help participation
Challenges
Challenges beyond
humans and technology
Factors
Families of factors without
relative importance

Objectives × Scientiﬁc Contributions
Motivation and Facilitation of Human Studies / Contributions
Thoroughly explored a research area
● Motivation and facilitation of participation in studies
Produced a presentational model
● Personalized stories model to motivate participation
Provided a unifying framework
● Mobile app design for longitudinal studies
● Chatbot design for health studies
Designed and developed a tool
● mQoL-Lab platform for human subject studies
Review literature on
participation
1
Propose model and
framework designs
2
Extend mQoL-Lab
platform
3

Part 2
Co-Calibration of Behavioural,
Health, and Quality of Life
Outcomes

Part 2
Co-Calibration of
Behavioural, Health, and
Quality of Life Outcomes
Background
Methods
Results
Reﬂections
Contributions

Project Goals
● Relieve caregiver pressure
● Increase participant wellbeing and autonomy
● Decrease participant risk of dementia
Context
Co-Calibration of Reported Outcomes / Background
Research Project
● AAL “Caregiver and ME” (CoME, No. 14-7)
○ 2015-2019
● For people of older age with mild impairment and their caregivers
○ Spain or Hungary, use a mobile phone
Livingston, G. et al. Dementia prevention, intervention, and care. Lancet 390.10113 (2017): 2673-2734.

Objectives
Co-Calibration of Reported Outcomes / Background
TechROs
Emerging
Gold Standard
PROs
?
Review the literature on
co-calibration
1
Review the literature on a
related QoL facet
2
Collect behavioural, health,
and Quality of Life PROs from
validated scales
3
Collect behavioural marker
TechROs using wearables
4
Assess quality properties of
the collected data
5
Observe PRO-TechRO
statistical relations
6
Derive a computational model
for co-calibration
7
Demonstrate the model's
feasibility in the study
8
Inform designs of future
longitudinal in situ studies
9

Measures
● Age, gender, ethnicity, profession, education,
cohabitants, height, weight, blood pressure,
cholesterol, smoking status, alcohol status,
medication, mild disease status, etc.
Profiles of Participants
Co-Calibration of Reported Outcomes / Methods
Profile
● Filled during the first visit at the study site
● Updated along the duration of the project

Measures (Validated Scales)
● Physical Activity (IPAQ)
● Social Support (MSPSS)
● Anxiety/Depression (GADS)
● Nutrition (PREDIMED, SelfMNA)
● Memory (MFE)
● Sleep Quality (PSQI)
● Health-Related Quality of Life (EQ-5D-3L)
Measured Patient-Reported Outcomes
Patient-Reported Measures (PROs)
● Filled during subsequent group visits

Measures (Daily Aggregates)
● Energy expenditure
● Steps
● Distance
● Sedentary duration
● Physical activity durations (light, fair, vigorous)
● Sleep duration
● Heart rate
Measured Technology-Reported Outcomes
Devices (TechROs)
● Fitbit Charge 2 consumer wearable for ownership

Interval durations
7, 14, 21, 28, 60, 120 days
1.1
Step 3A
Select PRO variables
- item
- sub-score
- score
7
Step 3B
Select TechRO variables
absolute: median
relative: geometric mean of composition
(centered log ratio)
For each interval duration
Use a leeway between:
- PRO administration date, and
- TechRO interval end date
Allow maximum 1 alignment per wave
Obtain max. 1 alignment / duration / participant / wave / leeway
Step 2 Align in time
PRO-TechRO Alignment
Using a leeway of 0, 7, 14, 21, 28, 60, 90, 120 days
•
• • • • •
• • • •
6 4 ... 2 7
Health outcomes (scale) PRO
Behavioural markers (Fitbit) TechRO
Statistical Correlations
e.g., Spearman rS
0.75
7 1.1
2.1
...
6.2
...
4
...
5
...
Set of Pairs
PRO-TechRO Pair
7
1.1
Patterns of Correlations
TechRO j TechRO k
PRO i 0.75
0.55
Participants
Construct
PRO-TechRO
bivariate sets
Step 1A Compute
PRO scores
Step 1B Select
TechRO aggregations
Step 4 Inference
statistical hypothesis testing
Step 5 Patterns
from correlations to patterns
4
2 1 ... 1 3
5 6 3 1
Sub-score Numeric
score
Categorical
score
Sub-score Sub-score
Metrics for patterns
1. Count signiﬁcant correlations 0.5+
- For all PROs and TechROs
2. Contours of signiﬁcant correlations 0.8+
- For all PROs but only ordered TechROs
coQoL Computational Model for Co-Calibration

Examples of Contour Correlations Metric |rS| ≥ 0.8

Participants (PRO)
Co-Calibration of Reported Outcomes / Results
Qualiﬁed
● N = 39 (age 70.0 ± 7.2)
○ At least one PRO or TechRO
○ 28 healthy, 11 with mild disease
Signed Up
● N = 42 (age 69.8 ± 7.4)

Waves of Answers
● Wave 1
○ Mid 2018
● Wave 2
○ End 2018
○ Start 2019
● Wave 3
○ Mid 2019
Data Quality: Waves of Answers

Physical Activity (IPAQ - Fitbit): Contours
Contours of PRO-TechRO correlations (0.8+)
● All participants
○ Domestic moderate
● Participants with mild disease
○ Work walking
○ Work moderate
○ Work vigorous
○ Garden vigorous
○ Leisure vigorous
○ Leisure total

Social Support (MSPSS - Fitbit): Contours
○ Signiﬁcant other social support
○ Family social support
● Healthy participants
○ Signiﬁcant other social support
○ Family social support
○ Friends social support

Anxiety-Depression (GADS - Fitbit): Contours
○ Q5A Sleeping poorly
○ Q7A Trembling

Mediterranean Diet (PREDIMED - Fitbit): Contours
○ Q12 Nuts consumption
○ Q3 Vegetables consumption

Nutrition (SelfMNA - Fitbit): Contours
○ Q1 Food intake declined
○ Q2 Weight loss
○ Q4 Stressed or severely ill

Memory (MFE - Fitbit): Contours
○ Q24 Forgetting where things are kept
○ Q14 Forgetting to do planned things
○ Q18 Forgetting to tell somebody
something important

Sleep Quality (PSQI - Fitbit): Contours
○ Q5F trouble due to feeling cold
○ Q5C trouble due to use of the bathroom
○ Q4 duration of actual sleep

Quality of Life (EQ-5D-3L - Fitbit): Contours
○ Q5 Anxiety and depression

Effects of Longitudinal Measurement
Co-Calibration of Reported Outcomes / Reflections
Example: MSPSS Q3 vs Relative Fair Activity
● A strong PRO-TechRO correlation (0.9)
○ “More family help ~ more fair activity”
○ PRO: MSPSS Q3 family trying to help
○ TechRO: relative fair physical activity
● Effect of interval and leeway on corrs.
○ correlates the highest at 28 days
○ increasing leeway, decreasing corrs.
Durations of Monitoring
● We reported strong correlations across
○ many TechRO durations (7-120 days)
○ many leeway durations (0-120 days)

Towards Personalized and Holistic Assessment
PROs TechROs
PROs TechROs
Wave 1
Worse
Mid
Better
Worse
Mid
Better
Worse
Mid
Better
Wave 2 Wave 3
Wave 1
Worse
Mid
Better
Worse
Mid
Better
Worse
Mid
Better
Wave 2 Wave 3
PROs
Wave 1
Worse
Mid
Better
Wave 2
Worse
Mid
Better
Wave 3
Worse
Mid
Better
TechROs

coQoL as a Methodological Approach
Advantages
Potential for personalized
and holistic assessment
Limitations
Small sample size led to
simple methods
Identiﬁed groups of
PRO-TechRO relations
Feasibility

Objectives × Scientiﬁc Contributions
Co-Calibration of Reported Outcomes / Contributions
Thoroughly explored a research area
● Prior work on assessing PROs and TechROs
● Energy and fatigue facet (see publications)
Provided empirical data
● Physical and psychological PROs (raw)
● Behavioural marker TechROs (aggregate)
● Patterns of PRO-TechRO statistical correlations
Produced a reusable computational model
● coQoL co-calibration model for PROs and TechROs
Designed and developed a tool
● Tools for validated scale data collection PRO
● Tools for consumer wearable data collection TechRO
co-calibration
1
a related QoL facet
2
Collect behavioural, health,
and Quality of Life PROs
from validated scales
3
Collect behavioural marker
TechROs using wearables
4
Assess quality properties
of the collected data
5
Observe PRO-TechRO
statistical relations
6
Derive a computational
model for co-calibration
7
Demonstrate the model's
feasibility in the study
8
Inform designs of future
longitudinal in situ studies
9

Challenges and Opportunities
Conclusions
Motivation and Facilitation
● Self-reported motivation to participate
○ Validate models that assess
motivation
● Motivation not perceived as central when
designing studies
○ Implement tools that facilitate and
measure participation
Co-calibration of Reported Outcomes
● Small sample size and simple methods
○ Powered studies specialized on
subsets of PROs and TechROs
○ More advanced methods, e.g.,
predict PROs based on TechRO
● coQoL for observational settings
○ Gold standard updates with
consumer wearables when valid
● coQoL for interventional settings
○ Proactive investigation of inﬂections

Vision Observational Study with coQoL
Conclusions
Behaviour change techniques
High-quality TechROs
approx. 1 month
Relevant PRO administration
at the end of the 1 month
coQoL PRO-TechRO subsets
Trigger
helps obtain
informs
Further co-calibration
facilitates *
longitudinal
study, 2+ years,
avoid initial 3
weeks
3 months of data
needed for the
less compliant
PRO repeated in
the same season
PRO repeated in
diﬀerent seasons

Vision Interventional Study with coQoL
Conclusions
*
longitudinal
study, avoid
initial 3 weeks
Gradual change in behaviour
Inﬂection criteria in TechRO
prior from co-calibrations
Relevant PRO administration
at the end of the 1 month
coQoL PRO-TechRO subsets
Trigger
helps trigger
informs
Participant known "state"
updates

Future Work
Conclusions
Motivation and Facilitation
● Data collected in a study (N = 100) on
motivation and facilitation of human
subject studies.
Co-calibration of Reported Outcomes
● Analysis complete in a study (N = 27) on
cardiac patients with Vital Beats.
● Additional studies in the lab leveraging
coQoL and its PRO-TechRO results.

Dissemination: Motivation and Facilitation
Conclusions
Workshop paper Vlad Manea, Mads Schnoor Hansen, Semahat Ece
Elbeyi, Katarzyna Wac. Towards Personalizing Participation in Health
Studies. Workshop on Multimedia for Personal Health and Health Care
(HealthMedia 2019), Conference on Multimedia (MM 2019). 8p. DOI:
https://doi.org/10.1145/3347444.3356241.2
Conference paper Allan Berrocal, Vlad Manea, Alexandre De Masi,
Katarzyna Wac. mQoL-Lab: Step-by-Step Creation of a Flexible Platform
to Conduct Studies Using Interactive, Mobile, Wearable and Ubiquitous
Devices. Conference on Mobile Systems and Pervasive Computing
(MobiSPC 2020). 9p. DOI: https://doi.org/10.1016/j.procs.2020.07.033.
Workshop paper Vlad Manea, Katarzyna Wac. mQoL: Mobile Quality of
Life lab: from Behavior Change to Quality of Life. Workshop on Mobile
Human Contributions (MHC 2018), Conference on Pervasive and
Ubiquitous Computing (UbiComp 2018). 6p. DOI:
https://doi.org/10.1145/3267305.3267549.3
Workshop call for papers Vlad Manea, Allan Berrocal, Alexandre De
Masi, Naja Holten Møller, Katarzyna Wac, Hannah Bayer, Sune Lehmann,
Euan Ashley. Call for Papers: LDC ’19: Workshop on Longitudinal Data
Collection in Human Subject Studies. Call for Papers for the Workshop
on Longitudinal Data Collection in Human Subject Studies (LDC 2019),
Conference on Pervasive and Ubiquitous Computing (UbiComp 2019). 4p.
DOI: https://doi.org/10.1145/3341162.3347758.4
Poster Vlad Manea, Vero Estrada-Galiñanes, Katarzyna Wac. mQoL:
Mobile Quality of Life lab. Poster and demo at the Digital Health
Conference (DH 2018).
Advised master thesis Alba Kejser Perez, Cecilie Rosentoft, Elisabeth
Brinth Refstrup. Designing for Participation in Longitudinal Health and
Well-being Studies. MSc thesis (2020, September). Department of Media,
Cognition, and Communication, University of Copenhagen.
Co-supervised master thesis Mads Schnoor Hansen. Engaging
Participants in the Recruitment Phase of Human Subject Health Studies –
mQoL-chat: a Chatbot Approach. MSc thesis (2019, September).
Department of Computer Science, University of Copenhagen.

Dissemination: Co-Calibration of Reported Outcomes
Conclusions
Journal paper Vlad Manea, Katarzyna Wac. Co-calibrating Physical and
Psychological Outcomes and Consumer Wearable Activity Outcomes in
Older Adults: An Evaluation of the coQoL Method. Journal of
Personalized Medicine, 10(4), MDPI, 2020. Special Issue: PROomics:
Patient Reported Outcome (PRO) and Self-Tracking for Personalized
Medicine. Impact factor 4.433, rank 10/102 (Q1) in Health Care Sciences
and Services. 41p. DOI: https://doi.org/10.3390/jpm10040203
Poster Vlad Manea, Katarzyna Wac. Using Consumer-Friendly Wearables
to Associate Patient-Reported Quality of Life and Tech-Reported
Physical Activity and Sleep in Healthy Seniors. Poster at the Conference
of the International Society for Quality of Life Research (ISOQOL 2020).
DOI: https://doi.org/10.1007/s11136-020-02626-y
Conference paper Vlad Manea, Allan Berrocal, Katarzyna Wac. Using
Consumer-Friendly Wearables to Associate Patient- and
Technology-Reported Physical Activity in Healthy Seniors. Conference
on Mobile Systems and Pervasive Computing (MobiSPC2020). 8p. DOI:
https://doi.org/10.1016/j.procs.2020.07.036
Book chapter Natalie Solomon, Vlad Manea. Energy and Fatigue:
Classiﬁcation and Assessment of Energy and Fatigue using Subjective,
Objective, and Mixed Methods towards Health and Quality of Life
(accepted). Book chapter in: Katarzyna Wac, Sharon Wulfovich (eds.),
Quantifying Quality of Life: Incorporating Daily Life into Medicine, Health
Informatics, Springer, Cham. 30p.
Advised master thesis Kirke Kjellberg. ConsistencyQoL: A Framework for
Modelling Consistency in Behavioural Data Collected with Wearables. MSc
thesis (2020, August). Department of Computer Science, University of
Copenhagen.

PhD Thesis Defence: From Participation Factors to Co-Calibration of Patient- and Wearable-Reported Outcomes in Behavioural, Health, and Quality of Life Studies

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PhD Thesis Defence: From Participation Factors to Co-Calibration of Patient- and Wearable-Reported Outcomes in Behavioural, Health, and Quality of Life Studies