Thank You for referencing this work, if you find it useful! Reference/Citation: Katarzyna Wac, Jenny-Margrethe Vej, Kimie Bodin Ryager, Quality of Life Technologies: From Fundamentals of Mobile Computing to Patterns of Sleep and Happiness (Poster), 5th EAI International Symposium on Pervasive Computing Paradigms for Mental Health (MindCare), Milan, Italy, September 2015. Additional Reference/Citation for a latest scientific paper: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.

1. Diverse Factors Influencing
Individual’s Sleep Quality and
Quantity
MSc project by Kimie Bodin Ryager
GOAL
Identify factors that positively or negatively
affect sleep quality and quantify among healthy
female IT managers in Denmark. Prioritize them
and derive design implications for a solution
that can help manage these factors.
METHODS
Online survey and a qualitative study
(interviews) with 10 participants including sleep
quality/quantity self-reports and auto-logging of
sleep (smartphone logger and BASIS watch).
TIMELINE August 2015 – January 2016
August: Online survey
September: Interviews with participants and first
list of the factors
October – November: Experiment with self-
reports & auto-logging & interviews
Quality of Life Technologies
Quality of Life by World Health
Organization “Individuals’ perception of
their position in life in the context of the culture
and value systems in which they live and in
relation to their goals, expectations, standards
and concerns”.
WHOQOL group, The World Health Organization Quality of Life
Assessment, Social science & Medicine 41.10: 1403-9, 1996
GOAL
Understand factors influencing expected and
experienced QoL, with emphasis on use of
technologies contributing (or not) to individual’s
QoL.
METHODS
Surveyed 85 ICT-literate individuals living/
working in Geneva; 50% being 25-35y old, 61%
females, 62% in relationship, 95% daily use an
Internet-enabled smartphone. Answers were
coded along WHOQOL, whenever possible.
RESULTS
75 individuals rate their QoL as “good” or “very
good”, only one as “poor”.
See the figures for the cumulative results.
LIMITATIONS & CONCLUSIVE
REMARKS
Limitations: convenience sample & explorative
study. More research is needed to understand
the role of technologies in QoL of an individual.
From Fundamentals of Mobile Computing to Patterns of Sleep and Happiness
1,2 Katarzyna Wac, 1 Jenny-Margrethe Vej, 1 Kimie Bodin Ryager {wac, jvej, kiry}@di.ku.dk
Abstract
We all consciously (or not) strive to improve the Quality of Life (QoL) for ourselves and our loved ones. In this process, we rely on a growing scale
on miniaturized technologies, including these enclosed in a smartphone. It enables us access any information anytime, anywhere and anyhow and
improves our capacity to make informed decisions across daily life activities. We have surveyed 85 smartphone users on their current perception of
QoL, as well as the role of technology in their QoL improvement. The results indicate that beyond their need for information, individuals strive
towards better relationships, more happiness and assurance of basic physiological needs like sleep. We presents the cumulative results and
delineate the future work areas in the area of QoL technologies, especially related to sleep: an ambulatory assessment of its quality and quantity
and factors influencing it.
1 FACULTY OF SCIENCE
UNIVERSITY OF COPENHAGEN, DENMARK
2 CENTER FOR INFORMATICS
UNIVERSITY OF GENEVA, SWITZERLAND
Smartphone-Based Ubiquitous
Assessment of Individual Sleep
Patterns
MSc project by Jenny-Margrethe Vej
GOAL
Develop an algorithm for an smartphone-based
sleep assessment (automatic, accurate, reliable
and in a privacy-preserving manner) and derive
design implications for a solution that can help
combat students’ unhealthy sleeping patterns.
METHODS
Modeling of data collected by Social Fabric
project (DK) and mQoL Living Lab (CH), and
conducting own experiment (smartphone logger
and BASIS watch) with 10 university students.
TIMELINE September 2015 – July 2016
November: First user experiment
January-March: First version of the algorithm for
sleep assessment (and its evaluation)
April: Follow up experiment; 2nd version of the
algorithm (and its evaluation)
QoL Domain Facets incorporated within QoL domains
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 act.
Physical environment (pollution / noise / traffic / climate)
Transport
WHOQOL scale: QoL domains/sub-domains
What are your future expectations for QoL?What is your current experience of QoL?
5th EAI International Symposium on Pervasive Computing Paradigms for Mental Health
Factors Influencing the Sleep Quality & Quantity (tentative)
What applications you currently use that support your QoL?
1 function sleepPatterns
2 define data as
3 phoneON && phoneTouches
&& ifMobility && location
&& appsUsed && lightLevel
&& WiFiUsed && CellID
&& ifCharging
4 define analyse as
5 accuracy && reliability
6 do analyse on data
7 return
8 probability of indoor
9 probability of atHome
10 probability of inBedroom
11 probability of sleeping