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Quality of Life Technologies: From Fundamentals of Mobile Computing to Patterns of Sleep and Happiness

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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.

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Quality of Life Technologies: From Fundamentals of Mobile Computing to Patterns of Sleep and Happiness

  1. 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

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