A Method for Simplified HRQOL Measurement by Smart Devices

Health-related quality of life (HRQOL) is a useful indicator that rates a person’s activities in various physical, mental and social domains. Continuously measuring HRQOL can help detect the early signs of declines in these activities and lead to steps to prevent such declines. However, it is difficult to continuously measure HRQOL by conventional methods, since its measurement requires each user to answer burdensome questionnaires. In this paper, we propose a simplified HRQOL measurement method for a continuous HRQOL measurement which can reduce the burden of questionnaires. In our method, sensor data from smart devices and the questionnaire scores of HRQOL are collected and used to construct a machine-learning model that estimates the score for each HRQOL questionnaire item. Our experiment result showed our method’s potential and found effective features for some questions.

[1]  Stuart Biddle,et al.  Physical activity and mental health: evidence is growing , 2016, World psychiatry : official journal of the World Psychiatric Association.

[2]  K. B. Hagen,et al.  Exercise in prevention and treatment of anxiety and depression among children and young people. , 2006, The Cochrane database of systematic reviews.

[3]  S. Saxena,et al.  The World Health Organization Quality of Life Assessment (WHOQOL): development and general psychometric properties. , 1998, Social science & medicine.

[4]  J. Vries,et al.  WHOQOL-Bref. Field trial version. Introduction, administration, scoring and generic version of the questionnaire , 1997 .

[5]  Ali H Mokdad,et al.  Associations between recommended levels of physical activity and health-related quality of life. Findings from the 2001 Behavioral Risk Factor Surveillance System (BRFSS) survey. , 2003, Preventive medicine.

[6]  A. Espay,et al.  A Viewpoint on Wearable Technology-Enabled Measurement of Wellbeing and Health-Related Quality of Life in Parkinson’s Disease , 2016, Journal of Parkinson's disease.

[7]  B. Spilker,et al.  Quality of life and pharmacoeconomics in clinical trials , 1996 .

[8]  Juhani Smolander,et al.  Associations between work ability, health-related quality of life, physical activity and fitness among middle-aged men. , 2008, Applied ergonomics.

[9]  M. Malik.  Heart Rate Variability , 1996, Clinical cardiology.

[10]  Joaquim Goncalves,et al.  Data mining and electronic devices applied to quality of life related to health data , 2015, 2015 10th Iberian Conference on Information Systems and Technologies (CISTI).

[11]  Akane Sano,et al.  Stress Recognition Using Wearable Sensors and Mobile Phones , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[12]  Oscar Mayora-Ibarra,et al.  Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step , 2015, IEEE Journal of Biomedical and Health Informatics.

[13]  Eiji Yamamoto,et al.  Patient-Motivated Prevention of Lifestyle-Related Disease in Japan , 2007 .

[14]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .