The prevalence of lifestyle-related health problems is increasing rapidly. Many of the diseases and health risks could be prevented or alleviated by making changes toward healthier lifestyles. We have developed the Wellness Diary (WD), a concept for personal and mobile wellness management based on Cognitive-Behavioral Therapy (CBT). Two implementations of the concept were made for the Symbian Series 60 (S60) mobile phone platform, and their usability, usage, and acceptance were studied in two 3-month user studies. Study I was related to weight management and study II to general wellness management. In both the studies, the concept and its implementations were well accepted and considered as easy to use and useful in wellness management. The usage rate of the WD was high and sustained at a high level throughout the study. The average number of entries made per day was 5.32 (SD = 2.59, range = 0-14) in study I, and 5.48 (SD = 2.60, range = 0-17) in study II. The results indicate that the WD is well suited for supporting CBT-based wellness management.

We propose a general purpose home area sensor network and monitoring platform that is intended for e-Health applications, ranging from elderly monitoring to early homecoming after a hospitalization period. Our monitoring platform is multipurpose, meaning that the system is easily configurable for various user needs and is easy to set up. The system could be temporarily rented from a service company by, for example, hospitals, elderly service providers, specialized physiological rehabilitation centers, or individuals. Our system consists of a chosen set of sensors, a wireless sensor network, a home client, and a distant server. We evaluated our concept in two initial trials: one with an elderly woman living in sheltered housing, and the other with a hip surgery patient during his rehabilitation phase. The results prove the functionality of the platform. However, efficient utilization of such platforms requires further work on the actual e-Health service concepts.

We developed a system consisting of both wearable and ambient technologies designed to monitor personal wellbeing for several months during daily life. The variables monitored included bodyweight, blood pressure, heart-rate variability and air temperature. Two different user groups were studied: there were 17 working-age subjects participating in a vocational rehabilitation programme and 19 elderly people living in an assisted living facility. The working-age subjects collected data for a total of 1406 days; the average participation period was 83 days (range 43-99). The elderly subjects collected data for a total of 1593 days; the average participation period was 84 days (range 19-107). Usage, technical feasibility and usability of the system were also studied. Some technical and practical problems appeared which we had not expected such as thunder storm damage to equipment in homes and scheduling differences between staff and the subjects. The users gave positive feedback in almost all their responses in a questionnaire. The study suggests that the data-collection rate is likely be 70-90% for typical health monitoring data.

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The objective of the study was to investigate the validity of 3-D-accelerometry-based Berg balance scale (BBS) score estimation. In particular, acceleration patterns of BBS tasks and gait were the targets of analysis. Accelerations of the lower back were measured during execution of the BBS test and corridor walking for 54 subjects, consisting of neurological patients, older adults, and healthy young persons. The BBS score was estimated from one to three BBS tasks and from gait-related data, separately, through assessment of the similarity of acceleration patterns between subjects. The work also validated both approaches' ability to classify subjects into high- and low-fall-risk groups. The gait-based method yielded the best BBS score estimates and the most accurate BBS-task-based estimates were produced with the stand to sit, reaching, and picking object tasks. The proposed gait-based method can identify subjects with high or low risk of falling with an accuracy of 77.8% and 96.6%, respectively, and the BBS-task based method with corresponding accuracy of 89.5% and 62.1%.