Abstract
Smart wellness services collect various types of lifelog data, such as the number of steps taken and sleep duration, via smart devices. However, most existing smart wellness services simply display each individual lifelog to users, limiting their ability to support overall user understanding. In this article, the authors develop a lifelogs-based daily wellness score (LDWS) to resolve such limitations by combining various lifelog data to calculate a score that represents overall daily health behaviors. LDWS was developed as part of a smart wellness service for college students in collaboration with an IT company. Lifelog data of 41 college students were collected through a four-week trial and were subsequently fitted to a random effects model. Based on the model estimates, LDWS was determined by linearly aggregating seven behavior variables. The utility of the developed LDWS was validated through a second trial of the service. The authors also discuss other potential uses of LDWS and the factors to be considered for developing a lifelogs-based wellness score for a smart wellness service.
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