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In order to be able to use the aroma hand massage as a skill that can be done by a nurse who does not have a special aromatherapy technique, we examine anti-stress effects of simplified aroma hand massage for healthy subjects. We evaluated the anti-stress action of aroma hand massage and the different components of the procedure in 20 healthy women in their twenties. We used autonomic nervous function measured via electrocardiogram as an index of stress. After conducting a baseline electrocardiogram, we induced stress in the participants by asking them to spend 30 minutes completing Kraepelin's arithmetic test. We then administered various treatments and examined the anti-stress effects. Kraepelin's test significantly increased sympathetic nervous function and significantly reduced parasympathetic nervous function. Compared with massage without essential oil or aroma inhalation, aroma hand massage significantly increased parasympathetic nervous function and significantly decreased sympathetic nervous function. The effect of the aroma hand massage persisted when the procedure was simplified. The anti-stress action of the aroma hand massage indicates that it might have beneficial application as a nursing technique. There are several limitations in this study; ambiguities of low component/high component ratio of heart rate variability and bias by small subjects groups of the same women.

The information of social media is not often written in ordinary web pages. Nevertheless, it is difficult to extract such information from social media because such services include so much information. Furthermore, various topics are mixed in social media communities. The authors designate such important and unique information related to social media as tip information. In this paper, they aim to propose a method to extract tip information that has been classified by topic from social networking services as a first step in extracting tip information from social media.

Themes of many kinds exist in a social media community because users write contents freely. Then the authors first detect the topics from the community and cluster the comment based on the topics. Subsequently, they extract tip information from each cluster. In this time, the tip information is include a user's experience and it has common important words.

The authors used an experiment to confirm that their proposed method can extract appropriate tip information from a community that a user specifies. The average precision is 69 per cent. A comparison of the authors' proposed method and baseline which is without detection of topic and clustering, the average precision obtained using the authors' proposed method is 18 per cent greater than the baseline.

The authors have three points to extract tip information from social media: topic detection and clustering from the social media using LDA method; extracting an author's actual experiences; and creation of a tip keyword dictionary from user experiments.