杉浦 雛姫

This study examines the relationship between cultural activities in daily life and the mental and physical well-being of respondents. We conducted a survey on cultural activities and health among residents of Takayama City, Gifu Prefecture, Japan. The survey includes both structured responses and free-text descriptions covering demographic data, hobbies, artistic activities, and emotional experiences. In particular, we focus on emotionally moving experiences, referred to in Japanese as 感動 (kandõ), which denotes experiences that strongly impress and deeply move one emotionally. Given the combination of numerical and textual data, direct statistical analysis is challenging. To address this, we convert the survey responses into text format and develop a regression model using BERT, a large-scale language model, to estimate mental and physical well-being. Training data are categorized into four groups: demographic data, hobbies, artistic activities, and emotional experiences. Our findings show that the regression model based on emotional experiences provides the most accurate estimations of both mental and physical well-being. SHAP analysis further reveals that experiences related to nature, such as “flowers,” “nature,” “growth,” and “sky,” are strongly associated with well-being, reflecting the significance of culturally and emotionally experiences (感動) in everyday life. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.34(2026) (online) DOI http://dx.doi.org/10.2197/ipsjjip.34.239 ------------------------------ This study examines the relationship between cultural activities in daily life and the mental and physical well-being of respondents. We conducted a survey on cultural activities and health among residents of Takayama City, Gifu Prefecture, Japan. The survey includes both structured responses and free-text descriptions covering demographic data, hobbies, artistic activities, and emotional experiences. In particular, we focus on emotionally moving experiences, referred to in Japanese as 感動 (kandõ), which denotes experiences that strongly impress and deeply move one emotionally. Given the combination of numerical and textual data, direct statistical analysis is challenging. To address this, we convert the survey responses into text format and develop a regression model using BERT, a large-scale language model, to estimate mental and physical well-being. Training data are categorized into four groups: demographic data, hobbies, artistic activities, and emotional experiences. Our findings show that the regression model based on emotional experiences provides the most accurate estimations of both mental and physical well-being. SHAP analysis further reveals that experiences related to nature, such as “flowers,” “nature,” “growth,” and “sky,” are strongly associated with well-being, reflecting the significance of culturally and emotionally experiences (感動) in everyday life. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.34(2026) (online) DOI http://dx.doi.org/10.2197/ipsjjip.34.239 ------------------------------

This study examines the relationship between cultural activities in daily life and the mental and physical well-being of respondents. We conducted a survey on cultural activities and health among residents of Takayama City, Gifu Prefecture, Japan. The survey includes both structured responses and free-text descriptions covering demographic data, hobbies, artistic activities, and emotional experiences. In particular, we focus on emotionally moving experiences, referred to in Japanese as 感動 (kandõ), which denotes experiences that strongly impress and deeply move one emotionally. Given the combination of numerical and textual data, direct statistical analysis is challenging. To address this, we convert the survey responses into text format and develop a regression model using BERT, a large-scale language model, to estimate mental and physical well-being. Training data are categorized into four groups: demographic data, hobbies, artistic activities, and emotional experiences. Our findings show that the regression model based on emotional experiences provides the most accurate estimations of both mental and physical well-being. SHAP analysis further reveals that experiences related to nature, such as “flowers, ” “nature, ” “growth, ” and “sky, ” are strongly associated with well-being, reflecting the significance of culturally and emotionally experiences (感動) in everyday life.