Are Physical Activities Associated With Perceived Stress? The Evidence From the China Health and Nutrition Survey

Psychological stress is a negative affective state. The association between physical activity and psychological stress was commonly reported in previous researches. Few published studies with large sample sizes have explored such an association in Chinese population. The current research aims to assess the association between perceived stress and physical activity preferences, as well as the association between risk of high perceived stress and physical activity behaviors (e.g., sports-, transport-, occupational-related physical activity, sedentary activities and time in bed). The data were collected from the China Health and Nutrition Survey (CHNS) in 2015. Generalized linear models and logistic regression models were used to explore the association between perceived stress and physical activity. In total, 11,066 participants were included in the current analysis. For physical activity preferences, we found that “like” preference of all six mentioned domains of activities contributed to lower perceived stress (i.e., lower perceived stress scale −14 score, all β < 0 and p < 0.05). For physical activity behaviors, the results indicated that none/low intensity physical activity behaviors (e.g., sports-, occupational- related, sedentary) were associated with risk of high perceived stress, except that low intensity of transport-related activities seemed to be protective from high stress. However, the association between intensity of physical activity behaviors and perceived stress was not simply the higher the better. High intensity of all these physical activity behaviors was also associated with high perceived stress. Our findings suggest that positive preferences and moderate physical activity behaviors were associated with low perceived stress. The findings herein highlight the effect of regulating physical activity on perceived stress, as well as inform potential strategies to reduce psychological stress.

Does Consumer Need for Uniqueness Influence Travel Decision-Making?

The present paper aims to assess the impact of Consumer Need for Uniqueness (CNFU) in tourists on travel decision-making, particularly, with regard to preferred destination types and activity preferences. The impact of self-attributed need for uniqueness (SANU) in moderating the aforementioned relationships is also examined. The data has been collected using a cross sectional survey on a sample of 288 tourists from various destinations in India. The hypotheses were tested through CFA and SEM using SPSS. The findings indicate that an individual with more need for uniqueness, both as a psychological trait and as a consumer disposition, may prefer unique destinations and unique activities in comparison to those with lower SANU and CNFU. This has important implications for travel and tourism marketers who may appeal to the NFU trait in tourists while promoting travel destinations and activity packages that provide unique, curated experiences for better acquisition and retention efforts.

Understanding Chinese tourist mobility and consumption-related behaviours in London using Sina Weibo check-ins

In this paper, we detail an individual-level analysis of under-exploited location-based social network (LBSN) data extracted from Sina Weibo, a comprehensive source for data-driven research focused on Chinese populations. The richness of the Sina Weibo data, coupled with high-quality venue and attraction information from Foursquare, enables us to track Chinese tourists visiting London and understand behaviours and mobility patterns revealed by their activities and venue-based ‘check-ins’. We use these check-ins to derive a series of indicators of mobility which reveal aggregate and individual-level behaviours associated with Chinese tourists in London, and which act as a tool to segment tourists based on those behaviours. Our data-driven tourist segmentation indicates that different groups of Chinese tourists have distinctive activity preferences and travel patterns. Our primary interest is in tourists’ consumption behaviours, and we reveal that tourists with similar activity preferences still exhibit individualised behaviours with regards to the nature and location of key consumption activities such as shopping and dining out. We aim to understand more about Chinese tourist shopping behaviours as a secondary activity associated with multi-purpose trips, demonstrating that these data could permit insights into tourist behaviours and mobility patterns which are not well captured by official tourism statistics, especially at a localised level. This analysis could be up-scaled to incorporate additional LBSN data sources and broader population subgroups in order to support data-driven urban analytics related to tourist mobilities and consumption behaviours.