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Local food and changes in tourist eating habits in a sun-and-sea destination: a segmentation approach

Purpose: Little is known about how tourists’ eating habits change between everyday life and holidays. This study aims to identify market segments based on changes in food consumption and experiences of a sun-and-sea destination’s local food. The authors evaluate to what extent tourists consume local food and assess the contribution of local food experiences to the tourists’ overall experience. Design/methodology/approach: The target population was all tourists visiting the Algarve in the Summer 2018 and included both domestic and international sun-and-sea tourists. A sample of 378 valid questionnaires was collected. Data analysis included descriptive analysis, statistical tests and cluster analysis. Findings: Cluster analysis identified three segments: non-foodies, selective foodies and local gastronomy foodies. Results indicate that tourists change their eating habits during holidays, eating significantly more seafood and fish and less legumes, meat, fast food and cereals and their derivatives. International and domestic sun-and-sea tourists reported that eating local food contributes significantly to their overall tourism experience. Practical implications: Sun-and-sea destinations should promote the offer of local dishes, especially those that include locally produced fish and seafood, to improve the tourist experience, differentiate the destination and increase sustainability. Originality/value: The authors address three identified research gaps: a posteriori segmentation based on tourists’ food consumption behaviour; measurement of changes in eating practices between home and in a sun-and-sea destination; and assessment of the role of food experiences to overall tourism experience of tourists visiting a sun-and-sea destination.

Runner’s perceptions of reasons to quit running

Physical inactivity has become a major public health concern and, consequently, the awareness of striving for a healthy lifestyle has increased. As a result, the popularity of recreational sports, such as running, has increased. Running is known for its low threshold to start and its attractiveness for a heterogeneous group of people. Yet, one can still observe high drop-out rates among (novice) runners. To understand the reasons for drop-out as perceived by runners, we investigate potential reasons to quit running among short distance runners (5 km and 10 km) (n = 898). Data used in this study were drawn from the standardized online Eindhoven Running Survey 2016 (ERS16). Binary logistic regressions were used to investigate the relation between reasons to quit running and different variables like socio-demographic variables, running habits and attitudes, interests, and opinions (AIOs) on running. Our results indicate that, not only people of different gender and age show significant differences in perceived reasons to quit running, also running habits, (e.g., running context and frequency) and AIOs are related to perceived reasons to quit running too. With insights into these related variables, potential drop-out reasons could help health professionals in understanding and lowering drop-out rates among recreational runners

Empowering Citizens to Adopt Healthy Lifestyle Habits

Smarter smartphones: understanding and predicting user habits from GPS sensor data

Interpersonal determinants of eating behaviours in Dutch older adults living independently: a qualitative study

Background Eating behaviour of older adults is influenced by a complex interaction of determinants. Understanding the determinants of a specific target group is important when developing targeted health-promoting strategies. The aim of this study was to explore interpersonal determinants of eating behaviours in older adults living independently in a specific neighbourhood in the Netherlands. Methods In the neighbourhood of interest, populated by relatively many older adults, fifteen semi-structured interviews were conducted with independently living older adults (aged 76.9 ± 6.4y). Interviews were complemented with observations among the target group: three occasions of grocery shopping and three collective eating occasions in the neighbourhood. A thematic approach was used to analyse the qualitative data. Results When we asked the older adults unprompted why they eat what they eat, the influence of interpersonal determinants did not appear directly; respondents rather mentioned individual (e.g. habits) and environmental factors (e.g. food accessibility). Key findings regarding interpersonal factors were: 1) Behaviours are shaped by someone’s context; 2) Living alone influences (determinants of) eating behaviour via multiple ways; 3) There is a salient norm that people do not interfere with others’ eating behaviour; 4) Older adults make limited use of social support (both formal and informal) for grocery shopping and cooking, except for organised eating activities in the neighbourhood. In this particular neighbourhood, many facilities (e.g. shops at walking distance) are present, and events (e.g. dinners) are organised with and for the target group, which likely impact (determinants of) their behaviours. Conclusions The study showed that older adults do not directly think of interpersonal factors influencing their eating behaviour, but rather of individual or environmental factors. However, multiple interpersonal factors did appear in the interviews and observations. Moreover, neighbourhood-specific factors seem to play a role, which underlines the need to understand the specific (social) setting when developing and implementing intervention programmes. Insights from this study can assist in developing health-promoting strategies for older adults, taking into account the context of the specific neighbourhood.

Smarter smartphones: understanding and predicting user habits from GPS sensor data

Smartphones and similar devices allow access to a wealth of information. Navigating this wealth of information is problematic. Semantic locations, assigned to observed GPS user movements, can help in providing inforamtion that is useful to the user at a specific time or place. This paper shows how a stream of sensor data can be processed and interpreted to determine (i) the locations of interest for a user, such as home, work, etc, and (ii) to predict the expected future transitions between such locations. We have implemented our algorithms in a fully functional prototype smartphone app and backend, and we present results based on actual usage data gathered over the past few months. We conclude that inferred semantic location information allows a smart device to offer personalized, contextual, information without the need for the user to perform any explicit query. Our system is open source, and can be used to build context-aware recommender systems that suggest content which is at the right time and at the right place.