Dynamic Risk Perceptions in Times of Avian and Seasonal Influenza Epidemics: A Repeated Cross-Sectional Design.

Infectious diseases pose a serious threat to humans. Therefore, it is crucial to understand how accurately people perceive these risks. However, accuracy can be operationalized differently depending on the standard of comparison. The present study investigated accuracy in risk perceptions for three infectious diseases (avian influenza, seasonal influenza, common cold) using three different standards for accuracy: Social comparison (self vs. others' risk perceptions), general problem level (risk perceptions for diseases with varying threat levels), and dynamic problem level (risk perceptions during epidemics/seasons vs. nonepidemic/off-season times). Four online surveys were conducted using a repeated cross-sectional design. Two surveys were conducted during epidemics/seasons of avian influenza, seasonal influenza, and common cold in 2006 (n = 387) and 2016 (n = 370) and two surveys during nonepidemic/off-season times for the three diseases in 2009 (n = 792) during a swine flu outbreak and in 2018 (n = 422) during no outbreak of zoonotic influenza. While on average participants felt less at risk than others, indicating an optimistic bias, risk perceptions matched the magnitude of risk associated with the three infectious diseases. Importantly, a significant three-way interaction indicated dynamic accuracy in risk perceptions: Participants felt more at risk for seasonal influenza and common cold during influenza and cold seasons, compared with off-season times. However, these dynamic increases were more pronounced in the perceived risk for others than for oneself (optimistic bias). The results emphasize the importance of using multiple approaches to assess accuracy of risk perception as they provided different information on how accurately people gauge their risk when facing infectious diseases.

Why We Eat What We Eat: Assessing Dispositional and In-the-Moment Eating Motives by Using Ecological Momentary Assessment.

BACKGROUND: Why do we eat? Our motives for eating are diverse, ranging from hunger and liking to social norms and affect regulation. Although eating motives can vary from eating event to eating event, which implies substantial moment-to-moment differences, current ways of measuring eating motives rely on single timepoint questionnaires that assess eating motives as situation-stable dispositions (traits). However, mobile technologies including smartphones allow eating events and motives to be captured in real time and real life, thus capturing experienced eating motives in-the-moment (states). OBJECTIVE: This study aimed to examine differences between why people think they eat (trait motives) and why they eat in the moment of consumption (state motives) by comparing a dispositional (trait) and an in-the-moment (state) assessment of eating motives. METHODS: A total of 15 basic eating motives included in The Eating Motivation Survey (ie, liking, habit, need and hunger, health, convenience, pleasure, traditional eating, natural concerns, sociability, price, visual appeal, weight control, affect regulation, social norms, and social image) were assessed in 35 participants using 2 methodological approaches: (1) a single timepoint dispositional assessment and (2) a smartphone-based ecological momentary assessment (EMA) across 8 days (N=888 meals) capturing eating motives in the moment of eating. Similarities between dispositional and in-the-moment eating motive profiles were assessed according to 4 different indices of profile similarity, that is, overall fit, shape, scatter, and elevation. Moreover, a visualized person × motive data matrix was created to visualize and analyze between- and within-person differences in trait and state eating motives. RESULTS: Similarity analyses yielded a good overall fit between the trait and state eating motive profiles across participants, indicated by a double-entry intraclass correlation of 0.52 (P<.001). However, although trait and state motives revealed a comparable rank order (r=0.65; P<.001), trait motives overestimated 12 of 15 state motives (P<.001; d=1.97). Specifically, the participants assumed that 6 motives (need and hunger, price, habit, sociability, traditional eating, and natural concerns) are more essential for eating than they actually were in the moment (d>0.8). Furthermore, the visualized person × motive data matrix revealed substantial interindividual differences in intraindividual motive profiles. CONCLUSIONS: For a comprehensive understanding of why we eat what we eat, dispositional assessments need to be extended by in-the-moment assessments of eating motives. Smartphone-based EMAs reveal considerable intra- and interindividual differences in eating motives, which are not captured by single timepoint dispositional assessments. Targeting these differences between why people think they eat what they eat and why they actually eat in the moment may hold great promise for tailored mobile health interventions facilitating behavior changes.

Polarized but illusory beliefs about tap and bottled water: A product- and consumer-oriented survey and blind tasting experiment.

BACKGROUND: Despite the rigorous control of tap water quality, substantial price differences, and environmental concerns, bottled water consumption has increased in recent decades. To facilitate healthy and sustainable consumer choices, a deeper understanding of this "water consumption paradox" is needed. Therefore, the aim of the two present studies was to examine health-related beliefs and risk perceptions and their accuracy by implementing a combined product- and consumer-oriented approach. METHODS: An online survey (N = 578) and a blind taste test (N = 99) assessed perceptions and behaviors for tap and bottled water within primarily tap and bottled water consumers in a fully crossed design. The combined product- and consumer-oriented approach yielded significant consumer × product interaction effects. RESULTS: The two consumer groups showed "polarized" ratings regarding perceived quality/hygiene, health risks and taste for bottled and tap water, indicating that the two consumer groups substantially diverged in their beliefs. However, in the blind taste test, neither consumer group was able to distinguish tap from bottled water samples (consumer perspective). Moreover, tap or bottled water samples did not systemically vary in their ascribed health-risk or taste characteristics (product perspective). CONCLUSIONS: Although the two consumer groups differ greatly in their beliefs, the perceived health risk and taste differences seem to reflect illusionary beliefs rather than actual experiences or product characteristics. Public health campaigns should address these illusions to promote healthy and sustainable consumer choices.