In the current context of the global pandemic of coronavirus disease-2019 (COVID-19), health professionals are working with social scientists to inform government policy on how to slow the spread of the virus. An increasing amount of social scientific research has looked at the role of public message framing, for instance, but few studies have thus far examined the role of individual differences in emotional and personality-based variables in predicting virus-mitigating behaviors. In this study we recruited a large international community sample (N = 324) to complete measures of self-perceived risk of contracting COVID-19, fear of the virus, moral foundations, political orientation, and behavior change in response to the pandemic. Consistently, the only predictor of positive behavior change (e.g., social distancing, improved hand hygiene) was fear of COVID-19, with no effect of politically-relevant variables. We discuss these data in relation to the potentially functional nature of fear in global health crises.

Behavioral observation techniques which relate action to personality have long been neglected (Furr and Funder in Handbook of research methods in personality psychology, The Guilford Press, New York, 2007) and, when employed, often use human judges to code behavior. In the current study we used an alternative to human coding (biomechanical research techniques) to investigate how personality traits are manifest in gait. We used motion capture technology to record 29 participants walking on a treadmill at their natural speed. We analyzed their thorax and pelvis movements, as well as speed of gait. Participants completed personality questionnaires, including a Big Five measure and a trait aggression questionnaire. We found that gait related to several of our personality measures. The magnitude of upper body movement, lower body movement, and walking speed, were related to Big Five personality traits and aggression. Here, we present evidence that some gait measures can relate to Big Five and aggressive personalities. We know of no other examples of research where gait has been shown to correlate with self-reported measures of personality and suggest that more research should be conducted between largely automatic movement and personality.

The COVID-19 pandemic caused lifestyle changes, with unknown effect on pets’ quality of life (QoL). Between May and July 2020, we distributed an online survey to investigate the role of several factors on feline and canine QoL, including lockdown-related factors. We used existing scales to measure human and pets’ personalities (Reinforcement Sensitivity Theory Personality Questionnaire, RST-PQ; RST-Dog; RST-Cat) and the human–animal relationship (Lexington Attachment to Pets Scale, LAPS) and the Milan Pet Quality of Life instrument (MPQL). Overall, 235 participants reported about 242 adult pets (N_cats = 78, N_dogs = 164). Factor analysis confirmed the structure and internal reliability of the existing scales (RST-PQ, RST-Dog, RST-Cat, LAPS) and suggested a four-factor structure for the MPQL (physical, psychological, social, environmental). The results indicate that the pets’ psysical QoL was largely explained by pet-related elements (pets’ demographics and life experience, and pets’ personality). Conversely, the pets’ psychological QoL was explained mostly by owner-related elements, such as the owners’ demographics, COVID-19-related changes, and the owners’ personality. Predictably, the pets’ environmental QoL is mostly explained by environmental factors, such as the outdoor access in the home environment and the country. Finally, the pets’ social QoL was explained by the larger combination of models: pets’ characteristics and personality, environment and COVID-19-related changes, and the pet–human relationship. These findings can be explained by two non-mutually exclusive mechanisms. The reported changes may be a by-product of the COVID-19 pandemic’s psychological and lifestyle effects on the owners, which in turn alter the way the owners interact with their pets and look after them. However, the owners’ characteristics and mood may bias their answers regarding their pets.

Background: Advances in smartphone technology have facilitated an increase in the number of commercially available smartphone and tablet apps that enable the collection of physiological and biomechanical variables typically monitored in sport and exercise settings. Currently, it is not fully understood whether individuals collect data using mobile devices and tablets, independent of additional hardware, in their practice. Objective: This study aims to explore the use of smartphone and tablet software apps to collect data by individuals working in various sport and exercise settings, such as sports coaching, strength and conditioning, and personal training. Methods: A total of 335 practitioners completed an electronic questionnaire that surveyed their current training practices, with a focus on 2 areas: type of data collection and perceptions of reliability and validity regarding app use. An 18-item questionnaire, using a 5-point Likert scale, evaluated the perception of app use. Results: A total of 204 respondents reported using apps to directly collect data, with most of them (196/335, 58.5%) collecting biomechanical data, and 41.2% (138/335) respondents reported using at least one evidence-based app. A binomial general linear model determined that evidence accessibility (β=.35, 95% CI 0.04-0.67; P=.03) was significantly related to evidence-based app use. Age (β=−.03, 95% CI −0.06 to 0.00; P=.03) had a significant negative effect on evidence-based app use. Conclusions: This study demonstrates that practitioners show a greater preference for using smartphones and tablet devices to collect biomechanical data such as sprint velocity and jump performance variables. When it is easier to access information on the quality of apps, practitioners are more likely to use evidence-based apps. App developers should seek independent research to validate their apps. In addition, app developers should seek to provide clear signposting to the scientific support of their software in alternative ways.