AUTHOR=Bertollo Maurizio , Forzini Fabio , Biondi Sara , Di Liborio Massimiliano , Vaccaro Maria Grazia , Georgiadis Emmanouil , Conti Cristiana TITLE=How Does a Sport Psychological Intervention Help Professional Cyclists to Cope With Their Mental Health During the COVID-19 Lockdown? JOURNAL=Frontiers in Psychology VOLUME=12 YEAR=2021 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.607152 DOI=10.3389/fpsyg.2021.607152 ISSN=1664-1078 ABSTRACT=
All around the world in March, due to COVID-19, competitive sport calendars were suddenly canceled, jeopardizing the training programs of athletes. Moreover, in Italy, the government banned all non-essential travel across the entire country from the beginning of March. Consequently, Italian cyclists were banned from leaving their homes and therefore unable to perform their ordinary training activities. The Italian Association of Professional Cyclists (ACCPI) early on during that period noticed that several cyclists were experiencing a worrying decrease in their mental well-being and asked the authors to set up an online Sport Psychology Intervention (SPI) during lockdown to enhance the athletes' mental health. Through a number of unprecedented events and considerations, the aim of the current investigation was to assess the Italian cyclists' mental health during the lockdown and its changes after the SPI. We validated the Italian version of the Sport Mental Health Continuum Short Form (Sport MHC-SF)—presented in Study 1—and then applied it to a sample of Italian professional cyclists—presented in Study 2—prior to and after the SPI. To achieve these objectives, the reliability and construct validity of the Italian version of the Sport MHC-SF were tested in Study 1. RM-MANOVA tests were run to evaluate the effect of SPI on cyclists in Study 2. A total of 185 Italian athletes were involved in the validation of the MHC in Study 1 and 38 professional cyclists in Study 2. Results from Study 1 suggested a three-factor higher order model of Sport MHC-SF [Model fit: χ2(df) = 471.252 (252),