Gustavo Yuki
1 
, Luiz Hespanhol
2,3* , Lisa Mohr
4, Adelle Kemlall Bhundoo
5, David Jiménez-Pavón
6,7,8, Bernhard Novak
9, Stefano Nuccio
10, Jose Daniel Jiménez García
6,7, Julian David Pillay
5, Lorenzo Rum
10, Celso Sánchez Ramírez
11, Lutz Vogt
4, Jan Wilke
4,121 Masters and Doctoral Programs in Physical Therapy, Universidade Cidade de São Paulo (UNICID), Sao Paulo, Brazil
2 Department of Physical Therapy, Faculty of Medicine, University of Sao Paulo (USP), Sao Paulo, Brazil
3 Amsterdam Collaboration on Health & Safety in Sports, Department of Public and Occupational Health, Amsterdam, Movement Sciences, Amsterdam University Medical Centers (UMC) location VU University Medical Center Amsterdam (VUmc), Amsterdam, The Netherlands
4 Department of Sports Medicine and Exercise Physiology, Goethe University Frankfurt, Frankfurt/Main, Germany
5 Department of Basic Medical Sciences, Durban University of Technology, South Africa
6 ImFine Research Group, Department of Health and Human Performance, Universidad Politécnica de Madrid; Exercise is Medicine, Spain
7 MOVE-IT Research Group, Department of Physical Education, Faculty of Education Sciences University of Cádiz, Spain
8 CIBER of Frailty and Healthy Aging (CIBERFES), Madrid, Spain
9 Institute of Human Movement Science, Sport and Health, University of Graz, Austria
10 Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome, Italy
11 Sciences of Physical Activity, Sports and Health School, University of Santiago of Chile (USACH), Chile
12 Institute of Occupational, Social and Environmental Medicine, Goethe University Frankfurt, Frankfurt/Main, Germany
*Corresponding Author: Amsterdam Collaboration on Health & Safety in Sports, Department of Public and Occupational Health, Amsterdam, Movement Sciences, Amsterdam University Medical Centers (UMC) location VU University Medi Email l.hespanhol@usp.br
Abstract
Background: Online home exercises represent opportunities to increase physical activity levels. However, high dropout rates are commonly reported in such programmes. This study aimed to investigate the predictors of dropping out from an online home exercise programme.
Methods: A total of 760 individuals from nine countries participated in this 8-week prospective cohort study derived from a randomised controlled trial. The participants were randomised into “4-week live-streamed exercise –>4-week recorded exercise” or “4-week no intervention –>4-week recorded exercise” group. Repeated measurements using weekly questionnaires were performed. Pain intensity, disability, mental well-being score, exercise motivation, sleep quality, impulsiveness/anxiety, and physical activity level were analysed.
Results: A total of 53.8% (95% confidence interval [CI] 50.3%–57.3%) participants dropped out from the programme. The identified predictors of dropping out from the programme were: well-being (odds ratio [OR] 0.94, 95% CI 0.91–0.97) and disability (OR 1.02, 95% CI 1.002–1.04) at baseline considering the first 4 weeks; age (0.98; 95% CI 0.96–1.00) and baseline well-being (0.93; 95% CI 0.89–0.97) considering the entire follow-up (8 weeks); exercise motivation (0.92; 95% CI 0.87 to 0.97) and general impulsiveness/anxiety (1.04; 95% CI 1.01–1.07) repeated measured over time.
Conclusion: About half of the participants dropped out from the online home exercise programme. Higher baseline scores in mental well-being and age predicted a reduction in dropping out. Higher baseline disability predicted an increase in dropping out. During the follow-up, higher exercise motivation was associated with a reduction in dropping out, and higher impulsiveness and anxiety were associated with an increase in dropping out.