Statistical analysis
REGRESSION, SECONDARY, DISEASE CHARACTERISTIC
We estimated that a sample size of 212 participants (106 per arm) would ensure 80% power to detect a 0.5 h/day ST difference between the two arms, using a two-sided type I error rate of 0.05 and assuming a standard deviation of 1.18 h/day. The calculation was based on prior studies and assumed a 15% dropout rate [Primary outcome analysis was performed on an intention-to-treat basis. Between-group difference in the change in ST from pre- to post-CR was evaluated using linear mixed model analysis using random intercepts with time (pre- to post-CR) as categorical variable. Subgroup analyses were performed based on patient characteristics, including sex, age, employment status, living environment and education level as well as disease characteristics, including index diagnosis and treatment. We evaluated the difference between the control- and SIT LESS group in the proportion of patients with ST ≥ 9.5 h/day at post-CR by logistic regression analysis and adjustment for baseline ST. For other secondary outcomes, we used mixed model analysis to assess changes in the number of prolonged sedentary bouts per day; time spent in LIPA and MVPA per day; daily step count; HeartQoL, PAM score and SMART risk score from pre- to post-CR. Within the SIT LESS group, week-to-week differences in ST and prolonged sedentary bouts were investigated using mixed model analyses using random intercepts with time as continuous variable.Normally distributed continuous variables were presented as mean ± standard deviation and non-normally variables as median [interquartile range]. Categorical variables were expressed as numerical values and percentages. All statistical tests were two-sided, confidence intervals were at the 95% level and 