Here is the design of a clinical trial:

Official Title: Evaluation of the Implementation and Effectiveness of a Multidisciplinary Lifestyle Treatment for Inpatients With Mental Illness (MULTI+)

Included conditions:
- Mental Illness
- Lifestyle

Study Armgroups:
- {'label': 'TAU', 'type': 'NO_INTERVENTION', 'description': 'Treatment as usual (TAU); consist mainly of pharmacological treatment and psychotherapy.'}
- {'label': 'MULTI+', 'type': 'EXPERIMENTAL', 'description': 'Lifestyle treatment', 'interventionNames': ['Behavioral: MULTI+']}

Interventions:
- {'type': 'BEHAVIORAL', 'name': 'MULTI+', 'description': 'MULTI+ is a multidisciplinary, multicomponent treatment which aims to improve lifestyle factors through a holistic lifestyle approach, by focusing on 10 core components. The MULTI+ will be integrated into daily treatment to promote uptake and sustainability.', 'armGroupLabels': ['MULTI+']}

Primary Outcomes:
- {'measure': 'QRISK3 score', 'description': 'Algorithm that estimates the probability of developing cardiovascular disease over the next 10 years by taking multiple risk factors into account.', 'timeFrame': '4 measurements semi-annually over a period of 24 months'}

Please estimate the sample size based on the assumption: 
A conservative dropout rate of 10%, a correlation of 0.1 between clusters, a power of 80%, and a significance level of 5% (p < 0.05).
Sample size
        Our sample size considerations are based on demonstrating a difference in body weight between conditions. The reason is that we did not have any relevant data on changes in QRISK3 for this population, whereas body weight has been used in our previous research [32]. Furthermore, body weight is a key component of the QRISK3 and, given the nature of MULTIâ+â, it is likely that other components of the QRISK3 will change in the same direction. Therefore, using body weight can be seen as a more robust and more conservative approach than using uncertain assumptions about changes in QRISK3.
        Our research showed that 53.8% of the patients who participated in MULTI lostââ¥â5% of their initial bodyweight, as opposed to 16.3% of the patients who followed TAU. Given the broader implementation of MULTIâ+â, with patients who spend less time in healthcare than those in the earlier study, we expect to find effects in a relative smaller number of patients in this study. Based on a small-scale follow-up, we will assume 32% of patients will loseââ¥â5% of their initial bodyweight instead of 53.8%. Because metabolic risk factors are a routinely measured outcome, we expect a conservative dropout rate of 10%. Because there is insufficient knowledge about the correlations between clusters, we will assume a correlation of 0.1. Based on these data, a power of 80%, a significance level of 5% (pâ<â0.05), three clusters, the minimal sample size should consist of 846 patients. Given the high patient turnover (nâ2000), this seems feasible.