Here is the design of a clinical trial:

Official Title: On-site Multi-component Intervention to Improve Productivity and Reduce the Economic and Personal Burden of Neck Pain in Swiss Office-Workers

Included conditions:
- Neck Pain
- Headache
- Musculoskeletal Diseases
- Adherence, Patient
- Absenteeism
- Workplace
- Health Promotion
- Ergonomics
- Efficiency
- Presenteeism
- Occupational Health
- Surveys and Questionnaires
- Adult
- Exercise Therapy
- Work Performance

Study Armgroups:
- {'label': 'Intervention', 'type': 'OTHER', 'description': 'Multi-component intervention', 'interventionNames': ['Other: Multi-component intervention']}
- {'label': 'Control', 'type': 'OTHER', 'description': 'Control', 'interventionNames': ['Other: Control']}

Interventions:
- {'type': 'OTHER', 'name': 'Multi-component intervention', 'description': "Participants' workstation ergonomics will be assessed using an observation-based ergonomics assessment checklist for office-workers adapted to Swiss guidelines. Based on the initial assessment, best practice ergonomics will be applied individually using existing infrastructure. Participants will attend health promotion information group workshops for approximately one hour per week for 12 weeks. it. Participants will receive an individual progressive exercise programme aimed at conditioning the muscles of the neck and shoulder girdle. The exercises will be performed in groups (maximum of ten per group) at the workplace in a dedicated room, for approximately one hour (3x20 minutes) per week; once per week supervised by a physiotherapist, a human movement scientist, or a health scientist, and twice per week individually. Workshop session attendance will be recorded as an indication of adherence to health promotion. Adherence to neck exercises will be recorded with the Physitrack® app.", 'armGroupLabels': ['Intervention'], 'otherNames': ['Workstation Ergonomics, Health promotion, Neck exercise, Adherence to intervention']}
- {'type': 'OTHER', 'name': 'Control', 'description': 'No intervention takes place during the control phase.', 'armGroupLabels': ['Control']}

Primary Outcomes:
- {'measure': 'Change in NP-related productivity loss', 'description': 'Change in NP-related productivity loss will be measured in percentages of the working time, using the Work Productivity and Activity Impairment Questionnaire for Specific Health Problem (WPAI German version) and converted into monetary units using individual earnings. Absenteeism is assessed by asking the participants about the number of hours missed because of NP as well as the number of hours they have actually worked. To assess presenteeism, the participants are asked to indicate on a 10-score scale how much the NP affected productivity while working, with 0 indicating no effect on productivity and 10 indicating total disability. Scoring rules of the developers of the questionnaire will then be used to obtain self-reported absenteeism and presenteeism expressed as impairment percentages. The monetary value for the lost productivity will be obtained for each individual by multiplying the impairment percentages by the individual gross wage.', 'timeFrame': 'change from baseline at 16 months'}

Please estimate the sample size based on the assumption: 
Cluster size of seven subjects, intraclass correlations (Rho [1] = 0.1 or 0.2 and Rho [2] = 0.2 and 0.3), three or four steps, Type I Error (alpha) = 0.05, Type II Error (beta) = 0.20 (Power = 0.80), and an attrition rate of nearly 20%.
Sample size calculation
        Based on the baseline results of an Australian study, we assumed a baseline productivity of 90% and an intervention attributable increase in productivity of 5% [23]. Also, in line with the Australian study, the cluster size was set to seven subjects. In order to test the sensitivity of the sample size calculations, we used varying cluster-specific and subject-specific intraclass correlations (Rho [1] = 0.1 or 0.2 and Rho [2] = 0.2 and 0.3 respectively) as well as varying number of steps (three or four steps). The underlying statistical model that was used in the simulations was a standard closed cohort mixed effects model comprising a random effect for the clusters, a random effect for the repeated measurements on the same cohort of individuals, a fixed effect to account for time trends, and a fixed effect representing the treatment effect [77, 78]. The linear mixed effect method from the R-package lme4 was used to estimate the models [79]. Furthermore, the acceptable probability for a Type I Error to occur was set to alphaâ=â0.05 and the acceptable probability for a Type II Error to occur was set to betaâ=â0.20 (Powerâ=â0.80). From the four assessed scenarios, the solution with 72 participants, 12 clusters and three steps are optimal in the sense that three steps put much less burden on participants than four steps, i.e., there are less measurements per subject.
        An Australian study reported an attrition rate of nearly 20%. In order to prevent the risk to under-power our study, we will increase the number of clusters from 12 to 15 (>â20%) and the number of subjects per cluster from 6 to 8 (>â20%) [23]. Consequently, we aim to enrol and follow 120 participants in 15 clusters over four measurements (one baseline and three steps from the control to the intervention arm of the study) which yields a total of 420 observations.