Measurements Before and After Participation in a Health Intervention Programme in Halsnaes Municipality

The study will recruit citizens who are offered a health programme provided by Halsnaes Municipality and invite all citizens who live up to the inclusion criteria in the project period (with inclusion of a maximum of 200 citizens). Thus, purposive sampling, where the study staff target people who represent a specific demographic for study participation, is used. Statistical considerations: A power calculation based on WHODAS 2.0 shows that, using a two-sided paired t-test and aiming to detect a minimal clinically relevant difference of 3.0 points in functional ability (ΔSD = 9.1), with 90% power, the study requires complete data from 97 participants. Accounting for an expected dropout rate and/or missing data of 50%, the project needs to include 194 participants. A descriptive analysis of the study population's demographics will be conducted based on descriptive data from the municipality's electronic health record system, national registers, and information from citizen interviews and questionnaire responses, in order to characterize the participant population. To examine whether the organizational changes planned in Steno Tvaers lead to more citizens participating in and being retained in a municipal health programme, the uptake in the subproject population with the current uptake in the municipality will be compared. To examine whether the organizational changes lead to downstream health effects at the individual level, differences in functional ability (muscle strength, well-being, health-related quality of life, as well as WHODAS-assessed functioning and disability) from the start to the end of a municipal health programme will be analyzed. Similarly, changes in blood glucose and blood pressure among citizens with diabetes will be analyzed. Changes in medical adherence (attendance and medication use) among citizens with diabetes will be measured as the number of annual check-ups each citizen attends, as well as the proportion of prescriptions redeemed relative to the number prescribed between baseline and end of the health programme. Multiple linear regression (linear mixed models) will be used to analyze these changes over time (as there is no control group, only changes over time can be analyzed), including relevant covariates such as sociodemographic and health-related data from registers. Stratified analyses based on sociodemographic and health-related strata will also be conducted if relevant. The primary analyses will be performed after completion of the subproject but will also be conducted midway through the subproject period for those citizens who have completed a health programme by that time, although without linkage to national registers. Researchers from SDCC will assist with analyses and interpretation.