Statistical Analyses
forgetfulness
All analyses were conducted using SAS 9.4 (SAS Institute Inc). As our outcomes are exploratory, we present a post hoc determination of treatment-response trends for behavior change models indicated by unidirectional 95% CIs, which do not overlap the null value. This exploratory approach avoids confirmatory statistical significance conclusions based on We used random-intercept mixed linear models that accounted for repeated measures and clustering effects to assess between-group and within-group differences in the 1-year study period for all outcome variables. Data checks ensured that the underlying assumptions of the statistical modeling used in our data were not violated. Cluster effects for all outcomes were tested by calculating the intraclass correlation coefficients and their statistical significance with α set at <.05. Losses to follow-up were handled as intent-to-treat, and missing data attributable to unsystematic factors (eg, monitoring malfunction, sickness, improper device placement, forgetfulness to wear devices, and log work hours) were imputed using joint multiple imputation [In addition, sensitivity analyses included participants who completed the 1-year intervention (n total day=42: 13 controls, 13 sit-to-stand desks, and 16 treadmill desks; and n workday=35: 10 controls, 12 sit-to-stand desks, and 13 treadmill desks). These analyses were conducted for all outcomes to determine if the overall treatment-response trends of the 2 interventions were altered when examining an ideal but less conservative scenario of complete intervention compliance and to evaluate the sensitivity of the results to the handling of missing data and covariate adjustment [