Current trend of population ageing at global level is accompanied by increased prevalence of chronic diseases and higher rates of early retirement and labor market exit. In particular, the lifestyle of office workers is characterized by prolonged sitting and overall sedentary life, which alone is a high risk factor for cardiometabolic diseases, obesity and other related chronic diseases. The SmartWork unobtrusive monitoring system allows for continuous monitoring of various lifestyle, health, behavioural and work related parameters of office workers targeting to empower work ability sustainability. The large amounts of collected data in such systems are often characterized by the presence of missing entries. This work is an exploratory study on the potential of a Laplacian matrix completion variant for data imputation on the multi-channel time-series data collected with wearable or work devices in the SmartWork system.