Secreted by pancreatic β-cells, insulin is the major anabolic hormone, regulating the metabolism of fats, proteins, and carbohydrates. Defects in insulin production or action can lead to diabetes characterized by derangements in glucose handling and metabolic disease. Diabetes affects 420 million people worldwide, increasing morbidity, mortality and placing a burden on healthcare of nations. There is a need for rapid and accurate monitoring of insulin levels to optimize diabetes management and facilitate early diagnosis of insulin related chronic diseases. Conventional strategies such as HPLC, MALDI-TOF, ELISA, etc. used for insulin detection are not suitable for point-of-care testing (POCT) as they are expensive, and require sample preparation, sophisticated instruments, and skilled personnel. Our goal was to develop techniques to allow POCT for insulin in real time. In this study we developed two lateral flow assays (LFAs) based POCT platforms using aptamers as the biorecognition molecules for colorimetric and fluorescent detection of insulin. A range of conditions were tested such as concentrations of aptamers, reporter molecules used, volume of sample required, and assay time to obtain quantify insulin levels using a standard LFA reader. The colorimetric LFAs had linear detection range of 0.01-1 ng.mL-1 and LOD of 0.01 ng.mL-1. The fluorescent LFAs exhibited a linear detection range of 0-4 ng.mL-1 and 0.1 ng.mL-1 LOD. Various signal amplification strategies were incorporated, ie., gold-silver amplification technique and rolling circular amplification (RCA) to further enhance the signal. The developed colorimetric LFAs were successfully used for insulin quantification in rat blood, human blood, and human saliva samples. Although insulin levels were quantified within 12 min, some issues arose such as coagulation, need for dilution, and non-uniform flow through the test strips. Further work is required to optimize blood handling to progress an insulin POCT in real time. Future work could develop a multiplexed strip for detection of different analytes such as HbA1c, glucose, and C-peptide for better management of diabetes, along with a smartphone reader App. This research goes some way to addressing the challenge of providing a reliable and rapid approach for highly sensitive and specific detection of insulin for POCT applications.