Wireless sensor technology has revolutionised healthcare practices to deal with the increasing number of chronically ill patients. Real-time and continuous monitoring of health parameters can help in early diagnosis and timely treatment. Sensor nodes having limited resources in health monitoring systems are equipped with number of sensors which generates huge amount of data. An increase in data results in an increase in power consumption and memory requirement. An efficient data compression algorithm can be applied to reduce the power consumption and memory requirement. Minimalist, Adaptive and Streaming (MAS) algorithm proposed in literature can reduce significant power consumption during data transmission. In current work, MAS algorithm is further optimised to propose O-MAS-R algorithm by introducing R-bit to take advantage of consecutive repetition of data samples. MAS and O-MAS-R algorithms are applied on Electrocardiography (ECG), Electromyography (EMG) and accelerometer (Acc) datasets to compare the performance in terms of compression ratio (CR). O-MAS-R has shown 7.21 % average increase in CR of ECG datasets, 8.25% increase in EMG datasets and 45.24% increase in Acc datasets as compare to MAS algorithm.