The development of continuous glucose monitoring (CGM) devices has facilitated research of data-driven glucose prediction, which is useful in managing type 1 diabetes mellitus (T1DM). Existing research has investigated using historical-value based machine learning approaches to predict short-term glucose excursion and adapting pre-trained models to unseen patients via meta-learning. This study aims to investigate if time-index based modeling could improve the effectiveness and efficiency to the same objective. Different to existing methods that take the past glucose sequence as the input and output the future sequence, the method we apply — referred to as the meta-optimised time-index model (MOTIM) — takes time-index features as the input and predicts the glucose value at that time. We argue that the reduction of modeling complexity from sequence-level to value-level could ease the learning difficulty and increase the efficiency. In our experiments on the OhioT1DM and UMT1DM datasets, the MOTIM achieves comparable performance to the state-of-the-art models (e.g., 1-hour excursion averaged mean absolute percentage error of 10.25 on OhioT1DM and 12.50 on UMT1DM) while consuming a substantially lower cost (e.g., only 1.3% model parameters and more than 10 times faster inference). These promising findings encourage further work on time-index modeling in glucose prediction for T1DM, and to encourage it, we have released our codes at https://github.com/r-cui/MOTIMGluPred under the MIT license.