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When developing an application for production, end-user questionnaires are a rapid way of acquiring user evaluations about the product’s usability. These questionnaires include topics such as the base of the application, user interface, and various ailments related to the application, to say the least. Multiple types of questionnaires and methods have been developed to assist developers with feedback acquisition. However, with such a wide-range of disciplines, there is not a standard used by all developers in the research community. Furthermore, as applications are delivered using various devices, such as mobile, desktop, and now XR, new questionnaires have arisen. Compiling these evaluations leads to a large number of questions (100+), a multitude of redundancies, and difficulty comparing related applications due to lack of a solidified system. Furthermore, XR development requires unique feedback that typical presentations of data and engagement do not contain. In this work, we create a concise subset of questions, on a 5-point likert scale, that remove redundancies and allows for comprehensive statistical analysis, allowing developers to measure differences in populations more readily. 

With recent advances in Deep Learning (DL) models, the healthcare domain has seen an increased adoption of neural networks for clinical diagnosis, monitoring, and prediction. Deep Learning models have been developed for various tasks using 1D (one-dimensional) time-series signals. Time-series healthcare data, typically collected through sensors, have specific structures and characteristics such as frequency and amplitude. The nature of these features, including varying sampling rates that depend on the instruments used for sensing, poses challenges in handling them. Electrocardiograms (ECG), a class of 1D time-series signals representing the electrical activity of the heart, have been used to develop heart condition classification decision support systems. The sampling rate of these signals, influenced by different ECG instruments as well as their calibrations, can greatly impact the learning functions of deep learning models and subsequently, their decision outcomes. This hinders the development and deployment of generalized, DL-based ECG classifiers that can work with data from a variety of ECG instruments, particularly when the sampling rate of the training data remains unknown to users. Moreover, DL models are not designed to recognize the sampling rate of the testing data on which they are being deployed, further complicating their effective application across diverse clinical settings. In this study, we investigated the effect of different sampling rates of time-series ECG signals on DL-based ECG classifiers. To the best of our knowledge, this is the first work to understand how varying sampling rates affect the performance of DL-based models for classifying 1D time-series ECG signals. Through our comprehensive experiments, we showed that accuracy can drop by as much as 20% when the training and testing sampling rates are different. We provide visual explanations to understand the differences in learned model features through activation maps when the sampling rates for training and testing data are different. We also investigated potential strategies to address the challenges posed by different sampling rates: (i) transfer learning, (ii) resampling, and (iii) training a DL model using ECG data at different sampling rates.