# Comparative Analysis of MoCA and DigiMoCA Test Results: A Pilot Study
###### Abstract
These disorders often tend to persist even during the remission of depressive symptoms [10]. These deficits are thought to be clinically relevant and seem to be in the same order of magnitude of deficits observed in other well-known disorders involving cognitive dysfunction (e.g., mild cognitive impairment, attention-deficit hyperactivity disorder) [11,12]. Digital technology has been demonstrated to have a great impact on cognitive assessment and health care [13], as it facilitates repeated and continuous assessments, as well as the collection of clinical data in a much more convenient and cost-effective way than paper-and-pencil assessments [14]. Some previous research efforts have addressed innovative solutions for the screening of mental health disorders, specially depression, by means of voice interaction only, utilising both commercial and experimental devices [15]. In this context, the DigiMoCA, an implementation of the phone version of the Montreal cognitive assessment test (T-MoCA) as a skill for Amazon's Alexa, was developed as a proof of concept to illustrate the implementation of scripted conversations by means of off-the-shelf smart assistants for the screening of cognitive impairment [16]. The DigiMoCA utilises Alexa's speech recognition and natural language understanding (NLU) services, adapting the speech rate to the user with prosodic annotations, collecting user responses to the test, as well as additional variables not captured with the original test (e.g., response times, delays, voice level), and storing sessions for future retrieval in Amazon's persistent database system. A pilot study was conducted where the DigiMoCA was evaluated in terms of (i) performance, evidencing that it understands the user 90% of the time; (ii) usability and acceptability, with average scores in the Technology Acceptance and Post-Study System Usability questionnaires over 3/5 and 5/7, respectively; and (iii) validity, with a correlation factor with a T-MoCA of r = 0.88 and an area under the ROC curve of 0.79 for MCI detection. The motivation of this research originated from the evidence obtained with the study referenced above [16], which provided initial evidence that the DigiMoca may enhance the precision and efficiency of cognitive assessments by providing standardised administration and scoring, consequently reducing the impact of confounding factors like the white-coat effect, human errors, and variability. Digital platforms based on smart assistants also incorporate adaptive conversation techniques, which become adaptive testing techniques in this context, tailoring the difficulty of tasks to the subject's performance, which may result in more accurate and personalised assessments. Additionally, these tools greatly facilitate the collection of rich, real-time data both on cognitive dimensions and the testing process itself (e.g., response times, delays, etc. ), enabling more comprehensive and detailed analysis. In fact, our experience with machine learning analysis [17] helped us to identify patterns and changes in cognitive function that were not adequately addressed by traditional methods. Additionally, digital tools may improve accessibility to cognitive evaluations, allowing for remote administration and monitoring, which is particularly beneficial for older adults who may have mobility issues or live in rural areas. To sum up, the adoption of intelligent agent-based cognitive tests has the potential to improve the early detection, diagnosis, and ongoing management of MCI, ultimately enhancing the quality of care for the elderly. This research aimed to provide a comprehensive assessment of cognitive dimensions using the MoCA in groups of individuals with depressive symptoms (DCss) and MCI, while also exploring the added value that the digital administration tool DigiMoCA may offer in the collection and analysis of cognitive data.