The Value of a Virtual Assistant to Improve Engagement in Computerized Cognitive Training at Home: Exploratory Study.

BACKGROUND: Impaired cognitive function is observed in many pathologies, including neurodegenerative diseases such as Alzheimer disease. At present, the pharmaceutical treatments available to counter cognitive decline have only modest effects, with significant side effects. A nonpharmacological treatment that has received considerable attention is computerized cognitive training (CCT), which aims to maintain or improve cognitive functioning through repeated practice in standardized exercises. CCT allows for more regular and thorough training of cognitive functions directly at home, which represents a significant opportunity to prevent and fight cognitive decline. However, the presence of assistance during training seems to be an important parameter to improve patients' motivation and adherence to treatment. To compensate for the absence of a therapist during at-home CCT, a relevant option could be to include a virtual assistant to accompany patients throughout their training. OBJECTIVE: The objective of this exploratory study was to evaluate the interest of including a virtual assistant to accompany patients during CCT. We investigated the relationship between various individual factors (eg, age, psycho-affective functioning, personality, personal motivations, and cognitive skills) and the appreciation and usefulness of a virtual assistant during CCT. This study is part of the THERADIA (Thérapies Digitales Augmentées par l'Intelligence Artificielle) project, which aims to develop an empathetic virtual assistant. METHODS: A total of 104 participants were recruited, including 52 (50%) young adults (mean age 21.2, range 18 to 27, SD 2.9 years) and 52 (50%) older adults (mean age 67.9, range 60 to 79, SD 5.1 years). All participants were invited to the laboratory to answer several questionnaires and perform 1 CCT session, which consisted of 4 cognitive exercises supervised by a virtual assistant animated by a human pilot via the Wizard of Oz method. The participants evaluated the virtual assistant and CCT at the end of the session. RESULTS: Analyses were performed using the Bayesian framework. The results suggest that the virtual assistant was appreciated and perceived as useful during CCT in both age groups. However, older adults rated the assistant and CCT more positively overall than young adults. Certain characteristics of users, especially their current affective state (ie, arousal, intrinsic relevance, goal conduciveness, and anxiety state), appeared to be related to their evaluation of the session. CONCLUSIONS: This study provides, for the first time, insight into how young and older adults perceive a virtual assistant during CCT. The results suggest that such an assistant could have a beneficial influence on users' motivation, provided that it can handle different situations, particularly their emotional state. The next step of our project will be to evaluate our device with patients experiencing mild cognitive impairment and to test its effectiveness in long-term cognitive training.

Episodic memory assessment: effects of sex and age on performance and response time during a continuous recognition task.

Introduction: Continuous recognition tasks (CRTs) assess episodic memory (EM), the central functional disturbance in Alzheimer's disease and several related disorders. The online MemTrax computerized CRT provides a platform for screening and assessment that is engaging and can be repeated frequently. MemTrax presents complex visual stimuli, which require complex involvement of the lateral and medial temporal lobes and can be completed in less than 2 min. Results include number of correct recognitions (HITs), recognition failures (MISSes = 1-HITs), correct rejections (CRs), false alarms (FAs = 1-CRs), total correct (TC = HITs + CRs), and response times (RTs) for each HIT and FA. Prior analyses of MemTrax CRT data show no effects of sex but an effect of age on performance. The number of HITs corresponds to faster RT-HITs more closely than TC, and CRs do not relate to RT-HITs. RT-HITs show a typical skewed distribution, and cumulative RT-HITs fit a negative survival curve (RevEx). Thus, this study aimed to define precisely the effects of sex and age on HITS, CRs, RT-HITs, and the dynamics of RTs in an engaged population. Methods: MemTrax CRT online data on 18,255 individuals was analyzed for sex, age, and distributions of HITs, CRs, MISSes, FAs, TC, and relationships to both RT-HITs and RT-FAs. Results: HITs corresponded more closely to RT-HITs than did TC because CRs did not relate to RT-HITs. RT-FAs had a broader distribution than RT-HITs and were faster than RT-HITs in about half of the sample, slower in the other half. Performance metrics for men and women were the same. HITs declined with age as RT-HITs increased. CRs also decreased with age and RT-FAs increased, but with no correlation. The group over aged 50 years had RT-HITs distributions slower than under 50 years. For both age ranges, the RevEx model explained more than 99% of the variance in RT-HITs. Discussion: The dichotomy of HITs and CRs suggests opposing cognitive strategies: (1) less certainty about recognitions, in association with slower RT-HITs and lower HIT percentages suggests recognition difficulty, leading to more MISSes, and (2) decreased CRs (more FAs) but faster RTs to HITs and FAs, suggesting overly quick decisions leading to errors. MemTrax CRT performance provides an indication of EM (HITs and RT-HITs may relate to function of the temporal lobe), executive function (FAs may relate to function of the frontal lobe), processing speed (RTs), cognitive ability, and age-related changes. This CRT provides potential clinical screening utility for early Alzheimer's disease and other conditions affecting EM, other cognitive functions, and more accurate impairment assessment to track changes over time.

Study design and protocol of a low to high intensity computer-based cognitive training at home in supplement to standard care in patients with AD.

INTRODUCTION: Recent studies on cognitive training in patients with Alzheimer's disease (AD) showed positive long-term effects on cognition and daily living, suggesting remote computer-based programmes to increase training sessions while reducing patient's travelling. The aim of this study is to examine short-term and long-term benefits of computer-based cognitive training at home in patients with mild to moderate AD, as a complement to the training in speech and language therapists' (SLT) offices. The secondary purpose is to study training frequency required to obtain noticeable effects. METHODS AND ANALYSES: This is a national multicentre study, conducted in SLT offices. The patients follow training in one of three conditions: once a week in SLT office only (regular condition) and once a week in SLT office plus one or three times per week at home. The trainings' content in SLT office and at home is identical. For all three groups near and far transfer will be compared with evaluate training frequency's effect. Our primary outcome is executive and working memory scores in experimental tasks, and the secondary is neuropsychological tests and questionnaires' scores. Linear models' analyses are considered for all measures with a random intercept for patients and another for per practice. The fixed effects will be: three modality groups and time, repeated measures, (T0-pretraining, T1-post-training, T2-long-term follow-up) and the interaction pairs. ETHICS AND DISSEMINATION: The study got ethics approval of the national ethical committee CPP Sud Méditerranée III (No 2019-A00458-49) and of the National Commission for Information Technology and Liberties (No 919217). Informed consent is obtained from each participant. Results will be disseminated in oral communications or posters in international conferences and published in scientific journals. TRIAL REGISTRATION NUMBER: NCT04010175.

Targeted cognitive game training enhances cognitive performance in multiple sclerosis patients treated with interferon beta 1-a.

BACKGROUND: Prevention of cognitive decline in Multiple Sclerosis (MS) is of major importance. We explored the effect of a 6 months computerized game training program on cognitive performance in MS patients with mild cognitive impairment. METHODS: This was a single-center, randomized prospective study. We enrolled in this study 100 eligible MS patients treated with Interferon-beta-1a (Rebif). All had mild cognitive impairment in either executive function or information processing speed. Patients were randomized 1:1 to either use the cognitive games platform by HappyNeuron (HN) or receive no intervention. Executive function and information processing speed scores were measured at 3 and 6 months from baseline to evaluate the effect of game training on cognitive scores. RESULTS: In both executive function and information processing speed, the game Training group showed significant improvement after 3 and 6 months. The Non-Training group showed mild deterioration in both domains at 3 months, and further deterioration that became significant at 6 months in executive function. Furthermore, at 6 months, the percent of patients in the Training group that improved or remained stable in both cognitive domains was significantly higher compared to the Non-Training group. CONCLUSIONS: Our findings suggest that cognitive game training has a beneficial effect on cognitive performance in MS patients suffering from mild cognitive impairment. While further evaluation is required to assess the longevity of that effect, we nonetheless recommend to MS patients to be engaged in cognitive gaming practice as part of a holistic approach to treating their condition.

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification.

BACKGROUND: Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient management. OBJECTIVE: Our primary aim was to utilize machine learning in determining initial viable models to serve as complementary instruments in demonstrating efficacy of the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodic-memory screening and assessing cognitive impairment. METHODS: We used an existing dataset subset (n = 18,395) of demographic information, general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adults who took the M-CRT test. M-CRT performance and participant features were used as independent attributes: true positive/negative, percent responses/correct, response time, age, sex, and recent alcohol consumption. For predictive modeling, we used demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy), based on the screening questions. RESULTS: ANOVA revealed significant differences among HealthQScore groups for response time true positive (p = 0.000) and true positive (p = 0.020), but none for true negative (p = 0.0551). Both % responses and % correct had significant differences (p = 0.026 and p = 0.037, respectively). Logistic regression was generally the top-performing learner with moderately robust prediction performance (AUC) for HealthQScore (0.648-0.680) and selected general health questions (0.713-0.769). CONCLUSION: Our novel application of supervised machine learning and predictive modeling helps to demonstrate and validate cross-sectional utility of MemTrax in assessing early-stage cognitive impairment and general screening for AD.

A Computerized Continuous-Recognition Task for Measurement of Episodic Memory.

Based on clinical observations of severe episodic memory (EM) impairment in dementia of Alzheimer's disease (AD), a brief, computerized EM test was developed for AD patient evaluation. A continuous recognition task (CRT) was chosen because of its extensive use in EM research. Initial experience with this computerized CRT (CCRT) showed patients were very engaged in the test, but AD patients had marked failure in recognizing repeated images. Subsequently, the test was administered to audiences, and then a two-minute online version was implemented (http://www.memtrax.com). The online CCRT shows 50 images, 25 unique and 25 repeats, which subjects respectively either try to remember or indicate recognition as quickly as possible. The pictures contain 5 sets of 5 images of scenes or objects (e.g., mountains, clothing, vehicles, etc.). A French company (HAPPYneuron, SAS) provided the test for 2 years, with these results. Of 18,477 individuals, who indicated sex and age 21-99 years and took the test for the first time, 18,007 individuals performed better than chance. In this group, age explained 1.5% of the variance in incorrect responses and 3.5% of recognition time variance, indicating considerable population variability. However, when averaging for specific year of age, age explained 58% of percent incorrect variance and 78% of recognition time variance, showing substantial population variability but a major age effect. There were no apparent sex effects. Further studies are indicated to determine the value of this CCRT as an AD screening test and validity as a measure of EM impairment in other clinical conditions.

The Flow Engine Framework: A Cognitive Model of Optimal Human Experience.

Flow is a well-known concept in the fields of positive and applied psychology. Examination of a large body of flow literature suggests there is a need for a conceptual model rooted in a cognitive approach to explain how this psychological phenomenon works. In this paper, we propose the Flow Engine Framework, a theoretical model explaining dynamic interactions between rearranged flow components and fundamental cognitive processes. Using an IPO framework (Inputs - Processes - Outputs) including a feedback process, we organize flow characteristics into three logically related categories: inputs (requirements for flow), mediating and moderating cognitive processes (attentional and motivational mechanisms) and outputs (subjective and objective outcomes), describing the process of the flow. Comparing flow with an engine, inputs are depicted as flow-fuel, core processes cylinder strokes and outputs as power created to provide motion.

Feasibility of BCI Control in a Realistic Smart Home Environment.

Smart homes have been an active area of research, however despite considerable investment, they are not yet a reality for end-users. Moreover, there are still accessibility challenges for the elderly or the disabled, two of the main potential targets for home automation. In this exploratory study we design a control mechanism for smart homes based on Brain Computer Interfaces (BCI) and apply it in the "Domus" smart home platform in order to evaluate the potential interest of users about BCIs at home. We enable users to control lighting, a TV set, a coffee machine and the shutters of the smart home. We evaluate the performance (accuracy, interaction time), usability and feasibility (USE questionnaire) on 12 healthy subjects and 2 disabled subjects. We find that healthy subjects achieve 77% task accuracy. However, disabled subjects achieved a better accuracy (81% compared to 77%).

Feature extraction of the first difference of EMG time series for EMG pattern recognition.

This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2-8%.