Background: Cognitive and behavioral symptoms are the clinical hallmarks of neurocognitive disorders. Cognitive training may be offered to reduce the risks of cognitive decline and dementia and to reduce behavioral symptoms, such as apathy. Information and communication technology approaches, including serious games, can be useful in improving the playful aspect of computerized cognitive training and providing motivating solutions in elderly patients. Objective: The objective of this study was to assess the effectiveness of employing the MeMo (Memory Motivation) Web app with regard to cognitive and behavioral symptoms in patients with neurocognitive disorders. Methods: MeMo is a Web app that can be used on any Web browser (computer or tablet). The training activities proposed in MeMo are divided into the following two parts: memory and mental flexibility/attention. The study included 46 individuals (mean age 79.4 years) with a diagnosis of neurocognitive disorders at the Institut Claude Pompidou Memory Center in Nice. This randomized controlled study compared the evolution of cognition and behavior between patients not using MeMo (control group) and patients using MeMo (MeMo group) for 12 weeks (four sessions per week). Patients underwent memory and attention tests, as well as an apathy assessment at baseline, week 12 (end of the training period), and week 24 (12 weeks after the end of the training sessions). In addition, to assess the impact of high and low game uses, the MeMo group was divided into patients who used MeMo according to the instructions (about once every 2 days; active MeMo group) and those who used it less (nonactive MeMo group). Results: When comparing cognitive and behavioral scores among baseline, week 12, and week 24, mixed model analysis for each cognitive and behavioral score indicated no significant interaction between testing time and group. On comparing the active MeMo group (n=9) and nonactive MeMo group (n=13), there were significant differences in two attention tests (Trial Making Test A [P=.045] and correct Digit Symbol Substitution Test items [P=.045]) and in the Apathy Inventory (AI) (P=.02). Mixed analysis (time: baseline, week 12, and week 24 × number of active days) indicated only one significant interaction for the AI score (P=.01), with a significant increase in apathy in the nonactive MeMo group. Conclusions: This study indicates that the cognitive and behavioral efficacies of MeMo, a Web-based training app, can be observed only with regular use of the app. Improvements were observed in attention and motivation. Trial Registration: ClinicalTrials.gov NCT04142801; https://clinicaltrials.gov/ct2/show/NCT04142801

Background: During the COVID-19 pandemic, health professionals have been directly confronted with the suffering of patients and their families. By making them main actors in the management of this health crisis, they have been exposed to various psychosocial risks (stress, trauma, fatigue, etc). Paradoxically, stress-related symptoms are often underreported in this vulnerable population but are potentially detectable through passive monitoring of changes in speech behavior. Objective: This study aims to investigate the use of rapid and remote measures of stress levels in health professionals working during the COVID-19 outbreak. This was done through the analysis of participants' speech behavior during a short phone call conversation and, in particular, via positive, negative, and neutral storytelling tasks. Methods: Speech samples from 89 health care professionals were collected over the phone during positive, negative, and neutral storytelling tasks; various voice features were extracted and compared with classical stress measures via standard questionnaires. Additionally, a regression analysis was performed. Results: Certain speech characteristics correlated with stress levels in both genders; mainly, spectral (ie, formant) features, such as the mel-frequency cepstral coefficient, and prosodic characteristics, such as the fundamental frequency, appeared to be sensitive to stress. Overall, for both male and female participants, using vocal features from the positive tasks for regression yielded the most accurate prediction results of stress scores (mean absolute error 5.31). Conclusions: Automatic speech analysis could help with early detection of subtle signs of stress in vulnerable populations over the phone. By combining the use of this technology with timely intervention strategies, it could contribute to the prevention of burnout and the development of comorbidities, such as depression or anxiety.

Objectives: The roles of procalcitonin (PCT) and C-reactive protein (CRP) in febrile cancer patients is currently unclear. Our aim was to assess these in febrile patients with solid tumors and to identify cut-off values for ruling out infection.Methods: We retrospectively evaluated patients with solid tumors admitted to hospital due to fever. They were divided into those with Fever with microbiologically documented infection (FMDI), Fever with clinically documented infection (FCDI) and Tumor-related fever (TRF). PCT and CRP levels were compared. Receiver-operating curves were plotted to define the best cut-off values for discriminating between infection-related and cancer-related fever.Results: Between January 2015 to November 2018, 131 patients were recorded (mean age 68 years, 67% male, 86% with metastasis). Patients with FMDI or FCDI had significantly higher baseline levels of PCT and lower CRP/PCT than those with TRF. A PCT cut-off value of 0.52 ng/mL for discriminating between infection and cancer-associated fever yielded 75% sensitivity, 55% specificity, 77% positive predictive value (PPV), and 52% negative predictive value (NPV). A CRP/PCT ratio with a cut-off value of 95 showed 56% sensitivity, 70% specificity, 79% NPV, and 44% PPV.Discussion: PCT is a sensitive marker of sepsis or localized infection in patients with solid tumors, but its specificity is poor. The CRP/PCT ratio improves specificity, thus providing a reliable means of ruling out infection for values above 95.

Background: Neurocognitive disorders are often accompanied by behavioral symptoms such as anxiety, depression, and/or apathy. These symptoms can occur very early in the disease progression and are often difficult to detect and quantify in nonspecialized clinical settings. Objective: We focus in this study on apathy, one of the most common and debilitating neuropsychiatric symptoms in neurocognitive disorders. Specifically, we investigated whether facial expressivity extracted through computer vision software correlates with the severity of apathy symptoms in elderly subjects with neurocognitive disorders. Methods: A total of 63 subjects (38 females and 25 males) with neurocognitive disorder participated in the study. Apathy was assessed using the Apathy Inventory (AI), a scale comprising 3 domains of apathy: loss of interest, loss of initiation, and emotional blunting. The higher the scale score, the more severe the apathy symptoms. Participants were asked to recall a positive and a negative event of their life, while their voice and face were recorded using a tablet device. Action units (AUs), which are basic facial movements, were extracted using OpenFace 2.0. A total of 17 AUs (intensity and presence) for each frame of the video were extracted in both positive and negative storytelling. Average intensity and frequency of AU activation were calculated for each participant in each video. Partial correlations (controlling for the level of depression and cognitive impairment) were performed between these indexes and AI subscales. Results: Results showed that AU intensity and frequency were negatively correlated with apathy scale scores, in particular with the emotional blunting component. The more severe the apathy symptoms, the less expressivity in specific emotional and nonemotional AUs was displayed from participants while recalling an emotional event. Different AUs showed significant correlations depending on the sex of the participant and the task’s valence (positive vs negative story), suggesting the importance of assessing male and female participants independently. Conclusions: Our study suggests the interest of employing computer vision-based facial analysis to quantify facial expressivity and assess the severity of apathy symptoms in subjects with neurocognitive disorders. This may represent a useful tool for a preliminary apathy assessment in nonspecialized settings and could be used to complement classical clinical scales. Future studies including larger samples should confirm the clinical relevance of this kind of instrument.