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OBJECTIVE: This work explores the advances in conversational agents aimed at the detection of mental health disorders, and specifically the screening of depression. The focus is put on those based on voice interaction, but other approaches are also tackled, such as text-based interaction or embodied avatars. METHODS: PRISMA was selected as the systematic methodology for the analysis of existing literature, which was retrieved from Scopus, PubMed, IEEE Xplore, APA PsycINFO, Cochrane, and Web of Science. Relevant research addresses the detection of depression using conversational agents, and the selection criteria utilized include their effectiveness, usability, personalization, and psychometric properties. RESULTS: Of the 993 references initially retrieved, 36 were finally included in our work. The analysis of these studies allowed us to identify 30 conversational agents that claim to detect depression, specifically or in combination with other disorders such as anxiety or stress disorders. As a general approach, screening was implemented in the conversational agents taking as a reference standardized or psychometrically validated clinical tests, which were also utilized as a golden standard for their validation. The implementation of questionnaires such as Patient Health Questionnaire or the Beck Depression Inventory, which are used in 65% of the articles analyzed, stand out. CONCLUSIONS: The usefulness of intelligent conversational agents allows screening to be administered to different types of profiles, such as patients (33% of relevant proposals) and caregivers (11%), although in many cases a target profile is not clearly of (66% of solutions analyzed). This study found 30 standalone conversational agents, but some proposals were explored that combine several approaches for a more enriching data acquisition. The interaction implemented in most relevant conversational agents is text-based, although the evolution is clearly towards voice integration, which in turns enhances their psychometric characteristics, as voice interaction is perceived as more natural and less invasive.

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Introduction: The Geriatric Depression Scale is an instrument used to identify depression in people of an older age. The original English version of this scale has been translated into Spanish (GDS-VE); two shorter versions of 5- (GDS-5) and 15-items (GDS-15) have been developed. Aim of the study: To assess the validity and compare the 5- and 15-item Spanish versions of the GDS among the Spanish population. Materials and methods: 573 Galicia residents aged >50 years participated in this study. The following instruments were applied: the 19-item Control, Autonomy, Self-Realization and Pleasure scale, the Subjective Memory Complaints Questionnaire, the Mini-Mental State Examination test, the GDS-5, and the GDS-15. Results: We found differences in total score between GDS-5 and GDS-15 regarding the variable sex. Internal reliability for GDS-5 and GDS-15 was 0.495 and 0.715, respectively. Sensitivity and specificity for GDS-5 - with a cut-off value of 1 - was 0.517 and 0.650, respectively; for GDS-15 - with a cut-off value of 3 points - sensitivity was 0.755 and specificity 0.668. GDS-5 has a ROC curve of 0.617 and GDS-15 of 0.764. Conclusion: GDS-15, and to a greater extent GDS-5, should be revised or even reformulated to improve their diagnostic usefulness by choosing higher discriminative ability items or even include new items with greater sensitivity that consider currently prevailing psychosocial factors.

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Introduction and objectives: The experiences and changes that come along with old age may lead to a feeling of loneliness, usually followed by negative physical and mental manifestations. In this systematic review, we evaluated the existing tools to assess loneliness in older adults. Methods: We performed a literature search in the Web of Science, Medline, and PsycINFO, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After, we examined the psychometric properties of the instruments with a focus on reliability, validity, and main conclusions. Results: We included 27 articles published between 1996 and 2021. Conclusion: To date, there are few instruments to assess loneliness in older adults. In general, they present adequate psychometric properties, although it is true that some scales show somewhat low levels of reliability and validity.

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PURPOSE: Pancreas transplantation (PT) is one of the few ways to restore euglycemia within diabetic patients; however, the high morbidity caused by surgical complications and the need for immunosuppressive therapy has raised controversy about PT improving the health-related quality-of-life (HRQoL). The aim of this study is to assess the long-term (≥ 5 years after PT) HRQoL and to identify the factors affecting it. METHODS: A single-center, cross-sectional study of 49 sequential PT was performed. All patients conducted a telephone interview to fulfill the modification of Medical Outcome Health Survey Short Form questionnaire (SF-36v2) and were compared to similar post-PT studies from the literature. RESULTS: Patients with a history of replacement renal therapy (RRT) or neuropathy undergoing a PT were associated to a worse bodily pain (P = 0.03) and physical function (P = 0.04), respectively, whereas those with retinopathy showed an improved Role Emotional (P = 0.04). Multivariate analysis revealed the presence of RRT as the only independent prognostic factor for a worse bodily pain [relative risk = 3.9; 95% confidence interval (1.1-14.6)], (P = 0.04). Furthermore, nearly all PT recipients (91.8%) claimed an overall better health than prior to PT. CONCLUSION: Our study confirms that PT recipients' HRQoL improves after PT, showing similar HRQoL scores across different populations and suggests that patients in predialysis could benefit from an improved HRQoL if transplanted on the early stages of the disease.

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OBJECTIVE: To determine whether smart conversational agents can be used for detection of neuropsychiatric disorders. Therefore, we reviewed the technologies used, targeted mental disorders and validation procedures of relevant proposals in this field. METHODS: We searched Scopus, PubMed, Pro-Quest, IEEE Xplore, Web of Science, CINAHL and the Cochrane Library using a predefined search strategy. Studies were included if they focused on neuropsychiatric disorders and involved conversational data for detection and diagnosis. They were assessed for eligibility by independent reviewers and ultimately included if a consensus was reached about their relevance. RESULTS: 2356 references were initially retrieved. Eventually, 17 articles - referring 9 smart conversational agents - met the inclusion criteria. Out of the selected studies, 7 are targeted at neurocognitive disorders, 7 at depression and 3 at other conditions. They apply diverse technological solutions and analysis techniques (82.4% use Artificial Intelligence), and they usually rely on gold standard tests for criterion validity assessment. Acceptability, reliability and other aspects of validity were rarely addressed. CONCLUSION: The use of smart conversational agents for the detection of neuropsychiatric disorders is an emerging and promising field of research, with a broad coverage of mental disorders and extended use of AI. However, the few published studies did not undergo robust psychometric validation processes. Future research in this field would benefit from more rigorous validation mechanisms and standardized software and hardware platforms.

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OBJECTIVES: To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not. DESIGN: Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1 score, and Cohen's kappa) were computed for each algorithm, and graphic metrics (ROC and precision-recall curves) and features analysis were computed for the best-performing algorithm. SETTING: Primary care health centers. PARTICIPANTS: 128 participants: 78 cognitively unimpaired and 50 with MCI. MEASUREMENTS: Diagnosis at baseline, months from the baseline assessment until the 3rd follow-up or development of dementia, gender, age, Charlson Comorbidity Index, Neuropsychiatric Inventory-Questionnaire (NPI-Q) individual items, NPI-Q total severity, and total stress score and Geriatric Depression Scale-15 items (GDS-15) total score. RESULTS: 30 participants developed dementia, while 98 did not. Most of the participants who developed dementia were diagnosed at baseline with amnestic multidomain MCI. The Random Forest Plot model provided the metrics that best predicted conversion to dementia (e.g. accuracy=.88, F1=.67, and Cohen's kappa=.63). The algorithm indicated the importance of the metrics, in the following (decreasing) order: months from first assessment, age, the diagnostic group at baseline, total NPI-Q severity score, total NPI-Q stress score, and GDS-15 total score. CONCLUSIONS: ML is a valuable technique for detecting the risk of conversion to dementia in MCI patients. Some NPS proxies, including NPI-Q total severity score, NPI-Q total stress score, and GDS-15 total score, were deemed as the most important variables for predicting conversion, adding further support to the hypothesis that some NPS are associated with a higher risk of dementia in MCI.

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INTRODUCTION: Alzheimer's disease is a degenerative brain disease and the most common cause of dementia. Today, 47 million people live with dementia worldwide. This number is projected to increase to more than 131 million by 2050, as populations age. Therefore, the World Health Organization considers serious cognitive deterioration a public health priority. OBJECTIVES: Advanced cognitive evaluation mechanisms are needed to help make an early diagnosis. These new mechanisms should overcome the limitations of current neuropsychological tests, including delayed detection; being perceived as intrusive; being non-ecological; being dependent on confounding factors; or their administration being expensive, among others. A promising novel approach consists of the introduction of serious games based on virtual reality and machine learning able to assess cognitive traits relevant to the diagnosis of mild cognitive impairment and Alzheimer's disease. METHODS: As a result of a preliminary pilot experiment, promising evidence was obtained about the predictive power of this solution. However, for these new serious games to be effective, evidence has to be gathered on the player experience by senior adults, avoiding the limitations of traditional tests at the same time. This study addresses these aspects with the participation of 74 senior users and 15 test administrators. RESULTS: Main findings confirm the usability and playability of Panoramix, a game battery designed according to the principles discussed above, its technological acceptability and its accessibility. For example, in relation to acceptability, the game battery was scored 4.39 in a 5-point scale, while its average usability score was 4.45 regardless of socio-cultural level or previous experience with digital technologies. In addition, health professionals confirm both, usability and playability, levels with an average score of 6.5 in a 7-point scale. Participants' willingness of using this kind of systems for cognitive evaluation was also confirmed. CONCLUSION: Promising results obtained pave the way for additional work to confirm the diagnostic validity according to clinical standards of these new cognitive assessment tools.

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INTRODUCTION: Assessment of episodic memory is traditionally used to evaluate potential cognitive impairments in senior adults. The present article discusses the capabilities of Episodix, a game to assess the aforementioned cognitive area, as a valid tool to discriminate among mild cognitive impairment (MCI), Alzheimer's disease (AD) and healthy individuals (HC); that is, it studies the game's psychometric validity study to assess cognitive impairment. MATERIALS AND METHODS: After a preliminary study, a new pilot study, statistically significant for the Galician population, was carried out from a cross-sectional sample of senior adults as target users. A total of 64 individuals (28 HC, 16 MCI, 20 AD) completed the experiment from an initial sample of 74. Participants were administered a collection of classical pen-and-paper tests and interacted with the games developed. A total of six machine learning classification techniques were applied and four relevant performance metrics were computed to assess the classification power of the tool according to participants' cognitive status. RESULTS: According to the classification performance metrics computed, the best classification result is obtained using the Extra Trees Classifier (F1 = 0.97 and Cohen's kappa coefficient = 0.97). Precision and recall values are also high, above 0.9 for all cognitive groups. Moreover, according to the standard interpretation of Cohen's kappa index, classification is almost perfect (i.e., 0.81-1.00) for the complete dataset for all algorithms. LIMITATIONS: Weaknesses (e.g., accessibility, sample size or speed of stimuli) detected during the preliminary study were addressed and solved. Nevertheless, additional research is needed to improve the resolution of the game for the identification of specific cognitive impairments, as well as to achieve a complete validation of the psychometric properties of the digital game. CONCLUSION: Promising results obtained about psychometric validity of Episodix, represent a relevant step ahead towards the introduction of serious games and machine learning in regular clinical practice for detecting MCI or AD. However, more research is needed to explore the introduction of item response theory in this game and to obtain the required normative data for clinical validity.

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Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. These devices include sensors to gather data on physiological signals such as heart rate, skin temperature or galvanic skin response. By applying data analytics techniques to these kinds of signals, it is possible to obtain estimations of higher-level aspects of human behavior. In the literature, there are several works describing the use of physiological data collected using clinical devices to obtain information on sleep patterns or stress. However, it is still an open question whether data captured using COTS wrist wearables is sufficient to characterize the learners' psychological state in educational settings. This paper discusses a protocol to evaluate stress estimation from data obtained using COTS wrist wearables. The protocol is carried out in two phases. The first stage consists of a controlled laboratory experiment, where a mobile app is used to induce different stress levels in a student by means of a relaxing video, a Stroop Color and Word test, a Paced Auditory Serial Addition test, and a hyperventilation test. The second phase is carried out in the classroom, where stress is analyzed while performing several academic activities, namely attending to theoretical lectures, doing exercises and other individual activities, and taking short tests and exams. In both cases, both quantitative data obtained from COTS wrist wearables and qualitative data gathered by means of questionnaires are considered. This protocol involves a simple and consistent method with a stress induction app and questionnaires, requiring a limited participation of support staff.

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INTRODUCTION: Assessment of episodic memory has been traditionally used to evaluate potential cognitive impairments in senior adults. Typically, episodic memory evaluation is based on personal interviews and pen-and-paper tests. This article presents the design, development and a preliminary validation of a novel digital game to assess episodic memory intended to overcome the limitations of traditional methods, such as the cost of its administration, its intrusive character, the lack of early detection capabilities, the lack of ecological validity, the learning effect and the existence of confounding factors. MATERIALS AND METHODS: Our proposal is based on the gamification of the California Verbal Learning Test (CVLT) and it has been designed to comply with the psychometric characteristics of reliability and validity. Two qualitative focus groups and a first pilot experiment were carried out to validate the proposal. RESULTS: A more ecological, non-intrusive and better administrable tool to perform cognitive assessment was developed. Initial evidence from the focus groups and pilot experiment confirmed the developed game's usability and offered promising results insofar its psychometric validity is concerned. Moreover, the potential of this game for the cognitive classification of senior adults was confirmed, and administration time is dramatically reduced with respect to pen-and-paper tests. LIMITATIONS: Additional research is needed to improve the resolution of the game for the identification of specific cognitive impairments, as well as to achieve a complete validation of the psychometric properties of the digital game. CONCLUSION: Initial evidence show that serious games can be used as an instrument to assess the cognitive status of senior adults, and even to predict the onset of mild cognitive impairments or Alzheimer's disease.

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OBJECTIVE: To design, implement, and test a solution to provide social and health services for the elderly at home based on smart TV technologies and access to all services. METHODS: The architecture proposed is based on an open software platform and standard personal computing hardware. This provides great flexibility to develop new applications over the underlying infrastructure or to integrate new devices, for instance to monitor a broad range of vital signs in those cases where home monitoring is required. RESULTS: An actual system as a proof-of-concept was designed, implemented, and deployed. Applications range from social network clients to vital signs monitoring; from interactive TV contests to conventional online care applications such as medication reminders or telemedicine. CONCLUSION: In both cases, the results have been very positive, confirming the initial perception of the TV as a convenient, easy-to-use technology to provide social and health care. The TV set is a much more familiar computing interface for most senior users, and as a consequence, smart TVs become a most convenient solution for the design and implementation of applications and services targeted to this user group. SIGNIFICANCE: This proposal has been tested in real setting with 62 senior people at their homes. Users included both individuals with experience using computers and others reluctant to them.

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