Comprehensive analysis of gastrointestinal side effects in COVID-19 patients undergoing combined pharmacological treatment with azithromycin and hydroxychloroquine: a systematic review and network meta-analysis.

During the COVID-19 pandemic, several drugs were repositioned and combined to quickly find a way to mitigate the effects of the infection. However, the adverse effects of these combinations on the gastrointestinal tract are unknown. We aimed investigate whether Hydroxychloroquine (HD), Azithromycin (AZ), and Ivermectin (IV) used in combination for the treatment of COVID-19, can lead to the development of gastrointestinal disorders. This is a systematic review and network meta-analysis conducted using Stata and Revman software, respectively. The protocol was registered with PROSPERO (CRD42023372802). A search of clinical trials in Cochrane Library databases, Embase, Web of Science, Lilacs, PubMed, Scopus and Clinicaltrials.gov conducted on November 26, 2023. The eligibility of the studies was assessed based on PICO criteria, including trials that compared different treatments and control group. The analysis of the quality of the evidence was carried out according to the GRADE. Six trials involving 1,686 COVID-19 patients were included. No trials on the association of HD or AZ with IV met the inclusion criteria, only studies on the association between HD and AZ were included. Nausea, vomiting, diarrhea, abdominal pain and increased transaminases were related. The symptoms of vomiting and nausea were evaluated through a network meta-analysis, while the symptom of abdominal pain was evaluated through a meta-analysis. No significant associations with these symptoms were observed for HD, AZ, or their combination, compared to control. Low heterogeneity and absence of inconsistency in indirect and direct comparisons were noted. Limitations included small sample sizes, varied drug dosages, and potential publication bias during the pandemic peak. This review unveils that there are no associations between gastrointestinal adverse effects and the combined treatment of HD with AZ in the management of COVID-19, as compared to either the use of a control group or the administration of the drugs individually, on the other hand, highlighting the very low or low certainty of evidence for the evaluated outcomes. To accurately conclude the absence of side effects, further high-quality randomized studies are needed.

Health Care Professional-Supported Co-Design of a Mime Therapy-Based Serious Game for Facial Rehabilitation.

This research letter presents the co-design process for RG4Face, a mime therapy-based serious game that uses computer vision for human facial movement recognition and estimation to help health care professionals and patients in the facial rehabilitation process.

Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review.

Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement classic methods of mental health assessment and monitoring. This research area proposes innovative methods that perform multimodal sensing of multiple situations of interest (e.g., sleep, physical activity, mobility) to health professionals. In this paper, we present a Systematic Literature Review (SLR) to recognize, characterize and analyze the state of the art on DPMH using multimodal sensing of multiple situations of interest to professionals. We searched for studies in six digital libraries, which resulted in 1865 retrieved published papers. Next, we performed a systematic process of selecting studies based on inclusion and exclusion criteria, which selected 59 studies for the data extraction phase. First, based on the analysis of the extracted data, we describe an overview of this field, then presenting characteristics of the selected studies, the main mental health topics targeted, the physical and virtual sensors used, and the identified situations of interest. Next, we outline answers to research questions, describing the context data sources used to detect situations, the DPMH workflow used for multimodal sensing of situations, and the application of DPMH solutions in the mental health assessment and monitoring process. In addition, we recognize trends presented by DPMH studies, such as the design of solutions for high-level information recognition, association of features with mental states/disorders, classification of mental states/disorders, and prediction of mental states/disorders. We also recognize the main open issues in this research area. Based on the results of this SLR, we conclude that despite the potential and continuous evolution for using these solutions as medical decision support tools, this research area needs more work to overcome technology and methodological rigor issues to adopt proposed solutions in real clinical settings.

Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review.

Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.

A Telemedicine Platform for Aphasia: Protocol for a Development and Usability Study.

BACKGROUND: Aphasia is a central disorder of comprehension and expression of language that cannot be attributed to a peripheral sensory deficit or a peripheral motor disorder. The diagnosis and treatment of aphasia are complex. Interventions that facilitate this process can lead to an increase in the number of assisted patients and greater precision in the therapeutic choice by the health professional. OBJECTIVE: This paper describes a protocol for a study that aims to implement a computer-based solution (ie, a telemedicine platform) that uses deep learning to classify vocal data from participants with aphasia and to develop serious games to treat aphasia. Additionally, this study aims to evaluate the usability and user experience of the proposed solution. METHODS: Our interactive and smart platform will be developed to provide an alternative option for professionals and their patients with aphasia. We will design 2 serious games for aphasia rehabilitation and a deep learning-driven computational solution to aid diagnosis. A pilot evaluation of usability and user experience will reveal user satisfaction with platform features. RESULTS: Data collection began in June 2022 and is currently ongoing. Results of system development as well as usability should be published by mid-2023. CONCLUSIONS: This research will contribute to the treatment and diagnosis of aphasia by developing a telemedicine platform based on a co-design process. Therefore, this research will provide an alternative method for health care to patients with aphasia. Additionally, it will guide further studies with the same purpose. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40603.

Efeitos da fisioterapia aquática no equilíbrio de pacientes pós-acidente vascular encefálico: uma revisão integrativa / Effects of aquatic physical therapy on balance in post-cerebrovascular accident patients: an integrative review

Objetivo: Atualizar a literatura sobre os efeitos da terapia aquática no equilíbrio de pacientes pós-AVE e verificar os métodos avaliativos mais empregados. Métodos: A busca na literatura foi realizada em 6 bases de dados Pubmed, Web of Science, Scopus, Medline, PEDro e Cochrane, utilizando a associação de descritores, palavras-chave e operadores booleanos "Stroke" AND "Hydrotherapy" OR "Hydrokinesiotherapy" OR "Aquatic Physiotherapy" AND "Balance", estipulando critérios de inclusão e exclusão. Resultados: Dos 259 estudos identificados, foram selecionados 14 para análise e síntese qualitativa. No geral, os resultados evidenciaram diferenças significativas no equilíbrio de indivíduos com AVE após terapia aquática. Conclusão: Quando comparada às técnicas de fisioterapia neurofuncional convencionais, a fisioterapia aquática apresenta superioridade de eficácia. Os meios avaliativos mais utilizados são a Berg Balance Scale e a Timed Up and Go por se tratarem de ferramentas de rápida e fácil aplicação, além de alta eficácia, demonstrando a relevância do estudo em aspectos de reabilitação funcional em meio a disfunções advindas de comprometimentos neurológicos.

Neural Networks to Recognize Patterns in Topographic Images of Cortical Electrical Activity of Patients with Neurological Diseases.

Software such as EEGLab has enabled the treatment and visualization of the tracing and cortical topography of the electroencephalography (EEG) signals. In particular, the topography of the cortical electrical activity is represented by colors, which make it possible to identify functional differences between cortical areas and to associate them with various diseases. The use of cortical topography with EEG origin in the investigation of diseases is often not used due to the representation of colors making it difficult to classify the disease. Thus, the analyses have been carried out, mainly, based on the EEG tracings. Therefore, a computer system that recognizes disease patterns through cortical topography can be a solution to the diagnostic aid. In view of this, this study compared five models of Convolutional Neural Networks (CNNs), namely: Inception v3, SqueezeNet, LeNet, VGG-16 and VGG-19, in order to know the patterns in cortical topography images obtained with EEG, in Parkinson's disease, Depression and Bipolar Disorder. SqueezeNet performed better in the 3 diseases analyzed, with Parkinson's disease being better evaluated for Accuracy (88.89%), Precison (86.36%), Recall (91.94%) and F1 Score (89.06%), the other CNNs had less performance. In the analysis of the values of the Area under ROC Curve (AUC), SqueezeNet reached (93.90%) for Parkinson's disease, (75.70%) for Depression and (72.10%) for Bipolar Disorder. We understand that there is the possibility of classifying neurological diseases from cortical topographies with the use of CNNs and, thus, creating a computational basis for the implementation of software for screening and possible diagnostic assistance.

Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation.

People at risk of suicide tend to be isolated and cannot share their thoughts. For this reason, suicidal ideation monitoring becomes a hard task. Therefore, people at risk of suicide need to be monitored in a manner capable of identifying if and when they have a suicidal ideation, enabling professionals to perform timely interventions. This study aimed to develop the Boamente tool, a solution that collects textual data from users' smartphones and identifies the existence of suicidal ideation. The solution has a virtual keyboard mobile application that passively collects user texts and sends them to a web platform to be processed. The platform classifies texts using natural language processing and a deep learning model to recognize suicidal ideation, and the results are presented to mental health professionals in dashboards. Text classification for sentiment analysis was implemented with different machine/deep learning algorithms. A validation study was conducted to identify the model with the best performance results. The BERTimbau Large model performed better, reaching a recall of 0.953 (accuracy: 0.955; precision: 0.961; F-score: 0.954; AUC: 0.954). The proposed tool demonstrated an ability to identify suicidal ideation from user texts, which enabled it to be experimented with in studies with professionals and their patients.

Motor Rehabilitation of Upper Limbs Using a Gesture-Based Serious Game: Evaluation of Usability and User Experience.

Objective: Gesture-based serious games can be based on playful and interactive scenarios to enhance user engagement and experience during exercises, thereby increasing efficiency in the motor rehabilitation process. This study aimed to develop the Rehabilite Game (RG) as a complementary therapy tool for upper limb rehabilitation in clinics and home environments and to evaluate aspects of usability and user experience of it. Materials and Methods: The evaluation consisted of the use of a gesture-based serious game with motor rehabilitation sessions managed in a web platform. Thirty-three participants were recruited (21 physiotherapists and 12 patients). The protocol allowed each participant to have the experience of playing sessions with different combinations of settings. The User Experience Questionnaire (UEQ) was used to evaluate aspects of usability and user experience. The study was approved by the Research Ethics Board of the Federal University of Piaui (number 3,429,494). Results: The level of satisfaction with the RG was positive, with an excellent Net Promoter Score for 85.7% of physiotherapists and 100% of patients. All six UEQ scales (attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty) reflected acceptance. Conclusion: The study demonstrated that, according to the results obtained in the experiments, the RG had positive feedback from physiotherapists and patients, indicating that the game can be used in a clinical trial to be compared with other rehabilitation techniques.

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review.

BACKGROUND: Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients' interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies. OBJECTIVE: This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective. METHODS: We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends. RESULTS: A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal. CONCLUSIONS: This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

A computer vision-based mobile tool for assessing human posture: A validation study.

BACKGROUND AND OBJECTIVE: Non-invasive methods for postural assessment are tools used for tracking and monitoring the progression of postural deviations. Different computer-based methods have been used to assess human posture, including mobile applications based on images and sensors. However, such solutions still require manual identification of anatomical points. This study aims to present and validate the NLMeasurer, a mobile application for postural assessment. This application takes advantage of the PoseNet, a solution based on computer vision and machine learning used to estimate human pose and identify anatomical points. From the identified points, NLMeasurer calculates postural measures. METHODS: Twenty participants were photographed in front view while using surface markers over anatomical landmarks. Then, the surface markers were removed, and new photos were taken. The photos were analyzed by two examiners, and six postural measurements were computed with NLMeasurer and a validated biophotogrammetry software. One-sample t-test and Bland Altman procedure were used to assess agreement between the methods, and Intraclass Correlation Coefficient (ICC) was used to assess inter- and intra-rater reliability. RESULTS: Postural measurements calculated using the NLMeasurer were in agreement with the biophotogrammetry software. Furthermore, there was good inter- and intra-rater reliability for most photos without surface markers. CONCLUSIONS: NLMeasurer demonstrated to be a valid tool method to assess postural measurements in the frontal view. The use of surface markers on specific anatomical landmarks (i.e., ears, iliac spines and ankles) can facilitate the digital identification of these landmarks and improve the reliability of the postural measurements performed with NLMeasurer.

Dempster-Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing.

The Internet of Things (IoT) has emerged from the proliferation of mobile devices and objects connected, resulting in the acquisition of periodic event flows from different devices and sensors. However, such sensors and devices can be faulty or affected by failures, have poor calibration, and produce inaccurate data and uncertain event flows in IoT applications. A prominent technique for analyzing event flows is Complex Event Processing (CEP). Uncertainty in CEP is usually observed in primitive events (i.e., sensor readings) and rules that derive complex events (i.e., high-level situations). In this paper, we investigate the identification and treatment of uncertainty in CEP-based IoT applications. We propose the DST-CEP, an approach that uses the Dempster-Shafer Theory to treat uncertainties. By using this theory, our solution can combine unreliable sensor data in conflicting situations and detect correct results. DST-CEP has an architectural model for treating uncertainty in events and its propagation to processing rules. We describe a case study using the proposed approach in a multi-sensor fire outbreak detection system. We submit our solution to experiments with a real sensor dataset, and evaluate it using well-known performance metrics. The solution achieves promising results regarding Accuracy, Precision, Recall, F-measure, and ROC Curve, even when combining conflicting sensor readings. DST-CEP demonstrated to be suitable and flexible to deal with uncertainty.

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals.

Traditionally, mental health specialists monitor their patients' social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson's correlation coefficient >70%) with individuals' social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.

Mental health ubiquitous monitoring supported by social situation awareness: A systematic review.

Traditionally, the process of monitoring and evaluating social behavior related to mental health has based on self-reported information, which is limited by the subjective character of responses and various cognitive biases. Today, however, there is a growing amount of studies that have provided methods to objectively monitor social behavior through ubiquitous devices and have used this information to support mental health services. In this paper, we present a Systematic Literature Review (SLR) to identify, analyze and characterize the state of the art about the use of ubiquitous devices to monitor users' social behavior focused on mental health. For this purpose, we performed an exhaustive literature search on the six main digital libraries. A screening process was conducted on 160 peer-reviewed publications by applying suitable selection criteria to define the appropriate studies to the scope of this SLR. Next, 20 selected studies were forwarded to the data extraction phase. From an analysis of the selected studies, we recognized the types of social situations identified, the process of transforming contextual data into social situations, the use of social situation awareness to support mental health monitoring, and the methods used to evaluate proposed solutions. Additionally, we identified the main trends presented by this research area, as well as open questions and perspectives for future research. Results of this SLR showed that social situation-aware ubiquitous systems represent promising assistance tools for patients and mental health professionals. However, studies still present limitations in methodological rigor and restrictions in experiments, and solutions proposed by them have limitations to be overcome.

Can human posture and range of motion be measured automatically by smart mobile applications?

Human posture and Range of Motion (ROM) are important components of a physical assessment and, from the collected data, it is possible to identify postural deviations such as scoliosis or joint and muscle limitations, hence identifying risks of more serious injuries. Posture assessment and ROM measures are also necessary metrics to monitor the effect of treatments used in the motor rehabilitation of patients, as well as to monitor their clinical progress. These evaluation processes are more frequently performed through visual inspection and manual palpation, which are simple and low cost methods. These methods, however, can be optimized with the use of tools such as photogrammetry and goniometry. Mobile solutions have also been developed to help health professionals to capture more objective data and with less risk of bias. Although there are already several systems proposed for assessing human posture and ROM in the literature, they have not been able to automatically identify and mark Anatomical and Segment Points (ASPs). The hypothesis presented here considers the development of a mobile application for automatic identification of ASPs by using machine learning algorithms and computer vision models associated with technologies embedded in smartphones. From ASPs identification, it will be possible to identify changes in postural alignment and ROM. In this context, our view is that an application derived from the hypothesis will serve as an additional tool to assist in the physical assessment process and, consequently, in the diagnosis of disorders related to postural and movement changes.

Time estimation exposure modifies cognitive aspects and cortical activity of attention deficit hyperactivity disorder adults.

AIM: This study investigated whether time-estimation task exposure influences the severity of Attention Deficit Hyperactivity Disorder (ADHD), as well as theta band activity in the dorsolateral prefrontal cortex and ventrolateral prefrontal cortex. MATERIAL AND METHODS: Twenty-two patients with ADHD participated in a crossover experiment with a visual time-estimation task under control conditions (without exposure to time estimation tasks) and experimental (thirty days exposure to time-estimation tasks) in association with electroencephalographic analysis of theta band. RESULTS: ADHD patients with thirty days of time-estimation task exposure presented a worse performance of the time-estimation task, as revealed by the measurements of the absolute error and relative error (p ≤ 0.05). However, our findings show the improvement of self-reported symptoms of attention, impulsivity, and emotional control in patients after the time-estimation task exposure (p = 0.0001). Moreover, the theta band oscillations in the right dorsolateral prefrontal cortex and in the ventrolateral prefrontal increased with thirty days of time-estimation task exposure (p ≤ 0.05). CONCLUSION: We propose that the decrease in EEG theta power may indicate an efficient accumulation of temporal pulses, which could be responsible for the improvement in the patient cognitive aspects as demonstrated by the current study. Time-estimation task improves ADHD cognitive symptoms, with a substantial increase in cortical areas activity related to attention and memory, suggesting its use as a tool for cognitive timing function management and non-invasive therapeutic aid in ADHD.

Methylphenidate modifies activity in the prefrontal and parietal cortex accelerating the time judgment.

Methylphenidate produces its effects via actions on cortical areas involved with attention and working memory, which have a direct role in time estimation judgment tasks. In particular, the prefrontal and parietal cortex has been the target of several studies to understand the effect of methylphenidate on executive functions and time interval perception. However, it has not yet been studied whether acute administration of methylphenidate influences performance in time estimation task and the changes in alpha band absolute power in the prefrontal and parietal cortex. The current study investigates the influence of the acute use of methylphenidate in both performance and judgment in the time estimation interpretation through the alpha band absolute power activity in the prefrontal and parietal cortex. This is a double-blind, crossover study with a sample of 32 subjects under control (placebo) and experimental (methylphenidate) conditions with absolute alpha band power analysis during a time estimation task. We observed that methylphenidate does not influence task performance (p > 0.05), but it increases the time interval underestimation by over 7 s (p < 0.001) with a concomitant decrease in absolute alpha band power in the ventrolateral prefrontal cortex and dorsolateral prefrontal cortex and parietal cortex (p < 0.001). Acute use of methylphenidate increases the time interval underestimation, consistent with reduced accuracy of the internal clock mechanisms. Furthermore, acute use of methylphenidate influences the absolute alpha band power over the dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, and parietal cortex.

Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness.

Current mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient's daily routine (e.g., "studying", "at work", "working out"). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations.