Studies on the impact of assistive communication devices on the quality of life of patients with amyotrophic lateral sclerosis

Tese de doutoramento, Ciências Biomédicas (Neurociências), Universidade de Lisboa, Faculdade de Medicina, 2016Amyotrophic Lateral Sclerosis (ALS) is a progressive neuromuscular disease with rapid and generalized degeneration of motor neurons. Patients with ALS experiment a relentless decline in functions that affect performance of most activities of daily living (ADL), such as speaking, eating, walking or writing. For this reason, dependence on caregivers grows as the disease progresses. Management of the respiratory system is one of the main concerns of medical support, since respiratory failure is the most common cause of death in ALS. Due to increasing muscle weakness, most patients experience dramatic decrease of speech intelligibility and difficulties in using upper limbs (UL) for writing. There is growing evidence that mild cognitive impairment is common in ALS, but most patients are self-conscious of their difficulties in communicating and, in very severe stages, locked-in syndrome can occur. When no other resources than speech and writing are used to assist communication, patients are deprived of expressing needs or feelings, making decisions and keeping social relationships. Further, caregivers feel increased dependence due to difficulties in communication with others and get frustrated about difficulties in understanding partners’ needs. Support for communication is then very important to improve quality of life of both patients and caregivers; however, this has been poorly investigated in ALS. Assistive communication devices (ACD) can support patients by providing a diversity of tools for communication, as they progressively lose speech. ALS, in common with other degenerative conditions, introduces an additional challenge for the field of ACD: as the disease progresses, technologies must adapt to different conditions of the user. In early stages, patients may need speech synthesis in a mobile device, if dysarthria is one of the initial symptoms, or keyboard modifications, as weakness in UL increases. When upper limbs’ dysfunction is high, different input technologies may be adapted to capture voluntary control (for example, eye-tracking devices). Despite the enormous advances in the field of Assistive Technologies, in the last decade, difficulties in clinical support for the use of assistive communication devices (ACD) persist. Among the main reasons for these difficulties are lack of assessment tools to evaluate communication needs and determine proper input devices and to indicate changes over disease progression, and absence of clinical evidence that ACD has relevant impact on the quality of life of affected patients. For this set of reasons, support with communication tools is delayed to stages where patients are severely disabled. Often in these stages, patients face additional clinical complications and increased dependence on their caregivers’ decisions, which increase the difficulty in adaptation to new communication tools. This thesis addresses the role of assistive technologies in the quality of life of early-affected patients with ALS. Also, it includes the study of assessment tools that can improve longitudinal evaluation of communication needs of patients with ALS. We longitudinally evaluated a group of 30 patients with bulbar-onset ALS and 17 caregivers, during 2 to 29 months. Patients were assessed during their regular clinical appointments, in the Hospital de Santa Maria-Centro Hospitalar Lisboa_Norte. Evaluation of patients was based on validated instruments for assessing the Quality of Life (QoL) of patients and caregivers, and on methodologies for recording communication and measuring its performance (including speech, handwriting and typing). We tested the impact of early support with ACD on the QoL of patients with ALS, using a randomized, prospective, longitudinal design. Patients were able to learn and improve their skills to use communication tools based on electronic assistive devices. We found a positive impact of ACD in psychological and wellbeing domains of quality of life in patients, as well as in the support and psychological domains in caregivers. We also studied performance of communication (words per minute) using UL. Performance in handwriting may decline faster than performance in typing, supporting the idea that the use of touchscreen-based ACD supports communication for longer than handwriting. From longitudinal recordings of speech and typing activity we could observe that ACD can support tools to detect early markers of bulbar and UL dysfunction in ALS. Methodologies that were used in this research for recording and assessing function in communication can be replicated in the home environment and form part of the original contributions of this research. Implementation of remote monitoring tools in daily use of ACD, based on these methodologies, is discussed. Considering those patients who receive late support for the use of ACD, lack of time or daily support to learn how to control complex input devices may hinder its use. We developed a novel device to explore the detection and control of various residual movements, based on sensors of accelerometry, electromyography and force, as input signals for communication. The aim of this input device was to develop a tool to explore new communication channels in patients with generalized muscle weakness. This research contributed with novel tools from the Engineering field to the study of assistive communication in patients with ALS. Methodologies that were developed in this work can be further applied to the study of the impact of ACD in other neurodegenerative diseases that affect speech and motor control of UL

Quantification of the Voicescape: A Person-centric Approach to Describing Real-life Behaviour Patterns - A Case Study Comparing Two Age Groups

The human voice is a fundamental part of the everyday auditory environment. A measure of all voice activity that a person produces or perceives in the environment, i.e., the person’s voicescape, might provide an informative, low cost, ecologically valid, and person-centric approach to characterizing patterns of socially-relevant behaviour in real life. In this paper, we use the measure ratio of voice activity (rva) and present results of data acquired from N=20 subjects of 2 different age groups as they engaged in their usual daily life activities over 4 consecutive days. The data show no differences in total voice activity but significant between-group differences in its daily distribution. We propose that measurement of the voicescape can, even without knowledge of specific voice sources, serve as a useful indicator of person- or group specific activity patterns for purposes of describing significant aspects of variation and within- and between-group differences in patterns of everyday behaviour and, potentially, for identifying change in patterns that have health-related implications. Future work will target automatic detection and identification of voice sources and the use of privacy-preserving processing methods

Systematic review

Publisher Copyright: © Salome Azevedo, Teresa Cipriano Rodrigues, Ana Rita Londral. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 19.08.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.Background: The COVID-19 pandemic catalyzed the adoption of home telemonitoring to cope with social distancing challenges. Recent research on home telemonitoring demonstrated benefits concerning the capacity, patient empowerment, and treatment commitment of health care systems. Moreover, for some diseases, it revealed significant improvement in clinical outcomes. Nevertheless, when policy makers and practitioners decide whether to scale-up a technology-based health intervention from a research study to mainstream care delivery, it is essential to assess other relevant domains, such as its feasibility to be expanded under real-world conditions. Therefore, scalability assessment is critical, and it encompasses multiple domains to ensure population-wide access to the benefits of the growing technological potential for home telemonitoring services in health care. Objective: This systematic review aims to identify the domains and methods used in peer-reviewed research studies that assess the scalability of home telemonitoring-based interventions under real-world conditions. Methods: The authors followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines and used multiple databases (PubMed, Scopus, Web of Science, and EconLit). An integrative synthesis of the eligible studies was conducted to better explore each intervention and summarize relevant information concerning the target audience, intervention duration and setting, and type of technology. Each study design was classified based on the strength of its evidence. Lastly, the authors conducted narrative and thematic analyses to identify the domains, and qualitative and quantitative methods used to support scalability assessment. Results: This review evaluated 13 articles focusing on the potential of scaling up a home telemonitoring intervention. Most of the studies considered the following domains relevant for scalability assessment: problem (13), intervention (12), effectiveness (13), and costs and benefits (10). Although cost-effectiveness was the most common evaluation method, the authors identified seven additional cost analysis methods to evaluate the costs. Other domains were less considered, such as the sociopolitical context (2), workforce (4), and technological infrastructure (3). Researchers used different methodological approaches to assess the effectiveness, costs and benefits, fidelity, and acceptability. Conclusions: This systematic review suggests that when assessing scalability, researchers select the domains specifically related to the intervention while ignoring others related to the contextual, technological, and environmental factors, which are also relevant. Additionally, studies report using different methods to evaluate the same domain, which makes comparison difficult. Future work should address research on the minimum required domains to assess the scalability of remote telemonitoring services and suggest methods that allow comparison among studies to provide better support to decision makers during large-scale implementation.publishersversionpublishe

A wireless user- computer interface to explore various sources of biosignals and visual biofeedback for severe motor impairment

Severe speech and motor impairments caused by several neurological disorders can limit communication skills to simple yes/no replies. Variability among patients’ physical and social conditions justifies the need of providing multiple sources of signals to access to Augmentative and Alternative Communication (AAC) systems. Our study presents the development of a new user-computer interface that can be controlled by the detection of various sources of biosignals. Wireless sensors are placed on the body and users learn to enhance the control of detected signals by visual biofeedback, on a switch based control approach. Experimental results in four patients with just few residual movements showed that different sensors can be placed in different body locations and detect novel communication channels, according to each person’s physiological and social condition. Especially in progressive conditions, this system can be used by therapists to anticipate progression and assess new channels for communication.Peer Reviewe

Image Analysis System for Early Detection of Cardiothoracic Surgery Wound Alterations Based on Artificial Intelligence Models

Funding Information: This work is part of a research project funded by Fundação para a Ciência e Tecnologia, which aims to design and implement a post-surgical digital telemonitoring service for cardiothoracic surgery patients. The main goals of the research project are: to study the impact of daily telemonitoring on early diagnosis, to reduce hospital readmissions, and to improve patient safety, during the 30-day period after hospital discharge. This remote follow-up involves a digital remote patient monitoring kit which includes a sphygmomanometer, a scale, a smartwatch, and a smartphone, allowing daily patient data collection. One of the daily outcomes was the daily photographs taken by patients regarding surgical wounds. Every day, the clinical team had to analyze the image of each patient, which could take a long time. The automatic analysis of these images would allow implementing an alert related to the detection of wound modifications that could represent a risk of infection. Such an alert would spare time for the clinical team in follow-up care. Funding Information: This research has been supported by Fundação para a Ciência e Tecnologia (FCT) under CardioFollow.AI project (DSAIPA/AI/0094/2020), Lisboa-05-3559-FSE-000003 and UIDB/04559/2020. Publisher Copyright: © 2023 by the authors.Cardiothoracic surgery patients have the risk of developing surgical site infections which cause hospital readmissions, increase healthcare costs, and may lead to mortality. This work aims to tackle the problem of surgical site infections by predicting the existence of worrying alterations in wound images with a wound image analysis system based on artificial intelligence. The developed system comprises a deep learning segmentation model (MobileNet-Unet), which detects the wound region area and categorizes the wound type (chest, drain, and leg), and a machine learning classification model, which predicts the occurrence of wound alterations (random forest, support vector machine and k-nearest neighbors for chest, drain, and leg, respectively). The deep learning model segments the image and assigns the wound type. Then, the machine learning models classify the images from a group of color and textural features extracted from the output region of interest to feed one of the three wound-type classifiers that reach the final binary decision of wound alteration. The segmentation model achieved a mean Intersection over Union of 89.9% and a mean average precision of 90.1%. Separating the final classification into different classifiers was more effective than a single classifier for all the wound types. The leg wound classifier exhibited the best results with an 87.6% recall and 52.6% precision.publishersversionpublishe

Estudio de diferentes parámetros para la detección de esclerosis lateral amiotrófica a partir del movimiento articulatorio

Amyotrophic lateral sclerosis (ALS) is a degenerative neuromuscular disease, one of its early symptoms being a progressive difficulty to speak (ALS dysarthria). To improve its diagnosis and monitoring, a new method based on articulatory movement estimation has been developed. As a result, two articulatory movement parameters are presented as well as their relationship with the illness grade.La esclerosis lateral amiotrófica (ELA) es una enfermedad degenerativa de tipo neuromuscular, uno de cuyos primeros síntomas es la dificultad para hablar. Recientemente se ha presentado un nuevo método para determinar el movimiento articulatorio a partir de la estimación de los formantes. En este artículo se presentan dos parámetros obtenidos mediante el modelado del movimiento articulatorio y se evalúan con respecto al grado de la enfermedad

a before-after design analysis

Funding FVH, FM, and ARL acknowledge funding from the Portuguese National Funding Agency for Science, Research, and Technology (FCT) and public ESF funding with reference LISBOA-05–3559-FSE-000003. SG acknowledges funding from DSAIPA project FrailCare.AI (DSAIPA/0106/2019/02) with the fnancial support of FCT.BACKGROUND: Emergency department (ED) High users (HU), defined as having more than ten visits to the ED per year, are a small group of patients that use a significant proportion of ED resources. The High Users Resolution Group (GRHU) identifies and provides care to HU to improve their health conditions and reduce the frequency of ED visits by delivering patient-centered case management integrated care. The main objective of this study was to measure the impact of the GRHU intervention in reducing ED visits, outpatient appointments, and hospitalizations. As secondary objectives, we aimed to compare the GRHU intervention costs against its potential savings or additional costs. Finally, we intend to study the impact of this intervention across different groups of patients. METHODS: We studied the changes triggered by the GRHU program in a retrospective, non-controlled before-after analysis of patients' hospital utilization data on 6 and 12-month windows from the first appointment. RESULTS: A total of 238 ED HU were intervened. A sample of 152 and 88 patients was analyzed during the 6 and 12-month window, respectively. On the 12-month window, GRHU intervention was associated with a statistically significant reduction of 51% in ED visits and hospitalizations and a non-statistically significant increase in the total number of outpatient appointments. Overall costs were reduced by 43.56%. We estimated the intervention costs to be €79,935.34. The net cost saving was €104,305.25. The program's Return on Investment (ROI) was estimated to be €2.3. CONCLUSION: Patient-centered case management for ED HU seems to effectively reduce ED visits and hospitalizations, leading to better use of resources.publishersversionpublishe

collaborative development and implementation of Remote Patient Monitoring pilot initiatives to increase access to follow-up care

Funding Information: This research has been supported by Fundação para a Ciência e Tecnologia (FCT) under CardioFollow.AI project (DSAIPA/AI/0094/2020) and Lisboa-05-3559-FSE-000003. Acknowledgments Funding Information: In 2020, in the scope of the COVID-19 pandemic, the Portuguese Foundation for Science and Technology (Fundação Portuguesa para a Ciência e Tecnologia - FCT) launched a tender to support Research and Development (R&D) projects in the areas of data science and artificial intelligence (AI) in Public Administration (). The main objective was to promote projects that could cope with pandemic-imposed challenges, improve public health services, and support citizens in better decision-making concerning health behaviors. FCT required the participation of at least one public administration entity providing health care committed to using the project results and the R&D activities. Another requirement was to provide a Data Management Plan that preserved the use of data ethical and legal aspects, such as privacy and consent issues in citizens’ data access, data sharing across different sources, and transparency of the analysis and utilization. The projects could last 24 to 36 months with a maximum funding limit per project of 240 thousand euros. This tender allocated 3 million euros from a national-based fund budget. Publisher Copyright: 2022 Azevedo, Guede-Fernández, von Hafe, Dias, Lopes, Cardoso, Coelho, Santos, Fragata, Vital, Semedo, Gualdino and Londral.Background: COVID-19 increased the demand for Remote Patient Monitoring (RPM) services as a rapid solution for safe patient follow-up in a lockdown context. Time and resource constraints resulted in unplanned scaled-up RPM pilot initiatives posing risks to the access and quality of care. Scalability and rapid implementation of RPM services require social change and active collaboration between stakeholders. Therefore, a participatory action research (PAR) approach is needed to support the collaborative development of the technological component while simultaneously implementing and evaluating the RPM service through critical action-reflection cycles. Objective: This study aims to demonstrate how PAR can be used to guide the scalability design of RPM pilot initiatives and the implementation of RPM-based follow-up services. Methods: Using a case study strategy, we described the PAR team’s (nurses, physicians, developers, and researchers) activities within and across the four phases of the research process (problem definition, planning, action, and reflection). Team meetings were analyzed through content analysis and descriptive statistics. The PAR team selected ex-ante pilot initiatives to reflect upon features feedback and participatory level assessment. Pilot initiatives were investigated using semi-structured interviews transcribed and coded into themes following the principles of grounded theory and pilot meetings minutes and reports through content analysis. The PAR team used the MoSCoW prioritization method to define the set of features and descriptive statistics to reflect on the performance of the PAR approach. Results: The approach involved two action-reflection cycles. From the 15 features identified, the team classified 11 as must-haves in the scaled-up version. The participation was similar among researchers (52.9%), developers (47.5%), and physicians (46.7%), who focused on suggesting and planning actions. Nurses with the lowest participation (5.8%) focused on knowledge sharing and generation. The top three meeting outcomes were: improved research and development system (35.0%), socio-technical-economic constraints characterization (25.2%), and understanding of end-user technology utilization (22.0%). Conclusion: The scalability and implementation of RPM services must consider contextual factors, such as individuals’ and organizations’ interests and needs. The PAR approach supports simultaneously designing, developing, testing, and evaluating the RPM technological features, in a real-world context, with the participation of healthcare professionals, developers, and researchers.publishersversionpublishe

Scale-up of Digital Innovations in Health Care: Expert Commentary on Enablers and Barriers

Health care delivery is undergoing a rapid change from traditional processes toward the use of digital health interventions and personalized medicine. This movement has been accelerated by the COVID-19 crisis as a response to the need to guarantee access to health care services while reducing the risk of contagion. Digital health scale-up is now also vital to achieve population-wide impact: it will only accomplish sustainable effects if and when deployed into regular health care delivery services. The question of how sustainable digital health scale-up can be successfully achieved has, however, not yet been sufficiently resolved. This paper identifies and discusses enablers and barriers for scaling up digital health innovations. The results discussed in this paper were gathered by scientists and representatives of public bodies as well as patient organizations at an international workshop on scaling up digital health innovations. Results are explored in the context of prior research and implications for future work in achieving large-scale implementations that will benefit the population as a whole

Expert Commentary on Enablers and Barriers

©Hannes Schlieter, Lisa A Marsch, Diane Whitehouse, Lena Otto, Ana Rita Londral, Gisbert Wilhelm Teepe, Martin Benedict, Joseph Ollier, Tom Ulmer, Nathalie Gasser, Sabine Ultsch, Bastian Wollschlaeger, Tobias Kowatsch. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.03.2022.Health care delivery is undergoing a rapid change from traditional processes toward the use of digital health interventions and personalized medicine. This movement has been accelerated by the COVID-19 crisis as a response to the need to guarantee access to health care services while reducing the risk of contagion. Digital health scale-up is now also vital to achieve population-wide impact: it will only accomplish sustainable effects if and when deployed into regular health care delivery services. The question of how sustainable digital health scale-up can be successfully achieved has, however, not yet been sufficiently resolved. This paper identifies and discusses enablers and barriers for scaling up digital health innovations. The results discussed in this paper were gathered by scientists and representatives of public bodies as well as patient organizations at an international workshop on scaling up digital health innovations. Results are explored in the context of prior research and implications for future work in achieving large-scale implementations that will benefit the population as a whole.publishersversionpublishe