Health-Related Quality of Life in Romanian Patients with Dystonia: An Exploratory Study.

Background/Objectives: Dystonia is a neurological movement disorder characterized by involuntary muscle contractions that lead to abnormal movements and postures; it has a major impact on patients' health-related quality of life (HRQoL). The aim of this study was to examine the HRQoL of Romanian patients with dystonia using the EQ-5D-5L instrument. Methods: Responses to the EQ-5D-5L and the visual analogue scale (VAS) were collected alongside demographic and clinical characteristics. Health profiles were analyzed via the metrics of the EQ-5D-5L, severity levels, and age groups. Using Shannon's indexes, we calculated informativity both for patients' health profile as a whole and each individual dimension. Level sum scores (LSS) of the EQ-5D-5L were calculated and compared with scores from the EQ-5D-5L index and VAS. The HRQoL measures were analyzed through demographic and clinical characteristics. Descriptive statistics, Spearman correlation, and non-parametric tests (Mann-Whitney U or Kruskall-Wallis H) were used. The level of agreement between HRQoL measures was assessed using their intraclass correlation coefficient (ICC) and Bland-Altman plots. Results: A sample of 90 patients was used, around 75.6% of whom were female patients, and the mean age at the beginning of the survey was 58.7 years. The proportion of patients reporting "no problems" in all five dimensions was 10%. The highest frequency reported was "no problems" in self-care (66%), followed by "no problems" in mobility (41%). Shannon index and Shannon evenness index values showed higher informativity for pain/discomfort (2.07 and 0.89, respectively) and minimal informativity for self-care (1.59 and 0.68, respectively). The mean EQ-5D-5L index, LSS, and VAS scores were 0.74 (SD = 0.26), 0.70 (SD = 0.24), and 0.61 (SD = 0.21), respectively. The Spearman correlations between HRQoL measures were higher than 0.60. The agreement between the EQ-5D-5L index and LSS values was excellent (ICC = 0.970, 95% CI = 0.934-0.984); the agreement was poor-to-good between the EQ-5D-5L index and VAS scores (ICC = 683, 95% CI = 0.388-0.820), and moderate-to-good between the LSS and VAS scores (ICC = 0.789, 95% CI = 0.593-0.862). Conclusions: Our results support the utilization of the EQ-5D-5L instrument in assessing the HRQoL of dystonia patients, and empirical results suggest that the EQ-5D-5L index and LSS measure may be used interchangeably. The findings from this study highlight that HRQoL is complex in patients with dystonia, particularly across different age groups.

Monitoring and Predicting Health Status in Neurological Patients: The ALAMEDA Data Collection Protocol.

(1) Objective: We explore the predictive power of a novel stream of patient data, combining wearable devices and patient reported outcomes (PROs), using an AI-first approach to classify the health status of Parkinson's disease (PD), multiple sclerosis (MS) and stroke patients (collectively named PMSS). (2) Background: Recent studies acknowledge the burden of neurological disorders on patients and on the healthcare systems managing them. To address this, effort is invested in the digital transformation of health provisioning for PMSS patients. (3) Methods: We introduce the data collection journey within the ALAMEDA project, which continuously collects PRO data for a year through mobile applications and supplements them with data from minimally intrusive wearable devices (accelerometer bracelet, IMU sensor belt, ground force measuring insoles, and sleep mattress) worn for 1-2 weeks at each milestone. We present the data collection schedule and its feasibility, the mapping of medical predictor variables to wearable device capabilities and mobile application functionality. (4) Results: A novel combination of wearable devices and smartphone applications required for the desired analysis of motor, sleep, emotional and quality-of-life outcomes is introduced. AI-first analysis methods are presented that aim to uncover the prediction capability of diverse longitudinal and cross-sectional setups (in terms of standard medical test targets). Mobile application development and usage schedule facilitates the retention of patient engagement and compliance with the study protocol.

Shared Decision-Making to Improve Health-Related Outcomes for Adults with Stroke Disease.

Stroke is one of the leading causes of disability and death worldwide, a severe medical condition for which new solutions for prevention, monitoring, and adequate treatment are needed. This paper proposes a SDM framework for the development of innovative and effective solutions based on artificial intelligence in the rehabilitation of stroke patients by empowering patients to make decisions about the use of devices and applications developed in the European project ALAMEDA. To develop a predictive tool for improving disability in stroke patients, key aspects of stroke patient data collection journeys, monitored health parameters, and specific variables covering motor, physical, emotional, cognitive, and sleep status are presented. The proposed SDM model involved the training and consultation of patients, medical staff, carers, and representatives under the name of the Local Community Group. Consultation with LCG members, consists of 11 representative people, physicians, nurses, patients and caregivers, which led to the definition of a methodological framework to investigate the key aspects of monitoring the patient data collection journey for the stroke pilot, and a specific questionnaire to collect stroke patient requirements and preferences. A set of general and specific guidelines specifying the principles by which patients decide to use wearable sensing devices and specific applications resulted from the analysis of the data collected using the questionnaire. The preferences and recommendations collected from LCG members have already been implemented in this stage of ALAMEDA system design and development.

Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review.

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

Sustainable Health-Related Quality of Life in Older Adults as Supported by the vINCI Technology.

The vINCI technology represents an innovative instrument developed specifically but not exclusively for older adults by technology researchers together with a medical team specialized in geriatrics and gerontology. It was designed to be independently and effortlessly used by older adults in the comfort and safety of their own environment. It is a modular and flexible platform that can integrate a large array of various sensors and can easily adapt to specific healthcare needs. The pilot study tested sensors and standardized instruments capable of evaluating several care-related parameters and of generating personalized feedback for the user dedicated to optimizing physical activity level, social interaction, and health-related quality of life. Moreover, the system was able to detect and signal events and health-related aspects that would require medical assistance. This paper presents how the innovative vINCI technology improves quality of life in older adults. This is evidenced by the results obtained following the clinical validation of the vINCI technology by older adults admitted to the Ana Aslan National Institute of Gerontology and Geriatrics (NIGG) in Bucharest.

Latent profile analysis for quality of life in older patients.

BACKGROUND: Quality of life (QOL) is a complex concept known for being influenced by socio-demographic characteristics, individual needs, perceptions and expectations. The study investigates influences of such heterogeneous variables and aims to identify and describe subgroups of older patients who share similar response patterns for the four domains (physical health, psychological health, social relationships and environment) of World Health Organization Quality of Life instrument, Short Form (WHOQOL-BREF). METHODS: The sample used included older Romanian patients (N = 60; equal numbers of men and women; mean age was 71.95, SD = 5.98). Latent Profile Analysis (LPA) was conducted to explore quality of life profiles with the four WHOQOL-BREF domains as input variables. Differences between profiles were analysed by MANOVA and ANOVAs as a follow-up. RESULTS: The LPA results showed that the three-profile model was the most suitable and supported the existence of three distinct QOL profiles: low and very low (28.3%), moderate (63.3%) and high (8.4%). The relative entropy value was high (0.86), results pointed to a good profile solution and the three profiles differed significantly from one another. CONCLUSION: Our results reveal heterogeneity within the older adult sample and provide meaningful information to better tailor QOL improvement programs to the needs of older patient groups, especially those designed for patients of profiles related to poorer QOL in different domains.

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments.

This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge-cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge-cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.

Human Physical Activity Recognition Using Smartphone Sensors.

Because the number of elderly people is predicted to increase quickly in the upcoming years, "aging in place" (which refers to living at home regardless of age and other factors) is becoming an important topic in the area of ambient assisted living. Therefore, in this paper, we propose a human physical activity recognition system based on data collected from smartphone sensors. The proposed approach implies developing a classifier using three sensors available on a smartphone: accelerometer, gyroscope, and gravity sensor. We have chosen to implement our solution on mobile phones because they are ubiquitous and do not require the subjects to carry additional sensors that might impede their activities. For our proposal, we target walking, running, sitting, standing, ascending, and descending stairs. We evaluate the solution against two datasets (an internal one collected by us and an external one) with great effect. Results show good accuracy for recognizing all six activities, with especially good results obtained for walking, running, sitting, and standing. The system is fully implemented on a mobile device as an Android application.