Systematic design of health monitoring systems centered on older adults and ADLs.

BACKGROUND: Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life. METHOD: A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain. RESULTS: The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning. CONCLUSIONS: The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

A semi-automatic mHealth system using wearable devices for identifying pain-related parameters in elderly individuals.

BACKGROUND: Mobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored. OBJECTIVE: To design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease. METHODS: A multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions. RESULTS: The multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores. CONCLUSION: The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies.

Spatio-temporal dynamics of vehicles: Fusion of traffic data and context information.

We present a dataset for vehicle tracking in a rural area. Specifically, in the Barranco de Poqueira region, which includes the municipalities of Pampaneira, Bubión, and Capileira in the Sierra Nevada National Park, Granada, Spain. Four Hikvision License Plate Recognition (LPR) cameras collect vehicle entries and exits to each village. Additional contextual data, including vacation calendars, vehicle origins, and socio-demographic information, enrich the dataset. The dataset comprises three files covering nine months from February to October 2022: one with raw data directly extracted from the cameras, another aggregated at the visit level and including context information, and a third aggregated by vehicles with context information. These datasets can be useful for mobility studies, urban planning, tourism, and socio-demographic analysis.

Rectus Femoris Cross-Sectional Area and Phase Angle asPredictors of 12-Month Mortality in Idiopathic Pulmonary Fibrosis Patients.

BACKGROUND: The value of the phase angle (PhA), measured via bioelectrical impedance analysis (BIA), could be considered a good marker of the cell mass and the cell damage of a patient; however, there are new techniques, such as muscle ultrasonography, that allow the quantity and quality of muscle to be assessed in a minimally invasive way. The aim of this study is to determine the prognostic value of morphofunctional techniques in the prognosis of mortality in patients with idiopathic pulmonary fibrosis (IPF). METHODS: This multicenter, cross-sectional study included 86 patients with idiopathic pulmonary fibrosis with a mean age of 71 years, 82.7% of whom were male. The nutritional risk of the patients was assessed by means of questionnaires, such as the Subjective Global Assessment (SGA), and non-invasive functional techniques, including BIA, nutritional ultrasound, and hand grip strength (HGS). Statistical analysis of the sample was performed using JAMOVI version 2.3.22. RESULTS: Correlations were made between the RF-CSA techniques with PhA (r = 0.48, p < 0.001), BCM (r = 0.70, p < 0.001), SMI (r = 0.64, p < 0.001), and HGS (r = 0.54, p < 0.001). The cut-off points for 12-month mortality were PhA = 4.5° (AUC = 0.722, sensitivity of 72.7% and specificity of 66.6%), BCM = 28.8 kg (AUC = 0.609, sensitivity of 32.4% and specificity of 100.0%), RF-CSA = 3.00 cm2 (AUC = 0.857, sensitivity of 64.4% and specificity of 100.0%), 6MMW = 420 m (AUC = 0.830, sensitivity of 63.27% and specificity of 100.0%), and TUG = 7.2 s (AUC = 0.771, sensitivity of 100.0% and specificity of 56.67%). In addition, a multivariate analysis was performed with RF-CSA, HR = 8.11 (1.39-47.16, p = 0.020), and PhA of 6.35 (1.29-31.15, p = 0.023), taking into account age, sex, and BMI to determine mortality. Finally, a Kaplan-Meier survival analysis was conducted with low or normal values for classical parameters (GAP and T6MM) and new parameters (PhA, BCM, RF-CSA, and TUG). CONCLUSION: RF-CSA and PhA were shown to be good prognostic markers of 12-month mortality and could, therefore, be useful screening tools to complement the nutritional assessment of IPF patients.

Achieving clinical outcomes with benralizumab in severe eosinophilic asthma patients in a real-world setting: orbe II study.

BACKGROUND: The ORBE II study aimed to describe the characteristics and clinical outcomes of adult patients with severe eosinophilic asthma (SEA) treated with benralizumab in a real-world setting in Spain. METHODS: ORBE II (NCT04648839) was an observational, retrospective cohort study in adult SEA patients who had been prescribed benralizumab. Demographic and clinical data of 204 SEA patients were collected 12 months prior to benralizumab initiation (baseline) and at follow-up. Exacerbation rate, asthma symptoms, maintenance oral corticosteroid (OCS) use and lung function were evaluated, among other variables. RESULTS: A total of 204 SEA patients were evaluated. Mean (standard deviation, SD) age of the study population was 56.4 (12.4) years, 62.3% were women and mean (SD) duration of asthma was 15.1 (12.7) years. Median (Q1-Q3) follow-up duration was 19.5 (14.2-24.2) months. At baseline, 72.6% of the overall population (OP) presented blood eosinophil counts ≥ 300 cells/µL; 36.8% had comorbid chronic rhinosinusitis with nasal polyps (CRSwNP); 84.8% reported at least one severe exacerbation, and 29.1% were OCS-dependent. At 1 year of follow-up, patients receiving benralizumab treatment had a 85.6% mean reduction in exacerbations from baseline, and 81.4% of patients achieved zero exacerbations. We also found a clinically relevant mean (SD) increase in pre-bronchodilator (BD) FEV1 of 331 (413) mL, with 66.7% of patients achieving a pre-BD FEV1 increase ≥ 100 mL, and 46.3% of patients achieving a pre-BD FEV1 ≥ 80% of predicted. Regarding symptom control, 73.8% of the OP obtained an ACT score ≥ 20 points. After 1 year of follow-up, mean reduction in the daily OCS dose was 70.5%, and complete OCS withdrawal was achieved by 52.8% of the OCS-dependent patients. Almost half (43.7%) of the OP on benralizumab met all four criteria for clinical remission. Patients with concomitant CRSwNP obtained similar or enhanced outcomes. CONCLUSIONS: These data support the real-world benefits of benralizumab in SEA patients, and particularly in those with concomitant CRSwNP. TRIAL REGISTRATION: NCT04648839.

Technical note: A mobile collaborative workspace to assist forensic experts in disaster victim identification scenarios.

Integrated approaches to disaster victim identification (DVI) management have led to a need for technologies to improve interaction among parties involved in post-mortem (PM) and ante-mortem (AM) data collection through better communication and coordination. Mobile Forensic Workspace© (MFW) is a collaborative mobile system that not only facilitates the systematic collection of high-quality data, but also allows DVI professionals to coordinate activities and exchange data through secure real-time communication at major disaster scenarios in accordance with security, privacy and legal protocols. MFW is adaptable to any communication format (text, voice calls, photographs, etc.) and is dynamically self-reconfigurable when connectivity problems arise. It also allows data integration and backup through secure communication channels between local and remote servers. The feasibility of the system has been demonstrated through implementation of MFW on the iOS platform for iPhone, iPod Touch and iPad terminals. A further strength of MFW is that it provides out-of-the-box support for INTERPOL DVI forms. The application of information and communication technologies for DVI was shown to be useful in improving DVI management by enhancing the quality of data collection and enabling non-Internet dependent real-time data sharing and communication.

A psycho-educational intervention programme for parents with SGA foetuses supported by an adaptive mHealth system: design, proof of concept and usability assessment.

BACKGROUND: Technology-based approaches during pregnancy can facilitate the self-reporting of emotional health issues and improve well-being. There is evidence to suggest that stress during pregnancy can affect the foetus and result in restricted growth and preterm birth. Although a number of mobile health (mHealth) approaches are designed to monitor pregnancy and provide information about a specific aspect, no proposal specifically addresses the interventions in parents at risk of having small-for-gestational-age (SGA) or premature babies. Very few studies, however, follow any design and usability guidelines which aim to ensure end-user satisfaction when using these systems. RESULTS: We have developed an interactive, adaptable mHealth system to support a psycho-educational intervention programme for parents with SGA foetuses. The relevant results include a metamodel to support the task of modelling current or new intervention programmes, an mHealth system model with runtime adaptation to changes in the programme, the design of a usable app (called VivEmbarazo) and an architectural design and prototype implementation. The developed mHealth system has also enabled us to conduct a proof of concept based on the use of the mHealth systems and this includes data analysis and assesses usability and acceptance. CONCLUSIONS: The proof of concept confirms that parents are satisfied and that they are enthusiastic about the mHealth-supported intervention programme. It helps to technically validate the results obtained in the other stages relating to the development of the solution. The data analysis resulting from the proof of concept confirms that the stress experienced by parents who followed the mHealth-supported intervention programme was significantly lower than among those who did not follow it. This implies an improvement in the emotional health not only of the parents but also of their child. In fact, the babies of couples who followed the mHealth-supported programme weigh more than the babies of couples under traditional care. In terms of user acceptance and usability, the analysis confirms that mothers place greater value on the app design, usefulness and ease of use and are generally more satisfied than their partners. Although these results are promising in comparison with more traditional and other more recent technology-based approaches.

Treatment persistence and exacerbations in patients with asthma initiating treatment with inhaled corticosteroids and beta-adrenergic agonists: retrospective cohort study.

OBJECTIVE: To determine treatment persistence and exacerbations in patients initiating inhaler treatment with fixed-dose combinations of inhaled corticosteroids/long-acting beta-2-adrenergic agonists (ICS/LABA) for the treatment of asthma. DESIGN: Retrospective observational study conducted by review of electronic medical records (database: Fundación RediSS). SETTING: Retrospective cohort study. The follow-up period was 1 year. PARTICIPANTS: The study included patients aged ≥18 years who started treatment with ICS/LABA and met the inclusion/exclusion criteria. MAIN OUTCOMES AND MEASURES: The study groups were fluticasone propionate/salmeterol (FP/SAL), beclomethasone/formoterol (BDP/FORM), budesonide/formoterol (BUD/FORM), fluticasone furoate/vilanterol (FF/VI) and fluticasone propionate/formoterol (FP/FORM). The main measurements were persistence, medication possession ratio (MPR) and exacerbations. Statistical significance was established as p<0.05. RESULTS: In total, 3203 patients were recruited for the study. By groups, 31.1% FP/SAL, 28.6% BDP/FORM, 25.0% BUD/FORM, 8.2% FF/VI and 7.0% FP/FORM. The mean age was 52.2 years, 60.8% were female and 44.9% had persistent-moderate asthma. Treatment persistence was 61.7% (95% CI 60.0% to 63.4%) and by study group it was FP/SAL: 60.7%, BDP/FORM: 61.2%, BUD/FORM: 60.3%, FF/VI: 66.7% and FP/FORM: 67.6% (p=0.046). MPR by study group was FP/SAL: 74.3%, BDP/FORM: 73.8%, BUD/FORM: 74.6%, FF/VI: 79.4% and FP/FORM: 80.6% (p=0.028). The mortality rate was 2.9%. By treatment group, exacerbations were FP/SAL: 21.9% (95% CI 19.3% to 24.5%), BDP/FORM: 22.2% (95% CI 19.5% to 24.9%), BUD/FORM: 22.8% (95% CI 19.9% to 25.7%), FF/VI: 17.9% (95% CI 14.9% to 20.7%) and FP/FORM: 16.0% (95% CI 12.2% to 19.3%), p=0.036. CONCLUSIONS: Patients undergoing treatment with FP/FORM and FF/VI versus FP/SAL, BDP/FORM and BUD/FORM were associated with greater treatment adherence (persistence, MPR) and lower rates of exacerbations. However, further studies will be needed to strengthen the consistency of the results.

A machine learning approach for semi-automatic assessment of IADL dependence in older adults with wearable sensors.

BACKGROUND AND OBJECTIVE: The assessment of dependence in older adults currently requires a manual collection of data taken from questionnaires. This process is time consuming for the clinicians and intrudes the daily life of the elderly. This paper aims to semi-automate the acquisition and analysis of health data to assess and predict the dependence in older adults while executing one instrumental activity of daily living (IADL). METHODS: In a mobile-health (m-health) scenario, we analyze whether the acquisition of data through wearables during the performance of IADLs, and with the help of machine learning techniques could replace the traditional questionnaires to evaluate dependence. To that end, we collected data from wearables, while older adults do the shopping activity. A trial supervisor (TS) labelled the different shopping stages (SS) in the collected data. We performed data pre-processing techniques over those SS and analyzed them with three machine learning algorithms: k-Nearest Neighbors (k-NN), Random Forest (RF) and Support Vector Machines (SVM). RESULTS: Our results confirm that it is possible to replace the traditional questionnaires with wearable data. In particular, the best learning algorithm we tried reported an accuracy of 97% in the assessment of dependence. We tuned the hyperparameters of this algorithm and used embedded feature selection technique to get the best performance with a subset of only 10 features out of the initial 85. This model considers only features extracted from four sensors of a single wearable: accelerometer, heart rate, electrodermal activity and temperature. Although these features are not observational, our current proposal is semi-automatic, because it needs a TS labelling the SS (with a smartphone application). In the future, this labelling process could be automatic as well. CONCLUSIONS: Our method can semi-automatically assess the dependence, without disturbing daily activities of elderly people. This method can save clinicians' time in the evaluation of dependence in older adults and reduce healthcare costs.

Acute Lung Injury Biomarkers in the Prediction of COVID-19 Severity: Total Thiol, Ferritin and Lactate Dehydrogenase.

SARS-CoV-2 (COVID-19) patients who develop acute respiratory distress syndrome (ARDS) can suffer acute lung injury, or even death. Early identification of severe disease is essential in order to control COVID-19 and improve prognosis. Oxidative stress (OS) appears to play an important role in COVID-19 pathogenesis; we therefore conceived a study of the potential discriminative ability of serum biomarkers in patients with ARDS and those with mild to moderate disease (non-ARDS). 60 subjects were enrolled in a single-centre, prospective cohort study of consecutively admitted patients: 29 ARDS/31 non-ARDS. Blood samples were drawn and marker levels analysed by spectrophotometry and immunoassay techniques. C-reactive protein (CRP), lactate dehydrogenase (LDH), and ferritin were significantly higher in ARDS versus non-ARDS cases at hospital admission. Leukocytes, LDH, ferritin, interleukin 6 (IL-6) and tumour necrosis factor alpha (TNF-α) were also significantly elevated in ARDS compared to non-ARDS patients during the hospital stay. Total thiol (TT) was found to be significantly lower in ARDS. Conversely, D-dimer, matrix metalloproteinase-9 (MMP-9) and advanced glycosylated end products (AGE) were elevated. Leukocytes, LDH, CRP, ferritin and IL-6 were found to be significantly higher in non-survivors. However, lymphocyte, tumour necrosis factor beta (TGF-ß), and TT were lower. In summary, our results support the potential value of TT, ferritin and LDH as prognostic biomarkers for ARDS development in COVID-19 patients, distinguishing non-ARDS from ARDS (AUCs = 0.92; 0.91; 0.89) in a fast and cost-effective manner. These oxidative/inflammatory parameters appear to play an important role in COVID-19 monitoring and can be used in the clinical management of patients.

Reducing Response Time in Motor Imagery Using A Headband and Deep Learning.

Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to machines. Recently, some commercial low-intrusive BCI headbands have appeared, but with less electrodes than the regular BCIs. Some works have proved the ability of the headbands to detect basic motor imagery. However, all of these works have focused on the accuracy of the detection, using session sizes larger than 10 s, in order to improve the accuracy. These session sizes prevent actuators using the headbands to interact with the user within an adequate response time. In this work, we explore the reduction of time-response in a low-intrusive device with only 4 electrodes using deep learning to detect right/left hand motion imagery. The obtained model is able to lower the detection time while maintaining an acceptable accuracy in the detection. Our findings report an accuracy above 83.8% for response time of 2 s overcoming the related works with both low- and high-intrusive devices. Hence, our low-intrusive and low-cost solution could be used in an interactive system with a reduced response time of 2 s.

Insights on early mutational events in SARS-CoV-2 virus reveal founder effects across geographical regions.

Here we aim to describe early mutational events across samples from publicly available SARS-CoV-2 sequences from the sequence read archive and GenBank repositories. Up until 27 March 2020, we downloaded 50 illumina datasets, mostly from China, USA (WA State) and Australia (VIC). A total of 30 datasets (60%) contain at least a single founder mutation and most of the variants are missense (over 63%). Five-point mutations with clonal (founder) effect were found in USA next-generation sequencing samples. Sequencing samples from North America in GenBank (22 April 2020) present this signature with up to 39% allele frequencies among samples (n = 1,359). Australian variant signatures were more diverse than USA samples, but still, clonal events were found in these samples. Mutations in the helicase, encoded by the ORF1ab gene in SARS-CoV-2 were predominant, among others, suggesting that these regions are actively evolving. Finally, we firmly urge that primer sets for diagnosis be carefully designed, since rapidly occurring variants would affect the performance of the reverse transcribed quantitative PCR (RT-qPCR) based viral testing.

A Microservices e-Health System for Ecological Frailty Assessment Using Wearables.

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.

Author Correction: Chloropicophyceae, a new class of picophytoplanktonic prasinophytes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Scrippsiella acuminata versus Scrippsiella ramonii: A Physiological Comparison.

Scrippsiella is a cosmopolitan dinoflagellate genus that is able to form Harmful Algal Blooms in coastal waters. The large physiological, morphological, and genetic variability that characterizes this genus suggest the existence of cryptic species. In this study, flow cytometric analyses were carried out to compare the cell cycle and life cycle of two Scrippsiella strains from two different species: Scrippsiella ramonii (VGO1053) and Scrippsiella acuminata (S3V). Both species were also investigated by internally transcribed spacer rDNA sequencing and high-performance liquid chromatography-based pigment analyses. The reddish-brown color of S. acuminata and yellowish-green hue of S. ramonii were consistent with the quantitative differences determined in their pigment profiles. Our results indicate that the cell cycle is light-controlled and that it differs in the two species. S-phase was detected during the light period in both, whereas the G2/M phase occurred during the light period in S. ramonii but under dark conditions in S. acuminata. The detection of 4C stages, mobile zygotes (planozygotes), and resting cysts in S. ramonii (nonclonal) provided convincing evidence of sexuality in this species. Sexual related processes were not found in the clonal S. acuminata strain, suggesting its heterothallic behavior (i.e., the need for outcrossing). The differences in the genome size of these species were examined as well. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Comparative ecophysiology of Dinophysis acuminata and D. acuta (DINOPHYCEAE, DINOPHYSIALES): effect of light intensity and quality on growth, cellular toxin content, and photosynthesis.

Dinoflagellates of the genus Dinophysis are the most persistent producers of lipophilic shellfish toxins in Western Europe. Their mixotrophic nutrition requires a food chain of cryptophytes and plastid-bearing ciliates for sustained growth and photosynthesis. In this study, cultures of D. acuminata and D. acuta, their ciliate prey Mesodinium rubrum and the cryptophyte, Teleaulax amphioxeia, were subject to three experimental settings to study their physiological response to different combinations of light intensity and quality. Growth rates, pigment analyses (HPLC), photosynthetic parameters (PAM-fluorometry), and cellular toxin content (LC-MS) were determined. Specific differences in photosynthetic parameters were observed in Dinophysis exposed to different photon fluxes (10-650 µmol photons · m-2  · s-1 ), light quality (white, blue and green), and shifts in light regime. Dinophysis acuta was more susceptible to photodamage under high light intensities (370-650 µmol photons · m-2  · s-1 ) than D. acuminata but survived better with low light (10 µmol photons · m-2  · s-1 ) and to a prolonged period (28 d) of darkness. Mesodinium rubrum and T. amphioxeia showed their maximal growth rate and yield under white and high light whereas Dinophysis seemed better adapted to grow under green and blue light. Toxin analyses in Dinophysis showed maximal toxin per cell under high light after prey depletion at the late exponential-plateau phase. Changes observed in photosynthetic light curves of D. acuminata cultures after shifting light conditions from low intensity-blue light to high intensity-white light seemed compatible with photoacclimation in this species. Results obtained here are discussed in relation to different spatiotemporal distributions observed in field populations of D. acuminata and D. acuta in northwestern Iberia.

Chloropicophyceae, a new class of picophytoplanktonic prasinophytes.

Prasinophytes are a paraphyletic group of nine lineages of green microalgae that are currently classified either at the class or order level or as clades without formal taxonomic description. Prasinophyte clade VII comprises picoplanktonic algae that are important components of marine phytoplankton communities, particularly in moderately oligotrophic waters. Despite first being cultured in the 1960s, this clade has yet to be formally described. Previous phylogenetic analyses using the 18S rRNA gene divided prasinophyte clade VII into three lineages, termed A, B and C, the latter formed by a single species, Picocystis salinarum, that to date has only been found in saline lakes. Strains from lineages A and B cannot be distinguished by light microscopy and have very similar photosynthetic pigment profiles corresponding to the prasino-2A pigment group. We obtained phenotypic and genetic data on a large set of prasinophyte clade VII culture strains that allowed us to clarify the taxonomy of this important marine group. We describe two novel classes, the Picocystophyceae and the Chloropicophyceae, the latter containing two novel genera, Chloropicon and Chloroparvula, and eight new species of marine picoplanktonic green algae.