Caracterización experimental de las propiedades de transmisión en fibras ópticas de plástico de índice gradual (GI-POF)

El proyecto desarrollado en esta memoria se enmarca dentro de la línea de trabajo de caracterización experimental de fibras ópticas de plástico (POF) que se lleva a cabo en el grupo de POF de la Universidad de Zaragoza. El eje central del trabajo es la fibra óptica de plástico de índice gradual (GI-POF), ya que hasta la actualidad no se han analizado sus propiedades en profundidad. Por ello, el objetivo de este trabajo es realizar una caracterización experimental completa de los parámetros de transmisión propios de este tipo de fibra que dote al grupo de un conocimiento más detallado de ella, a la vez que permita obtener un modelo teórico en el futuro. Dentro del conjunto de medidas a realizar, en primer lugar se obtiene la atenuación espectral en diferentes longitudes y se compara con el tipo de fibra de plástico más extendida, la de salto de índice (SI POF). Los resultados obtenidos demuestran que la GI POF presenta aproximadamente el doble de atenuación que la SI-POF. Seguidamente el trabajo se centra en la obtención de la respuesta frecuencial y del ancho de banda en función de la longitud. Para llevar a cabo esta parte es necesario realizar un estudio previo del montaje experimental, tanto de su distribución como de sus elementos para establecer una configuración que permita obtener los mejores resultados en un amplio rango de frecuencias. Las medidas obtenidas permiten apreciar que la GI POF presenta un ancho de banda notablemente mayor que el presentado por la SI POF. Además, se ha observado una gran variabilidad en ellas lo que nos ha llevado a estudiar la dependencia con las condiciones de inyección. En este sentido, se han tenido en cuenta los efectos del pulido, la inyección mediante otro tramo de fibra y el uso de un mezclador de modos o scrambler. En la última fase de la caracterización se han realizado las medidas del campo lejano para analizar la distribución espacial de potencia óptica a la salida de la fibra según su longitud. Esta parte del trabajo permite conocer cómo se propaga la luz a través de la fibra y con ello dar explicación a los resultados obtenidos en la caracterización de las respuestas frecuenciales. Para concluir el trabajo se ha establecido en el laboratorio un enlace de POF con dispositivos comercializados para dicho fin y se ha medido la tasa de error binario (BER, Bit Error Rate), la cual nos permite verificar las buenas prestaciones en cuanto a ancho de banda de la fibra estudiada. Como conclusión general de este trabajo podemos afirmar que en los escenarios actuales los enlaces basados en GI-POF tienen limitada su longitud principalmente por atenuación. Sin embargo, es previsible que en un futuro próximo surjan aplicaciones en las que esta fibra encuentre un segmento de mercado

Automatic assessment of the 2-minute walk distance for remote monitoring of people with multiple sclerosis

The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes

Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis

The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes

The feasibility of implementing remote measurement technologies in psychological treatment for depression: a mixed-methods study on engagement

Background Remote Measurement Technologies (RMTs) such as smartphones and wearables, can help improve treatment for depression by providing more objective, continuous, and ecologically valid insight into mood and behavior. Engagement with RMTs is varied and highly context-dependent, yet few studies have investigated their feasibility in the context of treatment. Objective A mixed-methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on two different types of engagement: study attrition (engagement with study protocol), and patterns of missing data (engagement with digital devices) which we termed data availability. Qualitative interviews were conducted to help interpret engagement differences. Methods Sixty-six people undergoing psychological therapy for depression were followed up for 7 months. Active data in the form of weekly questionnaires, speech, and cognitive tasks were generated, and passive data were gathered from smartphone sensors and a Fitbit wearable device Results Overall study retention was 60%. Higher-intensity treatment and higher baseline anxiety were associated with increased attrition, but depression severity was not. A trend towards significance was found for the association between longer treatments and increased attrition. Data availability was higher for active data than passive data but declined at a sharper rate (90% to 30% drop in 7 months). Within passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability compared to smartphone-based data, which remained stable at the 20-40% range throughout. Missing data was more prevalent in GPS location data, followed by Bluetooth, than accelerometry. For active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. Participants in treatment provided less Fitbit data but higher active data during treatment than those on the waiting list. Conclusions Different data streams showed varied patterns of missing data despite being gathered from the same device. Longer and more complex treatments as well as clinical characteristics like higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in healthcare settings, as well as for the generalisability and accuracy of the data collected by these methods, feature construction, and the appropriateness of their use in the long-term

Autonomic response to walk tests is useful for assessing outcome measures in people with multiple sclerosis

Objective: The aim of this study was to evaluate the association between changes in the autonomic control of cardiorespiratory system induced by walk tests and outcome measures in people with Multiple Sclerosis (pwMS).Methods: Electrocardiogram (ECG) recordings of 148 people with Relapsing-Remitting MS (RRMS) and 58 with Secondary Progressive MS (SPMS) were acquired using a wearable device before, during, and after walk test performance from a total of 386 periodical clinical visits. A subset of 90 participants repeated a walk test at home. Various MS-related symptoms, including fatigue, disability, and walking capacity were evaluated at each clinical visit, while heart rate variability (HRV) and ECG-derived respiration (EDR) were analyzed to assess autonomic nervous system (ANS) function. Statistical tests were conducted to assess differences in ANS control between pwMS grouped based on the phenotype or the severity of MS-related symptoms. Furthermore, correlation coefficients (r) were calculated to assess the association between the most significant ANS parameters and MS-outcome measures.Results: People with SPMS, compared to RRMS, reached higher mean heart rate (HRM) values during walk test, and larger sympathovagal balance after test performance. Furthermore, pwMS who were able to adjust their HRM and ventilatory values, such as respiratory rate and standard deviation of the ECG-derived respiration, were associated with better clinical outcomes. Correlation analyses showed weak associations between ANS parameters and clinical outcomes when the Multiple Sclerosis phenotype is not taken into account. Blunted autonomic response, in particular HRM reactivity, was related with worse walking capacity, yielding r = 0.36 r = 0.29 (RRMS) and r > 0.5 (SPMS). A positive strong correlation r > 0.7 r > 0.65 between cardiorespiratory parameters derived at hospital and at home was also found.Conclusion: Autonomic function, as measured by HRV, differs according to MS phenotype. Autonomic response to walk tests may be useful for assessing clinical outcomes, mainly in the progressive stage of MS. Participants with larger changes in HRM are able to walk longer distance, while reduced ventilatory function during and after walk test performance is associated with higher fatigue and disability severity scores. Monitoring of disorder severity could also be feasible using ECG-derived cardiac and respiratory parameters recorded with a wearable device at home

A wearable device perspective on the standard definitions of disability progression in multiple sclerosis

INTRODUCTION: Multiple sclerosis (MS) is a leading cause of disability among young adults, but standard clinical scales may not accurately detect subtle changes in disability occurring between visits. This study aims to explore whether wearable device data provides more granular and objective measures of disability progression in MS.METHODS: Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADAR-CNS) is a longitudinal multicenter observational study in which 400 MS patients have been recruited since June 2018 and prospectively followed up for 24 months. Monitoring of patients included standard clinical visits with assessment of disability through use of the Expanded Disability Status Scale (EDSS), 6-minute walking test (6MWT) and timed 25-foot walk (T25FW), as well as remote monitoring through the use of a Fitbit.RESULTS: Among the 306 patients who completed the study (mean age, 45.6 years; females 67%), confirmed disability progression defined by the EDSS was observed in 74 patients, who had approximately 1392 fewer daily steps than patients without disability progression. However, the decrease in the number of steps experienced over time by patients with EDSS progression and stable patients was not significantly different. Similar results were obtained with disability progression defined by the 6MWT and the T25FW.CONCLUSION: The use of continuous activity monitoring holds great promise as a sensitive and ecologically valid measure of disability progression in MS.</p

The Impact of COVID-19 Lockdown on Adults with Major Depressive Disorder from Catalonia: A Decentralized Longitudinal Study

The present study analyzes the effects of each containment phase of the first COVID-19 wave on depression levels in a cohort of 121 adults with a history of major depressive disorder (MDD) from Catalonia recruited from 1 November 2019, to 16 October 2020. This analysis is part of the Remote Assessment of Disease and Relapse-MDD (RADAR-MDD) study. Depression was evaluated with the Patient Health Questionnaire-8 (PHQ-8), and anxiety was evaluated with the Generalized Anxiety Disorder-7 (GAD-7). Depression's levels were explored across the phases (pre-lockdown, lockdown, and four post-lockdown phases) according to the restrictions of Spanish/Catalan governments. Then, a mixed model was fitted to estimate how depression varied over the phases. A significant rise in depression severity was found during the lockdown and phase 0 (early post-lockdown), compared with the pre-lockdown. Those with low pre-lockdown depression experienced an increase in depression severity during the "new normality", while those with high pre-lockdown depression decreased compared with the pre-lockdown. These findings suggest that COVID-19 restrictions affected the depression level depending on their pre-lockdown depression severity. Individuals with low levels of depression are more reactive to external stimuli than those with more severe depression, so the lockdown may have worse detrimental effects on them

Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder

This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of [Formula: see text] , a sensitivity of [Formula: see text] and a specificity of [Formula: see text] , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate