Use of Smart Technology Tools for Supporting Public Health Surveillance: From Development of a Mobile Health Platform to Application in Stress Prediction

BACKGROUND Traditional public health data collection methods are typically based on self-reported data and may be subject to limitations such as biases, delays between collection and reporting, costs, and logistics. These may affect the quality of collected information and the ability of public health agencies to monitor and improve the health of populations. An alternative may be the use of personal, off-the-shelf smart devices (e.g., smartphones and smartwatches) as additional data collection tools. These devices can collect passive, continuous, real-time and objective health-related data, mitigating some of the limitations of self-reported information. The novel data types can then be used to further study and predict a condition in a population through advanced analytics. In this context, this thesis’ goal is to investigate new ways to support public health through the use of consumer-level smart technologies as complementary survey, monitoring and analyses tools, with a focus on perceived stress. To this end, a mobile health platform (MHP) that collects data from devices connected to Apple Health was developed and tested in a pilot study collecting self-reported and objective stress-related information, and a number of Machine Learning (ML) models were developed based on these data to monitor and predict the stress levels of participants. METHODS The mobile platform was created for iOS using the XCode software, allowing users to self-report their stress levels based on the stress subscale of the Depression, Anxiety and Stress Scale (DASS-21) as well as a single-item LIKERT-based scale. The platform also collects objective data from sensors that integrate with Apple Health, one of the most popular mobile health data repositories. A pilot study with 45 participants was conducted that uses the platform to collects stress self-reports and variables associated with stress from Apple Health, including heart rate, heart rate variability, ECG, sleep, blood pressure, weight, temperature, and steps. To this end, participants were given an iPhone with the platform installed as well as an Apple Watch, Withings Sleep, Withings Thermos, Withings BPM Connect, Withings Body+, and an Empatica E4 (the only device that does not connect to Apple Health but included due to its wide use in research). Participants were instructed to take device measurements and self-report stress levels 6 times per day for 14 days. Several experiments were conducted involving the development of ML models to predict stress based on the data, using Random Forests and Support Vector Machines. In each experiment, different subsets of the data from the full sample of 45 participants were used. 3 approaches to model development were followed: a) creating generalized models with all data; b) a hybrid approach using 80% of participants to train and 20% to test the model c) creating individualized user-specific models for each participant. In addition, statistical analyses of the data – specifically Spearman correlation and repeated measures ANOVA – were conducted. RESULTS Statistical analyses did not find significant differences between groups and only weak significant correlations. Among the Machine Learning models, the approach of using generalized models performed well, with f1-macro scores above 60% for several of the samples and features investigated. User-specific models also showed promise, with 82% achieving accuracies higher than 60% (the bottom limit of the state-of-the-art). While the hybrid approach had lower f1-macro scores, suggesting the models could not predict the two classes well, the accuracy of several of these models was in line with the state-of-the-art. Apple Watch sleep features, as well as weight, blood pressure, and temperature, were shown to be important in building the models. DISCUSSION AND CONCLUSION ML-based models built with data collected from the MHP in real-life conditions were able to predict stress with results often in line with state-of-the- art, showing that smart technology data can be a promising tool to support public health surveillance. In particular, the approaches of creating models for each participant or one generalized model were successful, although more validation is needed in future studies (e.g., with more purposeful sampling) for increased generalizability and validity on the use of these technologies in the real-world. The hybrid approach had good accuracy but lower f1-scores, indicating results could potentially be improved (e.g., possibly with less missing or noisy data, collected in more controlled conditions). For feature selection, important features included sleep data as well as weight, blood pressure and temperature from mobile and wearable devices. In summary, this study indicates that a platform such as the MHP, collecting data from smart technologies, could potentially be a novel tool to complement population-level public health data collection. The predictive stress modelling might be used to monitor stress levels in a population and provide personalized interventions. Although more validation may be needed, this work represents a step in this direction

Multimodal response to levodopa treatment in advanced and late Parkinson’s disease

Parkinson’s disease (PD) is a progressive age-dependent neurodegenerative disease. Life expectancy increasing and a better knowledge in PD treatment management, including the advent of device-aided therapies, are likely to increase the number of patients who can reach an advanced disease stage and eventually enter the late stage (LS) of the disease in the next decades. LSPD is a recently recognized disease stage, in which patients are severely disable and dependent on activities of daily life (ADLs) due to the presence of poor treatment responsive motor and non-motor symptoms (NMS) thus highly impacting caregiver’s burden and social/health care system. Hence an operational clinical criteria to identify LSPD patients has been recently proposed suggesting adopt a Schwab and England activity of daily life score (S&E) < 50 in the MED ON condition. LSPD patients’ treatment management is challenging. Treatment-related adverse effects (AEs) are frequent and few evidence in terms of phamacological and non-pharmacological treatment efficacy are available as they are barely included in clinical or research studies and even the participation into routine hospital-based visits can be an unsurmountable limit. At the same time, even if general PD disease severity milestones have been described, we do not know how LSPD patients specifically progress, if they do evolve and if there are clinical markers or biomarkers of poor outcome that could be useful to focus specific therapeutic interventions for this specific disease stage. We aimed to deeply characterize the clinical phenotype, needs along with clinical markers or biomarkers of poor outcome of LSPD patients. As levodopa (L-dopa) is the mainstay of PD treatment and a simplification of treatment regimen in later disease stages has been suggested, we also aimed to investigate the real effect of L-dopa on motor symptoms and NMS among LSPD patients, if compared to advanced stage patients. Among NMS, we focused our work particularly on speech impairment, exploring speech response to L-dopa among LSPD patients and to fine stimulation parameters adjustment, in combination with L-dopa, in advanced PD patients submitted to deep brain stimulation (DBS). Participants were LSPD (Schwab and England ADL Scale [S&E] 3 in “MED ON” state) and advanced stage PD patients previously submitted to DBS. Cross-sectional data were obtained by means of a comprehensive clinical assessment including a L-dopa challenge test with a suprathreshold dose. A subgroup of thirteen LSPD patients underwent a neuroimaging study in order to study neuromelanin (NM) substantia nigra (SN) area changes in the latest disease stage if compared to previous ones. Automated analysis of speech were used to study the effect of a supramaximal L-dopa dose in twenty-four LSPD patients as well as L-dopa and frequency stimulation adjustment in twenty deep brain stimulated patients. Longitudinal data were collected only for LSPD patients. Descriptive, regression and survival curves analysis were performed. Fifty LSPD patients (female 46%) were included. Mean age was 77.5 ± 5.9 years and mean disease duration was 15.5± 6.5 years. At baseline, 76% had L-dopa-induced motor complications (MCs), mainly non-troublesome, 68%were demented, 54% had psychosis and 68% depression. Caregiver distress was high. L-dopa responsiveness was mild (18% ± 12 of improvement on MDS-UPDRS-III) and present only for appendicular signs, being tremor and rigidity the most responsive ones, while axial signs did not change. The clinical significance of this better motor response was marginal according to the Clinical Global Improvement Scale and the change in the S&E between OFF and ON state. The magnitude of L-dopa response correlated with the acute appearance of dyskinesias and the severity of MCs. After one-year, 20% of the patients were dead, 18% institutionalized in nursing home and 6% passed to a HY 5. MDS-UPDRS-motor mean score worsened 7.2 ± 10.3 points, corresponding to a 15.7% (±23.0) increase, with no difference between tremor-dominant versus akinetic-rigid phenotype or PD patients with/without dementia (PDD/non-PDD) at baseline. However, there was heterogeneity between patients in terms of disease progression, as 12 patients (37.5%) had a motor deterioration ≤ 3 points and 14 (43%) ≤ 5 points with concomitant worsening of the MDS-UPDRS-II (Motor Aspects of Experiences of Daily Living), of 2.1±4.1. Conversely, eleven cases (32%) did not deteriorate and, in fact, 10 of these improved between 1-6 points at the MDS-UPDRS-III. Overall NMS worsened, mostly in cognition/mood, urinary and gastrointestinal domains. Conversely, MCs improved despite similar L-dopa equivalent dose. Functional independence and quality of life worsened. Dysphagia severity at baseline predicted a poor combined outcome (death, being institutionalized or developing HY 5) (Hazard ratio 2.3, 95% CI 1.12- 4.4; p = 0.01) or death alone (Hazard ratio of 2.9, 95% CI 1.12- 8.6, p=0.04), whereas magnitude of L-dopa response of LSPD patients did not. SN area evaluated by NM-sensitive magnetic resonance imaging (MRI), resulted able to differentiate LSPD patients from both de novo PD patients and controls, though not founding statistical differences between LSPD patients and patients with two-five year disease duration. Performing an indirect comparison of the effect of L-dopa on motor symptoms and NMS among twenty LSPD patients and twenty-two, not-matched, advanced PD patients, a milder response on motor symptoms (11% vs. 37% of improvement on MDS-UPDRS-III) and an absence of response on NMS, namely anxiety, fatigue and pain, were found among LSPD patients, with concomitant higher frequency of drug-related AEs. Indeed orthostatic hypotension (OH) or drowsiness occurred among 35% of LSPD patients versus 13% of advanced PD patients, who still presented a benefit from L-dopa intake on pain and anxiety, while fatigue did not change. Scales applicability and blood pressure assessment while standing resulted challenging among LSPD patients with consequent missing data on depression, anxiety, pain and OH identification and possible underestimation of those symptoms. No effect of L-dopa was found on speech and voice by means of both automated analysis and clinical evaluation in LSPD patients. Respiratory support for speech and voice stability were the most affected speech and voice features among LSPD patients. Among axial symptoms, speech seemed to be the most L-dopa unresponsive one. Speech unresponsiveness to L-dopa was confirmed also among subthalamic (STN)-DBS treated patients with both mild and severe dysarthria, at least in combination with stimulation. Conversely, PD patients with severe dysarthria under chronic STN-DBS treatment showed a benefit of lowering frequency of stimulation from 130 Hz (High frequency stimulation [HFS]) to 60Hz (low frequency stimulation [LFS]), with concomitant increment of voltage, in order to keep constant the total energy delivered. Indeed speech intelligibility and articulatory diadochokinesis presented an acute improvement passing from HFS to LFS, as assessed by automated speech analysis and such a benefit, when present and clinically meaningful, lasted during six months with no motor worsening, though requiring medication adjustment. The present study provides further evidence to better delineate a recently recognized and poorly described PD stage. An extensive cross-sectional and longitudinal observation is proposed. LSPD patients clearly differ from previous stages in terms of both clinical features, needs, therapeutic response and drugs’ tolerability profile. Over one year, a heterogeneous disease progression of motor symptoms is still present and it seems even steeper if compared to previous stages, while functional independence globally worsened. As well as mild motor improvements are still possible with treatment adjustment, it is also possible to identify a clinical phenotype of LSPD patients who are likely to have a better response to L-dopa if compared to the other ones. Clinical assessment and therapeutic interventions for swallowing problems should be a priority. PDD or living in a nursing home remain other indicators of poor outcome. In the next few years the number of LSPD patients who have been previously submitted to device-aided therapies is expected to increase, bringing new clinical scenarios, such as the fine parameters adjustment of invasive treatment for challenging motor and NMS and the difficult management or eventual interruption of those treatments among elderly and frail LSPD patients. Overall, future research and fund allocations should be specifically oriented on LSPD patients, usually not included or considered in clinical trials or research studies, and on L-dopa not-responsive aspects and caregivers’ need

Manipulador aéreo con brazos antropomórficos de articulaciones flexibles

[Resumen] Este artículo presenta el primer robot manipulador aéreo con dos brazos antropomórficos diseñado para aplicarse en tareas de inspección y mantenimiento en entornos industriales de difícil acceso para operarios humanos. El robot consiste en una plataforma aérea multirrotor equipada con dos brazos antropomórficos ultraligeros, así como el sistema de control integrado de la plataforma y los brazos. Una de las principales características del manipulador es la flexibilidad mecánica proporcionada en todas las articulaciones, lo que aumenta la seguridad en las interacciones físicas con el entorno y la protección del propio robot. Para ello se ha introducido un compacto y simple mecanismo de transmisión por muelle entre el eje del servo y el enlace de salida. La estructura en aluminio de los brazos ha sido cuidadosamente diseñada de forma que los actuadores estén aislados frente a cargas radiales y axiales que los puedan dañar. El manipulador desarrollado ha sido validado a través de experimentos en base fija y en pruebas de vuelo en exteriores.Ministerio de Economía y Competitividad; DPI2014-5983-C2-1-