Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987

Predizione di cadute reali tramite l’uso di sensori indossabili

Le cadute, intese come mancanza improvvisa di equilibrio e stabilità senza perdita di coscienza, rappresentano un serio problema per le persone anziane. Le conseguenze fisiche e psicologiche della caduta determinano una riduzione dell’indipendenza e una perdita della qualità della vita della persona anziana. Questa tesi si pone l’obiettivo di studiare i meccanismi di caduta in una persona anziana, al fine di trovare un modo efficace, efficiente ed implementabile in real-time per predire la caduta al fine di poter attivare un meccanismo di protezione (airbag) che consenta di evitare, o almeno attenuare, le sue conseguenze più spiacevoli. Il progetto di tesi è stato diviso in due fasi: la fase di training e la fase di testing. Nella fase di training sono stati implementati diversi algoritmi di Fall Prediction, sia basati su soglie sia su Machine Learning. Questi algoritmi sono stati testati su dati reali registrati da persone anziane tramite un sensore indossabile al fine di valutarne le performance e l’effettiva utilità nella vita quotidiana. Successivamente, nella fase di testing gli algoritmi risultati migliori sono stati testati al fine di valutarne le performance su dati non considerati nella fase di training e sui quali gli algoritmi non sono stati effettivamente addestrati. Un aspetto molto importante degli algoritmi implementati è il trade-off tra sensibilità e falsi allarmi, ovvero tra la capacità di predire le cadute in tempo da attivare eventuali meccanismi di protezione e la capacità di ridurre al minimo il numero di falsi allarmi, i quali porterebbero ad aperture indesiderate dell’airbag

Realizzazione e collaudo di un sensore di ozono controllato da Arduino

Nell’ambito della sterilizzazione da agenti patogeni, l’ozono riveste un ruolo fondamentale grazie al suo grande potere ossidante ed è di fondamentale importanza misurare quanto ozono viene prodotto mediante una scarica DBD. Scopo di questa tesi è stato quello di realizzare un sensore dedicato alla misurazione della concentrazione di ozono, controllato da Arduino Uno. Il setup realizzato risulta economico, facilmente trasportabile e flessibile in quanto è utilizzabile in diverse modalità: è possibile salvare i dati tramite Arduino per elaborarli successivamente; permette di collegare l’uscita all’oscilloscopio per monitorare i segnali elettrici nel tempo; consente di leggere la concentrazione di ozono misurata real-time su display lcd. Infine sono state effettuati dei confronti tra il setup attualmente presente in laboratorio e il setup realizzato, e i risultati sono stati soddisfacenti

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated.We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms.Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987

Internet and social media use among patients with colorectal diseases (ISMAEL): a nationwide survey

Aim: Social media are used daily by both healthcare workers and patients. Online platforms have the potential to provide patients with useful information, increase their engagement and potentially revolutionize the patient–physician relationship. This survey aimed to evaluate the impact of the Internet and social media (I&amp;SM) on patients affected by colorectal and proctological diseases to define a pathway to develop an evidence-based communications strategy. Method: A 31-item anonymous electronic questionnaire was designed. It consisted of different sections concerning demographics and education, reason for the visit, knowledge of the diseases, frequency of I&amp;SM use and patients' opinions about physicians' websites. Results: Over a 5-month period, 37 centres and 105 surgeons took part in the survey, and a total of 5800 patients enrolled. Approximately half of them reported using the Internet daily, and 74.6% of the study population used it at least once per week. There was a correlation (P&nbsp;&lt;&nbsp;0.001) between those who used the Internet for work and those who had knowledge of both symptoms and the likely diagnosis before consultation. Patients who used the Internet daily were more likely to request a consultation within 6&nbsp;months of symptom onset (P&nbsp;&lt;&nbsp;0.0001). Patients with anorectal diseases were more likely to know about their disease and symptoms before the visit (P&nbsp;&lt;&nbsp;0.001). Conclusion: Colorectal patients use I&amp;SM to look for health-related information mainly after their medical visit. Surgeons and hospital networks should plan a tailored strategy to increase patient engagement, delivering appropriate information on social media