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This study primarily aimed to explore the capabilities of digitalisation in the healthcare context, focusing on a specific disease. In this case, the study examined the potential of remote monitoring of gait to address the sensitivity of multiple sclerosis progression to gait characteristics by adopting a non-invasive approach to remotely quantify gait disturbances in a patient's daily life. To better understand the managerial aspects associated with this approach, the researchers conducted a literature review along with a set of semi-structured interviews. The target population included MS patients as well as the key agents involved in their care: patients' family members, neurologists, MS nurses, physiotherapists, medical directors, and pharmacist. The study identifies the perceived barriers and drivers that could contribute to the successful deployment of PSS remote gait monitoring as a healthcare service: i) At mega-level governance. Implications on privacy and security data are notable barriers missing on the speech. ii) At macro level, funding is highlighted as main barrier. The cost and lack of health system subsidies may render initiatives unsustainable, as emphasised by the interviewees. iii) At meso level, useable data is recognised as a driver. The data collection process can align with diverse interests to create value and business opportunities for the ecosystem actors, enhance care, attract stakeholders, such as insurers and pharma, and form partnerships. iv) At micro-level processes, we find two potential barriers: wearable device and app usability (comfort, navigation, efficiency) and organisational/behavioural aspects (training, digital affinity, skills), which are crucial for value creation in innovation ecosystems among patients and healthcare professionals. Finally, we find an interesting gap in the literature and interviews. Stakeholders' limited awareness of technological demands, especially from information technologies, for a successful long-term service, can be consider two key barriers for PSS., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: Data on coronavirus disease 2019 (COVID-19) incidence in patients with multiple sclerosis (MS) during the first wave have been published but are scarce for the remaining waves. Factors associated with COVID-19 infection of any grade are also poorly known. The aim of this study was to analyze the incidence, clinical features, and risk factors for COVID-19 infection of any grade in patients with MS (pwMS) during waves 1-5., Methods: This study prospectively analyzes the cumulative incidence of COVID-19 from the first to the fifth waves by periodic case ascertainment in pwMS followed at the University Hospital of Getafe (UHG). Global and stratified cumulative incidence was calculated. Logistic regression models were used to estimate the weight of selected variables as risk and prognostic factors., Results: We included 431 pwMS, of whom 86 (20%) were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The overall cumulative incidence of confirmed cases was similar to that of Madrid (13,689 vs. 13,307 per 100,000 habitants) but 3 times higher during the first wave and slightly lower from the second to the fifth waves. The majority (86%) of pwMS developed mild forms of COVID-19. Smoking was the only factor associated with a decreased risk of SARS-CoV2 infection of any grade [odds ratio (OR) 0.491; 95% CI 0.275-0.878; p = 0.017]. Risk factors associated with severe forms were Expanded Disability Severity Scale (EDSS) ≥3.5 (OR 7.569; 95% CI 1.234-46.440) and pulmonary disease (OR 10.763; 95% CI 1.27-91.254)., Conclusion: The incidence of COVID-19 was similar in this MS cohort to the general population. Smoking halved the risk of being infected. Higher EDSS and pulmonary comorbidity were associated with an increased risk of severe forms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Pilo De La Fuente, González Martín-Moro, Martín Ávila, Méndez Burgos, Ramos Barrau, Thuissard, Torrejón Martín and Aladro Benito.)

Introduction: There are some validated rating scales to assess severity of Essential tremor (ET), the most common cause of action tremor. Clinical evaluation through telematic consultations has been expanding in the last decade. Patients' self-assessment of tremor severity at home could constitute a useful tool in telemedicine. This paper aims to assess intrarater and interrater reliability of ET severity using Fahn-Tolosa Marin Tremor Rating Scale (FTMTRS) for patients' and neurologists' ratings., Material and Methods: Patients were instructed on how to perform and rate the FTMTRS tasks. Supervised by neurologists, each patient performed one FTMTRS self-assessment at the hospital, which was rated in a blinded way by two neurologists, and six more self-assessments at home afterwards. Postural, intention and specific-tasks tremor were rated. A cumulative linked mixed model was used to assess intrarater and interrater reliability., Results: A total of 161 self-assessments from 19 patients were analyzed. Intrarater reliability of patients' self-ratings at home showed ICCs between 0.843 and 0.962. Interrater ICCs of neurologists' ratings were also excellent for all tremor types (0.903-0.987). Concordance between neurologists' and patients' assessments showed ICCs ranging from 0.407 to 0.824, with the higher agreement for writing/drawing-related tremor (0.824; CI 95% 0.634-0.989)., Conclusions: The rating of ET severity from FTMTRS self-assessments performed by well-trained patients at home could be a suitable clinical measure to assess tremor in non-face-to-face medical consultations. The assessment of tremor during specific tasks could be the most efficient measure for the patient self-assessment at home. These results could be useful in telemedicine., (Copyright © 2022 Elsevier Ltd. All rights reserved.)