Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping

BackgroundLongitudinal tracking of multiple sclerosis (MS) symptoms in an individual’s environment may improve self-monitoring and clinical management for people with MS. Conventional symptom tracking methods rely on self-reports and clinical visits, which can be infrequent, subjective, and burdensome. Digital phenotyping using passively collected sensor data from smartphones and fitness trackers offers a promising alternative for continuous, real-time symptom monitoring with minimal patient burden.ObjectiveWe aimed to develop and evaluate a machine learning (ML)–based digital phenotyping approach to monitor the severity of clinically-relevant MS symptoms. We used passive sensing data to predict short-term fluctuations in patient-reported symptoms, including depressive symptoms, global MS symptom burden, severe fatigue, and poor sleep quality. Further, we examined the impact of incorporating behavioral context features and ecological momentary assessments on prediction performance.MethodsWe conducted a 12- to 24-week longitudinal study involving 104 people with MS, collecting passive sensor and behavioral health data. Smartphone sensors recorded call activity, location, and screen use, while fitness trackers captured heart rate, sleep patterns, and step count. We extracted patient-level behavioral features and categorized them into 2 feature sets: one from the prediction period (called action) and one from the preceding period (called context). Using an ML pipeline based on support vector machines and AdaBoost, we evaluated the predictive performance of sensor-based models, both with and without ecological momentary assessment inputs.ResultsBetween November 16, 2019, and January 24, 2021, overall, 104 people with MS (women: n=88, 84.6%; non-Hispanic White: n=97, 93.3%; mean age 44, SD 11.8 years) from a clinic-based cohort completed 12 weeks of data collection, including a subset of 44 participants (women: n=39, 89%; non-Hispanic White: n=42, 95%; mean age 45.7, SD 11.2 years) who completed 24 weeks of data collection. In total, we collected approximately 12,500 days of passive sensor and behavioral health data from the participants. Among the best-performing models with the least sensor data requirement, the ML algorithm predicted depressive symptoms with an accuracy of 80.6% (F1-score=0.76), high global MS symptom burden with an accuracy of 77.3% (F1-score=0.78), severe fatigue with an accuracy of 73.8% (F1-score=0.74), and poor sleep quality with an accuracy of 72.0% (F1-score=0.70). Further, sensor data were largely sufficient for predicting symptom severity, while the prediction of depressive symptoms benefited from minimal active patient input in the form of responses to 2 brief questions on the day before the prediction point.ConclusionsOur digital phenotyping approach using passive sensors on smartphones and fitness trackers may help patients with real-world, continuous self-monitoring of common symptoms in their own environment and assist clinicians with better triage of patient needs for timely interventions in MS and potentially other chronic neurological disorders.

Background:Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual’s own environment may improve self-monitoring and clinical management for people with MS (pwMS).Objective:We present a machine learning approach that enables longitudinal monitoring of clinically relevant patient-reported symptoms for pwMS by harnessing passively collected data from sensors in smartphones and fitness trackers.Methods:We divide the collected data into discrete periods for each patient. For each prediction period, we first extract patient-level behavioral features from the current period (action features) and the previous period (context features). Then, we apply a machine learning (ML) approach based on Support Vector Machine with Radial Bias Function Kernel and AdaBoost to predict the presence of depressive symptoms (every two weeks) and high global MS symptom burden, severe fatigue, and poor sleep quality (every four weeks).Results:Between November 16, 2019, and January 24, 2021, 104 pwMS (84.6% women, 93.3% non-Hispanic White, 44.0±11.8 years mean±SD age) from a clinic-based MS cohort completed 12-weeks of data collection, including a subset of 44 pwMS (88.6% women, 95.5% non-Hispanic White, 45.7±11.2 years) who completed 24-weeks of data collection. In total, we collected approximately 12,500 days of passive sensor and behavioral health data from the participants. Among the best-performing models with the least sensor data requirement, ML algorithm predicts depressive symptoms with an accuracy of 80.6% (35.5% improvement over baseline; F1-score: 0.76), high global MS symptom burden with an accuracy of 77.3% (51.3% improvement over baseline; F1-score: 0.77), severe fatigue with an accuracy of 73.8% (45.0% improvement over baseline; F1-score: 0.74), and poor sleep quality with an accuracy of 72.0% (28.1% improvement over baseline; F1-score: 0.70). Further, sensor data were largely sufficient for predicting symptom severity, while the prediction of depressive symptoms benefited from minimal active patient input in the form of response to two brief questions on the day before the prediction point.Conclusions:Our digital phenotyping approach using passive sensors on smartphones and fitness trackers may help patients with real-world, continuous, self-monitoring of common symptoms in their own environment and assist clinicians with better triage of patient needs for timely interventions in MS (and potentially other chronic neurological disorders).

COVID-19 in MS and NMOSD: A multicentric online national survey in Chile

Background: Previous studies have addressed multiple sclerosis (MS) symptom management and improved health-related quality of life (HrQOL). Yet lowered estrogen levels in post-menopasual women with MS may further worsen physical function and symptomology and not all types of pain management have been examined. Objectives: For post-menopausal women with MS, we evaluated the extent to which smoking is associated with worsened health outcomes and HrQOL, and the extent to which menopausal hormone treatment (MHT) improves health outcomes and HrQOL. For all adult men and women with clinically diagnosed MS, we systematically reviewed pharmacological and non-pharmacological strategies for the reduction of pain. Methods: We identified 256 post-menopausal women with MS in the Women's Health Initiative Observational Study and examined changes from baseline to 3 years in activities of daily living, physical activity, SF-36 mental and physical component scales (MCS, PCS), and menopausal symptoms. In all adults, experimental studies published after 1965 were included if the sample was not restricted to participants with spasticity or trigeminal neuralgia and participant-reported pain was a primary or secondary outcome. Pain scores were reported as Cohen’s d. Results: Nine percent of post-menopausal women with MS were current smokers and 51% reported current MHT use. Smoking and MHT use had no effect on physical functioning, activities of daily living, or menopausal symptoms. Women with early age at smoking initiation experienced declines in MCS (adjusted β <20 vs. ≥ 25 years: -10.50, 95% Confidence Interval (CI) -2.1 to -18.1; adjusted β 20-24 vs. ≥ 25 years: -8.81, 95% CI: 0.6 to -17.4), but not in PCS. Relative to never MHT users, ever MHT users had higher MCS scores at year 3 compared to baseline (adjusted β: 3.0, 95% CI: 0.4 to 5.6), but no change in PCS. For all adults, transcutaneous electrical nerve stimulation (TENS; Cohen’s d: -3.34), nabixomols (Cohen’s d: -0.61), and dextromethorphan/quinidine (Cohen’s d: -0.22) were reported effective in reducing pain. Conclusions: Smoking prevention efforts should be increased for women with MS. Women with MS may also experience HrQOL gains with MHT, but contemporaneous data on MHT use is needed. TENS may be more effective than pharmacological methods in reducing MS pain.

Objective:In this cohort of individuals with and without multiple sclerosis (MS), we illustrate some of the novel approaches that smartphones provide to monitor patients with chronic neurologic disorders in their natural setting.Methods:Thirty-eight participant pairs (MS and cohabitant) aged 18–55 years participated in the study. Each participant received an Android HTC Sensation 4G smartphone containing a custom application suite of 19 tests capturing participant performance and patient-reported outcomes (PROs). Over 1 year, participants were prompted daily to complete one assigned test.Results:A total of 22 patients with MS and 17 cohabitants completed the entire study. Among patients with MS, low scores on PROs relating to mental and visual function were associated with dropout (p < 0.05). We illustrate several novel features of a smartphone platform. First, fluctuations in MS outcomes (e.g., fatigue) were assessed against an individual's ambient environment by linking responses to meteorological data. Second, both response accuracy and speed for the Ishihara color vision test were captured, highlighting the benefits of both active and passive data collection. Third, a new trait, a person-specific learning curve in neuropsychological testing, was identified using spline analysis. Finally, averaging repeated measures over the study yielded the most robust correlation matrix of the different outcome measures.Conclusions:We report the feasibility of, and barriers to, deploying a smartphone platform to gather useful passive and active performance data at high frequency in an unstructured manner in the field. A smartphone platform may therefore enable large-scale naturalistic studies of patients with MS or other neurologic diseases.

Multiple sclerosis (MS) is a chronic neurological disorder, often affecting young people. Comorbid disorders such as depression, anxiety and hypertension are common and can affect disease course, treatment, and quality of life (QOL) of people with MS (PwMS). The associations between comorbidities, body mass index (BMI) and health outcomes are not well studied in MS, although research shows most PwMS are overweight. Most data on the prevalence of comorbidities and obesity in PwMS comes from North American populations. This study describes the prevalence of comorbidities, overweight and obesity and associations with modifiable factors in an international sample of PwMS recruited online through social media, MS societies and websites. The online survey consisted of validated and researcher-devised instruments to assess self-reported health outcomes and lifestyle behaviors. Of the 2399 respondents, 22.5% were overweight, 19.4% were obese and 67.2% reported at least one comorbidity, with back pain (36.2%), depression (31.7%), anxiety (29.1%) and arthritis (13.7%) most prevalent and most limiting in daily activities. Obesity and most comorbid disorders were significantly more prevalent in North America. Obese participants were more likely to have comorbidities, especially diabetes (OR 4.8) and high blood pressure (OR 4.5) but also depression (OR 2.2). Being overweight, obese, or a former, or current smoker was associated with an increase in the number of comorbidities; while healthy diet, physical activity (borderline significant) and moderate alcohol consumption were associated with decreased number of comorbidities. Increasing number of comorbidities was related to worse QOL, increased odds of disability and prior relapse. Obese PwMS had higher odds of disability and lower QOL. The associations between BMI, comorbidities and health outcomes are likely to be bi-directional and associated with lifestyle behaviors. Preventing and treating comorbid disorders and obesity in PwMS is warranted, and advice regarding healthy and risky lifestyle may assist in improving health outcomes.

Multiple sclerosis (MS) is a chronic neurological disorder, which can lead to a wide range of disabling symptoms. The condition has a significant negative impact on health-related quality of life, and the economic cost of the disease is substantial. Decision-making regarding treatments for MS, and particularly disease-modifying interventions, has been hampered by limitations in the data and evaluative framework for assessing their cost effectiveness. Whilst attention has been drawn to these weaknesses, the scope and extent of the challenges in this area have not been fully set out to date. The aims of this review were to identify all published economic evaluations of MS treatments in order to provide a statement on the scope and characteristics of the cost-effectiveness literature in the area of MS and to provide a basis on which to suggest practical recommendations for future research to aid decision-making. A systematic search was undertaken to identify economic evaluations of treatments for people with MS published in English up to December 2011. Included studies were reviewed to provide a comprehensive description of the characteristics of the currently applied framework for cost effectiveness in MS, with the following key methodological components considered: methods for estimating disease progression, the impact of treatment and health outcomes and costs associated with MS. Thirty-seven papers were identified. Most studies (n = 32) were model-based evaluations of disease-modifying drugs. All models used disability stages defined by the Expanded Disability Status Scale (EDSS) to characterise disease progression, and the impact of treatment was based on data from clinical trials and epidemiological cohorts. Outcomes were primarily based on quality-adjusted life-years (n = 22) and/or related to relapse (n = 14). Estimates for health state utility values (HSUVs), costs and the impact of treatment on the course of MS varied considerably between studies, depending on the data sources used and the methods used to incorporate data into models. The scope of the studies was narrow, with a sparsity of economic evaluations of symptomatic and/or non-pharmacological interventions; exclusion of direct non-medical, indirect and informal care costs from analyses; and a narrow view of the potential impact of treatment, concentrating on disability, according to the EDSS, and relapses. In addition, there were issues concerning how to capture losses in HSUVs due to relapses in a way that reflects their salience to people with MS, the wide variation in costs and outcomes from different sources and from potentially unrepresentative samples and modelling disease progression from natural history data from over 30 years ago. There are many complexities for those designing and reporting cost-effectiveness studies of treatments for MS. Analysts, and ultimately decision makers, face multiple data and methodological challenges. Policy makers, technology developers, clinicians, patients and researchers need to acknowledge and address these challenges and to consider recommendations that will improve the current scenario. There is a need for further research that can constructively inform decision-making regarding the funding of treatments for MS.

People with multiple sclerosis (MS) have up to a 15 times higher risk of being frail compared to age-matched individuals without MS. Frailty is a biological syndrome of decreased physiological reserve and resilience that increases the vulnerability to adverse clinical outcomes and leads to a lower quality of life. Recent studies have begun investigating frailty in the context of MS, highlighting several associations between frailty and adverse events, such as falls, and common MS-related symptoms involving the physical health domain, such as walking and sleeping problems. However, there is a critical knowledge gap regarding the relationship between mental health and frailty in people with MS. This mini-review article aimed to shed light on the potential relationships between MS, frailty, and mental health. Despite the dearth of studies on this topic, indirect evidence strongly suggests that the association between frailty and mental health in people with MS is likely bidirectional in nature. Specifically, mental health disorders such as depression and anxiety may be involved in the etiology of frailty in people with MS. However, they could also be exacerbated by the detrimental effects of frailty on overall health. The complex relationship between frailty and mental health in MS underscores the multifaceted challenges people with MS face. Conducting further research to untangle such a relationship is critical to developing early detection and intervention strategies for improving well-being and medical outcomes in people with MS.

Chronic neurological disorders may affect various cognitive processes, including religiosity or superstitious belief. We investigated whether superstitious beliefs are equally prevalent in patients with Parkinson's disease (PD), people with epilepsy (PWE), patients with multiple sclerosis (MS) and healthy controls (HCs). From late 2014 to early 2023 we conducted a cross-sectional in-person anonymous paper-based survey at the tertiary clinic of Vilnius University Hospital Santaros Klinikos among outpatients and HCs by asking them to ascribe meaning or report belief for 27 culturally adapted statements (9 omens and 18 superstitions). The sum of items that a respondent believes in was labeled the superstition index (SI). The SI was compared between groups by means of the Kruskal-Wallis (H) test and negative binomial regression modeling. A two-step cluster analysis was performed to discern different subgroups based on answers to the items of the SI. There were 553 respondents who completed the questionnaire (183 PWE, 124 patients with PD, 133 with MS and 113 HCs). Complete SI scores were collected for 479 (86.6%) participants and they were lower in patients with PD (n = 96, Md = 1, IQR = 0-5.75) in comparison to those with epilepsy (n = 155, Md = 6, IQR = 1-14), MS (n = 120, Md = 4, IQR = 0-12) or HCs (n = 108, Md = 4.5, IQR = 1-10), H (3) = 26.780, p < 0.001. In a negative binomial regression model (n = 394, likelihood ratio χ2 = 35.178, p < 0.001), adjusted for sex, place of residence, income and education, female sex was the only characteristic associated with the SI (β = 0.423, OR = 1.526, 95% CI = 1.148 to 2.028). Both female sex (β = 0.422, OR = 1.525, 95% CI = 1.148 to 2.026) and Parkinson's disease (β = -0.428, OR = 0.652, 95% CI = 0.432 to 0.984) were significant predictors of the SI when age was removed from the model. Two-step cluster analysis resulted in individuals with PD being grouped into "extreme non-believer," "non-believer" and "believer" rather than "non-believer" and "believer" clusters characteristic for PWE, patients with MS and HCs. Our study suggests that individuals with PD believe in less superstitions than patients with MS, PWE or HCs. The results of this investigation should be independently confirmed after adjusting for PD-specific variables.

Background: There has been increased interest in palliative care for Individuals with chronic neurological disorders, yet there are no specific case reports or illustrations on detailed palliative care practices. Palliative care services for neurological disorders in India are limited. Aim: To illustrate the generalist palliative care practice for persons with chronic neurological disorders in a tertiary care hospital. Materials and Methods: A case study design was used to demonstrate generalist palliative care in neurological disorders using five case studies. CARE guidelines used for reporting case studies. Results: Degenerative neurological conditions such as dementia, motor neuron diseases, Parkinson's disease, multiple sclerosis, Duchenne muscular dystrophy and other conditions such as progressive supranuclear palsy, sub-acute sclerosing pan encephalitis, mitochondrial disease, multiple system atrophy, stroke with poor prognosis need immense palliative care. Early identification of palliative care needs from the beginning of diagnosis, and the barriers in facilitating palliative care in tertiary care settings were discussed. The psychiatric social workers provided generalist palliative care such as communicating prognosis, addressing caregiver burden, end-of-life care issues, advance care planning, and appropriate referral to hospice care and other palliative care services. Conclusion: Palliative care for chronic neurological disorders is in its infant stage in India. There is limited awareness about the need for palliative care for chronic neurological disorders among social work trainees, neurology trainees, and other health care providers. Hence, there is a strong need to increase awareness and access to palliative care for persons with life limiting or life threatening chronic neurological disorders. It is feasible to provide generalist palliative care for chronic neurological disorders in a tertiary care setting.Keywords: Palliative care, neurological disorders, psychiatric social work interventions, end-of-life care

Background: Little is known about place of death in chronic neurological diseases. Mortality statistics are ideal for examining trends in place of death, but analyses are limited by coding rule changes. Aim: To examine the relationship between place of death and underlying cause of death in Parkinson’s disease, multiple sclerosis and motor neurone disease and the impact of coding rule changes on analysis of place of death. Design: Population-based study. Proportion ratios for death in hospice, home, care home and hospital were calculated according to underlying cause of death, using multivariable Poisson regression. Participants: Deaths in England (1993–2010) with any mention of Parkinson’s disease, multiple sclerosis or motor neurone disease as a cause of death, identified from national mortality data. Results: In this study, 125,242 patients with Parkinson’s disease, 23,501 with multiple sclerosis, and 27,030 with motor neurone disease were included. Home deaths ranged from 9.7% (Parkinson’s disease) to 27.1% (motor neurone disease), hospice deaths ranged from 0.6% (Parkinson’s disease) to 11.2% (motor neurone disease) and hospital deaths ranged from 43.4% (Parkinson’s disease) to 55.8% (multiple sclerosis). In Parkinson’s disease and multiple sclerosis, cancer as underlying cause of death increased likelihood of hospice death (proportion ratio (PR): 18.8, 95% confidence interval (CI) = 16.1–22.0; 8.88, 95% CI = 7.49–10.5) and home death (PR: 1.91, 95% CI = 1.80–2.04; 1.71, 95% CI = 1.56–1.88). Dementia as underlying cause of death increased likelihood of care home death in Parkinson’s disease (PR: 1.25, 95% CI = 1.19–1.32), multiple sclerosis (PR: 1.73, 95% CI = 1.22–2.45) and motor neurone disease (PR: 2.36, 95% CI = 1.31–4.27). Conclusions: Underlying cause of death has a marked effect on place of death. The effects of coding rule changes are an essential consideration for all research using underlying cause of death to study place of death.

Emerging role of neuregulin-1beta1 in pathogenesis and progression of multiple sclerosis

BackgroundRegular outpatient rehabilitation is prescribed for many patients with chronic neurological disorders, such as Parkinson’s disease or multiple sclerosis, to constantly support patients and their proxies in disease management. Due to the coronavirus disease 2019 pandemic, federal institutions and governments worldwide have directed local or nationwide lockdowns. During these times, the provision of regular outpatient rehabilitation service is drastically limited, making it actually impossible for community-dwelling patients with neurological disorders to receive prescribed rehabilitation interventions.Case presentationA 67-year-old White Swiss man with two chronic neurological diseases, Parkinson’s disease and multiple sclerosis, underwent a 4-week inpatient rehabilitation in our hospital. The main rehabilitation goals were related to improvements of mobility and a decrease in the risk of falls. The patient gained significant functional improvements that he maintained over the following months, supported by the continuation of physiotherapy in the domestic environment. Due to a coronavirus disease 2019 pandemic-related interruption of the regular ambulatory rehabilitation for several weeks during the first coronavirus disease 2019 wave in Switzerland, the patient’s functional abilities decreased significantly. Thus, the patient was again referred to our hospital for intensive inpatient rehabilitation to regain his physical functioning and mobility capacity. At hospital discharge, the patient improved most of his physical functioning to a prepandemic level.ConclusionsThe interruption of a rehabilitation service due to a pandemic-related lockdown can significantly impact the functional abilities of patients with chronic neurological diseases. This case report supports the claim for continuous access to rehabilitation services for all people with rehabilitation needs.

Studying sexual health in the UK

Multiple sclerosis (MS) is a chronic neurologic disorder with significant implications for public health as being the first cause of nontraumatic neurologic disability in young adults. Although the global prevalence of MS has been increasing, recent temporal trends in incidence remain unclear. We aimed to evaluate current MS incidence trends in France over 11 years using the Système National des Données de Santé, a nationwide administrative database covering 99% of the French population. We used a published algorithm that incorporates multiple data sources, including benefits from long-term diseases, specific disease-modifying treatment prescriptions, and hospital discharge, to identify incident MS cases from January 1, 2011, to December 31, 2021. Sex-standardized and age-standardized incidence and prevalence were estimated using a "specific" and a "sensitive" definition providing bounds on the evolution of recent incidence. We used multivariable Poisson regression models to estimate temporal trends in incidence rates, calculating incidence rate ratios (IRRs) along with corresponding 95% CIs. In a sensitivity analysis, the time lag between past visits to neurologists and the database recording of MS was analyzed to ensure that the diagnosis extraction date was reliable. A total of 67,311 individuals with suspected MS were identified between 2011 and 2021, with 50,320 individuals classified as incident MS using the specific definition. The sensitive definition identified 56,918 individuals with incident cases. The median age at diagnosis was 40.6 years for the specific definition and 41.5 years for the sensitive definition. The study found stable incidence of MS over the 11-year period (adjusted IRR: 0.998 [95% CI 0.996-1.001] for the specific cohort). The female-to-male ratio of incident MS cases remained stable while sex-standardized and age-standardized prevalence of MS continued to rise. The median time lag between probable diagnosis and database recording was estimated to be less than 18 months, with variations depending on age and method of patient identification. This study provides a comprehensive analysis of MS epidemiology in France, demonstrating stable incidence and sex ratio. The increase in prevalence suggests improved management and survival and highlights the ongoing need for health care systems to support the growing population of individuals with MS.