Continuous monitoring with wearables in multiple sclerosis reveals an association of cardiac autonomic dysfunction with disease severity

Heart Rate Variability Applications in Medical Specialties: A Narrative Review

HRV is clinically considered to be a surrogate measure of the asymmetrical interplay of the sympathetic and parasympathetic nervous system. While HRV has become an increasingly measured variable through commercially-available wearable devices, HRV is not routinely monitored or utilized in healthcare settings at this time. The purpose of this narrative review is to discuss and evaluate the current research and potential future applications of HRV in several medical specialties, including critical care, cardiology, pulmonology, nephrology, gastroenterology, endocrinology, infectious disease, hematology and oncology, neurology and rehabilitation, sports medicine, surgery and anesthesiology, rheumatology and chronic pain, obstetrics and gynecology, pediatrics, and psychiatry/psychology. A narrative literature review was conducted with search terms including HRV and relevant terminology to the medical specialty in question. While HRV has demonstrated promise for some diagnoses as a non-invasive, easy to use, and cost-effective metric for early disease detection, prognosis and mortality prediction, disease monitoring, and biofeedback therapy, several issues plague the current literature. Substantial heterogeneity exists in the current HRV literature which limits its applicability in clinical practice. However, applications of HRV in psychiatry, critical care, and in specific chronic diseases demonstrate sufficient evidence to warrant clinical application regardless of the surmountable research issues. More data is needed to understand the exact impact of standardizing HRV monitoring and treatment protocols on patient outcomes in each of the clinical contexts discussed in this paper.

Digital remote monitoring of people with multiple sclerosis

Introduction

Multiple sclerosis (MS) is a chronic neurodegenerative disease that affects over 2.8 million people globally, leading to significant motor and non-motor symptoms. Effective disease monitoring is critical for improving patient outcomes but is often hindered by the limitations of infrequent clinical assessments. Digital remote monitoring tools leveraging big data and AI offer new opportunities to track symptoms in real time and detect disease progression.

Methods

This narrative review explores recent advancements in digital remote monitoring of motor and non-motor symptoms in MS. We conducted a PubMed search to collect original studies aimed at evaluating the use of AI and/or big data for digital remote monitoring of pwMS. We focus on tools and techniques applied to data from wearable sensors, smartphones, and other connected devices, as well as AI-based methods for the analysis of big data.

Results

Wearable sensors and machine learning algorithms show significant promise in monitoring motor symptoms, such as fall risk and gait disturbances. Many studies have demonstrated their reliability not only in clinical settings and for independent execution of motor assessments by patients, but also for passive monitoring during everyday life. Cognitive monitoring, although less developed, has seen progress with AI-driven tools that automate the scoring of neuropsychological tests and analyse passive keystroke dynamics. However, passive cognitive monitoring is still underdeveloped, compared to monitoring of motor symptoms. Some preliminary evidence suggests that application of AI and big data to other understudied aspects of MS (namely sleep and circadian autonomic patterns) may provide novel insights.

Conclusion

Advances in AI and big data offer exciting possibilities for improving disease management and patient outcomes in MS. Digital remote monitoring has the potential to revolutionize MS care by providing continuous, long-term granular data on both motor and non-motor symptoms. While promising results have been demonstrated, larger-scale studies and more robust validation are needed to fully integrate these tools into clinical practice and generalise their results to the wider MS population.

Measuring Disease Progression in Multiple Sclerosis Clinical Drug Trials and Impact on Future Patient Care

Multiple sclerosis (MS) is a chronic immune-mediated disease of the central nervous system characterised by inflammation, demyelination and neurodegeneration. Although several drugs are approved for MS, their efficacy in progressive disease is modest. Addressing disease progression as a treatment goal in MS is challenging due to several factors. These include a lack of complete understanding of the pathophysiological mechanisms driving MS and the absence of sensitive markers of disease progression in the short-term of clinical trials. MS usually begins at a young age and lasts for decades, whereas clinical research often spans only 1-3 years. Additionally, there is no unifying definition of disease progression. Several drugs are currently being investigated for progressive MS. In addition to new medications, the rise of new technologies and of adaptive trial designs is enabling larger and more integrated data collection. Remote assessments and decentralised clinical trials are becoming feasible. These will allow more efficient and large studies at a lower cost and with less burden on study participants. As new drugs are developed and research evolves, we anticipate a concurrent change in patient care at various levels in the foreseeable future. We conducted a narrative review to discuss the challenges of accurately measuring disease progression in contemporary MS drug trials, some new research trends and their implications for patient care.

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study

BACKGROUND

Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations.

OBJECTIVE

We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting.

METHODS

Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA.

RESULTS

All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001).

CONCLUSIONS

Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.3390/clockssleep6010010.

Heart Rate Variability (HRV) serves as an objective correlate of distress and symptom burden in multiple sclerosis

Background

Autonomic nervous system (ANS) dysfunction is frequently seen in people living with multiple sclerosis (MS). Heart rate variability (HRV) is an easy and objective index for evaluating ANS functioning, and it has been previously used to explore the association between ANS and the experience of symptom burden in other chronic diseases. Given ANS functioning can be influenced by physical and psychological factors, this study investigated whether emotional distress and/or the presence of ANS dysfunction is associated with symptom severity in people living with MS.

Methods

Participants with MS and healthy controls (HC) with no history of cardiac conditions were recruited to self-collect HR data sampled from a chest strap HR monitor (PolarH10). Short-term HR signal was collected for five minutes, and time and frequency HRV analyses were performed and compared between groups. HRV values were then compared to self-reported distress (Kessler Psychological Distress Scale) and MS participants' self-reported measures of symptom burden (SymptoMScreen).

Results

A total of n = 23 adults with MS (51 ± 12 years, 65 % female, median Patient Determined Disease Steps [PDDS]: 3.0) and n = 23 HCs (43 ± 18 years, 40 % female) completed the study procedures. All participants were able to complete the chest strap placement and HR data capture independently. Participants with MS, compared to the HC participants, had a significantly lower parasympathetic activation as shown by lower values of the root mean square of successive differences between normal heartbeats (RMSSD: 21.86 ± 9.84 vs. 43.13 ± 20.98 ms, p = 0.002) and of high-frequency (HF) power band (HF-HRV: 32.69 ± 12.01 vs. 42.39 ± 7.96 nu, p = 0.016), indicating an overall lower HRV in the MS group. Among individuals with MS, HF-HRV was significantly correlated with the severity of self-reported MS symptoms (r = -0.548, p = 0.010). Participants with MS also reported higher levels of distress compared to HC participants (18.32 ± 6.05 vs. 15.00 ± 4.61, p = 0.050), and HRV correlated with the severity of distress in MS participants (r = -0.569, p = 0.007). A significant mediation effect was also observed, with emotional distress fully mediating the association between HRV and symptom burden.

Conclusions

These findings suggest the potential for ANS dysfunction, as measured by HRV (i.e., lower value of HF power), to be utilized as an objective marker of symptom burden in people living with MS. Moreover, it is apparent that the relationship between HRV and symptom burden is mediated by emotional distress.

Modeling multiple sclerosis using mobile and wearable sensor data

Multiple sclerosis (MS) is a neurological disease of the central nervous system that is the leading cause of non-traumatic disability in young adults. Clinical laboratory tests and neuroimaging studies are the standard methods to diagnose and monitor MS. However, due to infrequent clinic visits, it is fundamental to identify remote and frequent approaches for monitoring MS, which enable timely diagnosis, early access to treatment, and slowing down disease progression. In this work, we investigate the most reliable, clinically useful, and available features derived from mobile and wearable devices as well as their ability to distinguish people with MS (PwMS) from healthy controls, recognize MS disability and fatigue levels. To this end, we formalize clinical knowledge and derive behavioral markers to characterize MS. We evaluate our approach on a dataset we collected from 55 PwMS and 24 healthy controls for a total of 489 days conducted in free-living conditions. The dataset contains wearable sensor data - e.g., heart rate - collected using an arm-worn device, smartphone data - e.g., phone locks - collected through a mobile application, patient health records - e.g., MS type - obtained from the hospital, and self-reports - e.g., fatigue level - collected using validated questionnaires administered via the mobile application. Our results demonstrate the feasibility of using features derived from mobile and wearable sensors to monitor MS. Our findings open up opportunities for continuous monitoring of MS in free-living conditions and can be used to evaluate and guide the effectiveness of treatments, manage the disease, and identify participants for clinical trials.

Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis

Typical assessments of balance impairment are subjective or require data from cumbersome and expensive force platforms. Researchers have utilized lower back (sacrum) accelerometers to enable more accessible, objective measurement of postural sway for use in balance assessment. However, new sensor patches are broadly being deployed on the chest for cardiac monitoring, opening a need to determine if measurements from these devices can similarly inform balance assessment. Our aim in this work is to validate postural sway measurements from a chest accelerometer. To establish concurrent validity, we considered data from 16 persons with multiple sclerosis (PwMS) asked to stand on a force platform while also wearing sensor patches on the sacrum and chest. We found five of 15 postural sway features derived from the chest and sacrum were significantly correlated with force platform-derived features, which is in line with prior sacrum-derived findings. Clinical significance was established using a sample of 39 PwMS who performed eyes-open, eyes-closed, and tandem standing tasks. This cohort was stratified by fall status and completed several patient-reported measures (PRM) of balance and mobility impairment. We also compared sway features derived from a single 30-second period to those derived from a one-minute period with a sliding window to create individualized distributions of each postural sway feature (ID method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the ID method establish additional relationships with PRMs, detect differences in more tasks, and distinguish between fall status groups. Overall, the chest was found to be a valid location to monitor postural sway and we recommend utilizing the ID method over single-observation analyses.