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

Abstract

Background: Dysfunction of the autonomic nervous system is common in multiple sclerosis patients, and probably present years before diagnosis, but its role in the disease is poorly understood.

Objectives: To study the autonomic nervous system in patients with multiple sclerosis using cardiac autonomic regulation measured with a wearable.

Methods: In a two-week study, we present a method to standardize the measurement of heart rate variability using a wearable sensor that allows the investigation of circadian trends. Using this method, we investigate the relationship of cardiac autonomic dysfunction with clinical hallmarks and subjective burden of fatigue and autonomic symptoms.

Results: In 55 patients with multiple sclerosis and 24 healthy age- and gender-matched controls, we assessed the cumulative circadian heart-rate variability trend of two weeks. The trend analysis revealed an effect of inflammation (P = 0.0490, SMD = -0.5466) and progressive neurodegeneration (P = 0.0016, SMD = 1.1491) on cardiac autonomic function. No association with subjective symptoms could be found.

Conclusions: Trend-based heart rate variability measured with a wearable provides the opportunity for unobtrusive long-term assessment of autonomic functions in patients with multiple sclerosis. It revealed a general dysregulation in patients with multiple sclerosis.

Keywords: autonomic nervous system; cardiac autonomic dysfunction; heart rate variability; multiple sclerosis; progressive; wearable.

Figure 2.

Left: Pearson correlation of the median from the ten approximated trend segments using all possible combinations of available days compared to the full data for all three metrics. The approximations quickly converge, showing that one week of data collection may be sufficient. Right: An example of trend approximation (line) for one patient using the super-imposed daily data (dots) for each metric. Grey dots represent sleep and wake data replicated to the margins to avoid spurious values in the polynomial fit.

Figure 1.

Flow chart of the recruitment process and overview of excluded participants.

Figure 3.

A standardized day starting with waking up in the morning and ending with waking up the day after represented by ten segments (20%), five during awake and five during sleep. The median of the approximated HRV trends of participants groups is shown in the line diagram. The histogram corresponds to the total data availability per segment. The top heat map shows optimal single time windows, the bottom one is the optimal windows for adaptive assessment, ranking the top 10 best AUCs. HC refers to healthy controls, pwMS to patients with multiple sclerosis. During awake hours either early morning or late evening shows differences in the HRV metrics between subgroups, meanwhile signals are converging during a majority of the daytime. Patients with a progressive disease course show a substantially different circadian adaptation, predominantly during sleep.

Figure 4.

Effect size of difference in pwMS per metric displayed as standardized mean difference with 95% CI. Mann-Whitney-U test was applied and * signifies a p-value <0.05, ** a p-value <0.01, after Benjaminin-Hochberg correction. COMPASS-31 results are displayed both as differences between HC and subjective AD and within patients with or without subjective AD. Other effects where performed only within pwMS.