Analysis of Gender Differences in HRV of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Mobile-Health Technology

Abstract

In a previous study using mobile-health technology (mHealth), we reported a robust association between chronic fatigue symptoms and heart rate variability (HRV) in female patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study explores HRV analysis as an objective, non-invasive and easy-to-apply marker of ME/CFS using mHealth technology, and evaluates differential gender effects on HRV and ME/CFS core symptoms. In our methodology, participants included 77 ME/CFS patients (32 men and 45 women) and 44 age-matched healthy controls (19 men and 25 women), all self-reporting subjective scores for fatigue, sleep quality, anxiety, and depression, and neurovegetative symptoms of autonomic dysfunction. The inter-beat cardiac intervals are continuously monitored/recorded over three 5-min periods, and HRV is analyzed using a custom-made application (iOS) on a mobile device connected via Bluetooth to a wearable cardiac chest band. Male ME/CFS patients show increased scores compared with control men in all symptoms and scores of fatigue, and autonomic dysfunction, as with women in the first study. No differences in any HRV parameter appear between male ME/CFS patients and controls, in contrast to our findings in women. However, we have found negative correlations of ME/CFS symptomatology with cardiac variability (SDNN, RMSSD, pNN50, LF) in men. We have also found a significant relationship between fatigue symptomatology and HRV parameters in ME/CFS patients, but not in healthy control men. Gender effects appear in HF, LF/HF, and HFnu HRV parameters. A MANOVA analysis shows differential gender effects depending on the experimental condition in autonomic dysfunction symptoms and HF and HFnu HRV parameters. A decreased HRV pattern in ME/CFS women compared to ME/CFS men may reflect a sex-related cardiac autonomic dysfunction in ME/CFS illness that could be used as a predictive marker of disease progression. In conclusion, we show that HRV analysis using mHealth technology is an objective, non-invasive tool that can be useful for clinical prediction of fatigue severity, especially in women with ME/CFS.

Cardiopulmonary and metabolic responses during a 2-day CPET in myalgic encephalomyelitis/chronic fatigue syndrome: translating reduced oxygen consumption to impairment status to treatment considerations

“…The goal of pacing, as opposed to graded exercise therapy, is symptom reduction to improve well-being and overall function through self-regulatory behavior [ 162 ]. Effective pacing can reduce fatigue, psychological distress, depression, and improve overall physical function [ 163 ]. Activity pacing relies on basic journaling of symptoms and activities to provide a ‘look-back’ in instances where PEM symptoms emerge to understand possible triggers of symptom exacerbation and inform future pacing-related decisions.…”

Cardiovascular and haematological pathology in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): A role for viruses

“…Additionally, females are also more affected than males [ 182 ], as is observed in ME/CFS populations [ 183 ]. The effect of sex-specific physiology in the context of these two post-viral diseases is acknowledged [ [184] , [185] , [186] ], but requires further study and elucidation. The two syndromes exhibit striking similarities, so much so that many Long COVID patients meet the diagnostic criteria for ME/CFS [ 8 , 9 ].…”

“A Switch Went off in my Whole Body”: Lived Experiences of Fatigue and Post-Exertional Malaise in Long Covid

“…Many ML techniques have been used for classifying the severity of chronic fatigue based for instance on immune biomarkers captured through invasive tools [48]. In contrast, less work has focused on non-invasive tools such as mobile health technologies [8] integrating multimodal sensing data and self-reports for fatigue assessment [29], or HRV and accelerometer data for detecting fatigue severity [21]. We can imagine new algorithms taking as input the data from the EMA, wearables, or less explored environmental sensors to predict the worsening of the symptoms and the likelihood of PEM occurring.…”