Relationship Between Chronic Stress and Heart Rate Over Time Modulated by Gender in a Cohort of Office Workers: Cross-Sectional Study Using Wearable Technologies

Abstract

Background Chronic stress is increasing in prevalence and is associated with several physical and mental disorders. Although it is proven that acute stress changes physiology, much less is known about the relationship between physiology and long-term stress. Continuous measurement of vital signs in daily life and chronic stress detection algorithms could serve this purpose. For this, it is paramount to model the effects of chronic stress on human physiology and include other cofounders, such as demographics, enabling the enrichment of a population-wide approach with individual variations. Objective The main objectives of this study were to investigate the effect of chronic stress on heart rate (HR) over time while correcting for weekdays versus weekends and to test a possible modulation effect by gender and age in a healthy cohort. Methods Throughout 2016 and 2017, healthy employees of technology companies were asked to participate in a 5-day observation stress study. They were required to wear two wearables, of which one included an electrocardiogram sensor. The derived HR was averaged per hour and served as an output for a mixed design model including a trigonometric fit over time with four harmonics (periods of 24, 12, 8, and 6 hours), gender, age, whether it was a workday or weekend day, and a chronic stress score derived from the Perceived Stress Scale (PSS) as predictors. Results The study included 328 subjects, of which 142 were female and 186 were male participants, with a mean age of 38.9 (SD 10.2) years and a mean PSS score of 13.7 (SD 6.0). As main effects, gender (χ21=24.02, P<.001); the hour of the day (χ21=73.22, P<.001); the circadian harmonic (χ22=284.4, P<.001); and the harmonic over 12 hours (χ22=242.1, P<.001), over 8 hours (χ22=23.78, P<.001), and over 6 hours (χ22=82.96, P<.001) had a significant effect on HR. Two three-way interaction effects were found. The interaction of age, whether it was a workday or weekend day, and the circadian harmonic over time were significantly correlated with HR (χ22=7.13, P=.03), as well as the interaction of gender, PSS score, and the circadian harmonic over time (χ22=7.59, P=.02). Conclusions The results show a relationship between HR and the three-way interaction of chronic stress, gender, and the circadian harmonic. The modulation by gender might be related to evolution-based energy utilization strategies, as suggested in related literature studies. More research, including daily cortisol assessment, longer recordings, and a wider population, should be performed to confirm this interpretation. This would enable the development of more complete and personalized models of chronic stress.

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

“…The bidirectional relationship between emotion and stress is well-known, with many papers reporting the influence emotion has over the autonomic nervous system (Kreibig et al, 2010). The physiological response from acute stress is often protective; however, chronic stress is known to facilitate numerous physical and mental health illnesses, which has a significant economic impact [ 17 , 28 ]. The understanding of chronic stress impact on the body has driven researchers to continue to develop new ways to detect and monitor stress, typically relying on the sympathetic nervous system physiological responses induced by stress, including changes in heart rate, heart rate variability, skin temperature, and conductance (van Kraaij et al, 2020).…”

“…A total of 15 studies were identified for inclusion in this review based on the search term “stress”. Similar to anxiety, the use of cardiac metrics, namely heart rate and heart rate variability, were the predominant physiological markers of stress detection in 10 of the 15 studies which detected stress [ 9 , 12 , 14 , 17 , 18 , 20 , 22 , 23 , 26 , 28 ]. It has been reported that altered HRV measurements are related to ANS dysregulation associated with many cardiovascular diseases including cardiac ischemia, myocardial infarction and heart failure, diabetes, and obesity, as well as mental health conditions including anxiety and depression [ 9 , 46 ].…”

“…Whilst the findings by Lucas et al (2019) and Pakhomov et al (2020) suggest HR may be useful in detecting stress, both studies were limited by the young age of their subjects; thus, the impact of comorbidities on HR, and therefore stress detection, may not be reflective of the general population [ 20 , 22 ]. The study by van Kraajj et al (2020) supports this, using two separate wearables: an unspecified chest patch for HR measurement and a wristband (Chillband) for detecting activity, with the study reporting that there was a significant relationship between HR and the three-way interaction between chronic stress, gender, and circadian rhythm [ 28 ]. Further, it is known that maximum HR decreases linearly with age [ 50 ], with sleep and stress levels fluctuating majorly throughout life, which further supports this relationship.…”

Toward Designs of Workplace Stress Management Mobile Apps for Frontline Health Workers During the COVID-19 Pandemic and Beyond: Mixed Methods Qualitative Study

“…They can provide easy access to self-help reference materials while preserving privacy needs and time constraint issues of individuals [ 12 ]. Wearable devices, on the other hand, can also passively monitor behaviors to assess user’s needs and, in response, curate context-aware, personalized, adaptive, and anticipatory interventions [ 13 ]. In other words, mobile apps provide the flexibility to deliver just-in-time and suitable interventions in users’ context.…”

Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors

“… 27 – 30 Recent studies demonstrated the potential of using physiological features that were extracted from wearable sensors to predict subjective stress levels and stress resilience. 28 , 29 , 31 For instance, a machine learning algorithm predicted next-day stress levels in a group of 104 college students with an accuracy of 81.5% using wearable sensor data on skin conductance and body temperature alongside data collected from surveys, smartphone logs, and daily weather. 32 Similarly, a machine learning algorithm that used wearable sensor data on skin conductance and body temperature was able to classify participants into high versus low stress groups with 78.3% accuracy in a sample of 201 healthy participants.…”