Sedentary Work Style and Heart Rate Variability:

A Short Term Analysis

Aleksandar Matic¹, Pietro Cipresso², Venet Osmani¹, Silvia Serino²,

Andrei Popleteev¹, Andrea Gaggioli², Oscar Mayora¹ and Giuseppe Riva²

CREATE-NET, Via alla Cascata 56/D, 38123, Trento, Italy

IRCCS Istituto Auxologico Italiano, Via G. Pellizza da Volpedo 41, 20149, Milano, Italy

Abstract. Emerging studies suggest that sedentary work style is often

associated with deleterious physiological implications, including diabetes, high

blood pressure and obesity. However, only few studies linked prolonged

periods of sitting with psychological responses thus the implications of

sedentary behavior on mental health still remain highly unexplored.

In this study, we investigated the relation between sedentary time and Heart

Rate Variability (HRV) parameters, which are considered important biological

markers of psychological processes including cognitive and emotional aspects.

In this manner, we aim to explore factors that may indicate that sedentary

behavior causes responses at psychological level. Recent progress in the

sophistication and usability of wearable sensors offers the opportunity to

continuously record ECG parameters and accelerometer data in daily-life

settings, such as at workplace.

1 Introduction

Physical inactivity leads to a number of health complications and various media

campaigns are designed to encourage increase in physical activity levels and promote

healthy lifestyle. However, a general increase in physical activities is not sufficient to

improve health. An important component of physical activity is the work style. In

developed economies, knowledge workers typically have a work style that requires

sitting for prolonged periods of time. As a number of studies have shown [1]; [10];

[13] sedentary work style leads to an array of health complications, including

diabetes, high blood pressure and obesity. The negative effects on health due to

sedentary work style occur even if people follow the guidelines on physical activity

and lead a healthy lifestyle outside of the workplace [1]; [10]. However, the impact of

prolonged periods of sitting on mental health remains a largely unexplored area of

research [18].

In this study, we explore how sedentary behavior affects a biological marker that

is indicative for processes at physiological (and maybe emotional and cognitive)

levels, namely Heart Rate Variability (HRV). Being suitable for a short term analysis

of cognitive and emotional responses, HRV might provide cues for the potential

Matic A., Cipresso P., Osmani V., Serino S., Popleteev A., Gaggioli A., Mayora O. and Riva G..

Sedentary Work Style and Heart Rate Variability: A Short Term Analysis.

DOI: 10.5220/0003893000960101

In Proceedings of the 2nd International Workshop on Computing Paradigms for Mental Health (MindCare-2012), pages 96-101

ISBN: 978-989-8425-92-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

psychological impacts of sedentary behavior thus motivating further investigations in

this area.

Wearable devices to measure ECG are readily available and are unobtrusive to

use, finding applications beyond health monitoring (for instance sport performance

monitoring), therefore becoming a powerful tool for both research and clinical studies

[4]. The clinical relevance of HRV was first noted in 1965 [9] and continues to be

used today to examine neuropathy in diabetic patients, risk of cardiovascular

mortality and a number of other physiological and pathological conditions [4]. On the

other hand, recent studies have found strong correlation between HRV and mental

stress [7], visual stimulations [3] and mood states [5]; [6]. In addition, the influence of

physical activities on HRV has been widely studied, including the effects of habitual

physical activity and also HRV analysis during pre-scheduled activities. However,

less emphasis has been placed on the physical inactivity, especially in workplace.

2 Background

Heart rate variability represents variations in inter-beat intervals (IBI) and provides a

significant measure of physiological and pathological conditions [4], furthermore

psychological responses. It mirrors the relationship between sympathetic and

parasympathetic branches of autonomic nervous system; the former stimulates

organs’ functioning and causes increase in heart rate (HR), while the later inhibits

functioning of organs and causes a decrease in heart rate [3]; [15]; [16]. The balance

between sympathetic and parasympathetic systems constantly changes when the body

attempts to achieve an optimum state corresponding to all internal and external

stimuli [2]; [17]. Therefore, HRV is considered a measure of changes in system

balance and consequently as a measure of body responses to internal and external

provocations [2]; [17].

These studies have demonstrated that lower HRV values often suggest

sympathetic dominance, higher stress and negative emotions, according to

experiments where subjects were asked to watch horror movies [3] or perform mental

tasks [7], [15]. Higher HRV values, to the contrary, indicate domination of

parasympathetic system and positive emotions that were typically evoked by

watching delightful movies, such as love stories [5] or landscape scenes [3].

3 Materials and Methods

3.1 Classification of Sedentary Time

Accelerometers are widely available in newer generations of smart phones, typically

used for their role in user interfaces [14]. They provide an important research tool

able to reliably measure and classify a number of physical activities, including

walking, jogging, sitting, standing [11], and more complex activities such as

estimation of metabolic energy expenditure, sit-to stand transfers, and assessment of

balance and intensity of physical activity [12].

For our study, it was important to distinguish only sitting periods from all other

physical activities and to provide precise duration and timestamp of each segment.

We analyzed acceleration data and calculated standard deviation of resultant

accelerations over each one-minute interval [12] - the square roots of the sum of the

values of each axis (x, y and z) squared [11]. In most cases it was easy to distinguish

periods characterized by very low intensity movements that were considered

sedentary periods.

All the activities related to usage of the phone itself, such as making phone calls or

sending texts, were also recorded; the accelerometer data for these periods was

discarded to avoid confusion with physical activities.

3.2 HRV Measures

In order to acquire HRV, we used Shimmer Wireless ECG sensor [8]; [17], connected

with the mobile phone via Bluetooth. Due to limited performance of the mobile

device, the maximum ECG sampling rate that it could process (along with the data

from other sensors, including accelerometer and location) did not allow us to use

frequency domain analysis [4]; [15]; [16]. Therefore, our focus was on time domain

analysis of HRV. In order to prevent the sensor battery from running out during

subjects’ working time, we recorded ECG data for 1 minute during a time frame of 5

minutes. Before the calculation of time domain measures of HRV took place, all

abnormal heart beats and artefacts were removed from consideration; the signal

suffered high noise usually when the subject was moving intensively.

In the ECG recordings, each interval between neighboring beats, called NN

interval was detected. We analyzed the following measures [4]; [15]; [16]:

SDNN[ms] – Standard deviation of the NN interval, i.e. the square root of variance.

RMSSD[ms]- The square root of the mean squared differences of successive NN

intervals.

pNN50[%] – The proportion derived by dividing NN50 by the total number of NN

intervals, where NN50 represents the number of interval differences of successive NN

intervals greater than 50ms.

4 Experiments and Results

We recruited 6 participants from our research centre (4 males and 2 females), with

ages between 26 and 35, with an average age of 29. They were all knowledge workers

with no major differences either in the type of job regarding sedentary routines or in

the number of working hours. None of the subjects was a cigarette smoker, nor had a

chronic disease.

A total of 47 recordings have been collected among the six subjects. Descriptive

statistics have been reported in Table 1.

For each one of these recording we calculated three HRV indexes, namely SDNN,

RMSSD and pNN50, as before described, and an activity index (NonSedTime) to

measure non-sedentary time, that is the percent of time spent in non sedentary

activities. Since sedentary and non-sedentary time indexes are counter-proportional,

selecting one of the two is sufficient to investigate the correlation between sedentary

behavior and HRV parameters.

Table 1. Descriptive statistics of activity and HRV indexes, used in our study.

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

NonSedTime 47 0.00 46.85 13.38 10.49

SDNN Mean 47 0.05 0.18 0.09 0.04

RMSSD Mean 47 0.63 0.87 0.76 0.06

pNN50 Mean 47 0.25 0.49 0.37 0.06

The analysis (Table 2) showed a positive correlation between non-sedentary time

and HRV indexes – SDNN and pNN50 indexes increase as NonSedTime increases

and vice versa, i.e. the lower amount of time spent in non-sedentary activities the

lower the values of SDNN and pNN50. A lower levels of SDNN and pNN50 is

known to be associated with higher stress levels [4]; [15]; [16] and negative emotions

[3-7]. Therefore, the results indicate that sedentary workstyle lead the subjects to be

more prone to negative emotions and stress, measuring the stress level according to

the Task Force of the European Society of Cardiology and the North American

Society of Pacing Electrophysiology [4]. However, due to the small sample size of

this pilot study, the results should be considered only as an indication for the

association between sedentary behavior and psychological processes.

Table 2. Correlation analysis between non-sedentary time and HRV indexes.

Correlations

SDNN Mean RMSSD Mean pNN50 Mean

NonSedTime .442

0.046 0.262

SDNN Mean 1 .305

.740

RMSSD Mean .305

1 .576

pNN50 Mean .740

.576

. Correlation is significant at the 0.10 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

Table 2 also shows a strong internal coherence for HRV indexes, in fact the

correlations between these indexes are always highly statistically significant. This

means that the relationship between SDNN, RMSSD, and pNN50 is typically strong,

and the phenomena revealed with one of the indexes should also apply for the others,

even though it is indirect.

Furthermore, since the recording entries are hierarchical within participants, we

estimated, with hierarchical linear regression, the relationship between the non-

sedentary time and HRV, using SDNN as a dependent variable. Results are showed in

Table 3.

Table 3. Hierarchical linear regression.

Parameter Estimates

Parameter B Std. Error

95% Wald Confidence

Interval

Hypothesis Test

Lower Upper Wald Chi-Square df Sig.

(Intercept) .072 .0085 .056 .089 71.594 1 .000

NonsedTime .002 .0003 .001 .002 25.827 1 .000

(Scale) .001

5 Conclusions

Emerging studies suggest that sedentary work style is often associated with

deleterious health complications, including diabetes, high blood pressure and obesity

[1]; [3]; [5]; [9]; [10]; [11]; [19]. In this study, we aimed to explore the correlation

between sedentary time and HRV parameters, which are considered biological

markers of both physical and mental health. In particular, recent studies demonstrated

that lower HRV values often suggest sympathetic dominance, higher stress and

negative emotions [3-7]. Recent progress in the sophistication and usability of

wearable biosensors offers the opportunity to continuously record ECG parameters

and accelerometer data in daily-life settings, such us at work place. In this experiment

we used a Shimmer Wireless ECG sensor, connected with the mobile phone via

Bluetooth, to investigate the correlation between sedentary time and HRV parameters

at workplace, thus exploring possible cues for the association between sedentary

behavior and psychological processes. Results showed a strong relationship with

HRV parameters, in particular with SDNN and pNN50, suitable for a short term

analysis. Such evidences suggest the use of wearable devices to measure ECG indexes

in naturalistic environment to explore new possibilities to encourage a healthy work

style. However, due to the small sample size, these results provide only cues about the

examined correlations and we believe that they might motivate for future

investigation on the correlation between sedentary time and HRV but also on the

impacts of sedentary behavior on psychological response including emotions, stress

and the mood.

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