A PILOT STUDY OF SHORT-TERM EFFECTS

OF NON-SEDENTARY BEHAVIOUR ON MOOD CHANGES

Aleksandar Matic, Venet Osmani, Andrei Popleteev and Oscar Mayora-Ibarra

CREATE-NET, Via alla Cascata 56/D, 38133, Trento, Italy

Keywords: Sedentary behaviour, Accelerometers, Mood changes, Mental health, Pervasive computing.

Abstract: Sedentary behaviour is considered to be a major factor impacting a number of health outcomes while use of

technology in the workplace is increasingly contributing to the amount of sedentary time. Even if guidelines

on the amount of physical activity are followed outside of workplace, sedentary work style still has a

deleterious impact on health. There have been a number of studies that investigated the impact of sedentary

behaviour on general health while no study has reported investigation of effects of sedentary behaviour on

mood changes. The aim of this pilot study, which included 14 knowledge workers, was to objectively

measure sedentary time using accelerometers and investigate correlation with the self-reported mood

changes in short term. The results show that sedentary behaviour has negative effects on the mood even

over a short period of one day. Although a pilot study, this is the first study to examine short term

psychological implications of sedentary behaviour. Randomised controlled experiments are needed to

further clarify these findings.

1 INTRODUCTION

Conveniences of modern life in developed countries

are having an adverse effect on health. This is

particularly manifested through decreased physical

activity, where people spend significant amount of

time in sedentary activities. Physical inactivity leads

to a number of health outcomes and various media

campaigns are designed to encourage increase in

physical activity levels and promote healthy

lifestyle. However, a general increase in physical

activities outside work hours is not sufficient to

compensate for deleterious effects of sedentary time

in workplaces. Sedentary work style has adverse

health risks, where negative effects on health occur

even if people follow the guidelines on physical

activity outside of the workplace (Tremblay et al.,

2010; Healy et al., 2008). As a number of studies

have shown (Sanchez-Villegas et al., 2008;

Tremblay et al., 2010; Hamilton et al., 2007;

Hamburg et al., 2007; Zwart et al., 2007; Hu et al.,

2003; Healy et al., 2008) prolonged sitting leads to

an array of health complications, including diabetes,

high blood pressure and obesity.

Office workers typically adhere to a work style

that requires sitting for prolonged periods of time.

Therefore, monitoring sedentary patterns in

workplace provides a good basis for polices that

encourage healthy work style and minimize the time

spent in sedentary activities. In addition to physical

health effects, sedentary behaviour also has a

negative effect on mental health. The closest study

to our work has linked prolonged sitting time with

mental health disorders (Sanchez-Villegas et al.,

2008), however, no study has been reported that has

investigated correlation between sedentary patterns

and mood changes.

The aim of our pilot study was to investigate

how sedentary work style affects office workers’

subjectively reported mood. Measuring mood

changes is important when considering mood shifts

are linked to serious diseases, such as depression, bi-

polar disease but also cost economies an average of

$44 billion per year in Lost Productive Time (LPT)

(Stewart et al., 2003).

In our study we used well-established

technologies to monitor sitting time, namely mobile

phones with embedded accelerometers. Using

mobile phones as a sensing device not only allows

for precise measurements of physical activities and

sedentary periods (thus overcoming recall biases and

floor effects (Tudor-Locke and Myers, 2001)), but

also provides an unobtrusive monitoring platform

that does not interfere with the typical office

299

Matic A., Osmani V., Popleteev A. and Mayora-Ibarra O..

A PILOT STUDY OF SHORT-TERM EFFECTS OF NON-SEDENTARY BEHAVIOUR ON MOOD CHANGES.

DOI: 10.5220/0003874702990304

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2012), pages 299-304

ISBN: 978-989-8425-88-1

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

workers’ routines. In this manner, we were able to

measure precisely sedentary behaviour at workplace

and correlate the findings with the reported mood

changes. The study described in this paper is the first

to have investigated this issue and we hope it will

motivate further research in the implications of

sedentary behaviour on psychological response.

2 BACKGROUND AND RELATED

WORK

Sedentary behaviour (from the Latin “sedere”

meaning “sitting”) refers to the category of

behaviours for which the energy expenditure is low,

including prolonged sitting periods at work, home,

in car, bus, and in leisure time (Tremblay et al.,

2010). Sedentary behaviour, characterized by the

SITT formula (Sedentary behaviour frequency,

Intensity, Time and Type), is often associated with

adverse health outcomes. Emerging studies suggest

that independently and qualitatively different from a

lack of moderate or intense physical activity,

sedentary behaviour has effects on human

metabolism, physical function and health outcomes

and therefore it should be considered as a separate

and unique phenomenon (Tremblay et al., 2010).

Even if one meets physical activity guidelines,

sitting for prolonged periods increases the risks for

obesity, metabolic syndrome, type 2 diabetes and

cardiovascular disease (Tremblay et al., 2010;

Hamilton et al., 2007).

Previous research initiatives have relied on self-

reporting methods to acquire information about

activities that correspond to sitting routines. Self-

reporting of physical activity have a number of

drawbacks including floor effects and recall bias

(Tudor-Locke and Myers, 2001) while continuous

monitoring over long periods of time may disturb

habitual physical activity (Baranowski, 1988). On

the other hand, accelerometers can provide

recognition of physical activities such as sitting,

running, standing (Kwapisz et al., 2010; Mathie et

al., 2004) and in addition record further details such

as duration, frequency and intensity of movements.

Accelerometers have become critical in investigation

of sedentary habits that are difficult to recall with the

questionnaire method. With the advancement of

technology, accelerometers have been used to

capture sedentary patterns in an objective manner,

thus overcoming the drawbacks of self-reporting.

Usage of accelerometers allowed significant results

in establishing the influence of sedentary behaviour

on metabolism (Hamburg et al., 2007), vascular

health (Hamburg et al., 2007) and bone mineral

content (Zwart et al., 2007). Moreover, recent

studies link sedentary lifestyle and obesity (Hu et al.,

2003), cancer (Gierach et al., 2009) and

psychosocial health (Martin et al., 2009). Sanchez-

Villegas et al. (Sanchez-Villegas et al., 2008) found

that the risk of mental disorders was 31% higher for

prolonged patterns of sitting in front of a computer

or a TV, comparing subjects that spend more than

42h/week in front of a TV with those watching less

than 10.5h/week. The extensive literature survey on

the implications of sedentary lifestyle, provided by

Tremblay et al. (Tremblay et al., 2010), concludes

that there is a need to understand the factors of

sedentary behaviour and to implement interventions

to reduce population-wide levels of sedentary

behaviour, shifting some proportion of time from

sitting to various types of physical activity. The

authors (Tremblay et al., 2010) suggest that this

should be done empirically. By conducting an

empirical study that investigates non-sitting periods

during working time, we have studied the correlation

between sedentary patterns at workplace and office

workers’ mood changes, which may form the basis

for strategies to reduce prolonged sitting periods.

Our work is the first to investigate the influence of

sedentary behaviour on mood changes, opening up a

research avenue to explore psychological effects of

increasing prevalence of sedentary habits.

3 MOTIVATION

Examining the influence of sedentary behaviour on

psychological responses of an individual stemmed

from an immense research body that was focused on

physiological implications of sedentary lifestyle

(Sanchez-Villegas et al., 2008; Tremblay et al.,

2010; Hamilton et al., 2007; Hamburg et al., 2007;

Zwart et al., 2007; Hu et al., 2003; Healy et al.,

2008). The postulates of physiological psychology

that interrelate physiological and psychological

processes (Andreassi, 2006) prompted us to

investigate whether prolonged periods of sitting may

also cause psychological responses.

The current literature provides number of

directions for the possible correlation between

sedentary behaviour and psychological reactions:

• Effects of sedentary behavior on insulin

sensitivity are extensively examined (Hamburg

et al., 2007; Balkau et al., 2008; Jennings et al.,

1986) indicating that even single portions of

prolonged inactivity, such as days or weeks,

decrease insulin sensitivity in normal subjects.

HEALTHINF 2012 - International Conference on Health Informatics

300

On the other hand, recent studies have shown

that insulin affects the secretion of serotonin in

the brain. It also impacts memory and mood

(Reagan, 2007) and causes mood changes

(Gonder-Fredericl et al., 1989), while even

postpartum mood disorders were attributed to

decreased insulin levels (Balkau et al., 2008).

• The lipid metabolism and prolonged periods of

inactivity are related according to the animal

studies (Bey and Hamilton, 2003) which

indicated the correlation even for shorter

periods of inactivity such as 4 hours or several

days. Wells et al. (Wellsl et al., 1998) claim

that the presence of lipids can induce

sleepiness and mood changes.

• The effects of prolonged sitting on sympathetic

activity are demonstrated in (Jennings et al.,

1986). On the other hand, close relationship

between anxiety and sympathetic nervous

system activity is well-established (Wellsl et

al., 1998) while there were also attempts to

correlate sympathetic activity with mood states

(Yu et al., 2008) and diurnal variations

(Rechlin et al., 1995).

However, it would be inappropriate to claim

correlation between sedentary behaviour and the

mood based solely on the physiological parameters

as an underlying factor. Rather, this provided us the

motivation to examine the effects of sedentary

behaviour on psychological responses independently

from physiological parameters.

4 STUDY DESIGN

4.1 Mood Assessment

The mood may depend on a number of different

factors, such as circadian rhythms (Clark et al.,

1999), type of environment (Adan and Guardia,

1993), quality of sleep (Volkers et al.,, 1998), state

of health, private problems or other factors

incomprehensible not only to direct measurement

but also difficult for an individual to identify.

Therefore, it may be impossible to consider all the

factors that influence the mood and provide the

ultimate conclusion about the exact cause of one’s

state of mood. For this reason, our approach is to

follow relative changes of mood rather than focus on

an absolute mood state. This is because we assume

that the interval between two mood assessments of a

couple of hours (in our design) is not sufficient for a

significant change in “background” factors. These

factors, such as private problems for example, are

likely to be constantly present during relatively

longer periods of time while, the events within that

period have pre-dominant influence on relative

changes of mood. In our mood study we aim to

capture sedentary patterns during the day that

correlate with similar responses in individuals’

mood. Our method for assessing mood fluctuations

during the day is based on EMA (Ecologically

Momentary Assessment) approach in order to

compare retrospective and momentary mood data

(Smyth and Stone, 2003). The EMA approach,

which involves asking participants to report their

psychological state multiple times a day, reduces the

critical issue of retrospective recall of extended time

intervals. The retrospective recall issue is related to

cognitive and emotive limitations that bias the recall

of autobiographical memory (Smyth and Stone,

2003) influencing subject’s report by most salient

events during the recall interval. The questionnaire

we used was derived from a well-established scale

for mood study – the Profile of Mood States

(POMS) scale that consists of 65 items in its

standard version. However, long and repeated mood

questionnaires become a burden on subjects,

therefore a short version of the POMS scale was

used. We derived 8 adjectives from the POMS scale,

namely cheerful, sad, tensed, fatigued, energetic,

relaxed, annoyed and friendly that were rated on 5-

point scale (1-not at all, 2- a little, 3- moderately, 4

quite a bit, 5- extremely). The scores were summed

across the items related to positive and negative

expression in order to generate a single score for

each mood report. The difference of that score

(between two sequential questionnaires) was taken

as a measure of relative change of subject’s mood.

The questionnaires were administered three times a

day, scheduled to best fit with the work routines,

while also allowing the user to manually invoke the

questionnaire. Typically, the questionnaires were

answered in the morning, after lunch and at the end

of working day.

4.2 Sedentary Time

Accelerometers provide an important research tool

able to reliably measure and classify a number of

physical activities, including walking, jogging,

sitting, standing (Kwapisz et al., 2010), and more

complex activities such as estimation of metabolic

energy expenditure, sit-to stand transfers, and

assessment of balance and intensity of physical

activity (Kwapisz et al., 2010). In our study we

focused on distinguishing between sedentary and

A PILOT STUDY OF SHORT-TERM EFFECTS OF NON-SEDENTARY BEHAVIOUR ON MOOD CHANGES

301

non-sedentary time. Typical approach is recording

accelerometer data in 1-min epochs and a threshold

of <100 counts per minute (CPM) is chosen to

classify sedentary time (Tremblay et al., 2010; Healy

et al., 2008). Total sedentary time is calculated as a

sum of all sedentary minutes, while each minute

interval where number of accelerometer counts is

above 100 is considered a non-sedentary break

(Healy et al., 2008). The current studies

investigating sedentary behavior typically use

dedicated devices such as ActiGraph, TriTrac,

Caltrac, Actiwatch or Actical (Trost et al., 2005) that

directly provide the number of counts. On the other

hand, accelerometers are widely available in smart

phones due to their role in user interfaces (Mobile

Dev&Design, 2011). Since mobile phones are

providing the raw accelerometer signal, instead of

calculating counts we opted for a simpler approach

of analyzing standard deviation of accelerometer

signal (Kwapisz et al., 2010). However, we

compared the performance of the two approaches for

inference of sedentary time in our experimental

settings and they demonstrated comparable

performance.

In our study we used a mobile phone; not only

because it does not require an additional sensing

device to be carried but also it does not impact the

typical behavior routines of office workers. Clearly,

an issue with technological monitoring is that

subjects may not always wear the device and this is

an issue that we will address in the upcoming

studies. The typical approach to deal with this issue

of using a 20-minute inactivity criterion to identify a

non-wearing period was not applicable in our study

as office workers may sit continuously for more than

20 minutes.

5 EXPERIMENTS AND RESULTS

We recruited 14 participants (9 males, 4 females), all

of them office workers not connected with this

study, for 5 working days (characteristics of the

sample are shown in Table 1).

Table 1: Characteristics of the sample.

Age (years) 31.5±8.2

Work hours per week 38.5±2.0

Duration between two

questionnaires (min)

203.1±39.4

Non-sedentary time in

one interval (min)

39.3±19.2

The participants filled in the mood

questionnaires in the beginning, in the middle and at

the end of working day. There were no significant

differences between men and women either in the

relevant parameters (such as age, number of working

hours or type of the job regarding sedentary

routines) or in the measures (such as a number of

reported positive/negative mood changes and

average non sedentary time within one monitored

interval). None of the participants was a cigarette

smoker nor reported health problems. After

discarding intervals due to non-completed reports,

the data analyzed contained 151 monitored intervals,

74 and 77 intervals of positive and negative mood

changes respectively. Self-reported mood change,

measured as a difference in scores between two

consecutive questionnaires, was analyzed regarding

sedentary time, acquired using accelerometer

embedded in smart phones. Statistical analysis was

performed using SPSS while sedentary patterns were

inferred with R-2.13.

Figure 1, Figure 2 and Figure 3 show the

distributions of Spearman correlation between non-

sedentary time and reported change in mood. The

mean correlation between non-sedentary time and

positive/negative/overall mood changes was

0.18±0.36 (min=-0.34, max=0.86), -0.23±0.37

(min=-0.90, max=0.33) and 0.35±0.38 (min=-0.48,

max=0.86) respectively.

Figure 1: Distribution of Spearman correlation between

non-sedentary time and positive mood score change.

The non-sedentary time/positive mood changes

and non-sedentary-time/overall mood changes

distributions were significantly greater than 0

(t=1.753, t=3.451 respectively, P<0.1, P<0.005)

while non-sedentary time/negative mood changes

distribution was significantly less than 0 (t=-2.339

HEALTHINF 2012 - International Conference on Health Informatics

302

P<0.05). None of the distributions was significantly

skewed.

Figure 2: Distribution of Spearman correlation between

non-sedentary time and negative mood score change.

The results suggest that the time spent in non-

sedentary activities is positively correlated with

changes in reported positive/overall mood and

negatively correlated with changes in reported

negative mood. On the other hand, the reported

mood at the beginning of monitored intervals had

moderately low impact on the sedentary behaviour

in the following interval (r=0.24, r=-0.16 and r=0.23

respectively for initial positive/negative/overall

mood score and non-sedentary time across subjects).

This suggests that sedentary patterns were not

influenced by subjects’ mood

Figure 3: Distribution of Spearman correlation between

non-sedentary time and overall mood score change.

6 CONCLUSIONS

Our pilot study has shown correlation between

sedentary patterns at workplace and mood changes.

Considering the potential health implications, this

study provides a basis to establish new health

recommendations and create work place policies that

minimize sedentary work style, so that wellbeing of

office workers is further improved. Use of mobile

phones will allow workers to get feedback on their

mood change scores and correlation with non-

sedentary periods. This will form part of a

persuasive feedback application that will be

developed to encourage a healthy work style.

REFERENCES

A. Sanchez-Villegas, I. Ara, F. Guille´n-Grima, M. Bes-

Rastrollo, J.J.Varo-Cenarruzabeitia, M. A. Martı´nez-

Gonza´lez, “Physical activity, sedentary index, and

mental disorders in the SUN cohort study”. Med. Sci.

Sports Exerc. Vol 40(5): 827–834, 2008.

W. F. Stewart, J. A. Ricci, E. Chee, S. R. Hahn, and D.

Morganstein, “Cost of lost productive work time along

US workers with depression”. JAMA, Vol. 289,

Number 23 (2003), 3135-3144

M. S. Tremblay, R. C. Colley, T. J. Saunders, G. N. Healy,

and Neville Owen,”Psychological and health

implications of a sedentary lifestyle”. Applied

Physiology, Nutrition, and Metabolism, vol. 35(6), pp

725-740, December 2010.

Tudor-Locke CE, Myers AM. Challenges and

opportunities for measuring physical activity in

sedentary adults. Sports Med 2001;31:91–100

Hamilton, M. T., Hamilton, D. G., and Zderic, T. W. 2007.

Role of low energy expenditure and sitting in obesity,

metabolic syndrome, type 2 diabetes, and

cardiovascular disease. Diabetes, 56(11): 2655–2667.

doi:10.2337/db07-0882. PMID:17827399.

Baranowski T. Validity and reliability of self report

measures of physical activity: an information-

processing perspective. Res Q Exerc Sport 1988;

59:314–27

J. Kwapisz, G. M. Weiss, S. A. Moore, “Activity

recognition using cell phone accelerometers”, Human

Factors, pp 10-18, 2010. (Retrieved from

http://storm.cis.fordham.edu/~gweiss/papers/sensorK

DD-2010.pdf)

M. J. Mathie, A. C. F. Coster, B. H. Lovell, and B. G.

Celler, “Accelerometry: providing an integrated,

practical method for long-term, ambulatory monitoring

of human movement”, Journal of Physiological

Measurement, vol 25(2), 2004.

N. M. Hamburg, C. J. McMackin, A. L. Huang, S. M.

Shenouda. M. E. Widlansky, E. Schulz, et al.,

“Physical inactivity rapidly induces insulin resistance

A PILOT STUDY OF SHORT-TERM EFFECTS OF NON-SEDENTARY BEHAVIOUR ON MOOD CHANGES

303

and microvascular dysfunction in healthy volunteers”,

Arterioscler. Thromb. Vasc. Biol. 27(12):2650–2656,

2007.

S. R. Zwart, A. R. Hargens, S. M. Lee, B. R. Macias, D. E.

Watenpaugh, K. Tse, S. M. Smith, “Lower body

negative pressure treadmill exercise as a

countermeasure for bed rest-induced bone loss in

female identical twins”, Bone, vol 40(2), pp 529-537,

2007.

F. B. Hu, Y. T. Li, G. A. Golditz, W. C. Willett, and J. E.

Manson, “Television watching and other sedentary

behaviors in relation to risk of obesity and type 2

diabetes mellitus in women”, JAMA, vol 289 14), pp

1785–1791, 2003.

G. L. Gierach, S. C. Chang, L. A. Brinton, J. V. Lacey, A.

R. Hollenbeck, A. R., A. Schatzkin, M. F. Leitzmann,

“Physical activity, sedentary behavior, and

endometrial cancer risk in the NIH–AARP Diet and

Health Study”. Int. J. Cancer, vol 124(9), pp 2139–

2147, 2009.

C. K. Martin, T. S., Church, A. M Thompson, C. P.

Earnest, S. N. Blair, “Exercise dose and quality of life:

a randomized controlled trial”. Arch. Intern. Med. Vol

169(3), pp 269–278, 2009.

L. A. Clark, D. Watson, J. Leeka, “Diurnal variation in the

positive affect”, Motivation and Emotion, vol 13(3),

pp 205-234, 1999.

A. Adan, and J. Guardia, “Circadian variation of

selfreported activations: a multidimensional

approach”.Chronobiologia, Volume 20, Number 3-4,

233-24, 1993

A. C. Volkers, J. H. M. Tulen, and W. W. Van Den Broek,

”Relationships between sleep quality and diurnal

variations in mood inhealthy subjects”.

http://www.nswo.nl/userfiles/files/publications/

jaarboek-1998/volkers.pdf, (1998), 1-5

J. M. Smyth and A. A. Stone, “Ecological momentary

assessment research in behavioral medicine”. Journal

of Happiness Studies, Vol. 4, pp 35-52, 2003.

“Stand Up Australia: Sedentary behavior in workers”,

Brochure on the study undertaken by Baker IDI Heart

and Diabetes Institute and the Cancer Prevention

Research Centre at the University of Queensland in

Partnership with Meadibank Private. August 2009..

G. N. Healy, D. W. Dunstan, J. Salmon, and E. Cerin,

“Breaks in sedentary time: Beneficial associations

with metabolic risk”. Diabetes care, Vol. 31, number

4, 2008.

S. G. Trost, K. L. Mciver, and R. R. Pate, “Conducting

accelerometer-based activity assessments in field-

based research”, Medicine & Science in Sports &

Exercise, DOI: 10.1249/01.mss0000185657.86065.98,

2005.

Mobile Dev & Design, http://mobiledecdesign.com

[accessed: February 2011]

J. Sivula, W. B. Lake, R. A. Betzold, "Method and

apparatus for implementing activity sensing in a pulse

generator", US Patent number 5.052.388, 1989.

B. Balkau, L. Mhamdi, J. M. Oppert, J. Nolan, A. Golay,

F. Porcellati, et al. “Physical activity and insulin

sensitivity: the RISC study”, Diabetes, 57(10): pp.

2613–2618. 2008.

G. Jennings, L. Nelson, P. Nestel, M. Esler, P. Korner, D.

Burton, and J. Bazelmans, ”The effects of changes in

physical activity on major cardiovascular risk factors,

hemodynamics, symphatetic function, and glucose

utilization in man: a controlled study of four levels of

activity”, Circulation, Vol 73, pp 30-40, 1986.

L. Reagan, “Insulin signaling effects on memory and

mood”, Current Opinion in Parmacology, Col 7, pp

633-637, 2007.

L. A. Gonder-Fredericl, D. J. Cox, S. A. Bobbitt, and J.

Pennebaker, “Mood changes associated with blood

glucose fluctuations in insulin-dependend diabetes

mellitus”, Health Psychology, Vol. 8(1), pp 45-59,

1989.

A. Wells, N. W. Read, and A. Macdonald, “Effects of

Carbohydrate and Lipid on Resting Energy

Expenditure, Heart Rate, Sleepiness, and Mood”,

Physiological Behavior, Vol 63(4), pp 621-628, 1998.

L. Bey, and M. T. Hamilton, “Suppression of skeletal

muscle lipoprotein lipase activity during physical

inactivity: a molecular reason to maintain daily low-

intensity activity”. The Journal of Physiology, Vol.

551, pp: 673-682, Sep 1, 2003.

B. H. Yu, E. H. Kang, M. G. Ziegler, P. J. Mills, and J. E.

Dimsdale, “Mood states, sympathetic acriviy, and in

vivo b-adrenergic receptor function in a normal

population”. Depression and Anxiety, Vol. 25(7), pp:

5590564, 2008.

T. Rechlin, M. Weis, and W. P. Kaschka, “Is diurnal

variation of mood associated with parasympathetic

activity?”. Journal of Affective Disorders, Vol. 34(3),

pp: 249-255, June 8, 1995.

J. L. Andreassi, “Psycholophysiology: human behavior

and physiological response”. Routledge, 2006.

HEALTHINF 2012 - International Conference on Health Informatics

304