Parametrization of Physical Activity Aggregation

Monika

Simaityt

, Andrius Petr

enas

1,2

and Vaidotas Marozas

1,2

Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania

Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, Lithuania

Keywords:

Physical Activity Distribution, Physical Activity Proﬁle, Smart Wristband, Steps, Sedentary Behaviour,

Long-term Monitoring, Cardiovascular Disease.

Abstract:

This work introduces a novel approach to parametrization of physical activity proﬁle. The proposed parameter,

named as physical activity aggregation, is useful for evaluating a distribution of daily or weekly physical

activity. The parameter takes a large value for a highly accumulated physical activity, whereas is much lower

for an evenly spread activity over the monitoring period. The parameter was investigated on step data obtained

using a smart wristband on a group of 71 participants with cardiovascular disease. The results of the pilot

study show that the proposed parameter is capable of discriminating among different physical activity proﬁles,

including sedentary behaviour, going to and from work, walking in a park and being active the entire day.

Moreover, the results demonstrate the tendency that middle-aged and older women are associated with lower

aggregation values, suggesting that they probably spend less time in sedentary behaviour compared to men of

the same age. The proposed parameter has potential to be useful for characterizing physical activity proﬁle,

as well as, for investigating its relation to health outcomes, e.g., during ambulatory rehabilitation after major

cardiovascular events.

1 INTRODUCTION

Physical inactivity is often considered among the lea-

ding risk factors of chronic diseases, and is closely

related to all cause mortality (Biswas et al., 2015;

de Souto Barreto et al., 2017). Unfortunately, ac-

cording to the World Health Organization, more than

80% of the population are insufﬁciently physically

active. It is widely accepted that even low physi-

cal activity is beneﬁcial for health (Sattelmair et al.,

2011), thus it is recommended to avoid sedentary be-

haviour as much as possible to reduce the risk of a

hazardous outcome (Biswas et al., 2015).

Based on the latest report by the Physical Acti-

vity Guidelines Advisory Committee [PAG] (2008),

there is an absence of research on optimal physical

activity for healthy and unhealthy individuals. There-

fore, optimization may potentially lead to an impro-

ved general health status and reduced number of de-

aths (Alves et al., 2016). Positive effect on health is

observed for at least of 150 min weekly moderate acti-

vity (PAG, 2008). However, there is no consensus on

whether 30 minutes in 5 days or 50 minutes in 3 days

are more beneﬁcial for health. In most studies, a mea-

sure of the total time spent in physical activity is usu-

ally employed for investigating causal relationships

with chronic diseases and mortality (Warburton et al.,

2010; Wilmot et al., 2012). Nevertheless, such factors

as activity session frequency and duration may also be

desirable to account for in order to comprehensively

evaluate physical activity proﬁle (PAG, 2008).

Rapid development of electronics and cloud

technology has given rise to various means of long-

term physical activity monitoring (Piwek et al., 2016).

Therefore, the conventional approach for collecting

information on physical activity via questionnaires

can now be replaced by objective evaluation. The ad-

vancements in technology, capable of tracking phy-

sical activity (e.g., smart wristbands, smart watches,

smartphones), have led to compact, user-friendly and

inexpensive devices, which are especially suitable for

monitoring for extended periods of time (months, ye-

ars). Most of them provide information about the

number of steps, sedentary time, climbed ﬂoors, tra-

velled distances, etc. It has been shown that these

devices are sufﬁciently accurate in tracking physi-

cal activity, therefore, they are becoming increasingly

popular for use in research and medical applicati-

ons (Leininger et al., 2016; Althoff et al., 2017; Leth

et al., 2017).

190

Šimaityte, M., Petr

enas, A. and Marozas, V.

Parametrization of Physical Activity Aggregation.

DOI: 10.5220/0006632101900194

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS, pages 190-194

ISBN: 978-989-758-279-0

Time

Steps

A = 0

Time

Steps

A = 0.5

Time

Steps

A = 0.25

Time

Steps

A = 0.7

Time

Accumulated steps

Time

Accumulated steps

Time

Accumulated steps

Time

Accumulated steps

Figure 1: Physical activity proﬁles (above) with corresponding accumulated steps (below) of actual (a) and reference uniform

(u) distributions: a) uniform distribution over the entire monitoring period, b) a continuous single episode taking half of the

total monitoring period, c) two episodes of equal intensity, d) two episodes of unequal intensity.

In this paper, we propose a novel parameter for

an objective evaluation of physical activity aggrega-

tion, allowing to express the distribution of physical

activity over time in terms of a single number. The

acquired new information can potentially be useful

for long-term tracking of changes in the physical acti-

vity proﬁle, as well as, for investigating its relations-

hip to health status.

2 METHODS

2.1 Physical Activity Aggregation

In this study, we deﬁne physical activity as a number

of steps in a time interval. Physical activity aggrega-

tion is given by

A =

∑

i=1

− u

|, (1)

where a

and u

are accumulated steps of actual and

uniform distributions. The latter is used as a reference

for computing the aggregation of actual distribution.

S – the total number of steps in a time period under

analysis (e.g., day or week), N – the total number of

time intervals.

The function of accumulated steps of actual distri-

bution a

is expressed by

i, j+1

i+ j

∑

k=i

, i = 1, ..., N − j, j = 0, ..., N − 1, (2)

= (a

i, j

)

max

, i, j = 1, ..., N, (3)

where s – the number of steps in a time interval k.

The function of accumulated steps of reference

uniform distribution u

is expressed by

∑

k=1

, i = 1, ..., N. (4)

Physical activity aggregation A takes values bet-

ween 0 and 1. Values close to 0 indicate low physical

activity aggregation. This applies for physical activity

proﬁles with steps evenly spread over the monitoring

period (Fig. 1 a). In contrast, values close to 1 indi-

cate maximal temporal aggregation, which is inherent

for physical activity proﬁles with a single continuous

activity episode (Fig. 1 d). It should be noted, that

the aggregation parameter depends on the duration of

an episode, i.e., A becomes larger for shorter physical

activity episodes.

2.2 Study Population

Seventy-one participants (37 women), 51 ± 13 ye-

ars old, with a body mass index of 27.1 ± 4.7 kg/m

were enrolled in the study. Most of the participants

were diagnosed with serious cardiovascular disea-

ses, namely, congestive heart failure, angina pectoris,

myocardial ischemia, atrial ﬁbrillation, hypertension,

etc. A signed, written consent to participate in a study

was obtained from all the participants.

In order to investigate the differences in physical

activity proﬁles among different age women and men,

the participants were assigned to three age groups (see

Table 1).

Parametrization of Physical Activity Aggregation

191

Figure 2: Acquisition of step data and evaluation of daily physical activity aggregation.

Table 1: The number of the participants in each age group.

Age Women Men

<50 years 11 16

50-60 years 13 12

>60 years 13 6

2.3 Data Acquisition

Minute-by-minute step data was obtained using a Fit-

bit Charge 2 (Fitbit, San Francisco, CA, the US) smart

wristband. Then, the data was processed and used for

estimating the physical activity aggregation, see block

diagram in Fig. 2. Since no major activity is expected

during the night, this time period was excluded from

the computation of A . The onset of the night was set

when the number of steps per hour decreased to less

than 20. Similarly, the end of the night was set when

the number of steps per hour exceeded 20.

3 RESULTS

The obtained A values for various physical activity

proﬁles clearly show that the aggregation parameter

is sensitive to various step distributions (see Fig. 3).

For example, a physical activity proﬁle, dominated by

a low intensity physical activity, results in a very low

aggregation value (Fig. 3 a). Another physical acti-

vity proﬁle (Fig. 3 b) demonstrates the case when the

largest number of steps is aggregated during time pe-

riods corresponding to going to and from work. Such

a proﬁle is especially common among those working

in an ofﬁce. Similarly, the proﬁle in Fig. 3 c repre-

sents an ordinary workday, however, with additional

physical activity in the evening due to a 2 h walk in

a park. Since half of the total daily activity is aggre-

gated in the evening, the parameter value approaches

0.5. The example in Fig. 3 d stands for the proﬁle

when the participant is only active for a short period

of time, therefore, almost all activity is aggregated in

a continuous episode starting from 12 pm to 4 pm.

For this reason, a highly aggregated physical activity

results in a large A value.

12 am 6 am 12 pm 6 pm 12 am

Steps

120

A = 0.05

12 am 6 am 12 pm 6 pm 12 am

Steps

120

A = 0.34

12 am 6 am 12 pm 6 pm 12 am

Steps

120

A = 0.48

12 am 6 am 12 pm 6 pm 12 am

Steps

120

A = 0.70

Figure 3: Different physical activity proﬁles with computed

physical activity aggregation: a) uniformly distributed acti-

vity, b) aggregated during the time periods corresponding

to going to and from work, c) aggregated during walking in

the park at the evening, d) most of the activity aggregated

in the afternoon.

Figure 4 displays physical activity aggregation

among different age women and men. The re-

sults show that physical activity aggregation is larger

among participants over 60 years compared to those

under 50 years, which is obvious since younger indi-

viduals are more likely to be physically active, spre-

ading physical activity over the day. Moreover, the

results demonstrate the tendency of physical activity

aggregation being lower for women than men, sug-

gesting that women spend less time in sedentary be-

haviour.

BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing

192

<50 years 50-60 years >60 years

0.25

0.5

Women Men

Figure 4: Physical activity aggregation among different age

groups. Results are expressed as mean ± two-sided 95%

conﬁdence interval.

4 DISCUSSION

The goal of this work is to propose a parameter for

an objective evaluation of a physical activity distribu-

tion. With such a parameter daily or weekly physical

activity proﬁle is characterized by a single number.

The results of the pilot study show that the aggrega-

tion parameter is capable of differentiating among dif-

ferent physical activity proﬁles. Therefore, such in-

formation, collected over an extended period of time

(months, years), could be useful for answering the

core question what physical activity proﬁle is optimal

for different patient groups.

It is widely agreed that regularly performed phy-

sical activity extends life expectancy after major car-

diovascular events, such as myocardial infarction. A

study on leisure time physical activity inﬂuence on

survival after myocardial infarction has shown that

different survival rates are associated with regular and

irregular physical activity, suggesting that a physi-

cal activity pattern may play a signiﬁcant role in life

expectancy (Gerber et al., 2011). Therefore, long-

term monitoring of physical activity, and required cor-

rections of an activity proﬁle may improve ambula-

tory rehabilitation. Since a conventional approach

using questionnaires is neither sufﬁciently accurate,

nor convenient for both the patient and the physician,

objective monitoring of aggregation using smart wris-

tbands may be considered as a promising replacement

for conventional methods.

The approach for quantifying temporal aggre-

gation of speciﬁc events was ﬁrst introduced for

the purpose to characterize the distribution of self-

terminating atrial arrhythmia episodes (Charitos et al.,

2012; Charitos et al., 2013). Differently from the ori-

ginal approach, in which a single arrhythmia episode

shorter than the total monitored time is always assig-

ned to maximal aggregation, our approach is ﬂexible

and duration-dependent. That is, aggregation increa-

ses when the duration of a continuous physical acti-

vity episode decreases. This parameter update is mo-

tivated by the rationale that there is a major difference

between a single very short episode (e.g., 5 min) and

a long one (e.g., 2 h). Therefore, it is incorrect to as-

sign such diverse physical activity proﬁles to the same

aggregation value.

Large aggregation values represent physical acti-

vity proﬁles dominated by the sedentary behaviour.

However, since the aggregation parameter is not af-

fected by physical activity intensity, but rather by dis-

tribution itself, the value is the same both for low and

high intensities. Based on the current agreement that

“some activity is good, but more is better” (Sattelmair

et al., 2011; PAG, 2008), the aggregation parameter

can be even more valuable if studied with respect to

other parameters, such as, physical activity intensity.

4.1 Limitations

The major limitation of this study is the small num-

ber of women and men participants assigned to diffe-

rent age groups. In addition, a study cohort preferably

should cover a larger span of age to draw a more reli-

able insights on gender-related physical activity pro-

ﬁle.

5 CONCLUSIONS

This study shows that the physical activity aggrega-

tion parameter is useful for an objective evaluation of

a physical activity proﬁle. The proposed parameter is

especially suitable for implementation in devices, ca-

pable of tracking physical activity (smart wristbands,

smartphones), therefore, can provide additional infor-

mation on physical activity relationship with health

status.

ACKNOWLEDGEMENTS

This work was partially supported by the Lithuanian

Business Support Agency (LBSA) under the Intellect

LT measure (Agreement No. J05-LVPA-K-01-0254),

and the Research Council of Lithuania (agreement

No. MIP088/15).

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