Real Time Dose-Response Assessment Tool

Applicable in Exercise Therapy

ao Santinha

, Joana Sousa

, Hugo Gamboa

1,2

and Hugo Silva

2,3

Physics Department, Faculty of Sciences and Technology

New University of Lisbon, 2829-516 Caparica, Portugal

PLUX - Wireless Biosignals, S.A.

Av. 5 de Outubro n. 70 - 6, 1050-059 Lisboa, Portugal

Instituto de Telecomunicac¸

oes - IST/UTL

Av. Rovisco Pais, n. 1, 1049-001 Lisboa, Portugal

Abstract. In this paper we present a new tool, which enables the study of the

dose-response relationship in real time, through the assessment of the level of

physical activity using METs, and applicable in exercise therapy. A validation

protocol for METs algorithm was performed, and METs values were obtained

for various real-world and lifestyle and sporting activities. These values were

compared with the results from the state-of-the-art work in the ﬁeld for the same

activities. Results have proven to be accurate, according to the model by Crouter,

allowing the assessment of physical activity level. Our tool is intended for prac-

titioners working addressing exercise activities and exercise therapy in a wide

range of areas, one of which is psychology. According to the obtained results, the

base hardware holds comparable performance with the advantage of being wear-

able and wireless, and thus convenient to be used on the daily monitoring of the

patients daily routines.

1 Introduction

Associated with both morbidity and mortality, depression is a major public health prob-

lem throughout the world and is characterized by lowered mood, loss of capacity to

experience pleasure, increased sense of worthlessness, fatigue, and preoccupation with

death and suicide [1]. Exercise therapy is known to play a major role in the reduc-

tion of morbidity and mortality [1]; several studies consistently found relation between

physical activity and disorders such as depression [1–5] and it has become generally

accepted that regular physical exercise ultimately results in beneﬁts to participants [2,

3]. Evidence of a dose-response relationship between physical activity and protection

against symptoms of depression and anxiety has also been documented [4, 6].

Biosignal measurement and analysis stand as an important resource, for practition-

ers to better support prevention and intervention strategies aimed at decreasing depres-

sion and anxiety disorders. Strategies applicable to populations inexpensively and with-

out side effects, are needed, as current estimates point psychological disorders such

depression to be the leading burden of disease worldwide by the year 2020 [4]. In this

Santinha J., Sousa J., Gamboa H. and Silva H..

Real Time Dose-Response Assessment Tool Applicable in Exercise Therapy.

DOI: 10.5220/0003892000910095

In Proceedings of the 2nd International Workshop on Computing Paradigms for Mental Health (MindCare-2012), pages 91-95

ISBN: 978-989-8425-92-8

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

paper we present a tool capable of monitoring the dose of physical activity in real-time

and record data for future analysis. This will allow practitioners to better study the

dose-response of physical activity in exercise therapies, and to be used in the future by

patients to monitor their activity level. The rest of the paper is organized as follows:

Section 2 describes the tool targeted at the study of dose-response relationship between

physical activity and depression; Section 3 presents the acquisition system and valida-

tion procedures; Section 4 outlines the main results, and Section 5 highlights the overall

conclusions .

2 Measuring Physical Activity

Our proposed tool is capable of monitoring and evaluate the level of physical activity

based on the metabolic equivalent for task (MET). METs are used to estimate the level

of physical activity, and it is deﬁned as the resting metabolic rate, that is, the amount

of oxygen consumed at rest. As such, exercise at 2 METs requires twice the resting

metabolism, and so on [7]. METs can be computed as a function of the magnitude of

the accelerometer signal. For this, a real time bandpass ﬁlter was implemented, based on

a Inﬁnite Impulse Response model determined for a 2

order Butterworth ﬁlter within

the 0.25-1.4 Hz passing band. This ﬁlter was used since these frequencies correspond

to the range of human activities are performed.

Based on uniaxial Actigraph devices, the input signal corresponds to the accelera-

tion signal in a range of 0.05-2 G, and the output signal to the ﬁltered acceleration sig-

nal, which is subsampled at 10 Hz.. Then, counts × min

−1

and the coefﬁcient of vari-

ation of counts, c

, are determined each 10 seconds over a period of one minute.

For this operation, the range of the accelerometer is divided into levels of 0.001664

g, being each level considered 1 count. The number of counts is determined by how

many levels the difference of the magnitude of the acceleration between samples corre-

sponds to during this period [8]. Then the counts × min

−1

are converted into METs

through a non-linear signal processing algorithm using two regression equations based

on the method described by Crouter et al. [9].

The output values presented to the user, in the visual display alongside with the

raw data signals, are the METs, as shown in Figure 1. The raw data is represented in a

window with a dimensionless auto-scale y-axis. If the user chooses to record the data,

the parameters recorded are the counts × min

−1

and METs. Table 1 shows the typical

classiﬁcation of the physical activity according with the METs results [10].

Table 1. Classiﬁcation of activity level using the METs values.

METs Activity level

≤ 3 Light

3 > METs ≥ 6 Moderate

METs > 6 Vigorous

Fig. 1. Example of the display window with multilpe physical activity assessment parameters.

3 Experimental Evaluation

We used a bioPLUX motion data acquisition system [11]. This wireless system is also

responsible for the signal’s analog to digital conversion, using a 12 bit ADC, and Blue-

tooth transmission of data to a computer. This system can acquire data from an inte-

grated 3-axis accelerometer at a maximum sampling rate of 1000 Hz. The bioPLUX al-

lows to visualize the raw signal in real-time and save data in .txt ﬁle to post-processing.

With this system we can see the METs values in real-time and compare these values

with the ofﬂine values from the raw data. The comparison between real-time and of-

ﬂine values allowed to validate if the algorithm in real-time are correctly implemented.

To validate the real time algorithm for METs calculation based on the model de-

ﬁned by Crouter [9], a set of various lifestyle and sporting activities were performed.

The selected activities were Lying, Standing, Computer work, Filling papers, Ascend-

ing/Descending stairs, Slow walk, Brisk walk and Slow run. These activities were se-

lected based on those used by Crouter to validate his model. Each of these activities had

a duration of 10 minutes, and were repeated only once producing a total of 60 METs

values per repetition, since the algorithm determines the METs each 10 seconds. The

mean value and standard deviation of METs per min were calculated to compare with

Crouter model results for each activity.

It is intended that this tool is used by practitioners to study the dose-effect in dif-

ferent physical activity levels. The protocol used in these studies must be done by the

practitioners according with their knowledge and intended therapeutic model.

4 Results and Discussion

Table 2 shows the mean and standard deviation (SD) METs values obtained by Crouter

and by our METs algorithm for each activity [10]. As we can observe, the METs results

from our algorithm for Lying, Standing, Computer work and Slow run are equal to those

obtained by Crouter. For the Filling papers activity it is possible to verify a difference

of 0.07 METs. For the activities of Slow walk, Brisk walk and Ascending/Descending

stairs a difference of 0.27, 0.33 and 0.67 METs was found, respectively.

These differences can be explained by the fact that these activities involve free

movement and/or depend heavily on the locomotion of the individual. Therefore, more

tests must be done, preferably also using a gold standard device, but preliminarily

we were able to prove experimentally the correct functioning of the algorithm. The

lower values of SD obtained using METs algorithm are justiﬁed by the use of only one

repetion instead of the ﬁfteen performed by Crouter. The dose-response relationship

study results will be obtained in future by practitioners using this tool, and will help to

provide a better understanding of this relationship. This fact would be the most help-

ful for practitioners advising patients about the beneﬁts of physical activity for both

somatic and psychologic well-being [1]. Since this tool is already portable, it would

be ready for use in the daily life of patients, allowing the real-time adjustment of the

exercise therapy.

Table 2. Results from Crouter and METs algorithm.

Crouter METs METs alg.

Activity Mean (SD) Mean (SD)

Lying 1.00 (0.00) 1.00 (0.00)

Standing 1.00 (0.00) 1.00 (0.00)

Computer work 1.00 (0.00) 1.00 (0.00)

Filling papers 1.30 (0.67) 1.23 (0.56)

Slow walk 3.73 (0.42) 3.46 (0.05)

Brisk walk 4.71 (0.58) 4.38 (0.06)

Asc./Desc. stairs 6.08 (1.29) 5.41 (1.15)

Slow run 7.76 (0.96) 7.76 (0.27)

5 Conclusions

In this paper we presented a tool for real time evaluation of physical activity, supported

in an inexpensive and miniaturized base hardware device. We were able to validate

the METs algorithm for the calculation of physical activity estimates in real-time; ex-

perimental results on real-world data enabled us to validate our results and prove the

accurate operation of the system as our results are within the conﬁdence intervals of the

reference work by Crouter for the same activities. As future work, we will implement

this algorithm in Android operation system in order to improve the portability and us-

ability of the system and take advantage of smartphone technology. Our work enables

practitioners to better study the dose-response relationship in physical activity, since

it allows the quantiﬁcation of the activity level, and, use the collected information to

better support the evaluation and prescription of exercise therapy based techniques.

Acknowledgements

This work was partially supported by National Strategic Reference Framework (NSRF-

QREN) under projects ”LUL” and ”Affective Mouse”, by Seventh Framework Pro-

gramme (FP7) program under project ICT4Depression and under the grant SFRH/BD/

65248/2009 from Fundac¸

ao para a Ci

encia e Tecnologia (FCT) , whose support the

authors gratefully acknowledge.

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