Advances in Building BodyInNumbers Exercise and Wellness Health

Strategy Framework

Petr Br˚uha

1, 2

, Roman Mouˇcek

1,2

, V´ıtˇezslav Vacek

, Pavel

Snejdar

, Luk´aˇs Vaˇreka

1, 2

, V´aclav Kraft

and Peter Rehor

Department of Computer Science and Engineering, Faculty of Applied Sciences,

University of West Bohemia, Univerzitn´ı 8, Pilsen, Czech Republic

NTIS - New Technologies for the Information Society, Faculty of Applied Sciences,

University of West Bohemia, Univerzitn´ı 8, Pilsen, Czech Republic

Keywords:

Exercise and Wellness, Chronic Disease, Health Related Data, Brain Data, Health Information Systems, Body

In Numbers Software System, Physical Performance, Cognitive Performance, Data Security.

Abstract:

Smoking, excessive drinking, overeating and physical inactivity are well-established risk factors decreasing

human physical performance and increasing incidence of chronic diseases. Moreover, epidemiological work

has identiﬁed modiﬁable lifestyle factors, such as poor diet, physical and cognitive inactivity that are associ-

ated with the risk of reduced cognitive performance. Chronic diseases present an enormous burden to society

by increasing medical costs and human suffering. Exercise and wellness health strategy frameworks aiming at

inﬂuencing modiﬁable lifestyle risk factors in voluntarily enrolled individuals and thus decreasing incidence

of chronic diseases are then very beneﬁcial. However, such frameworks also need a supporting software in-

frastructure. The advances in building of such software infrastructure, the BodyInNumbers software system

for rapid collection and analysis of health related data, are presented in this paper. They include the changes

in the system architecture, redeﬁnition of user roles related to data and metadata security and design, imple-

mentation and integration of new modules for collection and management of electroencephalographic/P300

event-related potential data and new modules for collection and management of data from measurements of

physical strength and balance. The results of the system testing are ﬁnally described.

1 INTRODUCTION

Chronic diseases present an enormous burden to so-

ciety by increasing medical costs and human suf-

fering. Recent data estimate that physical inactivity

and poor diet caused 40,000 deaths in 2000 (Ellison

et al., 2016), ranking second only to tobacco. Nu-

merous studies link cardiovascular disease risk with

the high glycaemic index/load of carbohydrate-based

diets (Grasgruber et al., 2016). Approximately one

tenth of the world population suffer from obesity and

prevalence of obesity among children and adults has

doubled in 73 countries since 1980 (Afshin et al.,

2017). Physical activity and a balanced diet are ef-

fective interventions as an essential weapon in the

war on chronic disease. Clearly, there is overwhelm-

ing evidence linking most chronic diseases seen in the

world today to physical inactivity and inappropriate

diet consumption.

Over the past decades, considerable knowledge

has accumulated concerning the signiﬁcance of exer-

cise in the treatment of a number of diseases, includ-

ing diseases that do not primarily manifest as disor-

ders of the locomotive apparatus. Today, exercise is

indicated in the treatment of a large number of medi-

cal disorders. In the medical world, it is traditional to

prescribe the evidence-based treatment known to be

the most effective and entailing the fewest side effects

or risks. The evidence suggests that in the selected

case exercise therapy is just as effective as medical

treatment and in special situations more effective or

adds to the effect. In this context, exercise therapy

does not represent a paradigm change it is rather that

the accumulated knowledge is now so extensive that

it has to be implemented.

Data also suggest that aerobic exercise is associ-

ated with a reduced risk of cognitive impairment and

dementia; it may slow dementing illness. A com-

pelling argument can be made for this via two plau-

sible biologic pathways. First, a convergence of evi-

548

Br˚uha, P., Mou

cek, R., Vacek, V., Šnejdar, P., Va

reka, L., Kraft, V. and Rehor, P.

Advances in Building BodyInNumbers Exercise and Wellness Health Strategy Framework.

DOI: 10.5220/0006655205480554

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 548-554

ISBN: 978-989-758-281-3

dence from both animal and human studies suggests

that aerobic exercise may attenuate progression of

neurodegenerative processes and age-related loss of

synapses and neuropil. This may occur via a direct

inﬂuence on neurodegenerative disease mechanisms

or facilitation of neuroprotective neurotrophic factors

and neuroplasticity. Not to be overlooked, however,

is a second pathway, cerebrovascular disease. Cere-

brovascular burden contributes to dementia risk, es-

pecially via small vessel disease (e.g. lacunes and

leukoaraiosis). Vascular risk factors are well known

to be reduced by aerobic exercise. Thus, ongoing,

moderate-intensity physical exercise should be con-

sidered as a prescription for lowering cognitive risks

and slowing cognitivedecline across the age spectrum

(Matura et al., 2017).

Numerous noncognitive, nonvascular beneﬁts ad-

ditionally beneﬁt from exercise, which may be es-

pecially relevant to aging population. This includes

reduction of osteoporosis and fracture risk (Rizzoli

et al., 2009) age-related sarcopenia (Thomas, 2010)

and beneﬁts directed at depression (Thomas, 2010)

and anxiety (Conn, 2010). An exercise program

may improve behavioral management in seniors with

dementia (Dunn, 2010) and fall risk (Teri et al.,

2003). Importantly, long-term physical activity and

ﬁtness reduce mortality risk in the general popula-

tion. (Kokkinos et al., 2011; Allan et al., 2009).

Mounting evidence shows regular exercise helps

reduce levels of brain loss and helps our cognitive

abilities as we age. A Florida study demonstrated

that exercise at midlife may reduce the odds of de-

mentia in older adults by up to 60 percent (Lee et al.,

2010). Such extraordinary ﬁndings were corroborated

by several other studies, including University of Lis-

bon study that found that physical activity beneﬁts

happen independently of age, education, vascular his-

tory or diabetes (Andel et al., 2008).

To address modiﬁable lifestyle health risk factors,

many different wellness intervention projects around

the world have been introduced. This paper presents a

progress report of such a wellness project that is cur-

rently conducted at the Department of Computer Sci-

ence and Engineering, University of West Bohemia

in the Czech Republic, and is called BodyInNum-

bers (Bruha et al., 2017).

Its focus is on deﬁnition and automation of the

data collection process in order to capture a huge

amount of heterogeneous health related data from

many users in various environment in a short time.

The architecture of an underlying application has

been extended and changes in the architectural design

related to the management of user roles and related

data and metadata security have been made. A new

module for collection and management of electroen-

cephalographic/P300 event-related potential data and

new modules for collection and management of data

from measurements of physical strength and balance

have been designed, implemented and integrated into

the system. A questionnaire given to participants has

been digitized. Finally, the related mobile application

for rapid collection of health data has been improved.

The paper is organized in the following way. The

next section shortly deals with the state of the art in

the ﬁeld of publicly available health related applica-

tions that focus on cognitive and/or physical health of

its users. Section 3 takes a closer look on the archi-

tecture of the BodyInNumbers software system and

especially deals with the deﬁnition of user roles re-

lated to the data and metadata security issues. The 3.2

section brings changes in the system implementation.

The last section summarizes the parts of the system

that have been already implemented and introduces

the future steps.

2 STATE OF THE ART

The effects of a healthy lifestyle on physical and cog-

nitive functions are of interest not only to researchers

or physicians, but also to people who feel their own

responsibility for their health. Then a well designed,

user friendly and secure exercise and wellness sys-

tem containing a large collection of annotated hu-

man health related data could be suitable for fur-

ther analysis of lifestyle inﬂuence on human cognitive

and physical performance. The acquisition of human

health related data must be also efﬁcient and ﬂexible,

both in non-lab and lab conditions. Only a sufﬁcient

set of data and metadata (e.g. age, gender and sum-

mary of the participant’s current life style and health)

allows researchers to perform further analysis, e.g. to

detect early symptoms of starting chronic diseases.

There are many applications that allow collection

of health related data, e.g. the Apple Health App or

Google Fit are their well known representatives. An-

other prime example is Vitabot that specializes in nu-

trition programs and goal tracking, with the ability

to connect personal ﬁtness trainers with users, widely

used in the ﬁtness industry (Vitabot.com, 2017). In-

dares.com (Chmel´ık et al., 2017) has been developed

with the aim to support education and research in

the ﬁeld of physical activity. A variety of games

is usually used for cognitive training, e.g. the Lu-

mino City puzzle game (State of Play games, 2014) or

My Happy Neuron (HAPPYneuron, 2017). There are

also projects utilizing reaction time as a physiological

measure (e.g. (Harris et al., 2010; Bolandzadeh et al.,

Advances in Building BodyInNumbers Exercise and Wellness Health Strategy Framework

549

2015; Fenesi et al., 2016)).

In contrast to these systems and applications, our

BodyInNumbers software system is able to collect

and manage two very different data and metadata

groups - heterogeneous health related data (including

reaction times) and electroencephalographic (EEG)

/event-related potential data (ERP) recordings. To the

authors best knowledge, there are no systems publicly

available that would contain such various data: re-

action time data, P300 event-related component data

and other supportive health-related data (color vi-

sion, spirometry, electrocardiography, blood pressure,

blood glucose, body proportions and ﬂexibility) to-

gether with corresponding metadata (except others,

for example, a summary of the participant’s current

lifestyle and health).

The set of supportive health-related data was se-

lected from two points of view: it has to represent a

basic characteristics of human physical performance

and the data have to be easily collected also in non-

lab conditions.

3 BODY IN NUMBERS

SOFTWARE SYSTEM

3.1 Architecture and Design

The architecture of the BodyInNumberssoftware sys-

tem is shown in Figure 1. The system design fully

follows the strict legislative requirements for stor-

ing and managing personal and sensitive data and

metadata. The architecture of the system is thus de-

signed to ﬁt these needs and includes pseudonymiza-

tion, anonymization and encryption of all sensitive

and personal information stored or processed within

the system. A set of user roles is deﬁned to access

the system functionalities and the data and metadata

stored in the system.

The system itself is divided into ﬁve essential

components: kernel, data warehouse, remote logger,

API, and web interface.

3.1.1 User roles

On the basis of activities that a user can perform

within the BodyInNumbers software system and on

the basis of privileges when accessing the data and

metadata stored in the system the following user

groups have been proposed:

• Data acquisition group – people who are per-

mitted to set a data recording procedure/collect

data/manage and verify collected data.

– coordinator – a person responsible for the deﬁ-

nition of a data collection procedure, he/she can

determine the data collection procedure within

the application,

– leading experimenter – a person responsible for

the correct conduction of a speciﬁc measure-

ment and for the quality of resulting data and

metadata, he/she can view measured data and

edit them in the application,

– experimenter – a person responsible for the

measurement itself, he/she can insert new data

into the application.

• Control authority group – executives have access

to the data stored in the system.

– ethics committee – can view a list of exper-

iments and measured data in anonymous and

pseudonymous form,

– top management of the corresponding depart-

ments, faculties and research centers– can view

anonymous and pseudonymous measured data.

• Research group – people who work with mea-

sured data. They have access to any data accord-

ing to their permissions.

– data analyst – can view and export any data in

the anonymous form,

– nutritional counselor – can view any measured

person in the anonymous and pseudonymous

form,

– physiotherapist – can view any measured per-

son in the anonymous and pseudonymousform,

– cognitive trainer – can view any measured per-

son in the anonymous and pseudonymousform.

• Technical support group – people who can view

all data and have full access to the application.

– data manager – person with full permission to

view, edit and delete measured data. He/she is

the only person who can access personal data

and metadata via the web interface,

– security administrator – person with an access

inside server for conﬁguration,

– system operator – owner of the system.

• Participants group – people who participated in

the measurement can access only their data and

metadata.

3.1.2 Kernel

Kernel is the most vital and secured componentof the

system. It is accessible only within a private network

and direct access is granted to a very limited num-

ber of users and services. The main responsibility

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550

Figure 1: Software prototype architecture.

of the kernel is encryption and decryption of health

related data based on asymmetric cryptography and

anonymization of all personal data when these are

transferred out of the kernel.

Health related data processed inside the kernel are

divided into several groups (based on their content,

sensitivity, etc.). Each data group has its unique pair

of keys, one key for data encryption and the sec-

ond one for data decryption. The keys are associated

with the presented user roles and privileges within the

BodyInNumbers system.

For example, John who is a diabetes data analyst

has a privilege to read measured data inside the dia-

betes data group only, this privilege is associated with

the single decryption key of this data group. Jane as a

member of the data acquisition group has privileges

associated only with the encryption keys, i.e. she

is able to encrypt and send measured data but can-

not read them (or any other data) after their encryp-

tion. Moreover, the user does not bear any knowledge

about cryptography procedures running in the back-

ground.

Decryption is handled only by the kernel, while

encryptionmay be delegated to the client side in some

cases to decrease the server load. The data processed

and encrypted inside the kernel are persistently stored

in the data warehouse.

3.1.3 Data Warehouse

The collection of personal data and design and imple-

mentation of their storage are managed according to

law. The data warehouse component consists of sev-

eral database servers running on separate machines. It

ensures data replication to prevent possible data loss

and suitable data distribution, i.e., for example that the

subject’s informed consent containing subjects name

and contact is not stored on the same physical ma-

chine as subjects measured data to prevent any possi-

ble personal information leakage.

Advances in Building BodyInNumbers Exercise and Wellness Health Strategy Framework

551

3.1.4 API

The application programming interface (API) is a

component which serves as a gateway to the BodyIn-

Numbers system. Communication with API is en-

sured by using a secured channel (https, authentica-

tion tokens,...). The request or measured data are

passed to the kernel when authentication and autho-

rization is ﬁnished.

3.1.5 Web Interface

The web interface component (the bottom part of Fig-

ure 1) enables users to visualize, edit, insert and ex-

port health related data. It is also provides necessary

functionality for data acquisition group (scheduling

of measuring sessions, planning of technical and hu-

man resources etc.) and basic statistical and analytical

functionality. All requests of this component are han-

dled by the REST based API.

3.2 Implementation and Deployment

3.2.1 Kernel and API

The design takes into account the appropriate storage

and backup of health related data. The disadvantage is

that every operation over the data is expensive (dele-

gation of requests, multiple encryption). For this rea-

son, the optimized source code of server-side compo-

nents will be re-implemented in C++.

3.2.2 Web Interface

The web interface is based on the Flask micro python

framework and MVC pattern. All functionality is

structured into separated modules covering speciﬁc

parts of the system. The functions of modules are tied

to the kernel of the system. Every operation which re-

quires data must request the system kernel for them.

Rest API is deﬁned for collecting data from client de-

vices.

• The General module covers functionalities afford-

able also for non-logged users.

• The Admin module serves for the administration

of users and application setting.

• The Measurement module includes the deﬁnition

of the measurement procedure and overall data

management including viewing, adding, editing

and deleting records.

• The Experiment module provides features for

adding, editing and deleting experiments.

• Each experiment requires its own set of measur-

ing devices. The Equipment module stores them

in the database and provides tools for their man-

agement.

• The QR generator module generates QR codes

into a PDF document given to the participant.

Each person has his/her numerical identiﬁer in-

cluded in his/her QR code.

• The File Storage module provides an interface for

uploading and downloading ﬁles.

• The Brain module serves for the P300 event-

related potential data processing.

• The Reaction module has features for statisti-

cal processing of hand and leg reaction time and

graph view.

• The Cardio module computes basic statistics from

heart and blood data

• The Respiration module shows statistics from

spirometry and stress spirometry data.

• The Fitness module is processing body for pro-

portion, strength and balance data.

While the Brain and Fitness modules were newly

designed, implemented and integrated within the soft-

ware system, other modules were only partly re-

designed and reimplemented.

3.3 Testing

The software prototypehas been tested on 124 healthy

participants both in lab and non-lab environment (e.g.

during the Days of science and technologies 2017 that

were held on the Pilsen main square in September

2017) according to the following procedure.

After registering and signing the informed con-

sent within the mobile application, each participant

obtained a QR code ticket and continued to ﬁll in

an electronic motivational questionnaire containing

a set of 19 single choice questions to provide a basic

overview of participant’s current lifestyle and health

condition. Immediately after that participants took

part in individual measurements organized at nine in-

dividual sites located in a big tent. Each site was

equipped with appropriate hardware and software

tools related to the speciﬁc measurement. It was op-

erated by at least one human expert who also pro-

vided the participant with information about the re-

lated measurement. There was an information desk

that served both for registration of participants and

provision of measurements results.

Although there was a recommended route be-

tween individual measurement sites, in fact, the par-

ticipants could visit them in any order (see the schema

HEALTHINF 2018 - 11th International Conference on Health Informatics

552

of measurement sites and the recommended route in

Figure 2). They were also not required to complete

all the measurements and could have interrupted the

measurement cycle at any time. Only in the best case

they visited all the measurement sites and ﬁlled in all

questions in the questionnaire. The complete data col-

lection procedure took approximately 30 minutes.

Figure 2: Schema of measurement sites and recommended

route between them.

When a single measurement was completed (see

e.g. the schema for the Brain and senses measure-

ment site in Figure 3), the obtained data were inserted

using the web interface into the BodyInNumbers soft-

ware system. When the participant ﬁnished his/her

last measurement, he/she was provided with the re-

sults (measured values) from all visited measurement

sites. The results were organized on the web page ac-

cording to the participant’s QR code.

Figure 3: Schema of the measurement site Brain and senses.

4 CONCLUSIONS

In this paper we presented an extension of the

BodyInNumbers software system for the exercise and

wellness health strategy framework. This software

system serves not only for rapid collection of health

related data but ﬁnally for the wellness intervention

program aiming at modiﬁable lifestyle factors that of-

ten contribute to incidence of chronic diseases. The

most important extended parts of the software system

include the deﬁnition of the user roles that have a di-

rect impact on the security of the data and metadata

stored in the system and design, implementation and

integration of the Brain and Fitness modules that en-

able greater variability of collected data and metadata.

The system functionalities were validated during its

real deployment.

The next steps in the system design and develop-

ment include the revision of used terminologies and

extension of data processing methods. The system is

planned to be deployed within an intervention pro-

gram to store, analyze and visualize health-related

data and their interpretations.

ACKNOWLEDGMENTS

This publication was supported by the UWB grant

SGS-2016-018 Data and Software Engineering for

Advanced Applications and the project LO1506of the

Czech Ministry of Education, Youth and Sports under

the program NPU I.

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