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.,