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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information:
DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
2
[19][21][27][28][29][30][31]. Custom exergaming solutions
mostly follow design guidelines and best practices focused in
specific intervention types such as balance improvement
[8][9][15][18][30][32][33][34][35], instead of exploring
overall physical well-being of elders.
Summarizing, significant gaps emerge when examining
exergaming studies and interventions for active and healthy
aging. There are, conspicuously, only a few studies making
any attempt at formal assessment of the usability of
computer/web-based exergaming platforms [16][26]. What is
more, an overall “prototype” attitude [20] is often
encountered: studies focus on demonstrating efficacy on rather
small user samples rather than following formal usability
testing on large cohorts. Indeed, a significantly greater number
of studies explore intervention efficacies through standardized
survey methods [8][9][10][15][18][19][25][35][36], but with
methodological generalization obstacles in their findings due
to small participant cohorts and short evaluation periods
[7][8][9][13][14][15][18][19][22][24][25][26][27][28][29][30]
[31][32][33][34][35][36][37][38]. Quite obviously, follow up
(repeated measures) tests in specific time periods (e.g. 6-
mohths, 12 months, etc.) after initial trial would also be
building up on evidence.
Last but not least, recent neuroscientific [39] and
neuropsychological [40] reviews on game-based elderly
interventions emphasize the importance of naturalistic or
personally meaningful environments and designs that should
be inducing a mismatch of supply and demand, with high task
variability, fulfilling basic individual senior needs, but be
engaging, so as to maximize long-term adherence.
Presented here is the assessment of a rigorously designed, low
cost, custom exergaming platform, utilizing off-the-shelf
contemporary controller hardware. This work is the first in the
field that utilizes validated standard tests to assess intervention
impact as well as platform usability. Additionally, this is the
first controlled study combining relatively large sample sizes
(n>200) with a rigorous intervention program (2 months, 5
d/week) providing results which could be generalized and
form the baseline for future similar efforts. The objective of
this work is to evaluate whether elderly-tailored exergaming
systems can be user friendly and effective enough to achieve
good physical exercise adherence and to improve the quality
of life. This effectiveness requires rigorous adherence to
established exercise protocols and valid assessment of
physical status in order to dynamically adjust the physical
challenges to the elderly users according to their needs and
abilities [14][20][21][24][25]. The architectural challenge
stemming from this overall objective is the incorporation of
standard physical exercise protocols and standard physical
assessment tests in exergaming software engineering practice.
The remainder of this paper is structured as follows. In the
Methodology section, the FitForAll exergaming platform and
its architecture are presented alongside the design principles
and criteria used. In order to give a clear view at the
methodology used for the evaluation of the system in terms of
usability and efficacy, a section of the Methodology deals with
the intervention and the evaluation tools as well as the
participant samples. A final methodology subsection presents
details of the statistical analysis methodology to be used for
the extraction of results. In the results section the evaluation
outcome in terms of usability, efficacy and adherence are
illustrated. At the end of the paper a Discussion section puts
the threads together by shedding light on the conducted
evaluation and its outcomes in the light of current research
work on the field of serious exergaming for elderly, along with
research limitations and further envisaged work.
II. MATERIALS AND METHODS
A. The FitForAll exergaming platform
The FitForAll (FFA) platform consists of specifically designed
games aiming at elderly exercise and
maintenance/advancement of healthy physical status and well-
being. FFA offers elderly-specific exercises within an
engaging game environment aiming at promoting physical
exercise protocol adherence. Fig. 1 illustrates information as
well as interaction layers between the users (seniors) and the
FFA system. Through contemporary controllers (Nintendo Wii
Remote Controller, Nintendo Wii BalanceBoard -Fig. 1:
Hardware Layer), FFA: (i) captures sensory information such
as acceleration, or body mass transfer by translating user body
movements and postures (Fig. 1. Physical Layer); (ii) converts
them to game input (Fig. 1 Data Layer) and compares them
with the physical exercise and gameplay objectives (Fig. 1
Semantic Layer); (iii) updates the game scenario accordingly
(Fig. 1 Game Layer); and (iv) provides appropriate forms of
feedback (Fig. 1 Presentation Layer) to the senior (Fig. 1
Outcome Layer). The Fullerton [41], an overall physical
assessment test (Fig. 1 Data Layer), evaluates the seniors’
physical status progress, based on their profile (Fig. 1 User
Profile), and adjusts the exercise intensity and difficulty level
accordingly.
The implementation of the application was based on the .NET
framework. The main application encapsulated the
communication with peripherals input devices based on the
Managed Library for Nintendo's Wiimote [42]. Filtering and
algorithmic processing of the controllers’ acquired signal, as
well as, identification of inconsistencies in calculation of body
posture and gestures and their correction are accommodated
after the acquisition process. The Microsoft XNA Game
Studio [43] was employed for the rendering of 2D and 3D
graphics. The 2D graphics were images while the 3D objects
were simple 3D models edited in 3Ds Max Studio and Google
Sketchup.
B. FFA game design principles and considerations
The design of the games that comprise FFA was guided by
tapping into expert knowledge from established protocols of
exercise used for facilitating a healthy lifestyle for the elderly.
Specifically, several such physical training protocols were
dissected and the recommended standard physical exercises
that are their building blocks were identified and recorded
[3][5]. Afterwards, a game was designed for each exercise that
incorporated in its control scheme the required physical
exercise (cf. Fig. 2). The game encapsulated the exercise in an
interesting and attractive, for the specific age group, game
concept [44], by adhering (during design) to a cohort of
guidelines and recommendations. The latter were identified in
the literature [21] or provided by the experts’ experience.
TABLE I summarizes the identified guidelines and
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information:
DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
3
recommendations along with the FFA approaches respectively
[21]. The vast majority of them have been extracted by
implications in the literature [11], one session trials [14], focus
groups [24], discussion with elderly people [21],
multidisciplinary workshop and SWOT analysis [20] or short
trials with less than 15 participants [25].
Fig. 1 The FitForAll architecture concept. The hardware layer (FFA input)
captures user’s movement and transforms them to game input, which is tested against the physical exercise and gameplay objectives (semantic layer)
according to the user profile (data layer). The output of the FFA system
(presentation layer) provides the appropriate feedback to the senior to adjust his/her movements accordingly. Expert knowledge like depicted in Outcome
and Semantic Layers (protocols, recommendations) was taken into account.
C. The FFA game suite
The elementary component of the FFA platform is the game.
Focusing on the senior’s game experience, each game is
formed as a goal-oriented activity through a virtual
environment, with simple and understandable graphics (cf.
Fig. 5). The physical task/exercise (physical activity objective)
is accomplished in conjunction with the in-game goal (game
objective), while the player’s performance is tracked.
Interaction with the game is event based, triggered when a
game’s logic rule is met (cf. Fig. 5 A, B and C). Events
provide either real-time notifications about goal achievements
(result events) (cf. Fig. 5 C) or guidance on appropriate task
execution and successful completion (action events) (cf. Fig. 5
B). In FFA, the combination of games in an ordered sequence,
configured to specific physical exercise objectives, instantiates
a physical training “session” which may stand on its own or be
part of a whole intervention protocol, as FFA is integrated into
a full elderly assisted living system through the use of web
services [45] and (health) user records [46][47]. Health care
providers are able to add or modify games, sessions and
therapies through a native interface (cf. Fig. 3). Forming a
session is achieved by selecting a cohort of games and
defining their order and parameters which will affect the
overall exercise difficulty. A pool of supported games is
available to therapist-users during a session’s elaboration.
Fig. 2 Physical exercises identified in the literature and incorporated in the FFA games as strength training exercises (upper row), or stretching and
flexibility exercises (middle row), or balance training (bottom row; first two
(2) pictures) and aerobic (bottom row; last two (2) pictures) exercises.
The full game suite (cf. Fig. 5 and Fig. 6) is composed of
aerobic, strength, balance and flexibility computerized
exercises blended with games. The following game types
compose the game suite. In Hiking or Cycling (aerobic
exercises) seniors are supposed to march on the spot or cycle
on a stationary mini-bike; FFA makes use of an avatar moving
through a city landscape to render exercise enjoyable (cf. Fig.
5 A). In Ski Jump (strength, flexibility) the senior is to move
the center of mass to a specific position, thus controlling an
avatar’s jump performance (maximum length travelled) (cf.
Fig. 5 B). In the well-known Arkanoid (dynamic balance)
seniors control the horizontal position of a bar and attempt
hitting a moving ball (directed to destroy bricks) (cf. Fig. 5 C).
In another dynamic balance game, Apple Tree, seniors move
to control a basket picking apples from a tree (cf. Fig. 5 D).
Likewise, in Fishing (dynamic balance too) seniors control the
vertical position of a boat which attempts fishing the
horizontally moving fishes (cf. Fig. 5 E). In Mini-Golf seniors
move their center of mass and attempt to put a ball into a hole
by overcoming different barriers (cf. Fig. 5 F). Finally,
numerous exercise tasks increase upper and lower limb
strength by weightlifting and resistance gaming exercises,
while stretching and warm-up exercises account for flexibility
training. Senior feedback and overall reward is empowered by
pictures of positive valence which are revealed gradually with
increasing repetitions and upon completion in an effort to
engage seniors.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information:
DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
4
Fig. 3 Administration interfaces. Left: editing sessions and games in terms of parameters, difficutly and order, Right: Configure default connections for Wii
Remote Control and Wii BalanceBoard.
D. System setup, launching and game navigation
Since connectivity and setup of the Wii Devices, through the
Bluetooth Stack provided tool, is a tricky task for
inexperienced senior users, FitForAll implements an auto
connect functionality by utilizing the Bluesoleil Bluetooth
Stack API. The default controller devices and its parameters in
terms of auto connectivity are configured by the FitForAll
administration panel (Fig. 3).
Upon system launching, the senior is guided by an intuitive
interface to connect the controller devices (cf. Fig. 6 A). Once
connection is established, the senior is able to choose either
the training session that FFA automatically recommends or
one of the available training sessions (cf. Fig. 6 B). Before the
game environment is launched, auxiliary interfaces offer
instructions about the game play and the physical task to be
undertaken (cf. Fig. 6 D). After reading the instructions, the
senior may press the start button to continue. A short count-
down prepares the user before the game. Additionally, the
senior is able to pause or skip a game at any time by simply
touching the screen (cf. Fig. 6 F).
TABLE I LITERATURE GUIDELINES AND RECOMMENDATIONS FOR EXERGAMING DESIGNS FOR ELDERLY.
Guideline to follow, rationale and evidence
Description FFA approach
Physical condition
considerations
[14][20][21][24][25]
Limited extremities use (consider diseases or
injuries)
Capacity for use of just one arm/leg.
Care for exercises performed by standing or in sitting.
Flexibility for skipping exercises at user’s discretion (cf. Fig. 6 C).
Range Of Motion (ROM)-Adaptability [14][21][24]
Bigger tolerance at high precision gestures Configurable tolerance of movement range (cf. Fig. 3).
Continuous Player Support
[14] Avoid assumption that different gestures are
remembered over whole game period. Comprehensive illustrations of instructions through the interface
(physical activity and game objectives) (cf. Fig. 5 B).
Instructions screen pops-up on movement inconsistencies or total detection absence (cf. Fig. 6 D).
Avoid small/fast moving
objects [21][25] Produce strain and anxiety Well discriminated objects (cf. Fig. 5).
Well defined game start up (with count-down period)
Adequate time for user adjustment to virtual environment Clean user interface [21] Clear instructions
Avoid redundant information
Simple, well defined graphics (cf. Fig. 5).
Big, visible buttons for screen navigation (e.g. pause, skip) (cf. Fig. 6) Attractive and friendly user
interface [11][20] Engages user in-game Simple, illustrated instructions and graphics. (cf. Fig. 6)
Suitable topics[21] Topics adjusted to elderly people’s interests. Real life scenarios, e.g. collecting apples, catching fish etc (cf. Fig. 5). Provide audiovisual
feedback [11][21][24] Necessary to understand game interaction.
Positive feedback immediately after a task’s completion
Avoid negative feedback
Achievements Panel (cf. Fig. 5 D)
Auditory signals (whistle, clapping) declare action sequence
Supporting, rewarding, motivational pop up messages, delivered as text or color codified indicators, upon significant events (cf. Fig. 5 E)
Adjustable difficulty
[14][20][21][24][25] Appropriate activity/challenge level keeps
active players engaged while avoiding overstraining others
Avoid cognitive and motion complexity
Option of several difficulty levels on startup. (cf. Fig. 6 B)
Subset of Fullerton [41] assessment tests performed every 7 sessions, automatically suggests difficulty level.
Encourage social interaction
[11][21] Encouraging/cheering game partners increase
fun.
Score feedback increases competitiveness among the seniors. (cf. Fig.
6 E) Exertion Management
[14][20] Manage fatigue, prevent overexertion Alternating between more/less physically intense game periods to
allow recovery.
Pause or skip exercise capacity at any time (cf. Fig. 6 F).
Simple Setup [14] Easy menus, startup and shutdown. Intuitive, illustrated instructions for devices’ connection (cf. Fig. 6 A)
Start on single button press. (cf. Fig. 6 B)
Record/display user's past behavior [11]
Show historical information related to physical activity
Difficulty level progress implies physical improvement
Provide information at
opportune moments [11] Avoid annoying messages at inappropriate
times. Pop up messages only on game event occurrence game start-up and
completion. (cf. Fig. 5)
Game pauses for user focus on message (cf. Fig. 5)
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information:
DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
5
E. Physical intensity and difficulty level selection: an
evidence based step towards system adaptability
As was mentioned previously, a session has a specific
difficulty level. This comprises of two components: the
physical exercise intensity component (e.g. more repetitions
per exercise), which is the dominant one and the gameplay
difficulty (e.g. avoid obstacles during the golf game). Apart
from the option of creating/modifying interventions, FFA
incorporates a default intervention protocol, tailored to elderly
[3], which consists of four difficulty levels. The first level
promotes light exercise while the last one promotes more
intensive physical exercise. Periodically, a formative
assessment [48], by means of a short computerized subset of
the Fullerton test (cf. Fig. 4), is requested to be performed by
the seniors prior to the intervention’s session. Four of the six
Fullerton tasks are facilitated by the FFA through Wiimote
(attached on the arm or the leg) to measure number of arm’s
curls (cf. Fig. 4 A), number of stand-ups from a chair (cf. Fig.
4 B), time to cover predefined distance (cf. Fig. 4 C) and
number of steps in a predefined time allotment (cf. Fig. 4 D).
The other two of the six tasks are simply measured by the
seniors and entered manually to the system by an intuitive
virtual keyboard on the screen. Following the design
guidelines, each task is accompanied by detailed and
illustrated instructions. Performance improvement in at least
three Fullerton tasks is required for promoting the seniors to
the next difficulty level while deterioration to at least two
tasks is enough for a level decrease.
Fig. 4 Partial computerization of the Fullerton overall physical status
assessment test. FFA measures number of arm’s curls (A), number of stand-ups from a chair (B), time to cover predefined distance (C) and number of
steps in a predefined time allotment (D) by means of the Wii Remote
Controller.
F. Intervention
The FFA platform was widely used and evaluated during
the trials of the Long Lasting Memories (LLM) project funded
by EU [49][50][51]. In compliance to the ACSM/AHA
recommendations [3], an appropriate number of training
sessions was created. Each trainee had to accomplish 20mins
aerobic exercises, 8-10 resistance exercises, 10mins flexibility
exercise and a set of balance targeted exercises. The warm-up
and cool-down processes constituted the initial and final
session’s components respectively. Exercise intensity was
constant per session but was gradually increased, based on the
formative assessment, throughout the whole intervention to
meet fitness level improvements [3][4][52]. Exercises kick-off
at the light intensity level with a target to reach 50-60%
maximum heart rate (HRmax) and can proceed to the very
hard level with a target set at 80-90% of HRmax. The
intervention was conducted on a series of elderly user groups
(cf. Fig. 7) and was supervised by formal carers. For
comparison, an (active) control group was used which, instead
of the FFA intervention, received cognitive training, identical
in terms of total duration and session intensity as well as
grouping attributes. The whole study was ecologically valid
and was conducted in numerous settings in Thessaloniki
(Greece) within: day care centers of the Greek Association of
Alzheimer's Disease and Related Disorders; municipal social
care centers as well as senior centers; and local parish
community centers.
Fig. 5 FitForAll indicative game interfaces and in-game feedback. A. Hiking:
colored representation of action required. B. Ski Jump: user guidance. C.
Arkanoid: game event message. D. Apple Tree: achievements panel. E. Fishing: motivating messages on low performance. F. Mini-Golf: red color
indicates time end up.
G. Evaluation Method
Both subjective and objective measures were used to evaluate
the FFA platform. Evaluation was conducted mainly in two
fronts, the user level (usability, adherence) and the efficacy
level (fitness impact, Quality of Life Impact). User experience
was evaluated through the (standard) Software Usability
Measurement Inventory (SUMI) questionnaire [53]. SUMI
may report results on efficiency (to which extent users feel
that the software helps them), affect (user’s emotional
reaction), helpfulness (intuitiveness and ease of use), control
(how in-control of the application the users feel), learnability
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information:
DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
6
(how quickly users feel they were able to master the
application) and one overall scale (Global) (TABLE III).
Additionally, participants also completed the System Usability
Scale (SUS) [54], a “quick and dirty” survey scale on
usability. Finally, an ancillary set of questions, focusing on the
platform’s impact on the elderly, ease of use and its potential
as a commercial product.
An attendance log was used for measuring the adherence to
schedule. Each participant was asked to accomplish 5 daily
sessions per week. While not all of the participants managed
to follow this intensive schedule, all managed to complete the
required total sessions (set at a minimum of 16). Adherence
was measured as the ratio of the number of participation
sessions over the number of planned sessions.
On the efficacy front, the Senior Fitness (Fullerton) Test
[41] was chosen as an index of elderly fitness. Assessing
upper and lower body strength, upper and lower body
flexibility, agility/dynamic balance and level of aerobic
endurance, this test was performed to both participant groups
(FFA intervention and control). Additionally, improvements in
quality of life (QOL) were assessed through the WHOQoL-
BREF [56] questionnaire, which addresses four (4) QOL
domains: physical/psychological health, social relationships
and environment (4-20 range scale).
Fig. 6 Auxiliary/navigation interfaces. A. Illustrated guidance for connecting
the peripheral controller devices. B. Well discriminated buttons for selecting options. C and D. Illustrative guidance on how to utilize the wiimote and the
balance board. E. Overall scores and performance on session end. F. Game
pause when user taps screen- option to skip or restart.
H. Participants and system use
FFA was the Physical Training Component in the LLM
project [51]. Thus, a relatively large number (415) of
European senior citizens engaged with it for a minimum of 2-3
times per week for a total duration of 7-8 weeks according to
the LLM database records [46]. The system was used for 6231
sessions between 2010-2012; each session lasted for
approximately 60 mins. For the purposes of this paper,
however, the rigorous evaluation procedure described above
was trialed and successfully completed only by 116 of those
(28%) participants. Likewise, the control group for the
purposes of this study which was conducted in Thessaloniki
(Greece) consisted of a demographically similar group of 116
participants. Specifically, inclusion criteria for both the
intervention and the control group were: age ≥ 60, fluent
language skills, normal or corrected-to-normal vision and
hearing, examination and formal permission from a
cardiologist and time commitment followed by a signed
informed consent (obtained prior to trial commencement). A
dropout was considered by means of not achieving the
minimum number of sessions (16) or five (5) consecutive
absences (FFA dropout rate was 21.2% as opposed to 22.2%
for the controls). No financial incentive was provided to
participants. The protocol was approved by the Ethical Boards
of the Greek Association of Alzheimer's Disease and Related
Disorders and the Medical School of the Aristotle University
of Thessaloniki.
Fig. 7 Photos from the intervention in a day care center in Thessaloniki,
Greece. Participants utilize mini bicycles, weights and chairs according to the game’s suggestion.
I. Statistical analysis
Since standard usability tools were used, our analysis followed
well-defined literature procedures. SUMI questionnaire data
were transformed so as to be comparable against the
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information:
DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
7
SUMISCO standard database average, fixed at "50" for each
of the scales. Mean and standard deviation (SD) values of the
Global and the five (5) additional subscales were calculated.
Likewise, the SUS score was normalized and transformed to
percentiles, allowing the usability of the developed system to
be comparable against a corpus of more than 5,000 SUS
observations [55]. This provides an indicator of the overall
usability of FFA as a system.
Efficacy data were analyzed using the statistical software
SPSS v.21 for Windows. Chi-square analysis was used to test
for gender and age differences between intervention and
control groups. Non-parametric models were chosen for the
physical fitness and QOL data analysis as the majority of
variables were not normally distributed. For continuous
variables pre- and post-intervention changes, within groups,
were analyzed with the Wilcoxon signed rank test, while the
Mann-Whitney U test was used for the differences (post – pre
intervention and post – pre control) between intervention and
control groups respectively. Descriptive statistics for
continuous variables are represented by mean±SD, while an
effect was considered statistically significant if a p-value of
less than 0.05 was obtained.
III. RESULTS
In both groups, female participants have been the majority
(TABLE II) in agreement with what is widely reported in
relevant literature [9][16][18][31][36][57]. Although
participants of both groups were asked to use the system 5
days per week, average FFA attendance was 4 days/week,
achieving a total mean of some 25 sessions. No statistically
significant differences were observed between groups with
respect to age (p=0.287) or gender (p=0.331) (TABLE II).
TABLE II DESCRIPTION OF GROUP DEMOGRAPHICS AND SESSION
ATTENDANCE
Control Intervention
Number of participants 116 116
No of females (%) 89 (76.7%) 95 (81.9%) No of males (%) 27 (23.3%) 21 (18.1%)
Age (years) 69.08 ± 6.6 69.98 ± 6.2
Age (Min-Max) 60-83 60-87 Total intervention sessions
(~1 hour per session)
24± 4 25± 6
SUMI results appear in TABLE III. Values for Efficiency,
Helpfulness, Control, Learnability and Global score are all
above 60%. This puts the FFA platform well above the mean
scores of the SUMISCO database when considering levels of
user satisfaction.
TABLE III SUMI QUESTIONNAIRE RESULTS
Mean St
Dev
Median Minimum Maximum
Global 68.33 5.85 71.0 42 73
Efficiency 64.84 8.96 68.0 35 72
Affect 68.57 6.99 72.0 20 72 Helpfulness 65.73 5.38 68.0 36 72
Controllability 61.69 7.47 65.0 24 72
Learnability 60.09 12.45 65.0 18 71
The mean raw SUS score was 76.36 with no participant rating
below 50%, while the normalized score was 77.7% [55].
Training schedule adherence was found to be good enough,
that is, 82%, as shown in the histogram of Fig. 8.
TABLE IV contains efficacy results (post – pre intervention
differences). Concerning the physical assessment test, the
intervention group showed statistically significant difference
to all the domains of the test in contrast with the control group.
The 8-Foot-Up-And-Go refers to time and thus lower score is
translated to better mobility and dynamic balance.
The within group comparisons show statistically significant
differences regarding pre and post results to all the domains of
the intervention group, while only lower body strength was
improved in the control group (but in obviously lower levels
than the intervention group). Regarding QOL assessment, the
intervention group, compared with the control group, showed
statistically significant difference to the three first domains.
However the differences post-pre interventions within the
same group are statistically significant in both the intervention
and the control group in domain 4 suggesting that
improvement in this domain was observed on both the
intervention and the control group.
On the qualitative questionnaires, 96.6% reported that the
FFA intervention has improved their social life and helped
them meet new people and 85.4% perceived that the FFA
platform allows them to control their health better.
Additionally, 87.1% of the users reported that with a
maximum of 5 days familiarization with the platform they
were able to use it without help, while another 92.2% reported
that they considered this as a commercial level quality ICT
platform, worth paying for if it was ever marketed.
Fig. 8 Intervention adherence as a histogram of the proportion of sessions attended by FFA participants with respect to the planned sessions.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information:
DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
8
TABLE IV EFFICACY RESULTS IN TERMS OF FULLERTON AND WHOQOL AND THEIR SUBDOMAINS. BOLD P VALUES DENOTE STATISTICALLY SIGNIFICANT
DIFFERENCES.
Test Sub-domain Control (N=116) Intervention (N=116) Between groups differences (post-pre)
Pre mean±SD
Post mean±SD
Wilcoxon P value
Pre mean±SD
Post mean±SD
Wilcoxon P value
Mann-Whitney U-test
Fulle
rton
Chair Stand (lower body strength)
13.84±2.97 14.56±3.62 p=0.000, Z=3.506
13.03±3.99 16.35±5.34 p=0.000, Z=7.895
p=0.000, Z=-6.248
Arm Curl (upper body strength) 20.50±4.63 20.60±4.71 p=0.250, Z=1.150
15.90±4.31 22.09±5.48 p=0.000, Z=8.802
p=0.000, Z=-9.751
2-minute Walk in place (aerobic endurance)
75.06±21.87 74.27±21.56 p=0.955, Z=-0.056
70.02±24.17 86.59±28.43 p=0.000, Z=7.343
p=0.000, Z=-7.582
Chair Sit and Reach (lower body flexibility)
0.20±8.893 0.79±9.17 p=0.748, Z=0.321
-1.10±10.834 4.25±10.71 p=0.000, Z=7.856
p=0.000, Z=-6.892
Back Sctratch (upper body flexibility)
-10.02±11.85 -9.57±11.59 p=0.237, Z=1.182
-10.05±11.24 -7.42±10.39 p=0.000,
Z=6.149
p=0.000,
Z=-5.113 8 Foot Up And Go (complex
coordination, agility and dynamic balance)
5.49±1.25 5.47±1.22 p=0.715, Z=-0.366
6.55±1.81 5.78±2.18 p=0.000, Z=-7.568
p=0.000, Z=7.086
WH
OQ
oL
Physical 15.60±2.00 15.50±1.96 p=0.759, Z=-0.307
14.77±2.37 15.28±2.15 p=0.006, Z=2.746
p=0.012, Z=-2.502
Psychological 12.17±1.84 12.33±1.58 p=0.256, Z=1.136
11.66±1.71 12.28±1.69 p=0.000, Z=3.854
p=0.013, Z=-2.482
Social 5.62±1.36 5.44±1.30 p=0.120, Z=-1.557
5.22±1.10 5.40±0.93 p=0.109, Z=1.603
p=0.026, Z=-2.223
Environment 16.43±1.84 16.76±1.86 p=0.025,
Z=2.234 15.97±1.95 16.83±1.78 p=0.000,
Z=4.160 p=0.066, Z=-1.839
IV. DISCUSSION
To our knowledge, this is the first elderly-focused exergaming
platform evaluated on a daily basis through an 8-week
intervention with more than 100 participants. On the usability
front, the results are very encouraging indeed: they enhance
existing literature which lacks of exergames usability
assessments in a standardized way [16][26]. This is of
paramount importance if exergaming/serious games platforms
are supposed to be designed not only as prototype systems, but
as products with an aimed readiness level good enough to be
introduced into the healthy and active aging market [20].
The results obtained with FFA show that its market readiness
is good enough, as the global scores of SUMI and SUS
indicate, thereby interpreting the high levels of users’
perception on usability. As already mentioned before (cf.
TABLE I), commonly accepted design considerations for
developing exergames for elderly [11][14][21][22][25] are
highly related to usability, which seems to be a vulnerable
point in commercial platforms for general audiences
[12][21][24][28]. The high individual scores of the SUMI
scale imply that the FFA platform is tailored to the elderly
population. Controllability and learnability scores, the lowest
of the rest, while well above the average, are justified since the
user base consisted of elderly users with implicit issues in
acquiring new knowledge and subsequently utilizing novel
control schemes [16][58].
Our findings are also consistent with conclusions of earlier
studies that exalted the role of exergames usability and
enjoyment [15][59] to that of motivation and adherence to the
physical exercise protocol [8][10][12][14][25][28][30][32].
While the achieved adherence level (82%) is in line with
previous studies [19], our results should be considered within
the general context of intensive schedules requiring a 5 day
weekly attendance.
A multidimensional, moderately intense activity program that
includes endurance, strength, balance, and flexibility training
is generally considered optimal for older adults [10][52]
contrary to vigorous activity [2][3][4], which affects
adherence negatively [3]. Fullerton post-test improvements of
FFA participants along with the high schedule adherence
levels demonstrate that the FFA platform provided not only
the aforementioned high schedule adherence, but a high
physical exercise adherence too. Moreover, Fullerton results
show statistically significant improvements for all domains of
physical outcomes, in contrary to the vast majority of
literature which reports either improvements simply in
Balance or merely some trends in physical improvements
[10][18][25][27][28][30]. In our case, the FFA group
improves significantly with respect to the control group in all
levels. It is interesting to note, however, that there existed a
statistically significant post-intervention difference in lower
body strength in the control group as well. This could be
attributed to controls' engagement for routine walking activity
(the majority of controls also had to walk from home to pilot
site where the study was conducted a few times a week; thus
there were performing some latent routine aerobic exercise).
The greatest improvement of the WHOQoL-BREF score was
observed in overall living environment and psychology
(including but not limited to evaluation of positive/negative
feelings, self-esteem, memory and concentration) in
consistency with other findings [27]. This could possibly be
attributed to the positive effect of video game playing on
participants' mood as reported in recent literature [13][14].
Naturally and expectedly, the physical aspect of life quality
was significantly improved.
Finally the social domain of the WHOQoL-BREF survey
revealed no statistically significant improvement during the
intervention. However, the between group difference was
statistically significant, thereby probably indicating that FFA's
social impact was significantly more relevant than the simple
socialization experienced by the control group.
From the technical viewpoint, the architecture of this work
incorporated a computerized subset of Fullerton as a formative
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DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
9
assessment component (Fig. 1 Data Layer, Fullerton
Assessment) for purposes of augmented adaptability and
adjustable difficulty. Contrary to other works which consider
the difficulty only on the gameplay axis (objects’ speed and
size, controllers sensitivity, etc.) [9][21][33], the realization of
the FFA architecture perceived the difficulty in two axes: the
gameplay difficulty as well as the physical exercise load. In
order for an appropriately validated set of physical challenges
to be presented in each elderly user, an equally formal,
validated way needed to be incorporated as a criterion for
modifying the physical exercise load. In that context it was
essential that the introduction of a formal assessment
instrument such as the Fullerton, needed to be incorporated as
a formative assessment tool in the platform’s architecture.
Equally important is the fact that this is the first exergame, to
the best of the author’s knowledge, that incorporates in its
architecture design and implementation the standard physical
exercise component (Fig. 1 Semantic Layer, Physical Exercise
Objectives), which refers to planned, structured, and repetitive
movement to improve or maintain one or more components of
physical fitness, contrary to physical activity which refers to
body movement that is produced by the contraction of skeletal
muscles and that increases energy expenditure [2]. This
essentially opens up the way to the next generation of
exergaming platforms which may be adaptive in a smart way.
In fact, the augmented effects of the standard physical exercise
may be the cause of the high adherence since the elders
consider using a system if it is useful, reliable and provides
obvious benefits to their lifestyle [60]. Especially, perceived
usefulness and perceived ease of use are considered as the
main predictors of technology acceptance [61]. With this in
mind, the consideration of standard physical exercises during
design and implementation could serve as an additional
guideline for exergaming design. This implies, as also
suggested by our results regarding perception of better health
control, that incorporating standard physical exercises to
exergames augments the observable effect to the physical state
of the seniors. As a consequence, this would increase elderly
acceptance of exergaming as a legitimate intervention for
maintaining a healthy and independent lifestyle, and thus
perceived as useful for them. According to the literature [60],
such an effect could lead to increased adherence and finally to
successful promotion of exercise to the elderly people. This
could be complementary to the initial motivation which stems
from the joyful experience of the “gaming” component of the
exergames [7][8][9][10][11][12], and also could be the cause
for sustaining this motivation.
A. Limitations
Despite the relatively large sample size of this piece of work
and the exploitation of a cohort of validated tests, some
limitations must be outlined too. As mentioned, comparisons
were done against a non-physically active group that engaged
merely in cognitive exercises. A comparison with a physically
active control (conventional physical exercises, like dancing)
would further empower our findings. Additionally, this study
involved intervention groups with the added social factor
possibly influencing the elderly users’ perception regarding
adherence and QOL. Furthermore, even though this study
consists of one of the longest durations in the field of
exergaming studies, adherence would be better supported by
even longer trials (6 months or more). From the technical
viewpoint, the partial implementation of the assessment test
(four out of six tasks) was a hard limitation of the Wii devices
which is going to be alleviated as soon as technology permits.
V. CONCLUSION
This piece of work reports on the design, implementation and
thorough evaluation through a wide pilot deployment of the
FFA exergaming platform for senior users. Formal assessment
was carried out along two main axes, namely, on user and
efficacy levels. On the usability front, we provide strong
evidence that such interventions are feasible and may be
implemented through contemporary IT systems capable of
motivating seniors to engage with a healthy physical activity
program. On the efficacy axis, quite expectedly, our study
provides strong evidence of physical improvement by means
of clinical tests, but more surprisingly, an even more
significant discovery, is the evidence that the platform
improved the general wellness and quality of life of its user
base. Furthermore, the FFA demonstrated a viable architecture
for incorporating a standard of physical exercises as well as a
computerized subset of Fullerton for accomplishing the
physical status formative assessment towards adjustability and
adaptability. On top of this, the mapping of the guidelines and
the architecture concept to the actual implementation of FFA
provides a concrete illustrative proof of application for the
aforementioned design paradigms.
The relatively large sample size used, in tandem with the
exploited assessment battery may serve as a reference/baseline
for assessing similar platforms in the future. Evolving and
improving access to such a database by providing (semantic
web) ways to exploit the underlying big data structures offers
an unprecedented opportunity certainly worth exploiting [62].
Likewise, the multimodal evaluation methodology could
contribute to the evolution of a gold standard for exergames
evaluation methodology and the results could probably serve
as an initial base for future exergames’ evaluation since this
field gains great importance nowadays. Future studies could
also assess the neuropsychological and neuroscientific impact
of the FFA approach [39][63][64]. Last but not least, the
obtained herein qualitative feedback with regards to the likely
marketability of the product has not only been taken seriously
into account, but already lead to a series of business efforts in
Greece and elsewhere. We deem these achievements as key
response elements of the biomedical engineering and health
informatics scientific communities to the call for
technologically supporting active and healthy aging and the
adoption of future mobile/e-health systems.
ACKNOWLEDGMENT
This research was partially funded by the European
Commission Programme CIP-ICTPSP.2008.1.4 as the Long
Lasting memories (LLM) project (Project No.238904)
(www.longlastingmemories.eu). Authors would like to thank
the whole group of pilot facilitators: Evangelia
Romanopoulou; Maria Karagianni; Eirini Grigoriadou; Aristea
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DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
10
Ladas; Athina Kyrillidou; Anthoula Tsolaki; Stavroula
Fasnaki; Anastasia Semertzidou; Fotini Patera; Efstathios
Sidiropoulos.
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Evdokimos I. Konstantinidis received the
Diploma in electronic engineering from the
Technological Educational Institute of
Thessaloniki, in 2004 and the M.Sc. degree in
medical informatics in 2008 from the Aristotle
University of Thessaloniki, Greece. He is
currently working toward his Ph.D. degree in
the Laboratory of Medical Physics of Medicine, School of
Health Sciences, Aristotle University of Thessaloniki, Greece.
His current research interests lie predominately in the area of
Medical Informatics, particularly with respect to people with
special needs and especially elderly. Recent research interests
focus on intervention for elderly in the field of exergaming.
He has authored more than 30 publications in various
international peer-reviewed journals and conferences.
Antonis S. Billis received his diploma in
Electrical and Computer Engineering in 2007
and MSc in Medical Informatics in 2011, both
from Aristotle University of Thessaloniki,
Greece. He also holds a Ptychio in Business
Administration and Management from the
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information:
DOI 10.1109/JBHI.2014.2378814, IEEE Journal of Biomedical and Health Informatics
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Copyright (c) 2014 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
12
University of Macedonia, Greece. He is currently working
toward the Ph.D. degree in gerontechnology and works with
the Laboratory of Medical Physics, Medical School, AUTH.
His main research interests lie in the areas of medical decision
support systems, AAL technologies, exergaming and cloud
computing. He has been a member of the technical chamber of
Greece since November of 2007.
Christos A. Mouzakidis was born in
Thessaloniki, Hellas. He graduated from
the Department of Physical Education and
Sport Sciences of the Aristotle University
of Thessaloniki. MSc and PhD in designing
and implementing physical exercise
programs for patients with dementia. He
has participated in several research projects and granted by the
European Commission to implement a physical exercise
program in patients with Alzheimer’s disease. He was
included in the 18th edition of Who is Who in the World
(2001) for his research and scientific activity. He was a
teacher of Physical Education in the Department of Primary
Education, Aristotle University of Thessaloniki (1999-2001),
Physical Education and Sport Sciences, University of Thessaly
(2000-2007) and Military School of Officers (2005-06, 2007-
09 & 2010-...). He is author and co-author in several articles
and book chapters in Hellenic and international journals and
books. He is a reviewer of Alzheimer's Association
International Research Grant Program and Alzheimer’s
Association Journal “Alzheimer’s & Dementia”. Dr
Mouzakidis since 1997 gives lectures and runs workshops
about the development and implementation of exercise
programmes in demented patients and has conducted a lot of
announcements in National and International Conferences. He
is currently working at the Hellenic’s Association of
Alzheimer’s Disease and Related Disorders (Alzheimer
Hellas) Day Care Centre.
Vicky I. Zilidou received the Diploma in
the Department of Physical Education and
Sports Science, from the Aristotle
University of Thessaloniki. She is student at
Postgraduate Program of Studies of Medical
Informatics in School of Medicine, Aristotle
University of Thessaloniki. Her current
research interests are mainly in the Medical Informatics,
particularly in relation to elderly people. She is now working
in a program organized by Department of Physical Education
and Sports Science, Aristotle University of Thessaloniki,
entitled "Traditional dance and dementia".
Panagiotis E. Antoniou is a postdoctoral
research associate in the Lab of Medical
Physics, department of Medicine, Aristotle
University of Thessaloniki. He received a
degree in Physics from Aristotle University of
Thessaloniki in 1997, a M.Sc. degree in
Medical Physics in 2001 from the Democritus
University of Thrace and a Ph.D. degree in Medical Physics in
2004 from Democritus University of Thrace. His research
interests are in the area of Medical Informatics, in the fields of
virtual patients in medical education and in the field of the
elderly exergaming interventions. He has authored more than
25 publications in peer reviewed journals and conferences.
Panagiotis D. Bamidis (M’09) received the
Diploma degree in physics from the Aristotle
University of Thessaloniki (AUTH),
Thessaloniki, Greece in 1990, the M.Sc. (with
distinction) degree in medical physics from
the University of Surrey, Guildford, U.K., in
1992, and the Ph.D. degree in
bioelectromagnetism and functional brain analysis and
imaging from the Open University, Milton Keynes, U.K., in
1996.
He is currently an Assist. Professor in Medical Education
Informatics within the Laboratory of Medical Physics,
Medical School, AUTH. He has been the co-ordinator of large
European projects (www.meducator.net;
www.longlastingmemories.eu, www.epblnet.eu,
www.childrenhealth.eu) as well as the principal investigator
for a number of national and international funded projects
(more than 30 in total). His research interests are within
assistive technologies (silverscience, silvergaming, mobile
health, decision support, avatars), technology enhanced
learning in Medical Education (web2.0, semantic web, serious
games, virtual patients, PBL, learning analytics) and Affective
and Physiological Computing and HCI, (bio)medical
informatics with emphasis on neurophysiological sensing and
health information management (open health big data), and
Affective Neurosciences. In 2009, he was awarded the Prize of
the AUTH Research Committee for the Best Track Record in
funded research projects among AUTH young academic staff.
He has been the Chairman/Organiser of six international
conferences (iSHIMR2001, iSHIMR2005, MEDICON2010,
GASMA2010, SAN2011, MEI2012) and the Conference
Producer of the Medical Education Informatics Conference
and Spring School Series. He is a member of the Advisory
Board for the Open Knowledge Foundation (OKFN), a
founding member of OKFN Greek chapter, and a Treasurer
for the Greek Biomedical Technology Society.