Post on 18-Dec-2021
Development of Smartphone Applications for
Chronic Diseases of the Elderly
Myeon-Gyun Cho School of Information and Communication, Semyung University, Jecheon, Korea
Email: mg_cho@semyung.ac.kr
Abstract—Recently, the aging society is facing with the rapid
increase of chronic disease. Chronic diseases can have a
profound impact on the health and quality of life of the
elderly,not to mention the financial burden that is often
associated with long-term illness. The major 3 well known
chronic diseases for the elderly are Depression, Diabetes and
Dementia (3Ds). Despite the advanced medical technology,
chronic diseases have not comprehensively managed yet. In
this paper, we have developed smart phone based U-health
care application (App) for healthcare of the elderly with
chronic diseases, such as 3Ds. The suggested App for
chronic diseases composes Depression check, Diabetes
detection and Dementia screening using smart-phone, urine
test and bionic sensors. The propose App can not only
prescribe proper activity to control the chronic diseases but
also it can monitor physical activity of the elderly in
everyday life.
Index Terms—applications, smartphone, chronic diseases,
depression check, diabetes detection, dementia screening
I. INTRODUCTION
Chronic Disease is a long-lasting condition that can be
controlled but not cured. Chronic illness affects the
population worldwide. As described by the Centers for
Disease Control, chronic disease is the leading cause of
death and disability in the United States. It accounts for
70% of all deaths in the U.S., which is 1.7 million each
year. Data from the World Health Organization show that
chronic disease is also the major cause of premature death
around the world even in places where infectious disease
are rampant. Although chronic diseases are among the
most common and costly health problems, they are also
among the most preventable and most can be effectively
controlled [1].
All too often, because there are so many chronic
conditions that seem to afflict older persons, there is the
mistaken perception that diabetes, arthritis and the like,
are just “part of growing old”–and nothing can be done
about them. The truth is most of these diseases and
conditions are treatable and should be addressed by a
physician. According to the American Society of
Consultant Pharmacists, the most common chronic
diseases afflicting the elderly are: Depression, Adult
onset diabetes, Dementia and Parkinson’s disease [2]
Manuscript received February 22, 2015; revised August 25, 2015.
Depression poses as one of the most serious
psychological disorders and threatens the old’s quality of
life badly. Depression has a relapsing course that adds to
the morbidity, mortality and economic loss [3], [4]. In
recent year, depression detection algorithm based on a
neuro-fuzzy network was introduced, and it was
incorporated with HRV (heart rate variability) data [5]. A
new classifier using a Back Propagation Neural Network
(BPNN) technique was proposed in order to define
depression grade with more accuracy. Paper [6] proposed
to apply data mining technique to discover the association
rules among responded questionnaire and users' negative
emotion words.
Since sophisticated HRV and BPNN signal analysis
consumes a large quantity of the available CPU resources
and processing times, which makes the application
inappropriate for mobile and embedded applications. In
addition, diagnosis only depending on questionnaire
cannot guarantee the sufficient accuracy for detecting
Depression.
Urine test strips are used for analyzing health of
patients with chronic diseases and they consist of several
reagents which changed its color when immersed in urine
of the patients [7]. In order to analyze such color changes,
colors in a reactive strip are compared against the
reference color chart provided by the manufacturer. Since
this task is not easy to be done accurately with human
eyes only, expensive hardware is used in the hospital;
therefore, ordinary person is unable to use the urine test
strips easily. In order to solve the difficulty, simple smart
phone-based urine analysis system [8] has been
introduced recently. However, this system is required to
use specific box-shaped equipment and complex
computation power for an accurate analysis
Dementia is a general term for a decline in mental
ability severe enough to interfere with daily life. Memory
loss is an example. Alzheimer's disease and vascular
Dementia are the most common type of Dementia [9].
Lately, Dementia has caused a social problem due to the
rapid expanding of population aging. However, if it is
possible to detect the vascular dementia in its early stage,
the onset and progression of Dementia could be
prevented [10].
Depression is also a common problem in older adults.
Unfortunately, all too many depressed seniors fail to
recognize the symptoms of depression and try to cure
them, but the depression can also occur as part of medical
problems such as Dementia [11]. Thus, Depression in
Journal of Life Sciences and Technologies Vol. 3, No. 2, December 2015
53© 2015 Journal of Life Sciences and Technologiesdoi: 10.18178/jolst.3.2.53-57
elderly patients, especially late-onset appears to be a
strong predictor of Dementia [12]. New study has found
that low amounts of albumin in the urine, strongly predict
faster cognitive decline in older women. Especially,
group in which albumin was detected in urine test
revealed that scores of verbal fluency test has dropped
rapidly [13]. Therefore, incorporating information about
albumin along with kidney function should help
clinicians identify patients at high risk for Dementia.
Smart-phone based electronic questionnaire is also
introduced to screen dementia more accurately as well as
bionic sensors such as SpO2 and HRV (heart rate
variability) [14], [15].
Since it is difficult to diagnosis Dementia in one way,
we are attempting to increase the accuracy with adapting
diversified methods. In this paper, we introduce novel
smart phone based U-health care application (App) for
healthcare of the elderly with chronic diseases, such as
Depression, Diabetes and Dementia. Thus, in this paper
we have developed one-stop u-healthcare App for the
elderly with chronic diseases using urine-test strip,
Depression checking and bionic sensors on the smart
phone.
II. U-HEALTHCARE ALGORITHMS FOR THE CHRONIC
DISEASES OF THE ELDERLY
The interest in U-healthcare is increasing with the
spread of ubiquitous Information Technology (IT). In
addition, the need for a new healthcare system that is
usable anytime and anyplace is growing due to the
paradigm shift from health supervision to health
preservation, the increasing number of the elderly.
Figure 1. Flow diagram of the proposed apps for depression-diagnosis (Th1, Th2 : threshold values from clinical test)
A. Depression Checking Algorithm [11]
Since the conventional system adopted sophisticated
HRV and BPNN signal analysis, they requires a large
quantity of the available CPU resources and processing
times, which makes the application inappropriate for
mobile and embedded applications. In order to reduce the
CPU resource and processing time, we adopted negative
emotion extraction based on text message (SMS, SNS:
face book) in communication and used electric
questionnaire built in smart-phone. User’s mobility with
GPS and the frequency of call are also employed to
enhance the accuracy of diagnosis as shown in Fig. 1.
In order to detect or diagnose Depression at an early
stage, we propose and develop application for smart
phone (abbreviated to Apps) with android program.
The proposed Apps are composed of 3 parts:
1) DI(depression index) measurement part
Questionnaire based on K-BDI (Beck Depression
Inventory) with 20 questions on smart-phone [6]
Text recognition for negative emotion check from
SMS(short message service) and SNS(social
network service: face book) of user
Mobility check from GPS sensor in smart-phone
Frequency of call check for testing sociality
The final DI is calculated by equation (1),
𝐃𝐈 = ∑ 𝐃𝐤𝟒𝐤=𝟏 ∙ 𝐚𝐤 (a1=0.3, a2=0.3, a3=0.2, a4=0.2) (1)
2) Depression diagnosis part
Normal condition : if DI is lower than threshold 1
Early stage Depression: Th1 ≤ DI ≤ Th2
Severe stage Depression: if DI is larger than Th2
3) Depression alarm part
Send or represent warning message to user
according to severity of Depression
B. Diabetes Check Altorithm using Smarphone and
Urine Test Strip
Recent developments of healthcare technology require
smaller, simpler and cost-saving methods to satisfy
everybody using it without technical and economic
barriers. The most probable candidate is the paper-based
(Urine test strip) colorimetric method, such as pH test
papers and urine tests. A urine test is done simply by a
piece of the functionalized paper which senses analyses
and reports the signals ascolours readily read with the
naked eye. The paper kit is cheap to purchase, light to
carry and convenient to use [7]. However, such benefits
come with a cost, which is subjective uncertainty in
evaluating colors by eye. Generally, human sensibility is
considered to be quite accurate, however at the same time,
is subjective by personal and surrounding conditions,
which brings about uncertainties. Since conventional
spectrometers cannot make full use of the proximity of
the paper-based colorimetry, many people have looked
for alternative approaches in smart phones. As a smart
phone has a built-in camera with high resolution,
colorimetric data are acquired as a digital image, which in
turn are converted to analytic concentrations [8].
Even though this smart phone-based colorimetry
application can provide an analysis platform closer to the
ideal urine test, it possesses restrictions in practical usage.
As for first drawback, the colors can be digitized
differently upon the light source and the surroundings, so
the comparison with the pre-loaded colors is not made
properly. The second restriction is that an image miss-
located or taken with shake can fail to collect the colors at
the appointed positions.
Journal of Life Sciences and Technologies Vol. 3, No. 2, December 2015
54© 2015 Journal of Life Sciences and Technologies
Figure 2. Flow diagram of the proposed apps for urine test using smartphone and urine test strip
In order to cope with conventional drawback from
smart phone-based colorimetric application, we propose
the novel approach shown in Fig. 2 and listed bellows. At
first, the color of unused urine-test strip is set to reference,
and then we compare the color changed after embedded
urine. By doing so, we can examine Ascorbic acid,
leukocyte, glucose, protein, ketenes, bilirubin, PH and so
on. Using the results of the examination in the proposed
App, early screening of chronic disease can be possible
from the information of renal function, liver function,
acid-base balance, urinary tract infections.
To measure and compare the color change of urine
strip pad before and after the urine test, we read the value
of RGB coordinate in pixels for each test pad location
(red, green, and blue). In the Android bitmap
development environments, the extracted pixel
information with integer type is composed of Alpha, Red,
Green, Blue, each 1 byte; the final 4 bytes can be
calculated by equation (2) as below.
Red = (Pixel(x,y)>>16)&0xFF
Blue = Pixel(x,y)&0xFF
By use of equation (2), we can measure RGB values of
test pad before and after urine test. By setting a reference
value with urine color before the test, the degree of color
change is determined by calculating the difference
between before and after urinalysis. There are 3-steps of
diagnosis for urine test such as low risk(a1), high risk(a2)
and emergency(a3), and then determination of whether a
noticeable positive reaction (positive index) can be
calculated with Table I.
Figure 3. Index number for pad U[i] of urine test strip
Index i = 0 ~ 9, in order are representing the occult
blood, bilirubin, right borrowing cyanogens, ketone
compound, protein, nitrite, glucose, pH, specific gravity,
leukocyte respectively. U[i] is a reference value, and U '[i]
is value of after urine test. Two values have RGB values
and Fig. 3 shows the number of urine strip test pads.
TABLE I. DETERMINATION OF DIAGNOSIS(3-STEPS) FOR URINE TEST
Types 3step Conditions
occult
blood
a1
a2
a3
U[0]R -U'[0]R ≥ 43.8 && U[0]G -U'[0]G ≥ -7
U[0]R -U'[0]R ≥ 87 && U[0]G -U'[0]G ≥ 9.3
U[0]R -U'[0]R ≥ 108.5 && U[0]G -U'[0]G ≥ 57
bilirubin
a1
a2
a3
U[1]G-U'[1]G ≥ 2.6 && U[1]B -U'[1]B ≥ 1.8
U[1]G-U'[1]G ≥ 16 && U[1]B -U'[1]B ≥ 13
U[1]G-U'[1]G ≥ 48.5 && U[1]B -U'[1]B ≥ 34.5
cyanogen
a1
a2
a3
U[2]G-U'[2]G ≥ 5 && U[2]B -U'[2]B ≥ 10.5
U[2]G-U'[2]G ≥ 27 && U[2]B -U'[2]B ≥ 25
U[2]G-U'[2]G ≥ 62.5 && U[2]B -U'[2]B ≥ 55.8
ketone
a1
a2
a3
U[3]R -U'[3]R ≥ 0.8 && U[3]G -U'[3]G ≥ 36.5
&& U[3]B -U'[3]B ≥ 32.6
U[3]R -U'[3]R ≥ 44.3 && U[3]G -U'[3]G ≥ 109.6
&& U[3]B -U'[3]B ≥ 73.1
U[3]R -U'[3]R ≥ 82.3 && U[3]G -U'[3]G ≥ 111.3
&& U[3]B -U'[3]B ≥ 81
protein
a1
a2
a3
U[4]R -U'[4]R ≥ 13.3 && U[4]G -U'[4]G ≥ 1
&& U[4]B -U'[4]B ≥ -13.8
U[4]R -U'[4]R ≥ 39.8 && U[4]G -U'[4]G ≥ 9.5
&& U[4]B -U'[4]B ≥ -46.3
U[4]R -U'[4]R ≥ 64 && U[4]G -U'[4]G ≥ 33
&& U[4]B -U'[4]B ≥ -62.8
Nitrite
glucose
a3
a1
a2
a3
U[5]G-U'[5]G ≥ 28.5 && U[5]B -U'[5]B ≥ 23
U[6]G-U'[5]G ≥ 10 && U[6]B -U'[6]B ≥ 70.3
U[6]G-U'[6]G ≥ 31.6 && U[6]B -U'[6]B ≥ 85.3
U[6]G-U'[6]G ≥ 63.5
pH a2
a3
U[7]R -U'[7]R ≥ 95.6 && U[7]G -U'[7]G ≥ -8.6
&& U[7]B -U'[7]B ≦ 1
U[7]R -U'[7]R ≥ 102.6 && U[7]G -U'[7]G ≥ 7.6
&& U[7]B -U'[7]B ≦ -49
specific
gravity
a1
a2
a3
U[8]R -U'[8]R ≦ -24.3 && U[8]B -U'[8]B ≥ 17.6
U[8]R -U'[8]R ≦ -95.8 && U[8]B -U'[8]B ≥54.8
U[8]R -U'[8]R ≦ -128.5 && U[8]B -U'[8]B ≥ 57.6
leukocyte
a1
a2
a3
U[9]R -U'[9]R ≥ 0 && U[9]G -U'[9]G ≥ 3
&& U[9]B -U'[9]B ≥ -6
U[9]R -U'[9]R ≥ 6.5 && U[9]G -U'[9]G ≥ 14
&& U[9]B -U'[9]B ≥ -1
U[9]R -U'[9]R ≥ 55 && U[9]G -U'[9]G ≥ 71
&& U[9]B -U'[9]B ≥ 24.8
Journal of Life Sciences and Technologies Vol. 3, No. 2, December 2015
55© 2015 Journal of Life Sciences and Technologies
Green = (Pixel(x,y)>>8)&0xFF (2)
C. Dementia Screening Algorithm
New study has found that low amounts of albumin in
the urine, strongly predict faster cognitive decline in older
women. Especially, group in which albumin was detected
in urine test revealed that scores of verbal fluency test has
dropped rapidly. Therefore, incorporating information
about albumin along with kidney function should help
clinicians identify patients at high risk for Dementia [12]
[13]. Depression is also a common problem in older
adults. Unfortunately, all too many depressed seniors fail
to recognize the symptoms of depression and try to cure
them, but the depression can also occur as part of medical
problems such as Dementia [9]. Thus, Depression in
elderly patients, especially late-onset appears to be a
strong predictor of Dementia [10].
Figure 4. Structure of dementia screening method from DSI
Fig. 4 shows the structure of Dementia screening
method which is composed of Depression checking part
and Urine test for chronic-disease part. If we explain in
more detail, Depression checking part comprise
questionnaire, text message check, mobility monitoring
and call frequency check. Meanwhile, albumin detection,
diabetes mellitus check and hypertension check compose
urine test for chronic-disease part. At last, the final DSI
(dementia screening index) is calculated from DI
(depression index) and CI(chronic-disease index). By
doing this, we can figure out the seriousness of Dementia
for the old with their smart-phone, regardless of time and
place. The final DSI (Dementia Screening Index) is
calculated by equations (3)~(6).
𝐷𝐼 = ∑𝐷𝑘 ∙ 𝑑𝑘
4
𝑘=1
(d1=0.3, d2=0.3, d3=0.2, d4=0.2) (3)
𝐶𝐼 = ∑𝑈𝑘
3
𝑘=1
∙ 𝑐𝑘 (c1=0.7, c2=0.2, c3=0.1) (4)
𝐵𝐼 = ∑𝑈𝑘 ∙ 𝑏𝑘
2
𝑘=1
(b1=0.5, b2=0.5) (5)
𝐷𝑆𝐼 = 𝐹1 ∙ 𝐷𝐼 + 𝐹2 ∙ 𝐶𝐼 + 𝐹3 ∙ 𝐵𝐼 (6)
where, F1, F2 and F3 are 0.3, 0.4, 0.3 respectively.
According to DSI, the seriousness of Dementia can be
screened by 3 stages as shown in Table II. We set
threshold values for classifying Dementia stage 40 and 70.
However, these parameters can be changed after clinical
trials.
TABLE II. DECISION OF 3 SCREENING STAGES FROM FINAL DSI
Decision Screening Stage for Dementia
DSI < 40 Normal Stage
40 ≤ DSI <70 Warning Stage (Early stage of Dementia)
DSI ≥ 70 Critical Stage (Second stage of Dementia)
III. IMPLEMENTATION OF U-HEALTHCARE
APPLICATION FOR CHRONICAL DISEASES OF THE
ELDERLY
A. Dementia Screeing Application for Smartphone
Fig. 5 represents start screen for the proposed
Dementia screening application for smart phone user.
And we can see that it is composed of 3 parts; Urine test,
Depression checking and bionic sensors.
Figure 5. Start screen shot and component block for the proposed dementia screening application
In this paper, we have presented an innovative and still
experimental tool to support screening of Dementia by
means of urine test and depression checking program.
However, the proposed Apps still need to be revised to
enhance accuracy for screening Dementia, and more
elaborated clinical trials should be performed to
guarantee validity of the paper
B. Chronic Diseases Screening Application for
Smartphone
Since the developed smart-phone based application
uses Depression checking, Urine test and bionic sensors
such as SpO2 and HRV, they can not only prescribe
proper activity to control the chronic diseases but also it
can monitor physical activity of the elderly in everyday
life.
IV. RESULTS AND DISCUSSIONS
This paper presented a novel approach to smart-phone
as a diagnostic tool for Dementia using Urine test,
Depression check and bionic sensor inputs. It has been
successfully implemented on smart-phone with android
Journal of Life Sciences and Technologies Vol. 3, No. 2, December 2015
56© 2015 Journal of Life Sciences and Technologies
program and also registered in play store. Although we
didn’t provide rigorous verification for Dementia
screening, the proposed App did help the smart-phone to
be a Dementia-screening method for the elderly. Current
implementation of Apps for the proposed algorithm opens
space for further improvements like prevention and
treatment for Dementia. Beside these improvements,
clinical trials of proposed Apps should be performed in
order to verify the validity of ‘Dementia Screening’ as an
accurate and appropriate diagnosis-tool for Alzheimer’s
disease or Dementia. After performing the clinical trials,
weight parameters such as dn, bn, cn and Fn will be
optimized
By the successful management of chronic diseases
based on u-health system, we want to achieve the
expansion of disease-free and disability-free life
expectancy and a solution to reduce the burden of
medical expenses in modern society eventually. In the
future, we will adopt glucose meter, blood pressure meter
and body fat scale using Bluetooth connection to smart-
phone, and plan to implement the one-step bionic
detection system for chronic disease.
ACKNOWLEDGMENT
This research was supported by Basic Science
Research Program through the NRF funded by the
Ministry of Education, Science and ICT & Future
Planning (NO. 2012R1A1A1001704)
REFERENCES
[1] S. Cheng and Y. D. Wang, “The care of the elderly with chronic
disease based on electronic health recodes,” Information Technology in Medicine and Education, vol. 2, pp. 760-763, 2012.
[2] Y. Hu and G. H. Bai, “A cloud model for interoperable home based chronic diseases healthcare,” in Proc. World Symposium on
Computer Application & Research, 2014, pp. 1-6.
[3] M. B. Keller, “Depression: A long-term illness,” Br J. Psychiatry, pp. 9-15, 1994.
[4] Z. X. Zhang, X. W. Tian, and J. S. Lim, “New algorithm for the
depression diagnosis using HRV: A neuro-fuzzy approach,” in Proc. International Symposium on Bioelectronics and
Bioinformatics, 2011, pp. 283-286.
[5] S. Chattopadhyay, P. Kaur, F. Rabhi, and U. Rajendra Acharya, “An automated system to diagnose the severity of adult
depression,” in Proc. Second International Conference on Emerging Applications of Information Technology, 2011, pp. 121-
124.
[6] Y. C. Chung, M. K. Rhee, S. H. Park, C. H. Sohn, et al., “Standardization study for korean-beck depression inventory,” K.
Journal of Neuropsychiatry, vol. 4, no. 1, pp. 77-95, 1995
[7] S. H. Han, M. S. Ra, G. S. Lee, W. Y. Kim, and D. A. Kim, “A novel design of urine test strip with spatially adjacent reference
color chart,” in Proc. IEEE ISCE, 2014, pp. 1-2.
[8] J. I. Hong and B. Y. Chang, “Development of the smart phone-based colorimetry for multi-analytic sensing arrays,” Lap on Chip,
vol. 14, pp. 1725-1732, 2014.
[9] K. H. Lee, C. Y. Kim, and S. H. Kim, “Diagnosis and treatment of dementia,” Journal of the Korean Physical Therapy Science, vol.
9, no. 3, pp. 171-178, 2002.
[10] G. Waldemar, B. Dubois, and M. Emre, “Recommendations for the diagnosis and management of Alzheimer's disease and other
disorders associated with dementia: EFNS guideline,” European
Journal of Neurology, vol. 14, no. 1, pp. 1–26, 2007. [11] M. G. Cho, “Development of diagnosis application for depression
on smart-phone,” in Proc. ICEIC, Jan. 2014, pp. 557–558.
[12] D. A. V. Espiritu, H. Rashid, B. T. Mast, J. Fitzgerald, J. Steinberg, and P. A. Lichetnberg, “Depression, cognitive impairment and
function in Alzheimer’s disease,” International Journal of Geriatr
Psychiatry, vol. 16, pp. 1098-1103, 2001. [13] American Society of Nephrology. [Online]. Available:
http://www.eurekalert.org/pub_releases/2010-11/asonpit111010
php [14] M. G. Cho, H. S. Choi, and H. J. Kim, “
program for dementia diagnosis using bionic sensor and oxygen
chamber,” Journal of the Korea Academia-Industrial Cooperation Society, vol. 14, no. 11, pp. 5847-5855, 2013.
[15] M. G. Cho, “Development of dementia-screening application
using urine test and depression check in a smartphone,” in Proc. ICEIC, Jan. 2015, pp. 602–604.
Myeon-gyun Cho
He received his Bachelor’s degree of
electronic and communication engineering from Hanyang University in 1994, Master’s
degree of electronic and communication
engineering from Hanyang University in 1996, and Ph.D of electric and electronic
engineering from Yeonsei University in 2003.
He worked in 4G system Lab., Samsung Electronics Ltd. during March 1996~February
2008. He is now working as Associate Professor at the Department of
Information and Communication, Semyung University, Jecheon, ChungBuk, Korea. He is also an IEEE Member.
His research areas include Embedded S/W, Mobile Communication,
Medical & IT Convergence System, Development of Application for Smartphone
E-mail: mg_cho@semyung.ac.kr
Journal of Life Sciences and Technologies Vol. 3, No. 2, December 2015
57© 2015 Journal of Life Sciences and Technologies
Integration of application