Development of Smartphone Applications for Chronic ...

Development of Smartphone Applications for

Chronic Diseases of the Elderly

Myeon-Gyun Cho School of Information and Communication, Semyung University, Jecheon, Korea

Email: [email protected]

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.


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



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



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)

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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: [email protected]



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